The high temporal resolution of data acquisition by geostationary satellites and their capability to resolve the diurnal cycle allows for the retrieval of a valuable source of information about geophysical parameters.

This paper presents a new hybrid approach integrating the cross-entropy (CE) algorithm and the sequential quadratic programming (SQP) technique to solve the economic load dispatch (ELD) problem related to electrical power generating units. Due to the introduction of the valve-point effect in the ELD objective function, the optimization task requires tools appropriate for a nonconvex optimization landscape.

This work investigates the capability of supervised classification methods in detecting both major tissues and subcortical structures using multispectral brain magnetic resonance images. First, by means of a realistic digital brain phantom, we investigated the classification performance of various Discriminant Analysis methods, K-Nearest Neighbor and Support Vector Machine.

The problem of asymptotic features of front propagation in stirred media is addressed for laminar and turbulent velocity fields. In particular we consider the problem in two dimensional steady and unsteady cellular flows in the limit of very fast reaction and sharp front, i.e., in the geometrical optics limit. In the steady case we provide an analytical approximation for the front speed, vf, as a function of the stirring intensity, U, in good agreement with the numerical results.

We examine spatially explicit models described by reaction-diffusion partial differential equations for the study of predator-prey population dynamics. The numerical methods we propose are based on the coupling of a finite difference/element spatial discretization and a suitable partitioned Runge-Kutta scheme for the approximation in time. The RK scheme here implemented uses an implicit scheme for the stiff diffusive term and a partitioned RK symplectic scheme for the reaction term (IMSP scheme).