A mixed directed-undirected data structure for a parallel implementation of a domain decomposition algorithm

The choice of data structures influences the parallelization, efficiency and the manageability of a mesh refinement program. We introduce a mixed directed-undirected graph that combines both communication and scheduling needs. An inverted index is maintained for the directed graph to improve code performance and readability.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract No. W-7405-Eng-48.     

Dispersion modeling by kinematic simulation: Cloud dispersion model

A new technique has been developed to compute mean and fluctuating concentrations in complex turbulent flows (tidal current near a coast and deep ocean). An initial distribution of material is discretized into any small clouds which are advected by a combination of the mean flow and large scale turbulence. The turbulence can be simulated either by kinematic simulation (KS) or direct numerical simulation. The clouds also diffuse relative to their centroids; the statistics for this are obtained from a separate calculation of the growth of individual clouds in small scale turbulence, generated by KS. The ensemble of discrete clouds is periodically re-discretized, to limit the size of the small clouds and prevent overlapping. The model is illustrated with simulations of dispersion in uniform flow, and the results are compared with analytic, steady state solutions. The aim of this study is to understand how pollutants disperses in a turbulent flow through a numerical simulation of fluid particle motion in a random flow field generated by Fourier modes. Although this homogeneous turbulent is rather a “simple” flow, it represents a building block toward understanding pollutant dispersion in more complex flow. The results presented here are preliminary in nature, but we expect that similar qualitative results should be observed in a genuine turbulent flow.     

Symmetric Markov chains in ℤd: How fast can they move?

Summary Consider a reversible Markov chain X _n which takes values in a subset of ^d. If the steps of the chain are uniformly bounded and the invariant measure satisfies a mild regularity condition, Varopoulos, Carne and Kesten have obtained estimates on \lambda n^{1/2} )$$ " align="middle" border="0"> which exhibit a Gaussian tail in but blow up as n. Following Kesten's approach we derive bounds which are uniform in n in some special cases. Our main result, however, is an example which shows that in general the estimates of Varopoulos, Carne and Kesten are essentially best possible.Research partially supported by an S.E.R.C. (U.K.) visiting fellowship and an operating grant from N.S.E.R.C. of Canada     

Automated separation and measurement of radioxenon for the Comprehensive Test Ban Treaty

A fully automatic radioxenon sampler/analyzer (ARSA) has been developed and demonstrated for the collection and quantitative measurement of the four xenon radionuclides,^131mXe(11.9 d),^133mXe(2.2 d),¹³³Xe(5.2 d), and¹³⁵Xe(9.1 hr), in the atmosphere. These radionuclides are important signatures in monitoring for compliance to a Comprehensive Test Ban Treaty (CTBT). Activity ratios of these radionuclides permit source attribution. Xenon, continuously and automatically separated from the atmosphere, is automatically analyzed by electron-photon coincidence spectrometry providing a lower limit of detection of about 100 μBq/m³. The demonstrated detection limit is about 100 times better than achievable with reported laboratory-based procedures for the short-time collection intervals of interest.     

An in silico ensemble method for lead discovery: decision forest

Recent progress in combinatorial chemistry and parallel synthesis has radically changed the approach to drug discovery in the pharmaceutical industry. At present, thousands of compounds can be made in a short period, creating a need for fast and effective in silico methods to select the most promising lead candidates. Decision forest is a novel pattern recognition method, which combines the results of multiple distinct but comparable decision tree models to reach a consensus prediction. In this article, a decision forest model was developed using a structurally diverse training data set containing 232 compounds whose estrogen receptor binding activity was tested at the U.S. Food and Drug Administration (FDA)'s National Center for Toxicological Research (NCTR). The model was subsequently validated using a test data set of 463 compounds selected from the literature, and then applied to a large data set with 57,145 compounds as a screening example. The results show that the decision forest method is a fast, reliable and effective in silico approach, which could be useful in drug discovery.     

Environmental monitoring on shellfish using UV laser ablation ICP-MS

Laser ablation inductive coupled plasma – mass spectrometry (LA-ICP-MS) was used to analyse minor and trace elements in the hard parts of a shellfish, representing the environmental changes. A large, long-lived bivalve such as Arctica islandica can produce historical records of trace element fluctuations in seawater. Analytical traverses using LA-ICP-MS were performed from the inner to the outer wall of the shell at different locations. The development of the UV laser system now offers spatial resolution of craters of 10 m diameter, enabling several analyses between the various growthbands. The elemental changes between growthbands of the shell are discussed, showing the potential for precise determination of heavy-metal pollution over the years.