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    Eliciting Uncertain Resilience Information for Risk Mitigation

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    The literature of risk, mitigation, and resilience is rich in classifications and recommendations. The missing link is evaluation: ideally, data based; initially, based on expert judgment. We present a novel approach for eliciting probability distributions describing mitigation effectiveness. This approach can be used by subject matter experts (SMEs) who are not specialists in mathematics or engineering. A visual interface permits each expert to sketch a distribution by moving five colored dots on the user interface. The engine can weight and combine estimates from several SMEs into an aggregate density function suitable for presentation, and an aggregate cumulated distribution for use in Monte Carlo simulations. Additional supporting software adapts the tool for real-time support of virtual Delphi-type sessions involving multiple distributed experts. Use of the tool in a study aimed at controlling information and communication technology supply chain risks yields valuable information on those threats, and on the tool itself

    User Generated Multi-Dimensional Classification in an Adaptive Network Library Interface

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    Classification can be thought of as defining subject matter classes, and assigning information bearing items (IBEs) to those classes as a way to support organization and retrieval of those IBEs. This corresponds to a Platonic view in which subjects reside in a world of abstractions, and real world IBEs are mapped to them (many-te-many) as accurately as possible

    Progressive left ventricular remodeling for predicting mortality in children with dilated cardiomyopathy: The Pediatric Cardiomyopathy Registry

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    BACKGROUND: Pediatric dilated cardiomyopathy often leads to death or cardiac transplantation. We sought to determine whether changes in left ventricular (LV) end-diastolic dimension (LVEDD), LV end-diastolic posterior wall thickness, and LV fractional shortening (LVFS) over time may help predict adverse outcomes. METHODS AND RESULTS: We studied children up to 18 years old with dilated cardiomyopathy, enrolled between 1990 and 2009 in the Pediatric Cardiomyopathy Registry. Changes in LVFS, LVEDD, LV end-diastolic posterior wall thickness, and the LV end-diastolic posterior wall thickness:LVEDD ratio between baseline and follow-up echocardiograms acquired ≈1 year after diagnosis were determined for children who, at the 1-year follow-up had died, received a heart transplant, or were alive and transplant-free. Within 1 year after diagnosis, 40 (5.0%) of the 794 eligible children had died, 117 (14.7%) had undergone cardiac transplantation, and 585 (73.7%) had survived without transplantation. At diagnosis, survivors had higher median LVFS and lower median LVEDD CONCLUSIONS: Progressive deterioration in LV contractile function and increasing LV dilation are associated with both early and continuing mortality in children with dilated cardiomyopathy. Serial echocardiographic monitoring of these children is therefore indicated. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00005391

    Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision

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    In this paper, we present a computer vision-based approach to measure the sizes and growth rates of apple fruitlets. Measuring the growth rates of apple fruitlets is important because it allows apple growers to determine when to apply chemical thinners to their crops in order to optimize yield. The current practice of obtaining growth rates involves using calipers to record sizes of fruitlets across multiple days. Due to the number of fruitlets needed to be sized, this method is laborious, time-consuming, and prone to human error. With images collected by a hand-held stereo camera, our system, segments, clusters, and fits ellipses to fruitlets to measure their diameters. The growth rates are then calculated by temporally associating clustered fruitlets across days. We provide quantitative results on data collected in an apple orchard, and demonstrate that our system is able to predict abscise rates within 3.5% of the current method with a 6 times improvement in speed, while requiring significantly less manual effort. Moreover, we provide results on images captured by a robotic system in the field, and discuss the next steps required to make the process fully autonomous
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