Numerical Simulations of the Impact and Spreading of a Particulate Drop on a Solid Substrate

We present two-dimensional numerical simulations of the impact and spreading of a droplet containing a number of small particles on a flat solid surface, just after hitting the solid surface, to understand particle effects on spreading dynamics of a particle-laden droplet for the application to the industrial inkjet printing process. The Navier-Stokes equation is solved by a finite-element-based computational scheme that employs the level-set method for the accurate interface description between the drop fluid and air and a fictitious domain method for suspended particles to account for full hydrodynamic interaction. Focusing on the particle effect on droplet spreading and recoil behaviors, we report that suspended particles suppress the droplet oscillation and deformation, by investigating the drop deformations for various Reynolds numbers. This suppressed oscillatory behavior of the particulate droplet has been interpreted with the enhanced energy dissipation due to the presence of particles

Sp1 up-regulates cAMP-response-element-binding protein expression during retinoic acid-induced mucous differentiation of normal human bronchial epithelial cells.

CREB [CRE (cAMP-response element)-binding protein] is an important transcription factor that is differentially regulated in cells of various types. We recently reported that RA (retinoic acid) rapidly activates CREB without using RARs (RA receptors) or RXRs (retinoid X receptors) in NHTBE cells (normal human tracheobronchial epithelial cells). However, little is known about the role of RA in the physiological regulation of CREB expression in the early mucous differentiation of NHTBE cells. In the present study, we report that RA up-regulates CREB gene expression and that, using 5\u27-serial deletion promoter analysis and mutagenesis analyses, two Sp1 (specificity protein 1)-binding sites located at nt -217 and -150, which flank the transcription initiation site, are essential for RA induction of CREB gene transcription. Furthermore, we found that CREs located at nt -119 and -98 contributed to basal promoter activity. Interestingly, RA also up-regulated Sp1 in a time- and dose-dependent manner. Knockdown of endogenous Sp1 using siRNA (small interfering RNA) decreased RA-induced CREB gene expression. However, the converse was not true: knockdown of CREB using CREB siRNA did not affect RA-induced Sp1 gene expression. We conclude that RA up-regulates CREB gene expression during the early stage of NHTBE cell differentiation and that RA-inducible Sp1 plays a major role in up-regulating human CREB gene expression. This result implies that co-operation of these two transcription factors plays a crucial role in mediating early events of normal mucous cell differentiation of bronchial epithelial cells

Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCN) have been proposed for this problem, we point out that they significantly rely on the assumption that the decades-old balance theory always holds in the real-world. To address this limitation, we propose a novel GCN-based SNE approach, named as TrustSGCN, which corrects for incorrect embedding propagation in GCN by utilizing the trustworthiness on edge signs for high-order relationships inferred by the balance theory. The proposed approach consists of three modules: (M1) generation of each node's extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings. Furthermore, TrustSGCN learns the node embeddings by leveraging two well-known societal theories, i.e., balance and status. The experiments on four real-world signed network datasets demonstrate that TrustSGCN consistently outperforms five state-of-the-art GCN-based SNE methods. The code is available at https://github.com/kmj0792/TrustSGCN.Comment: 12 pages, 8 figures, 9 table

A Factor Analysis of Urban Railway Casualty Accidents and Establishment of Preventive Response Systems

AbstractSince the commencement of urban railways in 1974 and KTX service in 2014, the use of railways has been steadily increasing. The number of people using rail transportation has been steadily rising. As a result, this has also led to an increase in the number of passenger-related accidents that are occurring within railway stations. In an effort to prevent such accidents, much of the rail operation system is now automated. Nevertheless, the potential risks of railway accidents are very much present today. This study has utilized the railway accident databases of rail operators to allow for analysis of different types of railway accidents, age of accident victims, gender of accident victims, pedestrian facilities involved in accidents, passengers involved in accidents, and underlying causes of rail accidents. Based on these statistics and analyses, this paper proposes the development of a railway safety education program and the establishment of railway safety education centers as a means of preventing railway accidents