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.

Extensions of Banach contraction principle to partial cone metric spaces over a non-normal solid cone

Abstract: In this paper, we present some extensions of Banach contraction principle to partial cone metric spaces over a non-normal solid cone, which improve many recent fixed point results in cone metric spaces and partial cone metric spaces. An example is given to support the usability of our results.

Heavy metals uptake and transport by native wild plants: implications for phytoremediation and restoration

Abstract: Knowing the characteristics and relationships that are important in the accumulation and transfer of heavy metals among native wild plants is beneficial for screening potential accumulator plants and guiding remediation. In this research, nine native wild plants were collected from the Xikuangshan mine in Hunan Province, and the content of heavy metals, bioconcentration factors and translocation factors were determined via analysis. The results showed that the plant rhizosphere was polluted with heavy metals, especially Sb, Hg and Cd. P. aquilinum showed a strong ability to take up and transfer As, Cd, Pb and Zn. I. cylindrica effectively removed Sb from soils, P. vittata removed As and Cd, and D. erythrosora removed Hg and Cd. Native wild plants showed cooperativity to accumulate Cd, Cr, Pb and Zn, similar to As–Cd, As–Cr and As–Pb. There was a significant positive correlation for translocation in Hg–Cd, Hg–Zn, Hg–Pb, Cd–Pb, Cd–Zn, Zn–Cr and Zn–Pb. Knowledge of the ability of native wild plants to take up and transfer heavy metals is useful in screening for potential phytoremediation, and identifying the relationship between heavy metals accumulation and transfer among species will guide the selection of multiple heavy-metal remediation plants.

Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection

Abstract: Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network architecture. To selectively fuse the CNNs features extracted from both RGB and depth as a final result, the state-of-the-art (SOTA) bi-stream networks usually consist of two independent subbranches; i.e., one subbranch is used for RGB saliency and the other aims for depth saliency. However, its depth saliency is persistently inferior to the RGB saliency because the RGB component is intrinsically more informative than the depth component. The bi-stream architecture easily biases its subsequent fusion procedure to the RGB subbranch, leading to a performance bottleneck. In this paper, we propose a novel data-level recombination strategy to fuse RGB with D (depth) before deep feature extraction, where we cyclically convert the original 4-dimensional RGB-D into \textbf{D}GB, R\textbf{D}B and RG\textbf{D}. Then, a newly lightweight designed triple-stream network is applied over these novel formulated data to achieve an optimal channel-wise complementary fusion status between the RGB and D, achieving a new SOTA performance.