Optimizing Battery Charging Using Neural Networks in the Presence of Unknown States and Parameters.

This work investigates the effectiveness of deep neural networks within the realm of battery charging. This is done by introducing an innovative control methodology that not only ensures safety and optimizes the charging current, but also substantially reduces the computational complexity with respect to traditional model-based approaches. In addition to their high computational costs, model-based approaches are also hindered by their need to accurately know the model parameters and the internal states of the battery, which are typically unmeasurable in a realistic scenario.

EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers.

In the current Information Age, it is usual to access our personal and professional information, such as bank account data or private documents, in a telematic manner. To ensure the privacy of this information, user authentication systems should be accurately developed. In this work, we focus on biometric authentication, as it depends on the user's inherent characteristics and, therefore, offers personalized authentication systems.

FGDB: a comprehensive graph database of ligand fragments from the Protein Data Bank.

This work presents Fragment Graph DataBase (FGDB), a graph database of ligand fragments extracted and generated from the protein entries available in the Protein Data Bank (PDB). FGDB is meant to support and elicit campaigns of fragment-based drug design, by enabling users to query it in order to construct ad hoc, target-specific libraries. In this regard, the database features more than 17 000 fragments, typically small, highly soluble and chemically stable molecules expressed via their canonical Simplified Molecular Input Line Entry System (SMILES) representation.

Performance evaluation for the design of a hybrid cloud based distance synchronous and asynchronous learning architecture.

The COVID-19 emergency suddenly obliged schools and universities around the world to deliver on-line lectures and services. While the urgency of response resulted in a fast and massive adoption of standard, public on-line platforms, generally owned by big players in the digital services market, this does not sufficiently take into account privacy-related and security-related issues and potential legal problems about the legitimate exploitation of the intellectual rights about contents. However, the experience brought to attention a vast set of issues, which have been addressed by implementing these services by means of private platforms.

Labile PT-INR in a Covid-19 Patient Under Long-term Vitamin K Antagonist Therapy: a Case Report.

The COronaVIrus Disease-19 (COVID-19) has been associated with thromboembolic complications, but evidence on its impact on hemorrhagic risk are still scarce. We describe a case of COVID-19 with hemorrhagic complication in the post-acute phase of the disease. The patient was a 93-year-old woman admitted to a post-acute care unit for COVID-19 patients in northern Italy.