The production of electric energy has been increasingly deriving from renewable sources, and it is projected that this trend will continue over the next years. Among these sources, the use of solar energy is supposed to be considered the main future solution to global climate change and fossil fuel emissions. Since current photovoltaic (PV) panels are estimated to have an average life of 25–30 years, their disposal is very important for the recovery of materials already used and for introducing them again into other processing cycles. Innovative solutions are therefore needed to minimize the emissions of pollutants derived from the recycling of photovoltaic panels that no longer work. In this research, an analysis of data related to durability, recyclability rates, different possible design layouts and materials used in the design and manufacture of PV panels was conducted. Through a Design for Recycling (DfR) and a Design for Durability (DfD), the authors identified the optimal materials, the best geometries and geometric proportions as well as the most convenient geometric and dimensional tolerances in the couplings between the layers and the components that comprise the panel to attain the most current, efficient and effective solutions for recycling end-of-life (EoL) PV panels and for longer durability.

The dynamic behavior of a Powered Two-Wheeler (PTW) is much more complicated than that of a car, which is due to the strong coupling between the longitudinal and lateral dynamics produced by the large roll angles. This makes the analysis of the dynamics, and therefore the design and synthesis of the controller, particularly complex and difficult. In relation to assistance in dangerous situations, several recent manuscripts have suggested devices with limitations of cornering velocity by proposing restrictive models. However, these models can lead to repulsion by the users of PTW vehicles, significantly limiting vehicle performance. In the present work, the authors developed an Advanced Rider-cornering Assistance System (ARAS) based on the skills learned by riders running across curvilinear trajectories using Artificial Intelligence (AI) and Neural Network (NN) techniques. New algorithms that allow the value of velocity to be estimated by prediction accuracy of up to 99.06% were developed using the K-Nearest Neighbor (KNN) Machine Learning (ML) technique.