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7 results for “mill scale”
Sp(2N) Yang-Mills theories on the lattice: scale setting and topology—data release
<p>This release contains all data and metadata used to prepare the publications <a href="https://arxiv.org/abs/2205.09254">Topological susceptibility in Yang-Mills theories</a> and <a href="https://arxiv.org/abs/2205.09364">Sp(2N) Yang-Mills theories on the lattice: scale setting and topology</a>.</p> <p>Included are:</p> <ul> <li>The raw log output from the Wilson flow computation, as well as metadata describing the ensembles used, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form. The archive contains a separate `README.md` describing the layout of the data.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `datapackage.h5`.</li> <li>The data presented in all tables in both papers, in CSV format, as described in more detail below.</li> </ul> <p>Further details are given in the file README.md.</p>
Text-fig. A1. a: Ulmus longifolia UNGER, 1847 (Unger 1847: pl. 26, fig. 5). b: Ulmus braunii HEER, 1856 (Heer 1856: pl. 79, fig. 17). c: Ulmus affinis A.MASSAL., 1853 (Massallongo 1854: pl. 4, fig. 8). d, e: Ulmus carpinifolia GLED., 1773 syn. of Ulmus minor MILL., 1768, (herbarium K566057), UK. Scale bars 30 mm (a–e). in The Late Early Pleistocene Flora Of Oriolo, Faenza (Italy): Assembly Of The Modern Forest Biome
Text-fig. A1. a: Ulmus longifolia UNGER, 1847 (Unger 1847: pl. 26, fig. 5). b: Ulmus braunii HEER, 1856 (Heer 1856: pl. 79, fig. 17). c: Ulmus affinis A.MASSAL., 1853 (Massallongo 1854: pl. 4, fig. 8). d, e: Ulmus carpinifolia GLED., 1773 syn. of Ulmus minor MILL., 1768, (herbarium K566057), UK. Scale bars 30 mm (a–e).
Text-fig. A2. a: Ulmus carpinifolia GLED., 1773 syn. of Ulmus minor MILL., 1768, (herbarium E00824885). b: Ulmus carpinifolia GLED., 1773 syn. of Ulmus minor MILL., 1768, (herbarium E00824885). c: Ulmus carpinifolia GLED., 1773 syn. of Ulmus minor MILL., 1768, (herbarium E00824885 detail of (a)). d, e: Ulmus elliptica K.KOCH, 1849 (herbarium E00034393). f, g: Ulmus lancifolia ROXB., 1814, nom. inval. (herbarium NMNH03413489). h: Ulmus lancifolia ROXB., 1814, nom. inval. (herbarium NMNH03413488). Asterisks indicate different types of asymmetric leaf base. Scale bars 50 mm (a, d, e, f), 10 mm (b, c, g, h). in The Late Early Pleistocene Flora Of Oriolo, Faenza (Italy): Assembly Of The Modern Forest Biome
Text-fig. A2. a: Ulmus carpinifolia GLED., 1773 syn. of Ulmus minor MILL., 1768, (herbarium E00824885). b: Ulmus carpinifolia GLED., 1773 syn. of Ulmus minor MILL., 1768, (herbarium E00824885). c: Ulmus carpinifolia GLED., 1773 syn. of Ulmus minor MILL., 1768, (herbarium E00824885 detail of (a)). d, e: Ulmus elliptica K.KOCH, 1849 (herbarium E00034393). f, g: Ulmus lancifolia ROXB., 1814, nom. inval. (herbarium NMNH03413489). h: Ulmus lancifolia ROXB., 1814, nom. inval. (herbarium NMNH03413488). Asterisks indicate different types of asymmetric leaf base. Scale bars 50 mm (a, d, e, f), 10 mm (b, c, g, h).
