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DDC: 510 × Type: Text × WGL Institute: PIK ×

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Now showing 1 - 10 of 27
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    Predicting the data structure prior to extreme events from passive observables using echo state network
    (Lausanne : Frontiers Media, 2022) Banerjee, Abhirup; Mishra, Arindam; Dana, Syamal K.; Hens, Chittaranjan; Kapitaniak, Tomasz; Kurths, Jürgen; Marwan, Norbert
    Extreme events are defined as events that largely deviate from the nominal state of the system as observed in a time series. Due to the rarity and uncertainty of their occurrence, predicting extreme events has been challenging. In real life, some variables (passive variables) often encode significant information about the occurrence of extreme events manifested in another variable (active variable). For example, observables such as temperature, pressure, etc., act as passive variables in case of extreme precipitation events. These passive variables do not show any large excursion from the nominal condition yet carry the fingerprint of the extreme events. In this study, we propose a reservoir computation-based framework that can predict the preceding structure or pattern in the time evolution of the active variable that leads to an extreme event using information from the passive variable. An appropriate threshold height of events is a prerequisite for detecting extreme events and improving the skill of their prediction. We demonstrate that the magnitude of extreme events and the appearance of a coherent pattern before the arrival of the extreme event in a time series affect the prediction skill. Quantitatively, we confirm this using a metric describing the mean phase difference between the input time signals, which decreases when the magnitude of the extreme event is relatively higher, thereby increasing the predictability skill.
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    Synchronization Patterns in Modular Neuronal Networks: A Case Study of C. elegans
    (Lausanne : Frontiers Media, 2019) Pournaki, Armin; Merfort, Leon; Ruiz, Jorge; Kouvaris, Nikos E.; Hövel, Philipp; Hizanidis, Johanne
    We investigate synchronization patterns and chimera-like states in the modular multilayer topology of the connectome of Caenorhabditis elegans. In the special case of a designed network with two layers, one with electrical intra-community links and one with chemical inter-community links, chimera-like states are known to exist. Aiming at a more biological approach based on the actual connectivity data, we consider a network consisting of two synaptic (electrical and chemical) and one extrasynaptic (wireless) layers. Analyzing the structure and properties of this layered network using Multilayer-Louvain community detection, we identify modules whose nodes are more strongly coupled with each other than with the rest of the network. Based on this topology, we study the dynamics of coupled Hindmarsh-Rose neurons. Emerging synchronization patterns are quantified using the pairwise Euclidean distances between the values of all oscillators, locally within each community and globally across the network. We find a tendency of the wireless coupling to moderate the average coherence of the system: for stronger wireless coupling, the levels of synchronization decrease both locally and globally, and chimera-like states are not favored. By introducing an alternative method to define meaningful communities based on the dynamical correlations of the nodes, we obtain a structure that is dominated by two large communities. This promotes the emergence of chimera-like states and allows to relate the dynamics of the corresponding neurons to biological neuronal functions such as motor activities. © Copyright © 2019 Pournaki, Merfort, Ruiz, Kouvaris, Hövel and Hizanidis.
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    How Price-Based Frequency Regulation Impacts Stability in Power Grids: A Complex Network Perspective
    (London : Hindawi, 2020) Ji, Peng; Zhu, Lipeng; Lu, Chao; Lin, Wei; Kurths, Jürgen
    With the deregulation of modern power grids, electricity markets are playing a more and more important role in power grid operation and control. However, it is still questionable how the real-time electricity price-based operation affects power grid stability. From a complex network perspective, here we investigate the dynamical interactions between price-based frequency regulations and physical networks, which results in an interesting finding that a local minimum of network stability occurs when the response strength of generators/consumers to the varying price increases. A case study of the real world-based China Southern Power Grid demonstrates the finding and exhibits a feasible approach to network stability enhancement in smart grids. This also provides guidance for potential upgrade and expansion of the current power grids in a cleaner and safer way. © 2020 Peng Ji et al.
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    Transitions in a genetic transcriptional regulatory system under Lévy motion
    (London : Nature Publishing Group, 2016) Zheng, Y.; Serdukova, L.; Duan, J.; Kurths, J.
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    Analysis, simulation and prediction of multivariate random fields with package randomfields
    (Los Angeles, Calif. : UCLA, Dept. of Statistics, 2015) Schlather, Martin; Malinowski, Alexander; Menck, Peter J.; Oesting, Marco; Strokorb, Kirstin
    Modeling of and inference on multivariate data that have been measured in space, such as temperature and pressure, are challenging tasks in environmental sciences, physics and materials science. We give an overview over and some background on modeling with crosscovariance models. The R package RandomFields supports the simulation, the parameter estimation and the prediction in particular for the linear model of coregionalization, the multivariate Matérn models, the delay model, and a spectrum of physically motivated vector valued models. An example on weather data is considered, illustrating the use of RandomFields for parameter estimation and prediction.
