Most of Quantum Secret Sharing(QSS) are (n, n) threshold 2-level schemes, in which the 2-level secret cannot be reconstructed until all n shares are collected. In this paper, we propose a (t, n) threshold d-level QSS scheme, in which the d-level secret can be reconstructed only if at least t shares are collected. Compared with (n, n) threshold 2-level QSS, the proposed QSS provides better universality, flexibility, and practicability. Moreover, in this scheme, any one of the participants does not know the other participants’ shares, even the trusted reconstructor Bob
1 is no exception. The transformation of the particles includes some simple operations such as d-level CNOT, Quantum Fourier Transform(QFT), Inverse Quantum Fourier Transform(IQFT), and generalized Pauli operator. The transformed particles need not to be transmitted from one participant to another in the quantum channel. Security analysis shows that the proposed scheme can resist intercept-resend attack, entangle-measure attack, collusion attack, and forgery attack. Performance comparison shows that it has lower computation and communication costs than other similar schemes when 2 < t < n − 1.
The expectation from automated vehicles has been increasing in recent years. Potential advantages for automated vehicles include driving safety, traffic efficiency, and positive environmental outcomes. To evaluate the extent to which automated vehicles are advantageous against conventional vehicles, this paper presents a simple car-following model, named the linked vehicle model, which reflects the capabilities of automated vehicles and the interaction between adjacent automated vehicles. The idea behind linked vehicle model is inspired by the collective motion of self-propelled particles in nature and the study of vehicle platooning from the Safe Road Trains for the Environment project. Moreover, the model is integrated into a traffic simulator in order to evaluate the performance of automated vehicles in an ideal circumstance. Comparisons with other representative car-following models are also provided, and they show that linked vehicle model achieves both optimal microscopic and macroscopic performance. Simulated results also show that linked vehicle model attains the best fuel economy and the lowest pollutant emissions.
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