Simulation cases of a lab-scale wet-operated stirred media mill using coupled CFD-DEM
<p>Simulation cases described in the article, "Coupled CFD-DEM simulation of pin-type wet stirred media mills using immersed boundary approach and hydrodynamic lubrication force", DOI: <a href="https://doi.org/10.1016/j.powtec.2024.120060" rel="nofollow">https://doi.org/10.1016/j.powtec.2024.120060</a></p> <p><strong>Pre-requisites:</strong> LIGGGHTS, OpenFOAM-6, cfdemCoupling, and their corresponding dependencies, Python (>3.6)</p> <p>*The versions of simulation softwares used in the simulation cases are taken from Institute for Particle Technology's (iPAT) GitLab repository: <a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a></p> <p>To run the simulations in this repository, one should first install the pre-requisites i.e., LIGGGHTS, OpenFOAM-6 and cfdemCoupling. The repositiries can be found at Institute for Particle Technology's GitLab (<a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a>) if not, they shall be requested.</p> <p>Running the simulations in the repositories includes, generation of the cases in "Base_Cases_Init", using the "generateCases.py" file (Python3), then run the "variables_Modify.py" file. Running of the "jobfile_Modify.py" and "jrun.py", sequentially, will submit the simulations to a HPC cluster. After the successful run of these simulations, the cases in the folders "Base_Cases_Stable" and "Base_Cases_Stable_Lubrication" can be launched in the same manner as described above, i.e., sequentially running "generateCases.py", "variables_Modify.py", "jobfile_Modify.py" and "jrun.py" (one needs to check if the corresponding restart files are existing in the Base_Cases_Stable*/Base_Case_Stable/Restart folder, which are generated from the "Base_Cases_Init" runs). Following this, the cases in "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" can be run using the same method as described above (one needs to check if the corresponding restart files are existing in the Base_Cases_Run*/Base_Case_Run/Restart folder, which are generated from the "Base_Cases_Stable" runs). After successfully running of the simulations the python file "generateAndRunPostFiles.py", in each of the corresponding "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" folders should be run.</p> <p> </p> <p> </p> <p> </p> <p><strong>Description:</strong> This repository provides the simulation cases to generate and run the simulation cases of the "stirred media mill" (MiniCeR). The simulations are setup to couple the CFD and DEM via two-way coupling and the corresponding files in the "Run" folder contain the post-processing scripts to extract the "collision/stress energies" and assemble them into a "collision/stress energy distribution". The simulations are setup in three stages, namely, "Init", "Stable", and "Run". The combinations of operating settings can be easily modified and the respective cases can be generated using the python scripts in the corresponding repositories. The scripts to run the simulations on the HPC-cluster systems are also added.</p> <p><strong><em>a. Init:</em></strong> This stage is to initialize the system with the particles. Three insertion faces are used to generate and insert the required number of particles (calculated according to their size and filling degree) into the system. The "base case" folder contains the necessary DEM scripts of the case setup and the required CAD (geometry) files. The python script "generateCases.py" generates the requested simulation cases according to the specified operating settings. It uses the help of "MakeCases.sh". The "variables_Modify.py" file modifies the variables in the generated folders of the simulation cases to alter the operation setting values. The "jobfile_Modify.py", and the "jrun.py" are used to modify the cluster job files and run the submit the simulation jobs onto the cluster, respectively.</p> <p><strong><em>b. Stable:</em></strong> This is the first stage couples the CFD and DEM. The restart files generated in the "Init" stage are used to start the coupling and run for a specified time. It follows the similar system as init, i.e., to generate the cases and modify the variables, but with additional generation and modifications in the CFD folder i.e., the mesh generation, etc. The simulations are launched in the same way as described above and the corresponding restart files are extracted.</p> <p><strong><em>c. Run:</em></strong> This second stage of the coupling of CFD and DEM launches the srabilized system and extracts the collision energies and stores them in ".txt" files which are postprocessed later to assemble the stress energy distribution. The post-processing to extract the stress energy distribution is done using the "Stress_Energy_Calculation.py" and "generateAndRunPostFiles.py", which generate corresponding folders of post-processing in each of the corresponding case folders.</p>
Data from: Evidence of divergent selection for drought and cold tolerance at landscape and local scales in Abies alba Mill. in the French Mediterranean Alps
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Data from: Fine-scale spatial genetic structure across the species range reflects recent colonization of high elevation habitats in silver fir (Abies alba Mill.)
<p class="western"><span>Variation in genetic diversity across species ranges has long been recognized as highly informative for assessing populations' resilience and adaptive potential. The spatial distribution of genetic diversity within populations, referred to as fine-scale spatial genetic structure (FSGS), also carries information about recent demographic changes, yet it has rarely been connected to range scale processes. We studied eight silver fir (<i>Abies alba </i>Mill.<i>)</i> population pairs (sites), growing at high and low elevations, representative of the main genetic lineages of the species. A total of 1368 adult trees and 540 seedlings were genotyped using 137 and 116 single nucleotide polymorphisms (SNPs), respectively. Sites revealed a clear east-west isolation-by-distance pattern consistent with the post-glacial colonization history of the species. Genetic differentiation among sites (<i>F</i><sub>CT</sub>=0.148) was an order of magnitude greater than between elevations within sites (<i>F</i><sub>SC</sub>=0.031), nevertheless high elevation populations consistently exhibited a stronger FSGS. Structural equation modeling revealed that elevation and, to a lesser extent, post-glacial colonization history, but not climatic and habitat variables, were the best predictors of FSGS across populations. These results suggest that high elevation habitats have been colonized more recently across the species range. Additionally, paternity analysis revealed a high reproductive skew among adults and a stronger FSGS in seedlings than in adults, suggesting that FSGS may conserve the signature of demographic changes for several generations. Our results emphasize that spatial patterns of genetic diversity within populations provide information about demographic history complementary to non-spatial statistics, and could be used for genetic diversity monitoring, especially in forest trees.</span></p>
Data from: Fine-scale spatial genetic structure across the species range reflects recent colonization of high elevation habitats in silver fir (Abies alba Mill.)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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