Xu Y.,University of Tennessee at Knoxville |
Shaw S.-L.,University of Tennessee at Knoxville |
Zhao Z.,University of Tennessee at Knoxville |
Yin L.,CAS Shenzhen Institutes of Advanced Technology |
And 6 more authors.
Annals of the American Association of Geographers | Year: 2016
Activity space is an important concept in geography. Recent advancements of location-aware technologies have generated many useful spatiotemporal data sets for studying human activity space for large populations. In this article, we use two actively tracked cellphone location data sets that cover a weekday to characterize people’s use of space in Shanghai and Shenzhen, China. We introduce three mobility indicators (daily activity range, number of activity anchor points, and frequency of movements) to represent the major determinants of individual activity space. By applying association rules in data mining, we analyze how these indicators of an individual’s activity space can be combined with each other to gain insights of mobility patterns in these two cities. We further examine spatiotemporal variations of aggregate mobility patterns in these two cities. Our results reveal some distinctive characteristics of human activity space in these two cities: (1) A high percentage of people in Shenzhen have a relatively short daily activity range, whereas people in Shanghai exhibit a variety of daily activity ranges; (2) people with more than one activity anchor point tend to travel further but less frequently in Shanghai than in Shenzhen; (3) Shenzhen shows a significant north–south contrast of activity space that reflects its urban structure; and (4) travel distance in both cities is shorter around noon than in regular work hours, and a large percentage of movements around noon are associated with individual home locations. This study indicates the benefits of analyzing actively tracked cellphone location data for gaining insights of human activity space in different cities. © 2016 by American Association of Geographers.
Zhang L.,National University of Singapore |
Dong Z.,Institute of Materials Research and Engineering of Singapore |
Wang Y.M.,Institute of Materials Research and Engineering of Singapore |
Liu Y.J.,Institute of Materials Research and Engineering of Singapore |
And 5 more authors.
Nanoscale | Year: 2015
We present a novel strategy capable of dynamically configuring the plasmon-induced transparency (PIT) effect with a polarization-dependent controllability based on a nanoring dimer array. The controllable coupling strength between the superradiant and subradiant modes is due to the polarization-dependent field distributions. It is shown that this dynamically controlled PIT is realized with a modulation depth as high as 95%, and a linear dependence of the coupling strength on polarization angle is deduced using a coupled-oscillator model. We believe that our results will inspire further exciting achievements that utilize various polarization states of the electromagnetic wave and pave a way towards applications using PIT with dynamic controllability such as slow light, optical nonlinearities and chemical/bio-sensing. This journal is © The Royal Society of Chemistry.
You Z.-H.,Shenzhen UniversityGuangdong |
Li S.,Hong Kong Polytechnic University |
Gao X.,Suzhou Institute of Biomedical Engineering and TechnologyJiangsu |
Luo X.,Hong Kong Polytechnic University |
Ji Z.,Shenzhen UniversityGuangdong
BioMed Research International | Year: 2014
Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions.However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection. Copyright © 2014 Zhu-Hong You et al.
Wang Q.,Shenzhen UniversityGuangdong |
Zhen Li J.,Shenzhen UniversityGuangdong
Optics Communications | Year: 2015
In this paper, the analytical vector Laguerre-Gaussian (LG) solutions are obtained in strongly nonlocal nonlinear media by variational approach. The comparisons of analytical solutions with numerical results show that the analytical vector LG solutions are in good agreement with the numerical simulations. Furthermore, we numerically proved that the completely stationary vector LG soliton, scalar LG soliton and even (odd) LG soliton can be obtained only in strong nonlocal media. For the general and weakly nonlocal cases, the single LG beam breaks up and the single even LG beam expands during propagation, only the LG beam pairs can reduce to a quasistable soliton due to the stabilizing mutual attraction between its components. © 2015 Elsevier B.V. All rights reserved.