Quasilinear gyrokinetic theory: A derivation of QuaLiKiz

In order to predict and analyze turbulent transport in tokamaks, it is important to model transport that arises from microinstabilities. For this task, quasilinear codes have been developed that seek to calculate particle, angular momentum, and heat fluxes both quickly and accurately. In this tutorial, we present a derivation of one such code known as QuaLiKiz, a quasilinear gyrokinetic transport code. The goal of this derivation is to provide a self-contained and complete description of the underlying physics and mathematics of QuaLiKiz from first principles. This work serves both as a comprehensive overview of QuaLiKiz specifically as well as an illustration for deriving quasilinear models in general.Comment: 52 page

Fast modeling of turbulent transport in fusion plasmas using neural networks

We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.Comment: 18 pages, 11 figures, Physics of Plasmas, ICDDPS 2019 conference pape

Use of GLP1 receptor agonists in early pregnancy and reproductive safety: a multicentre, observational, prospective cohort study based on the databases of six Teratology Information Services.

OBJECTIVES Glucagon-like peptide 1 receptor agonists (GLP1-RA) are indicated for the treatment of type 2 diabetes and more recently for weight loss. The aim of this study was to assess the risks associated with GLP1-RA exposure during early pregnancy. DESIGN This multicentre, observational prospective cohort study compared pregnancy outcomes in women exposed to GLP1-RA in early pregnancy either for diabetes or obesity treatment with those in two reference groups: (1) women with diabetes exposed to at least one non-GLP1-RA antidiabetic drug during the first trimester and (2) a reference group of overweight/obese women without diabetes, between 2009 and 2022. SETTING Data were collected from the databases of six Teratology Information Services. PARTICIPANTS This study included 168 pregnancies of women exposed to GLP1-RA during the first trimester, alongside a reference group of 156 pregnancies of women with diabetes and 163 pregnancies of overweight/obese women. RESULTS Exposure to GLP1-RA in the first trimester was not associated with a risk of major birth defects when compared with diabetes (2.6% vs 2.3%; adjusted OR, 0.98 (95% CI, 0.16 to 5.82)) or to overweight/obese (2.6% vs 3.9%; adjusted OR 0.54 (0.11 to 2.75)). For the GLP1-RA group, cumulative incidence for live births, pregnancy losses and pregnancy terminations was 59%, 23% and 18%, respectively. In the diabetes reference group, corresponding estimates were 69%, 26% and 6%, while in the overweight/obese reference group, they were 63%, 29% and 8%, respectively. Cox proportional cause-specific hazard models indicated no increased risk of pregnancy losses in the GLP1-RA versus the diabetes and the overweight/obese reference groups, in both crude and adjusted analyses. CONCLUSIONS This study offers reassurance in cases of inadvertent exposure to GLP1-RA during the first trimester of pregnancy. Due to the limited sample size, larger studies are required to validate these findings

Improving Data Collection in Pregnancy Safety Studies: Towards Standardisation of Data Elements in Pregnancy Reports from Public and Private Partners, A Contribution from the ConcePTION Project.

INTRODUCTION AND OBJECTIVE The ConcePTION project aims to improve the way medication use during pregnancy is studied. This includes exploring the possibility of developing a distributed data processing and analysis infrastructure using a common data model that could form a foundational platform for future surveillance and research. A prerequisite would be that data from various data access providers (DAPs) can be harmonised according to an agreed set of standard rules concerning the structure and content of the data. To do so, a reference framework of core data elements (CDEs) recommended for primary data studies on drug safety during pregnancy was previously developed. The aim of this study was to assess the ability of several public and private DAPs using different primary data sources focusing on multiple sclerosis, as a pilot, to map their respective data variables and definitions with the CDE recommendations framework. METHODS Four pregnancy registries (Gilenya, Novartis; Aubagio, Sanofi; the Organization of Teratology Information Specialists [OTIS]; Aubagio, Sanofi; the Dutch Pregnancy Drug Register, Lareb), two enhanced pharmacovigilance programmes (Gilenya PRIM, Novartis; MAPLE-MS, Merck Healthcare KGaA) and four Teratology Information Services (UK TIS, Jerusalem TIS, Zerifin TIS, Swiss TIS) participated in the study. The ConcePTION primary data source CDE includes 51 items covering administrative functions, the description of pregnancy, maternal medical history, maternal illnesses arising in pregnancy, delivery details, and pregnancy and infant outcomes. For each variable in the CDE, the DAPs identified whether their variables were: identical to the one mentioned in the CDE; derived; similar but with a divergent definition; or not available. RESULTS The majority of the DAP data variables were either directly taken (85%, n = 305/357, range 73-94% between DAPs) or derived by combining different variables (12%, n = 42/357, range 0-24% between DAPs) to conform to the CDE variables and definitions. For very few of the DAP variables, alignment with the CDE items was not possible, either because of divergent definitions (1%, n = 3/357, range 0-2% between DAPs) or because the variables were not available (2%, n = 7/357, range 0-4% between DAPs). CONCLUSIONS Data access providers participating in this study presented a very high proportion of variables matching the CDE items, indicating that alignment of definitions and harmonisation of data analysis by different stakeholders to accelerate and strengthen pregnancy pharmacovigilance safety data analyses could be feasible

Candidate positioning and voter choice.

