39 research outputs found
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Networks from Flows - From Dynamics to Topology
Complex network approaches have recently been applied to continuous spatial dynamical systems, like climate, successfully uncovering the system's interaction structure. However the relationship between the underlying atmospheric or oceanic flow's dynamics and the estimated network measures have remained largely unclear. We bridge this crucial gap in a bottom-up approach and define a continuous analytical analogue of Pearson correlation networks for advection-diffusion dynamics on a background flow. Analysing complex networks of prototypical flows and from time series data of the equatorial Pacific, we find that our analytical model reproduces the most salient features of these networks and thus provides a general foundation of climate networks. The relationships we obtain between velocity field and network measures show that line-like structures of high betweenness mark transition zones in the flow rather than, as previously thought, the propagation of dynamical information
Testing the detectability of spatio–temporal climate transitions from paleoclimate networks with the START model
A critical challenge in paleoclimate data analysis is the fact that the proxy data are
heterogeneously distributed in space, which affects statistical methods that
rely on spatial embedding of data. In the paleoclimate network approach nodes
represent paleoclimate proxy time series, and links in the network are given
by statistically significant similarities between them. Their location in
space, proxy and archive type is coded in the node attributes.
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We develop a semi-empirical model for <b>S</b>patio-<b>T</b>emporally
<b>A</b>utoco<b>R</b>related <b>T</b>ime series, inspired by the
interplay of different Asian Summer Monsoon (ASM) systems. We use an ensemble
of transition runs of this START model to test whether and how
spatio–temporal climate transitions could be detectable from (paleo)climate
networks. We sample model time series both on a grid and at locations at
which paleoclimate data are available to investigate the effect of the
spatially heterogeneous availability of data. Node betweenness centrality,
averaged over the transition region, does not respond to the transition
displayed by the START model, neither in the grid-based nor in the scattered
sampling arrangement. The regionally defined measures of regional node degree
and cross link ratio, however, are indicative of the changes in both
scenarios, although the magnitude of the changes differs according to the
sampling.
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We find that the START model is particularly suitable for pseudo-proxy
experiments to test the technical reconstruction limits of paleoclimate data
based on their location, and we conclude that (paleo)climate networks are
suitable for investigating spatio–temporal transitions in the dependence
structure of underlying climatic fields
Characterizing the evolution of climate networks
Peer reviewedPublisher PD
On the influence of spatial sampling on climate networks
Peer reviewedPublisher PD
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Characterizing the evolution of climate networks
Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, ErdA's-Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970-2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks
Selection of radio pulsar candidates using artificial neural networks
Radio pulsar surveys are producing many more pulsar candidates than can be
inspected by human experts in a practical length of time. Here we present a
technique to automatically identify credible pulsar candidates from pulsar
surveys using an artificial neural network. The technique has been applied to
candidates from a recent re-analysis of the Parkes multi-beam pulsar survey
resulting in the discovery of a previously unidentified pulsar.Comment: Accepted for publication in Monthly Notices of the Royal Astronomical
Society. 9 pages, 7 figures, and 1 tabl
Shared pooled mobility: expert review from nine disciplines and implications for an emerging transdisciplinary research agenda
Shared pooled mobility has been hailed as a sustainable mobility solution that uses digital innovation to efficiently bundle rides. Multiple disciplines have started investigating and analyzing shared pooled mobility systems. However, there is a lack of cross-community communication making it hard to build upon knowledge from other fields or know which open questions may be of interest to other fields. Here, we identify and review 9 perspectives: transdisciplinary social sciences, social physics, transport simulations, urban and energy economics, psychology, climate change solutions, and the Global South research and provide a common terminology. We identify more than 25 000 papers, with more than 100 fold variation in terms of literature count between research perspectives. Our review demonstrates the intellectual attractivity of this as a novel perceived mode of transportation, but also highlights that real world economics may limit its viability, if not supported with concordant incentives and regulation. We then sketch out cross-disciplinary open questions centered around (1) optimal configuration of ride-pooling systems, (2) empirical studies, and (3) market drivers and implications for the economics of ride-pooling. We call for researchers of different disciplines to actively exchange results and views to advance a transdisciplinary research agenda
Temporal and spatial analysis of the 2014-2015 Ebola virus outbreak in West Africa
West Africa is currently witnessing the most extensive Ebola virus (EBOV) outbreak so far recorded. Until now, there have been 27,013 reported cases and 11,134 deaths. The origin of the virus is thought to have been a zoonotic transmission from a bat to a two-year-old boy in December 2013 (ref. 2). From this index case the virus was spread by human-to-human contact throughout Guinea, Sierra Leone and Liberia. However, the origin of the particular virus in each country and time of transmission is not known and currently relies on epidemiological analysis, which may be unreliable owing to the difficulties of obtaining patient information. Here we trace the genetic evolution of EBOV in the current outbreak that has resulted in multiple lineages. Deep sequencing of 179 patient samples processed by the European Mobile Laboratory, the first diagnostics unit to be deployed to the epicentre of the outbreak in Guinea, reveals an epidemiological and evolutionary history of the epidemic from March 2014 to January 2015. Analysis of EBOV genome evolution has also benefited from a similar sequencing effort of patient samples from Sierra Leone. Our results confirm that the EBOV from Guinea moved into Sierra Leone, most likely in April or early May. The viruses of the Guinea/Sierra Leone lineage mixed around June/July 2014. Viral sequences covering August, September and October 2014 indicate that this lineage evolved independently within Guinea. These data can be used in conjunction with epidemiological information to test retrospectively the effectiveness of control measures, and provides an unprecedented window into the evolution of an ongoing viral haemorrhagic fever outbreak.status: publishe
Kingdom of Cambodia Grenade attack on peaceful demonstration
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