We use advanced statistical tools of time-series analysis to characterize the dynamical complexity of the transition to optical wave turbulence in a fiber laser. Ordinal analysis and the horizontal visibility graph applied to the experimentally measured laser output intensity reveal the presence of temporal correlations during the transition from the laminar to the turbulent lasing regimes. Both methods unveil coherent structures with well-defined time scales and strong correlations both, in the timing of the laser pulses and in their peak intensities. Our approach is generic and may be used in other complex systems that undergo similar transitions involving the generation of extreme fluctuations.
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http://dx.doi.org/10.1103/PhysRevLett.116.033902 | DOI Listing |
Sci Rep
December 2024
Department of Civil Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions.
View Article and Find Full Text PDFNat Commun
December 2024
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Probing regional glycogen metabolism in humans non-invasively has been challenging due to a lack of sensitive approaches. Here we studied human muscle glycogen dynamics post-exercise with a spatial resolution of millimeters and temporal resolution of minutes, using relayed nuclear Overhauser effect (glycoNOE) MRI. Data at 5T showed a homogeneous distribution of glycogen in resting muscle, with an average concentration of 99 ± 13 mM.
View Article and Find Full Text PDFNat Commun
December 2024
Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, Zurich, Switzerland.
Resting-state functional connectivity (rsFC) has been essential to elucidate the intricacy of brain organization, further revealing clinical biomarkers of neurological disorders. Although functional magnetic resonance imaging (fMRI) remains a cornerstone in the field of rsFC recordings, its interpretation is often hindered by the convoluted physiological origin of the blood-oxygen-level-dependent (BOLD) contrast affected by multiple factors. Here, we capitalize on the unique concurrent multiparametric hemodynamic recordings of a hybrid magnetic resonance optoacoustic tomography platform to comprehensively characterize rsFC in female mice.
View Article and Find Full Text PDFTransl Psychiatry
December 2024
School of Computer Science and Technology (School of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China.
Bipolar disorder (BD) is a neuropsychiatric disorder characterized by severe disturbance and fluctuation in mood. Dynamic functional connectivity (dFC) has the potential to more accurately capture the evolving processes of emotion and cognition in BD. Nevertheless, prior investigations of dFC typically centered on larger time scales, limiting the sensitivity to transient changes.
View Article and Find Full Text PDFJ Exerc Sci Fit
January 2025
Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, People's Republic of China.
Background: Basketball is an attractive sport required both cooperative and antagonistic motor skills. However, the neural mechanism of basketball proficiency remains unclear. This study aimed to examine the brain functional and structural substrates underlying varying levels of basketball capacity.
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