In the last years, several safety automotive concepts have been proposed, for instance the cruise control and the automatic brakes systems. The proposed systems are able to take the control of the vehicle when a dangerous situation is detected. Less effort was produced in driver aggressiveness in order to mitigate the dangerous situation. In this paper we propose an approach in order to identify the driver aggressiveness exploring the usage of unsupervised machine learning techniques. A real world case study is performed to evaluate the effectiveness of the proposed method.

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 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.

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner.

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.

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.

This poster describes some activities developed by CNR and its third party within EoCoE

This presentation describes activities developed by CNR within EoCoE

A general methodology, which consists in deriving two-dimensional finite-difference schemes which involve numerical fluxes based on Dirichlet-to-Neumann maps (or Steklov-Poincare operators), is first recalled. Then, it is applied to several types of diffusion equations, some being weakly anisotropic, endowed with an external source. Standard finite-difference discretizations are systematically recovered, showing that in absence of any other mechanism, like e.g.