SearcharxivSearch

arXiv subjects

Zhiqiang Sun

Publications and source records attributed to Zhiqiang Sun.

2 recordsLinked to original sources

Emergent quantum phenomena in two-dimensional 1T-TaS2

Strong electron correlation drives 1T-TaS2 from a half-filled metallic state into a Mott insulating phase, coexisting with a charge density wave at low temperatures. Under external stimuli such as pressure or ionic gating, superconductivity emerges in 1T-TaS2, exhibiting an intricate relationship of competition and coexistence with the charge density wave order. In the two-dimensional (2D) limit, enhanced quantum fluctuations can stabilize a quantum spin liquid (QSL) state in the Mott insulator. This review summarizes recent advances in understanding these quantum states in 2D 1T-TaS2 from the perspective of angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy (STM), with a focus on the dimensionality effect on its electronic structure. We outline the signatures of QSL state in electronic spectra and discuss how this state can be revealed in the family of this material through experimental approaches beyond conventional probes such as neutron scattering. The role of Kondo effect in detecting spinon excitations is further discussed. Finally, we suggest future experimental directions and highlight how external perturbations such as gating and light excitation offer versatile pathways to control and exploit these intertwined quantum states.

cond-mat.str-el

Real-time Monitoring and Early Warning Analysis of Urban Railway Operation Based on Multi-parameter Vital Signs of Subway Drivers in Plateau Environment

In order to ensure the personal safety of the drivers and passengers of rail transit in plateau environment, the vital signs and train conditions of the drivers and passengers are taken as the research object, and the dynamic relationship between them is studied and analyzed. In this paper, subway drivers under normal operation conditions are taken as research objects to establish the vital signs monitoring and early warning system. The vital signs data of the subway drivers, such as heart rate (HR), respiratory rate (RR), body temperature (T) and blood oxygen saturation (SPO2) of the subway driver are collected by the head-mounted sensor, and the least mean square adaptive filtering algorithm is used to preprocess the data and eliminate the interference information. Based on the improved BP (Back Propagation) neural network algorithm, a prediction model is established to predict the vital signs of subway drivers in real-time. We use the early warning score evaluation method to measure the risk of subway drivers' vital signs, and then the necessary judgment basis can be provided to dispatchers in the control center. Experiments show that the system developed in this paper can accurately predict the evolution of subway drivers' vital signs, and timely warn the abnormal states. The predicted value of vital signs is consistent with the actual value, and the absolute error of prediction is less than 0.5 which is within the allowable range.

eess.SY