Determining stress and strain in a component of a fusion power plant involves defining boundary conditions for the mechanical equilibrium equations, which implies the availability of a full reactor model for defining those conditions. To address this fundamental challenge of reactor design, a finite element method model for the Mega-Ampere Spherical Tokamak Upgrade fusion tokamak, operating at the Culham Campus of UKAEA, has been developed and applied to assess mechanical deformations, strain, and stress in the full tokamak structure, taken as a proxy for a fusion power plant. The model, handling 127 million finite elements using about 800 processors in parallel, illustrates the level of fidelity of structural simulations of a complex nuclear device made possible by the modern supercomputing systems. The model predicts gravitational and atmospheric pressure-induced deformations in broad agreement with observations, and enables computing the spectrum of acoustic vibrations of a tokamak, arising from mechanical disturbances like an earthquake or a plasma disruption. We introduce the notion of the density of stress to characterise the distribution of stress in the entire tokamak structure, and to predict the magnitude and locations of stress concentrations. The model enables defining computational requirements for simulating a whole operating fusion power plant, and provides a digital foundation for the assessment of reactor performance as well as for specifying the relevant materials testing programme.

Recent results from MAST Upgrade are presented, emphasising understanding the capabilities of this new device and deepening understanding of key physics issues for the operation of ITER and the design of future fusion power plants. The impact of MHD instabilities on fast ion confinement have been studied, including the first observation of fast ion losses correlated with Compressional and Global Alfvén Eigenmodes. High-performance plasma scenarios have been developed by tailoring the early plasma current ramp phase to avoid internal reconnection events, resulting in a more monotonic q profile with low central shear. The impact of m / n = 3/2, 2/1 and 1/1 modes on thermal plasma confinement and rotation profiles has been quantified, and scenarios optimised to avoid them have transiently reached values of normalised beta approaching 4.2. In pedestal and ELM physics, a maximum pedestal top temperature of ∼350 eV has been achieved, exceeding the value achieved on MAST at similar heating power. Mitigation of type-I ELMs with n = 1 RMPs has been observed. Studies of plasma exhaust have concentrated on comparing conventional and Super-X divertor configurations, while X-point target, X-divertor and snowflake configurations have been developed and studied in parallel. In L-mode discharges, the separatrix density required to detach the outer divertors is approximately a factor 2 lower in the Super-X than the conventional configuration, in agreement with simulations. Detailed analysis of spectroscopy data from studies of the Super-X configuration reveal the importance of including plasma-molecule interactions and D _2 Fulcher band emission to properly quantify the rates of ionisation, plasma-molecule interactions and volumetric recombination processes governing divertor detachment. In H-mode with conventional and Super-X configurations, the outer divertors are attached in the former and detached in the latter with no impact on core or pedestal confinement.

Mucociliary clearance is an essential defence mechanism against chronic airway infection and inflammation. Defects in ciliary motility are either primary, as in primary ciliary dyskinesia (PCD), or secondary. Identification of mucociliary clearance defects allows the implementation of appropriate management. High-speed video-microscopy (HSVM) is used to assess cilia motility from nasal biopsy samples. It is a time consuming and subjective requiring significant expertise. Computer vision can improve the identification of cilia motility defects by minimising subjectivity and reducing the cost and time to analyse samples. Using an artificial intelligence platform (Intel® Geti™), we have trained several models using archived HSVM videos from patients referred to the Royal Brompton Hospital who were diagnosed with PCD and display a range of ciliary motility phenotypes and non-PCD controls. The videos used are converted to optical flow to provide temporal information to the machine learning algorithm. We are training the platform to classify different categories of beat pattern: Immotile, Normal, Reduced Amplitude and Rotation. Models also include assessing sample quality and cilia beating orientation. The preliminary data based on projects currently in development are promising: the model classifying normal beating vs immotile cilia (around 30,000 frames) has a predictive accuracy of 100% and the beat pattern recognition model (around 25,000 frames) has a predictive accuracy of 97%. Further training and testing are ongoing, and more models are being developed to include a greater range of motility phenotypes and to encompass chronic inflammatory lung diseases.

