Xu Y.,Beihang University |
Li L.,Beihang University |
Luo S.,Beihang University |
Lu Q.,Dong Feng Commercial Vehicle Technical Center |
And 3 more authors.
Applied Surface Science | Year: 2017
Enhanced glow discharge plasma immersion ion implantation and deposition (EGD-PIII&D) have been proved to be highly effective for depositing diamond-like carbon (DLC) films on the inner surface of the slender quartz tube with a deposition rate of 1.3 μm/min. Such a high-efficiency DLC films deposition was explained previously as the short electrons mean free path to cause large collision frequency between electrons and neutral particles. However, in this paper, we found that the inner surface material of the tube itself play a vital role on the films deposition. To disclose the mechanism of this phenomenon, the effect of different inner surface materials on plasma discharge was experimentally and theoretically investigated. Then a self-enhancing plasma discharge is discovered. It is found that secondary electrons emitted from the inner surface material, whatever it is the tube inner surface or deposited DLC films, can dramatically enhance the plasma discharge to improve the DLC films deposition rate. © 2016 Elsevier B.V.
Tang X.,Shekou Peoples Hospital |
Jin J.,Shekou Peoples Hospital |
Tang Y.,Shekou Peoples Hospital |
Cao J.,Shekou Peoples Hospital |
Huang J.,Shenzhen UniversityGuangdong
Neuropsychiatric Disease and Treatment | Year: 2017
Background: Blood–oxygen-level dependent functional magnetic resonance imaging (BOLD-fMRI) maps cerebral activity by the hemodynamic response. Catechol-O-methyltransferase (COMT) gene is involved in the metabolism of dopamine. It is reported that both of these can be used to assess the aggression risk in patients with schizophrenia. However, these methods to assess the aggression risk patients with schizophrenia have not been established in China. Therefore, we deliver here a systematic review and meta-analysis based on the studies dealing with Chinese patients. Method: Nine fMRI studies and 12 gene studies were included. The data of each study were extracted and summarized. Odds ratios with 95% confidence intervals were estimated on allele, dominant, and recessive models. Publication bias was evaluated by Begg’s funnel plot. Results: Positive BOLD-fMRI values in the lower central neural system (CNS) and negative values in the high-level CNS were observed in the patients with aggression risk. A strong association was derived from the recessive gene model of COMT polymorphism rs4680 and risk in aggression behavior (odds ratio =2.10). No significant publication bias was identified. Conclusion: Aggression behavior in patients with schizophrenia can be indicated by positive BOLD-fMRI values in the lower CNS and negative values in the high-level CNS and by a recessive gene model in COMT polymorphism rs4680. A combined test of fMRI and COMT gene could increase the predictive value. © 2017 Tang et al.
Huang W.,Nanchang University |
Ding H.,Shenzhen UniversityGuangdong |
Chen G.,Xian Communications InstituteShaanxi
Signal Processing | Year: 2018
Moving human localization is the first pre-requisite step of human activity analysis in video surveillance. Identifying human targets accurately and efficiently is always of high demands in computer vision studies. Also, learning is often indispensable in contemporary moving human localization, and unknown parameters of proposed methods need to be properly adjusted to guarantee the final localization performance. Such a task can be facilitated with the help of popular deep learning techniques, especially when enormous surveillance video clips become commonly seen nowadays. In this study, the metric learning problem in moving human localization is emphasized, and a new deep multi-channel residual networks-based metric learning method is introduced for the first time. Specifically, the deep metric learning problem in this new method is solved within a ranking procedure via both the conventional stochastic gradient descent algorithm and a more efficient proximal gradient descent algorithm. Comprehensive experiments are conducted and this new method is compared with several other popular deep learning-based approaches. Qualitative and quantitative analysis are conducted from the statistical perspective, to evaluate all localization outcomes obtained by all compared methods based on two specific measurements. The localization performance of this new method is suggested to be promising after the comprehensive analysis. © 2017
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.
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.
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.
Cai A.,Guangdong Medical College |
Qi S.,Shenzhen UniversityGuangdong |
Su Z.,Guangdong Medical College |
Shen H.,Guangdong Medical College |
And 3 more authors.
Clinical and Translational Science | Year: 2016
Phototherapy has been widely used in treating neonatal jaundice, but detailed metabonomic profiles of neonatal jaundice patients and response to phototherapy have not been characterized. Our aim was to depict the serum metabolic characteristics of neonatal jaundice patients relative to controls and changes in response to phototherapy. A 1H nuclear magnetic resonance (NMR)-based metabonomic approach was employed to study the metabolic profiling of serum from healthy infants (n = 25) and from infants with neonatal jaundice (n = 30) pre- and postphototherapy. The acquired data were processed by multivariate principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA). The PLS-DA and OPLS-DA model identified nine metabolites capable of distinguishing patients from controls. In addition, 28 metabolites such as β-glucose, α-glucose, valine, and pyruvate changed in response to phototherapy. This study offers useful information on metabolic disorders in neonatal jaundice patients and the effects of phototherapy on lipids, amino acid, and energy metabolism. © 2016 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics