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6,452 results for “Application”
FIGURE 17 in Taxonomic application of macro and micro morphological characters of seeds in Astragalus L. (Galegeae, Fabaceae) in India
FIGURE 17. SEM-micrographs of Astragalus stipulatus: A, entire seed (150x); B, seed sculpture (3000x); C, seed sculpture (6000x). FIGURE 18. SEM-micrographs of Astragalus uttaranchalensis: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x).
FIGURE 7 in Taxonomic application of macro and micro morphological characters of seeds in Astragalus L. (Galegeae, Fabaceae) in India
FIGURE 7. SEM-micrographs of Astragalus candolleanus: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x). FIGURE 8. SEM-micrographs of Astragalus drasianus: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x).
FIGURE 1 in Taxonomic application of macro and micro morphological characters of seeds in Astragalus L. (Galegeae, Fabaceae) in India
FIGURE 1. SEM-micrographs of Astragalus multiceps: A, entire seed (150x); B, seed sculpture (3000x); C, seed sculpture (6000x). FIGURE 2. SEM-micrographs of Astragalus oplites: A, entire seed (50x); B, seed sculpture (3000x); C, seed sculpture (6000x).
FIGURE 9 in Taxonomic application of macro and micro morphological characters of seeds in Astragalus L. (Galegeae, Fabaceae) in India
FIGURE 9. SEM-micrographs of Astragalus malacophyllus: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x). FIGURE 10. SEM-micrographs of Astragalus pindreensis: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x).
FIGURE 11 in Taxonomic application of macro and micro morphological characters of seeds in Astragalus L. (Galegeae, Fabaceae) in India
FIGURE 11. SEM-micrographs of Astragalus rhizanthus: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x). FIGURE 12. SEM-micrographs of Astragalus lessertioides: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x).
FIGURE 27 in Taxonomic application of macro and micro morphological characters of seeds in Astragalus L. (Galegeae, Fabaceae) in India
FIGURE 27. SEM-micrographs of Astragalus tenuicaulis: A, entire seed (200x); B, seed sculpture (3000x); C, seed sculpture (6000x). FIGURE 28. SEM-micrographs of Astragalus hoffmeisteri: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x).
FIGURE 15 in Taxonomic application of macro and micro morphological characters of seeds in Astragalus L. (Galegeae, Fabaceae) in India
FIGURE 15. SEM-micrographs of Astragalus concretus: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x). FIGURE 16. SEM-micrographs of Astragalus graveolens: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x).
FIGURE 23 in Taxonomic application of macro and micro morphological characters of seeds in Astragalus L. (Galegeae, Fabaceae) in India
FIGURE 23. SEM-micrographs of Astragalus leucocephalus: A, entire seed (200x); B, seed sculpture (3000x); C, seed sculpture (6000x). FIGURE 24. SEM-micrographs of Astragalus sanjappae: A, entire seed (150x); B, seed sculpture (3000x); C, seed sculpture (6000x).
FIGURE 3 in Taxonomic application of macro and micro morphological characters of seeds in Astragalus L. (Galegeae, Fabaceae) in India
FIGURE 3. SEM-micrographs of Astragalus webbianus: A, entire seed (90x); B, seed sculpture (3000x); C, seed sculpture (6000x). FIGURE 4. SEM-micrographs of Astragalus zanskarensis: A, entire seed (100x); B, seed sculpture (3000x); C, seed sculpture (6000x).
Injecting Software Faults in Python Applications: The OpenStack Case Study - Supplemental Material (https://rdcu.be/cAPUh)
<p>Injecting Software Faults in Python Applications: The OpenStack Case Study - Supplemental Material<br> Link to the paper: <a href="https://rdcu.be/cAPUh">https://rdcu.be/cAPUh</a></p>
Tutorial videos on the application of XPS inelastic background for characterization of composition and morphology of nano-structures with QUASES software
<p>The tutorial videos illustrate the application of XPS inelastic background analysis for characterization of composition and morphology of nano-structures with QUASES software.</p> <p><strong>For an introduction to the method, see:</strong><br> S. Tougaard "Practical guide to the use of backgrounds in quantitative XPS." <em>J Vac Sci Technol A</em>. 2021;39:011201. doi:10.1116/6.0000661<br> S. Tougaard "Accuracy of the non-destructive surface nanostructure quantification technique based on analysis of the XPS or AES peak shape". <em>Surf Interface Anal</em>. 1998;26(4):249-269. doi:<a href="https://doi-org.proxy1-bib.sdu.dk/10.1002/(SICI)1096-9918(199804)26:4%3C249::AID-SIA368%3E3.0.CO;2-A">https://doi-org.proxy1-bib.sdu.dk/10.1002/(SICI)1096-9918(199804)26:4<249::AID-SIA368>3.0.CO;2-A</a></p> <p> </p> <p>The inelastic background analysis was done with the QUASES-Tougaard software . See www. quases.com.</p> <p>It is recommended to read first the short description in the pdf file: <strong>Guide to the 14 posted videos.pdf.</strong> This explains briefly the purpose of each video. </p> <p>To best understand what is done in Video 9 ("9.Practical procedure to correct for elastic electron scattering.mp4") you should first watch Video 8, then Video 6 and then Video 9.</p> <p> </p>
FIGURES 8–9 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURES 8–9. Examples of usages of our system. Fig. 8. Sketching, visualization and reconstruction of an open contour. (a) Haken's gordian knot (adapted from Fish & Lisitsa 2014); (b) our sketch traced over the original drawing (adapted from Burton
FIGURE 14 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 14. Male terminalia of Chrysopilus (Diptera, Rhagionidae): (a) Epandrium, dorsal view, of C. phaeopterus (from Santos & Amorim 2007); (b) our sketch traced over the original illustration; and (c) The 3D resulting model.
