Abstract-Loss of synchronization between wind farm and power grid during severe grid faults would cause wind farm tripping. In this paper, the mechanism of grid-synchronization is uncovered, described as motion of an autonomous nonlinear differential equation with specific initial states. The revealed mechanism indicates that even though steady state working point exists, improper initial states and poor system dynamic properties could lead to synchronization instability. In order to keep wind farm synchronous with the power grid during severe grid faults, special requirements on system dynamic properties are stated. Moreover, to satisfy all the requirements, a current injecting method is proposed. By adjusting active and reactive output currents of the wind farm, the proposed method could ensure system synchronization stability during severe grid faults. Implementation of the proposed method on PMSG and DFIG based wind farm is illustrated. Simulation results validate the analysis and the control method.

We found that the prognostic values of CA125, CA19.9, NSE, and SCC for stage I NSCLC are limited. CYFRA21.1 might be a hopeful prognostic serum tumor marker for stage I NSCLC.

Transient instability events of grid-tied converters probably occur while riding through grid faults. During lowvoltage ride through (LVRT) period, seeing from the converter terminal towards the grid, the Thévenin equivalent grid impedance becomes pretty significant, accordingly making the converter terminal voltage highly sensitive to the output current. Under such circumstances, it is challenging for the converter to resynchronize with the grid via a phase-locked loop (PLL). This paper develops a reduced-order nonlinear model to elaborate on the dynamic synchronization characteristic of the converters. By considering the impact of grid impedance and analysing spatial vector tracking relation, resynchronization principle of the converters during LVRT is revealed. Besides, the impacts of circuit parameters and controller parameters, including residual grid voltage, grid impedance, current references, and PLL parameters, on the transient stability of the converters are investigated. The results are verified by simulation and experimental results. Index Terms-Grid fault, low voltage ride through, phase-locked loop, power converter, synchronization, transient stability. I. IntroductIonG RID-TIED power converters are playing increasingly important roles in power systems, along with the rapid development of renewable power generation in recent years. Three-phase voltage source converters (VSCs), as one of the most crucial components of renewable energy generation units, often offer quite different performance from the conventional synchronous generator units, such as outstanding rapidity and controllability, but limited fault ride through capability [1], [2]. Grid codes for renewable power generation, in which various facets of the operating characteristics of grid-tied renewable energy generation units are standardized, have been formulated by independent system operators in order to ensure the power system security and stability [2]-[4]. One of the grid codes, namely low-voltage ride through (LVRT) requirement, requires renewable energy generation units to remain connected to the Manuscript received January 25, 2019.

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Esophageal squamous cell carcinoma (ESCC) is a deadly disease that requires extensive research. Here, we review the current understanding of the functions of the nuclear factor erythroid-derived 2-like 2 (NRF2) signaling pathway in the esophagus. Genomic data suggest that gene mutations and several other mechanisms result in NRF2 hyperactivation in human ESCC. As a consequence, NRF2 ESCC is more resistant to chemoradiotherapy and associated with poorer survival than NRF2 ESCC. Mechanistically, we believe NRF2, functioning as a transcription factor, causes an esophageal phenotype through regulation of gene transcription. We discuss metabolism, mitochondria, proteasomes, and several signaling pathways as downstream players that may contribute to an esophageal phenotype due to NRF2 hyperactivation. Finally, strategies are proposed to target the NRF2 signaling pathway for therapy of NRF2 ESCC.

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The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.