The problem of classifying multispectral image data is studied here. We propose a new Bayesian method for this. The method uses "a priori" spatial information modeled by means of a suitable Markov random field. The image data for each class are assumed to be i.i.d. following a multivariate Gaussian model with unknown mean and unknown diagonal covariance matrix. When the prior information is not used and the variances of the Gaussian model are equal, the method reduces to the standard K-means algorithm. All the parameters appearing in the posterior model are estimated simultaneously.

This paper is concerned with improving the performance of certain Markov chain algorithms for Monte Carlo simulation. We propose a new algorithm for simulating from multivariate Gaussian densities. This algorithm combines ideas from coupled Markov chain methods and from an existing algorithm based only on over-relaxation. The rate of convergence of the proposed and existing algorithms can be measured in terms of the square of the spectral radius of certain matrices. We present examples in which the proposed algorithm converges faster than the existing algorithm and the Gibbs sampler.

Recent studies have demonstrated the potential of dynamic contrast-enhanced magnetic resonance imaging (MRI) describing pulmonary perfusion. However, breathing motion, susceptibility artifacts, and a low signal-to-noise ratio (SNR) make automatic pixel-by-pixel analysis difficult. In the present work, we propose a novel method to compensate for breathing motion. In order to test the feasibility of this method, we enrolled 53 patients with pulmonary embolism (N = 24), chronic obstructive pulmonary disease (COPD) (N = 14), and acute pneumonia (N = 15).

During the last 20 years, three seismic sequences affected the Apenninic belt (central Italy): Colfiorito (1997-98), L'Aquila (2009) and Amatrice Visso-Norcia Campotosto (2016-17). They lasted for a long time, with a series of moderate-to-large earthquakes distributed over 40-60 km long Apenninic-trending segments. Their closeness in space and time suggested to study their aftershock sequences to highlight similarities and differences. Aftershock space migration and the distribution of aftershock inter-arrival time were studied.

The authors present a novel method for processing T1-weighted images acquired with Inversion-Recovery (IR) sequence. The method, developed within the Bayesian framework, takes into account a priori knowledge about the spatial regularity of the parameters to be estimated. Inference is drawn by means of Markov Chains Monte Carlo algorithms. The method has been applied to the processing of IR images from irradiated Fricke-agarose gels, proposed in the past as relative dosimeter to verify radiotherapeutic treatment planning systems.

Retinal gene therapy based on adeno-associated viral (AAV) vectors is safe and efficient in humans. The low intrinsic DNA transfer capacity of AAV has been expanded by dual vectors where a large expression cassette is split in two halves independently packaged in two AAV vectors. Dual AAV transduction efficiency, however, is greatly reduced compared to that obtained with a single vector. As AAV intracellular trafficking and processing are negatively affected by phosphorylation, this study set to identify kinase inhibitors that can increase dual AAV vector transduction.

Despite the development of new technologies in order to prevent the stealing of cars, the number of car thefts is sharply increasing. With the advent of electronics, new ways to steal cars were found. In order to avoid auto-theft attacks, in this paper we propose a machine learning based method to silently and continuously profile the driver by analyzing built-in vehicle sensors. We consider a dataset composed by 51 different features extracted by 10 different drivers, evaluating the efficiency of the proposed method in driver identification.

I fenomeni di trasporto, e la loro generalizzazione ai casi con reazione, costituiscono un capitolo molto importante della matematica applicata e trovano utilizzo in ambiti molto vari, che vanno dalla diffusione di sostanze inquinanti in atmosfera e in mare, ai processi industriali, alla biomatematica, alla propagazione di epidemie. Oltre alla loro rilevanza pratica, lo studio di tali fenomeni ha portato contributi molto importanti nella storia della fisica e della matematica.

We propose a mathematical model to describe enzyme-based tissue degradation in cancer therapies. The proposed model combines the poroelastic theory of mixtures with the transport of enzymes or drugs in the extracellular space. The effect of the matrix-degrading enzymes on the tissue composition and its mechanical response are accounted for. Numerical simulations in 1D, 2D and axisymmetric (3D) configurations show how an injection of matrix-degrading enzymes alters the porosity of a biological tissue.

We present a rigorous convergence result for smooth solutions to a singular semilinear hyperbolic approximation, called vector-BGK model, to the solutions to the incompressible Navier-Stokes equations in Sobolev spaces. Our proof deeply relies on the dissipative properties of the system and on the use of an energy which is provided by a symmetrizer, whose entries are weighted in a suitable way with respect to the singular perturbation parameter. This strategy allows us to perform uniform energy estimates and to prove the convergence by compactness.