| 1. |
- Bombarda, F., et al.
(author)
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Runaway electron beam control
- 2019
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In: Plasma Physics and Controlled Fusion. - : IOP Publishing. - 1361-6587 .- 0741-3335. ; 61:1
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Journal article (peer-reviewed)
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| 2. |
- Krasilnikov, A., et al.
(author)
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Evidence of 9 Be + p nuclear reactions during 2ω CH and hydrogen minority ICRH in JET-ILW hydrogen and deuterium plasmas
- 2018
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In: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 58:2
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Journal article (peer-reviewed)abstract
- The intensity of 9Be + p nuclear fusion reactions was experimentally studied during second harmonic (2ω CH) ion-cyclotron resonance heating (ICRH) and further analyzed during fundamental hydrogen minority ICRH of JET-ILW hydrogen and deuterium plasmas. In relatively low-density plasmas with a high ICRH power, a population of fast H+ ions was created and measured by neutral particle analyzers. Primary and secondary nuclear reaction products, due to 9Be + p interaction, were observed with fast ion loss detectors, γ-ray spectrometers and neutron flux monitors and spectrometers. The possibility of using 9Be(p, d)2α and 9Be(p, α)6Li nuclear reactions to create a population of fast alpha particles and study their behaviour in non-active stage of ITER operation is discussed in the paper.
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- 2018
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In: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 58:1
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Research review (peer-reviewed)
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Overview of the JET results
- 2015
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In: Nuclear Fusion. - : IOP Publishing. - 0029-5515 .- 1741-4326. ; 55:10
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Journal article (peer-reviewed)
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- 2018
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In: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 58:9
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- Kurzen, H, et al.
(author)
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What causes hidradenitis suppurativa?
- 2008
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In: Experimental dermatology. - : Wiley. - 1600-0625 .- 0906-6705. ; 17:5, s. 455-472
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Journal article (peer-reviewed)
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| 39. |
- Lyth, Johan, 1980-, et al.
(author)
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Trends in cutaneous malignant melanoma in Sweden 1997-2011: Thinner tumours and improved survival among men
- 2015
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In: British Journal of Dermatology. - : Wiley-Blackwell. - 0007-0963 .- 1365-2133. ; 172:3, s. 700-706
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Journal article (peer-reviewed)abstract
- Background: Both patient survival and the proportion of patients diagnosed with thin cutaneous malignant melanoma (CMM) have been steadily rising in Sweden as in most western countries, though the rate of improvement in survival appears to have declined in Sweden at the end of last millennium.Objectives: To analyse the most recent trends in the distribution of tumour thickness (T-category) as well as CMM-specific survival in Swedish patients diagnosed 1997-2011.Methods: This nationwide population-based study included 30 590 patients registered in the Swedish Melanoma Register (SMR) and diagnosed with a first primary invasive CMM 1997-2011. The patients were followed through 2012 in the national Cause-of-Death Register.Results: Logistic and Cox regression analyses adjusting for age at diagnosis, tumour site, and health care region were carried out. The odds ratio for being diagnosed with thicker tumours was significantly reduced (P = 0·0008) and the CMM-specific survival significantly improved in men diagnosed 2007-2011 compared to men diagnosed 1997-2001 (hazard ratio=0·81; 95% CI 0·72-0·91, P = 0·0009) while the corresponding differences for women were not significant. Women were diagnosed with significantly thicker tumours during 2002-2006 and a tendency towards decreased survival was observed compared to those diagnosed earlier 1997-2001 and later 2007-2011.Conclusion: In Sweden, the CMMs of men are detected earlier over time and this seems to be followed by an improved CMM-specific survival for men. Women are still diagnosed with considerably thinner tumours and they experience a better survival than men.
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| 43. |
- Tschandl, P., et al.
(author)
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Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study
- 2019
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In: The Lancet Oncology. - 1470-2045 .- 1474-5488. ; 20:7, s. 938-947
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Journal article (peer-reviewed)abstract
- Background: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. Methods: For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. Findings: Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p<0·0001) more correct diagnoses (17·91 [SD 3·42] vs 19·92 [4·27]). 27 human experts with more than 10 years of experience achieved a mean of 18·78 (SD 3·15) correct answers, compared with 25·43 (1·95) correct answers for the top three machine algorithms (mean difference 6·65, 95% CI 6·06–7·25; p<0·0001). The difference between human experts and the top three algorithms was significantly lower for images in the test set that were collected from sources not included in the training set (human underperformance of 11·4%, 95% CI 9·9–12·9 vs 3·6%, 0·8–6·3; p<0·0001). Interpretation: State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research. Funding: None. © 2019 Elsevier Ltd
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| 48. |
- Bonner, Richard, et al.
(author)
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Nonstochastic languages as projections of 2-tape quasideterministic languages
- 1998
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In: Mathematical Foundations of Computer Science 1998. - 3540648275 ; , s. 213-219
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Conference paper (peer-reviewed)abstract
- A language L(n) of n-tuples of words which is recognized by a n-tape rational finite-probabilistic automaton with probability 1-ε, for arbitrary ε > 0, is called quasideterministic. It is proved in [Fr 81], that each rational stochastic language is a projection of a quasideterministic language L(n) of n-tuples of words. Had projections of quasideterministic languages on one tape always been rational stochastic languages, we would have a good characterization of the class of the rational stochastic languages. However we prove the opposite in this paper. A two-tapequasideterministic language exists, the projection of which on the first tape is a nonstochastic language.
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