Recent progress with the MAST synthetic aperture imaging radiometer

The MAST Synthetic Aperture Microwave Imaging (SAMI) radiometer is an antenna array designed to image thermal microwave emission from MAST fusion plasmaaSAMl is now installed on MAST and preliminary data has been taken. This data clearly show the presence of electrostatic to electromagnetic mode conversion and the circumstances under which this mode conversion take place are strongly related to the pedestal density gradient and magnetic field. These quantities are valuable in understanding tokamak pedestal behaviour and are especially important to models of the Edge Localised Mode (ELM). This paper describes SAMIs design and construction, as well as fist signals showing the presence of mode conversion

Artificial intelligence in fracture detection: a systematic review and meta-analysis

Background: Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies apply artificial intelligence (AI) techniques to fracture detection as an adjunct to clinician diagnosis. Purpose: To perform a systematic review and meta-analysis comparing the diagnostic performance in fracture detection between AI and clinicians in peer-reviewed publications and the gray literature (ie, articles published on preprint repositories). Materials and Methods: A search of multiple electronic databases between January 2018 and July 2020 (updated June 2021) was performed that included any primary research studies that developed and/or validated AI for the purposes of fracture detection at any imaging modality and excluded studies that evaluated image segmentation algorithms. Meta-analysis with a hierarchical model to calculate pooled sensitivity and specificity was used. Risk of bias was assessed by using a modified Prediction Model Study Risk of Bias Assessment Tool, or PROBAST, checklist. Results: Included for analysis were 42 studies, with 115 contingency tables extracted from 32 studies (55061 images). Thirty-seven studies identified fractures on radiographs and five studies identified fractures on CT images. For internal validation test sets, the pooled sensitivity was 92% (95% CI: 88, 93) for AI and 91% (95% CI: 85, 95) for clinicians, and the pooled specificity was 91% (95% CI: 88, 93) for AI and 92% (95% CI: 89, 92) for clinicians. For external validation test sets, the pooled sensitivity was 91% (95% CI: 84, 95) for AI and 94% (95% CI: 90, 96) for clinicians, and the pooled specificity was 91% (95% CI: 81, 95) for AI and 94% (95% CI: 91, 95) for clinicians. There were no statistically significant differences between clinician and AI performance. There were 22 of 42 (52%) studies that were judged to have high risk of bias. Meta-regression identified multiple sources of heterogeneity in the data, including risk of bias and fracture type. Conclusion: Artificial intelligence (AI) and clinicians had comparable reported diagnostic performance in fracture detection, suggesting that AI technology holds promise as a diagnostic adjunct in future clinical practice

Observation of accelerated beam ion population during edge localized modes in the ASDEX Upgrade tokamak

The interaction between fast-ions and edge localized modes (ELMs) is investigated by means of fast-ion loss detector measurements. Fast-ion losses are increased during ELMs exhibiting a 3D filamentary-like behaviour. An accelerated beam ion population has been observed during ELMs in a tokamak for the first time. Tomographic inversion of the measured fast-ion losses reveal multiple velocity-space structures. Attending to the experimental observations, an acceleration mechanism is proposed based on a resonant interaction between the beam ions and parallel electric fields emerging during the ELM crash. The key experimental observations can be qualitatively reproduced by full-orbit following simulations of fast-ions in the presence of the ELM magnetic and electric perturbation fields. Our findings may shed light on the possible contribution of fast-ions to the ELM stability and the transient heat loads on plasma facing components.EUROfusion Consortium 633053Spanish Ministry of Economy and Competitiveness (Grant No. FIS2015-69362-P)H2020 Marie Sklodowska Curie programme (Grant No. 708257

Acceleration of beam ions during edge localized modes in the ASDEX Upgrade tokamak

EUROfusion Consortium 63305

GPU-Based Data Processing for 2-D Microwave Imaging on MAST

The Synthetic Aperture Microwave Imaging (SAMI) diagnostic is a Mega Amp Spherical Tokamak (MAST) diagnostic based at Culham Centre for Fusion Energy. The acceleration of the SAMI diagnostic data-processing code by a graphics processing unit is presented, demonstrating acceleration of up to 60 times compared to the original IDL (Interactive Data Language) data-processing code. SAMI will now be capable of intershot processing allowing pseudo-real-time control so that adjustments and optimizations can be made between shots. Additionally, for the first time the analysis of many shots will be possible