This contribution outlines current research aimed at developing models for personalized type 2 diabetes mellitus (T2D) prevention in the framework of the European project PRAESIIDIUM (Physics Informed Machine Learn-ing-Based Prediction and Reversion of Impaired Fasting Glucose Management) aimed at building a digital twin for preventing T2D in patients at risk.

The IMAGO project aims to develop an innovative system that utilizes Multispectral Imaging and Augmented Reality (AR) techniques for studying and preserving cultural heritage.

Starting from 2019, the Italian Space Agency (ASI) is supporting dedicated projects for the development of new methods, tools and competences for the interpretation and the exploitation of the future measurements of the FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) experiment. FORUM will be the ninth Earth Explorer mission of the European Space Agency, scheduled for launch on a polar orbiting satellite in 2027.

Surface and vegetation monitoring is a key activity in analyzing and understanding how climate change is impacting natural resources. Moreover, identifying vegetation stress using remote-sensed data has proven to be essential in assessing said understanding, as well as in the effort to prevent or act upon extreme phenomena, such as premature land and forest dryness due to summer heatwaves in the Mediterranean area.

We study the consistency and the oracle properties of the adaptive Lasso estimator for the coefficients of a linear AR(p) time series with a strictly stationary white noise (not necessarily described by i.i.d. r.v.'s). We apply the results to INAR(p) time series and to the non-parametric inference of the fertility function of a Hawkes point process. We present some numerical simulations to emphasize the advantages of the proposed procedure with respect to more classical ones and finally we apply it to a set of epidemiological data

In this paper we deal with pedestrian modeling, aiming at simulating crowd behavior in normal and emergency scenarios, including highly congested mass events. We are specifically concerned with a new agent-based, continuous-in-space, discrete-in-time, nondifferential model, where pedestrians have finite size and are compressible to a certain extent. The model also takes into account the pushing behavior appearing at extremely high densities. The main novelty is that pedestrians are not assumed to generate any kind of "field" which governs the dynamics of the others in the space around them.

In this paper, we study fluctuations and precise deviations of cumulative INAR time series, both in a non-stationary and in a stationary regime. The theoretical results are based on the recent mod- convergence theory as presented in Féray et al., 2016. We apply our findings to the construction of approximate confidence intervals for model parameters and to quantile calculation in a risk management context.

Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements of the electric field on the scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to the ill-posedness of the underlying mathematical problem. However, it is difficult to find systematic comparisons involving a wide variety of methods. Further, existing comparisons rarely take into account the variability of the results with respect to the input parameters.