We report on time-resolved measurements of the first order spatial coherence in an exciton-polariton Bose-Einstein condensate. Long-range spatial coherence is found to set in right at the onset of stimulated scattering, on a picosecond time scale. The coherence reaches its maximum value after the population and decays slowly, staying up to a few hundred picoseconds. This behavior can be qualitatively reproduced, using a stochastic classical field model describing interaction between the polariton condensate and the exciton reservoir within a disordered potential.
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http://dx.doi.org/10.1103/PhysRevLett.103.256402 | DOI Listing |
Sci Total Environ
January 2025
Universidad de Santiago de Chile, Santiago, Chile.
Assessing future snow cover changes is challenging because the high spatial resolution required is typically unavailable from climate models. This study, therefore, proposes an alternative approach to estimating snow changes by developing a super-spatial-resolution downscaling model of snow depth (SD) for Japan using a convolutional neural network (CNN)-based method, and by downscaling an ensemble of models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset. After assessing the coherence of the observed reference SD dataset with independent observations, we leveraged it to train the CNN downscaling model; following its evaluation, we applied the trained model to CMIP6 climate simulations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Satellite Application Division, Korea Aerospace Research Institute (KARI), Daejeon 34133, Republic of Korea.
For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Chemistry & Environmental Science, Jordan Hu College of Science and Liberal Arts, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
Nanoparticles (NPs) have been successfully used as drug delivery systems. To develop and optimize NP-based drug delivery systems, it is essential to understand the dynamics of cell-NP interactions. Quantitative phase imaging techniques enable label-free imaging and have the potential to reveal how cells interact with NPs.
View Article and Find Full Text PDFJ Pers Med
January 2025
Department of Ophthalmology, Mayo Clinic, Rochester, MN 55905, USA.
: Augmented reality (AR) may allow vitreoretinal surgeons to leverage microscope-integrated digital imaging systems to analyze and highlight key retinal anatomic features in real time, possibly improving safety and precision during surgery. By employing convolutional neural networks (CNNs) for retina vessel segmentation, a retinal coordinate system can be created that allows pre-operative images of capillary non-perfusion or retinal breaks to be digitally aligned and overlayed upon the surgical field in real time. Such technology may be useful in assuring thorough laser treatment of capillary non-perfusion or in using pre-operative optical coherence tomography (OCT) to guide macular surgery when microscope-integrated OCT (MIOCT) is not available.
View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
Group of Biomechatronics, Fachgebiet Biomechatronik, Technische Universität Ilmenau, D-98693 Ilmenau, Germany.
Anguilliform locomotion, an efficient aquatic locomotion mode where the whole body is engaged in fluid-body interaction, contains sophisticated physics. We hypothesized that data-driven modeling techniques may extract models or patterns of the swimmers' dynamics without implicitly measuring the hydrodynamic variables. This work proposes empirical kinematic control and data-driven modeling of a soft swimming robot.
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