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Institution

Chonbuk National University

EducationJeonju, South Korea
About: Chonbuk National University is a education organization based out in Jeonju, South Korea. It is known for research contribution in the topics: Apoptosis & Nanofiber. The organization has 14820 authors who have published 28884 publications receiving 554131 citations.


Papers
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Journal ArticleDOI
15 Apr 2009-Talanta
TL;DR: This paper reports the fabrication of highly-sensitive cholesterol biosensor based on cholesterol oxidase (ChOx) immobilization on well-crystallized flower-shaped ZnO structures composed of perfectly hexagonal-shapedZnO nanorods grown by low-temperature simple solution process.

180 citations

Journal ArticleDOI
TL;DR: The experimental results show that FOS-ELM has higher accuracy with fewer training time, better stability and short-term predictability than EOS- ELM.

180 citations

Journal ArticleDOI
A. Adare1, S. Afanasiev2, Christine Angela Aidala3, N. N. Ajitanand4  +390 moreInstitutions (55)
TL;DR: In this article, the PHENIX 2007 data set of J/psi yields at forward rapidity (1.2 < vertical bar y vertical bar < 2.2) in Au + Au collisions at root s(NN) = 200 GeV.
Abstract: Heavy quarkonia are observed to be suppressed in relativistic heavy-ion collisions relative to their production in p + p collisions scaled by the number of binary collisions. In order to determine if this suppression is related to color screening of these states in the produced medium, one needs to account for other nuclear modifications including those in cold nuclear matter. In this paper, we present new measurements from the PHENIX 2007 data set of J/psi yields at forward rapidity (1.2 < vertical bar y vertical bar < 2.2) in Au + Au collisions at root s(NN) = 200 GeV. The data confirm the earlier finding that the suppression of J/. at forward rapidity is stronger than at midrapidity, while also extending the measurement to finer bins in collision centrality and higher transverse momentum (p(T)). We compare the experimental data to the most recent theoretical calculations that incorporate a variety of physics mechanisms including gluon saturation, gluon shadowing, initial-state parton energy loss, cold nuclear matter breakup, color screening, and charm recombination. We find J/psi suppression beyond cold-nuclear-matter effects. However, the current level of disagreement between models and d + Au data precludes using these models to quantify the hot-nuclear-matter suppression.

179 citations

Journal ArticleDOI
TL;DR: This study presents an approach meant to encourage the end-to-end learning of a volumetric convolutional neural network (CNN) model for four binary classification tasks based on magnetic resonance imaging (MRI) and visualizes its outcomes in terms of the decision of the CNNs without any human intervention.
Abstract: Recently, deep-learning-based approaches have been proposed for the classification of neuroimaging data related to Alzheimer’s disease (AD), and significant progress has been made. However, end-to-end learning that is capable of maximizing the impact of deep learning has yet to receive much attention due to the endemic challenge of neuroimaging caused by the scarcity of data. Thus, this study presents an approach meant to encourage the end-to-end learning of a volumetric convolutional neural network (CNN) model for four binary classification tasks (AD vs. normal control (NC), progressive mild cognitive impairment (pMCI) vs. NC, stable mild cognitive impairment (sMCI) vs. NC and pMCI vs. sMCI) based on magnetic resonance imaging (MRI) and visualizes its outcomes in terms of the decision of the CNNs without any human intervention. In the proposed approach, we use convolutional autoencoder (CAE)-based unsupervised learning for the AD vs. NC classification task, and supervised transfer learning is applied to solve the pMCI vs. sMCI classification task. To detect the most important biomarkers related to AD and pMCI, a gradient-based visualization method that approximates the spatial influence of the CNN model’s decision was applied. To validate the contributions of this study, we conducted experiments on the ADNI database, and the results demonstrated that the proposed approach achieved the accuracies of 86.60% and 73.95% for the AD and pMCI classification tasks respectively, outperforming other network models. In the visualization results, the temporal and parietal lobes were identified as key regions for classification.

179 citations

Journal ArticleDOI
TL;DR: In this paper, cubic nickel oxide (NiO) nanoparticles with uniform size around 40-50 nm and well dispersion have been synthesized using a mixture of nickel acetate and polyvinyl acetate (PVAc) as precursor followed by heat treatment at 723 K.
Abstract: Cubic nickel oxide (NiO) nanoparticles with uniform size around 40–50 nm and well dispersion have been synthesized using a mixture of nickel acetate and poly(vinyl acetate) (PVAc) as precursor followed by heat treatment at 723 K. The structure, morphology and crystalline phase of the nickel oxide nanocrystals have been investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectrum, UV–visible absorption spectrum and FT-IR. TEM images showed that the nickel oxide nanoparticles have hexagonal structure with uniform size distribution around 40–50 nm. Phase pure, cubic nickel oxide formation was identified from the XRD data.

178 citations


Authors

Showing all 14943 results

NameH-indexPapersCitations
Hyun-Chul Kim1764076183227
Andrew Ivanov142181297390
Dong-Chul Son138137098686
C. Haber135150798014
Tae Jeong Kim132142093959
Alessandro Cerri1291244103225
Paul M. Vanhoutte12786862177
Jason Nielsen12589372688
Chi Lin1251313102710
Paul Lujan123125576799
Young Hee Lee122116861107
Min Suk Kim11997566214
Alexandre Sakharov11958256771
Yang-Kook Sun11778158912
Rui L. Reis115160863223
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202366
2022203
20212,069
20201,883
20191,798
20181,893