| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
- Anselm, Jonas, et al.
(författare)
-
Bannlys alla politiska beslut som ger mer klimatutsläpp
- 2014
-
Ingår i: Dagens Nyheter.
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Torftig valdebatt. Dagspolitiken klarar inte att hantera ödesfrågan om klimatet, vilket oroar oss. Vi föreslår därför ett ”utsläppsmoratorium”: inga beslut får tas som ökar utsläppen av växthusgaser. Principen måste kopplas till mål om exempelvis förnybar energi och grön infrastruktur, skriver 23 forskare och debattörer.
|
|
| 5. |
|
|
| 6. |
- Mejtoft, Thomas, 1976-, et al.
(författare)
-
Feedback in commercial educational applications : Guidelines and conceptual framework
- 2017
-
Ingår i: Proceedings of the European Conference on Cognitive Ergonomics 2017 (ECCE 2017). - New York, NY, USA : ACM Digital Library. - 9781450352567 ; , s. 113-120
-
Konferensbidrag (refereegranskat)abstract
- In recent years, schools made large investments to increase the integration and use of contemporary technologies in teaching and learning.Today iPads and educational applications are growing in popularity and are commonly used. These technologies have a potential beneficial effect on the learning, but studies show that some applications have flaws that hinder these positive effects, for example regarding the feedback provided to the user. This study has determined a number of guidelines for effective feedback in a learning situation and has further shown how well these guidelines can be reached in an educational application today. The study shows that there are both advantages and disadvantages of feedback in an application compared to giving feedback in a more traditional learning situation. Many of the guidelines for effective feedback were found to be possible to reach in an application, and the greatest advantage found in applications is that they can provide feedback timely on each action taken by the pupils.
|
|
| 7. |
|
|
| 8. |
- Bashir, Muwada Bashir Awad, et al.
(författare)
-
Computational phenotyping of obstructive airway diseases: protocol for a systematic review
- 2022
-
Ingår i: Systematic Reviews. - : Springer Nature. - 2046-4053. ; 11:1
-
Forskningsöversikt (refereegranskat)abstract
- Background: Over the last decade, computational sciences have contributed immensely to characterization of phenotypes of airway diseases, but it is difficult to compare derived phenotypes across studies, perhaps as a result of the different decisions that fed into these phenotyping exercises. We aim to perform a systematic review of studies using computational approaches to phenotype obstructive airway diseases in children and adults.Methods and analysis: We will search PubMed, Embase, Scopus, Web of Science, and Google Scholar for papers published between 2010 and 2020. Conferences proceedings, reference list of included papers, and experts will form additional sources of literature. We will include observational epidemiological studies that used a computational approach to derive phenotypes of chronic airway diseases, whether in a general population or in a clinical setting. Two reviewers will independently screen the retrieved studies for eligibility, extract relevant data, and perform quality appraisal of included studies. A third reviewer will arbitrate any disagreements in these processes. Quality appraisal of the studies will be undertaken using the Effective Public Health Practice Project quality assessment tool. We will use summary tables to describe the included studies. We will narratively synthesize the generated evidence, providing critical assessment of the populations, variables, and computational approaches used in deriving the phenotypes across studiesConclusion: As progress continues to be made in the area of computational phenotyping of chronic obstructive airway diseases, this systematic review, the first on this topic, will provide the state of the art on the field and highlight important perspectives for future works.Ethics and dissemination: No ethical approval is needed for this work is based only on the published literature and does not involve collection of any primary or human data.
|
|
| 9. |
|
|
| 10. |
- Mukhopadhyay, Ritwika, et al.
(författare)
-
Computation of prediction intervals for forest aboveground biomass predictions using generalized linear models in a large-extent boreal forest region
- 2024
-
Ingår i: Forestry (London). - : Oxford University Press. - 0015-752X .- 1464-3626.
-
Tidskriftsartikel (refereegranskat)abstract
- Remotely sensed data have an important application for estimation of forest variables, e.g. height, volume, and aboveground biomass (AGB). The increased use of remotely sensed data implemented along with model-based inference has shown improved efficiency in prediction and mapping of such forest variables. In this study, plot-level airborne laser scanning data and Swedish National Forest Inventory field reference data were used to predict AGB using generalized linear models (GLMs) assuming Gamma and Tweedie distributions for the field observed AGB. The GLMs were selected considering the convenience of not correcting transformation bias as it is required in other regression models with transformed response variable. To overcome the challenge in providing reliable uncertainty estimates for the estimated forest variable map products at individual pixel-scale, we focused on computing 95% prediction intervals (PIs) for Gamma and Tweedie GLMs with a square root link function. The relative uncertainties were computed as the ratio between the half-width of the PIs and the predicted AGBs. The AGB-airborne laser scanning models were developed with root mean square error values of 22.6 Mgha-1 (26%) and 21.7 Mgha-1 (25%), respectively, for the Gamma and Tweedie GLMs. Two methods were applied to compute PIs for the Gamma GLM, one using the R package 'ciTools' and another derived through asymptotic theory. It was found that the 95% PIs computed using 'ciTools' had the most accurate coverage probability in comparison to the other method. An extended version of these PIs was also utilized for the Tweedie GLMs. The range of PIs associated with the prediction of AGB were narrower for lower predicted AGB values compared with the length of higher predicted AGB values. Comparing the two fitted models, the Gamma GLM showed lower relative uncertainties for the lower range of predicted AGBs, whereas the Tweedie GLM showed lower relative uncertainties for the higher range of predicted AGBs. Overall, the Tweedie GLM provided a better model fit for AGB predictions.
|
|
