https://www.selleckchem.com/products/l-ornithine-l-aspartate.html The results demonstrate that total interfacial areas for natural media are typically much larger than those for ideal media comprising smooth surfaces due to the substantial contribution of surface roughness to wetting-film interfacial area. The degree to which fluid-fluid interfacial area is influenced by roughness is a function of fluid-retention characteristics and the nature of the rough surfaces. The full impact of roughness may be masked to some degree due to the formation of thick wetting films, which is explicitly quantified by the model. Application of the model provides insight into the importance of the interplay between pore-scale distribution and configuration of wetting fluid and the surface properties of solids.Convolutional neural networks (CNNs) have exhibited superior performance in various types of classification and prediction tasks, but their interpretability remains to be low despite years of research effort. It is crucial to improve the ability of existing models to interpret deep neural networks from both theoretical and practical perspectives and to develop new neural network models with interpretable representations. The aim of this paper is to propose a set of novel masked CNN (MCNN) models with better ability to interpret networks and more accurate prediction. The key ideas behind MCNNs are to introduce a latent binary network to extract informative regions of interest that contain important signals for prediction and to integrate the latent binary network with CNNs to achieve better prediction in various supervised learning problems. Extensive numerical studies demonstrate the competitive performance of the proposed MCNN models.Glass micromodels have been extensively used to simulate and investigate crude oil, brine, and surface interactions due to their homogeneous wettability, rigidity, and ability to precisely capture a reservoir's areal heterogeneity. Most micromodels