Cyanobacteria blooms in aquaculture ponds harm the harvesting of aquatic animals and threaten human health. Therefore, it is crucial to identify key drivers and develop methods to predict cyanobacteria blooms in aquaculture water management. In this study, we analyzed monitoring data from 331 aquaculture ponds in central China and developed two machine learning models - the least absolute shrinkage and selection operator (LASSO) regression model and the random forest (RF) model - to predict cyanobacterial abundance by identifying the key drivers. Simulation results demonstrated that both machine learning models are feasible for predicting cyanobacterial abundance in aquaculture ponds. The LASSO model (R² = 0.918, MSE = 0.354) outperformed the RF model (R² = 0.798, MSE = 0.875) in predicting cyanobacteria abundance. Farmers with well-equipped aquaculture ponds that have abundant water monitoring data can use the nine environmental variables identified by the LASSO model as an operational solution to accurately predict cyanobacteria abundance. For crude ponds with limited monitoring data, the three environmental variables identified by the RF model provide a convenient solution for useful cyanobacteria prediction. Our findings revealed that chemical oxygen demand (COD) and total organic carbon (TOC) were the two most important predictors in both models, indicating that organic carbon concentration had a close relationship with cyanobacteria growth and should be considered a key metric in water monitoring and pond management of these aquaculture ponds. We suggest that monitoring of organic carbon coupled with phosphorus reduction in feed usage can be an effective management approach for cyanobacteria prevention and to maintain a healthy ecological state in aquaculture ponds.

The leader-follower model is found efficient for robot swarm exploration tasks. When a swarm of mobile robots collaboratively explore an unknown environment with limited communication, such as communication delay or even message loss, it is necessary to adaptively re-group robots according to the dynamic communication conditions and task goals. In this paper, we propose to analyze the leader-follower topology in a graph fashion and employ Ratio Cut of the proposed graph for membership re-assignment. We take each robot as a node and consider both the communication delay and the goal, i.e., exploring towards unknown area, as the edge weight. Then we use the Laplacian matrix to bipartition the graph repeatedly to optimize group partition. Simulation results validate the effectiveness of the proposed method.

In this paper, we investigate the stabilization for infinite dimensional switched linear systems comprising two unstable subsystems. From the algebraic structure of the state space, two switching strategies are designed to stabilize the infinite dimensional switched linear systems. Under these strategies and suitable assumptions, the systems with time-varying disturbances possess a good robust stability. In addition, we can also obtain an arbitrarily low switching frequency for the switched systems.