Flow Simulation of Suspension Bridge Cable Based on Lattice-Boltzmann Method

Suspension bridge is a kind of bridge which uses cables as the main bearing structure. Suspension bridge has the characteristics of saving materials and weak stiffness. With the increase of the span of suspension bridge, wind induced vibration has resulted in injury of several suspension bridges, which leads to a significant loss. Thus, it is imperative to study the wind vibration mechanism of cables. As for this problem, this paper based on motion theory of mesoscopic particles performs flow simulation of cables by LBM which is different from traditional computing method of fluid mechanics. By calculating the distribution function of the distribution on the grid of uniform flow field, the macroscopic motion law of the flow field around cables can be obtained, which can provide reference for wind resistant design of suspension

LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity

Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion of the information of multi-hop neighbors to include more nodes with homophily. However, the heterophily might differ among nodes, which requires to consider the local topology. Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module. For better fusion, we propose a novel and efficient Initial Residual Difference Connection (IRDC) to extract more informative multi-hop information. Moreover, we provide theoretical analysis on the effectiveness of LocalSim representing node homophily on synthetic graphs. Extensive evaluations over real benchmark datasets show that our proposed method, namely Local Similarity Graph Neural Network (LSGNN), can offer comparable or superior state-of-the-art performance on both homophilic and heterophilic graphs. Meanwhile, the plug-and-play model can significantly boost the performance of existing GNNs. Our code is provided at https://github.com/draym28/LSGNN.Comment: The first two authors contributed equally to this work; IJCAI2

Hydrothermal Preparation of Visible-Light-Driven N-Br-Codoped TiO

Using a facile hydrothermal method, N-Br-codoped TiO2 photocatalyst that had intense absorption in visible region was prepared at low temperature (100°C), through a direct reaction between nanocrystalline anatase TiO2 solution and cetyltrimethylammonium bromide (CTAB). The results of X-ray photoelectron spectroscopy (XPS) showed the existence of N-Ti-N, O-Ti-N-R, Ti3+ (attribute to the doped Br atoms by charge compensation), and TiOxNy species, indicating the successful codoping of N and Br atoms, which were substituted for lattice oxygen without any influence on the crystalline phase of TiO2. In contrast to the N-doped sample, the N-Br-codoped TiO2 photocatalyst could more readily photodegrade methylene blue (MB) under visible-light irradiation. The visible-light catalytic activity of thus-prepared photocatalyst resulted from the synergetic effect of the doped nitrogen and bromine, which not only gave high absorbance in the visible-light range, but also reduced electron-hole recombination rate

Multiferroic Magnon Spin-Torque Based Reconfigurable Logic-In-Memory

Magnons, bosonic quasiparticles carrying angular momentum, can flow through insulators for information transmission with minimal power dissipation. However, it remains challenging to develop a magnon-based logic due to the lack of efficient electrical manipulation of magnon transport. Here we present a magnon logic-in-memory device in a spin-source/multiferroic/ferromagnet structure, where multiferroic magnon modes can be electrically excited and controlled. In this device, magnon information is encoded to ferromagnetic bits by the magnon-mediated spin torque. We show that the ferroelectric polarization can electrically modulate the magnon spin-torque by controlling the non-collinear antiferromagnetic structure in multiferroic bismuth ferrite thin films with coupled antiferromagnetic and ferroelectric orders. By manipulating the two coupled non-volatile state variables (ferroelectric polarization and magnetization), we further demonstrate reconfigurable logic-in-memory operations in a single device. Our findings highlight the potential of multiferroics for controlling magnon information transport and offer a pathway towards room-temperature voltage-controlled, low-power, scalable magnonics for in-memory computing

A Comprehensive X-ray Report on AT2019wey

The Galactic low-mass X-ray binary AT2019wey (ATLAS19bcxp, SRGA J043520.9+552226, SRGE J043523.3+552234, ZTF19acwrvzk) was discovered as a new optical transient in Dec 2019, and independently as an X-ray transient in Mar 2020. In this paper, we present comprehensive NICER, NuSTAR, Chandra, Swift, and MAXI observations of AT2019wey from ~1 year prior to the discovery to the end of September 2020. AT2019wey appeared as a ~1 mCrab source and stayed at this flux density for several months, displaying a hard X-ray spectrum that can be modeled as a power-law with photon index Gamma~1.8. In June 2020 it started to brighten, and reached ~20 mCrab in ~2 months. The inclination of this system can be constrained to i≾30 deg by modeling the reflection spectrum. Starting from late-August (~59082 MJD), AT2019wey entered into the hard-intermediate state (HIMS), and underwent a few week-long timescale outbursts, where the brightening in soft X-rays is correlated with the enhancement of a thermal component. Low-frequency quasi-periodic oscillation (QPO) was observed in the HIMS. We detect no pulsation and in timing analysis of the NICER and NuSTAR data. The X-ray states and power spectra of AT2019wey are discussed against the landscape of low-mass X-ray binaries