Results from direct numerical simulations (DNS) of particle relative dispersion in three-dimensional homogeneous and isotropic turbulence at Reynolds number Re?~300 are presented. We study point-like passive tracers and heavy particles, at Stokes number St=0.6,1 and 5. Particles are emitted from localised sources, in bunches of thousands, periodically in time, allowing an unprecedented statistical accuracy to be reached, with a total number of events for two-point observables of the order of 1011.

With the aim to describe the interaction between a couple of neurons a stochastic model is proposed and formalized. In such a model, maintaining statements of the Leaky Integrate-and-Fire framework, we include a random component in the synaptic current, whose role is to modify the equilibrium point of the membrane potential of one of the two neurons and when a spike of the other one occurs it is turned on. The initial and after spike reset positions do not allow to identify the inter-spike intervals with the corresponding first passage times.

We analyze a peer-assisted Video-on-Demand (VoD) system in which users contribute their upload bandwidth to the redistribution of a video that they are downloading or that they have cached locally. Our target is to characterize the additional bandwidth that servers must supply to immediately satisfy all requests to watch a given video. We develop an approximate fluid model to compute the required server bandwidth in the sequential delivery case, as well as in controlled nonsequential swarms.

We present and compare the performances of two many-core architectures: the Nvidia Kepler and the Intel MIC both in a single system and in cluster configuration for the simulation of spin systems. As a benchmark we consider the time required to update a single spin of the 3D Heisenberg spin glass model by using the Over-relaxation algorithm. We present data also for a traditional high-end multi-core architecture: the Intel Sandy Bridge.

This paper investigates the problem of quantifying longevity risk in a quantile perspective. In this field, the idea of deepening the expected changes of future mortality rates over a single year is gaining. In the following the authors propose an approach which combines a stochastic model for the evolution of mortality rates and a quantile analysis of the mortality distribution in order to capture the trend component of longevity. An ex post analysis is proposed, relying on the past mortality experience of the Italian male population measured in the period of 1954-2008.