Early and accurate diagnosis of Primary Ciliary Dyskinesia (PCD) allows appropriate multidisciplinary management and a reduction in lung function decline. Transmission Electron Microscopy (TEM) is essential in determining ciliary ultrastructural defects, when diagnosing PCD. This requires highly skilled specialists with considerable experience. Machine learning provides an excellent opportunity to reduce the time experts spend assessing cilia (1–2 hours) and improve accuracy of diagnosis. In collaboration with Intel®, we have used an Artificial Intelligence platform (Intel® Geti™), to develop a workflow called PCD-AID (PCD- Artificial Intelligence Diagnosis) that uses computer vision to aid in the diagnosis of PCD. This work is part of an organised ERS Clinical Research Collaboration with BEAT-PCD. The system was tested alongside the PCD diagnostic pathway (n=158) to determine diagnostic accuracy. The model has been trained with TEM images from over 21,000 cilia cross-sections to detect cilia and then classify them based on normal or abnormal ultrastructure or 'unusable' for diagnostic purposes (tilted or distorted images). Using retrospective and prospective patient samples, we have found PCD-AID can reliably identify ciliary ultrastructural defects (sensitivity of 0.87 and specificity of 0.88) and assess TEM images in under 1 minute per patient. It has good agreement with diagnostic specialists (> 75%) at identifying a range of ultrastructural defects and strikingly outperforms specialists at identifying subtle central pair defects associated with pathogenic mutations in HYDIN. Implementing computer vision artificial intelligence in the diagnostic pathway improved diagnosis of PCD.

Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulnerable to adversarial attacks due to strong financial incentives and the associated technological infrastructure. In this paper, we study previously unexplored factors affecting adversarial attack vulnerability of deep learning MedIA systems in three medical domains: ophthalmology, radiology, and pathology. We focus on adversarial black-box settings, in which the attacker does not have full access to the target model and usually uses another model, commonly referred to as surrogate model, to craft adversarial examples. We consider this to be the most realistic scenario for MedIA systems. Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model. Secondly, we study the influence of differences in development data between target and surrogate models. We further study the interaction of weight initialization and data differences with differences in model architecture. All experiments were done with a perturbation degree tuned to ensure maximal transferability at minimal visual perceptibility of the attacks. Our experiments show that pre-training may dramatically increase the transferability of adversarial examples, even when the target and surrogate's architectures are different: the larger the performance gain using pre-training, the larger the transferability. Differences in the development data between target and surrogate models considerably decrease the performance of the attack; this decrease is further amplified by difference in the model architecture. We believe these factors should be considered when developing security-critical MedIA systems planned to be deployed in clinical practice.

Adversarial attacks consist in maliciously changing the input data to mislead the predictions of automated decision systems and are potentially a serious threat for automated medical image analysis. Previous studies have shown that it is possible to adversarially manipulate automated segmentations produced by neural networks in a targeted manner in the white-box attack setting. In this article, we studied the effectiveness of adversarial attacks in targeted modification of segmentations of anatomical structures in chest X-rays. Firstly, we experimented with using anatomically implausible shapes as targets for adversarial manipulation. We showed that, by adding almost imperceptible noise to the image, we can reliably force state-of-the-art neural networks to segment the heart as a heart symbol instead of its real anatomical shape. Moreover, such heart-shaping attack did not appear to require higher adversarial noise level than an untargeted attack based the same attack method. Secondly, we attempted to explore the limits of adversarial manipulation of segmentations. For that, we assessed the effectiveness of shrinking and enlarging segmentation contours for the three anatomical structures. We observed that adversarially extending segmentations of structures into regions with intensity and texture uncharacteristic for them presented a challenge to our attacks, as well as, in some cases, changing segmentations in ways that conflict with class adjacency priors learned by the target network. Additionally, we evaluated performances of the untargeted attacks and targeted heart attacks in the black-box attack scenario, using a surrogate network trained on a different subset of images. In both cases, the attacks were substantially less effective. We believe these findings bring novel insights into the current capabilities and limits of adversarial attacks for semantic segmentation.

