Zhejiang Gongshang University

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.

Ecological effects of new-type urbanization in China

Abstract: The development of urbanization in China has changed from a traditional form of urbanization that focuses on the rate of growth to a new type of urbanization that stresses improvements in quality. To evaluate this new type of urbanization, this paper constructs a system that looks at the four dimensions of economy, population, society, and environment, and then, using provincial-level panel data, employs a dynamic spatial panel model to empirically test the ecological effects of the new type urbanization. The study finds that the new-type urbanization in China increased gradually from 2003 to 2017, focused on improvements to the ecological environment, and displayed obvious inter-provincial differences. Moreover, China's new-type urbanization has not only effectively reduced pollution emissions and improved energy efficiency but has also been significant in terms of its ecological effect. Moreover, economic urbanization, population urbanization, social urbanization and environmental urbanization exhibit the obvious ecological effects of “pollution reduction and efficiency improvement.” In the process of this new type of urbanization, both the government's “severe constraints” on pollution emissions and the active introduction of foreign capital are further important avenues that lead to achieving “pollution reduction and efficiency improvements.”

Dual Encoding for Zero-Example Video Retrieval

Abstract: This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval.

Direct Discovery of High Utility Itemsets without Candidate Generation

Abstract: Utility mining emerged recently to address the limitation of frequent itemset mining by introducing interestingness measures that reflect both the statistical significance and the user's expectation. Among utility mining problems, utility mining with the itemset share framework is a hard one as no anti-monotone property holds with the interestingness measure. The state-of-the-art works on this problem all employ a two-phase, candidate generation approach, which suffers from the scalability issue due to the huge number of candidates. This paper proposes a high utility itemset growth approach that works in a single phase without generating candidates. Our basic approach is to enumerate itemsets by prefix extensions, to prune search space by utility upper bounding, and to maintain original utility information in the mining process by a novel data structure. Such a data structure enables us to compute a tight bound for powerful pruning and to directly identify high utility itemsets in an efficient and scalable way. We further enhance the efficiency significantly by introducing recursive irrelevant item filtering with sparse data, and a lookahead strategy with dense data. Extensive experiments on sparse and dense, synthetic and real data suggest that our algorithm outperforms the state-of-the-art algorithms over one order of magnitude.

Privacy-Preserving-Outsourced Association Rule Mining on Vertically Partitioned Databases

Abstract: Association rule mining and frequent itemset mining are two popular and widely studied data analysis techniques for a range of applications. In this paper, we focus on privacy-preserving mining on vertically partitioned databases. In such a scenario, data owners wish to learn the association rules or frequent itemsets from a collective data set and disclose as little information about their (sensitive) raw data as possible to other data owners and third parties. To ensure data privacy, we design an efficient homomorphic encryption scheme and a secure comparison scheme. We then propose a cloud-aided frequent itemset mining solution, which is used to build an association rule mining solution. Our solutions are designed for outsourced databases that allow multiple data owners to efficiently share their data securely without compromising on data privacy. Our solutions leak less information about the raw data than most existing solutions. In comparison to the only known solution achieving a similar privacy level as our proposed solutions, the performance of our proposed solutions is three to five orders of magnitude higher. Based on our experiment findings using different parameters and data sets, we demonstrate that the run time in each of our solutions is only one order higher than that in the best non-privacy-preserving data mining algorithms. Since both data and computing work are outsourced to the cloud servers, the resource consumption at the data owner end is very low.

Antioxidant rich grape pomace extract suppresses postprandial hyperglycemia in diabetic mice by specifically inhibiting alpha-glucosidase.

Abstract: Postprandial hyperglycemia is an early defect of type 2 diabetes and one of primary anti-diabetic targets. Treatment of postprandial hyperglycemia can be achieved by inhibiting intestinal α-glucosidase, the key enzyme for oligosaccharide digestion and further glucose absorption. Grape pomace is winemaking byproduct rich in bioactive food compounds such as phenolic antioxidants. This study evaluated the anti-diabetic potential of two specific grape pomace extracts by determining their antioxidant and anti-postprandial hyperglycemic activities in vitro and in vivo. The extracts of red wine grape pomace (Cabernet Franc) and white wine grape pomace (Chardonnay) were prepared in 80% ethanol. An extract of red apple pomace was included as a comparison. The radical scavenging activities and phenolic profiles of the pomace extracts were determined through the measurement of oxygen radical absorbance capacity, DPPH radical scavenging activity, total phenolic content and flavonoids. The inhibitory effects of the pomace extracts on yeast and rat intestinal α-glucosidases were determined. Male 6-week old C57BLKS/6NCr mice were treated with streptozocin to induce diabetes. The diabetic mice were then treated with vehicle or the grape pomace extract to determine whether the oral intake of the extract can suppress postprandial hyperglycemia through the inhibition of intestinal α-glucosidases. The red grape pomace extract contained significantly higher amounts of flavonoids and phenolic compounds and exerted stronger oxygen radical absorbance capacity than the red apple pomace extract. Both the grape pomace extracts but not the apple pomace extract exerted significant inhibition on intestinal α-glucosidases and the inhibition appears to be specific. In the animal study, the oral intake of the grape pomace extract (400 mg/kg body weight) significantly suppressed the postprandial hyperglycemia by 35% in streptozocin-induced diabetic mice following starch challenge. This is the first report that the grape pomace extracts selectively and significantly inhibits intestinal α-glucosidase and suppresses postprandial hyperglycemia in diabetic mice. The antioxidant and anti-postprandial hyperglycemic activities demonstrated on the tested grape pomace extract therefore suggest a potential for utilizing grape pomace-derived bioactive compounds in management of diabetes.