| 1. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 2. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 3. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 4. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 5. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 6. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 7. |
- Aad, G., et al.
(författare)
-
- 2010
-
swepub:Mat__t (refereegranskat)
|
|
| 8. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 9. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 10. |
- Aad, G., et al.
(författare)
-
- 2010
-
swepub:Mat__t
|
|
| 11. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 12. |
- Aad, G., et al.
(författare)
-
- 2010
-
swepub:Mat__t
|
|
| 13. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 14. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t
|
|
| 15. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 16. |
|
|
| 17. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 18. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 19. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 20. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 21. |
- Aad, G., et al.
(författare)
-
- 2010
-
swepub:Mat__t
|
|
| 22. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 23. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 24. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 25. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 26. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 27. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 28. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 29. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 30. |
- Aad, G., et al.
(författare)
-
- 2010
-
swepub:Mat__t
|
|
| 31. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 32. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 33. |
- Aad, G., et al.
(författare)
-
- 2011
-
swepub:Mat__t (refereegranskat)
|
|
| 34. |
- Aad, G., et al.
(författare)
-
- 2012
-
Tidskriftsartikel (refereegranskat)
|
|
| 35. |
- Abdesselam, A., et al.
(författare)
-
Engineering for the ATLAS SemiConductor Tracker (SCT) end-cap
- 2008
-
Ingår i: Journal of Instrumentation. - 1748-0221. ; 3
-
Tidskriftsartikel (refereegranskat)abstract
- The ATLAS SemiConductor Tracker (SCT) is a silicon-strip tracking detector which forms part of the ATLAS inner detector. The SCT is designed to track charged particles produced in proton-proton collisions at the Large Hadron Collider (LHC) at CERN at an energy of 14 TeV. The tracker is made up of a central barrel and two identical end-caps. The barrel contains 2112 silicon modules, while each end-cap contains 988 modules. The overall tracking performance depends not only on the intrinsic measurement precision of the modules but also on the characteristics of the whole assembly, in particular, the stability and the total material budget. This paper describes the engineering design and construction of the SCT end-caps, which are required to support mechanically the silicon modules, supply services to them and provide a suitable environment within the inner detector. Critical engineering choices are highlighted and innovative solutions are presented - these will be of interest to other builders of large-scale tracking detectors. The SCT end-caps will be fully connected at the start of 2008. Further commissioning will continue, to be ready for proton-proton collision data in 2008.
|
|
| 36. |
|
|
| 37. |
- van Bragt, JJMH, et al.
(författare)
-
Characteristics and treatment regimens across ERS SHARP severe asthma registries
- 2020
-
Ingår i: The European respiratory journal. - : European Respiratory Society (ERS). - 1399-3003 .- 0903-1936. ; 55:1
-
Tidskriftsartikel (refereegranskat)abstract
- Little is known about the characteristics and treatments of patients with severe asthma across Europe, but both are likely to vary. This is the first study in the European Respiratory Society Severe Heterogeneous Asthma Research collaboration, Patient-centred (SHARP) Clinical Research Collaboration and it is designed to explore these variations. Therefore, we aimed to compare characteristics of patients in European severe asthma registries and treatments before starting biologicals.This was a cross-sectional retrospective analysis of aggregated data from 11 national severe asthma registries that joined SHARP with established patient databases.Analysis of data from 3236 patients showed many differences in characteristics and lifestyle factors. Current smokers ranged from 0% (Poland and Sweden) to 9.5% (Belgium), mean body mass index ranged from 26.2 (Italy) to 30.6 kg·m−2 (the UK) and the largest difference in mean pre-bronchodilator forced expiratory volume in 1 s % predicted was 20.9% (the Netherlands versus Hungary). Before starting biologicals patients were treated differently between countries: mean inhaled corticosteroid dose ranged from 700 to 1335 µg·day−1 between those from Slovenia versus Poland when starting anti-interleukin (IL)-5 antibody and from 772 to 1344 µg·day−1 in those starting anti-IgE (Slovenia versus Spain). Maintenance oral corticosteroid use ranged from 21.0% (Belgium) to 63.0% (Sweden) and from 9.1% (Denmark) to 56.1% (the UK) in patients starting anti-IL-5 and anti-IgE, respectively.The severe asthmatic population in Europe is heterogeneous and differs in both clinical characteristics and treatment, often appearing not to comply with the current European Respiratory Society/American Thoracic Society guidelines definition of severe asthma. Treatment regimens before starting biologicals were different from inclusion criteria in clinical trials and varied between countries.
|
|
| 38. |
- Rodríguez, A., et al.