In magnetic fusion devices, unwanted non-axisymmetric magnetic field perturbations, known as error fields (EF), can have detrimental effects on plasma stability and confinement. Such EFs may originate from several sources, such as axi-symmetric coil misalignments, coil feeds, 3D structures in the wall surrounding the plasma, presence of ferromagnetic materials near the plasma surface, blanket materials in future devices. To minimize their impact on plasma performance and on the available operational space, it is important to identify the EF sources and develop EF control strategies. MAST Upgrade (MAST-U) is a spherical tokamak which will operate in the near future after a series of enhancements from the previous MAST experiment (Morris A.W. et al., MAST accomplishments and upgrade for fusion next-steps. IEEE Transactions on Plasma Science, April 2014). To deliver a machine with EF amplitude low enough to allow a high quality experimental programme, systematic analysis of the intrinsic EF sources has been carried out for poloidal field (P) and divertor (D) coils. To deliver a machine with EF amplitude low enough to allow a high quality experimental programme, systematic analysis of the intrinsic EF sources has been carried out for poloidal field (P) and divertor (D) coils, whose magnetic field measurements were available when writing this paper. Such measurements reveal that P and D coils are 3D deformed and thus are responsible for intrinsic EFs, with mainly n = 1 and n = 2 toroidal mode numbers. In preparation to MAST-U operation, both passive and active EF control strategies have been adopted for n = 1 and n = 2 EFs compensation. Passive EF control consisted of installing finely each D and P coil within MAST-U device so as to minimize the intrinsic n = 1 EF amplitude. The optimal coil alignment has been determined based on magnetic field measurements and the corresponding 3D electro-magnetic modelling, and envisaged coil shift and tilt of 3.2 mm and 0.7 mrad, respectively, in the case of P coil named P4. Conversely, active control will be use during MAST-U operation to reduce the n = 2 EF which is associated mainly with P4 and P5, as well (Kirk A. et al. 2014 Plasma Phys. Control. Fusion 56, 104003). Since these coils have been re-used from the MAST device, studies attempting n = 2 EF control, based on MAST plasmas, have been modelled utilizing the MARS-F code (Liu Y.Q. et al. 2000 Phys. Plasmas 7, 3681) to interpret experimental results in MAST and to give hints for future n = 2 EF control studies in MAST-U. A model-based control set for n = 2 EF control has been identified which would allow for the minimization of rotation braking, of the resonant magnetic field at th = agand of the plasma displacement in MAST-U, simultaneously.

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Modern optical flow methods make use of salient scene feature points detected and matched within the scene as a basis for sparse-to-dense optical flow estimation. Current feature detectors however either give sparse, non uniform point clouds (resulting in flow inaccuracies) or lack the efficiency for frame-rate real-time applications. In this work we use the novel Dense Gradient Based Features (DeGraF) as the input to a sparse-to-dense optical flow scheme. This consists of three stages: 1) efficient detection of uniformly distributed Dense Gradient Based Features (DeGraF) [1]; 2) feature tracking via robust local optical flow [2]; and 3) edge preserving flow interpolation [3] to recover overall dense optical flow. The tunable density and uniformity of DeGraF features yield superior dense optical flow estimation compared to other popular feature detectors within this three stage pipeline. Furthermore, the comparable speed of feature detection also lends itself well to the aim of real-time optical flow recovery. Evaluation on established real-world benchmark datasets show test performance in an autonomous vehicle setting where DeGraF-Flow shows promising results in terms of accuracy with competitive computational efficiency among non-GPU based methods, including a marked increase in speed over the conceptually similar EpicFlow approach [3].

The scarcity of labeled data often limits the application of supervised deep learning techniques for medical image segmentation. This has motivated the development of semi-supervised techniques that learn from a mixture of labeled and unlabeled images. In this paper, we propose a novel semi-supervised method that, in addition to supervised learning on labeled training images, learns to predict segmentations consistent under a given class of transformations on both labeled and unlabeled images. More specifically, in this work we explore learning equivariance to elastic deformations. We implement this through: (1) a Siamese architecture with two identical branches, each of which receives a differently transformed image, and (2) a composite loss function with a supervised segmentation loss term and an unsupervised term that encourages segmentation consistency between the predictions of the two branches. We evaluate the method on a public dataset of chest radiographs with segmentations of anatomical structures using 5-fold cross-validation. The proposed method reaches significantly higher segmentation accuracy compared to supervised learning. This is due to learning transformation consistency on both labeled and unlabeled images, with the latter contributing the most. We achieve the performance comparable to state-of-the-art chest X-ray segmentation methods while using substantially fewer labeled images.