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    Taxing interacting externalities of ocean acidification, global warming, and eutrophication
    (Malden, Mass. : Wiley-Blackwell, 2021) Hänsel, Martin C.; Bergh, Jeroen C. J. M. van den
    We model a stylized economy dependent on agriculture and fisheries to study optimal environmental policy in the face of interacting external effects of ocean acidification, global warming, and eutrophication. This allows us to capture some of the latest insights from research on ocean acidification. Using a static two-sector general equilibrium model we derive optimal rules for national taxes on (Formula presented.) emissions and agricultural run-off and show how they depend on both isolated and interacting damage effects. In addition, we derive a second-best rule for a tax on agricultural run-off of fertilizers for the realistic case that effective internalization of (Formula presented.) externalities is lacking. The results contribute to a better understanding of the social costs of ocean acidification in coastal economies when there is interaction with other environmental stressors. Recommendations for Resource Managers: Marginal environmental damages from (Formula presented.) emissions should be internalized by a tax on (Formula presented.) emissions that is high enough to not only reflect marginal damages from temperature increases, but also marginal damages from ocean acidification and the interaction of both with regional sources of acidification like nutrient run-off from agriculture. In the absence of serious national policies that fully internalize externalities, a sufficiently high tax on regional nutrient run-off of fertilizers used in agricultural production can limit not only marginal environmental damages from nutrient run-off but also account for unregulated carbon emissions. Putting such regional policies in place that consider multiple important drivers of environmental change will be of particular importance for developing coastal economies that are likely to suffer the most from ocean acidification. © 2021 The Authors. Natural Resource Modeling published by Wiley Periodicals LLC.
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    Understanding the transgression of global and regional freshwater planetary boundaries
    (London : Royal Society, 2022) Pastor, A.V.; Biemans, H.; Franssen, W.; Gerten, D.; Hoff, H.; Ludwig, F.; Kabat, P.
    Freshwater ecosystems have been degraded due to intensive freshwater abstraction. Therefore, environmental flow requirements (EFRs) methods have been proposed to maintain healthy rivers and/or restore river flows. In this study, we used the Variable Monthly Flow (VMF) method to calculate the transgression of freshwater planetary boundaries: (1) natural deficits in which flow does not meet EFRs due to climate variability, and (2) anthropogenic deficits caused by water abstractions. The novelty is that we calculated spatially and cumulative monthly water deficits by river types including the frequency, magnitude and causes of environmental flow (EF) deficits (climatic and/or anthropogenic). Water deficit was found to be a regional rather than a global concern (less than 5% of total discharge). The results show that, from 1960 to 2000, perennial rivers with low flow alteration, such as the Amazon, had an EF deficit of 2–12% of the total discharge, and that the climate deficit was responsible for up to 75% of the total deficit. In rivers with high seasonality and high water abstractions such as the Indus, the total deficit represents up to 130% of its total discharge, 85% of which is due to withdrawals. We highlight the need to allocate water to humans and ecosystems sustainably. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.
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    Topology identification of complex network via chaotic ant swarm algorithm
    (New York, NY : Hindawi Publishing Corporation, 2013) Peng, H.; Li, L.; Kurths, J.; Li, S.; Yang, Y.
    Nowadays, the topology of complex networks is essential in various fields as engineering, biology, physics, and other scientific fields. We know in some general cases that there may be some unknown structure parameters in a complex network. In order to identify those unknown structure parameters, a topology identification method is proposed based on a chaotic ant swarm algorithm in this paper. The problem of topology identification is converted into that of parameter optimization which can be solved by a chaotic ant algorithm. The proposed method enables us to identify the topology of the synchronization network effectively. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed method.
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    Systems Medicine of Cancer: Bringing Together Clinical Data and Nonlinear Dynamics of Genetic Networks
    (London : Hindawi, 2016) Blyuss, Konstantin B.; Manchanda, Ranjit; Kurths, Jürgen; Alsaedi, Ahmed; Zaikin, Alexey
    Editorial
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    Influence of Sound on Empirical Brain Networks
    (Lausanne : Frontiers Media, 2021) Sawicki, Jakub; Schöll, Eckehard
    We analyze the influence of an external sound source in a network of FitzHugh–Nagumo oscillators with empirical structural connectivity measured in healthy human subjects. We report synchronization patterns, induced by the frequency of the sound source. We show that the level of synchrony can be enhanced by choosing the frequency of the sound source and its amplitude as control parameters for synchronization patterns. We discuss a minimum model elucidating the modalities of the influence of music on the human brain.