Abstract: We revisit the generation of a matter-antimatter asymmetry in the minimal extension of the Standard Model with two singlet heavy neutral leptons (HNL) that can explain neutrino masses. We derive an accurate analytical approximation to the solution of the complete linearized set of kinetic equations, which exposes the non-trivial parameter dependencies in the form of parameterization-independent CP invariants. The identification of various washout regimes relevant in different regions of parameter space sheds light on the relevance of the mass corrections in the interaction rates and clarifies the correlations of baryogenesis with other observables. In particular, by requiring that the measured baryon asymmetry is reproduced, we derive robust upper or lower bounds on the HNL mixings depending on their masses, and constraints on their flavour structure, as well as on the CP-violating phases of the PMNS mixing matrix, and the amplitude of neutrinoless double-beta decay. We also find certain correlations between low and high scale CP phases. Especially emphasizing the testable part of the parameter space we demonstrate that our findings are in very good agreement with numerical results. The methods developed in this work can help in exploring more complex scenarios.

Abstract: The recent observations from the James Webb Space Telescope have led to a surprising discovery of a significant density of massive galaxies with masses of M >= 10(10.5)M(circle dot) at redshifts of approximately z similar to 10. This corresponds to a stellar mass density of roughly rho* similar to 10(6)M(circle dot) Mpc(-3). Despite making conservative assumptions regarding galaxy formation, this finding may not be compatible with the standard.CDM cosmology that is favored by observations of CMB Anisotropies from the Planck satellite. In this paper, we confirm the substantial discrepancy with Planck's results within the.CDM framework. Assuming a value of is an element of = 0.2 for the efficiency of converting baryons into stars, we indeed find that the.CDM model is excluded at more than 99.7% confidence level (C.L.). An even more significant exclusion is found for is an element of similar to 0.1, while a better agreement, but still in tension at more than 95%, is obtained for is an element of = 0.32. This tension, as already discussed in the literature, could arise either from systematics in the JWST measurements or from new physics. Here, as a last-ditch effort, we point out that disregarding the large angular scale polarization obtained by Planck, which allows for significantly larger values of the matter clustering parameter sigma(8), could lead to better agreement between Planck and JWST within the.CDM framework. Assuming.CDM and no systematics in the current JWST results, this implies either an unknown systematic error in current large angular scale CMB polarization measurements or an unidentified physical mechanism that could lower the expected amount of CMB polarization produced during the epoch of reionization. Interestingly, the model compatible with Planck temperature-only data and JWST observation also favors a higher Hubble constant H-0 = 69.0 +/- 1.1 km/s/Mpc at 68% C.L., in better agreement with observations based on SN-Ia luminosity distances.

Abstract: Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-year mortality. The main aim of this work is to develop and validate machine-learning-based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Five machine-learning techniques were applied using a retrospective dataset. The evaluation was performed with five metrics computed by a resampling strategy: Accuracy, the area under the ROC curve, Specificity, Sensitivity, and the Balanced Error Rate. All models reported an AUC ROC from 0.857 to 0.91. Specifically, Gradient Boosting Classifier was the best model, producing an AUC ROC of 0.91, a sensitivity of 0.858, a specificity of 0.808, and a BER of 0.1687. Information from standard procedures at hospital admission combined with machine learning techniques produced models with competitive discriminative power. Our models reach the best results reported in the state of the art. These results demonstrate that they can be used as an accurate data-driven palliative care criteria inclusion.