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1,939 results on '"Liu, Yuying"'

4. Fault Leakage Risk Identification Method Based on K-Means++ and Neural Network

Author

Zhang, Penghe, Xue, Yang, Song, Runan, Sheng, Dejie, Liu, Yuying, Chen, Ruodan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, and Li, Jian, editor

Published
2025
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14. A novel peptide PDHK1-241aa encoded by circPDHK1 promotes ccRCC progression via interacting with PPP1CA to inhibit AKT dephosphorylation and activate the AKT-mTOR signaling pathway

Author

Huang, Bo, Ren, Junwu, Ma, Qiang, Yang, Feifei, Pan, Xiaojuan, Zhang, Yuying, Liu, Yuying, Wang, Cong, Zhang, Dawei, Wei, Ling, Ran, Lingyu, Zhao, Hongwen, Liang, Ce, Wang, Xiaolin, Wang, Shiming, Li, Haiping, Ning, Hao, Ran, Ai, Li, Wei, Wang, Yongquan, and Xiao, Bin

Published
2024
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15. Cerebral haemodynamics in symptomatic intracranial atherosclerotic disease: a narrative review of the assessment methods and clinical implications.

Author

Liu, Yuying, Li, Shuang, Tian, Xuan, Leung, Thomas, Liu, Liping, Liebeskind, David, and Leng, Xinyi

Subjects
atherosclerosis, perfusion, perfusion imaging, stroke, Humans, Stroke, Ischemic Attack, Transient, Brain Ischemia, Constriction, Pathologic, Risk Factors, Hemodynamics, Plaque, Atherosclerotic, Ischemic Stroke, Intracranial Arteriosclerosis
Abstract

Intracranial atherosclerotic disease (ICAD) is a common cause of ischaemic stroke and transient ischaemic attack (TIA) with a high recurrence rate. It is often referred to as intracranial atherosclerotic stenosis (ICAS), when the plaque has caused significant narrowing of the vessel lumen. The lesion is usually considered symptomatic ICAD/ICAS (sICAD/sICAS) when it has caused an ischaemic stroke or TIA. The severity of luminal stenosis has long been established as a prognostic factor for stroke relapse in sICAS. Yet, accumulating studies have also reported the important roles of plaque vulnerability, cerebral haemodynamics, collateral circulation, cerebral autoregulation and other factors in altering the stroke risks across patients with sICAS. In this review article, we focus on cerebral haemodynamics in sICAS. We reviewed imaging modalities/methods in assessing cerebral haemodynamics, the haemodynamic metrics provided by these methods and application of these methods in research and clinical practice. More importantly, we reviewed the significance of these haemodynamic features in governing the risk of stroke recurrence in sICAS. We also discussed other clinical implications of these haemodynamic features in sICAS, such as the associations with collateral recruitment and evolution of the lesion under medical treatment, and indications for more individualised blood pressure management for secondary stroke prevention. We then put forward some knowledge gaps and future directions on these topics.

Published
2023

20. Physics-Informed Koopman Network

Author

Liu, Yuying, Sholokhov, Aleksei, Mansour, Hassan, and Nabi, Saleh

Subjects
Computer Science - Machine Learning, Mathematics - Analysis of PDEs, Mathematics - Dynamical Systems, Mathematics - Operator Algebras
Abstract

Koopman operator theory is receiving increased attention due to its promise to linearize nonlinear dynamics. Neural networks that are developed to represent Koopman operators have shown great success thanks to their ability to approximate arbitrarily complex functions. However, despite their great potential, they typically require large training data-sets either from measurements of a real system or from high-fidelity simulations. In this work, we propose a novel architecture inspired by physics-informed neural networks, which leverage automatic differentiation to impose the underlying physical laws via soft penalty constraints during model training. We demonstrate that it not only reduces the need of large training data-sets, but also maintains high effectiveness in approximating Koopman eigenfunctions., Comment: 26 pages, 5 figures

Published
2022

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