185 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
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 (Csa´rdi 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
What do cyclists need to see to avoid single-bicycle crashes?
The number of single-bicycle crash victims is substantial in countries with high levels of cycling. To study the role of visual characteristics of the infrastructure, such as pavement markings, in single-bicycle crashes, a study in two steps was conducted. In Study 1, a questionnaire study was conducted among bicycle crash victims (n = 734). Logistic regression was used to study the relationship between the crashes and age, light condition, alcohol use, gaze direction and familiarity with the crash scene. In Study 2, the image degrading and edge detection method (IDED-method) was used to investigate the visual characteristics of 21 of the crash scenes. The results of the studies indicate that crashes, in which the cyclist collided with a bollard or road narrowing or rode off the road, were related to the visual characteristics of bicycle facilities. Edge markings, especially in curves of bicycle tracks, and improved conspicuity of bollards are recommended. Statement of Relevance: Elevated single-bicycle crash numbers are common in countries with high levels of cycling. No research has been conducted on what cyclists need to see to avoid this type of crash. The IDED-method to investigate crash scenes is new and proves to be a powerful tool to quantify 'visual accessibility'. © 2011 Taylor & Francis
Multiplex PageRank
(15 pages, 6 figures
Why Does Exercise “Triggerâ€? Adaptive Protective Responses in the Heart?
Numerous epidemiological studies suggest that individuals who exercise have decreased cardiac morbidity and mortality. Pre-clinical studies in animal models also find clear cardioprotective phenotypes in animals that exercise, specifically characterized by lower myocardial infarction and arrhythmia. Despite the clear benefits, the underlying cellular and molecular mechanisms that are responsible for exercise preconditioning are not fully understood. In particular, the adaptive signaling events that occur during exercise to “trigger� cardioprotection represent emerging paradigms. In this review, we discuss recent studies that have identified several different factors that appear to initiate exercise preconditioning. We summarize the evidence for and against specific cellular factors in triggering exercise adaptations and identify areas for future study
Is the astronomical forcing a reliable and unique pacemaker for climate? A conceptual model study
There is evidence that ice age cycles are paced by astronomical forcing,
suggesting some kind of synchronisation phenomenon. Here, we identify the type
of such synchronisation and explore systematically its uniqueness and
robustness using a simple paleoclimate model akin to the van der Pol relaxation
oscillator and dynamical system theory. As the insolation is quite a complex
quasiperiodic signal involving different frequencies, the traditional concepts
used to define synchronisation to periodic forcing are no longer applicable.
Instead, we explore a different concept of generalised synchronisation in terms
of (coexisting) synchronised solutions for the forced system, their basins of
attraction and instabilities. We propose a clustering technique to compute the
number of synchronised solutions, each of which corresponds to a different
paleoclimate history. In this way, we uncover multistable synchronisation
(reminiscent of phase- or frequency-locking to individual periodic components
of astronomical forcing) at low forcing strength, and monostable or unique
synchronisation at stronger forcing. In the multistable regime, different
initial conditions may lead to different paleoclimate histories. To study their
robustness, we analyse Lyapunov exponents that quantify the rate of convergence
towards each synchronised solution (local stability), and basins of attraction
that indicate critical levels of external perturbations (global stability). We
find that even though synchronised solutions are stable on a long term, there
exist short episodes of desynchronisation where nearby climate trajectories
diverge temporarily (for about 50 kyr). (...)Comment: 22 pages, 18 figure
- …