Publications by authors named "Xujie Shi"
Article Synopsis
- Harmful algal blooms have become more common, negatively impacting drinking water quality due to the release of toxins and unpleasant tastes, while measuring algal biomass using chlorophyll-a (Chl-a) faces challenges.
- This study developed a model using convolutional neural networks (CNNs) and 3D fluorescence data to accurately classify thirteen types of algae with over 99.5% accuracy, focusing on algal pigment regions.
- The model showed varying accuracy in determining Chl-a concentrations across different water backgrounds, and after calibration, it improved significantly, highlighting the importance of algal pigment fluorescence in measurement accuracy.
Satellite evidence indicates a global increase in lacustrine algal blooms. These blooms can drift with winds, resulting in significant changes of the algal biomass spatial distribution, which is crucial in bloom formation. However, the lack of long-term, large-scale observational data has limited our understanding of bloom drift.
View Article and Find Full Text PDFIn this paper, we investigate the nonlocal Kundu-nonlinear Schrödinger (Kundu-NLS) equation, which can be obtained from the reduction of the coupled Kundu-NLS system. Based on the analysis of the eigenfunctions, a Riemann-Hilbert problem is constructed to derive the N-soliton solutions of the coupled Kundu-NLS system. The N-soliton solutions of the nonlocal Kundu-NLS equation are then deduced with properly chosen symmetry relations on the scattering data.
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