The artificial intelligence (AI) techniques have been widely used in the transient stability analysis of a power system. They are recognized as the most promising approaches for predicting the post-fault transient stability status with the use of phasor measurement units data. However, the popular AI methods used for power systems are often ''black boxes,'' which result in the poor interpretation of the model. In this paper, a transient stability prediction method based on extreme gradient boosting is proposed. In this model, a decision graph and feature importance scores are provided to discover the relationship between the features of the power system and transient stability. Meanwhile, the key features are selected according to the feature importance scores to remove redundant variables. The simulation results on the New England 39-bus system have demonstrated the superiority of the proposed model over the prior methods in the computation speed and prediction accuracy. Finally, an algorithm is proposed to interpret the prediction results for a specific fault of the power system, which further improves the interpretability of the model and makes it attractive for real-time transient stability prediction. INDEX TERMSFeature importance scores, model interpretation, XGBoost model, transient stability prediction.

Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by hyperglycemia and dyslipidemia caused by impaired insulin secretion and resistance of the peripheral tissues. A major pathogenesis of T2DM is obesity-associated insulin resistance. Gynura divaricata (L.) DC. (GD) is a natural plant and has been reported to have numerous health-promoting effects on both animals and humans. In this study, we aimed to elucidate the regulatory mechanism of GD improving glucose and lipid metabolism in an obesity animal model induced by high-fat and high-sugar diet in combination with low dose of streptozocin and an insulin-resistant HepG2 cell model induced by dexamethasone. The study showed that the water extract of GD (GD extract A) could significantly reduce fasting serum glucose, reverse dyslipidemia and pancreatic damage, and regulate the body weight of mice. We also found that GD extract A had low toxicity in vivo and in vitro. Furthermore, GD extract A may increase glucose consumption in insulin-resistant HepG2 cells, markedly inhibit NF-κB activation, and decrease the impairment in signaling molecules of insulin pathway, such as IRS-1, AKT, and GLUT1. Overall, the results indicate that GD extract A is a promising candidate for the prevention and treatment of T2DM.

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Fault location is one of the most essential techniques to maintain the stable operation of power systems. A fast and accurate fault location allows operators to restore power grids faster and avoid economic losses. Conventional methods rely on expert knowledge to extract the necessary features (e.g. DWT, DFT). For large systems, more coupling effects of transmission lines require more complex feature engineering, and incomplete features can easily introduce large errors. To overcome this, a deep learning approach without manual feature extraction is introduced to the fault location model under big data application. Towards this end, in the proposed method, the attention mechanism, the Bi-GRU and a dual structure network are applied to analyze the current data from different perspectives. Complete information for the fault features is extracted for the fault location. Based on a time series model and benefit from the ability to internally acquire the information architecture of faulty line, the established model is adaptive to the power grids with very complex topologies. Simulation results indicate that the proposed double-structure model reduces the maximum error and is less affected by noise. In comparison with different structures and different models, the proposed method shows better performance in IEEE 39-bus system.

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Based on data available from phase measurement units (PMUs), this paper proposes an energy margin constrained online coordinated voltage control (CVC) algorithm. First, the energy function model is established, which is based on detailed device models that include the effects of shunt capacitor banks, reactive power output of generators and under-load-tap-changers (ULTCs). In order to improve the computational efficiency, a Lie derivative-based method is used to map the energy change into the system trajectory along the energy manifold. Then, the change of energy margin based on the Lie derivative is used as the objective function and the reactive power margin is used as a constraint to formulate a linear programming problem. A coordinated strategy is proposed to help achieve the optimal solution when the linear programming problem is solved. In comparison with the other control modes, the proposed method improves the stability margin and the control effectiveness of voltage profile. Numerical results are provided for a three-machine system, the IEEE-30 bus system and the New England-39 bus benchmark system. Index Terms-Coordinated strategy (CS), coordinated voltage control (CVC), energy margin, voltage stability.Qun-Ying Liu (M'13) received the B.Eng. degree and M.S. and Ph.D. degrees in electrical engineering from

In the context of big data, machine learning plays an important role in many fields. With the increasing scale of power system and capacity of power grid, it becomes more and more complicated to accurately evaluate the transient stability of power system. In this paper, a power system transient stability assessment method based on XGBOOST algorithm is proposed. The XGBOOST algorithm is introduced to train the decision tree model and evaluate the transient stability of power system by converting the simulated power system operating data into the characteristic variables of power system. The results show that the training model of the algorithm can solve this kind of problem accurately and quickly.

Background: Previous studies have shown that oxidative stress is an important factor in preeclampsia (PE). Heme oxygenase-1 (HO-1) and nuclear factor erythroid 2-related factor-2 (Nrf2) are protective proteins that are involved in combating oxidative stress in the body. Nrf2 is also an essential upstream transcription factor regulating HO-1. This study was aimed at exploring the physiological roles of HO-1 and Nrf2 in PE. Methods: Serum and placenta were collected from 30 patients who presented with severe PE and 30 healthy pregnant females. HO-1 and Nrf2 levels in placenta were measured. Following stimulation of the HTR-8/SVneo cell line with tert-butylhydroquinone (tBHQ), an Nrf2 activator, nuclear Nrf2 protein and HO-1 mRNA levels were determined. Results: Compared with the healthy pregnancy group, HO-1 protein and mRNA levels were increased in placental samples obtained from the severe PE group (p < 0.01, p < 0.05). Similar increases were also observed for Nrf2 protein levels (p < 0.01). Nuclear Nrf2 protein and HO-1 mRNA levels were both increased in the HTR-8/SVneo cell line following stimulation with tBHQ (p < 0.05). Conclusion: Patients with severe PE may be protected against oxidative injury following an elevation in HO-1 and Nrf2 levels. Nrf2 is likely to have a synergistic effect on HO-1 in PE.