Designing a Distributed Space Systems Simulation in Accordance with the Simulation Interoperability Standards Organization (SISO)

Simulations are essential for engineering design. These virtual realities provide characteristic data to scientists and engineers in order to understand the details and complications of the desired mission. A standard development simulation package known as Trick is used in developing a source code to model a component (federate in HLA terms). The runtime executive is integrated into an HLA based distributed simulation. TrickHLA is used to extend a Trick simulation for a federation execution, develop a source code for communication between federates, as well as foster data input and output. The project incorporates international cooperation along with team collaboration. Interactions among federates occur throughout the simulation, thereby relying on simulation interoperability. Communication through the semester went on between participants to figure out how to create this data exchange. The NASA intern team is designing a Lunar Rover federate and a Lunar Shuttle federate. The Lunar Rover federate supports transportation across the lunar surface and is essential for fostering interactions with other federates on the lunar surface (Lunar Shuttle, Lunar Base Supply Depot and Mobile ISRU Plant) as well as transporting materials to the desired locations. The Lunar Shuttle federate transports materials to and from lunar orbit. Materials that it takes to the supply depot include fuel and cargo necessary to continue moon-base operations. This project analyzes modeling and simulation technologies as well as simulation interoperability. Each team from participating universities will work on and engineer their own federate(s) to participate in the SISO Spring 2011 Workshop SIW Smackdown in Boston, Massachusetts. This paper will focus on the Lunar Rover federate

Radiation Effects in Metal Oxides and Carbides

MD simulations of SiO2, TiO2, Cr2O3, Al2O3, MgO, and SiC, are performed to: (a) calculate TDE probability distributions and dependence on crystallographic direction, and (b) determine the number and types of defects formed with low- and high-energy PKAs and projectiles. In addition, a qualitative comparison of the MD simulation results of radiation damage in TiO2, MgO, and crystalline and amorphous SiC thin films are compared with those of in situ TEM ion beam irradiation experiments at the Sandia National Laboratories’ I3TEM facility. The TDE probability distributions show strong anisotropy and those with 50% probability agree well with the reported experimental values. Results show that MgO and TiO2 are the most radiation hard of all metal oxides investigated and that the lost long-range order in TiO2 during the ballistic phase of interaction by a 46 keV Ti projectile, reemerges as most of the produced defects anneal within tens of picoseconds. The MD simulations of Si PKAs of up to 100 keV in 3C-SiC shows only dispersed subcascades forming. The sizes of defect clusters in 3C-SiC are in general agreement with the in situ TEM irradiation experiments using a 1.7 MeV Au3+ ion beam. The defect structures show contrast changes ranging from 9.1 to 83.5 nm2, which is in general agreement with the MD simulation values ranging from 5 to 76 nm2. SiC amorphization is not observed in the MD simulations. The stored potential energy due to defect production in 3C-SiC is ~10% of the 10-100 keV Si PKAs in the MD simulations, and indistinguishable in a-SiC. Simulations using single and multiple Au projectiles show that the extent of the defect cascades strongly depend on the number of projectiles and that defect structures are in general agreement with those induced by single 1.7 MeV Au3+ ion strikes in the in situ TEM experiments. The MD simulations with 10, 20-keV projectiles in MgO produces a void of 102,500 vacancies during the ballistic phase, decreasing to 5,000 vacancies after annealing. Simulated SAED patterns and RDFs show local amorphization of MgO at the peak of the ballistic phase, which partially recrystallizes during the annealing phase

Going the distance for protein function prediction: a new distance metric for protein interaction networks

Due to an error introduced in the production process, the x-axes in the first panels of Figure 1 and Figure 7 are not formatted correctly. The correct Figure 1 can be viewed here: http://dx.doi.org/10.1371/annotation/343bf260-f6ff-48a2-93b2-3cc79af518a9In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.MC, HZ, NMD and LJC were supported in part by National Institutes of Health (NIH) R01 grant GM080330. JP was supported in part by NIH grant R01 HD058880. This material is based upon work supported by the National Science Foundation under grant numbers CNS-0905565, CNS-1018266, CNS-1012910, and CNS-1117039, and supported by the Army Research Office under grant W911NF-11-1-0227 (to MEC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology

A distinct and active bacterial community in cold oxygenated fluids circulating beneath the western flank of the Mid-Atlantic ridge

The rock-hosted, oceanic crustal aquifer is one of the largest ecosystems on Earth, yet little is known about its indigenous microorganisms. Here we provide the first phylogenetic and functional description of an active microbial community residing in the cold oxic crustal aquifer. Using subseafloor observatories, we recovered crustal fluids and found that the geochemical composition is similar to bottom seawater, as are cell abundances. However, based on relative abundances and functional potential of key bacterial groups, the crustal fluid microbial community is heterogeneous and markedly distinct from seawater. Potential rates of autotrophy and heterotrophy in the crust exceeded those of seawater, especially at elevated temperatures (25 °C) and deeper in the crust. Together, these results reveal an active, distinct, and diverse bacterial community engaged in both heterotrophy and autotrophy in the oxygenated crustal aquifer, providing key insight into the role of microbial communities in the ubiquitous cold dark subseafloor biosphere

Differential limit on the extremely-high-energy cosmic neutrino flux in the presence of astrophysical background from nine years of IceCube data

We report a quasi-differential upper limit on the extremely-high-energy (EHE) neutrino flux above $5\times 10^{6}$ GeV based on an analysis of nine years of IceCube data. The astrophysical neutrino flux measured by IceCube extends to PeV energies, and it is a background flux when searching for an independent signal flux at higher energies, such as the cosmogenic neutrino signal. We have developed a new method to place robust limits on the EHE neutrino flux in the presence of an astrophysical background, whose spectrum has yet to be understood with high precision at PeV energies. A distinct event with a deposited energy above $10^{6}$ GeV was found in the new two-year sample, in addition to the one event previously found in the seven-year EHE neutrino search. These two events represent a neutrino flux that is incompatible with predictions for a cosmogenic neutrino flux and are considered to be an astrophysical background in the current study. The obtained limit is the most stringent to date in the energy range between $5 \times 10^{6}$ and $5 \times 10^{10}$ GeV. This result constrains neutrino models predicting a three-flavor neutrino flux of $E_\nu^2\phi_{\nu_e+\nu_\mu+\nu_\tau}\simeq2\times 10^{-8}\ {\rm GeV}/{\rm cm}^2\ \sec\ {\rm sr}$at$10^9\ {\rm GeV}$. A significant part of the parameter-space for EHE neutrino production scenarios assuming a proton-dominated composition of ultra-high-energy cosmic rays is excluded.Comment: The version accepted for publication in Physical Review