FIGURE 1 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 1. Categories supported as inputs for the reconstruction of a chromosome. Input strokes belong to three categories: (a) open contours and their respective 3D reconstruction; (b) closed contours and 3D surface reconstruction; (c) stripes sketched using our system and interactions and their 3D reconstruction; and (d) final 3D model combining the three categories.
FIGURE 16 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 16. Head of Austroleptis papaveroi (Diptera, Austroleptidae): (a) photograph using a microscope (from Fachin et al. 2018); (b) our closed contour selection based on the photograph; (c) colored layers; and (d)–(f) resulting 3D model.
FIGURES 2–7 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURES 2–7. Creation, visual enhancement and 3D reconstruction. Fig. 2. Strokes: (a) raw stroke; (b) default smoothing applied three times; (c) smoothing applied nine times; (d) oversketching through drawing a new curve near the original one; and (e) replacing the current sketch with the new curve. Fig. 3. Selection operation: (a) cross selection (green) for a closed contour; (b) brush selection (red) by painting the segments belonging to the same contour. Fig. 4. Reconstruction process of a 3D stripe: (a) lines drawn with twist annotation using inverted colors for stripe sides; (b) automatic creation of stripe twists; (c) 3D parametric reconstruction of the stripe. Fig. 5. Sketch inference: (a) triangles created combining points; (b) "headlight beam" effect created by painting triangles according to their angle. Fig. 6. Different depth effects: (a) contours using gray-scale to depict different layers; (b) halo effect; (c) hatching effect; (d) layer coloring from blue to yellow. Fig. 7. Smoothing operations of an open contour. (a) one time; (b) ten times; (c) fifty times; (d) one hundred times; and (e) two hundred times.
FIGURE 15 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 15. Male terminalia of Chrysopilus (Diptera, Rhagionidae): (a) hypopygium, ventral view, of C. balbii (from Santos & Amorim 2017); (b)–(c) our sketch traced over the original illustration; and (d)–(f) The resulting 3D model.
FIGURE 13 in A sketch-based system for modeling 3D objects: Applications to taxonomy
FIGURE 13. Female terminalia of Austroleptis papaveroi (Diptera, Austroleptidae): (a) photograph using a microscope; (b) drawing used as input for our 3D reconstruction (both from Fachin et al. 2018); (c)–(d) our closed contour selection traced from the adapted illustration; and (e)–(f) the resulting 3D model.
MyLake Application for Lake 227
<p>Code and data for the application of the MyLake model to Lake 227. Accompanies the paper: Warming combined with experimental eutrophication intensifies lake phytoplankton blooms</p>
Data from the thesis diagnosis of waste, application possibilities and future demand for green coconut shells, with evaluation of information uncertainty
<p>Information on the quantities of green coconut shells produced in coastal regions is not well known, which can make it difficult to manage these frequently wasted materials, as well as the understanding that they can be included in a production process as a raw material, with various possibilities of use and income generation. Given this situation, the objective of this research is to develop a diagnosis of the potential use of green coconut shells, after water consumption, in public places in municipalities in the southern and southern coast of Bahia. It is also intended to identify the future demand of the potential possibility for the recovery of the bark, and provide subsidies that can be used to organize the use of this material. In this context, the method applied in this research consists of five steps: (i) literature review; (ii) diagnosis of bark situation in the study region; (iii) evaluation of the uncertainty of the estimated amount of shells; (iv) possibilities for applying green coconut shells and preparing the analysis of strengths, weaknesses, opportunities and threats (SWOT); and (v) obtaining future demand for the potential output for the region through quantitative techniques for predicting time series with uncertainty assessment. The main scientific contributions of this work consist, therefore, (a) in the diagnosis of the waste of green coconut shells in the region; (b) the possibilities of application of coconut shells; (c) in the demand forecasting method elaborated for time series without trends and with high variability; and (d) in assessing the uncertainty of the quantitative information obtained. Thus, the study developed can be used as a subsidy, either for replicating the research process in other types of waste allocated in any region, or for the adoption of public policies to manage these materials, or even for the encouragement and creation of an appropriate productive scenario for the management of green coconut shells in the study region, which contributes to the understanding of the potential for generating environmental, economic and social value from the use and processing of these wasted materials.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.