(författare)
-
Implications of crop model ensemble size and composition for estimates of adaptation effects and agreement of recommendations
- 2019
-
Ingår i: Agricultural and Forest Meteorology. - : Elsevier BV. - 0168-1923 .- 1873-2240. ; 264, s. 351-362
-
Tidskriftsartikel (refereegranskat)abstract
- Climate change is expected to severely affect cropping systems and food production in many parts of the world unless local adaptation can ameliorate these impacts. Ensembles of crop simulation models can be useful tools for assessing if proposed adaptation options are capable of achieving target yields, whilst also quantifying the share of uncertainty in the simulated crop impact resulting from the crop models themselves. Although some studies have analysed the influence of ensemble size on model outcomes, the effect of ensemble composition has not yet been properly appraised. Moreover, results and derived recommendations typically rely on averaged ensemble simulation results without accounting sufficiently for the spread of model outcomes. Therefore, we developed an Ensemble Outcome Agreement (EOA) index, which analyses the effect of changes in composition and size of a multi-model ensemble (MME) to evaluate the level of agreement between MME outcomes with respect to a given hypothesis (e.g. that adaptation measures result in positive crop responses). We analysed the recommendations of a previous study performed with an ensemble of 17 crop models and testing 54 adaptation options for rainfed winter wheat (Triticum aestivum L.) at Lleida (NE Spain) under perturbed conditions of temperature, precipitation and atmospheric CO2 concentration. Our results confirmed that most adaptations recommended in the previous study have a positive effect. However, we also showed that some options did not remain recommendable in specific conditions if different ensembles were considered. Using EOA, we were able to identify the adaptation options for which there is high confidence in their effectiveness at enhancing yields, even under severe climate perturbations. These include substituting spring wheat for winter wheat combined with earlier sowing dates and standard or longer duration cultivars, or introducing supplementary irrigation, the latter increasing EOA values in all cases. There is low confidence in recovering yields to baseline levels, although this target could be attained for some adaptation options under moderate climate perturbations. Recommendations derived from such robust results may provide crucial information for stakeholders seeking to implement adaptation measures.
|
|
| 39. |
- Ruiz-Ramos, Miguel, et al.
(författare)
-
Adaptation response surfaces for managing wheat under perturbed climate and CO2 in a Mediterranean environment
- 2018
-
Ingår i: Agricultural Systems. - : Elsevier BV. - 0308-521X. ; 159, s. 260-274
-
Tidskriftsartikel (refereegranskat)abstract
- Adaptation of crops to climate change has to be addressed locally due to the variability of soil, climate and the specific socio-economic settings influencing farm management decisions. Adaptation of rainfed cropping systems in the Mediterranean is especially challenging due to the projected decline in precipitation in the coming decades, which will increase the risk of droughts. Methods that can help explore uncertainties in climate projections and crop modelling, such as impact response surfaces (IRSs) and ensemble modelling, can then be valuable for identifying effective adaptations. Here, an ensemble of 17 crop models was used to simulate a total of 54 adaptation options for rainfed winter wheat (Triticum aestivum) at Lleida (NE Spain). To support the ensemble building, an ex post quality check of model simulations based on several criteria was performed. Those criteria were based on the "According to Our Current Knowledge" (AOCK) concept, which has been formalized here. Adaptations were based on changes in cultivars and management regarding phenology, vernalization, sowing date and irrigation. The effects of adaptation options under changed precipitation (P), temperature (T), [CO2] and soil type were analysed by constructing response surfaces, which we termed, in accordance with their specific purpose, adaptation response surfaces (ARSs). These were created to assess the effect of adaptations through a range of plausible P, T and [CO2] perturbations. The results indicated that impacts of altered climate were predominantly negative. No single adaptation was capable of overcoming the detrimental effect of the complex interactions imposed by the P, T and [CO2] perturbations except for supplementary irrigation (sI), which reduced the potential impacts under most of the perturbations. Yet, a combination of adaptations for dealing with climate change demonstrated that effective adaptation is possible at Lleida. Combinations based on a cultivar without vernalization requirements showed good and wide adaptation potential. Few combined adaptation options performed well under rainfed conditions. However, a single sI was sufficient to develop a high adaptation potential, including options mainly based on spring wheat, current cycle duration and early sowing date. Depending on local environment (e.g. soil type), many of these adaptations can maintain current yield levels under moderate changes in T and P, and some also under strong changes. We conclude that ARSs can offer a useful tool for supporting planning of field level adaptation under conditions of high uncertainty.
|
|
| 40. |
- Fronzek, Stefan, et al.
(författare)
-
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
- 2018
-
Ingår i: Agricultural Systems. - : Elsevier BV. - 0308-521X. ; 159, s. 209-224
-
Tidskriftsartikel (refereegranskat)abstract
- Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (-2 to +9°C) and precipitation (-50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses.The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern.The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description.Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index.Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
|
|
