Ozone impact on the rubber contained in the tip cap of medical prefillable syringes.

Becton Dickinson company which makes and sells syringes needs to expand the shelf-life of a specific product. A brief evaluation of this statement conducts to the study of the impact of ozone on the rubber part of the syringe tip cap which is considered as the most prone to degradation. The formulation of the elastomer makes its susceptibility to ozone degradation high. As a result, a visual, material, and functional characterization is needed to estimate the impacts of ozone. Some theoretical elements were confirmed such as the impact of stress on the rubber, but others remain unclear like the impact on the mechanical properties. Thus, this thesis led to a greater understanding of ozone impact and enlighten further questions and analysis

Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System

This paper reports on advances to the state-of-the-art deep-learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced a 2019 Nature publication. In particular, the predictor now features not only the disruption score, as an indicator of the probability of an imminent disruption, but also a sensitivity score in real-time to indicate the underlying reasons for the imminent disruption. This adds valuable physics-interpretability for the deep-learning model and can provide helpful guidance for control actuators now that it is fully implemented into a modern Plasma Control System (PCS). The advance is a significant step forward in moving from modern deep-learning disruption prediction to real-time control and brings novel AI-enabled capabilities relevant for application to the future burning plasma ITER system. Our analyses use large amounts of data from JET and DIII-D vetted in the earlier NATURE publication. In addition to when a shot is predicted to disrupt, this paper addresses reasons why by carrying out sensitivity studies. FRNN is accordingly extended to use many more channels of information, including measured DIII-D signals such as (i) the n1rms signal that is correlated with the n =1 modes with finite frequency, including neoclassical tearing mode and sawtooth dynamics, (ii) the bolometer data indicative of plasma impurity content, and (iii) q-min, the minimum value of the safety factor relevant to the key physics of kink modes. The additional channels and interpretability features expand the ability of the deep learning FRNN software to provide information about disruption subcategories as well as more precise and direct guidance for the actuators in a plasma control system