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11篇 您的检索式:作者名="ChaoGe"
    题名 作者 年代 出处 被引量
1Cancer stem/progenitor cells are highly enriched in CD133<sup>+</sup>CD44<sup>+</sup> population in hepatocellular carcinoma显示文摘ZhengZhu XiangfangHao MingxiaYan MingYao ChaoGe JianrenGu JinjunLi 2010Int J Cancer2010,,9:3
2Detrital Zircon U-Pb Geochronology: New Insight into the Provenance of Sanya Formation in the Yinggehai Basin显示文摘Objective The NNW-SSE trending Yinggehai Basin,located on the continental shelf at water depths of 50-200 m in the northwestern South China Sea,is a Cenozoic conversion extensional basin.Over the past decades,a number of hydrocarbon reservoirs have been discovered in the deepwater area of the basin,including the Lingtou Formation(Eocene),Yacheng and LingshuiWANG Ce LIANG Xinquan FU Jiangang JIANG Ying DONG Chaoge 2016Acta Geologica Sinica(English Edition)2016,90,1:2
3Cancer stem/progenitor cells are highly enriched in CD133+CD44+ population in hepatocellular carcinoma显示文摘ZhengZhu XiangfangHao MingxiaYan MingYao ChaoGe JianrenGu JinjunLi 2010Int. J. Cancer2010,,:2
4Detrital Zircon Provenance of Pliocene Yinggehai Formation in the Ledong Gas Field of the Yinggehai-Song Hong Basin显示文摘1 Introduction Over the past decades,a number of hydrocarbon reservoirs have been discovered in the deepwater area of the Yinggehai-Song Hong Basin,South China Sea.The NW-oriented Yinggehai-Song Hong Basin,located on the continental shelf at water depths of 50-200 m in the northwestern South China Sea,is one of theWANG Ce LIANG Xinquan FU Jiangang ZHOU Yun JIANG Ying DONG Chaoge 2015Acta Geologica Sinica(English Edition)2015,89,A01:2
5Acutle infla natory reaction following experimental intacerebral morrhage in rats显示文摘ChaoG Jutian TH Richard FK 2000Ber2000,871,1:1
6Organizational Socialization:Its Content and Consequences显示文摘ChaoG T OLeary-kelly A M Wol? S Klein H J Gardner P D 1994Journal of Applied Psychology1994,79,5:1
7Foreskin development before adolescence in 2149 schoolboys显示文摘Teng-FH Chaog HC Shih SC 0,,07:1
8Research advance in sediment provenance of the Qiongdongnan Basin显示文摘1 Introduction Sediment provenance study,as an important part of basin analysis,is a key for source area definition,paleogeographic reconstruction,sediment transport route identification,and tectonic evolution(Haughton et al.,1991;Morton et al.,1999;Fontanelli et al.,2009;Cawood et al.,2012).The Qiongdongnan Basin(QDNB),lies in the northwestern passive continental margin of theWANG Ce LIANG Xinquan LIANG Xirong DONG Chaoge XIE Hao 2017Acta Geologica Sinica(English Edition)2017,91,S1:1
9Cancer stem/progenitor cells are highly enriched in CD133+CD44+ population in hepatocellular carcinoma显示文摘ZhengZhu XiangfangHao MingxiaYan MingYao ChaoGe JianrenGu JinjunLi 2010Cancer2010,,9:1
10NFT Security Matrix:Towards Modeling NFT Ecosystem Threat显示文摘Digital assets have boomed over the past few years with the emergence of Non-fungible Tokens(NFTs).To be specific,the total trading volume of digital assets reached an astounding$55.5 billion in 2022.Nevertheless,numerous security concerns have been raised by the rapid expansion of the NFT ecosystem.NFT holders are exposed to a plethora of scams and traps,putting their digital assets at risk of being lost.However,academic research on NFT security is scarce,and the security issues have aroused rare attention.In this study,the NFT ecological process is comprehensively explored.This process falls into five different stages encompassing the entire lifecycle of NFTs.Subsequently,the security issues regarding the respective stage are elaborated and analyzed in depth.A matrix model is proposed as a novel contribution to the categorization of NFT security issues.Diverse data are collected from social networks,the Ethereum blockchain,and NFT markets to substantiate our claims regarding the severity of security concerns in the NFT ecosystem.From this comprehensive dataset,nine key NFT security issues are identified from the matrix model and then subjected to qualitative and quantitative analysis.This study aims to shed light on the severity of NFT ecosystem security issues.The findings stress the need for increased attention and proactive measures to safeguard the NFT ecosystem.Peng Liao Chaoge Liu Jie Yin Zhi Wang Xiang Cui 2024Computer Modeling in Engineering & Sciences2024,139,6:0
11A lightweight DDoS detection scheme under SDN context显示文摘Software-defined networking(SDN),a novel network paradigm,separates the control plane and data plane into dif-ferent network equipment to realize the flexible control of network traffic.Its excellent programmability and global view present many new opportunities.DDoS detection under the SDN context is an important and challenging research field.Some previous works attempted to collect and analyze statistics related to flows,usually recorded in switches,to address DDoS threats.In contrast,other works applied machine learning-based solutions to identify DDos and achieved promising results.Generally,most previous works need to periodically request flow rules or packets to obtain flow statistics or features to detect stealthy exceptions.Nevertheless,the request for flow rules is very time-consuming and CPU-consuming;moreover may congest the communication channel between the controller and the switches.Therefore,we present FORT,a lightweight DDoS detection scheme,which spreads the rule-based detection algorithm at edge switches and determines whether to start it by periodically retrieving the ports state.A time-series algorithm,ARIMA,is utilized to determine the port statistics adaptively,and an SVM algorithm is applied to detect whether a DDoS attack does occur.Representative experiments demonstrate that FORT can significantly reduce the controller load and provide a reliable detection accuracy.Referring to the false alarm rate of 1.24%in the comparison scheme,the false alarm rate of this scheme is only 0.039%,which significantly reduces the probability of false alarm.Besides,by introducing the alarm mechanism,this scheme can reduce the load of the southbound chan-nel by more than 60%in the normal state.Kun Jia Chaoge Liu Qixu Liu Junnan Wang Jiazhi Liu Feng Liu 2023Cybersecurity2023,6,1:0
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