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Results 21-30 of 59 (Search time: 0.01 seconds).
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PreviewIssue dateOnline publication dateTitleAuthor(s)
alphafold_predicts_the_most_c-20230130153447798.pdf.jpg20222-Feb-2023AlphaFold predicts the most complex protein knot and composite protein knotsBrems, Maarten A.; Runkel, Robert; Yeates, Todd O.; Virnau, Peter
analysis_of_turbulence_report-20221115095603046.pdf.jpg202215-Nov-2022Analysis of turbulence reports and ERA5 turbulence diagnostics in a tropopause-based vertical frameworkKaluza, Thorsten; Kunkel, Daniel; Hoor, Peter
a_variational_framework_for_t-20221114154749214.pdf.jpg202214-Dec-2022A variational framework for the inverse Henderson problem of statistical mechanicsFrommer, Fabio; Hanke, Martin
existence_of_dissipative_solu-20221114154331543.pdf.jpg202214-Dec-2022Existence of dissipative solutions to the compressible Navier-Stokes system with potential temperature transportLukáčová-Medvid’ová, Mária; Schömer, Andreas
isovector_axial_form_factor_o-20230307095302585.pdf.jpg20223-May-2023Isovector axial form factor of the nucleon from lattice QCDDjukanovic, Dalibor; von Hippel, Georg; Koponen, Jonna; Meyer, Harvey B.; Ottnad, Konstantin; Schulz, Tobias; Wittig, Hartmut
first_concurrent_extraction_o-20230308093822347.pdf.jpg202227-Apr-2023First concurrent extraction of the leading-order scalar and spin proton polarizabilitiesMornacchi, E.; Rodini, S.; Pasquini, B.; Pedroni, P.
relative_energy_and_weakstron-20230127152341597.pdf.jpg202230-Jan-2023Relative energy and weak–strong uniqueness of a two-phase viscoelastic phase separation modelBrunk, Aaron; Lukáčová-Medvid’ová, Mária
amplitudes_within_causal_loop-20230307110926395.pdf.jpg20223-May-2023Amplitudes within causal loop-tree dualityKromin, Sascha; Schwanemann, Niklas; Weinzierl, Stefan
o___s___perturbative_and_nonp-20230307093701013.pdf.jpg20223-May-2023O(αs) perturbative and nonperturbative corrections to polarized semileptonic Λb decay distributionsFischer, M.; Groote, S.
fully_nonperturbative_charmqu-20230307094703551.pdf.jpg20223-May-2023Fully nonperturbative charm-quark tuning using machine learningHudspith, R. J.; Mohler, D.

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