The Budapest Neutron Center operates the cold neutron beam imaging station, Neutron Optics and Radiography for Material Analysis (NORMA), for non-destructive testing. For the NORMA station, there have been increasing requests to reach higher spatial resolution and the ability to follow time-dependent processes. Therefore, the system used successfully so far was completely redesigned and installed for a variety of tasks. The new system is based on the principle of three independent modules, allowing for highly configurable settings. It is to find the right balance between the necessary spatial resolution, a sufficiently shorter or longer temporal resolution, and a large enough field of view. The systematic study of the setups clearly shows the parameters' effects, helping to make the right choice for the measurement tasks. Among the rarely investigated parameters, we studied both the effect of the pixel binning and the change in the lens f-stop value on the spatial resolution. The newly improved NORMA facility allows the acquisition of high-quality neutron images for planned observations, e.g., local water kinetics in fuel cells.
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http://dx.doi.org/10.1063/5.0208844 | DOI Listing |
Sci Data
January 2025
Department of Earth and Environmental Engineering, Columbia University, New York, USA.
The Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) missions have provided estimates of Terrestrial Water Storage Anomalies (TWSA) since 2002, enabling the monitoring of global hydrological changes. However, temporal gaps within these datasets and the lack of TWSA observations prior to 2002 limit our understanding of long-term freshwater variability. In this study, we develop GRAiCE, a set of four global monthly TWSA reconstructions from 1984 to 2021 at 0.
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January 2025
Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4515 McKinley Ave., St. Louis, MO, 63110, USA.
Functional magnetic resonance imaging (fMRI) has dramatically advanced non-invasive human brain mapping and decoding. Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. HD-DOT grids have smaller inter-optode spacing (~ 13 mm) than sparse fNIRS (~ 30 mm) and therefore provide higher image quality, with spatial resolution ~ 1/2 that of fMRI, when using the several source-detector distances (13-40 mm) afforded by the HD-DOT grid.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Department of Medical Imaging, Pingyin people's Hospital, Jinan 250400, China.
Magnetic Resonance Imaging is a cornerstone of medical diagnostics, providing high-quality soft tissue contrast through non-invasive methods. However, MRI technology faces critical limitations in imaging speed and resolution. Prolonged scan times not only increase patient discomfort but also contribute to motion artifacts, further compromising image quality.
View Article and Find Full Text PDFUltramicroscopy
January 2025
Mechanical Engineering, University of Michigan, USA.
The objective of this work was to explore the capabilities of a field emission gun scanning electron microscope (FEG-SEM) equipped with a transmission scanning electron detector (TSEM) and energy dispersive spectroscopy (EDS) to identify nanoscale chemical heterogeneities in a gas atomization reaction synthesis (GARS) steel sample. The results of this analysis were compared to the same study conducted with scanning transmission electron microscopy (STEM) with EDS mapping. TSEM-EDS was performed using the standard spectral analysis approach, i.
View Article and Find Full Text PDFSci 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.
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