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Liu Y.,Guangdong University of Finance and Economics
Mathematical Methods in the Applied Sciences | Year: 2015

In this article, the existence of positive solutions of a boundary value problem for nonlinear singular fractional-order elastic beam equation is established. Here, f depends on t,x, and x'; f may be singular at t = 0 and t = 1; and f is a non-Carathéodory function. The results obtained are based upon fixed-point theorems in a cone in Banach space. An example is included to illustrate the main results. © 2015John Wiley & Sons, Ltd.

Nie P.-Y.,Guangdong University of Finance and Economics | Yang Y.-C.,Guangdong University of Finance and Economics
Applied Energy | Year: 2016

This article captures the effects of energy price fluctuations on the demand and supply of energy. By focusing on the industries that depend on energy inputs, we are able to apply these effects and analyze the Chinese energy industry. Four main sets of results are presented. First, rising energy prices reduce output and social welfare. Second, the energy industry in China has a vertical market structure, which acts as an amplifier of energy price fluctuations that increase with market power of energy firms. Third, environmental objectives can be achieved through energy price regulation, thereby weakening the market power of energy firms. Interestingly, the formula of price regulation needed to achieve the environmental objectives is given. Finally, entry-level regulation can reduce emissions. In summary, this article supports the concept of energy industry regulation by decision-makers. © 2015 Elsevier Ltd.

Wang C.,Huizhou University | Cai J.,Guangdong University of Finance and Economics
International Journal of Wavelets, Multiresolution and Information Processing | Year: 2014

In this paper, we investigate coefficient-based regularized least squares regression problem in a data dependent hypothesis space. The learning algorithm is implemented with samples drawn by unbounded sampling processes and the error analysis is performed by a stepping-stone technique. A new error decomposition technique is proposed for the error analysis. The regularization parameters in our setting provide much more flexibility and adaptivity. Sharp learning rates are addressed by means of l2-empirical covering numbers under a moment hypothesis condition. © 2014 World Scientific Publishing Company.

Chen S.,Guangdong University of Finance and Economics | Claramunt C.,French Naval Academy Research Institute | Ray C.,French Naval Academy Research Institute
Journal of Transport Geography | Year: 2014

The urban growth of large cities in China is at a critical stage with the booming of the economy and impressive increase of the population and traffic demand. This paper studies and qualifies the growth and accessibility of a rapid rail transit network, and characterizes the relations with urban development using a spatio-temporal modelling approach. Several measures of the network topological structure, i.e., beta index ( β), cyclomatic number ( μ), alpha index ( α) and gamma index ( γ), are selected in order to examine and quantify the overall metro network growth of the city of Guangzhou in China. The results show that the current spatial connectivity of the Guangzhou's metro network is relatively low, this stressing the need to augment the. reliability of. the connections between the network nodes, and to increase the number of circuits in the network. A travel-time matrix is modelled and evaluates the nodes accessibility and characterizes the spatio-temporal evolution of the metro network. The spatial interaction between the different nodes of the network, as well as nodes accessibility are analyzed and derived from a potential-based model. The extension of the metro network clearly shows a dramatic tendency of positive accessibility evolution but with regional differences. In particular, the core of the city is surrounded by areas with highest accessibility values and gradually expanding outward from the core, while the locations of transfer stations have significant influence on the variation of network time-based accessibility. Taking into account different network development scenarios, the approach reveals regional accessibility differences in the metropolitan area of the city of Guangzhou, this clearly illustrating the impact of network accessibility in urban development. © 2014 Elsevier Ltd.

Dai H.-L.,Guangdong University of Finance and Economics
Applied Soft Computing Journal | Year: 2015

The classification of imbalanced data is a major challenge for machine learning. In this paper, we presented a fuzzy total margin based support vector machine (FTM-SVM) method to handle the class imbalance learning (CIL) problem in the presence of outliers and noise. The proposed method incorporates total margin algorithm, different cost functions and the proper approach of fuzzification of the penalty into FTM-SVM and formulates them in nonlinear case. We considered an excellent type of fuzzy membership functions to assign fuzzy membership values and got six FTM-SVM settings. We evaluated the proposed FTM-SVM method on two artificial data sets and 16 real-world imbalanced data sets. Experimental results show that the proposed FTM-SVM method has higher G-Mean and F-Measure values than some existing CIL methods. Based on the overall results, we can conclude that the proposed FTM-SVM method is effective for CIL problem, especially in the presence of outliers and noise in data sets. © 2015 Elsevier B.V. All rights reserved.

