CNN disruption predictor at JET: Early versus late data fusion approach

This work focuses on the development of a data driven model, based on Convolutional Neural Networks (CNNs), for the real-time detection of disruptive events at JET. The predictor exploits the ability of CNNs in learning relevant spatiotemporal information straight from 1D plasma profiles, avoiding hand-engineered feature extraction procedures. In this paper, the radiation profiles from both the bolometer horizontal and vertical cameras have been considered amongst the predictor inputs, with the aim of discriminating between the core radiation due to impurity accumulations and the outboard radiation phenomena. Moreover, an innovative predictor architecture is proposed, where two separate CNNs are trained to focus on events with different timescales, that is, the destabilization of radiation, electron density and temperature profiles, and the mode-locking and current profile variations. The outputs of the two CNNs are combined with a logic OR function to provide the disruption alarm trigger. The advantages of this data fusion approach impact on the predictor performance, with a very limited number of false alarms (only 1 in the considered test set), and on the model output interpretability as the two different branches are triggered by different types of events

Effects of Uncertainty of Outlet Boundary Conditions in a Patient-Specific Case of Aortic Coarctation

Computational Fluid Dynamics (CFD) simulations of blood flow are widely used to compute a variety of hemodynamic indicators such as velocity, time-varying wall shear stress, pressure drop, and energy losses. One of the major advances of this approach is that it is non-invasive. The accuracy of the cardiovascular simulations depends directly on the level of certainty on input parameters due to the modelling assumptions or computational settings. Physiologically suitable boundary conditions at the inlet and outlet of the computational domain are needed to perform a patient-specific CFD analysis. These conditions are often affected by uncertainties, whose impact can be quantified through a stochastic approach. A methodology based on a full propagation of the uncertainty from clinical data to model results is proposed here. It was possible to estimate the confidence associated with model predictions, differently than by deterministic simulations. We evaluated the effect of using three-element Windkessel models as the outflow boundary conditions of a patient-specific aortic coarctation model. A parameter was introduced to calibrate the resistances of the Windkessel model at the outlets. The generalized Polynomial Chaos method was adopted to perform the stochastic analysis, starting from a few deterministic simulations. Our results show that the uncertainty of the input parameter gave a remarkable variability on the volume flow rate waveform at the systolic peak simulating the conditions before the treatment. The same uncertain parameter had a slighter effect on other quantities of interest, such as the pressure gradient. Furthermore, the results highlight that the fine-tuning of Windkessel resistances is not necessary to simulate the post-stenting scenario

Comparison of Safety of RADial comPRESSion Devices: A Multi-Center Trial of Patent Hemostasis following Percutaneous Coronary Intervention from Conventional Radial Access (RAD-PRESS Trial)

Although radial access is the current gold standard for the implementation of percutaneous coronary interventions (PCI), post-procedural radial compression devices are seldom compared with each other in terms of safety or efficacy. Our group aimed to compare a cost effective and potentially green method to dedicated radial compression devices, with respect to access site complications combined in a device oriented complex endpoint (DOCE), freedom from which served as our primary endpoint. Patients undergoing PCI were randomized to receive either the cost effective or a dedicated device, either of which were removed using patent hemostasis. Twenty-four hours after the procedure, radial artery ultrasonography was performed to evaluate the access site. The primary endpoint was assessed using a non-inferiority framework with a non-inferiority margin of five percentage points, which was considered as the least clinically meaningful difference. The cost-effective technique and the dedicated devices were associated with a comparably low rate of complications (freedom from DOCE: 83.3% vs. 70.8%, absolute risk difference: 12.5%, one-sided 95% confidence interval (CI): 1.11%). Composition of the DOCE (i.e., no complication, hematoma, pseudoaneurysm, and radial artery occlusion) and compression time were also assessed in superiority tests as secondary endpoints. Both the cost-effective technique and the dedicated devices were associated with comparably low rates of complications: p = 0.1289. All radial compression devices performed similarly when considering the time to complete removal of the respective device (120.0 (inter-quartile range: 100.0–142.5) for the vial vs. 120.0 (inter-quartile range: 110.0–180) for the dedicated device arm, with a median difference of [95% CI]: 7.0 [−23.11 to 2.00] min, p = 0.2816). In conclusion, our cost-effective method was found to be non-inferior to the dedicated devices with respect to safety, therefore it is a safe alternative to dedicated radial compression devices, as well as seeming to be similarly effective

eXplainable artificial intelligence applied to algorithms for disruption prediction in tokamak devices

Introduction: This work explores the use of eXplainable artificial intelligence (XAI) to analyze a convolutional neural network (CNN) trained for disruption prediction in tokamak devices and fed with inputs composed of different physical quantities.Methods: This work focuses on a reduced dataset containing disruptions that follow patterns which are distinguishable based on their impact on the electron temperature profile. Our objective is to demonstrate that the CNN, without explicit training for these specific mechanisms, has implicitly learned to differentiate between these two disruption paths. With this purpose, two XAI algorithms have been implemented: occlusion and saliency maps.Results: The main outcome of this paper comes from the temperature profile analysis, which evaluates whether the CNN prioritizes the outer and inner regions.Discussion: The result of this investigation reveals a consistent shift in the CNN's output sensitivity depending on whether the inner or outer part of the temperature profile is perturbed, reflecting the underlying physical phenomena occurring in the plasma

A Proof of Concept of a Non-Invasive Image-Based Material Characterization Method for Enhanced Patient-Specific Computational Modeling

PURPOSE: Computational models of cardiovascular structures rely on their accurate mechanical characterization. A validated method able to infer the material properties of patient-specific large vessels is currently lacking. The aim of the present study is to present a technique starting from the flow-area (QA) method to retrieve basic material properties from magnetic resonance (MR) imaging. METHODS: The proposed method was developed and tested, first, in silico and then in vitro. In silico, fluid-structure interaction (FSI) simulations of flow within a deformable pipe were run with varying elastic modules (E) between 0.5 and 32 MPa. The proposed QA-based formulation was assessed and modified based on the FSI results to retrieve E values. In vitro, a compliant phantom connected to a mock circulatory system was tested within MR scanning. Images of the phantom were acquired and post-processed according to the modified formulation to infer E of the phantom. Results of in vitro imaging assessment were verified against standard tensile test. RESULTS: In silico results from FSI simulations were used to derive the correction factor to the original formulation based on the geometrical and material characteristics. In vitro, the modified QA-based equation estimated an average E = 0.51 MPa, 2% different from the E derived from tensile tests (i.e. E = 0.50 MPa). CONCLUSION: This study presented promising results of an indirect and non-invasive method to establish elastic properties from solely MR images data, suggesting a potential image-based mechanical characterization of large blood vessels

Photochemically induced isomerisation in ruthenium polypyridyl complexes

The synthesis and characterisation of a series of ruthenium polypyridyl complexes containing pyridyltriazole ligands in different coordination modes are described. The electrochemical and electronic properties of the compounds are reported and discussed with respect to the coordination mode of the pyridyltriazole ligand. Upon photolysis of the complex containing the 1-methyl-3-(pyridin-2-yl)-1,2,4-triazole ligand irreversible ligand isomerisation is observed