Two machine learning approaches for predicting cyanobacteria abundance in aquaculture ponds.

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.

Stability Analysis of Switched Linear Singular Systems with Unstable and Stable Modes.

In this paper, stability is studied for a class of switched singular systems containing both stable and unstable modes. By introducing a time-varying piecewise Lyapunov function (TVPLF) and a mode-dependent average dwell time (ADT) switching rule, the computable sufficient conditions for system stability are derived. The time-varying piecewise Lyapunov functions are piecewise continuously differentiable on every mode (but may not be differentiable at the interpolating points of the dwell time). This Lyapunov function method is particularly advantageous in overcoming the limitations of traditional multiple Lyapunov function (MLF) methods, which may not have a feasible solution when dealing with switched systems containing only unstable modes. As such, the TVPLF offers greater flexibility in application. Compared with the conventional ADT switching rule, the mode-dependent ADT switching rule not only enables each mode to have its own ADT but also allows for its own switching strategy. Specifically, the stable mode adopts a slow switching strategy while the unstable mode adopts a fast one, thereby reducing the conservatism of the ADT switching rule. Furthermore, based on the stability analysis, the time-varying controllers are proposed to stabilize the switched singular system, which can be expressed as the sequential linear combination of a series of linear state feedback on each mode. The proposed controllers are continuous for each mode, which are different from the controllers designed through the traditional MLF and MDLF methods, where the controllers designed by traditional MLF are the time-invariant linear state feedback in each mode while the controllers designed by the MDLF are piecewise continuous for each mode.

Comparison of magnetic resonance imaging versus computed tomography-based thrombolysis treatment in patients with acute ischemic stroke.

PURPOSE: To evaluate the efficacy and safety of magnetic resonance imaging (MRI)-based comparing with computed tomography (CT)-based selection for intravenous thrombolysis in patients with acute ischemic stroke (AIS). METHODS: Totally 462 consecutive AIS patients treated with intravenous thrombolysis within a 4.5 h window from Jan. 2016 to Dec. 2019 were enrolled. The primary endpoint was the good functional outcome defined by a modified Rankin Scale (mRS) of 0-2 at 3 months. Secondary outcomes include the excellent functional outcome defined by a mRS of 0-1 at 3 months, occurrences of symptomatic intracranial hemorrhage (SICH), 7-day mortality, and 3-month mortality. RESULTS: Overall 172 patients received MRI and 290 received CT before they were treated with thrombolysis. The difference in the good or excellent functional outcome was not statistically significant between MRI and CT groups (both P > 0.05). The incidences of 7-day mortality (3.5% vs. 8.6%, P < 0.01), 30-day mortality (12.8% vs. 21.0%, P = 0.03), and SICH (12.2% vs. 20.3%, P < 0.01) were obviously lower for MRI-based regimen compared with CT-based regimen. Multivariate logistic regression indicated that MRI-based regimen was significantly associated with a lower risk of 7-day mortality (OR = 0.72, 95% CI: 0.53-0.91; P < 0.01), 30-day mortality (OR = 0.58, 95% CI: 0.47-0.73; P < 0.01), and SICH (OR = 0.44, 95% CI: 0.20-0.65; P < 0.01) after controlling for potential confounding factors. CONCLUSION: Despite MRI-based thrombolysis was not demonstrated to be associated with the good functional outcome, it significantly reduced risks of mortality and SICH in patients with AIS compared with CT-based thrombolysis.