| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
- Segersäll, Mikael
(författare)
-
Nickel-Based Single-Crystal Superalloys : the crystal orientation influence on high temperature properties
- 2013
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Superalloys are a group of materials that are used in high temperature applications, for example gas turbines and aero engines. Gas turbines are most commonly used for power generation, and it is only the very critical components which are exposed to the most severe conditions within the turbine, which are made from superalloy material.Today, energy consumption in many parts of the world is very high and is tending to increase. This implies that all power generating sources, including gas turbines, must aim for higher efficiency. For the gas turbine industry, it is a continuous challenge to develop more energy-efficient turbines. One way to do this is to increase the temperature within the hot stage of the turbine. However, increased temperature in the hot stage also challenges the materials that are used there. Today’s materials are already pushed to the limit, i.e. they cannot be exposed to the temperatures which are required to further increase the turbine efficiency. To solve this problem, research which later can lead to better superalloys that can withstand even higher temperatures, has to be conducted within the area of superalloys.The aim of this licentiate thesis is to increase our knowledge about deformation and damage mechanisms that occur in the microstructure in superalloys when they are subjected to high temperatures and loads. This knowledge can later be used when developing new superalloys. In addition, increased knowledge of what is happening within the material when it is exposed to those severe conditions, will facilitate the development of material models. Material models are used for FEM simulations, when trying to predict life times in gas turbine components during the design process.This licentiate thesis is based on results from thermomechanical fatigue (TMF) testing of Ni-based single-crystal superalloys. Results show that the deformation within the microstructure during TMF is localized to several deformation bands. In addition, the deformation mechanisms are mainly twinning and shearing of the microstructure. Results also indicate that TMF cycling seems to influence the creep rate of single-crystal superalloys.
|
|
| 5. |
|
|
| 6. |
- Kanai, M, et al.
(författare)
-
- 2023
-
swepub:Mat__t
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
|
|
| 10. |
|
|
| 11. |
- Bombarda, F., et al.
(författare)
-
Runaway electron beam control
- 2019
-
Ingår i: Plasma Physics and Controlled Fusion. - : IOP Publishing. - 1361-6587 .- 0741-3335. ; 61:1
-
Tidskriftsartikel (refereegranskat)
|
|
| 12. |
|
|
| 13. |
|
|
| 14. |
|
|
| 15. |
- Carninci, P, et al.
(författare)
-
The transcriptional landscape of the mammalian genome
- 2005
-
Ingår i: Science (New York, N.Y.). - : American Association for the Advancement of Science (AAAS). - 1095-9203 .- 0036-8075. ; 309:5740, s. 1559-1563
-
Tidskriftsartikel (refereegranskat)abstract
- This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5′ and 3′ boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.
|
|
| 16. |
|
|
| 17. |
|
|
| 18. |
|
|
| 19. |
|
|
| 20. |
|
|
| 21. |
|
|
| 22. |
- Gama, Fábio, Ass. Professor, 1980-, et al.
(författare)
-
Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice : Scoping Review
- 2022
-
Ingår i: Journal of Medical Internet Research. - Toronto, ON : JMIR Publications. - 1438-8871. ; 24:1
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice.Objective: This paper aims to identify the implementation frameworks used to understand the application of AI in health care practice.Methods: A scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies.Results: In total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation.Conclusions: This literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from the field of implementation science. ©Fábio Gama, Daniel Tyskbo, Jens Nygren, James Barlow, Julie Reed, Petra Svedberg.
|
|
| 23. |
|
|
| 24. |
- Gerkin, RC, et al.
(författare)
-
The best COVID-19 predictor is recent smell loss: a cross-sectional study
- 2020
-
Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundCOVID-19 has heterogeneous manifestations, though one of the most common symptoms is a sudden loss of smell (anosmia or hyposmia). We investigated whether olfactory loss is a reliable predictor of COVID-19.MethodsThis preregistered, cross-sectional study used a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified singular and cumulative predictors of COVID-19 status and post-COVID-19 olfactory recovery.ResultsBoth C19+ and C19-groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both single and cumulative feature models (ROC AUC=0.72), with additional features providing negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms, such as fever or cough. Olfactory recovery within 40 days was reported for ∼50% of participants and was best predicted by time since illness onset.ConclusionsAs smell loss is the best predictor of COVID-19, we developed the ODoR-19 tool, a 0-10 scale to screen for recent olfactory loss. Numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<OR<10), which can be deployed when viral lab tests are impractical or unavailable.
|
|
| 25. |
|
|
