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As discussed in the recent literature, several innovative car insurance concepts are proposed in order to gain advantages both for insurance companies and for drivers. In this context, the "pay how you drive" paradigm is emerging, but it is not thoroughly discussed and much less implemented. In this paper we propose an approach in order to identify the driver behaviour exploring the usage of unsupervised machine learning techniques. A real world case study is performed to evaluate the e↵ectiveness of the proposed solution. Furthermore, we discuss how the proposed model can be adopted as risk indicator for car insurance companies.

Most operational models in atmospheric physics, meteorology and climatology nowadays adopt spherical geodesic grids and require "ad hoc" developed interpolation procedures. The author does a comparison between chosen representatives of linear, distance-based and cubic interpolation schemes outlining their advantages and drawbacks in this specific application field. Numerical experiments on a standard test problem, while confirming a good performance of linear and distance-based schemes in a single interpolation step, also show their minor accuracy with respect to the cubic scheme in the more realistic simulation of advection of a meteorological field.

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Abstract. In this paper we introduce a new class of numerical schemes for the incompressible NavierStokes equations, which are inspired by the theory of discrete kinetic schemes for compressible fluids. For these approximations it is possible to give a stability condition, based on a discrete velocities version of the Boltzmann H-theorem. Numerical tests are performed to investigate their convergence and accuracy.Mathematics Subject Classification. 65M06, 76M20, 76R.

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. Crop vigor patterns, considered to be related to soils characteristics, and possibly indicative of yield potential, were derived by applying the different clustering algorithms to time series of Landsat images acquired on 94 agricultural fields near Rome (Italy). Two different approaches were applied and validated using Landsat 7 and 8 archived imagery. The first approach automatically extracts and calculates for each field of interest (FOI) the Normalized Difference Vegetation Index (NDVI), then exploits the standard K-means clustering algorithm to derive constant patterns at the field level. The second approach applies novel clustering procedures directly to spectral reflectance time series, in particular: (1) standard K-means; (2) functional K-means; (3) multivariate functional principal components clustering analysis; (4) hierarchical clustering. The different approaches were validated through cluster accuracy estimates on a reference set of FOIs for which yield maps were available for some years. Results show that multivariate functional principal components clustering, with an a priori determination of the optimal number of classes for each FOI, provides a better accuracy than those of standard clustering algorithms. The proposed novel functional clustering methodologies are effective and efficient for constant pattern retrieval and can be used for a sustainable management of agricultural fields, depending on farming systems and environmental conditions in different regions.

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