Using eddy covariance data to detect nuclear reactor operational status
Not Available
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Engineering topics
Casleton, Emily
Publications and source records attributed to Casleton, Emily.
Tracking the location of a road-constrained radioactive source with a network of detectors
Not Available
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Monitoring Nuclear Reactor and Reprocessing Facility Operations Externally using Radiation Detection [Slides]
Abstract not provided.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Framework for Analysis and Visualization of Simulation Data (VizAly)
As supercomputing resources increase, cosmological scientists are able to run more detailed and larger simulations generating massive amounts of data. Analyzing these simulations with an available open-source toolkit is important for collaborative Department of Energy scientific discovery across labs, universities, and other partners. Developed software as a part of this collection include: comparing data with other existing simulations, verifying and validating results with observation databases, new halo finder algorithms, and using analytical tools to get insights into the physics of the cosmological universe. The goal of this software project is to provide a set of open-source libraries, tools, and packages for large-scale cosmology that allows scientists to visualize, analyze, and compare large-scale simulation and observational data sets. Developed software will provide a variety of methods for processing, visualization, and analysis of astronomical observation and cosmological simulation data. These tools are intended for deployment on multiple scientific computing platforms, including but not limited to personal computers, cloud computing, experimental sites (telescopes) and high performance supercomputers.
Wendelberger, Joanne↗
A local structure model for network analysis
The statistical analysis of networks is a popular research topic with ever widening applications. Exponential random graph models (ERGMs), which specify a model through interpretable, global network features, are common for this purpose. In this study we introduce a new class of models for network analysis, called local structure graph models (LSGMs). In contrast to an ERGM, a LSGM specifies a network model through local features and allows for an interpretable and controllable local dependence structure. In particular, LSGMs are formulated by a set of full conditional distributions for each network edge, e.g., the probability of edge presence/absence, depending on neighborhoods of other edges. Additional model features are introduced to aid in specification and to help alleviate a common issue (occurring also with ERGMs) of model degeneracy. Finally, the proposed models are demonstrated on a network of tornadoes in Arkansas where a LSGM is shown to perform significantly better than a model without local dependence.
97 MATHEMATICS AND COMPUTING↗