T his article examines a fundamental aspect of democracy: the relationship between the policy positions of candidates and the choices of voters. Researchers have suggested three "A key characteristic of democracy," Dahl (1971, 1) noted, is the "responsiveness of the government to the preferences of its citizens." Two mechanisms play central roles in promoting responsiveness, thereby fostering congruence between the preferences of voters and the policy positions of candidates. Voters in a democracy can select candidates that represent their views, and candidates can compete for votes by strategically taking positions that appeal to the electorate. Both mechanisms are important; each depends on the criteria voters use to judge politicians on the issues. A lively debate has focused on three theories about how voters judge the policy stances of candidates. The first, proximity theory, assumes that citizens prefer candidates whose positions are closest to their own. For example, a voter who favors a 5% increase in government spending on health care will be happiest with a candidate who advocates the same level of spending. The more a candidate's position diverges from the voter's, the less satisfied the voter will feel. The presumed positive relationship between proximity and satisfaction, Michael Tomz is Associate Professor

Diagnosis and treatment of urticaria and angioedema: a worldwide perspective

Urticaria and angioedema are common clinical conditions representing a major concern for physicians and patients alike. The World Allergy Organization (WAO), recognizing the importance of these diseases, has contributed to previous guidelines for the diagnosis and management of urticaria. The Scientific and Clinical Issues Council of WAO proposed the development of this global Position Paper to further enhance the clinical management of these disorders through the participation of renowned experts from all WAO regions of the world. Sections on definition and classification, prevalence, etiology and pathogenesis, diagnosis, treatment, and prognosis are based on the best scientific evidence presently available. Additional sections devoted to urticaria and angioedema in children and pregnant women, quality of life and patient-reported outcomes, and physical urticarias have been incorporated into this document. It is expected that this article will supplement recent international guidelines with the contribution of an expert panel designated by the WAO, increasing awareness of the importance of urticaria and angioedema in medical practice and will become a useful source of information for optimum patient management worldwide

EUROfusion Integrated Modelling (EU-IM) capabilities and selected physics applications

International audienceRecent developments and achievements of the EUROfusion Code Development for Integrated Modelling project (WPCD), which aim is to provide a validated integrated modelling suite for the simulation and prediction of complete plasma discharges in any tokamak, are presented. WPCD develops generic complex integrated simulations, workflows, for physics applications, using the standardized European Integrated Modelling (EU-IM) framework. Selected physics applications of EU-IM workflows are illustrated in this paper

