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Since 2008, annual outbreaks of green tides in the Yellow Sea have had severe impacts on tourism, fisheries, water sports, and marine ecology, necessitating effective interception and removal measures. Satellite remote sensing has emerged as a promising tool for monitoring large-scale green tides due to its wide coverage and instantaneous imaging capabilities. Additionally, drift prediction techniques can forecast the location of future green tides based on remote sensing monitoring information. This monitoring and prediction information is crucial for developing an effective plan to intercept and remove green tides. One key aspect of this monitoring information is the green tide distribution envelope, which can be generated automatically and quickly using buffer analysis methods. However, this method produces a large number of envelope vertices, resulting in significant computational burden during prediction calculations. To address this issue, this paper proposes a simplification method based on azimuth difference and side length (SM-ADSL). Compared to the isometric and Douglas–Peucker methods with the same simplification rate, SM-ADSL exhibits better performance in preserving shape and area. The simplified distribution envelope can shorten prediction times and enhance the efficiency of emergency decision-making for green tide disasters. [ABSTRACT FROM AUTHOR]

In order to address the issue of large errors in predicting SST along the coast using numerical models, this study adopts LSTM, a deep learning method, to develop optimal SST prediction models. The Xiaomaidao Station is selected as an example, and then the method is then extended to 14 coastal stations along the Bohai Sea and the Yellow Sea. The results show that the SST prediction model based on LSTM effectively improves forecast accuracy. The mean absolute errors for 1–3-day SST forecasts of the optimal model at Xiaomaidao Station are 0.20 °C, 0.27 °C, and 0.31 °C, and the root mean square errors are 0.28 °C, 0.36 °C, and 0.41 °C, respectively, representing an average reduction of 78% compared to those of the numerical model. Extending this approach to other forecasting sites along the Bohai Sea and the Yellow Sea results in an average 61% reduction in forecast error when compared with the numerical model. Furthermore, it is found that utilizing an LSTM model can significantly save computational resources and improve the forecasting efficiency. [ABSTRACT FROM AUTHOR]

Adversarial pairwise learning has become the predominant method to enhance the discrimination ability of models against adversarial attacks, achieving tremendous success in various application fields. Despite excellent empirical performance, adversarial robustness and generalization of adversarial pairwise learning remain poorly understood from the theoretical perspective. This paper moves towards this by establishing the high-probability generalization bounds. Our bounds generally apply to various models and pairwise learning tasks. We give application examples involving explicit bounds of adversarial bipartite ranking and adversarial metric learning to illustrate how the theoretical results can be extended. Furthermore, we develop the optimistic generalization bound at order O(n -1 ) on the sample size n by leveraging local Rademacher complexity. Our analysis provides meaningful theoretical guidance for improving adversarial robustness through feature size and regularization. Experimental results validate theoretical findings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)