402 research outputs found
Zero frequency zonal flow excitation by energetic electron driven beta-induced Alfven eigenmode
Zero frequency zonal flow (ZFZF) excitation by trapped energetic electron
driven beta-induced Alfven eigenmode (eBAE) is investigated using nonlinear
gyrokinetic theory. It is found that, during the linear growth stage of eBAE,
resonant energetic electrons (EEs) not only effectively drive eBAE unstable,
but also contribute to the nonlinear coupling, leading to ZFZF excitation. The
trapped EE contribution to ZFZF generation is dominated by EE responses to eBAE
in the ideal region, and is comparable to thermal plasma contribution to
Reynolds and Maxwell stresses.Comment: submitted to Plasma Physics and Controlled Fusion (2020
Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection
Object detection on VHR remote sensing images plays a vital role in
applications such as urban planning, land resource management, and rescue
missions. The large-scale variation of the remote-sensing targets is one of the
main challenges in VHR remote-sensing object detection. Existing methods
improve the detection accuracy of high-resolution remote sensing objects by
improving the structure of feature pyramids and adopting different attention
modules. However, for small targets, there still be seriously missed detections
due to the loss of key detail features. There is still room for improvement in
the way of multiscale feature fusion and balance. To address this issue, this
paper proposes two novel modules: Guided Attention and Tucker Bilinear
Attention, which are applied to the stages of early fusion and late fusion
respectively. The former can effectively retain clean key detail features, and
the latter can better balance features through semantic-level correlation
mining. Based on two modules, we build a new multi-scale remote sensing object
detection framework. No bells and whistles. The proposed method largely
improves the average precisions of small objects and achieves the highest mean
average precisions compared with 9 state-of-the-art methods on DOTA, DIOR, and
NWPU VHR-10.Code and models are available at
https://github.com/Shinichict/GTNet.Comment: arXiv admin note: text overlap with arXiv:1705.06676,
arXiv:2209.13351 by other author
Low-frequency shear Alfv\'en waves at DIII-D: theoretical interpretation of experimental observations
The linear properties of the low-frequency shear Alfv\'en waves such as those
associated with the beta-induced Alfv\'en eigenmodes (BAEs) and the
low-frequency modes observed in reversed-magnetic-shear DIII-D discharges (W.
Heidbrink, et al 2021 Nucl. Fusion 61 066031) are theoretically investigated
and delineated based on the theoretical framework of the general fishbone-like
dispersion relation (GFLDR). By adopting representative experimental
equilibrium profiles, it is found that the low-frequency modes and BAEs are,
respectively, the reactive-type and dissipative-type unstable modes with
dominant Alfv\'enic polarization, thus the former being more precisely called
low-frequency Alfv\'en modes (LFAMs). More specifically, due to different
instability mechanisms, the maximal drive of BAEs occurs, in comparison to
LFAMs, when the minimum of the safety factor () deviates from a
rational number. Meanwhile, the BAE eigenfunction peaks at the radial position
of the maximum energetic particle pressure gradient, resulting in a large
deviation from the surface. Moreover, the ascending frequency
spectrum patterns of the experimentally observed BAEs and LFAMs can be
theoretically reproduced by varying and also be well interpreted
based on the GFLDR. The present analysis illustrates the solid predictive
capability of the GFLDR and its practical usefulness in enhancing the
interpretative capability of both experimental and numerical simulation
results
Typhoon Intensity Prediction with Vision Transformer
Predicting typhoon intensity accurately across space and time is crucial for
issuing timely disaster warnings and facilitating emergency response. This has
vast potential for minimizing life losses and property damages as well as
reducing economic and environmental impacts. Leveraging satellite imagery for
scenario analysis is effective but also introduces additional challenges due to
the complex relations among clouds and the highly dynamic context. Existing
deep learning methods in this domain rely on convolutional neural networks
(CNNs), which suffer from limited per-layer receptive fields. This limitation
hinders their ability to capture long-range dependencies and global contextual
knowledge during inference. In response, we introduce a novel approach, namely
"Typhoon Intensity Transformer" (Tint), which leverages self-attention
mechanisms with global receptive fields per layer. Tint adopts a
sequence-to-sequence feature representation learning perspective. It begins by
cutting a given satellite image into a sequence of patches and recursively
employs self-attention operations to extract both local and global contextual
relations between all patch pairs simultaneously, thereby enhancing per-patch
feature representation learning. Extensive experiments on a publicly available
typhoon benchmark validate the efficacy of Tint in comparison with both
state-of-the-art deep learning and conventional meteorological methods. Our
code is available at https://github.com/chen-huanxin/Tint.Comment: 8 pages, 2 figures, accepted by Tackling Climate Change with Machine
Learning: workshop at NeurIPS 202
Exploring the Roles of Aquaporins in Plant-Microbe Interactions
Aquaporins (AQPs) are membrane channel proteins regulating the flux of water and other various small solutes across membranes. Significant progress has been made in understanding the roles of AQPs in plants’ physiological processes, and now their activities in various plant⁻microbe interactions are receiving more attention. This review summarizes the various roles of different AQPs during interactions with microbes which have positive and negative consequences on the host plants. In positive plant⁻microbe interactions involving rhizobia, arbuscular mycorrhizae (AM), and plant growth-promoting rhizobacteria (PGPR), AQPs play important roles in nitrogen fixation, nutrient transport, improving water status, and increasing abiotic stress tolerance. For negative interactions resulting in pathogenesis, AQPs help plants resist infections by preventing pathogen ingress by influencing stomata opening and influencing defensive signaling pathways, especially through regulating systemic acquired resistance. Interactions with bacterial or viral pathogens can be directly perturbed through direct interaction of AQPs with harpins or replicase. However, whilst these observations indicate the importance of AQPs, further work is needed to develop a fuller mechanistic understanding of their functions
2,6-Bis(1H-benzimidazol-2-yl)pyridine methanol trisolvate
In the title compound, C19H13N5·3CH4O, the 2,6-bis(2-benzimidazolyl)pyridine molecule is essentially planar with an r.m.s. deviation for all non-H atoms of 0.185 Å. The crystal structure is stabilized by intermolecular O—H⋯O, O—H⋯N and N—H⋯O hydrogen bonds and weak π⋯π stacking interactions with centroid–centroid distances of 3.6675 (16) and 3.6891 (15) Å. The atoms of one of the methanol solvent molecules are disordered over two sites with refined occupancies of 0.606(8) and 0.394(8)
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