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We report the first simultaneous observations of total electron content (TEC), radio signal scintillation, and precise point positioning (PPP) variation associated with Strong Thermal Emission Velocity Enhancement (STEVE) emissions during a 26 March 2008 storm‐time substorm. Despite that the mid‐latitude trough TEC decreases during the substorm overall, interestingly, we found an unexpected TEC enhancement (by ∼2 TECU) during STEVE. Enhancement of vertical TEC and phase scintillation was highly localized to STEVE within a thin latitudinal band of 1°. As STEVE shifted equatorward, TEC enhancement was found at and slightly poleward of the optical emission. PPP exhibited enhanced variation across a 3° latitudinal range around STEVE and indicated increased GNSS positioning error. We suggest that TEC enhancement during STEVE creates local TEC structures in the ionosphere that degrade Global Navigation Satellite Systems (GNSS) signals and PPP performance. The TEC enhancement may be created by particle precipitation, Pedersen drift across STEVE, neutral wind, or plasma instability. Plain Language Summary: Global Navigation Satellite Systems (GNSS) signals experience phase and amplitude fluctuations known as scintillation when traveling through density irregularities in the ionosphere. The ionospheric density structures created during Strong Thermal Emission Velocity Enhancement (STEVE) have not yet been characterized in the literature. We combine ground‐based imaging and measurements of ionospheric density to show that STEVE creates unusually steep density gradients and small‐scale density irregularities. We find enhanced phase scintillation and increased GNSS positioning error in GNSS receiver stations where STEVE transects the line of sight between the receiver and satellite. The ionospheric density irregularities associated with STEVE are highly localized and suspected to be connected to particle precipitation and plasma drift during STEVE. These results quantify STEVE's impact on ionospheric dynamics and GNSS navigation for the first time. Key Points: A distinct TEC enhancement was found in the subauroral ionosphere during STEVEThe scintillation indices show significant GNSS phase and amplitude fluctuations during the TEC gradientsPrecise point positioning variations show enhanced GNSS positioning error during STEVE [ABSTRACT FROM AUTHOR]

Objective: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data. Method: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can advance our ability to accurately predict future suicidal behavior. Results: We provide a general overview of machine learning concepts, summarize exemplar studies, describe continued challenges, and propose innovative research directions. Conclusion: Machine learning has potential for improving estimation of suicide risk, yet important challenges and opportunities remain. Further research can focus on incorporating evolving methods for addressing data imbalances, understanding factors that affect generalizability across samples and healthcare systems, expanding the richness of the data, leveraging newer machine learning approaches, and developing automatic learning systems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Boudreaux, Rundensteiner, Liu, Wang, Larkin, Agu, Ghosh, Semeter, Simon and Davis-Martin.)

Small-scale dynamic auroras have spatial scales of a few km or less, and temporal scales of a few seconds or less, which visualize the complex interplay among charged particles, Alfvén waves, and plasma instabilities working in the magnetosphere-ionosphere coupled regions. We summarize the observed properties of flickering auroras, vortex motions, and filamentary structures. We also summarize the development of fundamental theories, such as dispersive Alfvén waves (DAWs), plasma instabilities in the auroral acceleration region, ionospheric feedback instabilities (IFI), and the ionospheric Alfvén resonator (IAR)., Supplementary Information: The online version contains supplementary material available at 10.1007/s11214-021-00796-w., (© The Author(s) 2021, corrected publication 2021.)

In this paper, we present a technique for dimensionality reduction in hyperspectral imaging during the data collection process. A four-channel hyperspectral imager using liquid crystal Fabry-Perot etalons has been built and used to verify this method for four applications: auroral imaging, plant study, landscape classification, and anomaly detection. This imager is capable of making measurements simultaneously in four wavelength ranges while being tunable within those ranges, and thus can be used to measure narrow contiguous bands in four spectral domains. In this paper, we describe the design, concept of operation, and deployment of this instrument. The results from preliminary testing of this instrument are discussed and are promising and demonstrate this instrument as a good candidate for hyperspectral imaging.

A four channel hyperspectral imager using Liquid Crystal Fabry-Perot (LCFP) etalons has been built and tested. This imager is capable of making measurements simultaneously in four wavelength ranges in the visible spectrum. The instrument was designed to make measurements of natural airglow and auroral emissions in the upper atmosphere of the Earth and was installed and tested at the Poker Flat Research Range in Fairbanks, Alaska from February to April 2014. The results demonstrate the capabilities and challenges this instrument presents as a sensor for aeronomical studies.