Evolution of the central safety factor during stabilized sawtooth instabilities at KSTAR

A motional Stark effect (MSE) diagnostic has recently been installed in the KSTAR tokamak. A difficulty faced at KSTAR and common to other MSE diagnostics is calibration of the system for absolute measurements. In this report we present our novel calibration routine and discuss first results, evaluating the evolution of the the central safety factor during sawtooth instabilities. The calibration scheme ensures that the bandpass filters typically used in MSE systems are aligned correctly and identifies and removes systematic offsets present in the measurement. This is verified by comparing the reconstructed safety factor profile against various discharges where the locations of rational q surfaces have been obtained from MHD markers. The calibration is applied to analyse the evolution of q 0 in a shot where the sawteeth are stabilized by neutral beam injection. Within the analysed sawtooth periods q 0 drops below unity during the quiescent phase and relaxes close to or slightly above unity at the sawtooth crash. This finding is in line with the classical Kadomtsev model of full magnetic reconnection and earlier findings at JET

Discriminative Analysis of Brain Function at Resting-State for Attention-Deficit/Hyperactivity Disorder

In this work, a discriminative model of attention deficit hyperactivity disorder (ADHD) is presented on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model consists of two parts, a classifier and an intuitive representation of discriminative pattern of brain function between patients and normal controls. Regional homogeneity (ReHo), a measure of brain function at resting-state, is used here as a feature of classification. Fisher discriminative analysis (FDA) is performed on the features of training samples and a linear classifier is generated. Our initial experimental results show a successful classification rate of 85%, using leave-one-out cross validation. The classifier is also compared with linear support vector machine (SVM) and Batch Perceptron. Our classifier outperforms the alternatives significantly. Fisher brain, the optimal projective-direction vector in FDA, is used to represent the discriminative pattern. Some abnormal brain regions identified by Fisher brain, like prefrontal cortex and anterior cingulate cortex, are well consistent with that reported in neuroimaging studies on ADHD. Moreover, some less reported but highly discriminative regions are also identified. We conclude that the discriminative model has potential ability to improve current diagnosis and treatment evaluation of ADHD. ? Springer-Verlag Berlin Heidelberg 2005.EI