• AIP Publishing
• AVS: Science & Technology of Materials, Interfaces, and Processing
• Acoustical Society of America
• American Association of Physicists in Medicine
• American Association of Physics Teachers
• American Crystallographic Association, Inc.
• Chinese Physical Society
• Chinese Society of Theoretical and Applied Mechanics
• Laser Institute of America
• The Society of Rheology

Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena. Chaos is committed to publish selective and high quality content that is accessible to researchers from a broad spectrum of disciplines. Topics cover nonlinear dynamical systems, neural networks and neuro-dynamics, climate and earth sciences, condensed matter, fluid dynamics, synchronization, turbulence, solitons and coherent structures, time-series analysis, and more.

Editor's Picks

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  Tipping point analysis of atmospheric oxygen concentration

  dx.doi.org/10.1063/1.4907185

  We apply tipping point analysis to nine observational oxygen concentration records around the globe, analyse their dynamics and perform projections under possible future scenarios, leading to oxygen deficiency in the atmosphere. The analysis is based on statistical physics framework with stochastic modelling, where we represent the observed data as a composition of deterministic and stochastic components estimated from the observed data using Bayesian and wavelet techniques.

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  How robust is dispersal-induced spatial synchrony?

  dx.doi.org/10.1063/1.4906951

  Many biological populations fluctuate in synchrony over large geographic regions. This behavior may increase the chance of extinction. The combination of time-scale separation between interacting species and weak spatial linear diffusive coupling is one mechanism that can generate synchrony; however, accounting for travel time between habitat patches may destabilize this synchrony. Here, we show that ubiquitous behavioral aspects of dispersal (e.g., predator avoidance), implemented as nonlinear diffusive coupling, may also destabilize synchrony. In addition, these aspects interact with travel-time delays and amplify mechanisms that destroy synchrony. Our work suggests that dispersal-induced synchrony is more rare than typically assumed.

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  Controllability and observability of Boolean networks arising from biology

  dx.doi.org/10.1063/1.4907708

  Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

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  Endogenous field feedback promotes the detectability for exogenous electric signal in the hybrid coupled population

  dx.doi.org/10.1063/1.4906545

  This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance.

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  Approximating high-dimensional dynamics by barycentric coordinates with linear programming

  dx.doi.org/10.1063/1.4906746

  The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.