Asymptotic bahavior for systems of nonlinear wave equations with multiple propagation speeds in three space dimensions

We consider the Cauchy problem for systems of nonlinear wave equations with multiple propagation speeds in three space dimensions. Under the null condition for such systems, the global existence of small amplitude solutions is known. In this paper, we will show that the global solution is asymptotically free in the energy sense, by obtaining the asymptotic pointwise behavior of the derivatives of the solution. Nonetheless we can also show that the pointwise behavior of the solution itself may be quite different from that of the free solution. In connection with the above results, a theorem is also developed to characterize asymptotically free solutions for wave equations in arbitrary space dimensions.Comment: The final version. 30 page

General Rule and Materials Design of Negative Effective U System for High-T_c Superconductivity

Based on the microscopic mechanisms of (1) charge-excitation-induced negative effective U in s^1 or d^9 electronic configurations, and (2) exchange-correlation-induced negative effective U in d^4 or d^6 electronic configurations, we propose a general rule and materials design of negative effective U system in itinerant (ionic and metallic) system for the realization of high-T_c superconductors. We design a T_c-enhancing layer (or clusters) of charge-excitation-induced negative effective $U$ connecting the superconducting layers for the realistic systems.Comment: 11 pages, 1 figures, 2 tables, APEX in printin

Seasonally and Fractionally Differenced Time Series

fractional differencing, Lagrange multiplier test, long memory, seasonal differencing, seasonal persistence

Asymptotic Prediction Mean Squared Error for Strongly Dependent Processes with Estimated Parameters

In this paper we deal with the prediction theory of long memory processes. After investigating the general theory relating to convergence of moments of the nonlinear least squares estimators, we evaluate the asymptotic prediction mean squared error of two predictors. One is defined by using the estimator of the differencing parameter and the other is defined by using a fixed, known differencing parameter, which is, in other words, one parametric predictor of the seasonally integrated autoregressive moving average (SARIMA) models. In this paper, results do not impose the normality assumption and deal not only with stationary time series but also with nonstationary ones. The finite sample behavior is examined by simulations using the computer program S-PLUS in terms of the asymptotic theory.Mean-squared prediction errosrs, Long memory, Seasonality, Nonlinear least squares estimators, Convergence of moments

Hyperons in neutron stars

Using the Dirac-Brueckner-Hartree-Fock approach, the properties of neutron-star matter including hyperons are investigated. In the calculation, we consider both time and space components of the vector self-energies of baryons as well as the scalar ones. Furthermore, the effect of negative-energy states of baryons is partly taken into account. We obtain the maximum neutron-star mass of $2.08\,M_{\odot}$, which is consistent with the recently observed, massive neutron stars. We discuss a universal, repulsive three-body force for hyperons in matter.Comment: 13 pages, 3 figures, 2 tables. arXiv admin note: text overlap with arXiv:1410.716

Closed-loop Identification of an Industrial Extrusion Process

This paper deals with the challenging problem of closed-loop identification for multivariable chemical processes and particularly the estimation of an open-loop plant model for a lab-scale industrial twin-screw extruder used in a powder coatings manufacturing line. The aim is to produce a low order efficient model in order to assist the scaling-up and the model-based control design of the manufacturing process. To achieve this goal, a two-stage indirect approach has been deployed which relies on the a-priori knowledge of the controller parameters in order to extract good estimates of the open-loop dynamics of the underlying process. As input excitation signals we have used multiple single variable step tests at various operating conditions (current industrial practice) carried out manually in order to generate the data-set which captures the dynamics of the extrusion process. In order to increase the efforts for obtaining a suitable plant model, we have employed various identification techniques, such as Prediction Error Methods (PEM) and Subspace Identification Methods (SIM) in order to generate candidate closed-loop models that fit to the original input-output process data. Then, a comparison of the estimated models was performed by means of the mean square error and data fitting criteria in order to select the model that best describes the dynamic behaviour of the extrusion process. Model validation based on closed-loop step responses also used as verification of the results