Anthropogenic sound has increased significantly in the past decade. However, only a few studies to date have investigated its effects on marine bivalves, with little known about the underlying physiological and molecular mechanisms. In the present study, the effects of different types, frequencies, and intensities of anthropogenic sounds on the digging behavior of razor clams (Sinonovacula constricta) were investigated. The results showed that variations in sound intensity induced deeper digging. Furthermore, anthropogenic sound exposure led to an alteration in the O:N ratios and the expression of ten metabolism-related genes from the glycolysis, fatty acid biosynthesis, tryptophan metabolism, and Tricarboxylic Acid Cycle (TCA cycle) pathways. Expression of all genes under investigation was induced upon exposure to anthropogenic sound at ~80 dB re 1 μPa and repressed at ~100 dB re 1 μPa sound. In addition, the activity of Ca²⁺/Mg²⁺-ATPase in the feet tissues, which is directly related to muscular contraction and subsequently to digging behavior, was also found to be affected by anthropogenic sound intensity. The findings suggest that sound may be perceived by bivalves as changes in the water particle motion and lead to the subsequent reactions detected in razor clams.

In light of the specific characteristics of the long‐term record and complex sequence nature of the ocean observation data, a new method was developed based on the original Dixon detection criteria to be specifically detect and remove data outliers. This method combines the two traditional methods of data quality control and Dixon detection theory and assumes that the second‐order differential sequence of parameter measurements passes an appropriate stationarity test. Thus, the measurement attributes are considered to be in the same physical state and to occupy a small range of time and space, equivalent to a parallel observation test. Provided that the observations over a small range of time and space correspond to the record of a sequence covering a short period of time, this short time sequence is treated as a sliding window in the proposed new method. Outliers are detected based on lookup‐table after an index parameter Q is calculated within the sliding window. A correlation analysis and the test results show that the proposed new method can effectively instantiate a sequence of outliers characterized by different phases. Compared with other existing methods, the new method proved to be computationally efficient and easily programmable for practical implementation. Further, this method preserves the original data because the outliers are replaced by an inverse distance‐weighted average of the recorded data within the window, while other data were intact.

Outliers are often present in large datasets of water quality monitoring time series data. A method of combining the sliding window technique with Dixon detection criterion for the automatic detection of outliers in time series data is limited by the empirical determination of sliding window sizes. The scientific determination of the optimal sliding window size is very meaningful research work. This paper presents a new Monte Carlo Search Method (MCSM) based on random sampling to optimize the size of the sliding window, which fully takes advantage of computers and statistics. The MCSM was applied in a case study to automatic monitoring data of water quality factors in order to test its validity and usefulness. The results of comparing the accuracy and efficiency of the MCSM show that the new method in this paper is scientific and effective. The experimental results show that, at different sample sizes, the average accuracy is between 58.70% and 75.75%, and the average computation time increase is between 17.09% and 45.53%. In the era of big data in environmental monitoring, the proposed new methods can meet the required accuracy of outlier detection and improve the efficiency of calculation.

Achieving accurate and efficient ship-type recognition is crucial for the development and management of modern maritime traffic systems. To overcome the limitations of existing methods that rely solely on AIS time series data or navigation trajectory images as single-modal approaches, this study introduces TrackAISNet, a multimodal ship classification model that seamlessly integrates ship trajectory images with AIS time series data for improved performance. The model employs a parallel structure, utilizing a lightweight neural network to extract features from trajectory images, and a specially designed TCN-GA (Temporal Convolutional Network with Global Attention) to capture the temporal dependencies and long-range relationships in the AIS time series data. The extracted image features and temporal features are then fused, and the combined features are fed into a classification network for final classification. We conducted experiments on a self-constructed dataset of variable-length AIS time series data comprising four types of ships. The results show that the proposed model achieved an accuracy of 81.38%, recall of 81.11%, precision of 80.95%, and an F1 score of 81.38%, outperforming the benchmark single-modal algorithms. Additionally, on a publicly available dataset containing three types of fishing vessel operations, the model demonstrated improvements in accuracy, recall, and F1 scores by 5.5%, 4.88%, and 5.88%, respectively.