The mega amp spherical tokamak (MAST) was a low aspect ratio device (R/a = 0.85/0.65 similar to 1.3) with similar poloidal cross-section to other medium-size tokamaks. The physics programme concentrates on addressing key physics issues for the operation of ITER, design of DEMO and future spherical tokamaks by utilising high resolution diagnostic measurements closely coupled with theory and modelling to significantly advance our understanding. An empirical scaling of the energy confinement time that favours higher power, lower collisionality devices is consistent with gyrokinetic modelling of electron scale turbulence. Measurements of ion scale turbulence with beam emission spectroscopy and gyrokinetic modelling in up-down symmetric plasmas find that the symmetry of the turbulence is broken by flow shear. Near the non-linear stability threshold, flow shear tilts the density fluctuation correlation function and skews the fluctuation amplitude distribution. Results from fast particle physics studies include the observation that sawteeth are found to redistribute passing and trapped fast particles injected from neutral beam injectors in equal measure, suggesting that resonances between the m = 1 perturbation and the fast ion orbits may be playing a dominant role in the fast ion transport. Measured D-D fusion products from a neutron camera and a charged fusion product detector are 40% lower than predictions from TRANSP/NUBEAM, highlighting possible deficiencies in the guiding centre approximation. Modelling of fast ion losses in the presence of resonant magnetic perturbations (RMPs) can reproduce trends observed in experiments when the plasma response and charge-exchange losses are accounted for. Measurements with a neutral particle analyser during merging-compression start-up indicate the acceleration of ions and electrons. Transport at the plasma edge has been improved through reciprocating probe measurements that have characterised a geodesic acoustic mode at the edge of an ohmic L-mode plasma and particle-in-cell modelling has improved the interpretation of plasma potential estimates from ball-pen probes. The application of RMPs leads to a reduction in particle confinement in L-mode and H-mode and an increase in the core ionization source. The ejection of secondary filaments following type-I ELMs correlates with interactions with surfaces near the X-point. Simulations of the interaction between pairs of filaments in the scrape-off layer suggest this results in modest changes to their velocity, and in most cases can be treated as moving independently. A stochastic model of scrape-off layer profile formation based on the superposition of non-interacting filaments is in good agreement with measured time-average profiles. Transport in the divertor has been improved through fast camera imaging, indicating the presence of a quiescent region devoid of filament near the X-point, extending from the separatrix to psi(n) similar to 1.02. Simulations of turbulent transport in the divertor show that the angle between the divertor leg on the curvature vector strongly influences transport into the private flux region via the interchange mechanism. Coherence imaging measurements show counter-streaming flows of impurities due to gas puffing increasing the pressure on field lines where the gas is ionised. MAST Upgrade is based on the original MAST device, with substantially improved capabilities to operate with a Super-X divertor to test extended divertor leg concepts. SOLPS-ITER modelling predicts the detachment threshold will be reduced by more than a factor of 2, in terms of upstream density, in the Super-X compared with a conventional configuration and that the radiation front movement is passively stabilised before it reaches the X-point. 1D fluid modelling reveals the key role of momentum and power loss mechanisms in governing detachment onset and evolution. Analytic modelling indicates that long legs placed at large major radius, or equivalently low B at the target compared with the X-point arc more amenable to external control. With MAST Upgrade experiments expected in 2019, a thorough characterisation of the sources of the intrinsic error field has been carried out and a mitigation strategy developed.

