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Institution

Istanbul Technical University

EducationIstanbul, Turkey
About: Istanbul Technical University is a education organization based out in Istanbul, Turkey. It is known for research contribution in the topics: Fuzzy logic & Large Hadron Collider. The organization has 12889 authors who have published 25081 publications receiving 518242 citations. The organization is also known as: İstanbul Teknik Üniversitesi & Technical University of Istanbul.


Papers
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Journal ArticleDOI
TL;DR: In this article, the influence of the affecting parameters during electrospinning on properties of electrospun gelatin was investigated, and it was shown that higher applied voltage resulted in higher zeta potential and diffusion coefficient values for dispersions containing electrospin gelatin nanofibers, whereas smooth nanofiber morphology without bead formation obtained at the highest voltage.

286 citations

Journal ArticleDOI
TL;DR: The first direct search for lepton-flavour-violating decays of the recently discovered Higgs boson (H) is described in this paper, where the search is performed in the H→μτ_e and H→mτ_h channels, where τeτe and τ_h are tau leptons reconstructed in the electronic and hadronic decay channels, respectively.

285 citations

Journal ArticleDOI
TL;DR: This paper includes four different fuzzy multi-attribute group decision-making methods extended to select the best computer integrated manufacturing system by taking into account both intangible and tangible factors.

285 citations

Journal ArticleDOI
TL;DR: In this paper, the suppression of individual nS states in PbPb collisions with respect to their yields in pp data has been measured, and the results demonstrate the sequential suppression of the Υ(nS) states from the dimuon invariant mass spectra.
Abstract: The suppression of the individual Υ(nS) states in PbPb collisions with respect to their yields in pp data has been measured. The PbPb and pp data sets used in the analysis correspond to integrated luminosities of 150 μb^(-1) and 230 nb^(-1), respectively, collected in 2011 by the CMS experiment at the LHC, at a center-of-mass energy per nucleon pair of 2.76 TeV. The Υ(nS) yields are measured from the dimuon invariant mass spectra. The suppression of the Υ(nS) yields in PbPb relative to the yields in pp, scaled by the number of nucleon-nucleon collisions, R_(AA), is measured as a function of the collision centrality. Integrated over centrality, the R_(AA) values are 0.56±0.08(stat)±0.07(syst), 0.12±0.04(stat)±0.02(syst), and lower than 0.10 (at 95% confidence level), for the Υ(1S), Υ(2S), and Υ(3S) states, respectively. The results demonstrate the sequential suppression of the Υ(nS) states in PbPb collisions at LHC energies.

282 citations

Proceedings ArticleDOI
01 Jan 2017
TL;DR: The task and evaluation methodology is defined, how the data sets were prepared, report and analyze the main results, and a brief categorization of the different approaches of the participating systems are provided.
Abstract: The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

281 citations


Authors

Showing all 13155 results

NameH-indexPapersCitations
David Miller2032573204840
H. S. Chen1792401178529
Hyun-Chul Kim1764076183227
J. N. Butler1722525175561
Andrea Bocci1722402176461
Bradley Cox1692150156200
Yang Gao1682047146301
J. E. Brau1621949157675
G. A. Cowan1592353172594
David Cameron1541586126067
Andrew D. Hamilton1511334105439
Jongmin Lee1502257134772
A. Artamonov1501858119791
Teresa Lenz1501718114725
Carlos Escobar148118495346
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023137
2022338
20211,860
20201,772
20191,834
20181,643