Lattice Boltzmann Model for The Volume-Averaged Navier-Stokes Equations

A numerical method, based on the discrete lattice Boltzmann equation, is presented for solving the volume-averaged Navier-Stokes equations. With a modified equilibrium distribution and an additional forcing term, the volume-averaged Navier-Stokes equations can be recovered from the lattice Boltzmann equation in the limit of small Mach number by the Chapman-Enskog analysis and Taylor expansion. Due to its advantages such as explicit solver and inherent parallelism, the method appears to be more competitive with traditional numerical techniques. Numerical simulations show that the proposed model can accurately reproduce both the linear and nonlinear drag effects of porosity in the fluid flow through porous media.Comment: 9 pages, 2 figure

Housing and household wealth inequality: Evidence from the People's Republic of China

We examine the issue of the widening wealth inequality in the People's Republic of China (PRC) from the perspective of housing. Using China Household Finance Survey (CHFS) data from 2011, we find that the PRC's wealth inequality including housing is much larger than income inequality. Housing value appreciation, in particular, contributes to wealth inequality by allowing households to enjoy equity market premium through investing more in equity markets and taking a higher position in risky assets

EGTSyn: Edge-based Graph Transformer for Anti-Cancer Drug Combination Synergy Prediction

Combination therapy with multiple drugs is a potent therapy strategy for complex diseases such as cancer, due to its therapeutic efficacy and potential for reducing side effects. However, the extensive search space of drug combinations makes it challenging to screen all combinations experimentally. To address this issue, computational methods have been developed to identify prioritized drug combinations. Recently, Convolutional Neural Networks based deep learning methods have shown great potential in this community. Although the significant progress has been achieved by existing computational models, they have overlooked the important high-level semantic information and significant chemical bond features of drugs. It is worth noting that such information is rich and it can be represented by the edges of graphs in drug combination predictions. In this work, we propose a novel Edge-based Graph Transformer, named EGTSyn, for effective anti-cancer drug combination synergy prediction. In EGTSyn, a special Edge-based Graph Neural Network (EGNN) is designed to capture the global structural information of chemicals and the important information of chemical bonds, which have been neglected by most previous studies. Furthermore, we design a Graph Transformer for drugs (GTD) that combines the EGNN module with a Transformer-architecture encoder to extract high-level semantic information of drugs.Comment: 15 pages,4 figures,6 table

Near-Infrared Chiral Plasmonic Metasurface Absorbers

Chirality plays an essential role in the fields of biology, medicine and physics. However, natural materials exhibit very weak chiroptical response. In this paper, near-infrared chiral plasmonic metasurface absorbers are demonstrated to selectively absorb either the left-handed or right-handed circularly polarized light for achieving large circular dichroism (CD) across the wavelength range from 1.3 µm to 1.8 µm. It is shown that the maximum chiral absorption can reach to 0.87 and that the maximum CD in absorption is around 0.70. The current chiral metasurface design is able to achieve strong chiroptical response, which also leads to high thermal CD for the local temperature increase. The high-contrast reflective chiral images are also realized with the designed metasurface absorbers. The demonstrated chiral metasurface absorbers can be applied in many areas, such as optical filters, thermal energy harvesting, optical communication, and chiral imaging