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| 1 | Stable isotopic analysis on sus bones from the Wanfabozi site, Tonghua, Jilin显示文摘In order to study the sus diets and explore the possibility to distinguish domestic pig from wild boar through dietary analysis, stable carbon and nitrogen isotopes of 28 pig bones from archaeological site of Wanfabozi in Tonghua City, Jilin Province were analyzed. The δ 13C and δ 15N values of uncontaminated bones show that the overall pigs in the site were generally herbivores and ate mainly C3 plants. Significant difference of δ15N values was observed between wild boar and domestic pigs, which may result from the higher consumption of animal protein in domestic pigs other than from that in wild boar, possibly from human leftover or waste,. The dietary difference between wild boar and domestic pigs has great potential to differentiate wild boar and domestic pigs in the early stage of pig domestication. | GUAN Li1, HU YaoWu2, TANG zhuoWei3, YANG YiMin2, DONG Yu1, CUI YaPing4 & WANG ChangSui2 1 Department of History of Science & Technology and Archaeometry University of Science and Technology of China, Hefei 230026, China 2 Department of Scientific History and Archaeometry School of the Humanities, Graduate University of Chinese Academy of Sci- ences, Beijing 100049, China 3 Laboratory of Environment Archaeology, Research Center for Chinese Frontier Archaeology Jilin University Changchun 130012, China 4 Museum of the West Han Dynasty Mausoleum of the Nanyue King, Guangzhou 510040, China | 2007 | Chinese Science Bulletin2007，52，24： | 3 |
| 2 | China's Economic Growth and Structural Transition since 1978显示文摘 | Zhang Ping NanYu | 2018 | China Economist2018，13，1： | 2 |
| 3 | Combustion reaction mechanism of four typical Chinese biomass by TG and DTG显示文摘 | Pei‐ShengLi QinWang QiaoXu WanYu Ya‐NanYue ZheLiang Xing‐ChenDong SongHu | 2012 | Asia‐Pac J Chem Eng2012，，： | 1 |
| 4 | Inhibition of 12- O -tetradecanoylphorbol-13-acetate-induced NF-κB activation by tea polyphenols, (–)-epigallocatechin gallate and theaflavins显示文摘 | Masaaki Nomura Wei-ya Ma Nanyue Chen Ann M. Bode | 2000 | Carcinogenesis2000，，： | 1 |
| 5 | LC Analysis of Lignans from Schisandra sphenanthera Rehd. et Wils.显示文摘 | Wei Gu Nanyu Wei Zhezhi Wang | 2008 | Chromatographia (-)2008，，11： | 1 |
| 6 | Five year study of cardiovascular risk factors in Japanese people: implications concerning new onset of metabolic syndrome显示文摘 | Suzuki A Kosuge K Nanyu O | 2010 | Intern Med2010，49，1： | 1 |
| 7 | Deficiency of c - Jun - NH(2)- terminal kinase - 1 in mice enhances skin tumor develop- ment by 12-0- tetradecanoylphorbol -13- acetate显示文摘 | Qing - Bai S Nanyue C Bode AM | 2002 | Cancer Res2002，62，5： | 1 |
| 8 | Five year study of cardiovas- cular risk factors in Japanese People: implicaflons concerning new onset of metabolic syndrome显示文摘 | SuzukiA KosugeK Nanyu O etal | 2010 | Intern Med2010，49，1： | 1 |
| 9 | Euler- Maclaurin summation formula and Its Application显示文摘 | Zhang Nanyue | 1985 | Math In Practice and Theory1985，1，： | 1 |
| 10 | PosFuzz:augmenting greybox fuzzing with effective position distribution显示文摘Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and effectiveness.Mutation-based greybox fuzzing generates input cases by mutating the input seed,i.e.,applying a sequence of mutation operators to randomly selected mutation positions of the seed.However,existing fruitful research work focuses on scheduling mutation operators,leaving the schedule of mutation positions as an overlooked aspect of fuzzing efficiency.This paper proposes a novel greybox fuzzing method,PosFuzz,that statistically schedules mutation positions based on their historical performance.PosFuzz makes use of a concept of effective position distribution to represent the semantics of the input and to guide the mutations.PosFuzz first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation operator.It then leverages two sampling methods in different mutating stages to select the positions from the distribution.We have implemented PosFuzz on top of AFL,AFLFast and MOPT,called Pos-AFL,-AFLFast and-MOPT respectively,and evaluated them on the UNIFUZZ benchmark(20 widely used open source programs)and LAVA-M dataset.The result shows that,under the same testing time budget,the Pos-AFL,-AFLFast and-MOPT outperform their counterparts in code coverage and vulnerability discovery ability.Compared with AFL,AFLFast,and MOPT,PosFuzz gets 21%more edge coverage and finds 133%more paths on average.It also triggers 275%more unique bugs on average. | Yanyan Zou Wei Zou JiaCheng Zhao Nanyu Zhong Yu Zhang Ji Shi Wei Huo | 2023 | Cybersecurity2023，6，4： | 0 |
| 11 | NDFuzz:a non-intrusive coverage-guided fuzzing framework for virtualized network devices显示文摘Network function virtualization provides programmable in-network middlewares by leveraging virtualization tech-nologies and commodity hardware and has gained popularity among all mainstream network device manufacturers.Yet it is challenging to apply coverage-guided fuzzing,one of the state-of-the-art vulnerability discovery approaches,to those virtualized network devices,due to inevitable integrity protection adopted by those devices.In this paper,we propose a coverage-guided fuzzing framework NDFuzz for virtualized network devices with a novel integrity protec-tion bypassing method,which is able to distinguish processes of virtualized network devices from hypervisors with a carefully designed non-intrusive page global directory inference technique.We implement NDFuzz atop of two black-box fuzzers and evaluate NDFuzz with three representative network protocols,SNMP,DHCP and NTP,on nine popular virtualized network devices.NDFuzz obtains an average 36%coverage improvement in comparison with its black-box counterparts.NDFuzz discovers 2 O-Day vulnerabilities and 11-Day vulnerability with coverage guidance while the black-box fuzzer can find only one of them.All discovered vulnerabilities are confirmed by corresponding vendors. | Yu Zhang Nanyu Zhong Wei You Yanyan Zou Kunpeng Jian Jiahuan Xu Jian Sun Baoxu Liu Wei Huo | 2023 | Cybersecurity2023，6，1： | 0 |
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