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Abstract A circular active noise barrier using a theoretically calculated control filter without real-time adaptation was proposed for noise reduction in a specific outdoor space. A compact circular barrier is used for the movable system to cope with changes in the location of the workspace, and noise in a wide frequency band can be reduced by conducting active noise control through control speakers arranged around a barrier. However, there was a significant performance gap compared with the maximum performance achieved by using the experimental fixed filter due to an extremely simplified theoretical model which ignores the interaction between the control speakers and the barrier. Therefore, this study tried to minimize the performance degradation when applying the theoretically calculated control filter. Another theoretical model is introduced to improve the noise reduction performance by considering the interaction between the control speaker and the barrier. Through experiment, it is confirmed that noise reduction performance is improved by about 2.6 dB in the frequency of interest.

Abstract Electrochemical water splitting has been considered one of the most promising methods of hydrogen production, which does not cause environmental pollution or greenhouse gas emissions. Oxygen evolution reaction (OER) is a significant step for highly efficient water splitting because OER involves the four electron transfer, overcoming the associated energy barrier that demands a potential greater than that required by hydrogen evolution reaction. Therefore, an OER electrocatalyst with large surface area and high conductivity is needed to increase the OER activity. In this work, we demonstrated an effective strategy to produce a highly active three-dimensional (3D)-printed NiFe-layered double hydroxide (LDH) pyramid electrode for OER using a three-step method, which involves direct-ink-writing of a graphene pyramid array and electrodeposition of a copper conducive layer and NiFe-LDH electrocatalyst layer on printed pyramids. The 3D pyramid structures with NiFe-LDH electrocatalyst layers increased the surface area and the active sites of the electrode and improved the OER activity. The overpotential (η) and exchange current density (i 0 ) of the NiFe-LDH pyramid electrode were further improved compared to that of the NiFe-LDH deposited Cu (NiFe-LDH/Cu) foil electrode with the same base area. The 3D-printed NiFe-LDH electrode also exhibited excellent durability without potential decay for 60 h. Our 3D printing strategy provides an effective approach for the fabrication of highly active, stable, and low-cost OER electrocatalyst electrodes.

Abstract The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categorized counties into multiple groups according to their population densities, and we divided the trajectory of COVID-19 into three stages: the outbreak stage, the social distancing stage, and the reopening stage. The study aimed to answer two research questions: (1) The extent to which the importance of heterogeneous features evolved at different stages; (2) The extent to which the importance of heterogeneous features varied across counties with different characteristics. We fitted a set of random forest models to determine weekly feature importance. The results showed that: (1) Social demographic features, such as gross domestic product, population density, and minority status maintained high-importance features throughout stages of COVID-19 across 2787 studied counties; (2) Within-county mobility features had the highest importance in counties with higher population densities; (3) The feature reflecting the social network structure (Facebook, social connectedness index), had higher importance for counties with higher population densities. The results showed that the data-driven machine learning models could provide important insights to inform policymakers regarding feature importance for counties with various population densities and at different stages of a pandemic life cycle.