19 research outputs found

    Application of an AIS to the problem of through life health management of remotely piloted aircraft

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    The operation of RPAS includes a cognitive problem for the operators(Pilots, maintainers, ,managers, and the wider organization) to effectively maintain their situational awareness of the aircraft and predict its health state. This has a large impact on their ability to successfully identify faults and manage systems during operations. To overcome these system deficiencies an asset health management system that integrates more cognitive abilities to aid situational awareness could prove beneficial. This paper outlines an artificial immune system (AIS) approach that could meet these challenges and an experimental method within which to evaluate it

    A New Species of \u3ci\u3eCelastrina\u3c/i\u3e from the Northwestern United States and Southwestern Canada with a Lectotype Designation of \u3ci\u3eLycaena pseudargiolus\u3c/i\u3e var. \u3ci\u3enigrescens\u3c/i\u3e Fletcher (Lycaenidae: Polyommatinae)

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    A new western azure species, Celastrina asheri, is described from the northwestern United States and southwestern Canada. A lectotype is designated for Lycaena pseudargiolus var. nigrescens Fletcher, 1903 to secure the identity of that name. C. asheri broadly overlaps with the northeastern range of C. echo (W. H. Edwards, 1864) but is usually very distinct from that species. C. lucia (W. Kirby, 1837) is narrowly sympatric with C. asheri in British Columbia, the southwest corner of Alberta and along the Rocky Mountains in Montana. As far as is known, C. asheri uses only red osier dogwood (Cornus sericea) and possibly oceanspray (Holodiscus discolor) as larval foodplants, while C. echo and C. lucia use a wide variety of larval foodplants. A lack of past research and confusion as a “form” of C. echo obscured the identity of C. asheri

    Monitoring concept study for aerospace power gear box drive train

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    Using a gearbox in a turbojet engine implies additional monitoring tasks due to new introduced failure modes. This paper outlines monitoring options to address technical diagnosis of the world’s most powerful aerospace gearbox. For this novel technology different monitoring options are assessed to enable the trade between technical effort and monitoring capability. In this paper options to monitor the gears and journal bearings are described. To detect gear wear, pitting, and gear teeth cracks the use of acceleration, acoustic emission sensors, and different methods will be assessed. First stage results are based on Back2Back test run results in occurring pitting and gear teeth loss [1]. The journal bearing mixed friction will be detected by the use of an acoustic emission sensor [3], [5]. Due to the location of the journal bearing in the rotating area of the gearbox a Wireless Data Transfer Unit (WDTU) must be introduced [6], [7]. Results of early subscale component test runs are used to define requirements to adjust the WDTU and accommodate the new power gearbox (PGB) requirements. The electronics of the WDTU must cope with challenges such as the environmental conditions of the gearbox. To extract the mixed friction pattern by the applied signal processing steps from the noise disturbance caused by gear mesh is a technical challenge. Finally the paper closes with a recommendation on how to monitor such a gearbox and provides an outlook to the next test campaign, where the WDTU will be applied based on a back2back configuration of a subscale planetary gearbox [8]

    The James Webb Space Telescope Mission

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    Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least 4m4m. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the 6.5m6.5m James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

    Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

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    Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

    Analysis of Short form Maintenance Records for NFF Using NLP, Phrase Matching, and Bayesian Learning

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    No Fault Found (NFF) is a well discussed phenomenon within the maintenance sector but which requires work to quantify how much of an issue it may be and provide metrics by which it may be tracked and various approaches to its reduction evaluated. Previous studies have relied on expert classification to identify NFF, however this approach is time consuming and costly. Maintainer classification (MC), expert classification (RC), phrase matching (PM), and Bayesian matching (NBPM) are all evaluated and contrasted as methods to identify NFF. The results demonstrate the utility of all 4 methods and discusses their place within a maintenance ecosystem
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