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- 2019
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Biurrun, Idoia, et al.
(författare)
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Benchmarking plant diversity of Palaearctic grasslands and other open habitats
- 2021
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Ingår i: Journal of Vegetation Science. - Oxford : John Wiley & Sons. - 1100-9233 .- 1654-1103. ; 32:4
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Tidskriftsartikel (refereegranskat)abstract
- Journal of Vegetation Science published by John Wiley & Sons Ltd on behalf of International Association for Vegetation Science.Aims: Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location: Palaearctic biogeographic realm. Methods: We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results: Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions: The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology. © 2021 The Authors.
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| 3. |
- Hoglund, L, et al.
(författare)
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Selective optical doping to predict the performance and reveal the origin of photocurrent peaks in quantum dots-in-a-well infrared photodetectors
- 2009
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Ingår i: INFRARED PHYSICS and TECHNOLOGY. - Exeter : Elsevier BV. - 1350-4495 .- 1879-0275. ; 52:6, s. 272-275
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Tidskriftsartikel (refereegranskat)abstract
- Resonant optical pumping across the band gap was used as artificial doping in InAs/In0.15Ga0.85As/GaAs quantum dots-in-a-well infrared photodetectors. Through efficient filling of the quantum dot energy levels by simultaneous optical pumping into the ground states and the excited states of the quantum dots, the response was increased by a factor of 10. Low temperature photocurrent peaks observed at 120 and 148 meV were identified as intersubband transitions emanating from the quantum dot ground state and the quantum dot excited state, respectively by a selective increase of the electron population in the different quantum dot energy levels.
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| 4. |
- Höglund, Linda, 1974-, et al.
(författare)
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Bias and temperature dependence of the escape processes in quantum dots-in-a-well infrared photodetectors
- 2008
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Ingår i: Applied Physics Letters. - New York : American Institute of Physics (AIP). - 0003-6951 .- 1077-3118. ; 93:10, s. 103501-
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Tidskriftsartikel (refereegranskat)abstract
- The performance of quantum dots-in-a-well infrared photodetectors (DWELL IPs) has been studied by means of interband and intersubband photocurrent measurements as well as dark current measurements. Using interband photocurrent measurements, substantial escape of electrons from lower lying states in the DWELL structure at large biases was revealed. Furthermore, a significant variation in the escape probability from energy states in the DWELL structure with applied bias was observed. These facts can explain the strong temperature and bias dependence of both photocurrent and dark currents in DWELL IPs.
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| 5. |
- Höglund, Linda, 1974-, et al.
(författare)
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Bias mediated tuning of the detection wavelength in asymmetrical quantum dots-in-a-well infrared photodetectors
- 2008
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Ingår i: Applied Physics Letters. - New York : AIP Publishing. - 0003-6951 .- 1077-3118. ; 93:20
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Tidskriftsartikel (refereegranskat)abstract
- Bias-mediated tuning of the detection wavelength within the infrared wavelength region is demonstrated for quantum dots-in-a-well and dots-on-a-well infrared photodetectors. By positioning the InAs quantum dot layer asymmetrically in an 8 nm wide In0.15Ga0.85As/GaAs quantum well, a shift in the peak detection wavelength from 8.4 to 10.3 mu m was observed when reversing the polarity of the applied bias. For a dots-on-a-well structure, the peak detection wavelength was tuned from 5.4 to 8 mu m with small changes in the applied bias. These tuning properties could be essential for applications such as modulators and dual-color infrared detection.
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| 6. |
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| 7. |
- Höglund, Linda, 1974-, et al.
(författare)
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Optical pumping as artificial doping in quantum dots-in-a-well infrared photodetectors
- 2009
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Ingår i: Applied Physics Letters. - New York : AIP Publishing. - 0003-6951 .- 1077-3118. ; 94:5, s. 053503-
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Tidskriftsartikel (refereegranskat)abstract
- Resonant optical pumping across the band gap was used as artificial doping in InAs/In0.15Ga0.85As/GaAs quantum dots-in-a-well infrared photodetectors. By selectively increasing the electron population in the different quantum dot energy levels, the low temperature photocurrent peaks observed at 120 and 148 meV, could be identified as intersubband transitions emanating from the quantum dot ground state and the quantum dot excited state, respectively. With efficient filling of the quantum dot energy levels through simultaneous optical pumping into the ground states and the excited states of the quantum dots, the response was increased by a factor of 10.
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| 8. |
- Höglund, Linda, 1974-, et al.
(författare)
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Origin of photocurrent in lateral quantum dots-in-a-well infrared photodetectors
- 2006
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Ingår i: Applied Physics Letters. - New York : American Institute of Physics. - 0003-6951 .- 1077-3118. ; 88:21
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Tidskriftsartikel (refereegranskat)abstract
- Interband and intersubband transitions of lateral InAs/In0.15Ga0.85As dots-in-a-well quantum dot infrared photodetectors were studied in order to determine the origin of the photocurrent. The main intersubband transition contributing to the photocurrent (PC) was associated with the quantum dot ground state to the quantum well excited state transition. By a comparison between intersubband PC measurements and the energy level scheme of the structure, as deduced from Fourier transform photoluminescence (FTPL) and FTPL excitation spectroscopies, the main transition contributing to the PC was identified.
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| 9. |
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| 10. |
- Wang, Leyao, et al.
(författare)
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A deep multiple kernel learning-based higher-order fuzzy inference system for identifying DNA N4-methylcytosine sites
- 2023
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Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 630, s. 40-52
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Tidskriftsartikel (refereegranskat)abstract
- N4-methylcytosine (4mC), as a DNA modification, plays a crucial role in epigenetic regulation. However, the existing experimentation methods for accurately identifying 4mC sites are inefficient and highly consumable, making them difficult to implement. Although a variety of new identification methods are continuously being proposed, existing techniques are not yet fully mature. Compared to traditional 4mC site predictors, based on support vector machine or convolutional neural network, we present an alternative computational approach. In this study, we propose a method based on a kernelized higher-order fuzzy inference system (KHFIS) and deep multiple kernel learning, called DMKL-HFIS, to improve the accuracy of 4mC site identification DNA sequences. We use PSTNP to process the benchmark datasets, and then apply KHFIS to obtain multiple fuzzy kernel matrices. A deep neural network is used to fuse multiple fuzzy kernel matrices. Finally, the predicted value is derived from the fused matrix. Our approach was compared with existing mainstream computational methods. On the benchmark datasets (G. subterraneus, D. melanogaster, E. coli, A. thaliana, and C. elegans), the accuracy of our approach exceeded that of a state-of-the-art method by 0.4%, 0.44%, 1.51%, 0.55%, and 0.25%, respectively. Compared to mainstream methods, our approach exhibits a higher level of accuracy and can therefore be considered an effective prediction tool. © 2023 Elsevier Inc. All rights reserved.
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