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Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.
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.
High coercive fields prevalent in wurtzite ferroelectrics present a significant challenge, as they hinder efficient polarization switching, which is essential for microelectronic applications.
This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.