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8,280 results on '"Zhou, Ting"'

53. Compartmentalized ocular lymphatic system mediates eye–brain immunity

Author

Yin, Xiangyun, Zhang, Sophia, Lee, Ju Hyun, Dong, Huiping, Mourgkos, George, Terwilliger, Gordon, Kraus, Aurora, Geraldo, Luiz Henrique, Poulet, Mathilde, Fischer, Suzanne, Zhou, Ting, Mohammed, Farrah Shalima, Zhou, Jiangbing, Wang, Yongfu, Malloy, Seth, Rohner, Nicolas, Sharma, Lokesh, Salinas, Irene, Eichmann, Anne, Thomas, Jean-Leon, Saltzman, W. Mark, Huttner, Anita, Zeiss, Caroline, Ring, Aaron, Iwasaki, Akiko, and Song, Eric

Published
2024
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56. 'Research progress in flame retardancy and smoke suppression of silicone rubber foam'

Author

KANG Fu-ru, TU Jia-yao, ZHAO Heng, ZHOU Ting-bin

Subjects
silicone rubber foam， metal hydro-xide flame retardant，phosphorus-based flame retardant， microcapsule， ceramicization， review，, Organic chemistry, QD241-441, Chemical engineering, TP155-156, Chemicals: Manufacture, use, etc., TP200-248
Abstract

The flame retardancy and smoke suppression performance of silicone rubber foam (SiFs) greatly affected its promotion and application in the field of high performance materials. Improving the flame retardancy and smoke suppression performance of SiFs had became a hot and difficult topic in recent years. The current research status of flame retardancy and smoke suppression of SiFs in flame retardant systems such as metal hydroxide flame retardants, boron- based flame retardants, phosphorus-and nitrogen-based flame retardants, platinum catalysts, microcapsule flame retardants, and ceramic-based flame retardants had been summarized with 67 references. Besides, the corresponding flame retardancy mechanisms had been expounded. Finally, the future research direction of flame retardancy and smoke suppression of SiFs was presented.

Published
2024
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60. Preconditioned USSOR Iterative Method for H-matrix Linear Systems

Author

Zhou, Ting, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, 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, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Zhang, Yonghong, editor, Qi, Lianyong, editor, Liu, Qi, editor, Yin, Guangqiang, editor, and Liu, Xiaodong, editor

Published
2024
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62. Integrating Satellites and Mobile Edge Computing for 6G Wide-Area Edge Intelligence: Minimal Structures and Systematic Thinking

Author

Lin, Yueshan, Feng, Wei, Zhou, Ting, Wang, Yanmin, Chen, Yunfei, Ge, Ning, and Wang, Cheng-Xiang

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

The sixth-generation (6G) network will shift its focus to supporting everything including various machine-type devices (MTDs) in an everyone-centric manner. To ubiquitously cover the MTDs working in rural and disastrous areas, satellite communications become indispensable, while mobile edge computing (MEC) also plays an increasingly crucial role. Their sophisticated integration enables wide-area edge intelligence which promises to facilitate globally-distributed customized services. In this article, we present typical use cases of integrated satellite-MEC networks and discuss the main challenges therein. Inspired by the protein structure and the systematic engineering methodology, we propose three minimal integrating structures, based on which a complex integrated satellite-MEC network can be treated as their extension and combination. We discuss the unique characteristics and key problems of each minimal structure. Accordingly, we establish an on-demand network orchestration framework to enrich the hierarchy of network management, which further leads to a process-oriented network optimization method. On that basis, a case study is utilized to showcase the benefits of on-demand network orchestration and process-oriented network optimization. Finally, we outline potential research issues to envision a more intelligent, more secure, and greener integrated network.

Published
2022

70. UI Layers Merger: Merging UI layers via Visual Learning and Boundary Prior

Author

Chen, Yun-nong, Zhen, Yan-kun, Shi, Chu-ning, Li, Jia-zhi, Chen, Liu-qing, Li, Ze-jian, Sun, Ling-yun, Zhou, Ting-ting, and Chang, Yan-fang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

With the fast-growing GUI development workload in the Internet industry, some work on intelligent methods attempted to generate maintainable front-end code from UI screenshots. It can be more suitable for utilizing UI design drafts that contain UI metadata. However, fragmented layers inevitably appear in the UI design drafts which greatly reduces the quality of code generation. None of the existing GUI automated techniques detects and merges the fragmented layers to improve the accessibility of generated code. In this paper, we propose UI Layers Merger (UILM), a vision-based method, which can automatically detect and merge fragmented layers into UI components. Our UILM contains Merging Area Detector (MAD) and a layers merging algorithm. MAD incorporates the boundary prior knowledge to accurately detect the boundaries of UI components. Then, the layers merging algorithm can search out the associated layers within the components' boundaries and merge them into a whole part. We present a dynamic data augmentation approach to boost the performance of MAD. We also construct a large-scale UI dataset for training the MAD and testing the performance of UILM. The experiment shows that the proposed method outperforms the best baseline regarding merging area detection and achieves a decent accuracy regarding layers merging., Comment: 15 pages, accepted to Frontiers of Information Technology & Electronic Engineering. This is a preprint version, the copyright belongs to the Springer Nature journals

Published
2022

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