An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing.

A novel and innovative solution addressing wind turbines' main bearing failure predictions using SCADA data is presented. This methodology enables to cut setup times and has more flexible requirements when compared to the current predictive algorithms. The proposed solution is entirely unsupervised as it does not require the labeling of data through work orders logs.

Efficient, fast and reabsorption-free perovskite nanocrystal-based sensitized plastic scintillators.

The urgency for affordable and reliable detectors for ionizing radiation in medical diagnostics, nuclear control and particle physics is generating growing demand for scintillator devices combining efficient scintillation, fast emission lifetime, high interaction probability with ionizing radiation and mitigated reabsorption losses in large-volume/high-density detectors. To date, the simultaneous achievement of all such features is still an open challenge. Here we realize this regime with poly(methyl methacrylate) nanocomposites embedding CsPbBr perovskite nanocrystals as sensitizers for a conjugated organic dye featuring a large Stokes shift and a fast emission lifetime in the red spectral region.