Complex special nuclear material (SNM) storage systems can benefit from automated monitoring and data integration systems that maximize safety and security and optimize system maintainability. Current methods of verification, which rely on physical access, are costly and labor intensive. A prototype data analysis. system for nuclear material monitoring is being developed through a joint effort by Argonne National Laboratory (ANL) and Lockheed Martin Idaho Technologies Company (LMITCO). The system synthesizes information from various sources and applies advanced data analysis to predict sensor faults and detect material instabilities and security/safeguards problems. The system makes use of Argonne's Multivariate State Estimation Technique, or MSET, to provide an early warning system for the performance of sensors and processes, The system is being implemented and tested at the Safeguard Technology Evaluation Laboratory (STEL) at ANL-W. The STEL was installed at a Fuel Manufacturing Facility (FMF) special nuclear materials vault at ANL-W in 1997 as part of a DOE Plutonium Focus Area Project. The STEL provides the infrastructure for the demonstration and integration of technologies for monitoring plutonium-bearing materials in various storage configurations. Real sensors located within the STEL are being used to ''calibrate'' and validate. software while simulated sensors are used to mockup larger-scale …

These topics are covered: Regulation of enzyme synthesis; Activation of acetate to acetyl-CoA; Biochemistry of acetyl-CoA cleavage; Electron transport; Other enzymes implicated in the pathway of acetate conversion to methane; and publications resulting from this work.

This paper presents an overview of the ozone human exposure and health risk analyses developed under sponsorship of the U.S. Environmental Protection Agency (EPA). These analyses are being used in the current review of the national ambient air quality standards (NAAQS) for ozone. The analyses consist of three principal steps: (1) estimating short-term ozone exposure for particular populations (exposure model); (2) estimating population response to exposures or concentrations (exposure-response or concentration-response models); and (3) integrating concentrations or exposure with concentration-response or exposure-response models to produce overall risk estimates (risk model). The exposure model, called the probabilistic NAAQS exposure model for ozone (pNEM/03), incorporates the following factors: hourly ambient ozone concentrations; spatial distribution of concentrations; ventilation state of individuals at time of exposure; and movement of people through various microenvironments (e.g., outdoors, indoors, inside a vehicle) of varying air quality. Exposure estimates are represented by probability distributions. Exposure-response relationships have been developed for several respiratory symptom and lung function health effects, based on the results of controlled human exposure studies. These relationships also are probabilistic and reflect uncertainties associated with sample size and variability of response among subjects. The analyses also provide estimates of excess hospital admissions in the New York …