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Abstract We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML‐AIM). ML‐AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020,https://doi.org/10.1029/2020JA027908), the FAC‐derived auroral conductance model of Robinson et al. (2020,https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993,https://doi.org/10.1029/92gl02109). The ML‐AIM inputs are 60‐min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML‐AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML‐AIM produces physically accurate ionospheric potential patterns such as the two‐cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML‐AIM, the Weimer (2005,https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data‐assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML‐AIM than others. ML‐AIM is unique and innovative because it predicts ionospheric responses to the time‐varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005,https://doi.org/10.1029/2004ja010884) designed to provide a quasi‐static ionospheric condition under quasi‐steady solar wind/IMF conditions. Plans are underway to improve ML‐AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.
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Molecular dynamics simulations to explore the structure and rheological properties of normal and hyperconcentrated airway mucus
Abstract We develop the first molecular dynamics model of airway mucus based on the detailed physical properties and chemical structure of the predominant gel‐forming mucin MUC5B. Our airway mucus model leverages the LAMMPS open‐source code [https://lammps.sandia.gov], based on the statistical physics of polymers, from single molecules to networks. On top of the LAMMPS platform, the chemical structure of MUC5B is used to superimpose proximity‐based, noncovalent, transient interactions within and between the specific domains of MUC5B polymers. We explore feasible ranges of hydrophobic and electrostatic interaction strengths between MUC5B domains with 9 nm spatial and 1 ns temporal resolution. Our goal here is to propose and test a mechanistic hypothesis for a striking clinical observation with respect to airway mucus: a 10‐fold increase in nonswellable, dense structures called flakes during progression of cystic fibrosis disease. Among the myriad possible effects that might promote self‐organization of MUC5B networks into flake structures, we hypothesize and confirm that the clinically confirmed increase in mucin concentration, from 1.5 to 5 mg/ml, alone is sufficient to drive the structure changes observed with scanning electron microscopy images from experimental samples. We postprocess the LAMMPS simulated data sets at 1.5 and 5 mg/ml, both to image the structure transition and compare with scanning electron micrographs and to show that the 3.33‐fold increase in concentration induces closer proximity of interacting electrostatic and hydrophobic domains, thereby amplifying the proximity‐based strength of the interactions.
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- PAR ID:
- 10448217
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Studies in Applied Mathematics
- Volume:
- 147
- Issue:
- 4
- ISSN:
- 0022-2526
- Format(s):
- Medium: X Size: p. 1369-1387
- Size(s):
- p. 1369-1387
- Sponsoring Org:
- National Science Foundation
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