| 1. |
- Abbafati, Cristiana, et al.
(författare)
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- 2020
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Tidskriftsartikel (refereegranskat)
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| 2. |
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| 3. |
- Kattge, Jens, et al.
(författare)
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TRY plant trait database - enhanced coverage and open access
- 2020
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Ingår i: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Tidskriftsartikel (refereegranskat)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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| 4. |
- Mehrabi, Ali, et al.
(författare)
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Spatiotemporal subsidence over Pabdana coal mine Kerman Province, central Iran using time-series of Sentinel-1 remote sensing imagery
- 2023
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Ingår i: Episodes. - : International Union of Geological Sciences. - 0705-3797 .- 2586-1298. ; 46:1, s. 19-33
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Tidskriftsartikel (refereegranskat)abstract
- Environmental monitoring of mining regions using satellite imagery is crucial for sustainable exploitation andnpreventing geohazards. Movements due to the failure of the roof in underground coal mining, by migrating upwards and outwards from the seam being mined, could eventually appear as ground deformation. To investigate the matter further, the surface deformation that occurred over the Pabdana mining area was monitored in three time periods, between October 2, 2014, and July 27, 2019. Persistent scatterer interferometry (PSI) was used based on 150 ascending and descending Sentinel-1A images. The maximum mining subsidence rate during the studied periods was about 30 to 35 mm/yr. The PSI analysis shows that the subsidence rate varied both temporally and spatially during the three studied periods. The time series and the displacement rate for various cross-sections highlight a clear quantitative relationship between coal extraction progress and subsidence, which proceeded southward throughout the three study periods. So, considering coal mining subsidence as a geohazard, land developments and structures over the mining area may be safeguarded. The approach used in this investigation can be implemented in other similar coal mining zones.
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| 5. |
- Mehrabi, Ali, et al.
(författare)
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Trust in higher guidance and suicidality in Afghan students under the Taliban regime
- 2023
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Ingår i: International Journal of Clinical and Health Psychology. - : ELSEVIER SCIENCE INC. - 1697-2600 .- 2174-0852. ; 23:4
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Tidskriftsartikel (refereegranskat)abstract
- Background/Objective: After years of war, political instability and natural catastrophes high rates of PTSD and depression have been found in the Afghan population. On this background, it was investigated whether religious beliefs (trust in higher guidance; TIHG) moderated the association between PTSD symptoms and/or depression and suicidal ideation.Method: A total of 279 Afghan university students (61.6% women; aged 18 to 30 years) took part in this crosssectional study between July and November 2022. Self-report measures of PTSD, depression, suicidal ideation, trust in higher guidance were used.Results: Severe PTSD symptoms were reported by 58.4%, clinically relevant depression symptoms were reported by 55.2% and current suicidal ideation was reported by 44.4% of the sample. TIHG moderated the impact of PTSD symptoms, on suicidal ideation. TIHG and depression were unrelated.Conclusion: PTSD symptoms, depression and suicidal ideation show an alarmingly high prevalence in this specific sample of Afghan students. TIHG seems to be a resilience factor of special importance.
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| 6. |
- Mehrabi, M., et al.
(författare)
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Modeling of condensation heat transfer coefficients and flow regimes in flattened channels
- 2021
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Ingår i: International Communications in Heat and Mass Transfer. - : Elsevier. - 0735-1933 .- 1879-0178. ; 126
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, an adaptive neuro-fuzzy inference system (ANFIS) with fuzzy C-means clustering (FCM) structure identification is proposed to model condensation heat transfer and flow regimes in flattened smooth tubes with different aspect ratios. The FCM-ANFIS model was trained by using experimental data points for six effective chosen parameters of saturation temperature, heat flux, mass flux, aspect ratio and hydraulic diameter of the flattened tube, and vapor quality. Three flow regimes of annular flow, stratified, and intermittent flow were linked to the effective parameters based on the experimental data. Two models were proposed to predict the condensation heat transfer coefficient and the flow regime of R134a and R410a in flattened smooth tubes. Three statistical criteria were used to ascertain the accurateness of the models compared to the experimental results. It is found that while among benchmarked cases, the proposed model for the condensation heat transfer coefficient performs well, the best result with the lowest error (MAE = 0.029, RMSE = 0.036 and MRE = 2.83%) is when T-sat = 45 degrees C, q ‘’ = 10 kW/m(2), G = 100 kg/m(2). s, beta = 6 and D-h = 2.3 mm. On the other hand, in the worst-performing case when the errors are MAE = 0.239, RMSE = 0.239 and MRE = 13.36%, the predicted results are still in the uncertainty range of the experimental result when T-sat = 45 degrees C, q ‘’ = 5 kW/m(2), G = 200 kg/m(2). s, beta = 6 and D-h = 2.3 mm.
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| 7. |
- Seal, M. K., et al.
(författare)
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Machine learning classification of in-tube condensation flow patterns using visualization
- 2021
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Ingår i: International Journal of Multiphase Flow. - : Elsevier. - 0301-9322 .- 1879-3533. ; 143
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Tidskriftsartikel (refereegranskat)abstract
- Identifying two-phase flow patterns is fundamental to successfully design and subsequently optimize highprecision heat transfer equipment, given that the heat transfer efficiency and pressure gradients occurring in such thermo-hydraulic systems are dependent on the flow structure of the working fluid. This paper shows that with visualization data and artificial neural networks, the flow pattern images of condensation of R-134a refrigerant in inclined smooth tubes can be classified with more than 98% accuracy. The study considers 10 classes of flow pattern images acquired from previous experimental works for a wide range of flow conditions and the full range of tube inclination angles. Although not the focus of this paper, the use of a Principal Component Analysis allowed feature dimensionality reduction, dataset visualization, and decreased associated computational cost when used together with multilayer perceptron neural networks. In addition, the superior two-dimensional spatial learning capability of convolutional neural networks allowed improved image classification and generalization performance. In both cases, the classification was performed sufficiently fast to enable real-time implementation in two-phase flow systems.
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