Probabilistic and deep learning approaches for conductivity-driven nanocomposite classification.

To foster greater trust and adoption of machine learning models, particularly neural networks, it is essential to develop approaches that quantify and report epistemic uncertainties alongside random uncertainties, which often affect the accuracy of Recurrent Neural Networks (RNNs). Addressing these challenges, this study proposes a hybrid approach integrating Bayesian techniques and deep learning to improve the classification of nanocomposites with a focus on evaluating their conductivity properties. The proposed framework begins with a Bayesian Network (BN) model, which provides probabilistic insights into the conductive behavior of nanocomposites by analyzing the distribution and interaction of their constituent nanoparticles.

On the controllability of a singular nonregular methaniser system.

The control and command of singular systems of non-regular type pose very complex problems for automation engineers. The classic concepts of controllability are not applicable because of the non-regularity of the response of such systems whose internal states are no longer controllable in the temporal or frequency plane. New concepts are rather considered such as R-Controllability and Imp-Controllability combined with [Formula: see text] type stabilization approaches.