Biyoinformatik ve Genetik Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/46

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Citation - WoS: 11
Citation - Scopus: 13
Holocene Climate Forcings and Lacustrine Regime Shifts in the Indian Summer Monsoon Realm
(Wıley, 2020) Prasad, Sushma; Marwan, Norbert; Eroğlu, Deniz; Goswami, Bedartha; Mishra, Praveen Kuma; Gaye, Birgit; Anoop, Akhil; Stebich, Martina; Jehangir, Arshid; Basavaiah, Nathani
Extreme climate events have been identified both in meteorological and long-term proxy records from the Indian summer monsoon (ISM) realm. However, the potential of palaeoclimate data for understanding mechanisms triggering climate extremes over long time scales has not been fully exploited. A distinction between proxies indicating climate change, environment, and ecosystem shift is crucial for enabling a comparison with forcing mechanisms (e.g. El-Nino Southern Oscillation). In this study we decouple these factors using data analysis techniques [multiplex recurrence network (MRN) and principal component analyses (PCA)] on multiproxy data from two lakes located in different climate regions - Lonar Lake (ISM dominated) and the high-altitude Tso Moriri Lake (ISM and westerlies influenced). Our results indicate that (i) MRN analysis, an indicator of changing environmental conditions, is associated with droughts in regions with a single climate driver but provides ambiguous results in regions with multiple climate/environmental drivers; (ii) the lacustrine ecosystem was 'less sensitive' to forcings during the early Holocene wetter periods; (iii) archives in climate zones with a single climate driver were most sensitive to regime shifts; (iv) data analyses are successful in identifying the timing of onset of climate change, and distinguishing between extrinsic and intrinsic (lacustrine) regime shifts by comparison with forcing mechanisms. Our results enable development of conceptual models to explain links between forcings and regional climate change that can be tested in climate models to provide an improved understanding of the ISM dynamics and their impact on ecosystems. (c) 2020 John Wiley & Sons, Ltd.
Citation - WoS: 22
Citation - Scopus: 23
Multifaceted Dynamics of Janus Oscillator Networks
(Amer Physical Soc., 2019) Nicolaou, Zachary G.; Eroğlu, Deniz; Motter, Adilson E.
Recent research has led to the discovery of fundamental new phenomena in network synchronization including chimera states explosive synchronization and asymmetry-induced synchronization. Each of these phenomena has thus far been observed only in systems designed to exhibit that one phenomenon which raises the questions of whether they are mutually compatible and if so under what conditions they co-occur. Here we introduce a class of remarkably simple oscillator networks that concurrently exhibit all of these phenomena. The dynamical units consist of pairs of nonidentical phase oscillators which we refer to as Janus oscillators by analogy with Janus particles and the mythological figure from which their name is derived. In contrast to previous studies these networks exhibit (i) explosive synchronization with identical oscillators, (ii) extreme multistability of chimera states including traveling intermittent and bouncing chimeras, and (iii) asymmetry-induced synchronization in which synchronization is promoted by random oscillator heterogeneity. These networks also exhibit the previously unobserved possibility of inverted synchronization transitions in which a transition to a more synchronous state is induced by a reduction rather than an increase in the coupling strength. These various phenomena are shown to emerge under rather parsimonious conditions and even in locally connected ring topologies which has the potential to facilitate their use to control and manipulate synchronization in experiments.
Network Dynamics Reconstruction From Data
(Scıentıfıc Technıcal Research Councıl Turkey-Tubıtak, 2020) Eroğlu, Deniz
We consider the problem of recovering the model of a complex network of interacting dynamical units from time series of observations. We focus on typical networks which exhibit heterogeneous degrees, i.e. where the number of connections varies widely across the network, and the coupling strength for a single interaction is small. In these networks, the behavior of each unit varies according to their connectivity. Under these mild assumptions, our method provides an effective network reconstruction of the network dynamics. The method is robust to a certain size of noise and only requires relatively short time series on the state variable of most nodes to determine: how well-connected a particular node is, the distribution of the nodes' degrees in the network, and the underlying dynamics.
Citation - WoS: 22
Citation - Scopus: 23
Revealing Dynamics, Communities, and Criticality From Data
(Amer Physical Soc, 2020) Eroğlu, Deniz; Tanzi, Matteo; van Strien, Sebastian; Pereira, Tiago
Complex systems such as ecological communities and neuron networks are essential parts of our everyday lives. These systems are composed of units which interact through intricate networks. The ability to predict sudden changes in the dynamics of these networks, known as critical transitions, from data is important to avert disastrous consequences of major disruptions. Predicting such changes is a major challenge as it requires forecasting the behavior for parameter ranges for which no data on the system are available. We address this issue for networks with weak individual interactions and chaotic local dynamics. We do this by building a model network, termed an effective network, consisting of the underlying local dynamics and a statistical description of their interactions. We show that behavior of such networks can be decomposed in terms of an emergent deterministic component and a fluctuation term. Traditionally, such fluctuations are filtered out. However, as we show, they are key to accessing the interaction structure. We illustrate this approach on synthetic time series of realistic neuronal interaction networks of the cat cerebral cortex and on experimental multivariate data of optoelectronic oscillators. We reconstruct the community structure by analyzing the stochastic fluctuations generated by the network and predict critical transitions for coupling parameters outside the observed range.

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