Data Mining Techniques to Predict Aircraft Damage Levels for Wildstrikes in United States

Wildlife strikes pose a major economic and threat to aviation safety all around the world. From 1990 through 2018 there were 209,950 wildlife strikes to aviation in the U.S. Approximately eight percent of those strikes caused damage to aircraft. A primary method to understand the magnitude of this economic and safety hazard is through data collection and analyses. Data mining methods can be used to predict the likelihood of events and significant factors of contribution based on historical datasets. Researchers will collect and analyze data (XXXX-2020) from the National Wildlife Strike Database. The purpose of this study is twofold: 1. To identify the potential predictors of damaging wildlife strikes to aviation in the U.S.; 2. To identify the potential predictors of substantial and minor damaging wildlife strikes to aviation in the U.S. Findings of the current study can help determine the nature and magnitude of this problem as well as provide the ground work for the development and implementation of integrated safety management and research efforts to improve aviation safety

MOSAIC: Spatially-multiplexed edge AI optimization over multiple concurrent video sensing streams

National Research Foundation, Singapor

MOSAIC: Spatially-Multiplexed Edge AI Optimization over Multiple Concurrent Video Sensing Streams

Sustaining high fidelity and high throughput of perception tasks over vision sensor streams on edge devices remains a formidable challenge, especially given the continuing increase in image sizes (e.g., generated by 4K cameras) and complexity of DNN models. One promising approach involves criticality-aware processing, where the computation is directed selectively to critical portions of individual image frames. We introduce MOSAIC, a novel system for such criticality-aware concurrent processing of multiple vision sensing streams that provides a multiplicative increase in the achievable throughput with negligible loss in perception fidelity. MOSAIC determines critical regions from images received from multiple vision sensors and spatially bin-packs these regions using a novel multi-scale Mosaic Across Scales (MoS) tiling strategy into a single canvas frame, sized such that the edge device can retain sufficiently high processing throughput. Experimental studies using benchmark datasets for two tasks, Automatic License Plate Recognition and Drone-based Pedestrian Detection, show that MOSAIC, executing on a Jetson TX2 edge device, can provide dramatic gains in the throughput vs. fidelity tradeoff. For instance, for drone-based pedestrian detection, for a batch size of 4, MOSAIC can pack input frames from 6 cameras to achieve (a) 4.75x higher throughput (23 FPS per camera, cumulatively 138FPS) with less than 1% accuracy loss, compared to a First Come First Serve (FCFS) processing paradigm.Comment: To appear in ACM Multimedia Systems 202

ACR appropriateness criteria(®) on abnormal vaginal bleeding

Synthesis of novel highly functionalized 2-oxo-(1,2,3,4-tetrahydropyridin-3-yl)acetic acids has been described via aza-annulation of both acyclic and cyclic α -oxo- and α -nitro-N,S- and -N,N-ketene acetals with itaconic anhydride

Methyl 1-imidazolecarbodithioate as a thiocarbonyl transfer reagent: a facile one-pot, three-component synthesis of novel 2-substituted-5-aryl-1-oxo-3-thioxo-1,2,3,5,11,11a-hexahydro-6H-imidazo-[1,5-b]-β-carbolines

An efficient one-pot, three-component synthesis of novel 2-substituted-5-aryl-1-oxo-3-thioxo-1,2,3,5,11,11a-hexahydro-6H-imidazo-[1,5-b]-β-carbolines employing 1-aryl-1,2,3,4-tetrahydro-β-carboline-3-carboxylates, primary amines (or amino acid esters) and methyl 1-imidazolecarbodithioate as the thiocarbonyl transfer reagent is reported