An X-ray computed μ-tomography analysis for the characterization of 3D-heart shape in a model of cardiac plasticity

The understanding of the crucial interplay between heart form and function through the development of new non-invasive methodologies for 3D morphological analysis has always aroused great interest in the cardio-scientists community. The X-ray computed μ- tomography (μ-CT) is a non-destructive 3D imaging technique utilizing X-rays to study the internal structures of materials, objects, or organisms. In this study, μ-CT has been used to investigate the 3D structural organization of the goldfish...

Enabling Technologies for Near Zero Power Renewable Energy Communities Model to Increase RES Penetration and Support the Electrical Power System

SDN-Assisted Client Selection to Enhance the Quality of Federated Learning Processes

An emerging modality, increasingly used by edge devices, to train machine learning models in a distributed and cooperative way is Federated Learning (FL). It combines an increase in the quality of the learning process with data privacy needs. Alongside the advantages of this emerging paradigm, however, there is a critical factor that risks seriously affecting its effectiveness in future 5G and 6G application scenarios: the possible delays deriving from the scarcity of communication resources ...

A Power Quality Event Classifier Based on Convolutional Neural Network

This article introduces a novel and accurate automatic classifier for Power Signal (PS) alterations. The efficient detection and accurate classification represent crucial steps in developing an automatic power quality (PQ) measurement system, which has become essential in contemporary settings. The objective of this article is to enhance alteration classification accuracy. This is accomplished by combining the Hilbert-Huang transform (HHT) and convolutional neural network (CNN). The HHT is em...