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.

Litchi flavonoids: isolation, identification and biological activity.

Abstract: The current status of the isolation, identification, biological activity, utilization and development prospects of flavonoids found in litchi fruit pericarp (LFP) tissues is reviewed. LFP tissues account for approximately 15% by weight of the whole fresh fruit and are comprised of significant amount of flavonoids. The major flavonoids in ripe LFP include flavonols and anthocyanins. The major flavanols in the LFP are reported to be procyanidin B4, procyanidin B2 and epicatechin, while cyanindin-3-rutinside, cyanidin-3-glucoside, quercetin-3-rutinosde and quercetin-3-glucoside are identified as the important anthocyanins. Litchi flavanols and anthocyanins exhibit good potential antioxidant activity. The hydroxyl radical and superoxide anion scavenging activities of procyanidin B2 are greater than those of procyanidin B4 and epicatechin, while epicatechin has the highest alpha,alpha-diphenyl-beta-picrylhydrazyl radical (DPPH*) scavenging activity. In addition to the antioxidant activity, LFP extract displays a dose- and time-dependent inhibitory effect on human breast cancer, which could be attributed, in part, to its inhibition of proliferation and induction of apoptosis in cancer cells through upregulation and down-regulation of multiple genes. Furthermore, various anticancer activities are observed for epicatechin, procyanidin B2, procyanidin B4 and the ethyl acetate fraction of LFP tissue extracts. Procyanidin B4 and the ethyl acetate fraction show a stronger inhibitory effect on HELF than MCF-7 proliferation, while epicatechin and procyanidin B2 have lower cytotoxicities towards MCF-7 and HELF than paclitaxel. It is therefore suggested that flavonoids from LFP might be potentially useful components for functional foods and/or anti-breast cancer drugs.

A novel recommendation method based on social network using matrix factorization technique

Abstract: The rapid development of information technology and the fast growth of Internet have facilitated an explosion of information which has accentuated the information overload problem. Recommender systems have emerged in response to this problem and helped users to find their interesting contents. With increasingly complicated social context, how to fulfill personalized needs better has become a new trend in personalized recommendation service studies. In order to alleviate the sparsity problem of recommender systems meanwhile increase their accuracy and diversity in complex contexts, we propose a novel recommendation method based on social network using matrix factorization technique. In this method, we cluster users and consider a variety of complex factors. The simulation results on two benchmark data sets and a real data set show that our method achieves superior performance to existing methods.

Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis.

Abstract: Fault detection and diagnosis (FDD) plays an essential role of maintaining large-scale heating ventilation air conditioning (HVAC) systems for industrial usage. The process of selecting important features is a crucial step for FDD methods of HVAC components, such as chillers. A suitable feature selection method selects the minimum number of features to save the number of installing sensors, and simultaneously maximizes the FDD accuracy. According to the surveyed related works, it is found that most existing works only focus on maximizing the classification accuracy, and miss two important points. First, the misclassification costs of false positive and false negative are different for chiller FDD. Second, the selected feature subsets must be sequential for real-world applications. In this study, a cost-sensitive and sequential feature selection algorithm for chiller FDD is proposed to select the most important features using a back-tracing sequential forward feature selection (BT-SFS) algorithm. The ASHRAE dataset collected by project number 1043-RP is utilized. Support vector machine (SVM), which is proved to be one of the most effective FDD classification method for chillers in existing works, is employed for accuracy measurement. This work fills in the gap between theoretical HVAC FDD methods and real-world HVAC FDD applications.