27 research outputs found

    Nuclear effects on J/{\psi} production in proton-nucleus collisions

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    The study of nuclear effects for J/{\psi} production in proton-nucleus collisions is crucial for a correct interpretation of the J/{\psi} suppression patterns experimentally observed in heavy-ion collisions. By means of three representative sets of nuclear parton distribution, the energy loss effect in the initial state and the nuclear absorption effect in the final state are taken into account in the uniform framework of the Glauber model. A leading order phenomenological analysis is performed on J/{\psi} production cross-section ratios RW/Be(xF) for the E866 experimental data. The J/{\psi} suppression is investigated quantitatively due to the different nuclear effects. It is shown that the energy loss effect with resulting in the suppression on RW/Be(xF) is more important than the nuclear effects on parton distributions in high xF region. The E866 data in the small xF keep out the nuclear gluon distribution with a large anti-shadowing effect. However, the new HERA-B measurement is not in support of the anti-shadowing effect in the nuclear gluon distribution. It is found that the J/{\psi}-nucleon inelastic cross section {\sigma} J/{\psi} abs depends on the kinematical variable xF, and increases as xF in the region xF > 0.2. 1 Introductio

    Resonance Raman microspectroscopy in biology

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    Acoustic Monitoring of Plasma Arcs in Direct Current Electric Arc Furnaces

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    In this article, the extraction of features from acoustic signals generated by a 60-kW direct current electric arc furnace and the use of these features to infer the arc length of the plasma jets in the furnace were considered. A sensor capable of such measurements would be more robust to the unobservable fluctuations of the arc length and would, in principle, allow better control of smelting operations. The collected data comprised sets of five separate 10-second recordings of the acoustic signal, furnace current, and voltage, each at nominal arc lengths of 5, 15, and 25 mm. In the approach, time-frequency features initially were obtained through filter bank analysis of the signals. Reduction of the dimensionality of these filter bank features was then performed using a nonlinear subspace method called kernel Fisher discriminant analysis.Finally, kernel discriminant features were used to infer the arc length via a nearest neighborclassification model that associated three classes of arc lengths (5, 15, and 25 mm) with theircorresponding features. The results of the small number of experiments suggest that a significantstatistical relationship exists between the length of a plasma arc and its acoustic signal despitepotentially large variations in arc phenomena inside the furnace
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