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Results for “PrIMe”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Inside Jupiter: What Juno Gravity Soundings Taught Us about the Gas Giant’s Interior during the Prime Mission
No abstract provided
PRIME: Powder Removal in Microgravity Environments Experiment
- In Additive Manufacturing (AM) with metal powders, layers of material are deposited and fused to form aerospace 3D printed parts - Remnant powder must be removed from interior cavities and channels to fully clean the part - AM cleaning methods have been a topic of interest
IXPE Science During the Prime Mission
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What's New in the Domestic Proton Access for SEE (>200 MeV prime)
This poster is a continuation of the ongoing tracking of proton capacity in the U.S. as it relates to radiation testing of electronics. The current state of access is presented.
PRIME-1 Annual Project Review
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A priming dose of protons alters the early cardiac cellular and molecular response to 56 Fe irradiation
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APEX: A Prime EXperiment at Jefferson Lab
APEX is an experiment at Thomas Jefferson National Accelerator Facility (JLab) in Virginia, USA, that searches for a new gauge boson (A' ) with sub-GeV mass and coupling to ordinary matter of g'~ (10{sup -6} - 10{sup -2} )e. Electrons impinge upon a fixed target of high-Z material. An A' is produced via a process analogous to photon bremsstrahlung, decaying to an e +e pair. A test run was held in July of 2010, covering m{sub A'} = 175 to 250 MeV and couplings g'/e > 10{sup -3} . A full run is approved and will cover m{sub A'} 65 to 525 MeV and g'/e > 2.3 x 10{sup -4} .
Effect of thermal aging on precipitation kinetics of gamma prime in alloy 617
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Photovoltaic system fault detection and diagnostics using laterally primed adaptive resonance theory neural network.
Abstract not provided.
Infrasound Observations From SPE 4 prime and Octocopter Operations.
Abstract not provided.
Laterally Primed Adaptive Resonance Theory
LAPART is an artificial neural network algorithm written in the Python programming language. The algorithm can learn patterns using multi-dimensional hyper boxes. It can also perfrom regression and classification calculations based on learned associations.
A-Dependence of Nuclear Transparency in Quasielastic A(e, e-prime p) at High Q**2
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CP-Violating Asymmetries in eta-prime K and Direct CP Violation Searches with BABAR in Charmless Hadronic B Meson Decays
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