Institution
Zhejiang Gongshang University
Education•Hangzhou, China•
About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.
Topics: Computer science, Chemistry, Adsorption, Catalysis, China
Papers published on a yearly basis
Papers
More filters
••
TL;DR: The concept of three-way decisions is introduced to provide a new interpretation of the three regions of rough set theory to construct acceptance, non-commitment, and rejection rules, respectively, from the positive, boundary, and negative regions.
73 citations
••
TL;DR: For weighted progressive iteration approximations, it is proved that the normalized B-basis of a space provides the fastest convergence rate among all normalized totally positive bases of the space.
73 citations
••
TL;DR: A portable and quantitative immunochromatographic assay (ICA) with a personal glucose meter (PGM) as readout for the detection of Escherichia coli O157:H7 as a model analyte can be easily developed to be a universal analysis system and applied to detection of a wide variety of foodborne pathogens and protein biomarkers.
73 citations
••
TL;DR: This paper proposes a privacy-preserving and multifunctional health data aggregation (PPM-HDA) mechanism with fault tolerance for cloud-assisted WBANs, and proposes a multifunctionAL health data additive aggregation scheme (MHDA+) to support additive aggregate functions, such as average and variance.
Abstract: Wireless body area networks (WBANs), as a promising health-care system, can provide tremendous benefits for timely and continuous patient care and remote health monitoring. Owing to the restriction of communication, computation and power in WBANs, cloud-assisted WBANs, which offer more reliable, intelligent, and timely health-care services for mobile users and patients, are receiving increasing attention. However, how to aggregate the health data multifunctionally and efficiently is still an open issue to the cloud server (CS). In this paper, we propose a privacy-preserving and multifunctional health data aggregation (PPM-HDA) mechanism with fault tolerance for cloud-assisted WBANs. With PPM-HDA, the CS can compute multiple statistical functions of users’ health data in a privacy-preserving way to offer various services. In particular, we first propose a multifunctional health data additive aggregation scheme (MHDA+) to support additive aggregate functions, such as average and variance. Then, we put forward MHDA $^{\oplus }$ as an extension of MHDA+ to support nonadditive aggregations, such as min/max, median, percentile, and histogram. The PPM-HDA can resist differential attacks, which most existing data aggregation schemes suffer from. The security analysis shows that the PPM-HDA can protect users’ privacy against many threats. Performance evaluations illustrate that the computational overhead of MHDA+ is significantly reduced with the assistance of CSs. Our MHDA $^{\oplus }$ scheme is more efficient than previously reported min/max aggregation schemes in terms of communication overhead when the applications require large plaintext space and highly accurate data.
73 citations
••
TL;DR: This review provided the most updated knowledge on dietary protein-phenolic interactions related with food science and human nutrition, including their mechanisms of complexation, analytical technologies, and alterations in the functionality and nutraceutical properties of both reacting partners.
Abstract: Dietary proteins and phenolic compounds are commonly co-existing components that readily interact with each other to yield complexes in a wide range of food systems The formed complexes play a cri
73 citations
Authors
Showing all 8318 results
Name | H-index | Papers | Citations |
---|---|---|---|
David Julian McClements | 131 | 1137 | 71123 |
Sajal K. Das | 85 | 1124 | 29785 |
Ye Wang | 85 | 466 | 24052 |
Xun Wang | 84 | 606 | 32187 |
Tao Jiang | 82 | 940 | 27018 |
Yueming Jiang | 79 | 452 | 20563 |
Mo Wang | 61 | 274 | 13664 |
Robert J. Linhardt | 58 | 1190 | 53368 |
Jiankun Hu | 57 | 493 | 11430 |
Xuming Zhang | 56 | 384 | 10788 |
Yuan Li | 50 | 352 | 8771 |
Chunping Yang | 49 | 173 | 8604 |
Duo Li | 48 | 329 | 9060 |
Matthew Campbell | 48 | 236 | 13448 |
Aiqian Ye | 48 | 163 | 6120 |