Tuning the band gap and magnetic properties of BN sheets impregnated with graphene flakes

The BN sheet is a nonmagnetic wide-band-gap semiconductor. Using density functional theory, we show that these properties can be fundamentally altered by embedding graphene flakes. Not only do graphene flakes preserve the two-dimensional (2D) planar structure of the BN sheet, but by controlling their shape and size, unexpected electronic and magnetic properties also emerge. The electronic band structure can be tuned from a direct gap to an indirect gap, the energy gap can be further modulated by changing the bonding patterns, and both hole injecting or electron injecting can be achieved by tailoring the triangular embedding pattern. Furthermore, the Lieb theorem still holds, and the embedded triangular graphene flakes become ferromagnetic with full spin polarizations of the introduced electrons or holes, opening the door to their use as spin filters. The study sheds new light on hybrid single-atomic-layer engineering for unprecedented applications of 2D nanomaterials

The estimation of vehicle speed and stopping distance by pedestrians crossing streets in a naturalistic traffic environment

The ability to estimate vehicle speed and stopping distance accurately is important for pedestrians to make safe road crossing decisions. In this study, a field experiment in a naturalistic traffic environment was conducted to measure pedestrians&#39; estimation of vehicle speed and stopping distance when they are crossing streets. Forty-four participants (18-45 years old) reported their estimation on 1043 vehicles, and the corresponding actual vehicle speed and stopping distance were recorded. In the speed estimation task, pedestrians&#39; performances change in different actual speed levels and different weather conditions. In sunny conditions, pedestrians tended to underestimate actual vehicle speeds that were higher than 40 km/h but were able to accurately estimate speeds that were lower than 40 km/h. In rainy conditions, pedestrians tended to underestimate actual vehicle speeds that were higher than 45 km/h but were able to accurately estimate speeds ranging from 35 km/h to 45 km/h. In stopping distance estimation task, the accurate estimation interval ranged from 60 km/h to 65 km/h, and pedestrians generally underestimated the stopping distance when vehicles were travelling over 65 km/h. The results show that pedestrians have accurate estimation intervals that vary by weather conditions. When the speed of the oncoming vehicle exceeded the upper bound of the accurate interval, pedestrians were more likely to underestimate the vehicle speed, increasing their risk of incorrectly deciding to cross when it is not safe to do so. (C) 2015 Elsevier Ltd. All rights reserved.</p

Physics-Based Deep Learning for Flow Problems

It is the tradition for the fluid community to study fluid dynamics problems via numerical simulations such as finite-element, finite-difference and finite-volume methods. These approaches use various mesh techniques to discretize a complicated geometry and eventually convert governing equations into finite-dimensional algebraic systems. To date, many attempts have been made by exploiting machine learning to solve flow problems. However, conventional data-driven machine learning algorithms require heavy inputs of large labeled data, which is computationally expensive for complex and multi-physics problems. In this paper, we proposed a data-free, physics-driven deep learning approach to solve various low-speed flow problems and demonstrated its robustness in generating reliable solutions. Instead of feeding neural networks large labeled data, we exploited the known physical laws and incorporated this physics into a neural network to relax the strict requirement of big data and improve prediction accuracy. The employed physics-informed neural networks (PINNs) provide a feasible and cheap alternative to approximate the solution of differential equations with specified initial and boundary conditions. Approximate solutions of physical equations can be obtained via the minimization of the customized objective function, which consists of residuals satisfying differential operators, the initial/boundary conditions as well as the mean-squared errors between predictions and target values. This new approach is data efficient and can greatly lower the computational cost for large and complex geometries. The capacity and generality of the proposed method have been assessed by solving various flow and transport problems, including the flow past cylinder, linear Poisson, heat conduction and the Taylor–Green vortex problem.</jats:p