The problem of inverse statistics (statistics of distances for which the signal fluctuations are larger than a certain threshold) in differentiable signals with power law spectrum, E(k) approximately k(-alpha), 3< or =alpha<5, is discussed. We show that for these signals, with random phases, exit-distance moments follow a bifractal distribution. We also investigate two dimensional turbulent flows in the direct cascade regime, which display a more complex behavior.

An efficient approach to the calculation of the E-entropy is proposed. The method is based on the idea of looking at the information content of a string nf data hv annalyzing the signal only nt thp instants when the fluctuations are larger than a certain threshold is an element of, i.e., by looking at the exit-time statistics. The practical and theoretical advantages of our method with respect to the usual one are shown by the examples of a deterministic map and a self-affine stochastic process.

We present an investigation of epsilon -entropy, h(epsilon), in dynamical systems, stochastic processes and turbulence, This tool allows for a suitable characterization of dynamical behaviours arising in systems with many different scales of motion. Particular emphasis is put on a recently proposed approach to the calculation of the epsilon -entropy based on the exit-time statistics. The advantages of this method are demonstrated in examples of deterministic diffusive maps, intermittent maps, stochastic self- and multi-affine signals and experimental turbulent data.

We review a recently proposed approach to the computation of the E-entropy of a given signal based on the exit-time statistics, i.e., one codes the signal by looking at the instants when the fluctuations are larger than a given threshold, epsilon. Moreover, we show how the exit-times statistics, when applied to experimental turbulent data, is able to highlight the intermediate-dissipative-range of turbulent fluctuations. (C) 2000 Elsevier Science B.V. All rights reserved.

We present a numerical study of two-dimensional turbulent flows in the enstropy cascade regime, with different large-scale energy sinks. In particular, we study the statistics of more-than-differentiable velocity fluctuations by means of two sets of statistical estimators, namely inverse statistics and second-order differences. In this way, we are able to probe statistical fluctuations that are not captured by the usual spectral analysis. We show that a new set of exponents associated to more-than-differentiable fluctuations of the velocity field exists.

The phase-separation process of a binary mixture with order-parameter-dependent mobility under shear flow is numerically studied. The ordering is characterized by an alternate stretching and bursting of domains which produce oscillations in the physical observables. The amplitude of such modulations reduce in time when the mobility vanishes in the bulk phase, disfavoring the growth of bubbles coming from bursted domains.

In a recent paper, we have introduced new sets of Sheffer and Brenke polynomial sequences based on higher order Bell numbers. In this paper, by using a more compact notation, we show another family of exponential polynomials belonging to the Sheffer class, called, for shortness, Sheffer-Bell polynomials. Furthermore, we introduce a set of logarithmic numbers, which are the counterpart of Bell numbers and their extensions.

In the past two decades, 7 coronaviruses have infected the human population, with two major outbreaks caused by SARS-CoV and MERS-CoV in the year 2002 and 2012, respectively. Currently, the entire world is facing a pandemic of another coronavirus, SARS-CoV-2, with a high fatality rate. The spike glycoprotein of SARS-CoV-2 mediates entry of virus into the host cell and is one of the most important antigenic determinants, making it a potential candidate for a vaccine. In this study, we have computationally designed a multi-epitope vaccine using spike glycoprotein of SARS-CoV-2.

We obtain sharp decay estimates in time in the context of Sobolev spaces for smooth solutions to the one-dimensional Jin Xin model under the diffusion scaling, which are uniform with respect to the singular parameter of the scaling. This provides the convergence to the limit nonlinear parabolic equation both for large time and for the vanishing singular parameter. The analysis is performed by means of two main ingredients.

In this paper, a reaction-diffusion prey-predator system including the fear effect of predator on prey population and group defense has been considered. The conditions for the onset of cross-diffusion-driven instability are obtained by linear stability analysis. The technique of multiple time scales is employed to deduce the amplitude equation near Turing bifurcation threshold by choosing the cross-diffusion coefficient as a bifurcation parameter.