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Institution

Zhejiang Gongshang University

EducationHangzhou, China
About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Adsorption & Supply chain. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.


Papers
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Journal ArticleDOI
TL;DR: The results illustrated the amorphous characteristic of cellulose was removed after acid hydrolysis and ultrasonic treatment, suggesting that electrostatic interactions played an important role in maintaining the stability and dispersibility of the nanocellulose particles.

86 citations

Proceedings ArticleDOI
15 Jun 2019
TL;DR: A hierarchical recurrent network structure is developed to simultaneously encodes local contexts of individual frames and global contexts of the sequence, which achieves more natural and accurate predictions over state-of-the-art methods.
Abstract: Anticipating the future motions of 3D articulate objects is challenging due to its non-linear and highly stochastic nature. Current approaches typically represent the skeleton of an articulate object as a set of 3D joints, which unfortunately ignores the relationship between joints, and fails to encode fine-grained anatomical constraints. Moreover, conventional recurrent neural networks, such as LSTM and GRU, are employed to model motion contexts, which inherently have difficulties in capturing long-term dependencies. To address these problems, we propose to explicitly encode anatomical constraints by modeling their skeletons with a Lie algebra representation. Importantly, a hierarchical recurrent network structure is developed to simultaneously encodes local contexts of individual frames and global contexts of the sequence. We proceed to explore the applications of our approach to several distinct quantities including human, fish, and mouse. Extensive experiments show that our approach achieves more natural and accurate predictions over state-of-the-art methods.

86 citations

Journal ArticleDOI
TL;DR: The homomorphic encryption and ID-based signature are employed to design a dynamic membership data aggregation (DMDA) scheme, which reduces the complexity on a new user's joining and an old user's quitting and is more suitable for next-generation smart grid and other Internet of Things environments.
Abstract: In order to protect the privacy of individual data, meantime guaranteeing the utility of big data, the privacy preserving data aggregation is widely researched, which is a feasible solution since it not only preserves the statistical feature of the original data, but also masks single user's data. With smart meter owning the capability of connecting to Internet, the aggregation area extends to the virtual area rather than a traditional physical area. However, in a virtual aggregation area, the users’ membership maybe frequently changes, if while executing the aggregation protocol for the traditional area, the overhead is not ignorable. In this paper, the homomorphic encryption and ID-based signature are employed to design a dynamic membership data aggregation (DMDA) scheme, which reduces the complexity on a new user's joining and an old user's quitting. In addition, the operation center obtains the sum of the data in the virtual aggregation area, meantime knows nothing about single user's data. Comparing with traditional privacy-preserving data aggregation scheme, DMDA is more suitable for next-generation smart grid and other Internet of Things environments.

86 citations

Journal ArticleDOI
TL;DR: Three substrates (glucose, peptone, and glycerol, representing carbohydrates, proteins, and lipids, respectively) were acidogenically fermented in this study, indicating that synergistic effects between microorganisms improved acidogenic fermentation.

85 citations

Journal ArticleDOI
TL;DR: This review provides the most updated information on dAGEs including their generation in processed foods, analytical and characterization techniques, metabolic fates, interaction with AGE receptors, implications on human health and reducing strategies, and roles played by concomitant compounds in the heat-processed foods.
Abstract: Dietary advanced glycation end products (dAGEs) are complex and heterogeneous compounds derived from nonenzymatic glycation reactions during industrial processing and home cooking. There is mounting evidence showing that dAGEs are closely associated with various chronic diseases, where the absorbed dAGEs fuel the biological AGEs pool to exhibit noxious effects on human health. Currently, due to the uncertain bioavailability and rapid renal clearance of dAGEs, the relationship between dAGEs and biological AGEs remains debatable. In this review, we provide the most updated information on dAGEs including their generation in processed foods, analytical and characterization techniques, metabolic fates, interaction with AGE receptors, implications on human health and reducing strategies. Available evidence demonstrating a relevance between dAGEs and food allergy is also included. AGEs are ubiquitous in foods and their contents largely depend on the reactivity of carbonyl and amino groups, along with surrounding condition mainly pH and heating procedures. Once being digested and absorbed into the circulation, two separate pathways can be involved in the deleterious effects of dAGEs: an AGE receptor-dependent way to stimulate cell signals, and an AGE receptor-independent way to dysregulate proteins via forming complexes. Inhibition of AGEs formation during food processing and reduction in the diet are two potent approaches to restrict health-hazardous dAGEs. To elucidate the biological role of dAGEs toward human health, the following significant perspectives are raised: molecular size and complexity of dAGEs; interactions between unabsorbed dAGEs and gut microbiota; and roles played by concomitant compounds in the heat-processed foods.

85 citations


Authors

Showing all 8318 results

NameH-indexPapersCitations
David Julian McClements131113771123
Sajal K. Das85112429785
Ye Wang8546624052
Xun Wang8460632187
Tao Jiang8294027018
Yueming Jiang7945220563
Mo Wang6127413664
Robert J. Linhardt58119053368
Jiankun Hu5749311430
Xuming Zhang5638410788
Yuan Li503528771
Chunping Yang491738604
Duo Li483299060
Matthew Campbell4823613448
Aiqian Ye481636120
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202325
2022153
2021937
2020770
2019627