Turbulent transport in tokamak advanced scenarios

Nuclear fusion has the potential of providing a high baseline, environmentally friendly, and sustainable source of energy. A plasma consisting of the hydrogen isotopes deuterium and tritium, heated to temperatures of ~ 108 K, can sustain fusion reactions at a sufficiently high rate for energy production. In a reactor the plasma must be confined and insulated from the walls. The leading confinement concept is the tokamak, where the plasma is trapped in a toroidal chamber by helical magnetic fields. The confinement is then limited by turbulent transport, which leads to leakage of heat and particles at a rate higher than expected from collisional transport. The poloidal component of the helical field is produced by toroidal current in the plasma itself. This current is in standard operation induced by a transformer in the centre of torus, where the plasma loop acts as the secondary circuit. The use of a transformer is not ideal for reactor operation; the discharge pulse times are limited since the transformer current cannot be infinitely ramped. The underlying question motivating the work in this thesis is thus: can an achievable tokamak operational scenario be developed which allows for significantly longer pulses, compatible with reactor requirements? One such scenario developed in present-day tokamaks is the ‘hybrid-scenario’. The scenario operates at reduced plasma current compared with fully inductive scenarios, and has an increased non-inductive current fraction through increased external current drive. Importantly, hybrid scenarios display better confinement than expected from empirical scaling laws, which can compensate the confinement lost due to the reduced plasma current. This holds great promise for the extrapolation of this scenario to future machines, and may even pave the way towards a long-pulse reactor scenario. This thesis focuses on an a particular factor which may partially explain the improved confinement - the impact of the broad current profile which characterises hybrid scenarios, on the turbulent transport which limits plasma confinement. The particular instabilities studied are those driven by the ion temperature gradient (ITG). This the dominant source of turbulence in the tokamak regimes studied. The current profile characteristics are represented by the following quantities derived thereof: the q-profile and magnetic shear (ˆs). Hybrid scenario q-profiles are characterised by high ˆs/q at high radii and low-ˆs at low radii. The beneficial effects of increased ˆs/q at high radii for increasing ITG critical gradient thresholds are quantified in an extrapolation of the hybrid scenario to the ITER tokamak (currently under construction) by integrated modelling with the CRONOS suite of codes coupled to the GLF23 transport model for predicted heat transport. The confinement is optimised by tailoring the q-profile with external current drive sources. It is predicted that for a mix of off-axis neutral beam injection (NBI) and electron cyclotron current drive (ECCD), an ITER hybrid scenario satisfying q > 1, Q = 5, and t > 3000 s can be achieved for an edge transport barrier (pedestal) temperature of Tped > 4keV . The fusion gain factor is defined as Q = Pfus Pinput. It is also predicted that ion cyclotron resonance heating (ICRH) and lower hybrid current drive (LHCD) are not beneficial for the ITER hybrid scenario main burn phase, since they do not provide beneficial q-profile shaping. These results are validated versus experimental discharges. Two pairs of hybrid scenario discharges from both the JET and ASDEX-Upgrade tokamaks are analysed. Each pair is characterised by similar pedestal heights but differences in q-profile and core confinement. The degree to which the differences in core confinement can be attributed to the ˆs/q effect is studied. CRONOS and GLF23 are used for predictive simulations of the discharges, as in the ITER extrapolation. These predictions are then compared to the actual experimental data. The effect of ˆs/q can be isolated in the simulations since all the various parameters and profiles in the simulations can be interchanged independently. Particularly for the JET pair, it is found that ˆs/q can indeed explain a major component of the core confinement difference. However, including the rotational flow shear turbulence suppression model in GLF23 leads to significant overprediction of the ion temperatures for all discharges studied. This raises questions regarding the importance of plasma shaping effects, and the validity of the parallel velocity gradient (PVG) destabilisation in the model. The beneficial effect of reduced temperature profile stiffness at low-ˆs in inner radii is assessed with non-linear modelling using the GENE gyrokinetic code. It is found that at low-ˆs the turbulence correlation lengths are decreased and the non-linear frequency broadening increases compared to high-ˆs cases. Both these effects may be related to the observed increase in zonal flow activity, and leads to a reduction in the predicted flux compared to the high-ˆs cases. With these results, the validity of an advanced quasilinear transport model, QuaLiKiz, was extended to low-ˆs parameter space. The underlying assumptions of the model are examined, and the mixing length rule improved with guidance from the non-linear simulations. This work improves the confidence in using the QuaLiKiz transport model in the future for hybrid scenario predictions. Finally, the experimentally observed stiffness reduction at low-ˆs and high flow shear was investigated with non-linear modelling. This topic is also of importance to hybrid scenarios, to further understand the relative importance of the various factors contributing to improved confinement. Hybrid scenarios on present-day machines also tend to be associated with high levels of flow shear. The simulations do not predict the degree of reduced stiffness as experimentally observed, and this question is thus still open. Experimental measurements of the poloidal rotation profile in this class of discharge may shed more light on the matter, as we assumed that the rotation is purely toroidal due to the expected neoclassical poloidal damping. Nevertheless, additional insights were gained into the non-linear nature of ITG turbulence. From electromagnetic simulations, the non-linear ße (ratio of electron pressure to magnetic pressure) stabilisation of ITG turbulence is observed to be significantly greater than the well-known linear ße stabilisation. This may be related to the observed increase in zonal flow activity within the ße range studied. Beyond the increase in fundamental understanding, this effect could also be important for furthering the interpretation of transport in hybrid scenarios, which tend to operate at higher ße than in inductive scenarios

Two step clustering for data reduction combining DBSCAN and k-means clustering

A novel combination of two widely-used clustering algorithms is proposed here for the detection and reduction of high data density regions. The Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used for the detection of high data density regions and the k-means algorithm for reduction. The proposed algorithm iterates while successively decrementing the DBSCAN search radius, allowing for an adaptive reduction factor based on the effective data density. The algorithm is demonstrated for a physics simulation application, where a surrogate model for fusion reactor plasma turbulence is generated with neural networks. A training dataset for the surrogate model is created with a quasilinear gyrokinetics code for turbulent transport calculations in fusion plasmas. The training set consists of model inputs derived from a repository of experimental measurements, meaning there is a potential risk of over-representing specific regions of this input parameter space. By applying the proposed reduction algorithm to this dataset, this study demonstrates that the training dataset can be reduced by a factor ~20 using the proposed algorithm, without a noticeable loss in the surrogate model accuracy. This reduction provides a novel way of analyzing existing high-dimensional datasets for biases and consequently reducing them, which lowers the cost of re-populating that parameter space with higher quality data