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Immobilized fibronectin and other natural proteins have been utilized to enhance substrate-mediated gene delivery, with apparent contributions from the intrinsic bioactivity and also physical properties of the immobilized proteins. In this report, we investigated the use of recombinant proteins, compared to the full-length fibronectin protein, as surface coatings for gene delivery to investigate the mechanisms by which fibronectin enhances gene transfer. The recombinant fibronectin fragment FNIII7−10(FNIII) contains the α5β1binding domain of fibronectin and supports cell adhesion, whereas the recombinant protein polymer PP-12 is also negatively charged and has a molecular weight similar to FNIII, but lacks cell binding domains. Transfection was compared on surfaces modified with FNIII, full-length fibronectin, or PP-12. The full-length fibronectin provided the greatest extent of transgene expression relative to FNIII or PP-12, which was consistent with the amount of DNA that associated with cells. FNIII had 4.2-fold or 4.7-fold lower expression levels relative to fibronectin for polyplexes and lipoplexes, respectively. PP-12 produced expression levels that were 317-fold and 12.0-fold less than fibronectin for polyplexes and lipoplexes, respectively. Although expression was greater on FNIII relative to PP-12, the levels of DNA associated per cell with FNIII were similar to or less than those with PP-12, suggesting that the bioactive sequences may contribute to an enhanced intracellular trafficking. For lipoplexes delivered on FNIII, the efficiency of intracellular trafficking and levels of caveolar DNA were greater than that observed with either the full-length fibronectin or PP-12. For polyplexes, fibronectin fragment resulted in greater intracellular trafficking efficiency compared to PP-12 protein polymer. Recombinant proteins can be employed in place of full-length extracellular matrix proteins for substrate-mediated gene delivery, and bioactive sequences can influence one or more steps in the gene delivery process to maximize transfection. [ABSTRACT FROM AUTHOR]

A disruption prediction algorithm, called disruption prediction using random forests (DPRF), has run in real-time in the DIII-D plasma control system (PCS) for more than 900 discharges. DPRF naturally provides a probability mapping associated with its predictions, i.e. the disruptivity signal, now incorporated in the DIII-D PCS. This paper discusses disruption prediction accomplishments in terms of shot-by-shot performances, by simulating alarms on each discharge as in the PCS framework. Depending on the optimised performance metric chosen to evaluate DPRF, we find that almost all disruptive discharges are detected on average with a few hundred milliseconds of warning time, but this comes at a high cost of false alarms produced. Performances do not satisfy ITER requirements, where the success rate has to be higher than 95%, but this is not completely unexpected. DPRF is trained on many years of major disruptions occurring during the flattop phase of the plasma current in DIII-D, but without any differentiation by cause. Furthermore, we find that DPRF is affected by a relatively high fraction of false alarms occurring during the first 500 milliseconds from the flattop onset. This subtle effect, more evident on discharges where DPRF is run in real-time, can be marginalised by taking specific precautions on the validity range of the predictions, and performances do improve. Even if presently burdened by some limitations, DPRF provides an incredible and novel advantage. Thanks to the feature contribution analysis (e.g. the identification of which signals contributed to triggering an alarm), it is possible to interpret and explain DPRF predictions. It is the first time that such interpretability features are exploited by a disruption predictor: by uncovering the causes of the disruption events, a better understanding of disruption dynamics is achieved, and a clear path toward the design of disruption avoidance strategies can be provided. [ABSTRACT FROM AUTHOR]

This paper reports on disruption prediction using a shallow machine learning method known as a random forest, trained on large databases containing only plasma parameters that are available in real-time on Alcator C-Mod, DIII-D, and EAST. The database for each tokamak contains parameters sampled  ∼106 times throughout  ∼104 discharges (disruptive and non-disruptive) over the last four years of operation. It is found that a number of parameters (e.g. , , , ) exhibit changes in aggregate as a disruption is approached on one or more of these tokamaks. However, for each machine, the most useful parameters, as well as the details of their precursor behaviors, are markedly different. When the prediction problem is framed using a binary classification scheme to discriminate between time slices ‘close to disruption’ and ‘far from disruption’, it is found that the prediction algorithms differ substantially in performance among the three machines on a time slice-by-time slice basis, but have similar disruption detection rates (∼80%–90%) on a shot-by-shot basis after appropriate optimisation. This could have important implications for disruption prediction and avoidance on ITER, for which development of a training database of disruptions may be infeasible. The algorithm’s output is interpretable using a method that identifies the most strongly contributing input signals, which may have implications for avoiding disruptive scenarios. To further support its real-time capability, successful applications in inter-shot and real-time environments on EAST and DIII-D are also discussed. [ABSTRACT FROM AUTHOR]

