
Yuxin Yang
Articles
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Dec 17, 2024 |
4spepublications.onlinelibrary.wiley.com | Chunhua Lou |Yuxin Yang |Siyu Jiang
CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest. REFERENCES 1, , , , . Static viscoelasticity of biomass polyethylene composites. Results Phys. 2017; 7: 2568-2574. 2, , , , . Properties of thermoplastic corn starch based green composites reinforced with barley (Hordeum vulgare L.) straw particles obtained by thermal compression. Fibers Polym. 2018; 19(9): 1970-1979. 3, .
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Nov 6, 2024 |
preprints.org | Jing Wang |Maosheng Yang |Yuxin Yang |Tiansong Wang
Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 6 November 2024 / Approved: 6 November 2024 / Online: 7 November 2024 (01:55:29 CET) Wang, J.; Yang, M.; Yang, Y.; Wang, T.; Hai, M.; Zhang, W.; Qin, Y.; Yang, Y.; Dong, Z.; Yan, Y.; Ma, R.; Wan, Q. The Anti-Asthmatic Immune Mechanisms of Mt.b ESAT6-Fc Fusion Protein via Intranasal Immunization in Murine Allergic Asthma. Preprints 2024, 2024110404.
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Oct 30, 2024 |
pubs.rsc.org | Chaojiang Li |Yuxin Yang |Rui Qu |Xun Cao
Recent Advances in Plasma Etching for Micro and Nano Fabrications of Silicon-based Materials: A Review The demands for precision machining of silicon-based materials are growing in various modern applications, including micro-electro-mechanical systems (MEMS), micromotors, sensors, bioelectronics, medical implants, and microfluidic devices.
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Oct 14, 2024 |
mdpi.com | Qixuan Zhang |Yuxin Yang |Xue Li |Pingping Wang
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Oct 8, 2024 |
biorxiv.org | Yuxin Yang |Abby Jerger |Song Feng |Zixu Wang
AbstractRecent years have witnessed the remarkable progress of deep learning within the realm of scientific disciplines, yielding a wealth of promising outcomes. A prominent challenge within this domain has been the task of predicting enzyme function, a complex problem that has seen the development of numerous computational methods, particularly those rooted in deep learning techniques.
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