Runaway electron generation during tokamak start-up

Tokamak start-up is characterized by low electron densities and strong electric fields, in order to quickly raise the plasma current and temperature, allowing the plasma to fully ionize and magnetic flux surfaces to form. Such conditions are ideal for the formation of superthermal electrons, which may reduce the efficiency of ohmic heating and prevent the formation of a healthy thermal fusion plasma. This is of particular concern in ITER where engineering limitations put restrictions on the allowable electric fields and limit the prefill densities during start-up. In this study, we present a new 0D burn-through simulation tool called STREAM (STart-up Runaway Electron Analysis Model), which self-consistently evolves the plasma density, temperature and electric field, while accounting for the generation and loss of relativistic runaway electrons. After verifying the burn-through model, we investigate conditions under which runaway electrons can form during tokamak start-up as well as their effects on the plasma initiation. We find that Dreicer generation plays a crucial role in determining whether a discharge becomes runaway-dominated or not, and that a large number of runaway electrons could limit the ohmic heating of the plasma, thus preventing successful burn-through or further ramp-up of the plasma current. The runaway generation can be suppressed by raising the density via gas fuelling, but only if done sufficiently early. Otherwise a large runaway seed may have already been built up, which can avalanche even at relatively low electric fields and high densities

Role of amino acid side chains in region 17-31 of Parathyroid Hormone (PTH) in binding to the PTH receptor.: J.Biol.Chem.

NRC publication: Ye

Toward Optimizing Risk Adjustment in the Dutch Surgical Aneurysm Audit

Background: To compare hospital outcomes of aortic aneurysm surgery, casemix correction for preoperative variables is essential. Most of these variables can be deduced from mortality risk prediction models. Our aim was to identify the optimal set of preoperative variables associated with mortality to establish a relevant and efficient casemix model. Methods: All patients prospectively registered between 2013 and 2016 in the Dutch Surgical Aneurysm Audit (DSAA) were included for the analysis. After multiple imputation for missing variables, predictors for mortality following univariable logistic regression were analyzed in a manual backward multivariable logistic regression model and compared with three standard mortality risk prediction models: Glasgow Aneurysm Score (GAS, mainly clinical parameters), Vascular Biochemical and Haematological Outcome Model (VBHOM, mainly laboratory parameters), and Dutch Aneurysm Score (DAS, both clinical and laboratory parameters). Discrimination and calibration were tested and considered good with a C-statistic > 0.8 and Hosmer-Lemeshow (H-L) P > 0.05. Results: There were 12,401 patients: 9,537 (76.9%) elective patients (EAAA), 913 (7.4%) acute symptomatic patients (SAAA), and 1,951 (15.7%) patients with acute rupture (RAAA). Overall postoperative mortality was 6.5%; 1.8% after EAAA surgery, 6.6% after SAAA, and 29.6% after RAAA surgery. The optimal set of independent variables associated with mortality was a mix of clinical and laboratory parameters: gender, age, pulmonary comorbidity, operative setting, creatinine, aneurysm size, hemoglobin, Glasgow coma scale, electrocardiography, and systolic blood pressure (C-statistic 0.871). External validation overall of VBHOM, DAS, and GAS revealed C-statistics of 0.836, 0.782, and 0.761, with an H-L of 0.028, 0.00, and 0.128, respectively. Conclusions: The optimal set of variables for casemix correction in the DSAA comprises both clinical and laboratory parameters, which can be collected easily from electronic patient files and will lead to an efficient casemix model