|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Preparation of ZrB_(2)-MoSi_(2) high oxygen resistant coating using nonequilibrium state powders by self-propagating high-temperature synthesis显示文摘To achieve high oxygen blocking structure of the ZrB_(2)-MoSi_(2) coating applied on carbon structural material,ZrB_(2)-MoSi_(2) coating was prepared by spark plasma sintering(SPS)method utilizing ZrB_(2)-MoSi_(2) composite powders synthesized by self-propagating high-temperature synthesis(SHS)technique as raw materials.The oxygen blocking mechanism of the ZrB_(2)-MoSi_(2) coatings at 1973 K was investigated.Compared with commercial powders,the coatings prepared by SHS powders exhibited superior density and inferior oxidation activity,which significantly heightened the structural oxygen blocking ability of the coatings in the active oxidation stage,thus characterizing higher oxidation protection efficiency.The rise of MoSi_(2) content facilitated the dispersion of transition metal oxide nanocrystals(5-20 nm)in the SiO_(2) glass layer and conduced to the increasing viscosity,thus strengthening the inerting impact of the compound glass layer in the inert oxidation stage.Nevertheless,the ZrB_(2)-40 vol% MoSi_(2) coating sample prepared by SHS powders presented the lowest oxygen permeability of 0.3% and carbon loss rate of 0.29×10^(6)g·cm^(-2)·s^(-1).Owing to the gradient oxygen partial pressure inside the coatings,the Si-depleted layer was developed under the compound glass layer,which brought about acute oxygen erosion. | Menglin ZHANG Xuanru REN Mingcheng ZHANG Songsong WANG Li WANG Qingqing YANG Hongao CHU Peizhong FENG | 2021 | Journal of Advanced Ceramics2021，10，5： | 2 |
| 2 | Network Protocol Recognition Based on Convolutional Neural Network显示文摘How to correctly acquire the appropriate features is a primary problem in network protocol recognition field.Aiming to avoid the trouble of artificially extracting features in traditional methods and improve recognition accuracy,a network protocol recognition method based on Convolutional Neural Network(CNN)is proposed.The method utilizes deep learning technique,and it processes network flows automatically.Firstly,normalization is performed on the intercepted network flows and they are mapped into two-dimensional matrix which will be used as the input of CNN.Then,an improved classification model named Ptr CNN is built,which can automatically extract the appropriate features of network protocols.Finally,the classification model is trained to recognize the network protocols.The proposed approach is compared with several machine learning methods.Experimental results show that the tailored CNN can not only improve protocol recognition accuracy but also ensure the fast convergence of classification model and reduce the classification time. | Wenbo Feng Zheng Hong Lifa Wu Menglin Fu Yihao Li Peihong Lin | 2020 | China Communications2020，17，4： | 2 |
| 3 | Artificial optical microfingerprints for advanced anticounterfeiting显示文摘Artificial optical microfingerprints,known as physically unclonable functions(PUFs)offer a groundbreaking approach for anti-counterfeiting.However,these PUFs artificial optical microfingerprints suffer from a limited number of challenge-response pairs,making them vulnerable to machine learning(ML)attacks when additional error-correcting units are introduced.This study presents a pioneering demonstration of artificial optical microfingerprints that combine the advantages of PUFs,a large encoding capacity algorithm,and reliable deep learning authentication against ML attacks.Our approach utilizes the triple-mode PUFs,incorporating bright-field,multicolor fluorescence wrinkles,and the topography of surface enhanced Raman scattering in the mechanical and optical layers.Notably,the quaternary encoding of these PUFs artificial microfingerprints allows for an encoding capacity of 6.43×10^(24082) and achieves 100%deep learning recognition accuracy.Furthermore,the PUFs artificial optical microfingerprints exhibit high resilience against ML attacks,facilitated by generative adversarial networks(GAN)(with mean prediction accuracy of~85.0%).The results of this study highlight the potential of utilizing up to three PUFs in conjunction with a GAN training system,paving the way for achieving encoded information that remains resilient to ML attacks. | Xueke Pang Qiang Zhang Jingyang Wang Xin Jiang Menglin Wu Mingyue Cui Zhixia Feng Wenxin Xu Bin Song Yao He | 2024 | Nano Research2024，17，5： | 0 |
| 4 | Progranulin regulation of autophagy contributes to its chondroprotective effect in osteoarthritis显示文摘Progranulin(PGRN)is a multifunctional growth factor involved in many physiolog-ical processes and disease states.The apparent protective role of PGRN and the importance of chondrocyte autophagic function in the progression of osteoarthritis(OA)led us to investi-gate the role of PGRN in the regulation of chondrocyte autophagy.PGRN knockout chondro-cytes exhibited a deficient autophagic response with limited induction following rapamycin,serum starvation,and IL-1b-induced autophagy.PGRN-mediated anabolism and suppression of IL-1b-induced catabolism were largely abrogated in the presence of the BafA1 autophagy inhibitor.Mechanistically,during the process of OA,PGRN and the ATG5eATG12 conjugate form a protein complex;PGRN regulates autophagy in chondrocytes and OA through,at least partially,the interactions between PGRN and the ATG5eATG12 conjugate.Furthermore,the ATG5eATG12 conjugate is critical for cell proliferation and apoptosis.Knockdown or knockout of ATG5 reduces the expression of ATG5eATG12 conjugate and inhibits the chondroprotective effect of PGRN on anabolism and catabolism.Overexpression of PGRN partially reversed this effect.In brief,the PGRN-mediated regulation of chondrocyte autophagy plays a key role in the chondroprotective role of PGRN in OA.Such studies provide new insights into the pathogen-esis of OA and PGRN-associated autophagy in chondrocyte homeostasis. | Yiming Pan Yuyou Yang Mengtian Fan Cheng Chen Rong Jiang Li Liang Menglin Xian Biao Kuang Nana Geng Naibo Feng Lin Deng Wei Zheng Fengmei Zhang Xiaoli Li Fengjin Guo | 2023 | Genes & Diseases2023，10，4： | 0 |
| 5 | Causal inference with marginal structural modeling for longitudinal data in laparoscopic surgery: A technical note显示文摘Causal inference prevails in the field of laparoscopic surgery.Once the causality between an intervention and outcome is established,the intervention can be applied to a target population to improve clinical outcomes.In many clinical scenarios,interventions are applied longitudinally in response to patients’conditions.Such longitudinal data comprise static variables,such as age,gender,and comorbidities;and dynamic variables,such as the treatment regime,laboratory variables,and vital signs.Some dynamic variables can act as both the confounder and mediator for the effect of an intervention on the outcome;in such cases,simple adjustment with a conventional regression model will bias the effect sizes.To address this,numerous statistical methods are being developed for causal inference;these include,but are not limited to,the structural marginal Cox regression model,dynamic treatment regime,and Cox regression model with time-varying covariates.This technical note provides a gentle introduction to such models and illustrates their use with an example in the field of laparoscopic surgery. | Zhongheng Zhang Peng Jin Menglin Feng Jie Yang Jiajie Huang Lin Chen Ping Xu Jian Sun Caibao Hu Yucai Hong | 2022 | Laparoscopic, Endoscopic and Robotic Surgery2022，5，4： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员