Wettability and capillary effects: Dynamics of pinch-off in unconstricted straight capillary tubes.

We study the interfacial evolution of immiscible two-phase flow within a capillary tube in the partial wetting regime using direct numerical simulation. We investigate the flow patterns resulting from the displacement of a more viscous fluid by a less viscous one under a wide range of wettability conditions. We find that beyond a wettability dependent critical capillary number, a uniform displacement by a less viscous fluid can transition into a growing finger that eventually breaks up into discrete blobs by a series of pinch-off events for both wetting and nonwetting contact angles.

Pore-scale study of water salinity effect on thin-film stability for a moving oil droplet.

Hypotheses: The interfacial dynamics in natural porous media are affected not only by the interplay between viscous and capillary forces but also the solid surface wettability. It has been hypothesized that the wettability alteration induced by changes in the water salinity is primarily caused by electric double-layer force expansion, which strongly affects the multiphase flow dynamics.

Simulations: We investigate the effect of water ionic composition and surface roughness on pore-scale wettability alteration.

End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification.

As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods.