A 10B-coated cathode double-GEM neutron detector (BGEM) was developed as a 3He-free cold-neutron beamline detector using a single 10B4C converter cathode and a 512-channel APV25 orthogonal-strip readout over 10 × 10cm2. The detector was tested at the HANARO Bio-REF beamline with a monochromatic 4.5Å beam (En=4.03meV). The detection efficiency relative to a 6Li-based Ce:LiCAF reference detector was ɛBGEM=(8.69±0.20)%(stat.). The pulse-height spectrum was qualitatively consistent with Geant4 energy-deposition simulations, and Cd-mask imaging yielded σ=(555⊕102)μm, corresponding to FWHMLSF=(1.31⊕0.24)mm. These results establish a cold-neutron beamline benchmark for a single-converter BGEM detector with full-strip APV25 readout.
Thermoelectric materials require precise doping to adjust the Fermi level in order to fully realize their thermoelectric potential. The effective mass (EM) model is commonly used to predict the maximum thermoelectric figure-of-merit (zT) and optimal carrier concentration, but its application is limited by the need for numerical Fermi integral solutions and Hall effect measurements. Since the thermopower (magnitude of the Seebeck coefficient, |S|) is effectively a measure of the Fermi level, it can be used as a direct descriptor of doping level in heavily doped semiconductors such as good thermoelectric materials. Here, we present a simple method to analyze thermoelectric transport using only the typical thermoelectric measurements: Seebeck coefficient, electrical conductivity and thermal conductivity. This enables evaluation of weighted mobility, quality factor (B), theoretical maximum zT, optimal thermopower, identification of anomalous scattering behavior; as well as a full prediction of zT, as a function of |S| given by
This study presents a graph-based framework for managing urban transport infrastructure and identifying potential mobility hubs within the Seoul metropolitan rail network in the Republic of Korea. The approach integrates traditional network centrality analysis with graph neural networks to capture both structural influence and flow mediation. Using operational schedule data, Bonacich power and random-walk betweenness centrality were embedded into a graph learning model to evaluate node importance beyond the limitations of conventional shortest-path assumptions. The results reveal that top nodes exhibit high multimodal potential, acting as strategic connectors within the metropolitan transit structure. By linking network-derived hub scores with public bicycle usage data, the analysis identifies spatial overlaps between structural centrality and micromobility demand. These findings support road space reallocation and multimodal integration strategies that enhance sustainable and inclusive accessibility. The proposed framework expands the methodological scope of transport planning by combining network science and machine learning. It provides a data-driven basis for infrastructure management and policy development toward low carbon dioxide, human-centred and resilient mobility systems. The proposed framework expands transport planning by combining network science and machine learning, providing a data-driven basis for infrastructure management, mobility hub planning and policy development toward low carbon dioxide and resilient urban transport systems.
HfO2/Al2O3 bilayer resistive random-access memory (RRAM) exhibits gradual resistive switching behavior, which is advantageous for achieving linear and symmetric conductance modulation required for reliable synaptic operation in neuromorphic computing. However, large device variability and poor endurance remain critical challenges that must be addressed for practical synaptic applications of RRAM. In this work, the performance of HfO2/Al2O3 bilayer RRAM was systematically improved through the sequential optimization of fabrication conditions. By combining postdeposition annealing (PDA) of the switching layer, optimization of the titanium (Ti) buffer layer thickness, and ultrathin molybdenum (Mo) deposition at the HfO2/Al2O3 interface, the optimized devices exhibited over 70% reduction in cycle-to-cycle (C2C) operational variability, excellent DC endurance (>10(3) cycles), a high on/off ratio (average 96.8), and robust retention (>4000 s at 85 degrees C). Cross-sectional transmission electron microscopy and energy-dispersive X-ray spectroscopy (EDS) analyses revealed that Mo nanoislands formed by the ultrathin Mo layer play a key role in suppressing the stochastic formation of conductive filaments. In addition, fitting of DC I-V characteristics indicated that direct tunneling, Fowler-Nordheim tunneling, and Poole-Frenkel emission are the dominant conduction mechanisms governing device operation. Finally, leveraging the obtained long-term potentiation and long-term depression characteristics, on-chip learning-based pattern recognition was evaluated using the Modified National Institute of Standards and Technology (MNIST) data set, achieving a maximum classification accuracy of 81.56%. These results demonstrate the strong potential of performance-optimized HfO2/Al2O3 bilayer RRAM as a synaptic device for neuromorphic computing applications.
In many cases, the distant collective transmission of Coronavirus Disease (COVID-19) primarily occurs through aerosolized respiratory particles. To elucidate the spatial dispersion of SARS-CoV-2-virions under indoor ventilation conditions, this study utilized Lagrangian particle tracking Computational Fluid Dynamics (CFD) simulations to analyse aerosols emitted from an infected host within a single room (dimensions: width x depth x height = 5 x 5 x 4 m) at three distinct levels of population density. The spatial risks of collective COVID-19 infection were accessed and compared across 13 distinct air-circulation settings. Our findings demonstrate that a mixed circulation combining a standing air purifier and ceiling outlets significantly outperforms other strategies in reducing the airborne infection. Conversely, ceiling-mounted air conditioning and natural ventilation methods are found to be less effective.
