Abstract: A Galactic cosmic-ray transport model featuring non-homogeneous transport has been developed over the latest years. This setup is aimed at reproducing gamma-ray observations in different regions of the Galaxy (with particular focus on the progressive hardening of the hadronic spectrum in the inner Galaxy) and was shown to be compatible with the very-high-energy gamma-ray diffuse emission recently detected up to PeV energies. In this work, we extend the results previously presented to test the reliability of that model throughout the whole sky. To this aim, we compare our predictions with detailed longitude and latitude profiles of the diffuse gamma-ray emission measured by Fermi-LAT for different energies and compute the expected Galactic nu diffuse emission, comparing it with current limits from the ANTARES collaboration. We emphasize that the possible detection of a Galactic nu component will allow us to break the degeneracy between our model and other scenarios featuring prominent contributions from unresolved sources and TeV halos.

Abstract: Neutron rich nuclei around Ca-48 have been measured with the CLARA-PRISMA setup, making use of Ca-48 on Ni-64 binary reactions, at 5.9 MeV/A. Angular distributions of gamma rays give evidence, in several transfer channels, for a large spin alignment (approximate to 70%) perpendicular to the reaction plane, making it possible to firmly establish spin and parities of the excited states. In the case of Ca-49, states arising from different types of particle-core couplings are, for the first time, unambiguously identified on basis of angular distribution, polarization and lifetime measurements. Shell model and particle-vibration coupling calculations are used to pin down the nature of the states. Evidence is found for the presence, in the same excitation energy region, of two types of coupled states, i.e. single particle coupled to either Ca-48 or Ca-50 simple configurations, and particle-vibration coupled states based on the 3- phonon of Ca-48.

Abstract: Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.