53 research outputs found

    Nonadiabatic approach to dimerization gap and optical absorption coefficient of the Su-Schrieffer-Heeger model

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    An analytical nonadiabatic approach has been developed to study the dimerization gap and the optical absorption coefficient of the Su-Schrieffer-Heeger model where the electrons interact with dispersive quantum phonons. By investigating quantitatively the effects of quantum phonon fluctuations on the gap order and the optical responses in this system, we show that the dimerization gap is much more reduced by the quantum lattice fluctuations than the optical absorption coefficient is. The calculated optical absorption coefficient and the density of states do not have the inverse-square-root singularity, but have a peak above the gap edge and there exist a significant tail below the peak. The peak of optical absorption spectrum is not directly corresponding to the dimerized gap. Our results of the optical absorption coefficient agree well with those of the experiments in both the shape and the peak position of the optical absorption spectrum.Comment: 14 pages, 7 figures. to be published in PR

    Childhood obesity intervention studies: A narrative review and guide for investigators, authors, editors, reviewers, journalists, and readers to guard against exaggerated effectiveness claims

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    Being able to draw accurate conclusions from childhood obesity trials is important to make advances in reversing the obesity epidemic. However, obesity research sometimes is not conducted or reported to appropriate scientific standards. To constructively draw attention to this issue, we present 10 errors that are commonly committed, illustrate each error with examples from the childhood obesity literature, and follow with suggestions on how to avoid these errors. These errors are as follows: using self-reported outcomes and teaching to the test; foregoing control groups and risking regression to the mean creating differences over time; changing the goal posts; ignoring clustering in studies that randomize groups of children; following the forking paths, subsetting, p-hacking, and data dredging; basing conclusions on tests for significant differences from baseline; equating “no statistically significant difference” with “equally effective”; ignoring intervention study results in favor of observational analyses; using one-sided testing for statistical significance; and stating that effects are clinically significant even though they are not statistically significant. We hope that compiling these errors in one article will serve as the beginning of a checklist to support fidelity in conducting, analyzing, and reporting childhood obesity research
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