Can Fractional Crystallization of a Lunar Magma Ocean Produce the Lunar Crust.

images[7]New techniques enable the study of Apollo samples and lunar meteorites in unprecedented detail, and recent orbital spectral data reveal more about the lunar farside than ever before, raising new questions about the supposed simplicity of lunar geology. Nevertheless, crystallization of a global-scale magma ocean remains the best model to account for known lunar lithologies. Crystallization of a lunar magma ocean (LMO) is modeled to proceed by two end-member processes – fractional crystallization from (mostly) the bottom up, or initial equilibrium crystallization as the magma is vigorously convecting and crystals remain entrained, followed by crystal settling and a final period of fractional crystallization (1). Physical models of magma viscosity and convection at this scale suggest that both processes are possible. We have been carrying out high-fidelity experimental simulations of LMO crystallization using two bulk compositions that can be regarded as end-members in the likely relevant range: Taylor Whole Moon (TWM) (2) and Lunar Primitive Upper Mantle (LPUM) (3). TWM is enriched in refractory elements by 1.5 times relative to Earth, whereas LPUM is similar to the terrestrial primitive upper mantle, with adjustments made for the depletion of volatile alkalis observed on the Moon. Here we extend our earlier equilibrium-crystallization experiments (4) with runs simulating full fractional crystallization.
Personal Author D. S. Draper J. F. Rapp For more info go to: or call NTIS 800-553-6847 Mon – Fri 8am to 5pm est.

Dynamic Phenotypic Plasticity in Photosynthesis and Biomass Patterns in Douglas-Fir Seedlings

images[1]As climate changes, understanding the mechanisms long-lived conifers use to adapt becomes more important. Light gradients within a forest stand vary constantly with the changes in climate, and the minimum light required for survival plays a major role in plant community dynamics. This study focuses on the dynamic plasticity of Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) seedlings grown in contrasting light environments. Plasticity in Douglas-fir seedlings was primarily achieved by a combination of the physiological processes: maximum photosynthesis, quantum yield, Fv/Fm, Km (the light constant), light compensation point, and the ratio of needle area to needle weight (specific leaf area). Specific leaf area was the most plastic of the biomass parameters measured. For more info please go to:
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Personal Author A. C. Koehn D. L. Adams D. L. Turner G. I. McDonald

Mining Tera-Scale Graphs: Theory, Engineering and Discoveries

images[2]How do we find patterns and anomalies, on graphs with billions of nodes and edges which do not fit in memory. How to use parallelism for such Tera- or Peta-scale graphs. In this thesis, we propose PEGASUS, a large scale graph mining system implemented on the top of the HADOOP platform, the open source version of MAPREDUCE. PEGASUS includes algorithms which help us spot patterns and anomalous behaviors in large graphs. PEGASUS enables the structure analysis on large graphs. We unify many different structure analysis algorithms, including the analysis on connected components, PageRank, and radius/diameter, into a general primitive called GIM-V. GIM-V is highly optimized, achieving good scale-up on the number of edges and available machines. We discover surprising patterns using GIM-V, including the 7-degrees of separation in one of the largest publicly available Web graphs, with 7 billion edges. PEGASUS also enables the inference and the spectral analysis on large graphs. We design an efficient distributed belief propagation algorithm which infer the states of unlabeled nodes given a set of labeled nodes. We also develop an eigensolver for computing top k eigenvalues and eigenvectors of the adjacency matrices of very large graphs. We use the eigensolver to discover anomalous adult advertisers in the who-follows-whom Twitter graph with 3 billion edges. In addition, we develop an efficient tensor decomposition algorithm and use it to analyze a large knowledge base tensor. Finally, PEGASUS allows the management of large graphs. We propose efficient graph storage and indexing methods to answer graph mining queries quickly. We also develop an edge layout algorithm for better compressing graphs.
Personal Author U. Kang
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