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Yanxiu Zuo(Yanxiu Zuo),Junxiang Qian(Junxiang Qian),Lichi Li(Lichi Li),Zhangguan Ni(Zhangguan Ni),Jun Wu(Jun Wu),Huiyun Zhang(Huiyun Zhang),Yufu Chen(Yufu Chen),Huiyun Zhang(Huiyun Zhang),Li Yao(Yao L 아시아사회과학학회 2022 Jornal of Asia Social Science Vol.9 No.1
Baihua Village is a typical mountainous village in the southwest part of Lujiang county, Longyang District, Baoshan City, Yunnan Province. Residents there made a living on the land, including growing sugarcane and planting maize, whose annual income was no more than 2000 yuan before 2006. Since then when a research institute has set it as one of the pilot villages for mango growing impetus with sci-tech. For the sake of “One village and One Product”, mango breeding and relevant techniques have been applied to daily work. Within years, the developed model of has been explored: simply “villages are the main carriers facilitated by the specialized cooperative for mango growing, back-up by science and technology. Technical trainings serve as the driving force for the leading growers, meanwhile, sellers work as the bridge link the producing-end and the markets”.
Zhang, Lichi,Wang, Qian,Gao, Yaozong,Li, Hongxin,Wu, Guorong,Shen, Dinggang Elsevier 2017 Neurocomputing Vol.229 No.-
<P><B>Abstract</B></P> <P>Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to the ambiguous intensity boundary between the hippocampus and surrounding anatomies. In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images. The contribution in our work is two-fold. <I>First</I>, each forest classifier is trained to label just a specific sub-region of the hippocampus, thus enhancing the labeling accuracy. <I>Second</I>, a novel forest selection strategy is proposed, such that each voxel in the test image can automatically select a set of optimal forests, and then dynamically fuses their respective outputs for determining the final label. <I>Furthermore</I>, we enhance the spatially-localized random forests with the aid of the auto-context strategy. In this way, our proposed learning framework can gradually refine the tentative labeling result for better performance. Experiments show that, regarding the large datasets of both adult and infant brain MR images, our method owns satisfactory scalability by segmenting the hippocampus accurately and efficiently.</P>
Hot Electron Field Emission <i>via</i> Individually Transistor-Ballasted Carbon Nanotube Arrays
Li, Chi,Zhang, Yan,Cole, Matthew T.,Shivareddy, Sai G.,Barnard, Jon S.,Lei, Wei,Wang, Baoping,Pribat, Didier,Amaratunga, Gehan A. J.,Milne, William I. American Chemical Society 2012 ACS NANO Vol.6 No.4
<P>We present electronically controlled field emission characteristics of arrays of individually ballasted carbon nanotubes synthesized by plasma-enhanced chemical vapor deposition on silicon-on-insulator substrates. By adjusting the source–drain potential we have demonstrated the ability to controllable limit the emission current density by more than 1 order of magnitude. Dynamic control over both the turn-on electric field and field enhancement factor have been noted. A hot electron model is presented. The ballasted nanotubes are populated with hot electrons due to the highly crystalline Si channel and the high local electric field at the nanotube base. This positively shifts the Fermi level and results in a broad energy distribution about this mean, compared to the narrow spread, lower energy thermalized electron population in standard metallic emitters. The proposed vertically aligned carbon nanotube field-emitting electron source offers a viable platform for X-ray emitters and displays applications that require accurate and highly stable control over the emission characteristics.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/ancac3/2012/ancac3.2012.6.issue-4/nn300111t/production/images/medium/nn-2012-00111t_0006.gif'></P>