The recognition of spatial patterns within agricultural fields, presenting similar yield potential areas, stable through time, is very important for optimizing agricultural practices. This study proposes the evaluation of different clustering methodologies applied to multispectral satellite time series for retrieving temporally stable (constant) patterns in agricultural fields, related to within-field yield spatial distribution. The ability of different clustering procedures for the recognition and mapping of constant patterns in fields of cereal crops was assessed.

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.

Biological interaction databases can be exploited by pathway-level enrichment methods for downstream analyses in biological and biomedical settings. Classical enrichment methods rely on predefined lists of pathways, biasing the search towards known pathways and risking to overlook unknown, yet important functional modules. To overcome this limitation, so-called de novo network enrichment approaches extract novel pathways from large, molecular interaction networks given molecular profiles of patients, e.g.

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.

We develop a modelling approach for the optimal spatiotemporal control of invasive species in natural protected areas of high conservation value. The proposed approach, based on diusion equations, is spatially explicit, and includes a functional response (Holling type II) which models the control rate as a function of the invasive species density. We apply a budget constraint to the control program and search for the optimal eort allocation for the minimization of the invasive species density.

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

The equations describing engineering and real-life models are usually derived in an approximated way. Thus, in most cases it is necessary to deal with equations containing some kind of perturbation. In this paper we consider fractional dfferential equations and study the eects on the continuous and numerical solution, of perturbations on the given function, over long-time intervals. Some bounds on the global error are also determined.

We describe the main issues found in the design of an efficient implementation, tailored to GPGPUs, of an Algebraic MultiGrid (AMG) preconditioner recently proposed by one of the authors and already available for CPU in the open-source BootCMatch code. The AMG method relies on a new approach for coarsening sparse symmetric positive definite matrices, which we refer as coarsening based on compatible weighted matching. It exploits maximum weight matching in the adjacency graph of the sparse matrix and the principle of compatible relaxation to define a pairwise aggregation of unknowns.

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.