The <Emphasis Type=Italic>p</Emphasis>-Daugavet property for function spaces

A natural extension of the Daugavet property for p-convex Banach function spaces and related classes is analysed. As an application, we extend the arguments given in the setting of the Daugavet property to show that no reflexive space falls into this class.     

On the approximate controllability of stochastic stokes systems

In this paper, we analyze the approximate controllability in quadratic mean of some systems governed by stochastic partial differential equations of the Stokes kind. When the noise is state-independent, we obtain satisfactory results, similar to those known for the corresponding deterministic system. In the more complicate case of a multiplicative noise, we are able to give (only) partial results. More precisely, we prove in this case that approximate controllability is equivalent to the unique continuation property for a particular backward (adjoint) stochastic system     

Measure-Preserving Functions and the Independence Copula

We solve a problem recently proposed by Kolesárová et al. Specifically, we prove that a necessary and sufficient condition for a given copula to be the independence or product copula is for the pair of measure-preserving transformations representing the copula to be independent as random variables.     

Evaluation of hip fracture risk using a hyper-parametric model based on the Locally Linear Embedding technique

The hip fracture is one of the most common diseases for elder people and also, one of the most worrying one since it usually is the starting point of further complications for both, the health of the patient and their daily life. Additionally, reports shown that there exist differences between people living in different regions, thus limiting the use of global models. In this work we propose a hip fracture prediction tool for a local region, using clinical data of the population of that region. The data is processed with a dimensionality reduction tool in combination with and hyper-parametrization process and the corresponding hyper-parameter optimization process for obtaining good predictions in the diagnoses, as the results shown.