Dynamical modulation of solar flare electron acceleration due to plasmoid-shock interactions in the looptop region

A fast-mode shock can form in the front of reconnection outflows and has been suggested as a promising site for particle acceleration in solar flares. Recent development of magnetic reconnection has shown that numerous plasmoids can be produced in a large-scale current layer. Here we investigate the dynamical modulation of electron acceleration in the looptop region when plasmoids intermittently arrive at the shock by combining magnetohydrodynamics simulations with a particle kinetic model. As plasmoids interact with the shock, the looptop region exhibits various compressible structures that modulate the production of energetic electrons. The energetic electron population varies rapidly in both time and space. The number of 5$-$10 keV electrons correlates well with the area with compression, while that of $>$50 keV electrons shows good correlation with strong compression area but only moderate correlation with shock parameters. We further examine the impacts of the first plasmoid, which marks the transition from a quasi-steady shock front to a distorted and dynamical shock. The number of energetic electrons is reduced by $\sim 20\%$ at 15$-$25 keV and nearly 40\% for 25$-$50 keV, while the number of 5$-$10 keV electrons increases. In addition, the electron energy spectrum above 10 keV evolves softer with time. We also find double or even multiple distinct sources can develop in the looptop region when the plasmoids move across the shock. Our simulations have strong implications to the interpretation of nonthermal looptop sources, as well as the commonly observed fast temporal variations in flare emissions, including the quasi-periodic pulsations.Comment: accepted for publication in ApJ

The Acceleration and Confinement of Energetic Electrons by a Termination Shock in a Magnetic Trap: An Explanation for Nonthermal Loop-top Sources during Solar Flares

Nonthermal loop-top sources in solar flares are the most prominent observational signature that suggests energy release and particle acceleration in the solar corona. Although several scenarios for particle acceleration have been proposed, the origin of the loop-top sources remains unclear. Here we present a model that combines a large-scale magnetohydrodynamic simulation of a two-ribbon flare with a particle acceleration and transport model for investigating electron acceleration by a fast-mode termination shock at the looptop. Our model provides spatially resolved electron distribution that evolves in response to the dynamic flare geometry. We find a concave-downward magnetic structure located below the flare termination shock, induced by the fast reconnection downflows. It acts as a magnetic trap to confine the electrons at the looptop for an extended period of time. The electrons are energized significantly as they cross the shock front, and eventually build up a power-law energy spectrum extending to hundreds of keV. We suggest that this particle acceleration and transport scenario driven by a flare termination shock is a viable interpretation for the observed nonthermal loop-top sources.Comment: submitted to ApJ

A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition

With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applications is essentially the fine-grained image classification task, which aims to learn effective discriminative features that can identify the subtle differences among similar visual samples. It is still challenging to solve for existing standard models troubled by oversized parameters and low accuracy performance. Therefore, in this paper, we propose a feature-enhanced attention neural network (Fe-Net) to handle the fine-grained image recognition of crop pests and diseases in innovative agronomy practices. This model is established based on an improved CSP-stage backbone network, which offers massive channel-shuffled features in various dimensions and sizes. Then, a spatial feature-enhanced attention module is added to exploit the spatial interrelationship between different semantic regions. Finally, the proposed Fe-Net employs a higher-order pooling module to mine more highly representative features by computing the square root of the covariance matrix of elements. The whole architecture is efficiently trained in an end-to-end way without additional manipulation. With comparative experiments on the CropDP-181 Dataset, the proposed Fe-Net achieves Top-1 Accuracy up to 85.29% with an average recognition time of only 71 ms, outperforming other existing methods. More experimental evidence demonstrates that our approach obtains a balance between the model’s performance and parameters, which is suitable for its practical deployment in precision agriculture art applications

A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition

With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applications is essentially the fine-grained image classification task, which aims to learn effective discriminative features that can identify the subtle differences among similar visual samples. It is still challenging to solve for existing standard models troubled by oversized parameters and low accuracy performance. Therefore, in this paper, we propose a feature-enhanced attention neural network (Fe-Net) to handle the fine-grained image recognition of crop pests and diseases in innovative agronomy practices. This model is established based on an improved CSP-stage backbone network, which offers massive channel-shuffled features in various dimensions and sizes. Then, a spatial feature-enhanced attention module is added to exploit the spatial interrelationship between different semantic regions. Finally, the proposed Fe-Net employs a higher-order pooling module to mine more highly representative features by computing the square root of the covariance matrix of elements. The whole architecture is efficiently trained in an end-to-end way without additional manipulation. With comparative experiments on the CropDP-181 Dataset, the proposed Fe-Net achieves Top-1 Accuracy up to 85.29% with an average recognition time of only 71 ms, outperforming other existing methods. More experimental evidence demonstrates that our approach obtains a balance between the model’s performance and parameters, which is suitable for its practical deployment in precision agriculture art applications

Phylogenetic relationships of group I introns inserted into phage <i>DNAP</i> genes (A) and their HNH proteins (B).

<p>All introns belong to IA2 subgoup of group I introns. The trees include only those HNH HEG-containing group I introns that are inserted in <i>Bacillus</i> phages (purple), <i>Enterobacteria</i> phages (blue), <i>Xanthomonas</i> phage (green) and cyanophage Pf-WMP3 (black). Horizontal distances are proportional to evolutionary divergence expressed as substitutions per site. Branch lengths higher than 0.1 are indicated on the tree branches. The scale bars represent 0.2 fixed mutations per amino acid position. GenBank accession numbers for intron sequences as follows: phiE (U04813.1), SP82 (BSU04812), SPO1 (M37686:), Bastille (AY256517), Pf-WMP3 (EF537008.1), phiI (AY769989.1), W31 (AY769990.1), phiL7 (EU717894.1). GenBank accession numbers for HEase sequences as follows: phiE (AAA56886.1), phiI (AAV53690.1), W31 (AAV53693.1). A phylogenetic tree was constructed using the neighbor-joining method in MEGA 4.</p

Sequence features of HEases inserted into phage <i>DNAP</i> gene introns.

<p>A. Shown is I-PfoP3I and highly similar HNH HEases encoded within mobile introns found in <i>DNAP</i> host genes from phages SPO1 (I-HmuI), SP82 (I-HmuII), Bastile (I-BasI), W31 (I-TslI) and phiL7 (phiL7_gp45). Black boxes indicate the positions of the HNH motif in these endonucleases. B. Amino acid sequence alignment of the conserved HNH motif. Brackets indicate number of residues in front of and following the aligned sequence. The black bar indicates residues of the active site domain and black spheres indicate the most conserved Asn and His residues. Alignment was generated with Clustalx1.83. Conserved residues are shaded using the BOXSHADE 3.21 program (<a href="http://www.ch.embnet.org/software/BOX_form.html" target="_blank">http://www.ch.embnet.org/software/BOX_form.html</a>). GenBank accession numbers: I-HmuI (YP_002300418.1), I-HmuII (AAA56884.1), I-BasI (AAO93095.1), I-TslI (AAV53690), phiL7_gp45 (ACE75785.1), I-PfoP3I (YP_001285778.1).</p