Institution
National Chung Hsing University
Education•Taichung, Taiwan•
About: National Chung Hsing University is a education organization based out in Taichung, Taiwan. It is known for research contribution in the topics: Catalysis & Thin film. The organization has 19443 authors who have published 24060 publications receiving 540154 citations. The organization is also known as: NCHU.
Topics: Catalysis, Thin film, Population, Apoptosis, Gene
Papers published on a yearly basis
Papers
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TL;DR: A conceptual model and hypotheses based on the theory of reasoned action and perceived values from the perspectives of software, hardware, and aesthetic design demonstrated relatively good explanatory power for purchase intention in the context of smartwatches.
138 citations
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TL;DR: The results of this study suggest that convenience, compatibility, and media richness all significantly contribute to dedicated e‐book reader acceptance.
Abstract: Purpose – Due to the rapid pace of development and innovation in information technology, the dedicated electronic book (e‐book) reader has become a new trend in reading. However, at present there is only a limited understanding of what factors drive user attitudes/willingness to use this new device for reading. Hence, this paper aims to explore what factors drive users to use dedicated e‐book readers for reading.Design/methodology/approach – The study proposes a causal model that explores how convenience, compatibility, and media richness affect users' attitudes towards the dedicated e‐book readers for reading.Findings – The results of this study suggest that convenience, compatibility, and media richness all significantly contribute to dedicated e‐book reader acceptance.Research limitations/implications – The study extends previous theories: the Technology Acceptance Model, Innovation Diffusion Theory, media richness theory and convenience. This helps one to better understand what factors affect usage of...
138 citations
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TL;DR: A composite of multwalled carbon nanotubes-chitosan (MWCNT-CHIT) was used as a matrix for entrapment of lactate dehydrogenase (LDH) onto a glassy carbon electrode in order to fabricate amperometric biosensor as discussed by the authors.
Abstract: A composite of multiwalled carbon nanotubes-chitosan (MWCNT-CHIT) was used as a matrix for entrapment of lactate dehydrogenase (LDH) onto a glassy carbon electrode in order to fabricate amperometric biosensor. The homogeneity of the resulting multiwalled carbon nanotubes-chitosan-lactate dehydrogenase (MWCNT-CHIT-LDH) nanobiocomposite film was investigated by atomic force microscopy (AFM). It shows that the enzyme is homogeneously immobilized within MWCNT-CHIT-LDH. The inclusion of MWCNT within MWCNT-CHIT-LDH exhibits the abilities to raise the current responses, to decrease the electrooxidation potential of β-nicotinamide adenine dinucleotide, reduced form (NADH), and to prevent the electrode surface fouling. The influence of several experimental parameters such as applied potential, solution pH value, NAD + concentration, and enzyme loading was explored to optimize the electroanalytical performance of the biosensor. The optimized biosensor for the determination of lactate shows a sensitivity of 0.0083 A M −1 cm −2 and a response time of about 3 s. The proposed biosensor retained 65% of its original response after 7 days.
138 citations
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TL;DR: In patients with HNSCC, coexpression of Snail and ERCC1 correlated with cisplatin resistance and a poor prognosis, and Activation of ER CC1 by Snail is critical in the generation of cis platin resistance of H NSCC cells.
Abstract: Purpose: We investigated the mechanism and clinical significance of the epithelial-mesenchymal transition (EMT)-induced chemoresistance in head and neck squamous cell carcinoma (HNSCC). Experimental Design: The correlation between the expression of different EMT regulators and chemoresistance genes, such as excision repair cross complementation group 1 ( ERCC1 ), was evaluated in cancer cell lines from the NCI-60 database and four human HNSCC cell lines. Ectopic expression of Snail or short-interference RNA-mediated repression of Snail or ERCC1 was done in HNSCC cell lines. Cell viability was examined for cells after cisplatin treatment. A luciferase reporter assay and chromatin immunoprecipitation were used to identify the transcriptional regulation of ERCC1 by Snail. Immunohistochemical analysis of Snail, Twist1, ERCC1, hypoxia inducible factor-1 α (HIF-1α), and NBS1 were done in samples from 72 HNSCC patients receiving cisplatin-based chemotherapy. Results: The correlation between the expression of Snail and ERCC1 was confirmed in different cell lines, including HNSCC cells. In HNSCC cell lines, overexpression of Snail in the low endogenous Snail/ERCC1 cell lines FaDu or CAL-27 increased ERCC1 expression, and hypoxia or overexpression of NBS1 also upregulated ERCC1. Knockdown of Snail in the high endogenous Snail/ERCC1 cell line OECM-1 downregulated ERCC1 expression and attenuated cisplatin resistance. Furthermore, suppression of ERCC1 in Snail- or NBS1-overexpressing HNSCC cells enhanced sensitivity to cisplatin. Snail directly regulated ERCC1 transcription. In patients with HNSCC, coexpression of Snail and ERCC1 correlated with cisplatin resistance and a poor prognosis. Conclusions: Activation of ERCC1 by Snail is critical in the generation of cisplatin resistance of HNSCC cells. Clin Cancer Res; 16(18); 4561–71. ©2010 AACR.
138 citations
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TL;DR: An interval type-2 fuzzy-neural network with support-vector regression (IT2FNN-SVR) for noisy regression problems that is optimized for structural-risk minimization using a two-phase linear SVR algorithm in order to endow the network with high generalization ability.
Abstract: This paper proposes an interval type-2 fuzzy-neural network with support-vector regression (IT2FNN-SVR) for noisy regression problems. The antecedent part in each fuzzy rule of an IT2FNN-SVR uses interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang (TSK) type. The use of interval type-2 fuzzy sets helps improve the network's noise resistance. The network inputs may be numerical values or type-1 fuzzy sets, with the latter being used for further improvements in robustness. IT2FNN-SVR learning consists of both structure learning and parameter learning. The structure-learning algorithm is responsible for online rule generation. The parameters are optimized for structural-risk minimization using a two-phase linear SVR algorithm in order to endow the network with high generalization ability. IT2FNN-SVR performance is verified through comparisons with type-1 and type-2 fuzzy-logic systems and other regression models on noisy regression problems.
138 citations
Authors
Showing all 19519 results
Name | H-index | Papers | Citations |
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Barry Halliwell | 173 | 662 | 159518 |
Chi-Huey Wong | 129 | 1220 | 66349 |
Meilin Liu | 117 | 827 | 52603 |
Wen-Hsiung Li | 106 | 461 | 61181 |
Pan-Chyr Yang | 102 | 786 | 46731 |
David A. Case | 102 | 364 | 74066 |
Jo Shu Chang | 99 | 639 | 37487 |
Wilhelm Gruissem | 94 | 325 | 32048 |
Pi-Tai Chou | 90 | 614 | 30922 |
Liang Tong | 81 | 342 | 21752 |
Tim H M Huang | 80 | 318 | 19905 |
De-en Jiang | 80 | 338 | 20466 |
Gwo-Hshiung Tzeng | 77 | 465 | 26807 |
Jianhua Yang | 74 | 554 | 27839 |
Gow-Chin Yen | 72 | 242 | 17303 |