We compute the metric fluctuations induced by a turbulent energy-matter tensor within the first order post-Minkowskian approximation. It is found that the turbulent energy cascade can in principle interfere with the process of black hole formation, leading to a potentially strong coupling between these two highly nonlinear phenomena.

The optimal Orlicz target space and the optimal rearrangement- invariant target space are exhibited for embeddings of fractional-order Orlicz-Sobolev spaces. Both the subcritical and the supercritical regimes are considered. In particular, in the latter case the relevant Orlicz-Sobolev spaces are shown to be embedded into the space of bounded continuous functions in R^n. This is a joint work with Andrea Cianchi, Lubos Pick and Lenka Slavikova.

Discussion on the meeting on "statistical approaches to inverse problem"

Given a bounded open set in [Formula presented], [Formula presented], and a sequence [Formula presented] of compact sets converging to an [Formula presented]-dimensional manifold [Formula presented], we study the asymptotic behaviour of the solutions to some minimum problems for integral functionals on [Formula presented], with Neumann boundary conditions on [Formula presented].

We present computer simulations of the response of a flexoelectric blue phase network, either in bulk or under confinement, to an applied field. We find a transition in the bulk between the blue phase I disclination network and a parallel array of disclinations along the direction of the applied field. Upon switching off the field, the system is unable to reconstruct the original blue phase but gets stuck in a metastable phase. Blue phase II is comparatively much less affected by the field.

In this paper we devise a microscopic (agent-based) mathematical model for reproducing crowd behavior in a specific scenario: a number of pedestrians, consisting of numerous social groups, flow along a corridor until a gate located at the end of the corridor closes. People are not informed about the closure of the gate and perceive the blockage observing dynamically the local crowd conditions. Once people become aware of the new conditions, they stop and then decide either to stay, waiting for reopening, or to go back and leave the corridor forever.

Models of networks play a major role in explaining and reproducing empirically observed patterns. Suitable models can be used to randomize an observed network while preserving some of its features, or to generate synthetic graphs whose properties may be tuned upon the characteristics of a given population. In the present paper, we introduce the Fitness-Corrected Block Model, an adjustable-density variation of the well-known Degree-Corrected Block Model, and we show that the proposed construction yields a maximum entropy model.