Recent studies have shown a worrying decline in the quantity and diversity of insects at a number of locations in Europe (Hallmann et al. 2017) and elsewhere (Lister and Garcia 2018). Although the downward trend that these studies show is clear, they are limited to certain insect groups and geographical locations. Most available studies (see overview in Sánchez-Bayo and Wyckhuys 2019) were performed in nature reserves, leaving rural and urban areas largely understudied. Most studies are based on the long-term collaborative efforts of entomologists and volunteers performing labor-intensive repeat measurements, inherently limiting the number of locations that can be monitored. We propose a monitoring network for insects in the Netherlands, consisting of a large number of smart insect cameras spread across nature, rural, and urban areas. The aim of the network is to provide a labor-extensive continuous monitoring of different insect groups. In addition, we aimed to develop the cameras at a relatively cheap price point so that cameras can be installed at a large number of locations and encourage participation by citizen science enthusiasts. The cameras are made smart with image processing, consisting of image enhancement, insect detection and species identification being performed, using deep learning based algorithms. The cameras take pictures of a screen, measuring ca. 30×40 cm, every 10 seconds, capturing insects that have landed on the screen (Fig. 1). Several screen setups were evaluated. Vertical screens were used to attract flying insects. Different screen colors and lighting at night, to attract night flying insects such as moths, were used. In addition two horizontal screen orientations were used (1) to emulate pan traps to attract several pollinator species (bees and hoverflies) and (2) to capture ground-based insects and arthropods such as beetles and spiders. Time sequences of images were analyzed semi-automatically, in the following way. First, single insects are outlined and cropped using boxes at every captured image. Then the cropped single insects in every image were preliminarily identified, using a previously developed deep-learning-based automatic species identification software, Nature Identification API (https://identify.biodiversityanalysis.nl). In the next step, single insects were linked between consecutive images using a tracking algorithm that uses screen position and the preliminary identifications. This step yields for every individual insect a linked series of outlines and preliminary identifications. The preliminary identifications for individual insects can differ between multiple captured images and were therefore combined into one identification using a fusing algorithm. The result of the algorithm is a series of tracks of individual insects with species identifications, which can be subsequently translated into an estimate of the counts of insects per species or species complexes. Here we show the first set of results acquired during the spring and summer of 2019. We will discuss practical experiences with setting up cameras in the field, including the effectiveness of the different set-ups. We will also show the effectiveness of using automatic species identification in the type of images that were acquired (see attached figure) and discuss to what extent individual species can be identified reliably. Finally, we will discuss the ecological information that can be extracted from the smart insect cameras.

In magnetic fusion devices, unwanted nonaxisymmetric magnetic field perturbations, known as error fields (EF), can have detrimental effects on plasma stability and confinement. To minimize their impact on plasma performance and on the available operational space, it is important to identify the EF sources and develop EF control strategies. MAST Upgrade is a spherical tokamak which will operate in the near future after a series of enhancements from the previous MAST experiment and will contribute to the programme of the exhaust and fast particle physics [Morris A. W. et al, ”MAST Accomplishments and Upgrade for Fusion Next-Steps” IEEE Transactions on Plasma Science, April 2014]. To deliver a machine with EF amplitude low enough to allow a high quality experimental programme, a careful analysis of the intrinsic EF sources in the poloidal field and in the divertor coils has been carried out. The 3D coil deformation has been characterized through high accuracy magnetic field measurements which reveal that the main EF harmonics have n=1 and n=2 toroidal mode numbers. Passive and active control strategies have been adopted to compensate them in preparation for MAST Upgrade operations. The passive EF control consisted in finding the optimal fine-scale coil alignment so as to minimize the intrinsic n=1 EF amplitude. The optimal coil alignment has been determined based on magnetic field measurements and the corresponding 3D electro-magnetic modelling. Conversely, active control will be adopted during MAST Upgrade operation to reduce the n=2 EF which is associated mainly to the manufacturing of the poloidal field coils named P4 and P5 [Kirk A. et al 2014 Plasma Phys. Control. Fusion 56 104003]. Since these coils have been re-used from the MAST device, the experience gained on n=2 EF control, from experimental studies and the corresponding ERGOS [Nardon E. et al 2007 J. Nucl. Mater. 363-365 1071] and MARS-F [Liu Y. Q. et al 2000 Phys. Plasmas 7 3681] modelling, has allowed the identification of the best n=2 EF control technique and to perform a modelbased optimization of its control parameters in preparation to MAST Upgrade operation.

We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1-5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and loss combined improve the performance substantially (8% ICC) compared to a more conventional regression network. We outperform traditional lung densitometry and two recently published methods for emphysema quantification by a large margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance. Moreover, our method generates emphysema segmentations that predict the spatial distribution of emphysema at human level.

We propose a computationally efficient approach for the extraction of dense gradient-based features based on the use of localized intensity-weighted centroids within the image. Whilst prior work concentrates on sparse feature derivations or computationally expensive dense scene sensing, we show that Dense Gradient-based Features (DeGraF) can be derived based on initial multi-scale division of Gaussian preprocessing, weighted centroid gradient calculation and either local saliency (DeGraF-α) or signal-to-noise inspired (DeGraF-β) final stage filtering. We present two variants (DeGraF-α / DeGraF-β) of which the signal-to-noise based approach is shown to perform admirably against the state of the art in terms of feature density, computational efficiency and feature stability. Our approach is evaluated under a range of environmental conditions typical of automotive sensing applications with strong feature density requirements.

