I have recently been elected as member of the Editorial Board of AIMS Mathematics. This is an international bimonthly publication devoted to publishing peer-reviewed, high quality, research articles in all major fields of mathematics. Its impact factor in 2019 is 0.882.

I recently obtained a Msc in Bioinformatics and Biostatitsics from Universitat Oberta de Catalunya and Universitat de Barcelona. My capstone project was entitled «Machine learning methods for characterizing single-particle trajectories with anomalous diffusion» under the supervision of Ferran Reverter Comes.

Single-Particle Tracking (SPT) appears to be a potential approach to studying different dynamic processes in the life sciences with recent advances in light microscopy. The physics of life molecules has an inherent instability due to the heterogeneity of free energy states that different types of molecules can show, far from thermal equilibrium and ranging at different scales, from the nanoscale of a single molecule up to the cellular or even organism level. The classification of trajectories is a relevant topic, not only in the biological field at the molecular level but also at the level of animal and human behavior. Besides, this topic combines physical principles with some degree of uncertainty, which habilitates us for exploring the use of ML techniques.

I have recently presented a contribution in the with Francisco Pedroche on rankings. We have particularized to Spotify. While people around the world stopped, music didn’t: we have shown that during the worst months of the pandemic, the list of the Top-200 hits on Spotify, produced from the streams registered on the popular app, had 18% more songs. The most played songs worldwide, during the months studied, were Dance Monkey (Tones and I), Blinding Lights (The Weeknd), and The Box (Roddy Ricch).

This means that, regardless of whether record companies published new material or not, listeners changed their preferences more often during the pandemic, thus causing an increase in the number of songs that made the lists. Thus, the first quarter of 2019 registered 474 songs on the Top 200 list, whereas in the same period of this year, the number of hits was 557, which represents an 18% increase. This figure reveals a significant increase when compared to the 1% decrease that took place during the same period from 2018 to 2019, or the 9% increase from 2017 to 2018.

Some links to the news on this paper: MUSIC NEWS, TIME24, La Vanguardia, and ABC.

It has already been published our paper «Community detection based deep neural network (CD-DNN) architectures: a fully automated framework for Likert scales» in the mathematical journal Mathematical Methods in Applied Science, where we apply network community detection in order a suitable infrastructure for an Artificial Neural Network. This permits to efficiently use raw data from psychological questionnaires based on Likert scales.