Tourists' Attitudes Towards Tea Tourism: A Case Study in Xinyang, China

Tea tourism as a new niche market has become more and more popular. Through a case study in Xinyang, China, this research explores tourists' attitudes and perceptions toward tea and tea tourism, identifies who the potential tea tourists are, and compares their attitudes with others. One hundred seventy-nine questionnaires were administered; one-way ANOVA and chi-square test were used based on their willingness of tea tourism. The results suggest that tea tourists and non-tea tourists have significant differences in terms of their attitudes toward tea drinking and their willingness of buying tea as souvenir. Tea tourists are mainly tea lovers driven by their high interest in tea and tea culture; they tend to be both males and females (yet females show a significant higher percentage than males), between ages 31-40, who have a positive attitude toward tea drinking, and who often drink tea. This research also provides some marketing suggestions for this niche market

DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations(wider than adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method

Detection of bound entanglement in continuous variable systems

We present several entanglement conditions in order to detect bound entangled states in continuous variable systems. Specifically, Werner and Wolf [Phys. Rev. Lett. 86, 3658 (2001)] and Horodecki and Lewenstein [Phys. Rev. Lett. 85, 2657 (2000)] have proposed examples of bound entangled Gaussian state and bound entangled non-Gaussian state, respectively, of which entanglement can be detected by using our entanglement conditions.Comment: 5 pages, 1 figur

Secure Quantum Secret Sharing Based on Reusable GHZ States as Secure Carriers

We show a potential eavesdropper can eavesdrop whole secret information when the legitimate users use secure carrier to encode and decode classical information repeatedly in the protocol [proposed in Bagherinezhad S and Karimipour V 2003 Phys. Rev. A \textbf{67} 044302]. Then we present a revised quantum secret sharing protocol by using Greenberger-Horne-Zeilinger state as secure carrier. Our protocol can resist Eve's attack