Several techniques have been developed in last years by automotive industry in order to protect drivers and car passengers. These methods, for instance the automatic brake systems and the cruise control, are able to intervene when there is a dangerous situation. With the aim to minimize these risks, in this paper we propose a method able to suggest to the driver the driving style to adopt in order to avoid dangerous situations.

The beneficial effects of physical activity for the prevention and management of several chronic diseases are widely recognized. Mathematical modeling of the effects of physical exercise in body metabolism and in particular its influence on the control of glucose homeostasis is of primary importance in the development of eHealth monitoring devices for a personalized medicine. Nonetheless, to date only a few mathematical models have been aiming at this specific purpose. We have developed a whole-body computational model of the effects on metabolic homeostasis of a bout of physical exercise.

Recent advances in omics profiling technologies yield ever larger amounts of molecular data. Yet, the elucidation of the molecular basis of human diseases remains an unsolved challenge. The analysis of multi-scale omics data requires integrative bioinformatic tools capable of multi-modal computing and multi-scale modeling. Unsupervised learning approaches are frequently employed to identify biomolecules and pathways involved in specific diseases.

The classic Brusselator model consists of four reactions in- volving six components A, B, D, E, X, Y. In a typical run, the final products D and E are removed instantly, while, the con- centrations of the reactants A and B are kept constant. Then, the classic Brusselator model consisting of two equations for the intermediate X and Y is obtained. When the component B is not considered constant, it is added to the mixture and the so-called full Brusselator model is considered. In this pa- per, the full Brusselator model is studied.

The Smoothed Particle Hydrodynamics (SPH) method is revisited within a Large Eddy Simulation (LES) perspective following the recent work of [1]. To this aim, LES filtering procedure is recast in a Lagrangian framework by defining a filter centred at the particle position that moves with the filtered fluid velocity.

In applied hydrodynamics it is presently a general common task to simulate flow around complex shaped ships with moving appendages. As an example the simulation of a turning circle manoeuvre of a full-appended combatant ship is common in manoeuvrability studies. Nevertheless the accurate numerical simulation of turbulent, unsteady flow around a full appended maneuvering complex-shaped hull is a challenging task, because of the geometrical complexity of the appendages present and their relative movement, generating a very complex hydrodynamic flow.

We analyze the stability of convolution quadrature methods for weakly singular Volterra integral equations with respect to a linear test equation. We prove that the asymptotic behavior of the numerical solution replicates the one of the continuous problem under some restriction on the stepsize. Numerical examples illustrate the theoretical results.

In this paper we revisit the problem of performing a QR-step on an unreduced Hessenberg matrix H when we know an "exact" eigenvalue ?0 of H. Under exact arithmetic, this eigenvalue will appear on diagonal of the transformed Hessenberg matrix H~ and will be decoupled from the remaining part of the Hessenberg matrix, thus resulting in a deflation. But it is well known that in finite precision arithmetic the so-called perfect shift can get blurred and that the eigenvalue ?0 can then not be deflated and/or is perturbed significantly.

Computing eigenvectors of graph Laplacian is a main computational kernel in data clustering, i.e., in identifying different groups such that data in the same group are similar and points in different groups are dissimilar with respect to a given notion of similarity. Data clustering can be reformulated in terms of a graph partitioning problem when the given set of data is represented as a graph, also known as similarity graph.

A genuinely two-dimensional discretization of general drift-diffusion (including incompressible Navier--Stokes) equations is proposed. Its numerical fluxes are derived by computing the radial derivatives of "bubbles" which are deduced from available discrete data by exploiting the stationary Dirichlet--Green function of the convection-diffusion operator. These fluxes are reminiscent of Scharfetter and Gummel's in the sense that they contain modified Bessel functions which allow one to pass smoothly from diffusive to drift-dominating regimes.