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During the COVID-19 pandemic, conflicting opinions on physical distancing swept across social media, affecting both human behavior and the spread of COVID-19. Inspired by such phenomena, we construct a two-layer multiplex network for the coupled spread of a disease and conflicting opinions. We model each process as a contagion. On one layer, we consider the concurrent evolution of two opinions — pro-physical-distancing and anti-physical-distancing — that compete with each other and have mutual immunity to each other. The disease evolves on the other layer, and individuals are less likely (respectively, more likely) to become infected when they adopt the pro-physical-distancing (respectively, anti-physical-distancing) opinion. We develop approximations of mean-field type by generalizing monolayer pair approximations to multilayer networks; these approximations agree well with Monte Carlo simulations for a broad range of parameters and several network structures. Through numerical simulations, we illustrate the influence of opinion dynamics on the spread of the disease from complex interactions both between the two conflicting opinions and between the opinions and the disease. We find that lengthening the duration that individuals hold an opinion may help suppress disease transmission, and we demonstrate that increasing the cross-layer correlations or intra-layer correlations of node degrees may lead to fewer individuals becoming infected with the disease. 

We examine the 2002 Yakutsk wildfire event and simulate the impacts of smoke aerosols on local radiative energy budget, using the WRF-Chem-SMOKE model. When comparing satellite retrievals (the Surface Radiation Budget (SRB) dataset) with model simulations, we found that the agreement is generally good, except for the regions where the model predicts too few clouds or SRB misclassifies strong smoke plumes as clouds. We also found that the smoke-induced changes in upward shortwave fluxes at top of atmosphere (TOA) vary under different burning and meteorological conditions. In the first period of the fire season (9–12 August), smoke particles cause a warming effect around 3 W/m2, mainly through functioning as ice nuclei, which deplete the cloud water amount in the frontal system. At the beginning of the second period of the fire season (19–20 August), large amounts of pre-existing smoke particles cause a strong cooling effect of −8 W/m2. This is offset by the warming effect caused by relatively small amounts of cloud condensation nuclei increases, which promotes the rain formation and depletes the cloud water amount. After the cloud decks are well mixed with smoke plumes (21–22 August), the first indirect and direct effects of smoke together lead to a cooling of −10 W/m2. These results highlight the importance of meso-scale modeling efforts in estimating the smoke-induced changes in the radiative energy budget over high latitudes. 

Direct propylene epoxidation using Au-based catalysts is an important gas-phase reaction and is clearly a promising route for the future industrial production of propylene oxide (PO). For instance, gold nanoparticles or clusters that consist of a small number of atoms demonstrate unique and even unexpected properties, since the high ratio of surface to bulk atoms can provide new reaction pathways with lower activation barriers. Support materials can have a remarkable effect on Au nanoparticles or clusters due to charge transfer. Moreover, Au (or Au-based alloy, such as Au–Pd) can be loaded on supports to form active interfacial sites (or multiple interfaces). Model studies are needed to help probe the underlying mechanistic aspects and identify key factors controlling the activity and selectivity. The current theoretical/computational progress on this system is reviewed with respect to the molecular- and catalyst-level aspects (e.g., first-principles calculations and kinetic modeling) of propylene epoxidation over Au-based catalysts. This includes an analysis of H2 and O2 adsorption, H2O2 (OOH) species formation, epoxidation of propylene into PO, as well as possible byproduct formation. These studies have provided a better understanding of the nature of the active centers and the dominant reaction mechanisms, and thus, could potentially be used to design novel catalysts with improved efficiency.