Multi-stage Coordinated Robust Optimization for Soft Open Point Allocation in Active Distribution Networks with PV

To optimize the placement of soft open points (SOPs) in active distribution networks (ADNs), many aspects should be considered, including the adjustment of transmission power, integration of distributed generations (DGs), coordination with conventional control methods, and maintenance of economic costs. To address this multi-objective planning problem, this study proposes a multi-stage coordinated robust optimization model for the SOP allocation in ADNs with photovoltaic (PV). First, two robust technical indices based on a robustness index are proposed to evaluate the operation conditions and robust optimality of the solutions. Second, the proposed coordinated allocation model aims to optimize the total cost, robust voltage offset index, robust utilization index, and voltage collapse proximity index. Third, the optimization methods of the multi-and single-objective models are coordinated to solve the proposed multi-stage problem. Finally, the proposed model is implemented on an IEEE 33-node distribution system to verify its effectiveness. Numerical results show that the proposed index can better reveal voltage offset conditions as well as the SOP utilization, and the proposed model outperforms conventional ones in terms of robustness of placement plans and total cost

Cell-free DNA in blood is a potential diagnostic biomarker of breast cancer

Breast cancer is a highly malignant disease in women. A convenient screening tool with high accuracy for early detection, not only in high-risk individuals but in the general population, is necessary. Two hundred breast cancer patients, 100 healthy controls and 100 hyperplasia patients were enrolled in this study. Samples were randomly assigned into training or testing cohorts. The receiver operating characteristic curve was used to explore the optimal concentration of cell-free DNA (GAPDH) in the training cohort and the cut-off point was validated in the testing cohort. The results showed that both in the training and testing cohorts, the overall accuracy of classification between cancer, healthy controls and hyperplasia was higher than 0.9. The sensitivity, specificity, positive predictive value and negative predictive value also reached 0.9, with the exception of the negative predictive value in the testing cohort. This study provides useful information on the use of concentration of free DNA for breast cancer detection. These findings need to be validated in a large prospective trial prior to clinical application