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Seaweed aquaculture produces enormous economic and ecological service benefits, making significant contributions to achieving global Sustainable Development Goals (SDGs). However, large-scale development of seaweed aquaculture and the unreasonable use of aquaculture rafts may trigger green tide, bringing negative ecological, social, and economic impacts. Therefore, it is vital to monitor the seaweed aquaculture industry accurately. Here, we mapped 10-m-resolution seaweed aquaculture along the Jiangsu coast of China based on active and passive remote sensing (Sentinel-1/2) and Random Forest using Google Earth Engine. The results demonstrate satisfactory model performance and data accuracy. The square seaweed aquaculture in the Lianyungang Offshore (Mode-I) has gradually expanded to the deep sea since 2016, with a maximum area of 194.06 km2 in 2018. Between 2021 and 2022, the area of the strip-shaped seaweed aquaculture in Subei radiation shoals (Mode-II) was considerably reduced, with most of the reduced land lying on the east side of the Dafeng Elk National Nature Reserve. In general, the area of the seaweed aquaculture in the prohibited breeding area was reduced from 20.32 km2 to 3.13 km2, and the area of the seaweed aquaculture in the restricted breeding area was reduced from 149.71 km2 to 33.15 km2. Results show that under the policy restriction, the scale of unsustainable seaweed aquaculture along the Jiangsu coast has been greatly reduced within seven years. This study can provide an efficient approach for the medium-scale extraction of seaweed aquaculture and provide decision support for the sustainable development of marine aquaculture.

With the maturity of the industrial robotic technology, robotic cells are gradually regarded as a kind of stand equipment to replace human work in every walk of life. How to obtain the maximum or approximate maximum throughput in a robotic cell is always the highlighted goal, especially in the rapid growing 3C industry market. In this paper, the objective is to get a 1-unit cycle sequence of robot actions that approximately minimizes the cycle time to produce a part and maximizes the throughput by using a new hybrid algorithm in the robotic cell with a dual-gripper robot. In this algorithm, different constrains are considered during computing the cycle time, including free/non-free process, allowed time window. The resulting diagrams provide very intuitive insights into the accuracy of the hybrid algorithm compared with the exact algorithm. Additional 100 simulation results prove the effectiveness of the hybrid algorithm, with a solid performance to achieve the maximum productivity of robotic cell.

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Prediction of Chlorophyll-a (Chl-a) concentration is significant for marine ecology and environmental protection. This paper presents an integrated approach to forecast seasonal Chl-a concentration in coastal waters. Before modeling, feature construction procedures, such as simplification, combination, and normalization, are conducted to identify the potentially significant features. The feature extraction method based on Random Forest (RF) and eXtreme Gradient BOOSTing (XGBoost) is applied to select relevant variables. Then, we propose a Cluster-stacking-based approach which includes a station-oriented clustering model and a stacking-based regression model. The former model is used to divide the observation stations into several groups, thus partitions the study region into several sub-regions and the study dataset into several subsets according to the corresponding stations. In each subset, single regression models including K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Multi-Layer Perceptron regression (MLP) and XGBoost are established in level 0 space and integrated by RF in level 1 space via stacked generalization. We compare the performance of the Cluster-stacking model with that of Cluster-KNN, Cluster-SVR, Cluster-MLP, Cluster-XGBoost and the regression stacking model without cluster. The model evaluation shows that the Cluster-stacking-based approach outperforms others in forecasting Chl-a concentration with a coefficient of determination (R 2) of 0.848 and a mean absolute error (MAE) of 0.665 ug/l. INDEX TERMS Chlorophyll-a concentration forecast, cluster-stacking-based approach, regression stacking, coastal waters.