Basal stem cluster bud induction and efficient regeneration for the Tibetan endemic medicinal plant Swertia conaensis

The artificial rapid propagation system for Swertia conaensis T. N. Ho et S. W. Liu was explored to screen the appropriate plant regeneration method and to provide an efficient propagation mode, useful for artificial breeding technology or for further research and development of the Tibetan endemic medicinal plant. In this study, the most suitable explant and hormone were chosen according to single factor test. Next, the effects of different hormone combinations on basal stem cluster bud induction, callus induction, adventitious bud occurrence and plant regeneration were investigated by using complete combination and orthogonal experiment. The obtained results showed that the explants suitable for in vitro of S. conaensis were stem tips with leaves, which were regenerated through the method of basal stem cluster bud occurrence in the MS medium with 2.0 mg∙L-1 6-BA, 0.5 mg∙L-1 NAA, but the proliferation coefficient was low, only 3.16 after 40 days of culture. Subsequently, the proliferation coefficient failed to improve, irrespective of change of the concentration ratio of 6-BA and NAA. Therefore, in the orthogonal experiment of adding ZT, the MS medium with 1.0 mg∙L-1 ZT, 0.5 mg∙L-1 NAA and 2.5 mg∙L-1 6-BA induced a large number of callus green and compact, with 86.30% callus occurrence rate. After 40 days of culture, the rate of adventitious bud occurrence was 96.55% and the proliferation coefficient was high (10.37). The rooting rate was 100% in the 1/2MS medium with 0.5 mg∙L-1 NAA. The survival rate of regenerated plants was more than 95%. Indirect organogenesis was more efficient than direct organogenesis in in vitro culture of S. conaensis. In this study, the efficient and stable regeneration system of S. conaensis was achieved through the method of explant to callus to adventitious buds, which provided an effective way to an endangered species

Investigation of nonlocal data-driven methods for subgrid-scale stress modelling in large eddy simulation

A nonlocal subgrid-scale stress (SGS) model is developed based on the convolution neural network (CNN), a powerful supervised data-driven approach. The CNN is an ideal approach to naturally consider nonlocal spatial information in prediction due to its wide receptive field. The CNN-based models used here only take primitive flow variables as input, then the flow features are automatically extracted without any $priori$ guidance. The nonlocal models trained by direct numerical simulation (DNS) data of a turbulent channel flow at $Re_{\tau}=178$ are accessed in both the $priori$ and $posteriori$ test, providing physically reasonable flow statistics (like mean velocity and velocity fluctuations) closing to the DNS results even when extrapolating to a higher Reynolds number $Re_{\tau}=600$. In our model, the backscatter is also predicted well and the numerical simulation is stable. The nonlocal models outperform local data-driven models like artificial neural network and some SGS models, e.g. the Smagorinsky model in actual large eddy simulation (LES). The model is also robust since stable solutions can be obtained when examining the grid resolution from one-half to double of the spatial resolution used in training. We also investigate the influence of receptive fields and suggest using the two-point correlation analysis as a quantitative method to guide the design of nonlocal physical models. To facilitate the combination of machine learning (ML) algorithms to computational fluid dynamics (CFD), a novel heterogeneous ML-CFD framework is proposed. The present study provides the effective data-driven nonlocal methods for SGS modelling in the LES of complex anisotropic turbulent flows.Comment: 17 pages, 10 figure

Iterative frequency domain equalization with generalized approximate message passing

An iterative frequency domain equalization approach for coded single-carrier block transmissions over frequency selective channels is developed by using the recently proposed generalized approximate message passing (GAMP) algorithm. Compared with the low-complexity iterative frequency domain linear minimum mean square error (FD-LMMSE) equalization, the proposed approach can achieve significant performance gain with slight complexity increase

Quantum switch for single-photon transport in a coupled superconducting transmission line resonator array

We propose and study an approach to realize quantum switch for single-photon transport in a coupled superconducting transmission line resonator (TLR) array with one controllable hopping interaction. We find that the single-photon with arbitrary wavevector can transport in a controllable way in this system. We also study how to realize controllable hopping interaction between two TLRs via a superconducting quantum interference device (SQUID). When the frequency of the SQUID is largely detuned from those of the two TLRs, the variables of the SQUID can be adiabatically eliminated and thus a controllable interaction between two TLRs can be obtained.Comment: 4 pages,3 figure

Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors through the way they spread, such as Recursive Neural Network (RvNN) and so on. However, these deep learning methods only take into account the patterns of deep propagation but ignore the structures of wide dispersion in rumor detection. Actually, propagation and dispersion are two crucial characteristics of rumors. In this paper, we propose a novel bi-directional graph model, named Bi-Directional Graph Convolutional Networks (Bi-GCN), to explore both characteristics by operating on both top-down and bottom-up propagation of rumors. It leverages a GCN with a top-down directed graph of rumor spreading to learn the patterns of rumor propagation, and a GCN with an opposite directed graph of rumor diffusion to capture the structures of rumor dispersion. Moreover, the information from the source post is involved in each layer of GCN to enhance the influences from the roots of rumors. Encouraging empirical results on several benchmarks confirm the superiority of the proposed method over the state-of-the-art approaches.Comment: 8 pages, 4 figures, AAAI 202