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Dimensional Analysis Meets AI for non-Newtonian Droplet Generation
Dimensional Analysis Meets AI for non-Newtonian Droplet Generation Non-Newtonian droplets are used across various applications, including pharmaceuticals, food processing, drug delivery and material science. However, predicting droplet formation using such complex fluids is challenging due to the intricate multiphase interactions between fluids with varying viscosities, elastic properties and geometrical constraints.
Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry
Introduction Pollen and tracheophyte spores are ubiquitous in the environment and present in air, soil and sediments. Accurate classification and quantification of pollen grains is critical for a range of palynological applications (Edwards et al., 2017) including the forecasting of aeroallergens, melissopalynology and forensics.
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