On the energy conversion efficiency in magnetic hyperthermia applications: A new perspective to analyze the departure from the linear regime

The success of magnetic hyperthermia cancer treatments rely strongly on the magnetic properties of the nanoparticles and their intricate dependence on the externally applied field. This is particularly more so as the response departs from the low field linear regime. In this paper we introduce a new parameter, referred to as the efficiency in converting electromagnetic energy into thermal energy, which is shown to be remarkably useful in the analysis of the system response, especially when the power loss is investigated as a function of the applied field amplitude. Using numerical simulations of dynamic hysteresis, through the stochastic Landau-Lifshitz model, we map in detail the efficiency as a function of all relevant parameters of the system and compare the results with simple-yet powerful-predictions based on heuristic arguments about the relaxation time. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4705392]

Optimal mean-variance control for discrete-time linear systems with Markovian jumps and multiplicative noises

In this paper, we consider the stochastic optimal control problem of discrete-time linear systems subject to Markov jumps and multiplicative noises under two criteria. The first one is an unconstrained mean-variance trade-off performance criterion along the time, and the second one is a minimum variance criterion along the time with constraints on the expected output. We present explicit conditions for the existence of an optimal control strategy for the problems, generalizing previous results in the literature. We conclude the paper by presenting a numerical example of a multi-period portfolio selection problem with regime switching in which it is desired to minimize the sum of the variances of the portfolio along the time under the restriction of keeping the expected value of the portfolio greater than some minimum values specified by the investor. (C) 2011 Elsevier Ltd. All rights reserved.