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).

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.

An inverse diffusion problem that appears in Magnetic Resonance dosimetry is studied. The problem is shown to be equivalent to a deconvolution problem with a known kernel. To cope with the singularity of the kernel, nonlinear regularization functionals are considered which can provide regular solutions, reproduce steep gradients and impose positivity constraints. A fast deterministic algorithm for solving the involved non-convex minimization problem is used.

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.

We propose a new iterative procedure to find the best time for re-initialization of meta-heuristic algorithms to solve combinatorial optimization problems. The sequence of algorithm executions with different random inizializations evolves at each iteration by either adding new independent executions or extending all existing ones up to the current maximum execution time. This is done on the basis of a criterion that uses a surrogate of the algorithm failure probability, where the optimal solution is replaced by the best so far one. Therefore, the new procedure can be applied in practice.

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.