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An Access Control Mechanism based on Permission Delegation in P2P Network
ZHANG Changyou,LIU Renfen,CAO Yuanda,LI Yanhua,CUI Liang 보안공학연구지원센터 2008 International Journal of Security and Its Applicat Vol.2 No.2
P2P(Peer-to-Peer) is a popular model in distributed computing. We present an access control mechanism based on permission delegation in this paper. This mechanism consists of three protocols, i.e. agency discovering protocol, permission delegating protocol and resource access protocol. Firstly, the task initiator decomposes the task into subtasks and chooses other peers in high trust degree with satisfied abilities to accomplish these subtasks. We call these neighbors as task agents. Then task initiator temporarily transfers some necessary permission to subtask agents by means of credit certificate and delegation certificate. Finally, the subtask agents consume resources of resource peers followed access protocol. These protocols are analyzed in Colored Petri-Net, and simulated with CPN Tools.
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Regulation Mechanism of Long Noncoding RNAs in Colon Cancer Development and Progression
Jiaming Zhu,Jingjing Liu,Xiaohuan Tang,Xiaofang Qiao,Chao Chen,Yuanda Liu 연세대학교의과대학 2019 Yonsei medical journal Vol.60 No.4
Colorectal cancer (CRC) is the second most common cause of cancer-related death worldwide, and its high rates of relapse andmetastasis are associated with a poor prognosis. Despite extensive research, the underlying regulatory mechanisms of CRC remainunclear. Long noncoding RNAs (lncRNAs) are a major type of noncoding RNAs that have received increasing attention inthe past few years, and studies have shown that they play a role in many biological processes in CRC. Here, we summarize recentstudies on lncRNAs associated with CRC and the signaling pathways and mechanisms underlying this association. We show thatdysregulated lncRNAs may be new prognostic and diagnostic biomarkers or therapeutic targets for clinical application. This reviewcontributes not only to our understanding of CRC, but also suggests novel signaling pathways associated with lncRNAs thatcan be targeted to block or eradicate CRC.
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Extracting Attributes of Named Entity from Unstructured Text with Deep Belief Network
Bei Zhong,Jin Liu,Yuanda Du,Yunlu Liaozheng,Jiachen Pu 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.5
Entity attribute extraction is a challenging research topic with broad application prospects. Many researchers had proposed rule based or statistic based approaches to deal with the extraction task in a variety of application areas. Recently, deep learning had shown its capacity to model high-level abstractions in data by using multiple processing layers network with complex structures. However there has no research reported to conduct entity attribute extraction with deep learning method. In this paper, we propose a new approach to extract the entities’ attributes from unstructured text corpus that was gathered from Web. The proposed method is an unsupervised machine learning method that extracts the entity attributes utilizing deep belief network (DBN). Experiment results show that, with our method, entity attributes can be extracted accurately and manual intervention can be reduced when compared with tradition methods.
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Jiao Wu,Shan Zhao,Xin Chen,Yuanda Jiu,Junfeng Liu,Jinglin Gao,Shijie Wang 한국식품과학회 2023 Food Science and Biotechnology Vol.32 No.13
Honey quality is in relation to botanical origin, and physicochemical properties, elemental composition, and antioxidant activity have been used for assessment and identification of honeys. The goal of this study is to contribute to the general analysis of five unifloral honeys from Cocos nucifera L., Dalbergia benthami Prain, Bombax ceiba L., Castanea mollissima Bl., and mangrove in Hainan province, China. Our results revealed that B. ceiba honey had the highest pH (4.27), color (139.33 mm Pfund), ash content (1.03 g/100 g), and electrical conductivity (1312.00 μS/cm) in five unifloral honeys. Furthermore, B. ceiba honey also contained the highest levels of total phenolic content (75.54 mg GAE/100 g) and total flavonoid content (29.22 mg RE/100 g), as well as the strongest antioxidant activity (DPPH IC50 value, 3.97 mg/mL; FRAP value, 6527.43 µmol TE/kg). Moreover, we revealed a considerable variation in element contents in honeys using ICP-MS, with potassium being the most predominant element. B. ceiba honey had the highest contents of K, Ca, Mg, and P, whereas the highest amount of Na was found in mangrove honey. Overall, our data indicated that B. ceiba honey deserves further research as a potential antioxidant agent.
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