4,642 research outputs found
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 guidance. The nonlocal models
trained by direct numerical simulation (DNS) data of a turbulent channel flow
at are accessed in both the and 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 . 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
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