1,566 research outputs found
Decaying magnetohydrodynamics: effects of initial conditions
We study the effects of homogenous and isotropic initial conditions on
decaying Magnetohydrodynamics (MHD). We show that for an initial distribution
of velocity and magnetic field fluctuations, appropriately defined structure
functions decay as power law in time. We also show that for a suitable choice
of initial cross-correlations between velocity and magnetic fields even order
structure functions acquire anomalous scaling in time where as scaling
exponents of the odd order structure functions remain unchanged. We discuss our
results in the context of fully developed MHD turbulence.Comment: To appear in Phys. Rev.
Asymptotic Properties of Minimum S-Divergence Estimator for Discrete Models
Robust inference based on the minimization of statistical divergences has
proved to be a useful alternative to the classical techniques based on maximum
likelihood and related methods. Recently Ghosh et al. (2013) proposed a general
class of divergence measures, namely the S-Divergence Family and discussed its
usefulness in robust parametric estimation through some numerical
illustrations. In this present paper, we develop the asymptotic properties of
the proposed minimum S-Divergence estimators under discrete models.Comment: Under review, 24 page
Higher Efficiency In Prediction Of TIBO Activity By Evolutionary Neural Network
The treatment of acquired immunodeficiency syndrome (AIDS) is a challenging medical problem. TIBO is a nonnucleoside reverse transcriptase inhibitor, which binds non-competitively to the hydrophobic pocket on the p66 subunit of RT enzyme. We used a dataset consisting of physicochemical properties and reverse transcriptase inhibitor activities of 88 set of 4,5,6,7-tetrahydro-y-imidazo-[4,5,1-jk][1,4]-x-benzodiazepin-2-(1h)one derivatives that are variously substituted by halogens, alkyl groups. The dataset was taken from the BIOBYTE database at (www.davidhoekman.com). The concentration of the compound leading to 50% effect has been measured and expressed as IC50. The logarithm of the inverse of this parameter has been used as biological end points (log 1/C) in the QSAR studies. The evolutionary neural network (ENN) is a new system for modeling multivariate data. The strengths of ENN’s are that they can extract insignificant predictors, choose the size of the hidden layers and nodes and fine tune the parameters needed in training the network. We have used an ENN to predict the biological activities of Reverse Transcriptase Inhibitors. We have found out that Evolutionary Neural networks are better predictor of activity values than Multiple linear regression and Multilayered Perceptrons. We have calculated the correlation coefficient of each of the methods where we have found ENNs are the best
Dynamo mechanism: Effects of correlations and viscosities
We analyze the effects of the background velocity and the initial magnetic
field correlations, and viscosities on the turbulent dynamo and the
\alpha-effect. We calculate the \alpha-coefficients for arbitrary magnetic and
fluid viscosities, background velocity and the initial magnetic field
correlations. We explicitly demonstrate that the general features of the
initial growth and late-time saturation of the magnetic fields due to the
non-linear feedback are qualitatively independent of these correlations. We
also examine the hydrodynamic limit of the magnetic field growth in a
renormalization group framework and discuss the possibilities of suppression of
the dynamo growth below a critical rotation. We demonstrate that for
Kolmogorov- (K41) type of spectra the Ekman number M >1/2 for dynamo growth to
occur.Comment: To appear in EPJB (2004
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