The Mega Amp Spherical Tokamak (MAST) is the centre piece of the UK fusion research programme. In 2010, a MAST Upgrade programme was initiated with three primary objectives, to contribute to: 1) Testing reactor concepts (in particular exhaust solutions via a flexible divertor allowing Super-X and other extended leg configurations); 2) Adding to the knowledge base for ITER (by addressing important plasma physics questions and developing predictive models to help optimise ITER performance of ITER) and 3) Exploring the feasibility of using a spherical tokamak as the basis for a fusion Component Test Facility. With the project mid-way through its construction phase, progress will be reported on a number of the critical subsystems. This will include manufacture and assembly of the coils, armour and support structures that make up the new divertors, construction of the new set coils that make up the centre column, installation of the new power supplies for powering the divertor coils and enhanced TF coil set, progress in delivering the upgraded diagnostic capability, the modification and upgrading of the NBI heating systems and the complete overhaul of the machine control infrastructure, including a new control room with full remote participation facilities.

The Mega Amp Spherical Tokamak (MAST) program and a major upgrade are aimed at using MASTs capabilities and parameter ranges to address specific issues for ITER physics, help find solutions for DEMO (in particular plasma exhaust), and develop a credible physics basis for possible ST-based Component Test Facilities/Fusion Nuclear Science Facilities. MAST also provides an attractive access point for scientists and engineers joining fusion. The general approach is to combine experiments, theory, and modeling intimately to help create usable predictive models for future devices. Physics results include edge-localized mode (ELM) mitigation with the flexible resonant magnetic perturbation coils (up to n = 6), where smaller, more frequent ELMs are generated, challenging conventional 2-D stability theory. Gyrokinetic theory and models are combined with experiments in the areas of pedestal stability, pellet fueling optimization, and impurity transport. Fast particle stability and confinement are used to optimize scenarios and neutral beam geometry. Exhaust modeling includes turbulence processes that enhance cross-field transport to help ease the power handling. The first major phase of the upgrade program is well under way, to increase the toroidal field and the transformer flux swing and especially implement the flexible exhaust physics platform by means of 17 new poloidal field coils and a closed pumpable divertor. Changes in the neutral beam systems (off-axis as well as on-axis) and new diagnostics especially of the divertor complete the present upgrade scope.

A potentially important feature in a divertor design for a high-power tokamak is an extended and expanded divertor leg. The upgrade to MAST will allow a wide range of such divertor leg geometries to be produced, and hence will allow the roles of greatly increased connection length and flux expansion to be experimentally tested. This will include testing the potential of the Super-X configuration [1]. The design process for the upgrade has required analysis of producing and controlling the magnetic configurations, and has included consideration of the roles that divertor closure and increasing magnetic connection length will play.

Following the completion in May 2011 of the shutdown for the installation of the beryllium wall and the tungsten divertor, the first set of JET campaigns have addressed the investigation of the retention properties and the development of operational scenarios with the new plasma-facing materials. The large reduction in the carbon content (more than a factor ten) led to a much lower Zeff (1.2–1.4) during L- and H-mode plasmas, and radiation during the burn-through phase of the plasma initiation with the consequence that breakdown failures are almost absent. Gas balance experiments have shown that the fuel retention rate with the new wall is substantially reduced with respect to the C wall. The re-establishment of the baseline H-mode and hybrid scenarios compatible with the new wall has required an optimization of the control of metallic impurity sources and heat loads. Stable type-I ELMy H-mode regimes with H98,y2 close to 1 and βN ∼ 1.6 have been achieved using gas injection. ELM frequency is a key factor for the control of the metallic impurity accumulation. Pedestal temperatures tend to be lower with the new wall, leading to reduced confinement, but nitrogen seeding restores high pedestal temperatures and confinement. Compared with the carbon wall, major disruptions with the new wall show a lower radiated power and a slower current quench. The higher heat loads on Be wall plasma-facing components due to lower radiation made the routine use of massive gas injection for disruption mitigation essential.