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From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging
No full textAdvancing alkaline water electrolysis by increasing the operating temperature
No full textsiehe Anlag
Future prospects for image-derived input function in molecular imaging quantification with the 7 T MR-BrainPET insert
No full textSurface structural evolution and its impact on the ion diffusion of 5 V high-voltage spinel LiNi0.5Mn1.5O4 cathodes in ether-enhanced ionic liquid electrolyte
No full textModeling gross primary production and transpiration from sun-induced chlorophyll fluorescence using a mechanistic light-response approach
No full textInfluence of mono- and divalent cations on heat induced gelation of protein from mealworm (Tenebrio molitor) at various structural length scales
No full text
Terahertz Electronic and Spin Currents in Wafer‐Scale Van der Waals Bi 2 Se 3 /WSe 2 Heterostructures and Polymorphs
No full textHybrid machine learning and physics-based modeling of pedestrian pushing behaviors
No full textIn high-density crowds, close proximity between pedestrians makes the steady state highly vulnerable to disruption by pushing behaviours, potentially leading to serious accidents. However, the scarcity of experimental data has hindered systematic studies of its mechanisms and accurate modelling. Using behavioural data from bottleneck experiments, we investigate pedestrian heterogeneity in pushing tendencies, showing that pedestrians tend to push under high-motivation and in wider corridors. We introduce a spatial discretization method to encode neighbour states into feature vectors, serving together with pedestrian pushing tendencies as inputs to a random forest model for predicting pushing behaviours. Through comparing speed-headway relationships, we reveal that pushing behaviours correspond to an aggressive space-utilization movement strategy. Consequently, we propose a hybrid machine learning and physics-based model integrating pushing tendencies heterogeneity, pushing behaviours prediction, and dynamic movement strategies adjustment. Validations show that the hybrid model effectively reproduces experimental crowd dynamics and fits to incorporate additional behaviours
On the High-Temperature Conduction in NASICON-Type
No full textLithium-based batteries are currently the leading battery technology. To develop more diverse and sustainable energy storage solutions, alternative battery chemistries and materials must be explored. In this work, we investigated the aliovalent substitution series of NASICON-type (x = 0, 0.125, 0.25, 0.375, 0.5, 0.75, and 1). To establish the structure–transport relations in the materials, a combination of impedance spectroscopy, direct current (DC) polarization measurements, and temperature-dependent powder X-ray diffraction (XRD) measurements up to 800 °C was used. At 800 °C, where the materials are most conductive, there is an increase of the ionic conductivity from x = 0 to 0.375, reaching a maximum of . For compositions with x ≥ 0.5, however, the conductivity decreases significantly. The maximum conductivity and its subsequent decrease are linked to an interplay of crystal–chemical factors, namely, the bottleneck size and the number of available vacancies. This study shows that it is possible to increase the ion conductivity through aliovalent substitution; however, due to distinct, crystallographic differences, design principles from lithium- or sodium-based NASICONs may not be directly applicable to their calcium analogues