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- Romanelli, F, et al.
(author)
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Overview of the JET results
- 2011
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In: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 51:9
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Journal article (peer-reviewed)abstract
- Since the last IAEA Conference JET has been in operation for one year with a programmatic focus on the qualification of ITER operating scenarios, the consolidation of ITER design choices and preparation for plasma operation with the ITER-like wall presently being installed in JET. Good progress has been achieved, including stationary ELMy H-mode operation at 4.5 MA. The high confinement hybrid scenario has been extended to high triangularity, lower ρ*and to pulse lengths comparable to the resistive time. The steady-state scenario has also been extended to lower ρ*and ν*and optimized to simultaneously achieve, under stationary conditions, ITER-like values of all other relevant normalized parameters. A dedicated helium campaign has allowed key aspects of plasma control and H-mode operation for the ITER non-activated phase to be evaluated. Effective sawtooth control by fast ions has been demonstrated with3He minority ICRH, a scenario with negligible minority current drive. Edge localized mode (ELM) control studies using external n = 1 and n = 2 perturbation fields have found a resonance effect in ELM frequency for specific q95values. Complete ELM suppression has, however, not been observed, even with an edge Chirikov parameter larger than 1. Pellet ELM pacing has been demonstrated and the minimum pellet size needed to trigger an ELM has been estimated. For both natural and mitigated ELMs a broadening of the divertor ELM-wetted area with increasing ELM size has been found. In disruption studies with massive gas injection up to 50% of the thermal energy could be radiated before, and 20% during, the thermal quench. Halo currents could be reduced by 60% and, using argon/deuterium and neon/deuterium gas mixtures, runaway electron generation could be avoided. Most objectives of the ITER-like ICRH antenna have been demonstrated; matching with closely packed straps, ELM resilience, scattering matrix arc detection and operation at high power density (6.2 MW m-2) and antenna strap voltages (42 kV). Coupling measurements are in very good agreement with TOPICA modelling. © 2011 IAEA, Vienna.
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- Romagnoni, A, et al.
(author)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Journal article (peer-reviewed)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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