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Reinwald M, Moseley B, Szenicer A, Nissen-Meyer T, Oduor S, Vollrath F, Markham A, Mortimer B. Seismic localization of elephant rumbles as a monitoring approach. J R Soc Interface 2021; 18:20210264. [PMID: 34255988 PMCID: PMC8277467 DOI: 10.1098/rsif.2021.0264] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/23/2021] [Indexed: 11/12/2022] Open
Abstract
African elephants (Loxodonta africana) are sentient and intelligent animals that use a variety of vocalizations to greet, warn or communicate with each other. Their low-frequency rumbles propagate through the air as well as through the ground and the physical properties of both media cause differences in frequency filtering and propagation distances of the respective wave. However, it is not well understood how each mode contributes to the animals' abilities to detect these rumbles and extract behavioural or spatial information. In this study, we recorded seismic and co-generated acoustic rumbles in Kenya and compared their potential use to localize the vocalizing animal using the same multi-lateration algorithms. For our experimental set-up, seismic localization has higher accuracy than acoustic, and bimodal localization does not improve results. We conclude that seismic rumbles can be used to remotely monitor and even decipher elephant social interactions, presenting us with a tool for far-reaching, non-intrusive and surprisingly informative wildlife monitoring.
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Affiliation(s)
| | - Ben Moseley
- Department of Computer Science, University of Oxford, Oxford, UK
| | | | | | | | - Fritz Vollrath
- Department of Zoology, University of Oxford, Oxford, UK
- Save the Elephants, Marula Manor, Karen, Nairobi, Kenya
| | - Andrew Markham
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Beth Mortimer
- Department of Zoology, University of Oxford, Oxford, UK
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Szenicer A, Fouhey DF, Munoz-Jaramillo A, Wright PJ, Thomas R, Galvez R, Jin M, Cheung MCM. A deep learning virtual instrument for monitoring extreme UV solar spectral irradiance. Sci Adv 2019; 5:eaaw6548. [PMID: 31616783 PMCID: PMC6774717 DOI: 10.1126/sciadv.aaw6548] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Accepted: 09/08/2019] [Indexed: 06/10/2023]
Abstract
Measurements of the extreme ultraviolet (EUV) solar spectral irradiance (SSI) are essential for understanding drivers of space weather effects, such as radio blackouts, and aerodynamic drag on satellites during periods of enhanced solar activity. In this paper, we show how to learn a mapping from EUV narrowband images to spectral irradiance measurements using data from NASA's Solar Dynamics Observatory obtained between 2010 to 2014. We describe a protocol and baselines for measuring the performance of models. Our best performing machine learning (ML) model based on convolutional neural networks (CNNs) outperforms other ML models, and a differential emission measure (DEM) based approach, yielding average relative errors of under 4.6% (maximum error over emission lines) and more typically 1.6% (median). We also provide evidence that the proposed method is solving this mapping in a way that makes physical sense and by paying attention to magnetic structures known to drive EUV SSI variability.
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Affiliation(s)
- Alexandre Szenicer
- Department of Earth Sciences, University of Oxford, Oxford, UK
- Frontier Development Lab, Mountain View, California, USA
| | - David F. Fouhey
- Frontier Development Lab, Mountain View, California, USA
- Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, Michigan, USA
| | - Andres Munoz-Jaramillo
- Frontier Development Lab, Mountain View, California, USA
- Southwest Research Institute, Boulder, Colorado, USA
| | - Paul J. Wright
- Frontier Development Lab, Mountain View, California, USA
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, USA
- SUPA School of Physics and Astronomy, University of Glasgow, Glasgow, UK
| | - Rajat Thomas
- Frontier Development Lab, Mountain View, California, USA
- Department of Psychiatry, Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Richard Galvez
- Frontier Development Lab, Mountain View, California, USA
- Center for Data Science, Center for Cosmology and Particle Physics, New York University, New York, NY, USA
| | - Meng Jin
- Frontier Development Lab, Mountain View, California, USA
- Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, California, USA
- SETI Institute, Mountain View, California, USA
| | - Mark C. M. Cheung
- Frontier Development Lab, Mountain View, California, USA
- Hansen Experimental Physics Laboratory, Stanford University, Stanford, California, USA
- Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, California, USA
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Abstract
We experimentally investigate the dynamics of capillary-driven flows at the nanoscale, using an original platform that combines nanoscale pores (⋍3 nm in diameter) and microfluidic features. In particular, we show that drying involves a fine coupling between thermodynamics and fluid mechanics that can be used to generate precisely controlled nanoflows driven by extreme stresses - up to 100 MPa of tension. We exploit these tunable flows to provide quantitative tests of continuum theories (e.g. Kelvin-Laplace equation and Poiseuille flow) across an unprecedented range and we isolate the breakdown of continuum as a negative slip length of molecular dimension. Our results show a coherent picture across multiple experiments including drying-induced permeation flows, imbibition and poroelastic transients.
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Affiliation(s)
- Olivier Vincent
- Cornell University, Robert Frederick Smith School of Chemical and Biomolecular Engineering, Ithaca, NY, USA.
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