309 research outputs found
Remote manipulator dynamic simulation
A simulator to generate the real time visual scenes required to perform man in the loop investigations of remote manipulator application and design concepts for the space shuttle is described. The simulated remote manipulator consists of a computed display system that uses a digital computer, the electronic scene generator, an operator's station, and associated interface hardware. A description of the capabilities of the implemented simulation is presented. The mathematical models and programs developed for the simulation are included
Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes
When network and graph theory are used in the study of complex systems, a
typically finite set of nodes of the network under consideration is frequently
either explicitly or implicitly considered representative of a much larger
finite or infinite region or set of objects of interest. The selection
procedure, e.g., formation of a subset or some kind of discretization or
aggregation, typically results in individual nodes of the studied network
representing quite differently sized parts of the domain of interest. This
heterogeneity may induce substantial bias and artifacts in derived network
statistics. To avoid this bias, we propose an axiomatic scheme based on the
idea of node splitting invariance to derive consistently weighted variants of
various commonly used statistical network measures. The practical relevance and
applicability of our approach is demonstrated for a number of example networks
from different fields of research, and is shown to be of fundamental importance
in particular in the study of spatially embedded functional networks derived
from time series as studied in, e.g., neuroscience and climatology.Comment: 21 pages, 13 figure
Investigating the topology of interacting networks - Theory and application to coupled climate subnetworks
Network theory provides various tools for investigating the structural or
functional topology of many complex systems found in nature, technology and
society. Nevertheless, it has recently been realised that a considerable number
of systems of interest should be treated, more appropriately, as interacting
networks or networks of networks. Here we introduce a novel graph-theoretical
framework for studying the interaction structure between subnetworks embedded
within a complex network of networks. This framework allows us to quantify the
structural role of single vertices or whole subnetworks with respect to the
interaction of a pair of subnetworks on local, mesoscopic and global
topological scales.
Climate networks have recently been shown to be a powerful tool for the
analysis of climatological data. Applying the general framework for studying
interacting networks, we introduce coupled climate subnetworks to represent and
investigate the topology of statistical relationships between the fields of
distinct climatological variables. Using coupled climate subnetworks to
investigate the terrestrial atmosphere's three-dimensional geopotential height
field uncovers known as well as interesting novel features of the atmosphere's
vertical stratification and general circulation. Specifically, the new measure
"cross-betweenness" identifies regions which are particularly important for
mediating vertical wind field interactions. The promising results obtained by
following the coupled climate subnetwork approach present a first step towards
an improved understanding of the Earth system and its complex interacting
components from a network perspective
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
We introduce the \texttt{pyunicorn} (Pythonic unified complex network and
recurrence analysis toolbox) open source software package for applying and
combining modern methods of data analysis and modeling from complex network
theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully
object-oriented and easily parallelizable package written in the language
Python. It allows for the construction of functional networks such as climate
networks in climatology or functional brain networks in neuroscience
representing the structure of statistical interrelationships in large data sets
of time series and, subsequently, investigating this structure using advanced
methods of complex network theory such as measures and models for spatial
networks, networks of interacting networks, node-weighted statistics or network
surrogates. Additionally, \texttt{pyunicorn} provides insights into the
nonlinear dynamics of complex systems as recorded in uni- and multivariate time
series from a non-traditional perspective by means of recurrence quantification
analysis (RQA), recurrence networks, visibility graphs and construction of
surrogate time series. The range of possible applications of the library is
outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure
Local Difference Measures between Complex Networks for Dynamical System Model Evaluation
Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD
Network centrality: an introduction
Centrality is a key property of complex networks that influences the behavior
of dynamical processes, like synchronization and epidemic spreading, and can
bring important information about the organization of complex systems, like our
brain and society. There are many metrics to quantify the node centrality in
networks. Here, we review the main centrality measures and discuss their main
features and limitations. The influence of network centrality on epidemic
spreading and synchronization is also pointed out in this chapter. Moreover, we
present the application of centrality measures to understand the function of
complex systems, including biological and cortical networks. Finally, we
discuss some perspectives and challenges to generalize centrality measures for
multilayer and temporal networks.Comment: Book Chapter in "From nonlinear dynamics to complex systems: A
Mathematical modeling approach" by Springe
Complex networks for climate model evaluation with application to statistical versus dynamical modeling of South American climate
Acknowledgments: This paper was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP. Furthermore, this work has been financially supported by the Leibniz Society (project ECONS), and the Stordalen Foundation (JFD). For certain calculations, the software packages pyunicorn (Donges et al. 2013a) and igraph (CsaÂŽrdi and Nepusz 2006) were used. The authors would like to thank Manoel F. Cardoso, Niklas Boers, and the reviewers for helpful comments on the manuscript. Open Access: This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.Peer reviewedPostprin
Trajectories of the Earth System in the Anthropocene
This is the final version of the article. Available from National Academy of Sciences via the DOI in this record.We explore the risk that self-reinforcing feedbacks could push the Earth System toward a planetary threshold that, if crossed, could prevent stabilization of the climate at intermediate temperature rises and cause continued warming on a "Hothouse Earth" pathway even as human emissions are reduced. Crossing the threshold would lead to a much higher global average temperature than any interglacial in the past 1.2 million years and to sea levels significantly higher than at any time in the Holocene. We examine the evidence that such a threshold might exist and where it might be. If the threshold is crossed, the resulting trajectory would likely cause serious disruptions to ecosystems, society, and economies. Collective human action is required to steer the Earth System away from a potential threshold and stabilize it in a habitable interglacial-like state. Such action entails stewardship of the entire Earth System-biosphere, climate, and societies-and could include decarbonization of the global economy, enhancement of biosphere carbon sinks, behavioral changes, technological innovations, new governance arrangements, and transformed social values.W.S. and C.P.S. are members of the Anthropocene Working Group.
W.S., J.R., K.R., S.E.C., J.F.D., I.F., S.J.L., R.W. and H.J.S. are members of the
Planetary Boundaries Research Network PB.net and the Earth Leagueâs EarthDoc
Programme supported by the Stordalen Foundation. T.M.L. was supported by
a Royal Society Wolfson Research Merit Award and the European Union
Framework Programme 7 Project HELIX. C.F. was supported by the Erlingâ
Persson Family Foundation. The participation of D.L. was supported by the
Haury Program in Environment and Social Justice and National Science
Foundation (USA) Decadal and Regional Climate Prediction using Earth
System Models Grant 1243125. S.E.C. was supported in part by Swedish Research
Council Formas Grant 2012-742. J.F.D. and R.W. were supported by
Leibniz Association Project DOMINOES. S.J.L. receives funding from Formas
Grant 2014-589. This paper is a contribution to European Research Council
Advanced Grant 2016, Earth Resilience in the Anthropocene Project 743080
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