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The eDICEML digital twin is proposed which emulates networks and hosts of an instrument-computing ecosystem. It runs natively on an ecosystem’s host or as a portable virtual machine.
Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.
Among the methods for point source carbon capture, the absorption of CO2 using aqueous amines (namely MEA) from the post-combustion gas stream is currently considered the most promising.
ORNL has developed a large area thermal neutron detector based on 6LiF/ZnS(Ag) scintillator coupled with wavelength shifting fibers. The detector uses resistive charge divider-based position encoding.
Electrochemistry synthesis and characterization testing typically occurs manually at a research facility.
This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.