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- Airila, M. I., et al.
(författare)
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ERO modelling of local deposition of injected C-13 tracer at the outer divertor of JET
- 2009
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Ingår i: Physica Scripta. - 0031-8949 .- 1402-4896. ; T138, s. 014021-
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Tidskriftsartikel (refereegranskat)abstract
- The 2004 tracer experiment of JET with the injection of (CH4)-C-13 into H-mode plasma at the outer divertor has been modelled with the Monte Carlo impurity transport code ERO. EDGE2D solutions for inter-ELM and ELM-peak phases were used as plasma backgrounds. Local two-dimensional (2D) deposition patterns at the vertical outer divertor target plate were obtained for comparison with post-mortem surface analyses. ERO also provides emission profiles for comparison with radially resolved spectroscopic measurements. Modelling indicates that enhanced re-erosion of deposited carbon layers is essential in explaining the amount of local deposition. Assuming negligible effective sticking of hydrocarbons, the measured local deposition of 20-34% is reproduced if re-erosion of deposits is enhanced by a factor of 2.5-7 compared to graphite erosion. If deposits are treated like the substrate, the modelled deposition is 55%. Deposition measurements at the shadowed area around injectors can be well explained by assuming negligible re-erosion but similar sticking behaviour there as on plasma-wetted surfaces.
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| 4. |
- Bartosch-Härlid, Anna, et al.
(författare)
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Artificial neural networks in pancreatic disease.
- 2008
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Ingår i: British Journal of Surgery. - : Oxford University Press (OUP). - 1365-2168 .- 0007-1323. ; 95:7, s. 817-826
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: An artificial neural network (ANNs) is a non-linear pattern recognition technique that is rapidly gaining in popularity in medical decision-making. This study investigated the use of ANNs for diagnostic and prognostic purposes in pancreatic disease, especially acute pancreatitis and pancreatic cancer. METHODS: PubMed was searched for articles on the use of ANNs in pancreatic diseases using the MeSH terms 'neural networks (computer)', 'pancreatic neoplasms', 'pancreatitis' and 'pancreatic diseases'. A systematic review of the articles was performed. RESULTS: Eleven articles were identified, published between 1993 and 2007. The situations that lend themselves best to analysis by ANNs are complex multifactorial relationships, medical decisions when a second opinion is needed and when automated interpretation is required, for example in a situation of an inadequate number of experts. CONCLUSION: Conventional linear models have limitations in terms of diagnosis and prediction of outcome in acute pancreatitis and pancreatic cancer. Management of these disorders can be improved by applying ANNs to existing clinical parameters and newly established gene expression profiles.
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