Nuclear multifragmentation within the framework of different statistical ensembles

The sensitivity of the Statistical Multifragmentation Model to the underlying statistical assumptions is investigated. We concentrate on its micro-canonical, canonical, and isobaric formulations. As far as average values are concerned, our results reveal that all the ensembles make very similar predictions, as long as the relevant macroscopic variables (such as temperature, excitation energy and breakup volume) are the same in all statistical ensembles. It also turns out that the multiplicity dependence of the breakup volume in the micro-canonical version of the model mimics a system at (approximately) constant pressure, at least in the plateau region of the caloric curve. However, in contrast to average values, our results suggest that the distributions of physical observables are quite sensitive to the statistical assumptions. This finding may help deciding which hypothesis corresponds to the best picture for the freeze-out stageComment: 20 pages, 7 figure

Prognosis after Myocardial Infarction - A Deep Look into Myocardial Tissue

info:eu-repo/semantics/publishedVersio

Chaotic and deterministic switching in a two-person game

We study robust long-term complex behaviour in the Rock-Scissors-Paper game with two players, played using reinforcement learning. The complex behaviour is connected to the existence of a heteroclinic network for the dynamics. This network is made of three heteroclinic cycles consisting of nine equilibria and the trajectories connecting them. We provide analytical proof both for the existence of chaotic switching near the heteroclinic network and for the relative asymptotic stability of at least one cycle in the network, leading to behaviour ranging from almost deterministic actions to chaotic-like dynamics. Our results are obtained by making use of the symmetry of the original problem, a new approach in the context of learning.learning process, dynamics, switching, chaos