Liu Y.,Guangdong University of Finance and Economics
Mathematical Methods in the Applied Sciences | Year: 2016

We point out some mistakes in a known paper. Some existence results for solutions of two classes of boundary value problems for nonlinear impulsive fractional differential equations are established. Our analysis relies on the well-known Schauder fixed point theorem. Examples are given to illustrate the main results. © 2016 John Wiley & Sons, Ltd.

Wang Z.,Guangdong University of Finance and Economics
WIT Transactions on Information and Communication Technologies | Year: 2014

People have different viewpoints on interdisciplinary recruited graduate student. Positive effects of multidisciplinary training are analyzed. Innovative talents training usually depends on a wide range of discipline knowledge, and interdisciplinary graduate students meet the growth law of creative talents. The negative effects are also summarized - Students' knowledge structure is not reasonable because their foundation of the new major is not solid, so they are difficult to catch professional trends. Finally, the reasons to form above characteristics are summarized. This helps to improve the multidisciplinary postgraduate training program. © 2014 WIT Press.

Peng R.,Guangdong University of Finance and Economics | Wu B.,Duke University
Journal of Aging and Health | Year: 2015

Objective: To examine rates of institutionalization of Chinese older adults aged 65+ and the impact of changes in health status on the likelihood of institutionalization. Method: Using data from the 2002, 2005, 2008, and 2011 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), admission rates for each 3-year interval between waves were calculated. Logistic regression models were used to assess the changes of five health status variables as risk factors. Results: Between the first (2002-2005) and third (2008-2011) intervals, the institutionalization rate increased from 0.5% to 0.8%. Risk of institutionalization increased 70% for respondents with declining ability to perform activities of daily living, 53% for those with declining cognitive function, and 44% for those with increasing number of chronic diseases. Discussion: Development of policies and programs to improve older adults' health status is essential to delay institutionalization. Quality of workforce is also critical in meeting the care needs. © The Author(s) 2015.

Cai J.,Guangdong University of Finance and Economics
International Journal of Wavelets, Multiresolution and Information Processing | Year: 2015

Kernel canonical correlation analysis (CCA) is a nonlinear extension of CCA, which aims at extracting information shared by two random variables. In this paper, a new notion of conditional kernel CCA is introduced. Conditional kernel CCA aims at analyzing the effect of variable Z to the dependence between X and Y. Rates of convergence of an empirical normalized conditional cross-covariance operator (empirical NCCCO) to the normalized conditional cross-covariance operator (NCCCO) are also investigated in this paper. Elaborate error analysis of conditional kernel CCA is elegantly conducted under mild decay conditions. Our refined analysis leads to satisfactory learning rates in a more general setting. © 2015 World Scientific Publishing Company.

Dai H.-L.,Guangdong University of Finance and Economics
IEEE Transactions on Nanobioscience | Year: 2015

Classification of protein sequences into functional and structural families based on machine learning methods is a hot research topic in machine learning and Bioinformatics. In fact, the underlying protein classification problem is a huge multiclass problem. Generally, the multiclass problem can be reduced to a set of binary classification problems. The protein in one class are seen as positive examples while those outside the class are seen as negative examples. However, the class imbalance problem will arise in this case because the number of protein in one class is usually much smaller than that of the protein outside the class. To handle the challenge, we propose a novel framework to classify the protein. We firstly use free scores (FS) to perform feature extraction for protein; then, the inverse random under sampling (IRUS) is used to create a large number of distinct training sets; next, we use a new ensemble approach to combine these distinct training sets with a new fuzzy total margin support vector machine (FTM-SVM) that we have constructed. we call the novel ensemble classifier as ensemble fuzzy total margin support vector machine (EnFTM-SVM). We then give a full description of our method, including the details of its derivation. Finally, experimental results on fourteen benchmark protein data sets indicate that the proposed method outperforms many state-of-the-art protein classifying methods. © 2015 IEEE.

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