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Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.
The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).
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.
This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.