The temperature-dependent edge magnetic susceptibility [Formula: see text] and the uniform magnetic susceptibility χ in zigzag graphene nanoribbons is studied within the Hubbard model on a honeycomb lattice. By using the determinant quantum Monte Carlo (DQMC) method, it is found that the ferromagnetic fluctuations at the zigzag edge dominate around half-filling, and that the fluctuations are strengthened markedly by the on-site Coulomb interaction U, which may lead to a possible high-temperature edge ferromagnetic behaviour in low-doped zigzag graphene nanoribbons.

In the curling sport, the coefficient of friction between the curling stone and pebbled ice is crucial to predict the motion trajectory. However, the theoretical and experimental investigations on stone–ice friction are limited, mainly due to the limitations of the field measurement techniques and the inadequacy of the experimental data from professional curling rinks. In this paper, on-site measurement of the stone–ice friction coefficient in a prefabricated ice rink for the Beijing Winter Olympics curling event was carried out based on computer vision technology. Firstly, a procedure to determine the location of the curling stone was proposed using YOLO-V3 (You Only Look Once, Version 3) deep neural networks and the CSRT Object tracking algorithm. Video data was recorded during the curling stone throwing experiments, and the friction coefficient was extracted. Furthermore, the influence of the sliding velocity on the friction coefficient was discussed. Comparison with published experimental data and models and verification of the obtained results, using a sensor-based method, were conducted. Results show that the coefficient of friction (ranging from 0.006 to 0.016) decreased with increasing sliding velocity, due to the presence of a liquid-like layer. Our obtained results were consistent with the literature data and the friction model of Lozowski. In addition, the experimental results of the computer vision technique method and the accelerometer sensor method showed remarkable agreement, supporting the accuracy and reliability of our proposed measurement procedure based on deep learning.

No abstract

No abstract