| 1. |
- Gonze, X., et al.
(författare)
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Recent developments in the ABINIT software package
- 2016
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Ingår i: Computer Physics Communications. - : Elsevier BV. - 0010-4655. ; 205, s. 106-131
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Tidskriftsartikel (refereegranskat)abstract
- ABINIT is a package whose main program allows one to find the total energy, charge density, electronic structure and many other properties of systems made of electrons and nuclei, (molecules and periodic solids) within Density Functional Theory (DFT), Many-Body Perturbation Theory (GW approximation and Bethe-Salpeter equation) and Dynamical Mean Field Theory (DMFT). ABINIT also allows to optimize the geometry according to the DFT forces and stresses, to perform molecular dynamics simulations using these forces, and to generate dynamical matrices, Born effective charges and dielectric tensors. The present paper aims to describe the new capabilities of ABINIT that have been developed since 2009. It covers both physical and technical developments inside the ABINIT code, as well as developments provided within the ABINIT package. The developments are described with relevant references, input variables, tests and tutorials. Program summary: . Program title: ABINIT. . Catalogue identifier: AEEU_v2_0. . Program summary URL: . http://cpc.cs.qub.ac.uk/summaries/AEEU_v2_0.html . . Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland. . Licensing provisions: GNU General Public License, version 3. . No. of lines in distributed program, including test data, etc.: 4845789. . No. of bytes in distributed program, including test data, etc.: 71340403. . Distribution format: tar.gz. . Programming language: Fortran2003, PERL scripts, Python scripts. . Classification: 7.3, 7.8. . External routines: (all optional) BigDFT [2], ETSF_IO [3], libxc [4], NetCDF [5], MPI [6], Wannier90 [7], FFTW [8]. . Catalogue identifier of previous version: AEEU_v1_0. . Journal reference of previous version: Comput. Phys. Comm. 180 (2009) 2582. . Does the new version supersede the previous version?: Yes. The abinit-7.10.5 version is now the up to date stable version of ABINIT. . Nature of problem: . This package has the purpose of computing accurately material and nanostructure properties: electronic structure, bond lengths, bond angles, primitive cell size, cohesive energy, dielectric properties, vibrational properties, elastic properties, optical properties, magnetic properties, non-linear couplings, electronic and vibrational life-times, and others. . Solution method: . Software application based on Density Functional Theory, Many-Body Perturbation Theory and Dynamical Mean Field Theory, pseudopotentials, with plane waves or wavelets as basis functions. . Reasons for new version: . Since 2009, the abinit-5.7.4 version of the code has considerably evolved and is not yet up to date. The abinit- 7.10.5 version contains new physical and technical features that allow electronic structure calculations impossible to carry out in the previous versions. . Summary of revisions: . •new physical features: quantum effects for the nuclei treated by the Path-integral Molecular Dynamics; finding transition states using image dynamics (NEB or string methods); two component DFT for electron-positron annihilation; linear response in a Projector Augmented-Wave approach -PAW-, electron-phonon interactions and temperature dependence of the gap; Bethe Salpeter Equation -BSE-; Dynamical Mean Field Theory (DMFT).•new technical features: development of a PAW approach for a wavelet basis; parallelisation of the code on more than 10,000 processors; new build system.•new features in the ABINIT package: tests; test farm; new tutorials; new pseudopotentials and PAW atomic data tables; GUI and postprocessing tools like the AbiPy and APPA libraries. . Running time: . It is difficult to answer to the question as the use of ABINIT is very large. On one hand, ABINIT can run on 10,000 processors for hours to perform quantum molecular dynamics on large systems. On the other hand, tutorials for students can be performed on a laptop within a few minutes. . References: . 1 http://www.gnu.org/copyleft/gpl.txt 2 http://bigdft.org 3 http://www.etsf.eu/fileformats 4 http://www.tddft.org/programs/octopus/wiki/index.php/Libxc 5 http://www.unidata.ucar.edu/software/netcdf 6 https://en.wikipedia.org/wiki/Message_Passing_Interface 7 http://www.wannier.org 8M. Frigo and S.G. Johnson, Proceedings of the IEEE, 93, 216-231 (2005). . .
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| 2. |
- Dengler, Juergen, et al.
(författare)
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GrassPlot - a database of multi-scale plant diversity in Palaearctic grasslands
- 2018
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Ingår i: Phytocoenologia. - : Schweizerbart. - 0340-269X. ; 48:3, s. 331-347
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Tidskriftsartikel (refereegranskat)abstract
- GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). GrassPlot collects plot records (releves) from grasslands and other open habitats of the Palaearctic biogeographic realm. It focuses on precisely delimited plots of eight standard grain sizes (0.0001; 0.001;... 1,000 m(2)) and on nested-plot series with at least four different grain sizes. The usage of GrassPlot is regulated through Bylaws that intend to balance the interests of data contributors and data users. The current version (v. 1.00) contains data for approximately 170,000 plots of different sizes and 2,800 nested-plot series. The key components are richness data and metadata. However, most included datasets also encompass compositional data. About 14,000 plots have near-complete records of terricolous bryophytes and lichens in addition to vascular plants. At present, GrassPlot contains data from 36 countries throughout the Palaearctic, spread across elevational gradients and major grassland types. GrassPlot with its multi-scale and multi-taxon focus complements the larger international vegetationplot databases, such as the European Vegetation Archive (EVA) and the global database " sPlot". Its main aim is to facilitate studies on the scale-and taxon-dependency of biodiversity patterns and drivers along macroecological gradients. GrassPlot is a dynamic database and will expand through new data collection coordinated by the elected Governing Board. We invite researchers with suitable data to join GrassPlot. Researchers with project ideas addressable with GrassPlot data are welcome to submit proposals to the Governing Board.
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| 3. |
- Schnabel, Renate B, et al.
(författare)
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Early diagnosis and better rhythm management to improve outcomes in patients with atrial fibrillation : the 8th AFNET/EHRA consensus conference
- 2023
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Ingår i: Europace. - : Oxford University Press. - 1099-5129 .- 1532-2092. ; 25:1, s. 6-27
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Tidskriftsartikel (refereegranskat)abstract
- Despite marked progress in the management of atrial fibrillation (AF), detecting AF remains difficult and AF-related complications cause unacceptable morbidity and mortality even on optimal current therapy. This document summarizes the key outcomes of the 8th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). Eighty-three international experts met in Hamburg for 2 days in October 2021. Results of the interdisciplinary, hybrid discussions in breakout groups and the plenary based on recently published and unpublished observations are summarized in this consensus paper to support improved care for patients with AF by guiding prevention, individualized management, and research strategies. The main outcomes are (i) new evidence supports a simple, scalable, and pragmatic population-based AF screening pathway; (ii) rhythm management is evolving from therapy aimed at improving symptoms to an integrated domain in the prevention of AF-related outcomes, especially in patients with recently diagnosed AF; (iii) improved characterization of atrial cardiomyopathy may help to identify patients in need for therapy; (iv) standardized assessment of cognitive function in patients with AF could lead to improvement in patient outcomes; and (v) artificial intelligence (AI) can support all of the above aims, but requires advanced interdisciplinary knowledge and collaboration as well as a better medico-legal framework. Implementation of new evidence-based approaches to AF screening and rhythm management can improve outcomes in patients with AF. Additional benefits are possible with further efforts to identify and target atrial cardiomyopathy and cognitive impairment, which can be facilitated by AI.
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