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This study introduces an agent‐based model (ABM) pedestrian simulation tool to assess the risk of close contact (6 feet) in dynamic indoor environments, specifically in urban settings with diverse social activities and spatial structures. Our approach uses machine learning‐based sensitivity analysis (SA) to identify factors impacting the number of individual contacts, such as individual stay time and area. In addition, we conducted an in‐depth quantitative analysis to evaluate how specific factors, such as the strategic placement of obstacles, dwell time, and stay time near the entrances, mitigate the number of contacts. This analysis provides valuable insights for developing practical guidelines to curb contact risks in indoor environments. Lastly, we share the model, validation methods, and associated data as an open‐source Python library, complete with comprehensive documentation. This aims at fostering collaborative research and enables the application of our model across various scenarios, contributing to the development of spatially explicit models. Such efforts enhance the understanding of contact risks in urban indoor settings and promote joint research efforts, thus advancing the field through shared knowledge and tools. [ABSTRACT FROM AUTHOR]

Recently, there has been a surge of interest in neuromorphic computation inspired by the extraordinary characteristics of the human brain, such as low energy consumption, parallelism, adaptivity, cognitive abilities, and learning capabilities. Significant research efforts have focused on exploring optical synaptic behaviors in various functional materials. In this study, the potential of red, green and blue (RGB)‐colored long‐persistent luminescence (LPL) in Sm3+/Er3+/La3+‐doped Ca2SnO4 is investigated for synaptic functionality. The luminescence of the samples is continuously enhanced under serial photoexcitation pulse applications, that is, the potentiation process, which is a key feature demonstrated in biological synapses. In addition, multichannel synaptic functionalities in the full‐color range is successfully demonstrated by integrating individual RGB‐colored Sm3+/Er3+/La3+‐doped Ca2SnO4 into a single quantity. To validate the optical synaptic behavior of the samples in neuromorphic computing applications, a reservoir computing (RC) simulation is performed for space‐time data processing using the unique responses of the samples under 4‐bit excitation pulses. The results demonstrated that the multi‐channel synaptic behaviors in the samples should be more valid for utilization in the RC layer than the single channel of synaptic behavior. We suggest this exploration holds promise for the advancement of synaptic devices employing LPL materials. [ABSTRACT FROM AUTHOR]

The oxygen evolution reaction (OER) plays a key role in determining the performance of overall water splitting, while a core technological consideration is the development of cost-effective, efficient, and durable catalysts. Here, we demonstrate a robust reduced Fe-oxide@NiCo 2 O 4 bilayered non-precious-metal oxide composite as a highly efficient OER catalyst in an alkaline medium. A bilayered oxide composite film with an interconnected nanoflake morphology (Fe 2 O 3 @NiCo 2 O 4 ) is reduced in an aqueous NaBH 4 solution, which results in a mosslike Fe 3 O 4 @NiCo 2 O 4 (reduced Fe-oxide@NiCo 2 O 4 ; rFNCO) nanostructured film with an enhanced electrochemical surface area. The rFNCO film demonstrates an outstanding OER activity with an extraordinary low overpotential of 189 mV at 10 mA cm -2 (246 mV at 100 mA cm -2 ) and a remarkably small Tafel slope of 32 mV dec -1 . The film also shows excellent durability for more than 50 h of continuous operation, even at 100 mA cm -2 . Furthermore, density functional theory calculations suggest that the unintentionally in situ doped Ni during the reduction reaction possibly improves the OER performance of the rFNCO catalyst shifting d -band centers of both Fe and Ni active sites.