Jinggangshan University

About: Jinggangshan University is a education organization based out in Ji’an, China. It is known for research contribution in the topics: Phosphor & Luminescence. The organization has 2068 authors who have published 1724 publications receiving 13974 citations.

A new model for relationship between irradiance and the rate of photosynthesis in Oryza sativa

Abstract: The calculated maximum net photosynthetic rate (P N) at saturation irradiance (I m) of 1 314.13 µmol m−2 s−1 was 25.49 µmol(CO2) m−2 s−1, and intrinsic quantum yield at zero irradiance was 0.103. The results fitted by nonrectangular hyperbolic model, rectangular hyperbolic method, binomial regression method, and the new model were compared. The maximum P N values calculated by nonrectangular hyperbolic model and rectangular hyperbolic model were higher than the measured values, and the I m calculated by nonrectangular hyperbolic model and rectangular hyperbolic model were less than measured values. Results fitted by new model showed that the response curve of P N to I was nonlinear at low I for Oryza sativa, P N increased nonlinearly with I below saturation value. Above this value, P N decreased nonlinearly with I.

Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection

Abstract: Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the level of public safety. Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.

Turn-On Luminescent Sensor toward Fe3+, Cr3+, and Al3+ Based on a Co(II) Metal-Organic Framework with Open Functional Sites.

Abstract: A novel Co-based metal–organic framework (MOF) with the formula of {[Co3(BIBT)3(BTC)2(H2O)2]·solvents}n (JXUST-2, where JXUST denotes Jiangxi University of Science and Technology, BIBT = 4,7-bi(1H-...