Peng Xu

Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.

A topological index is a numeric quantity related with the chemical composition claiming to correlate the chemical structure with different chemical properties. Topological indices serve to predict physicochemical properties of chemical substance. Among different topological indices, degree-based topological indices would be helpful in investigating the anti-inflammatory activities of certain chemical networks. In the current study, we determine the neighborhood second Zagreb index and the first extended first-order connectivity index for oxide network O X n, silicate network S L n, chain silicate network C S n, and hexagonal network H X n. Also, we determine the neighborhood second Zagreb index and the first extended first-order connectivity index for honeycomb network H C n.

This research improves our understanding of entrepreneurial intention in the Chinese cultural context. Drawing on career construction theory, we received 408 valid surveys from rural Chinese dwellers and examined the relationships rural Chinese have among “face consciousness,” indecisiveness, career adaptability, and entrepreneurial intention. We found that those who are fearful of losing face are less likely to have entrepreneurial intentions, but one’s desire to gain face has no significant direct impact on entrepreneurial intention. In addition, face consciousness and an indecisive personality interact to influence career adaptability and, in turn, entrepreneurial intention. In sum, this study supports the career construction perspective on understanding the formation of entrepreneurial intention and offers theoretical, practical, and policy implications for entrepreneurial career counseling and training.