A liquid droplet placed on a nonuniformly heated solid surface will migrate from a high-temperature region to a low-temperature region. This study reports the development of a theoretical model and experimental investigation on the migration behavior of paraffin oil droplets induced by the unidirectional thermal gradient. Thin-film lubrication theory is employed to determine the migration velocity of droplets, and temperature dependence of viscosity is taken into account. Comparisons between experimental and numerical results are presented. An effective approach for estimating the thermocapillary migration velocity of droplets on lubrication is proposed.
Motivation: In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of the discovery and treatment of disease. Since uncovering associations via traditional experimental methods is complicated and time-consuming, many computational methods have been proposed to identify the potential associations. However, there are still challenges in accurately determining potential associations between miRNA and disease by using multisource data.
Results: In this study, we develop a Multi-view Multichannel Attention Graph Convolutional Network (MMGCN) to predict potential miRNA–disease associations. Different from simple multisource information integration, MMGCN employs GCN encoder to obtain the features of miRNA and disease in different similarity views, respectively. Moreover, our MMGCN can enhance the learned latent representations for association prediction by utilizing multichannel attention, which adaptively learns the importance of different features. Empirical results on two datasets demonstrate that MMGCN model can achieve superior performance compared with nine state-of-the-art methods on most of the metrics. Furthermore, we prove the effectiveness of multichannel attention mechanism and the validity of multisource data in miRNA and disease association prediction. Case studies also indicate the ability of the method for discovering new associations.
Ferrofluids (FFs) are stable colloidal systems consisting of single-domain magnetic particles with a diameter of approximately 10 nm coated with surfactants and dispersed in a carrier liquid. By applying an external magnetic field, these fluids can be confined, positioned, shaped and, controlled at desired places. The load capacity of a lubricant film of FF can also be increased with an appropriate magnetic field. In this paper, Fe 3 O 4 -based FFs with different saturation magnetizations (M s ) were prepared by the co-precipitation technique. The tribological experiments of FFs under different magnets distributions were conducted on a ring-on-cylinder tribometer. The results show that the magnetic field intensity distributions on the rubbing surface have a significant influence on the tribological properties of FFs. The experimental results also indicate that FFs have a good friction-reduction performance in the presence of an external magnetic field compared with the carrier liquid and that its lifetime of friction can be greatly improved.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.