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| http://dx.doi.org/10.1152/japplphysiol.00454.2024 | DOI Listing |
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Education Research: Making a Tweetorial Fly: Features of Educational Social Media Posts Associated With High Sharing and Engagement.
Neurol Educ
December 2024
From the Departments of Neurology and Neurosurgery (C.S.W.A., E.C.L.), Emory University School of Medicine, Atlanta, GA; Division of Biostatistics (T.M.), Rollins School of Public Health, Emory University, Atlanta, GA; Department of Neurology (G.F.P.), University of Pittsburgh, PA; Department of Neurology (A.S.Z.), Weill Cornell Medical College, New York, NY; Emory University School of Medicine (N.D.), Atlanta, GA; Consulting Web Developer (S.M.), Scotland; Department of Neurology (A.S.), Wake Forest University, Winston-Salem, NC; Departments of Neurology and Neurosurgery (N.S.D), Icahn School of Medicine at Mount Sinai, New York, NY; Department of Neurology (A.L.B.), University of California, San Francisco; Department of Neurology (N.A.M.), University of Maryland School of Medicine, Baltimore, MD; and Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN.
Background And Objectives: Social media platforms such as X (formerly Twitter) are increasingly used in medical education. Characteristics of tweetorials (threaded teaching posts) associated with higher degrees of engagement are unknown. We sought to understand features of neurology-themed tweetorials associated with high sharing and engagement.
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BiLSTM-Filt: Neural network for radar word segmentation.
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College of Electronic Science and Technology, National University of Defense Technology, No. 109, Deya Road, Changsha, 410073, Hunan, China; College of Electronic Engineering, National University of Defense Technology, No. 460, Huangshan Road, Hefei, 230037, Anhui, China.
Radar word extraction is the analysis foundation for multi-function radars (MFRs) in electronic intelligence (ELINT). Although neural networks enhance performance in radar word extraction, current research still faces challenges from complex electromagnetic environments and unknown radar words. Therefore, in this paper, we propose a promising two-stage radar word extraction framework, consisting of segmentation and recognition.
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