Using the global Lagrangian version of the piecewise parabolic method‐magnetohydrodynamic (PPMLR‐MHD) model, we simulate two consecutive storms in December 2015, a moderate storm on 14–15 December and a strong storm on 19–22 December, and calculate the radial diffusion coefficients (
- Award ID(s):
- 1634133
- NSF-PAR ID:
- 10109518
- Date Published:
- Journal Name:
- Queueing systems
- Volume:
- 91
- ISSN:
- 0257-0130
- Page Range / eLocation ID:
- 15-47
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract D LL B z E φ B z E φ AE index in the recovery phase.B z E φ B z E φ L regions, with a strongerL dependence in theB z D LL D LL D LL B z E φ -
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Using GUI-based workflows for data analysis is an iterative process. During each iteration, an analyst makes changes to the workflow to improve it, generating a new version each time. The results produced by executing these versions are materialized to help users refer to them in the future. In many cases, a new version of the workflow, when submitted for execution, produces a result equivalent to that of a previous one. Identifying such equivalence can save computational resources and time by reusing the materialized result. One way to optimize the performance of executing a new version is to compare the current version with a previous one and test if they produce the same results using a workflow version equivalence verifier. As the number of versions grows, this testing can become a computational bottleneck. In this paper, we present Raven, an optimization framework to accelerate the execution of a new version request by detecting and reusing the results of previous equivalent versions with the help of a version equivalence verifier. Raven ranks and prunes the set of prior versions to quickly identify those that may produce an equivalent result to the version execution request. Additionally, when the verifier performs computation to verify the equivalence of a version pair, there may be a significant overlap with previously tested version pairs. Raven identifies and avoids such repeated computations by extending the verifier to reuse previous knowledge of equivalence tests. We evaluated the effectiveness of Raven compared to baselines on real workflows and datasets.more » « less
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Abstract Background As technology moves rapidly forward and our world becomes more interconnected, we are seeing increases in the complexity and challenge associated with scientific problems. More than ever before, scientists will need to be resilient and able to cope with challenges and failures en route to success. However, we still understand relatively little about how these skills manifest in STEM contexts broadly, and how they are developed by STEM undergraduate students. While recent studies have begun to explore this area, no measures exist that are specifically designed to assess coping behaviors in STEM undergraduate contexts at scale. Fortunately, multiple measures of coping do exist and have been previously used in more general contexts. Drawing strongly from items used in the COPE and Brief COPE, we gathered a pool of items anticipated to be good measures of undergraduate students’ coping behaviors in STEM. We tested the validity of these items for use with STEM students using exploratory factor analyses, confirmatory factor analyses, and cognitive interviews. In particular, our confirmatory factor analyses and cognitive interviews explored whether the items measured coping for persons excluded due to ethnicity or race (PEERs).
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/doi.org/10.1007/s11134-018-9588-8
