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Your search keyword '"Liu, Yichao"' showing total 176 results
Start Over Author "Liu, Yichao" Search Limiters Academic (Peer-Reviewed) Journals Publication Type Academic Journals Publication Type Periodicals
176 results on '"Liu, Yichao"'

39. A Thermal–EM Concentrator for Enhancing EM Signals and Focusing Heat Fluxes Simultaneously.

Author

Chen, Hanchuan, Liu, Yichao, Sun, Fei, Sun, Qianhan, Wu, Xiaoxiao, and Sun, Ran

Subjects
*HEAT flux, *TRANSFORMATION optics, *ANTENNAS (Electronics), *THERMODYNAMICS, *METAMATERIALS
Abstract

The simultaneous concentration of electromagnetic (EM) waves and heat fluxes in the same target region within an on‐chip system has substantial academic research importance and practical application value. However, the existing research is primarily aimed at the design and experimentation of concentrators for individual EM waves or temperature fields. In this study, a thermal‐EM concentrator capable of simultaneously concentrating EM waves and heat fluxes is designed using transformation optics and thermodynamics and fabricated using engineered thermal‐EM metamaterials. Both the numerically simulated and experimentally measured results demonstrate the concentrating capability of the proposed thermal‐EM concentrator, which can concentrate broadband transverse magnetic (TM)‐polarized EM waves ranging from 8 to 12 GHz and heat flux to the same target region within an on‐chip operating environment. The proposed thermal‐EM concentrator can be utilized for the efficient cooling of the specified component and for simultaneously enhancing the radiation and reception efficiency of the EM antenna within an on‐chip system. [ABSTRACT FROM AUTHOR]

Published
2024
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40. An Injectable, Self-Healing, Adhesive Multifunctional Hydrogel Promotes Bacteria-Infected Wound Healing.

Author

Zhang, Ling, Wang, Yan, Yang, Mingrui, Yu, Wen, Zhao, Zheng, and Liu, Yichao

Subjects
PHYTIC acid, SMALL molecules, HEALING, LIPOIC acid, WOUND healing, HYDROGELS, STAPHYLOCOCCUS aureus, COLLAGEN
Abstract

Bacterial infections have a serious impact on public health. It is urgent to develop antibacterial hydrogels with good biocompatibility to reduce the use of antibiotics. In this study, poly(lipoic acid-co-sodium lipoate)–phytic acid (P(LA-SL)-PA) hydrogels are prepared by a simple mixture of the natural small molecules lipoic acid (LA) and phytic acid (PA) in a mild and green reaction environment. The crosslinking network is constructed through the connection of covalent disulfide bonds as well as the hydrogen bonds, which endow the injectable and self-healing properties. The P(LA-SL)-PA hydrogels exhibit an adjustable compression modulus and adhesion. The in vitro agar plates assay indicates that the antibacterial rate of hydrogels against Escherichia coli and Staphylococcus aureus is close to 95%. In the rat-infected wound model, the P(LA-SL)-PA hydrogels adhere closely to the tissue and promote epithelialization and collagen deposition with a significant effect on wound healing. These results prove that the P(LA-SL)-PA hydrogels could act as effective wound dressings for promoting the healing of infected wounds. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Automatic Evaluation Method for Functional Movement Screening Based on Multi-Scale Lightweight 3D Convolution and an Encoder–Decoder.

Author

Lin, Xiuchun, Liu, Yichao, Feng, Chen, Chen, Zhide, Yang, Xu, and Cui, Hui

Subjects
HUMAN mechanics, EVALUATION methodology
Abstract

Functional Movement Screening (FMS) is a test used to evaluate fundamental movement patterns in the human body and identify functional limitations. However, the challenge of carrying out an automated assessment of FMS is that complex human movements are difficult to model accurately and efficiently. To address this challenge, this paper proposes an automatic evaluation method for FMS based on a multi-scale lightweight 3D convolution encoder–decoder (ML3D-ED) architecture. This method adopts a self-built multi-scale lightweight 3D convolution architecture to extract features from videos. The extracted features are then processed using an encoder–decoder architecture and probabilistic integration technique to effectively predict the final score distribution. This architecture, compared with the traditional Two-Stream Inflated 3D ConvNet (I3D) network, offers a better performance and accuracy in capturing advanced human movement features in temporal and spatial dimensions. Specifically, the ML3D-ED backbone network reduces the number of parameters by 59.5% and the computational cost by 77.7% when compared to I3D. Experiments have shown that ML3D-ED achieves an accuracy of 93.33% on public datasets, demonstrating an improvement of approximately 9% over the best existing method. This outcome demonstrates the effectiveness of and advancements made by the ML3D-ED architecture and probabilistic integration technique in extracting advanced human movement features and evaluating functional movements. [ABSTRACT FROM AUTHOR]

Published
2024
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