Jiangxi University of Finance and Economics

About: Jiangxi University of Finance and Economics is a education organization based out in Nanchang, China. It is known for research contribution in the topics: Fuzzy logic & China. The organization has 2865 authors who have published 3556 publications receiving 41567 citations.

Social Exclusion and Green Consumption: A Costly Signaling Approach

Abstract: This article is dedicated to examine the impact of social exclusion (i.e., being rejected, isolated, excluded or ignored by other individuals or groups in society) on consumers' intention of green consumption. Based on Costly Signaling Theory, three experiments have been conducted to explore one main effect and the corresponding mechanism together with two boundary conditions. Specifically, the first study tests the main effect and internal mechanism by manipulating the state of social exclusion. The results show that social exclusion enhances consumers' intention to buy green products and consumers' desire for self-sacrifice mediates that relationship. Study 2 manipulates audience state to examine the first boundary condition. The findings show that the effect of social exclusion on green consumption exists only in public purchasing scenarios. Study 3 tests the second boundary condition by manipulating the stability of exclusion causes. The results indicate that the main effect is significant only when causes of exclusion are not stable. The final part discusses theoretical contributions and practical implications of this study in the field of both social exclusion and green consumption.

Compensation Details-Based Injection Model for Remote Sensing Image Fusion

Abstract: Remote sensing image fusion has a potential spectral distortion problem due to the global/local spectral and spatial correlations between panchromatic (PAN) and multispectral (MS) images. To overcome this problem, in this letter, a compensation details-based injection (CDI) fusion model is presented from a new perspective of compensatory learning. In contrast to the traditional method, the two categories of details, namely, the PAN details and the CD, are considered to compensate for the spatial and spectral differences between low-resolution MS (LRMS) and high-resolution MS images. To obtain the CD, a robust sparse representation was employed to calculate the difference between the PAN and MS images during the fusion. The CD combined with the PAN details extracted by a multiscale-guided filter are then injected into the upsampled LRMS image to achieve a fused image. Extensive experiments were undertaken on several image data sets, and the results demonstrate the effectiveness of the proposed CDI method.

Random matrix theory for analyzing the brain functional network in attention deficit hyperactivity disorder.

Abstract: Attention deficit hyperactivity disorder (ADHD) is the most common childhood neuropsychiatric disorder and affects approximately 6-7% of children worldwide. Here, we investigate the statistical properties of undirected and directed brain functional networks in ADHD patients based on random matrix theory (RMT), in which the undirected functional connectivity is constructed based on correlation coefficient and the directed functional connectivity is measured based on cross-correlation coefficient and mutual information. We first analyze the functional connectivity and the eigenvalues of the brain functional network. We find that ADHD patients have increased undirected functional connectivity, reflecting a higher degree of linear dependence between regions, and increased directed functional connectivity, indicating stronger causality and more transmission of information among brain regions. More importantly, we explore the randomness of the undirected and directed functional networks using RMT. We find that for ADHD patients, the undirected functional network is more orderly than that for normal subjects, which indicates an abnormal increase in undirected functional connectivity. In addition, we find that the directed functional networks are more random, which reveals greater disorder in causality and more chaotic information flow among brain regions in ADHD patients. Our results not only further confirm the efficacy of RMT in characterizing the intrinsic properties of brain functional networks but also provide insights into the possibilities RMT offers for improving clinical diagnoses and treatment evaluations for ADHD patients.