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.

The Impact of the COVID-19 Pandemic on User Experience with Online Education Platforms in China

Abstract: During the COVID-19 pandemic, social education has shifted from face to face to online in order to avoid large gatherings and crowds for blocking the transmission of the virus. To analyze the impact of virus on user experience and deeply retrieve users’ requirements, this paper constructs a reasonable evaluation index system through obtaining user reviews about seven major online education platforms before and after the outbreak of COVID-19, and by combining the emotional analysis, hot mining technology, as well as relevant literature. At the same time, the variation coefficient method is chosen to weigh each index based on the difference of index values. Furthermore, this paper adopts the comprehensive evaluation method to analyze user experience before and after the outbreak of COVID-19, and finally finds out the change of users’ concerns regarding the online education platform. In terms of access speed, reliability, timely transmission technology of video information, course management, communication and interaction, and learning and technical support, this paper explores the supporting abilities and response levels of online education platforms during COVID-19, and puts forward corresponding measures to improve how these platforms function.

Learning Intents behind Interactions with Knowledge Graph for Recommendation

Abstract: Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained level of intents, and (2) exploit relation dependencies to preserve the semantics of long-range connectivity. In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN). Technically, we model each intent as an attentive combination of KG relations, encouraging the independence of different intents for better model capability and interpretability. Furthermore, we devise a new information aggregation scheme for GNN, which recursively integrates the relation sequences of long-range connectivity (i.e., relational paths). This scheme allows us to distill useful information about user intents and encode them into the representations of users and items. Experimental results on three benchmark datasets show that, KGIN achieves significant improvements over the state-of-the-art methods like KGAT [41], KGNN-LS [38], and CKAN [47]. Further analyses show that KGIN offers interpretable explanations for predictions by identifying influential intents and relational paths. The implementations are available at https://github.com/huangtinglin/Knowledge_Graph_based_Intent_Network.

Antioxidant properties and bioactive components of Norton (Vitis aestivalis) and Cabernet Franc (Vitis vinifera) wine grapes

Abstract: Three wine grapes, Norton ( Vitis aestivalis ), Cabernet Franc clone1, and Cabernet Franc clone313 ( Vitis vinifera ), collected from a Virginia vineyard were evaluated and compared for their antioxidant properties and phenolic profile. All grape extracts exerted remarkable antioxidant activities. Their oxygen radical absorbance capacity (ORAC) values were not significantly different from one another, ranging from 22.9 to 26.7 μmol TE/g of fresh weight. The Cabernet Franc clone1 had the strongest 1,1-diphenyl-2-picrylhydrazyl (DPPH ) radicals scavenging activity (8.8 μmol TE/g) compared to the Norton or Cabernet Franc clone313 grape extracts (7.9 μmol TE/g and 5.4 μmol TE/g, respectively). The Norton grape contained significantly higher total phenolic, anthocyanin, and flavonoid content than the Cabernet Franc grapes ( p

A review of advances in urban flood risk analysis over China

Abstract: China’s urban environments are particularly vulnerable to flooding due to climate change and rapid urbanization. Study of the urban flood risk analysis has significantly increased over the past decade, and this paper therefore reviews the main results (i.e. theoretical basis, methods, techniques, case studies) obtained in the literature from China. We focus on the following topics: (1) urban flood hazard analysis, (2) exposure and vulnerability analysis, and (3) urban flood risk assessment. Recent advances made in the research area are presented with suggestions for further research to improve the availability and reliability of urban flood risk analysis.