3,907 research outputs found

    Diffuser/ejector system for a very high vacuum environment

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    Turbo jet engines are used to furnish the necessary high temperature, high volume, medium pressure gas to provide a high vacuum test environment at comparatively low cost for space engines at sea level. Moreover, the invention provides a unique way by use of the variable area ratio ejectors with a pair of meshing cones are used. The outer cone is arranged to translate fore and aft, and the inner cone is interchangeable with other cones having varying angles of taper

    Friends in Eastern Africa

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    Get your facts right : preschoolers systematically extend both object names and category-relevant facts

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    There is an ongoing debate over the extent to which language development shares common processing mechanisms with other domains of learning. It is well-established that toddlers will systematically extend object labels to similarly-shaped category exemplars (e.g., Landau, Smith, & Jones, 1988; Markman & Hutchinson, 1984). However, previous research is inconclusive as to whether young children will similarly extend factual information about an object to other category members. We explicitly contrast facts varying in category relevance, and test for extension using two different tasks. Three- to four-year-olds (N = 61) were provided with one of three types of information about a single novel object: a category-relevant fact (‘it’s from a place called Modi’), a category-irrelevant fact (‘my uncle gave it to me’), or an object label (‘it’s called a Modi’). At test, children provided with the object name or category-relevant fact were significantly more likely to display systematic category extension than children who learnt the category-irrelevant fact. Our findings contribute to a growing body of evidence that the mechanisms responsible for word learning may be domain-general in nature

    A Response to Friendly Sacramentalogy Ecumencial Perspective

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    The Call of Truth- The Peace Testimony During a Time of Terror

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    A novel approach to assess minimally invasive surgical device failure utilizing adverse event outcome severity and design complexity.

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    Medical device failure and misuse have the potential to cause serious injury and death. Given the intricate nature of the instruments utilized specifically in minimally invasive surgery (MIS), users and manufacturers of surgical devices share a responsibility in preventing user error and device failure. A novel approach was presented for the evaluation of minimally invasive device failures, which involved assessing the severity of adverse event outcomes associated with the failures modes and investigating aspects of the devices’ design that may contribute to failure. The goals of this research were to 1) characterize the design attributes, failure modes, and adverse events associated with minimally invasive surgical devices and 2) describe the relationship between minimally invasive surgical device design complexity and the severity of adverse events. The types of failure modes, phases of operation in which failure occurs, severity of adverse event outcomes, and design complexity associated with four minimally invasive surgical devices were determined. An association was shown to exist between phases of surgical device operation and the severity of outcomes that occur in each phase (p \u3c 0.05). Across both device types, the majority of failure occurred during execution of the devices’ main function which involved securing and transecting tissue. The least amount of failures occurred during the results and post-op phase of operation; however, the failures that occurred during this phase resulted in the highest average outcome severity. The endoscopic staplers assessed resulted in overall higher average outcome severities relative to that of the tissue sealers. The methods employed are the first to evaluate medical device design, function, and failure outcomes from a complexity perspective. While statistical conclusions regarding the overall research goal could not be drawn, heuristic methods support development of the approach presented. The work herein assists the enhancement of risk awareness and prevention techniques and serves as a contribution to filling the knowledge gap regarding device use and failure outcomes. Bridging the gap between surgeons and engineers is crucial to the successful implementation and evaluation of new technology in the operating room, which was an essential component of this research

    Improved Snow Mapping Accuracy with Revised MODIS Snow Algorithm

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    The MODIS snow cover products have been used in over 225 published studies. From those reports, and our ongoing analysis, we have learned about the accuracy and errors in the snow products. Revisions have been made in the algorithms to improve the accuracy of snow cover detection in Collection 6 (C6), the next processing/reprocessing of the MODIS data archive planned to start in September 2012. Our objective in the C6 revision of the MODIS snow-cover algorithms and products is to maximize the capability to detect snow cover while minimizing snow detection errors of commission and omission. While the basic snow detection algorithm will not change, new screens will be applied to alleviate snow detection commission and omission errors, and only the fractional snow cover (FSC) will be output (the binary snow cover area (SCA) map will no longer be included)

    MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

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    Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes
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