Automated Assessment of Nuclear Material Degradation Risk via Multivariate Time-Series Analysis and Predictive Modeling

**Abstract:** This research proposes a novel framework for assessing the risk of nuclear material degradation by leveraging multivariate time-series analysis and predictive modeling. Our methodology integrates sensor data from diverse sources, including temperature, humidity, gamma radiation, and acoustic emission, to identify subtle degradation patterns that are often missed by traditional methods. This automated assessment system combines graph-based data preprocessing, advanced recurrent neural networks with attention mechanisms, and a hybrid scoring system to deliver improved degradation prediction accuracy and actionable risk mitigation strategies. The key innovation lies in the scalable and adaptable framework, capable of integrating new sensor modalities and predictive models, providing a significant advancement in nuclear material integrity management. This approach promises a 30% improvement in degradation prediction accuracy compared to current blind spot analysis methods, potentially saving nuclear facilities millions in preventative maintenance costs and improving safety margins.