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Institution

Fu Jen Catholic University

EducationTaipei, Taiwan
About: Fu Jen Catholic University is a education organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Population & Medicine. The organization has 6842 authors who have published 9512 publications receiving 171005 citations. The organization is also known as: FJU & Fu Jen.
Topics: Population, Medicine, Cancer, Hazard ratio, Apoptosis


Papers
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Journal ArticleDOI
TL;DR: This study utilizes ontology to construct user profiles and makes use of user profile ontology as the basis to reason about the interests of users, and takes advantage of the spreading activation model to search for other influential users in the community network environment.
Abstract: Following tremendous advancement in information technology, the speed of information development has become increasingly fast-paced. Yet the overabundance of information has forced users to spend more time and resources in searching for information relevant to their needs. Today, recommendation systems already exist that provide services like filtering, customization, and others to assist users in searching for the right information. This study proposes to use ontology and the spreading activation model for research paper recommendation, hoping that it can elevate the performance of the recommendation system and also improve the shortcomings of today's recommendation systems. This study utilizes ontology to construct user profiles and makes use of user profile ontology as the basis to reason about the interests of users. Furthermore, this study takes advantage of the spreading activation model to search for other influential users in the community network environment, making a study on their interests in order to provide recommendation on related information. Based on actual experiment results, the method of ontology network analysis that combines ontology and the spreading activation model is effective in knowing the research interests of users. Hence, using the mechanism proposed in this study can make up for the insufficiencies or shortcomings of other recommendation systems. Moreover, the precision rate can be up to 93% showing that our recommendation system has a positive effect on the effectiveness of the recommendation.

67 citations

Journal ArticleDOI
K. F. Chen1, W. S. Hou1, I. Adachi, H. Aihara2, T. Aushev3, A. M. Bakich4, Vladislav Balagura, A. Bay3, K. Belous, V. Bhardwaj5, M. Bischofberger6, A.E. Bondar7, A.E. Bondar8, A. Bozek9, M. Bračko10, Jolanta Brodzicka9, T. E. Browder, M. C. Chang11, P. Chang1, Y. Chao1, A. Chen12, Po-Hsun Chen1, B. G. Cheon13, C. C. Chiang1, R. Chistov, I. S. Cho14, Y. Choi15, J. Dalseno16, A. Drutskoy17, S.I. Eidelman7, S.I. Eidelman8, D. Epifanov7, D. Epifanov8, N. Gabyshev8, N. Gabyshev7, P. Goldenzweig17, H. Ha18, B. Y. Han18, K. Hayasaka19, H. Hayashii6, Masashi Hazumi, Y. Hoshi20, Y. B. Hsiung1, H. J. Hyun21, T. Iijima19, K. Inami19, R. Itoh, M. Iwabuchi14, Y. Iwasaki, T. Julius22, D. H. Kah21, Ju Hwan Kang, N. Katayama, C. Kiesling16, H. O. Kim21, Y. I. Kim21, Y. J. Kim23, K. Kinoshita17, B. R. Ko18, S. Korpar10, P. Kriaan24, P. Krokovny, Youngjoon Kwon, S. H. Kyeong14, J. S. Lange25, Sang Hoon Lee18, J. Li, C. Liu26, Yang Liu19, D. Liventsev, R. Louvot3, J. MacNaughton, A. Matyja9, S. McOnie4, K. Miyabayashi6, H. Miyata27, Y. Miyazaki19, R. Mizuk, Yasushi Nagasaka28, E. Nakano29, M. Nakao, S. Nishida, K. Nishimura, O. Nitoh30, T. Nozaki, S. Ogawa31, T. Ohshima19, S. Okuno32, G. Pakhlova, H. Park21, H. K. Park21, R. Pestotnik, Marko Petrič, L. E. Piilonen33, Sunmin Ryu34, H. Sahoo, K. Sakai27, Y. Sakai, O. Schneider3, C. Schwanda35, A. J. Schwartz17, R. Seidl, K. Senyo19, M. Shapkin, C. P. Shen, J. G. Shiu1, B.A. Shwartz7, B.A. Shwartz8, P. Smerkol, Andrey Sokolov, E. Solovieva, Samo Stanič36, M. Starič, K. Sumisawa, T. Sumiyoshi37, S. Suzuki38, Y. Teramoto29, K. Trabelsi, S. Uehara, T. Uglov, Y. Unno13, S. Uno, Y. Ushiroda, G. S. Varner, Kevin Varvell4, K. Vervink3, C. H. Wang39, M. Z. Wang1, P. Wang, Y. Watanabe32, Robin Wedd22, J. Wicht, E. Won18, Bruce Yabsley4, Y. Yamashita, C. Z. Yuan, Z. P. Zhang26, Vladimir Zhulanov8, Vladimir Zhulanov7, A. Zupanc, O. Zyukova8, O. Zyukova7 
TL;DR: In this article, the authors measured the production cross sections of the Belle detector at the KEKB collider and observed enhanced production in all three final states that does not agree well with the conventional $\ensuremath{\Upsilon}(10860)$ line shape.
Abstract: We measure the production cross sections for ${e}^{+}{e}^{\ensuremath{-}}\ensuremath{\rightarrow}\ensuremath{\Upsilon}(1S){\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}$, $\ensuremath{\Upsilon}(2S){\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}$, and $\ensuremath{\Upsilon}(3S){\ensuremath{\pi}}^{+}{\ensuremath{\pi}}^{\ensuremath{-}}$ as a function of $\sqrt{s}$ between 10.83 GeV and 11.02 GeV. The data consist of $8.1\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$ collected with the Belle detector at the KEKB ${e}^{+}{e}^{\ensuremath{-}}$ collider. We observe enhanced production in all three final states that does not agree well with the conventional $\ensuremath{\Upsilon}(10860)$ line shape. A fit using a Breit-Wigner resonance shape yields a peak mass of $[10\text{ }{888.4}_{\ensuremath{-}2.6}^{+2.7}(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}1.2(\mathrm{syst})]\text{ }\text{ }\mathrm{MeV}/{c}^{2}$ and a width of $[{30.7}_{\ensuremath{-}7.0}^{+8.3}(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}3.1(\mathrm{syst})]\text{ }\text{ }\mathrm{MeV}/{c}^{2}$.

