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- Huerta, E. A., et al.
(författare)
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Enabling real-time multi-messenger astrophysics discoveries with deep learning
- 2019
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Ingår i: Nature reviews physics. - : Springer Science and Business Media LLC. - 2522-5820. ; 1:10, s. 600-608
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Forskningsöversikt (refereegranskat)abstract
- Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics. A group of experts suggests ways in which deep learning can be used to enhance the potential for discovery in multi-messenger astrophysics.
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| 2. |
- Rudolfson, Niclas, et al.
(författare)
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Validating the Global Surgery Geographical Accessibility Indicator : Differences in Modeled Versus Patient-Reported Travel Times
- 2020
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Ingår i: World Journal of Surgery. - : Springer Science and Business Media LLC. - 0364-2313 .- 1432-2323. ; 44:7, s. 2123-2130
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Tidskriftsartikel (refereegranskat)abstract
- Background: Since long travel times to reach health facilities are associated with worse outcomes, geographic accessibility is one of the six core global surgery indicators; this corresponds to the second of the “Three Delays Framework,” namely “delay in reaching a health facility.” Most attempts to estimate this indicator have been based on geographical information systems (GIS) algorithms. The aim of our study was to compare GIS derived estimates to self-reported travel times for patients traveling to a district hospital in rural Rwanda for emergency obstetric care. Methods: Our study includes 664 women who traveled to undergo a Cesarean delivery in Kirehe, Rwanda. We compared self-reported travel time from home to the hospital (excluding waiting time) with GIS estimated travel times, which were computed using the World Health Organization tool AccessMod, using linear regression. Results: The majority of patients used multiple modes of transportation (walking = 48.5%, public transport = 74.2%, private transport = 2.9%, and ambulance 70.6%). Self-reported times were longer than GIS estimates by a factor of 1.49 (95% CI 1.40–1.57). Concordance was higher when the GIS model took into account that all patients in Rwanda are referred via their health center (β = 1.12; 95% CI 1.05–1.18). Conclusions: To our knowledge, in this largest to date GIS validation study for geographical access to healthcare in low- and middle-income countries, a standard GIS model was found to significantly underestimate real travel time, which likely is in part because it does not model the actual route patients are travelling. Therefore, previous studies of 2-h access to surgery will need to be interpreted with caution, and future studies should take local travelling conditions into account.
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