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| 1 | Neuroprotective effects of edaravone on early brain injury in rats after subarachnoid hemorrhage显示文摘背景在 subarachnoid 出血( SAH )以后的早 neurobiological 缺陷的内在的机制很好没被理解,但是反应的氧 superoxide ( ROS )的系统可能是 involved.Edaravone ( MC1-186 ),一个有势力阻止神经原的 apoptosis 的免费激进的 scavenger ,因此在这研究被使用在 120 只男 Sprague-Dawley 老鼠随机被分到四 groups:group 的老鼠 model.Methods 由于 SAH 在早大脑损害看见它的可能的治疗学的效果 1 。 | GAO Yang DING Xin-sheng XU Shu WANG Wei ZUO Qi-long KUAI Feng | 2009 | Chinese Medical Journal2009，，16： | 14 |
| 2 | Spatial correlation analysis of sea-bottom backscattering显示文摘The principle of the spatial correlation characteristics of sea-bottom backscattering was expounded.The Kirchhoff model is introduced to simulate sea-bottom backscattering and the spatial correlation is calculated.Some sediment characteristic parameters,such as the ratio of sediment mass density to water mass density,the ratio of sediment sound speed to water sound speed,the attenuation coefficient of sound waves,the strength and exponent of seabottom interface roughness spectrum,are considered while simulating and their influences on spatial correlation are analyzed.By establishing the expressions of the sediment characteristic parameters with the logarithmic sediment grain size,the variation tendency of the spatial correlation versus the logarithmic sediment grain size is obtained.The width of the major lobe of the spatial correlation function narrows as the logarithmic sediment grain size increases.The comparisons between simulated data and in-situ data collected in October 2007 have proven that their spatial correlation function waveforms agree well under the same sediment characteristics. | KUAI Duojie WANG Changhong FENG Lei WANG Yuling QIU Wei YI Huiqin CHEN Long | 2012 | Chinese Journal of Acoustics2012，31，3： | 9 |
| 3 | Development of an automatic monitoring system for rice light-trap pests based on machine vision显示文摘Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system. | YAO Qing FENG Jin TANG Jian XU Wei-gen ZHU Xu-hua YANG Bao-jun LU Jun XIE Yi-ze YAO Bo WU Shu-zhen KUAI Nai-yang WANG Li-jun | 2020 | Journal of Integrative Agriculture2020，19，10： | 8 |
| 4 | Detecting Fake News Over Online Social Media via Domain Reputations and Content Understanding显示文摘Fake news has recently leveraged the power and scale of online social media to effectively spread misinformation which not only erodes the trust of people on traditional presses and journalisms, but also manipulates the opinions and sentiments of the public. Detecting fake news is a daunting challenge due to subtle difference between real and fake news. As a first step of fighting with fake news, this paper characterizes hundreds of popular fake and real news measured by shares, reactions, and comments on Facebook from two perspectives:domain reputations and content understanding. Our domain reputation analysis reveals that the Web sites of the fake and real news publishers exhibit diverse registration behaviors, registration timing, domain rankings, and domain popularity. In addition, fake news tends to disappear from the Web after a certain amount of time. The content characterizations on the fake and real news corpus suggest that simply applying term frequency-inverse document frequency(tf-idf) and Latent Dirichlet Allocation(LDA) topic modeling is inefficient in detecting fake news,while exploring document similarity with the term and word vectors is a very promising direction for predicting fake and real news. To the best of our knowledge, this is the first effort to systematically study domain reputations and content characteristics of fake and real news, which will provide key insights for effectively detecting fake news on social media. | Kuai Xu Feng Wang Haiyan Wang Bo Yang | 2020 | Tsinghua Science and Technology2020，25，1： | 0 |
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