Browsing by Author "Yuan G"

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    Enhanced removal of arsenic and cadmium from contaminated soils using a soluble humic substance coupled with chemical reductant.
    (1/03/2023) Wei J; Tu C; Xia F; Yang L; Chen Q; Chen Y; Deng S; Yuan G; Wang H; Jeyakumar P; Bhatnagar A
    Soil washing is an efficient, economical, and green remediation technology for removing several heavy metal (loid)s from contaminated industrial sites. The extraction of green and efficient washing agents from low-cost feedback is crucially important. In this study, a soluble humic substance (HS) extracted from leonardite was first tested to wash soils (red soil, fluvo-aquic soil, and black soil) heavily contaminated with arsenic (As) and cadmium (Cd). A D-optimal mixture design was investigated to optimize the washing parameters. The optimum removal efficiencies of As and Cd by single HS washing were found to be 52.58%-60.20% and 58.52%-86.69%, respectively. Furthermore, a two-step sequential washing with chemical reductant NH2OH•HCl coupled with HS (NH2OH•HCl + HS) was performed to improve the removal efficiency of As and Cd. The two-step sequential washing significantly enhanced the removal of As and Cd to 75.25%-81.53% and 64.53%-97.64%, which makes the residual As and Cd in soil below the risk control standards for construction land. The two-step sequential washing also effectively controlled the mobility and bioavailability of residual As and Cd. However, the activities of soil catalase and urease significantly decreased after the NH2OH•HCl + HS washing. Follow-up measures such as soil neutralization could be applied to relieve and restore the soil enzyme activity. In general, the two-step sequential soil washing with NH2OH•HCl + HS is a fast and efficient method for simultaneously removing high content of As and Cd from contaminated soils.
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    Parameter-Free Extreme Learning Machine for Imbalanced Classification Authors Li, L - China Agric
    (Springer Science+Business Media, LLC, 2020-12) Li L; Zhao K; Sun R; Gan J; Yuan G; Liu T
    Imbalanced data distribution is a common problem in classification situations, that is the number of samples in different categories varies greatly, thus increasing the classification difficulty. Although many methods have been used for the imbalanced data classification, there are still problems with low classification accuracy in minority class and adding additional parameter settings. In order to increase minority classification accuracy in imbalanced problem, this paper proposes a parameter-free weighting learning mechanism based on extreme learning machine and sample loss values to balance the number of samples in each training step. The proposed method mainly includes two aspects: the sample weight learning process based on the sample losses; the sample selection process and weight update process according to the constraint function and iterations. Experimental results on twelve datasets from the KEEL repository show that the proposed method could achieve more balanced and accurate results than other compared methods in this work.