Evolution of Fermion Resonance in Thick Brane

In this work, we investigate numerical evolution of massive Kaluza-Klein (KK) modes of a Dirac field on a thick brane. We deduce the Dirac equation in five-dimensional spacetime, and obtain the time-dependent evolution equation and Schr\"odinger-like equation of the extra-dimensional component. We use the Dirac KK resonances as the initial data and study the corresponding dynamics. By monitoring the decay law of the left- and right-chiral KK resonances, we compute the corresponding lifetimes and find that there could exist long-lived KK modes on the brane. Especially, for the lightest KK resonance with a large coupling parameter and a large three momentum, it will have an extremely long lifetime.Comment: 26 pages, 12 figure

The Impact of Electronic Shopping Site\u27s Privacy Policy, Reputation, and Trust on Customers\u27 Purchase Intentions

With the advent of EC, Internet shopping becomes a way of business transactions. This research investigates how trust can affect customers’ purchase intentions. Having surveyed related literature, a conceptual framework has been proposed. The framework consists of four components: independent variables (privacy policy and reputation), intervening variables (cognitive and affective trust), dependent variable (customers’ purchase intentions), and moderating variable (perceived risk). The 2(with/without privacy policy)*2(good/bad reputation) experiments are employed to collect primary data to validate the proposed conceptual framework. The two levels privacy policy and reputation can significantly influence cognitive and affective trust. Moreover, the effect of cognitive trust and purchase intentions is more than that of affective trust. The study result also shows the interaction between privacy policy, reputation, and trust for the major product is not significant. Therefore, we suggest that electronic shopping sites’ managers should consider customers’ mentality in order to raise their purchase intentions

Building quantum neural networks based on swap test

Artificial neural network, consisting of many neurons in different layers, is an important method to simulate humain brain. Usually, one neuron has two operations: one is linear, the other is nonlinear. The linear operation is inner product and the nonlinear operation is represented by an activation function. In this work, we introduce a kind of quantum neuron whose inputs and outputs are quantum states. The inner product and activation operator of the quantum neurons can be realized by quantum circuits. Based on the quantum neuron, we propose a model of quantum neural network in which the weights between neurons are all quantum states. We also construct a quantum circuit to realize this quantum neural network model. A learning algorithm is proposed meanwhile. We show the validity of learning algorithm theoretically and demonstrate the potential of the quantum neural network numerically.Comment: 10 pages, 13 figure