Alarms periodically emerge for viral pneumonia infections due to coronavirus. In all cases, these are zoonoses passing the barrier between species and infect humans. The legitimate concern of the international community is due to the fact that the new identified coronavirus, named SARS-CoV-2 (previously called 2019-nCoV), has a quite high mortality rate, around 2%, and a strong ability to spread, with an estimated reproduction number higher than 2.

The problem of inverse statistics (statistics of distances for which the signal fluctuations are larger than a certain threshold) in differentiable signals with power law spectrum, E(k) approximately k(-alpha), 3< or =alpha<5, is discussed. We show that for these signals, with random phases, exit-distance moments follow a bifractal distribution. We also investigate two dimensional turbulent flows in the direct cascade regime, which display a more complex behavior.

We present a numerical study of two-dimensional turbulent flows in the enstropy cascade regime, with different large-scale energy sinks. In particular, we study the statistics of more-than-differentiable velocity fluctuations by means of two sets of statistical estimators, namely inverse statistics and second-order differences. In this way, we are able to probe statistical fluctuations that are not captured by the usual spectral analysis. We show that a new set of exponents associated to more-than-differentiable fluctuations of the velocity field exists.

A Round Robin exercise was implemented by ESA to compare different classification methods in detecting clouds from images taken by the PROBA-V sensor. A high-quality dataset of 1350 reflectances and Clear/Cloudy corresponding labels had been prepared by ESA in the framework of the exercise. Motivated by both the experience acquired by one of the authors in this exercise and the availability of such a reliable annotated dataset, we present a full assessment of the methodology proposed therein.

The Detweiler-Barack-Sago redshift function for particles moving along slightly eccentric equatorial orbits around a Kerr black hole is currently known up to the second order in eccentricity, second order in spin parameter, and the 8.5 post-Newtonian order. We improve the analytical computation of such a gaugeinvariant quantity by including terms up to the fourth order in eccentricity at the same post-Newtonian approximation level.

An efficient approach to the calculation of the E-entropy is proposed. The method is based on the idea of looking at the information content of a string nf data hv annalyzing the signal only nt thp instants when the fluctuations are larger than a certain threshold is an element of, i.e., by looking at the exit-time statistics. The practical and theoretical advantages of our method with respect to the usual one are shown by the examples of a deterministic map and a self-affine stochastic process.