WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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  • Article
    Citation - WoS: 215
    Citation - Scopus: 239
    Observation of Sequential Suppression in Pbpb Collisions
    (American Physical Society, 2012) CMS Collaboration; Karapınar, Güler
    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 150μb -1 and 230nb-1, respectively, collected in 2011 by the CMS experiment at the LHC, at a center-of-mass energy per nucleon pair of 2.76TeV. 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, RAA, is measured as a function of the collision centrality. Integrated over centrality, the RAA 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. © 2012 CERN.
  • Article
    Citation - WoS: 135
    Citation - Scopus: 157
    The Use of Ga-Anns in the Modelling of Compressive Strength of Cement Mortar
    (Elsevier Ltd., 2003) Akkurt, Sedat; Özdemir, Serhan; Tayfur, Gökmen; Akyol, Burak
    In this paper, results of a project aimed at modelling the compressive strength of cement mortar under standard curing conditions are reported. Plant data were collected for 6 months for the chemical and physical properties of the cement that were used in model construction and testing. The training and testing data were separated from the complete original data set by the use of genetic algorithms (GAs). A GA-artificial neural network (ANN) model based on the training data of the cement strength was created. Testing of the model was also done within low average error levels (2.24%). The model was subjected to sensitivity analysis to predict the response of the system to different values of the factors affecting the strength. The plots obtained after sensitivity analysis indicated that increasing the amount of C3S, SO3 and surface area led to increased strength within the limits of the model. C2S decreased the strength whereas C3A decreased or increased the strength depending on the SO3 level. Because of the limited data range used for training, the prediction results were good only within the same range. The utility of the model is in the potential ability to control processing parameters to yield the desired strength levels and in providing information regarding the most favourable experimental conditions to obtain maximum compressive strength.