Multidimensional Modeling of Atmospheric Effects and Surface Heterogeneities on Remote Sensing

The overall goal of this project is to establish a modeling capability that allows a quantitative determination of atmospheric effects on remote sensing including the effects of surface heterogeneities. This includes an improved understanding of aerosol and haze effects in connection with structural, angular, and spatial surface heterogeneities. One important objective of the research is the possible identification of intrinsic surface or canopy characteristics that might be invariant to atmospheric perturbations so that they could be used for scene identification. Conversely, an equally important objective is to find a correction algorithm for atmospheric effects in satellite-sensed surface reflectances. The technical approach is centered around a systematic model and code development effort based on existing, highly advanced computer codes that were originally developed for nuclear radiation shielding applications. Computational techniques for the numerical solution of the radiative transfer equation are adapted on the basis of the discrete-ordinates finite-element method which proved highly successful for one and two-dimensional radiative transfer problems with fully resolved angular representation of the radiation field

Continued fractions: Yet another tool to overcome the curse of dimensionality

The authors provide a rapid prediction method, in which a larger number of antecedents than currently considered is accounted for. To this end, they encode the successive (possibly rescaled) values of a time series, as the partial quotients of a continued fraction, resulting in a number from the unit interval. The accuracy of a ruled-based system utilizing this coding is investigated to some extent. Qualitative criteria for the applicability of the algorithm are formulated

Fuzzy fractals, chaos, and noise

To distinguish between chaotic and noisy processes, the authors analyze one- and two-dimensional chaotic mappings, supplemented by the additive noise terms. The predictive power of a fuzzy rule-based system allows one to distinguish ergodic and chaotic time series: in an ergodic series the likelihood of finding large numbers is small compared to the likelihood of finding them in a chaotic series. In the case of two dimensions, they consider the fractal fuzzy sets whose {alpha}-cuts are fractals, arising in the context of a quadratic mapping in the extended complex plane. In an example provided by the Julia set, the concept of Hausdorff dimension enables one to decide in favor of chaotic or noisy evolution

Fuzzy risk analysis for nuclear safeguards

Analysis of a safeguards system, based on the notion of fuzzy sets and linguistic variables, concerns such as complexity and inherent imprecision in estimating the possibility of loss or compromise. The automated risk analysis allows the risk to be determined for an entire system based on estimates for lowest level components and the component proportion. In addition, for each component (asset) the most effective combination of protection mechanisms against a given set of threats is determined. A distinction between bar and featured risk is made

Interesting Structures: Education and Outreach at the RCSB Protein Data Bank

The Research Collaboratory for Structural Bioinformatics Protein Data Bank is part of an international consortium that works not only to make the data describing the 3-D shape of biological macromolecules available worldwide, but also to increase their usefulness in science, medicine, and education

Smoke clearing by high energy laser beams

We describe the clearing phenomenon that occurs when a continuous wave (CW) high energy laser beam, incident upon a cloud of hygroscopic droplets, vaporizes these droplets. We consider the case when the incident wavelength is greater than the average droplet radius. Williams' model is used to describe the vaporization of a single droplet. The propagation of the laser beam is described by the radiative transfer equation in a slab geometry. The radiative transfer equation is solved using the method of successive orders of scattering

Hydrodynamics of evaporating aerosols irradiated by intense laser beams

An analysis is presented describing the interactions of atmospheric aerosols with a high-intensity laser beam propagating along an atmospheric path. For the case of moderate beam irradiances, diffusive mass transport and conductive energy transport dominate the aerosol-beam interactions. In this regime, the coupled aerosol-beam equations are solved numerically to obtain the spatic-temporal behavior of the propagating beam, and of the irradiated aerosols. For higher beam irradiances, convective transport of mass, energy and momentum away from the irradiated aerosols must be considered. The hydrodynamic equations are solved in the surrounding medium for this regime subject to appropriate ''jump conditions'' at the surface of the irradiated aerosol. Numerical examples illustrative of both regimes are given for the case of irradiated water aerosol droplets. 11 refs., 6 figs

RCSB PDB Mobile: iOS and Android mobile apps to provide data access and visualization to the RCSB Protein Data Bank.

SummaryThe Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) resource provides tools for query, analysis and visualization of the 3D structures in the PDB archive. As the mobile Web is starting to surpass desktop and laptop usage, scientists and educators are beginning to integrate mobile devices into their research and teaching. In response, we have developed the RCSB PDB Mobile app for the iOS and Android mobile platforms to enable fast and convenient access to RCSB PDB data and services. Using the app, users from the general public to expert researchers can quickly search and visualize biomolecules, and add personal annotations via the RCSB PDB's integrated MyPDB service.Availability and implementationRCSB PDB Mobile is freely available from the Apple App Store and Google Play (http://www.rcsb.org)

Simulation of Facility Operations and Materials Accounting for a Combined Reprocessing/Mox Fuel Fabrication Facility

We are developing a computer model of facility operations and nuclear materials accounting for a facility that reprocesses spent fuel and fabricates mixed oxide (MOX) fuel rods and assemblies from the recovered uranium and plutonium. The model will be used to determine the effectiveness of various materials measurement strategies for the facility and, ultimately, of other facility safeguards functions as well. This portion of the facility consists of a spent fuel storage pond, fuel shear, dissolver, clarifier, three solvent-extraction stages with uranium-plutonium separation after the first stage, and product concentrators. In this facility area mixed oxide is formed into pellets, the pellets are loaded into fuel rods, and the fuel rods are fabricated into fuel assemblies. These two facility sections are connected by a MOX conversion line in which the uranium and plutonium solutions from reprocessing are converted to mixed oxide. The model of the intermediate MOX conversion line used in the model is based on a design provided by Mike Ehinger of Oak Ridge National Laboratory (private communication). An initial version of the simulation model has been developed for the entire MOX conversion and fuel fabrication sections of the reprocessing/MOX fuel fabrication facility, and this model has been used to obtain inventory difference variance estimates for those sections of the facility. A significant fraction of the data files for the fuel reprocessing section have been developed, but these data files are not yet complete enough to permit simulation of reprocessing operations in the facility. Accordingly, the discussion in the following sections is restricted to the MOX conversion and fuel fabrication lines. 3 tabs

Plutonium isotopic determination from gamma-ray spectra

The use of low- and medium-resolution room-temperature detectors for the nondestructive assay of nuclear materials has widespread applications to the safeguarding of nuclear materials. The challenge to using these detectors is the inherent difficulty of the spectral analysis to determine the amount of specific nuclear materials in the measured samples. This is especially true for extracting plutonium isotopic content from low- and medium-resolution spectral lines that are not well resolved. In this paper, neural networks trained by stochastic and singular value decomposition algorithms are applied to retrieve the plutonium isotopic content from a simulated NaI spectra. The simulated sample consists of isotopes {sup 238}Pu, {sup 239}Pu, {sup 240}Pu, {sup 241}Pu, {sup 242}Pu, and {sup 241}Am. It is demonstrated that the neutral network optimized by singular value decomposition (SVD) and stochastic training algorithms is capable of estimating plutonium content consistently resulting in an average error much smaller than the error previously reported