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Hardware Trojans are intended malicious design modifications to integrated circuits, which can be used to launch powerful low-level attacks after being activated. Ａ new security threat of lightweight stealthy hardware Trojans leveraging discrete satisfiability don't care signals was demonstrated. These don't care could not be satisfied under normal operation and thus the circuit design with Trojan is functionally equivalent to the Trojan-free baseline. The attacker could activate the Trojan through simple yet effective fault injection. Experimental results on a 1024-bit RSA cryptographic core show that the proposed hardware Trojan can escape from logic synthesis optimization, and that the RSA private key can be retrieved by simply over-clocking the design. A defense technique that can effectively detect such stealthy Trojan design was provided.

Combining Lagrange particle tracking method with the ensemble forecasting concept, one ensemble trajectory model is developed for large jellyfish with vertical movement of jellyfish taken into account in the Yellow and Bohai Sea. Besides, this ensemble model can give quick simulation of drifting trajectories and potential affected range. The comparison between observation and simulation of jellyfish trajectories in the coastal waters of Qingdao and Qinhuangdao as the typical place of the Yellow Sea and Bohai Sea has been made. And the results indicate that the ensemble trajectory model is more reliable than single one, and could provide more useful information for emergency response of jellyfish bloom, especially under the circumstance that autonomic (vertical) movement of of jellyfish and its mechanism is not clear.

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]