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Akademický článek

Design of drying machine with co-heating and dehumidification and testing of its drying performance on shiitake mushrooms

Autor: BAO Huangjie, GUO Wenwen, LI Guoneng, SHI Yuxin, ZHANG Mengjie

Publikováno v: Shipin yu jixie, Vol 39, Iss 12, Pp 70-76 (2024)

Objective: In order to improve the mushroom drying efficiency and reduce the power consumption, a new type of co-heating and dehumidification drying machine was designed and built. Methods: The drying machine was a closed system, which took the semic

Externí odkaz: https://doaj.org/article/22cfa093a1c34d489ab9c6ec2ed251e3

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Akademický článek

Advancements and Challenges in Molten Salt Energy Storage for Solar Thermal Power Generation

Autor: Shi Yuxin

Publikováno v: MATEC Web of Conferences, Vol 404, p 01003 (2024)

Solar power, which is one of the most abundant and sustainable energy sources, has attracted a lot of attention for its clean and renewable attributes amid a growing global demand for renewable energy. Molten salt (MS) energy storage technology is an

Externí odkaz: https://doaj.org/article/099384be2f4148d29d0f74d54678e2b1

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Akademický článek

Nominal Lexemes Morphemes Reduplication of Mandarin

Autor: Shi Yuxin

Publikováno v: Okara: Jurnal Bahasa dan Sastra, Vol 13, Iss 2, Pp 203-212 (2019)

Morphologically, functions of Chinese reduplication may place categorically within the derivational domain of lexemes. In fact, whereas derivation typically forms new lexemes and can be category changing, reduplication often conveys values typically

Externí odkaz: https://doaj.org/article/55134654b8ae444b9b9901ec7e128202

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MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging

Autor: Liu, Yuanye, Gao, Zheyao, Shi, Nannan, Wu, Fuping, Shi, Yuxin, Chen, Qingchao, Zhuang, Xiahai

Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-view learning captures more information by analyzing multiple patches sim

Externí odkaz: http://arxiv.org/abs/2405.02918

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Fairness-Aware Job Scheduling for Multi-Job Federated Learning

Autor: Shi, Yuxin, Yu, Han

Federated learning (FL) enables multiple data owners (a.k.a. FL clients) to collaboratively train machine learning models without disclosing sensitive private data. Existing FL research mostly focuses on the monopoly scenario in which a single FL ser

Externí odkaz: http://arxiv.org/abs/2401.02740

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Fairness-Aware Client Selection for Federated Learning

Autor: Shi, Yuxin, Liu, Zelei, Shi, Zhuan, Yu, Han

Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients) to train machine learning models collaboratively without revealing private data. Since the FL server can only engage a limited number of clients in each training round, FL c

Externí odkaz: http://arxiv.org/abs/2307.10738

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A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging

Autor: Gao, Zheyao, Liu, Yuanye, Wu, Fuping, Shi, NanNan, Shi, Yuxin, Zhuang, Xiahai

Staging of liver fibrosis is important in the diagnosis and treatment planning of patients suffering from liver diseases. Current deep learning-based methods using abdominal magnetic resonance imaging (MRI) usually take a sub-region of the liver as a

Externí odkaz: http://arxiv.org/abs/2306.12054

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