4,642 research outputs found
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
Coordinated Transmit Beamforming for Multi-antenna Network Integrated Sensing and Communication
This paper studies a multi-antenna network integrated sensing and
communication (ISAC) system, in which a set of multi-antenna base stations
(BSs) employ the coordinated transmit beamforming to serve their respectively
associated single-antenna communication users (CUs), and at the same time reuse
the reflected information signals to perform joint target detection. In
particular, we consider two target detection scenarios depending on the time
synchronization among BSs. In Scenario \uppercase\expandafter{\romannumeral1},
these BSs are synchronized and can exploit the target-reflected signals over
both the direct links (from each BS to target to itself) and the cross links
(from each BS to target to other BSs) for joint detection. In Scenario
\uppercase\expandafter{\romannumeral2}, these BSs are not synchronized and can
only utilize target-reflected signals over the direct links for joint
detection. For each scenario, we derive the detection probability under a
specific false alarm probability at any given target location. Based on the
derivation, we optimize the coordinated transmit beamforming at the BSs to
maximize the minimum detection probability over a particular target area, while
ensuring the minimum signal-to-interference-plus-noise ratio (SINR) constraints
at the CUs, subject to the maximum transmit power constraints at the BSs. We
use the semi-definite relaxation (SDR) technique to obtain highly-quality
solutions to the formulated problems. Numerical results show that for each
scenario, the proposed design achieves higher detection probability than the
benchmark scheme based on communication design. It is also shown that the time
synchronization among BSs is beneficial in enhancing the detection performance
as more reflected signal paths are exploited
Vertices with the Second Neighborhood Property in Eulerian Digraphs
The Second Neighborhood Conjecture states that every simple digraph has a
vertex whose second out-neighborhood is at least as large as its first
out-neighborhood, i.e. a vertex with the Second Neighborhood Property. A cycle
intersection graph of an even graph is a new graph whose vertices are the
cycles in a cycle decomposition of the original graph and whose edges represent
vertex intersections of the cycles. By using a digraph variant of this concept,
we prove that Eulerian digraphs which admit a simple dicycle intersection graph
have not only adhere to the Second Neighborhood Conjecture, but have a vertex
of minimum outdegree that has the Second Neighborhood Property.Comment: fixed an error in an earlier version and made structural change
Optimal Coordinated Transmit Beamforming for Networked Integrated Sensing and Communications
This paper studies a multi-antenna networked integrated sensing and
communications (ISAC) system, in which a set of multi-antenna base stations
(BSs) employ the coordinated transmit beamforming to serve multiple
single-antenna communication users (CUs) and perform joint target detection by
exploiting the reflected signals simultaneously. To facilitate target sensing,
the BSs transmit dedicated sensing signals combined with their information
signals. Accordingly, we consider two types of CU receivers with and without
the capability of canceling the interference from the dedicated sensing
signals, respectively. In addition, we investigate two scenarios with and
without time synchronization among the BSs. For the scenario with
synchronization, the BSs can exploit the target-reflected signals over both the
direct links (BS-to-target-to-originated BS links) and the cross-links
(BS-to-target-to-other BSs links) for joint detection, while in the
unsynchronized scenario, the BSs can only utilize the target-reflected signals
over the direct links. For each scenario under different types of CU receivers,
we optimize the coordinated transmit beamforming at the BSs to maximize the
minimum detection probability over a particular targeted area, while
guaranteeing the required minimum signal-to-interference-plus-noise ratio
(SINR) constraints at the CUs. These SINR-constrained detection probability
maximization problems are recast as non-convex quadratically constrained
quadratic programs (QCQPs), which are then optimally solved via the
semi-definite relaxation (SDR) technique.Comment: arXiv admin note: text overlap with arXiv:2211.0108
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