Transformation cost spectrum for irregularly sampled time series

Irregularly sampled time series analysis is a common problem in various disciplines. Since conventional methods are not directly applicable to irregularly sampled time series, a common interpolation approach is used; however, this causes data distortion and consequently biases further analyses. We propose a method that yields a regularly sampled time series spectrum of costs with minimum information loss. Each time series in this spectrum is a stationary series and acts as a difference filter. The transformation costs approach derives the differences between consecutive and arbitrarily sized segments. After obtaining regular sampling, recurrence plot analysis is performed to distinguish regime transitions. The approach is applied to a prototypical model to validate its performance and to different palaeoclimate proxy data sets located around Africa to identify critical climate transition periods during the last 5 million years and their characteristic properties.Comment: 18 pages, 8 figures. Eur. Phys. J. Spec. Top. (2022

Connectivity-Driven Coherence in Complex Networks

We study the emergence of coherence in complex networks of mutually coupled non-identical elements. We uncover the precise dependence of the dynamical coherence on the network connectivity, on the isolated dynamics of the elements and the coupling function. These findings predict that in random graphs, the enhancement of coherence is proportional to the mean degree. In locally connected networks, coherence is no longer controlled by the mean degree, but rather on how the mean degree scales with the network size. In these networks, even when the coherence is absent, adding a fraction s of random connections leads to an enhancement of coherence proportional to s. Our results provide a way to control the emergent properties by the manipulation of the dynamics of the elements and the network connectivity.Comment: 4 pages, 2 figure

Network structural origin of instabilities in large complex systems

A central issue in the study of large complex network systems, such as power grids, financial networks, and ecological systems, is to understand their response to dynamical perturbations. Recent studies recognize that many real networks show nonnormality and that nonnormality can give rise to reactivity--the capacity of a linearly stable system to amplify its response to perturbations, oftentimes exciting nonlinear instabilities. Here, we identify network structural properties underlying the pervasiveness of nonnormality and reactivity in real directed networks, which we establish using the most extensive data set of such networks studied in this context to date. The identified properties are imbalances between incoming and outgoing network links and paths at each node. Based on this characterization, we develop a theory that quantitatively predicts nonnormality and reactivity and explains the observed pervasiveness. We suggest that these results can be used to design, upgrade, control, and manage networks to avoid or promote network instabilities.Comment: Includes Supplementary Material

Collective dynamics of random Janus oscillator networks

Janus oscillators have been recently introduced as a remarkably simple phase oscillator model that exhibits non-trivial dynamical patterns -- such as chimeras, explosive transitions, and asymmetry-induced synchronization -- that once were only observed in specifically tailored models. Here we study ensembles of Janus oscillators coupled on large homogeneous and heterogeneous networks. By virtue of the Ott-Antonsen reduction scheme, we find that the rich dynamics of Janus oscillators persists in the thermodynamic limit of random regular, Erd\H{o}s-R\'enyi and scale-free random networks. We uncover for all these networks the coexistence between partially synchronized state and a multitude of states displaying global oscillations. Furthermore, abrupt transitions of the global and local order parameters are observed for all topologies considered. Interestingly, only for scale-free networks, it is found that states displaying global oscillations vanish in the thermodynamic limit

Surface properties of mdf coated with calcite/cla y and effects of fire retardants on these properties

The coating of wood and wood panel surfaces basically serves for surface protection and its surface can be improved at various user areas. Different application methods are used for the coating of MDF surface. Th ese are methods such as curtain coating, spraying, rolling, knife, etc. In this study, suitability of pigment coating method instead of the traditional surface coating methods used for coating MDF panels and the effects of fi re retardants on surface properties of MDF coated by the best appropriate coating mixture was investigated. Calcite and clay were used as coating pigment and latex, urea formaldehyde, and melamine formaldehyde were used as adhesive. Coating/adhesive mixture obtained was applied to MDF panels with knives. Before the analysis, test samples were put to condition room and they were kept in there for 1 week. According to the results obtained, the best appropriate coating material was found as calcite. 22% concentrated melamine formaldehyde as adhesive and 0.25 mm as coating thickness was determined better than other combinations. Borax, boric acid and zinc borate as fi re retardant were added to calcite /melamine formaldehyde with 22% mixture. So, the effects of fi re retardant on surface characterization were determined. Surface properties of coated MDF panels tested were found lower than standard requirements except for abrasion resistance

Transformation-cost time-series method for analyzing irregularly sampled data

ACKNOWLEDGEMENTS We thank K.-H. Wyrwoll and F. McRobie from the School of Earth and Environment (UWA) for fruitful discussions. Moreover, we acknowledge for financial supports from TUBITAK under the 2214/A program (I.O.), from the Leibniz Association (WGL) under Grant No. SAW-2013-IZW-2542 (D.E.), as well as from the BMBF within the Potsdam Research Cluster for Georisk Analysis, Environmental Change and Sustainability (PROGRESS) Support Code No. 03IS2191B (N.M.).Peer reviewedPublisher PD

Measurement of cognitive dynamics during video watching through event-related potentials (ERPs) and oscillations (EROs)

Event-related potentials (ERPs) and oscillations (EROs) are reliable measures of cognition, but they require time-locked electroencephalographic (EEG) data to repetitive triggers that are not available in continuous sensory input streams. However, such real-life-like stimulation by videos or virtual-reality environments may serve as powerful means of creating specific cognitive or affective states and help to investigate dysfunctions in psychiatric and neurological disorders more efficiently. This study aims to develop a method to generate ERPs and EROs during watching videos. Repeated luminance changes were introduced on short video segments, while EEGs of 10 subjects were recorded. The ERP/EROs time-locked to these distortions were analyzed in time and time-frequency domains and tested for their cognitive significance through a long term memory test that included frames from the watched videos. For each subject, ERPs and EROs corresponding to video segments of recalled images with 25% shortest and 25% longest reaction times were compared. ERPs produced by transient luminance changes displayed statistically significant fluctuations both in time and time-frequency domains. Statistical analyses showed that a positivity around 450 ms, a negativity around 500 ms and delta and theta EROs correlated with memory performance. Few studies mixed video streams with simultaneous ERP/ERO experiments with discrete task-relevant or passively presented auditory or somatosensory stimuli, while the present study, by obtaining ERPs and EROs to task-irrelevant events in the same sensory modality as that of the continuous sensory input, produces minimal interference with the main focus of attention on the video stream