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  1. Deep learning’s performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most GNNs were created using distinct theories, direct comparisons are impossible. Prior research has primarily concentrated on categorizing existing models, with little attention paid to their intrinsic connections. The purpose of this study is to establish a unified framework that integrates GNNs based on spectral graph and approximation theory. The framework incorporates a strong integration between spatial- and spectral-based GNNs while tightly associating approaches that exist within each respective domain.

     
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    Free, publicly-accessible full text available May 31, 2025
  2. Free, publicly-accessible full text available August 4, 2024
  3. Abstract

    Radiative magnetohydrodynamic simulation includes sufficiently realistic physics to allow for the synthesis of remote sensing observables that can be quantitatively compared with observations. We analyze the largest flare in a simulation of the emergence of large flare-productive active regions described by Chen et al. The flare releases 4.5 × 1031erg of magnetic energy and is accompanied by a spectacular coronal mass ejection. Synthetic soft X-ray flux of this flare reaches M2 class. The eruption reproduces many key features of observed solar eruptions. A preexisting magnetic flux rope is formed along the highly sheared polarity inversion line between a sunspot pair and is covered by an overlying multipole magnetic field. During the eruption, the progenitor flux rope actively reconnects with the canopy field and evolves to the large-scale multithermal flux rope that is observed in the corona. Meanwhile, the magnetic energy released via reconnection is channeled down to the lower atmosphere and gives rise to bright soft X-ray post-flare loops and flare ribbons that reproduce the morphology and dynamic evolution of observed flares. The model helps to shed light on questions of where and when the a flux rope may form and how the magnetic structures in an eruption are related to observable emission properties.

     
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  4. Free, publicly-accessible full text available August 1, 2024
  5. Abstract

    We present a method of conducting data-driven simulations of solar active regions and flux emergence with the MURaM radiative magnetohydrodynamics (MHD) code. The horizontal electric field that is derived from the full velocity and magnetic vectors is implemented at the photospheric (bottom) boundary to drive the induction equation. The energy equation accounts for thermal conduction along magnetic fields, optically thin radiative loss, and heating of coronal plasma by viscous and resistive dissipation, which allows for a realistic presentation of the thermodynamic properties of coronal plasma that are key to predicting the observational features of solar active regions and eruptions. To validate this method, the photospheric data from a comprehensive radiative MHD simulation of solar eruption (the ground truth) are used to drive a series of numerical experiments. The data-driven simulation reproduces the accumulation of free magnetic energy over the course of flux emergence in the ground truth with an error of 3%. The onset time is approximately 8 minutes delayed compared to the ground truth. However, a precursor-like signature can be identified at the correct onset time. The data-driven simulation captures key eruption-related emission features and plasma dynamics of the ground truth flare over a wide temperature span, fromlog10T=4.5tolog10T>8. The evolution of the flare and coronal mass ejection as seen in synthetic extreme ultraviolet images is also reproduced with high fidelity. This method helps to understand the evolution of magnetic field in a more realistic coronal environment and to link the magnetic structures to observable diagnostics.

     
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  6. Free, publicly-accessible full text available June 4, 2024
  7. Free, publicly-accessible full text available June 29, 2024
  8. Weinert, Emily (Ed.)
    ABSTRACT When the oligotrophic microbial community was amended with Synechococcus -derived dissolved organic matter (SDOM) and incubated under the dark condition, archaea relative abundance was initially very low but made up more than 60% of the prokaryotic community on day 60, and remained dominant for at least 9 months. The archaeal sequences were dominated by Candidatus Nitrosopumilus , the Group I.1a Thaumarchaeota. The increase of Thaumarchaeota in the dark incubation corresponded to the period of delayed ammonium oxidation upon an initially steady increase in ammonia, supporting the remarkable competency of Thaumarchaeota in energy utilization and fixation of inorganic carbon in the ocean. IMPORTANCE Thaumarchaeota, which are ammonia-oxidizing archaea (AOA), are mainly chemolithoautotrophs that can fix inorganic carbon to produce organic matter in the dark. Their distinctive physiological traits and high abundance in the water column indicate the significant ecological roles they play in the open ocean. In our study, we found predominant Thaumarchaeota in the microbial community amended with cyanobacteria-derived lysate under the dark condition. Furthermore, Thaumarchaeota remained dominant in the microbial community even after 1 year of incubation. Through the ammonification process, dissolved organic matter (DOM) from cyanobacterial lysate was converted to ammonium which was used as an energy source for Thaumarchaeota to fix inorganic carbon into biomass. Our study further advocates the important roles of Thaumarchaeota in the ocean’s biogeochemical cycle. 
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