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| 1 | Human activated CD4 (+) T lymphoeytes increase IL 2 expression by downregulating microRNA-181e显示文摘 | Xue Q GuoZY LiW etal | 2011 | Mol Immunol2011，48，4： | 1 |
| 2 | The elements of human cyclin D1 promoter and regulation involved显示文摘 | GuoZY Hao X H Tan F F | | 0，，02： | 1 |
| 3 | A novel concept for convection heat transfer enhancement 显示文摘 | GUOZY WANGBX | 1998 | Int J Heat Mass Transfer1998，41，14： | 1 |
| 4 | A novel concept for convective heat transfer enhancement 显示文摘 | GUOZY LIDY WANG B X | 1998 | International Journal of Heat and Mass Transfer1998，41，14： | 1 |
| 5 | Entransy-APhysicalQuantityDescribingHeatTransferAbility显示文摘 | GUOZY ZHUHY LIANGXG | 2007 | InternationalJournalofHeatandMassTransfer2007，50，1314： | 1 |
| 6 | Effectiveness-thermalResistanceMethodforHeatExchangerDesignandAnalysis显示文摘 | GUOZY LIUXB TAOWQ etal | 2010 | InternationalJournalofHeatandMassTransfer2010，53，1314： | 1 |
| 7 | A novel concept for convective heat transfer enhancement显示文摘 | GUOZY LID Y WANG B X | 1998 | Int J of Heat and Mass Transfer1998，41，14： | 1 |
| 8 | A novel concept for convective heat transfer enhancement显示文摘 | GUOZY LID Y WANG B X | 1998 | Int J Heat Mass Transfer1998，41，14： | 1 |
| 9 | Entransy-a physical quantity describing heat transfer ability显示文摘 | GUOZY ZHUHY LIANGXG | | 0，，： | 1 |
| 10 | Entransy-a physical quantity describing heat transfer ability显示文摘 | GUOZY ZHUHY LIANGXG | | 0，，： | 1 |
| 11 | Enhancing the selectivity of benzene hydroxylation by tailoringthe chemical affinity of the MCM41 catalyst surface for the reactive molecules显示文摘 | HE J GUOZY MA H | 2002 | J Catal2002，212，1： | 1 |
| 12 | Entransy-a physical quantity describing heat transfer ability显示文摘 | GUOZY ZHU HY LIANGXG | | 0，，1314： | 1 |
| 13 | A novel con- cept for convective heat transfer enhancement 显示文摘 | GUOZY LID Y WANG BX | 1998 | International Journal of Heat and Mass Transfer1998，41，： | 1 |
| 14 | New theory of breathing control: a complex model integrates multi-systems显示文摘 | Sun XG GuoZY | 2011 | FASEB J2011，25，： | 1 |
| 15 | Sharing of Encrypted Lock Keys in the Blockchain-Based Renting House System from Time- and Identity-Based Proxy Reencryption显示文摘To design an efficient protocol for sharing the encrypted lock keys in the renting house system,we introduce a new notion called time-and identitybased proxy reencryption(TIPRE)and the blockchain platform.Our CPA secure TIPRE scheme is constructed from Green et al.’s identity-based proxy reencryption scheme by adding the time property.In every time period,a time stamp authority generates a public key embedded with the current time stamp for each user.In our protocol for the renting house system,the TIPRE scheme is the primary building block,and the blockchain platform serves instead of a trusted third party,such as a real estate agency between landlords and tenants.The TIPRE scheme allows the landlord to change the lock key at each time period for safety.The blockchain platform allows the landlords and tenants to directly interact,and all of the interactions are recorded in the blockchain database to provide the desired security requirements,such as nonrepudiation and unforgeability.Finally,we provide the secure analysis of our protocol and test its performance by implementing it in the MacBook Pro and the Intel Edison development platforms. | Zhiwei Wang Liping Qian Danwei Chen Guozi sun | 2022 | China Communications2022，19，5： | 0 |
| 16 | M-ISFCM:A Semisupervised Method for Anomaly Detection of MOOC Learning Behavior显示文摘Massive online courses(MOOCs)are becoming increasingly vital in the modern era,yet tools to track and detect MOOC learners’progress are inadequate.In reality,labeled MOOC data are difficult to acquire,whereas unlabeled data make up the majority of the data,and these massive unlabeled data are difficult to analyze,resulting in data waste.This paper tackles this issue by presenting a MOOC learning behavior anomaly detection model(M-ISFCM)for the supervision and inspection of MOOC learners’learning that combines semisupervised fuzzy C-mean clustering(SFCM)and an isolated forest algorithm.To optimize MOOC data usage,the model leverages unlabeled and labeledMOOCdata as prior assumptions.The MOOC detection runtimes are enhanced by integrating the outliers of the isolated forest approach in SFCM.The results show that the model has a higher precision rate,recall rate,andAUC than the traditional anomaly models in MOOC data.Therefore,the model is effective for recognizing anomalous MOOC learning behaviors. | Shichao Zhou Liefeng Cao Ruizhe Zhang Guozi Sun | 2022 | 国际计算机前沿大会会议论文集2022，，2： | 0 |
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