Transmission and reflection of Gaussian beams by anisotropic parallel plates

Explicit and compact expressions describing the reflection and the transmission of a Gaussian beam by anisotropic parallel plates are given. Multiple reflections inside the plate are taken into account as well as arbitrary optical axis orientation and angle of incidence.Comment: 20 page

Factors influencing the intention to use cryptocurrency payments: An examination of blockchain economy

In summary, this study has applied TAM model to examine cryptocurrency payment adoption in Taiwanese hotels, examining the factors that are more likely to affect the behavioral intent. The empirical results suggest that intent to adopt cryptocurrency payments is affected by perceived usefulness, and perceived ease of use of these payments. In turn, perceived usefulness is affected by trust towards these payments. Interestingly, perceived usefulness was not shown to be significantly affected by different types of risks associated with cryptocurrency payments, including financial risk, technological risk, and social risk. Perceived ease of use, in turn, is affected by convenience of cryptocurrency payments; and is not shown to be significantly affected by trust

An efficient surrogate model for emulation and physics extraction of large eddy simulations

In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. In this paper, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as {simplifying assumptions} for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations.Comment: Submitted to JASA A&C