67 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined the relationship between mentoring functions and resilience and investigated the moderating roles of the gender composition of the mentoring relationship and supervisory mentoring on this relationship.

67 citations

Journal ArticleDOI
TL;DR: The data suggest that hFOB cells may provide for researchers an easily available, homogeneous, and consistent in vitro model for study of human mesenchymal progenitor cells.
Abstract: The in vitro study of human bone marrow mesenchymal stromal cells (BMMSCs) has largely depended on the use of primary cultures. Although these are excellent model systems, their scarcity, heterogeneity, and limited lifespan restrict their usefulness. This has led researchers to look for other sources of MSCs, and recently, such a population of progenitor/stem cells has been found in mesodermal tissues, including bone. We therefore hypothesized that a well-studied and commercially available clonal human osteoprogenitor cell line, the fetal osteoblastic 1.19 cell line (hFOB), may have multilineage differentiation potential. We found that undifferentiated hFOB cells possess similar cell surface markers as BMMSCs and also express the embryonic stem cell-related pluripotency gene, Oct-4, as well as the neural progenitor marker nestin. hFOB cells can also undergo multilineage differentiation into the mesodermal lineages of chondrogenic and adipocytic cell types in addition to its predetermined pathway, the mature osteoblast. Moreover, as with BMMSCs, under neural-inducing conditions, hFOB cells acquire a neural-like phenotype. This human cell line has been a widely used model of normal osteoblast differentiation. Our data suggest that hFOB cells may provide for researchers an easily available, homogeneous, and consistent in vitro model for study of human mesenchymal progenitor cells.

67 citations

Journal ArticleDOI
TL;DR: PRP injection in the corpus cavernosum increased the number of myelinated axons and facilitated recovery of EF in the bilateral CN injury rat model, and reduced the apoptotic index.

67 citations


Authors

Showing all 6861 results

NameH-indexPapersCitations
P. Chang1702154151783
Christian Guilleminault13389768844
Pan-Chyr Yang10278646731
Po-Ren Hsueh92103038811
Shyi-Ming Chen9042522172
Peter J. Rossky7428021183
Chong-Jen Yu7257722940
Shuu Jiun Wang7150224800
Jaw-Town Lin6743415482
Lung Chi Chen6326713929
Ronald E. Taam5929012383
Jiann T. Lin5819010801
Yueh-Hsiung Kuo5761812204
San Lin You5517816572
Liang-Gee Chen5458212073
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202313
202233
2021726
2020666
2019571
2018528