Using data-driven methodology, we exploit the time series of relevant plasma parameters for a large set of disrupted and non-disrupted discharges to develop a classification algorithm for detecting disruptive phases in shots that eventually disrupt. Comparing the same methodology on different devices is crucial in order to have information on the portability of the developed algorithm and the possible extrapolation to ITER. Therefore, we use data from two very different tokamaks, DIII-D and Alcator C-Mod. We focus on a subset of disruption predictors, most of which are dimensionless and/or machine-independent parameters, coming from both plasma diagnostics and equilibrium reconstructions, such as the normalized plasma internal inductance ℓi and the n = 1 mode amplitude normalized to the toroidal magnetic field. Using such dimensionless indicators facilitates a more direct comparison between DIII-D and C-Mod. We then choose a shallow Machine Learning technique, called Random Forests, to explore the databases available for the two devices. We show results from the classification task, where we introduce a time dependency through the definition of class labels on the basis of the elapsed time before the disruption (i.e. ‘far from a disruption’ and ‘close to a disruption’). The performances of the different Random Forest classifiers are discussed in terms of several metrics, by showing the number of successfully detected samples, as well as the misclassifications. The overall model accuracies are above 97% when identifying a ‘far from disruption’ and a ‘disruptive’ phase for disrupted discharges. Nevertheless, the Forests are intrinsically different in their capability of predicting a disruptive behavior, with C-Mod predictions comparable to random guesses. Indeed, we show that C-Mod recall index, i.e. the sensitivity to a disruptive behavior, is as low as 0.47, while DIII-D recall is ∼0.72. The portability of the developed algorithm is also tested across the two devices, by using DIII-D data for training the forests and C-Mod for testing and vice versa. [ABSTRACT FROM AUTHOR]

Three-dimensional non-axisymmetric magnetic fields are purposely applied to toroidally symmetric fusion plasmas in order to modify transport of heat and particle fluxes to the plasma-facing components (PFCs), and specifically to control and suppress edge localized modes (ELMs) in H-mode tokamak plasmas. This paper presents a comparative study between two magnetic configurations available in the RFX-mod device, namely the original reversed-field pinch configuration and the more recent low-field circular ohmic tokamak. The paper presents a detailed analysis of the modulation of electrostatic turbulence and induced transport in the presence of an externally applied magnetic perturbation, as measured in both configurations. A detailed study of the underlying magnetic topology in both configurations is given through the field line tracing code FLiT. The electrostatic turbulence induced flux is observed to be modulated by the underlying topology, with an enhancement close to the O-point and a reduction at the X-point of the induced magnetic island. The transport modification are discussed in terms of relevant frequency and wavenumber spectrum. [ABSTRACT FROM AUTHOR]

A complete description of the effects of magnetic perturbation on the edge region of RFX-mod is here reported. The flexibility of the RFX-mod device [1] allows for the operation of the machine both as a reversed field pinch (RFP, with maximum current 2 MA) and as a low-current, circular ohmic tokamak (Ip,max = 0.15 MA). The present paper summarizes the most recent results obtained in both configurations with either spontaneous or induced edge radial magnetic perturbation. Emphasis will be devoted to the experimental characterization of the edge flow, focusing on the phase relation between flow and perturbed magnetic field. These informations are provided for natural and stimulated helical discharges in RFPs, and for tokamak safely operated, thanks to the unique RFX-mod MHD control system, in a wide range of edge safety factor 1.9 ≲ q(a) ⩽ 3.4 with externally imposed helical boundary. For the first time a detailed comparison between this phenomenology in tokamaks and RFPs will be presented, providing experimental measurement of the streamline of E × B flow around the magnetic perturbation and of the density modulation which exhibits the same periodicity of the perturbation. Strong new indication of the modification of the small scale turbulence in presence of magnetic perturbation is reported: this modification is deeply connected to the variation of turbulence induced particle transport. [ABSTRACT FROM AUTHOR]

One of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan–Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trained and tested on these databases. This survival analysis approach is applied to both smooth and noisy test data to highlight important features of the survival and hazard functions. In addition, this method is applied to three Alcator C-Mod plasma discharges and compared to a threshold-based scheme for triggering alarms. In one case, both techniques successfully predict the disruption; although, in another, neither warns of the impending disruption with enough time to mitigate. For the final discharge, the survival analysis approach could avoid the false alarm triggered by the threshold method. Limitations of this analysis and opportunities for future work are also presented. [ABSTRACT FROM AUTHOR]

The phenomenology of reconnection events, associated to relaxations in high-current (∼1.5 MA) plasmas of the reversed-field pinch device RFX–mod, is shown. Each relaxation event can be described as a series of stages starting in the core and propagating towards the plasma edge. In an initial stage (trigger), the evolution of the q profile brings resonant layers closer together, allowing for an interaction of the current sheets associated to tearing modes at the respective rational values of q. The phase locking of the resonant modes then initiates the reconnection process that, once started, changes the magnetic topology bringing the initially helical state to a more chaotic configuration. [ABSTRACT FROM AUTHOR]

Filament or blob structures have been observed in all magnetic configurations with very similar features despite the difference in the magnetic geometry, and are believed to play an important role in convecting particles and energy towards the wall. Despite their different generation mechanism, turbulent structures and edge-localized mode (ELM) filaments share some common physical features. The electromagnetic effects on filament structures deserve particular interest, among others reasons for the implication they could have for ELM, related for instance to their dynamics in the transition region between closed and open field lines or to the possibility, at high beta regimes, of causing line bending which could enhance the interaction of blobs with the first wall.A direct characterization of the effects of active modification of the edge topology on EM turbulent filament structures is presented, comparing reversed field pinch and tokamak configurations. Measurements are obtained in the RFX-mod device, which allows operation in both configurations and with different equilibria. The RFX-mod experiment versatility is exploited also from the point of view of the active control of the edge magnetic topology, equipped with an advanced system for edge boundary feedback control. Three different case studies of actively controlled magnetic perturbations are shown, focusing on the filament interaction with local magnetic islands. High-frequency fluctuations, characterizing electrostatic and magnetic filament features, and the associated transport coefficients have been observed to be strongly affected by the island proximity and topology. [ABSTRACT FROM AUTHOR]