Automated UF6 Cylinder Enrichment Assay: Status of the Hybrid Enrichment Verification Array (HEVA) Project: POTAS Phase II

Pacific Northwest National Laboratory (PNNL) intends to automate the UF6 cylinder nondestructive assay (NDA) verification currently performed by the International Atomic Energy Agency (IAEA) at enrichment plants. PNNL is proposing the installation of a portal monitor at a key measurement point to positively identify each cylinder, measure its mass and enrichment, store the data along with operator inputs in a secure database, and maintain continuity of knowledge on measured cylinders until inspector arrival. This report summarizes the status of the research and development of an enrichment assay methodology supporting the cylinder verification concept. The enrichment assay approach exploits a hybrid of two passively-detected ionizing-radiation signatures: the traditional enrichment meter signature (186-keV photon peak area) and a non-traditional signature, manifested in the high-energy (3 to 8 MeV) gamma-ray continuum, generated by neutron emission from UF6. PNNL has designed, fabricated, and field-tested several prototype assay sensor packages in an effort to demonstrate proof-of-principle for the hybrid assay approach, quantify the expected assay precision for various categories of cylinder contents, and assess the potential for unsupervised deployment of the technology in a portal-monitor form factor. We refer to recent sensor-package prototypes as the Hybrid Enrichment Verification Array (HEVA). The report provides an overview of the assay signatures and summarizes the results of several HEVA field measurement campaigns on populations of Type 30B UF6 cylinders containing low-enriched uranium (LEU), natural uranium (NU), and depleted uranium (DU). Approaches to performance optimization of the assay technique via radiation transport modeling are briefly described, as are spectroscopic and data-analysis algorithms

Automated UF6 Cylinder Enrichment Assay: Status of the Hybrid Enrichment Verification Array (HEVA) Project: POTAS Phase II

Pacific Northwest National Laboratory (PNNL) intends to automate the UF6 cylinder nondestructive assay (NDA) verification currently performed by the International Atomic Energy Agency (IAEA) at enrichment plants. PNNL is proposing the installation of a portal monitor at a key measurement point to positively identify each cylinder, measure its mass and enrichment, store the data along with operator inputs in a secure database, and maintain continuity of knowledge on measured cylinders until inspector arrival. This report summarizes the status of the research and development of an enrichment assay methodology supporting the cylinder verification concept. The enrichment assay approach exploits a hybrid of two passively-detected ionizing-radiation signatures: the traditional enrichment meter signature (186-keV photon peak area) and a non-traditional signature, manifested in the high-energy (3 to 8 MeV) gamma-ray continuum, generated by neutron emission from UF6. PNNL has designed, fabricated, and field-tested several prototype assay sensor packages in an effort to demonstrate proof-of-principle for the hybrid assay approach, quantify the expected assay precision for various categories of cylinder contents, and assess the potential for unsupervised deployment of the technology in a portal-monitor form factor. We refer to recent sensor-package prototypes as the Hybrid Enrichment Verification Array (HEVA). The report provides an overview of the assay signatures and summarizes the results of several HEVA field measurement campaigns on populations of Type 30B UF6 cylinders containing low-enriched uranium (LEU), natural uranium (NU), and depleted uranium (DU). Approaches to performance optimization of the assay technique via radiation transport modeling are briefly described, as are spectroscopic and data-analysis algorithms

EEG complexity as a biomarker for autism spectrum disorder risk

BACKGROUND: Complex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG) signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE) computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD. METHODS: Using mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months. RESULTS: Multiscale entropy appears to go through a different developmental trajectory in infants at high risk for autism (HRA) than it does in typically developing controls. Differences appear to be greatest at ages 9 to 12 months. Using several machine learning algorithms with mMSE as a feature vector, infants were classified with over 80% accuracy into control and HRA groups at age 9 months. Classification accuracy for boys was close to 100% at age 9 months and remains high (70% to 90%) at ages 12 and 18 months. For girls, classification accuracy was highest at age 6 months, but declines thereafter. CONCLUSIONS: This proof-of-principle study suggests that mMSE computed from resting state EEG signals may be a useful biomarker for early detection of risk for ASD and abnormalities in cognitive development in infants. To our knowledge, this is the first demonstration of an information theoretic analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder