Investigation on the Effect of Probability Distribution on the Dynamic Response of Liquefiable Soils

This study investigated the effect of different probabilistic distributions (Lognormal, Gamma, and Beta) to characterize the spatial variability of shear modulus on the soil liquefiable response. The parameter sensitivity analysis included the coefficient of variation and scale of fluctuation of soil shear modulus. The results revealed that the distribution type had no significant influence on the liquefication zone. In particular, the estimation with Beta distribution is the worst scenario. It illuminated that the estimation with Beta distribution can provide a conservative design if site investigation is absent.

Performance and Parameter Sensitivity Analysis of the PEMFC Flow Channel with Porous Baffles

In this paper, a method to improve the performance of PEMFCs using porous material as a flow channel baffle is proposed. The results show that PEMFCs with four porous baffles flow channels have better performance at high current density compared with the traditional flow channel. The structural parameters of the flow channel explored in this study include porosity, the thickness of the baffle and the number of baffles, and their influence on the performance of PEMFCs. Sensitivity analysis results show that the performance of the PEMFCs with the porous baffle channel is the most sensitive to baffle thickness, and the thickness and baffle could be appropriately adjusted. The number of plates and porosity of the baffle are adjusted to improve the performance of the PEMFCs.

COVID-19 vaccination strategies depend on the underlying network of social interactions

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, different mitigation and management strategies limiting economic and social activities have been implemented across many countries. Despite these strategies, the virus continues to spread and mutate. As a result, vaccinations are now administered to suppress the pandemic. Current COVID-19 epidemic models need to be expanded to account for the change in behaviour of new strains, such as an increased virulence and higher transmission rate. Furthermore, models need to account for an increasingly vaccinated population. We present a network model of COVID-19 transmission accounting for different immunity and vaccination scenarios. We conduct a parameter sensitivity analysis and find the average immunity length after an infection to be one of the most critical parameters that define the spread of the disease. Furthermore, we simulate different vaccination strategies and show that vaccinating highly connected individuals first is the quickest strategy for controlling the disease.