Uncertainty-aware and explainable machine learning for early prediction of battery degradation

Enhancing lifetime is a vital aspect in battery design and development. Lifetime evaluation requires prolonged cycling experiments. Early prediction of battery cell ageing can accelerate the development timeline as well as optimal charging schedule planning, and battery cell and pack production towards an extended lifetime. We demonstrate that an autoregressive model can be trained with limited data for early prediction capacity. Our approach robustly models the capacity degradation over the entire lifetime and outperforms previous approaches in accuracy for EOL (end of life) prediction. Our model captures the uncertainty in the prediction, allowing appropriate and reliable deployment. Explainability analysis of the proposed deep model provides cognizance of the interplay between multiple cell degradation mechanisms. Finally, we show that the model aligns with existing chemical insights into the rationale for early EOL despite not being trained for this or having received prior chemical knowledge

Low-dose CT pulmonary angiography on a 15-year-old CT scanner: a feasibility study

Background Computed tomography (CT) low-dose (LD) imaging is used to lower radiation exposure, especially in vascular imaging; in current literature, this is mostly on latest generation high-end CT systems. Purpose To evaluate the effects of reduced tube current on objective and subjective image quality of a 15-year-old 16-slice CT system for pulmonary angiography (CTPA). Material and Methods CTPA scans from 60 prospectively randomized patients (28 men, 32 women) were examined in this study on a 15-year-old 16-slice CT scanner system. Standard CT (SD) settings were 100 kV and 150 mAs, LD settings were 100 kV and 50 mAs. Attenuation of the pulmonary trunk, various anatomic landmarks, and image noise were quantitatively measured; contrast-to-noise ratios (CNR) and signal-to-noise ratios (SNR) were calculated. Three independent blinded radiologists subjectively rated each image series using a 5-point grading scale. Results CT dose index (CTDI) in the LD series was 66.46% lower compared to the SD settings (2.49 ± 0.55 mGy versus 7.42 ± 1.17 mGy). Attenuation of the pulmonary trunk showed similar results for both series (SD 409.55 ± 91.04 HU; LD 380.43 HU ± 93.11 HU; P = 0.768). Subjective image analysis showed no significant differences between SD and LD settings regarding the suitability for detection of central and peripheral PE (central SD/LD, 4.88; intra-class correlation coefficients [ICC], 0.894/4.83; ICC, 0.745; peripheral SD/LD, 4.70; ICC, 0.943/4.57; ICC, 0.919; all P > 0.4). Conclusion The LD protocol, on a 15-year-old CT scanner system without current high-end hardware or post-processing tools, led to a dose reduction of approximately 67% with similar subjective image quality and delineation of central and peripheral pulmonary arteries

The New Transatlantic Agenda: Transatlantic Security Relations between Post-Hegemonic Cooperation and Interdependence

The New Transatlantic Agenda: Transatlantic Security Relations Between Post-Hegemonic Cooperation and Interdependenc