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      Biophotons and Emergence of Quantum Coherence—A Diffusion Entropy Analysis

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          Abstract

          We study the emission of photons from germinating seeds using an experimental technique designed to detect light of extremely small intensity. We analyze the dark count signal without germinating seeds as well as the photon emission during the germination process. The technique of analysis adopted here, called diffusion entropy analysis (DEA) and originally designed to measure the temporal complexity of astrophysical, sociological and physiological processes, rests on Kolmogorov complexity. The updated version of DEA used in this paper is designed to determine if the signal complexity is generated either by non-ergodic crucial events with a non-stationary correlation function or by the infinite memory of a stationary but non-integrable correlation function or by a mixture of both processes. We find that dark count yields the ordinary scaling, thereby showing that no complexity of either kinds may occur without any seeds in the chamber. In the presence of seeds in the chamber anomalous scaling emerges, reminiscent of that found in neuro-physiological processes. However, this is a mixture of both processes and with the progress of germination the non-ergodic component tends to vanish and complexity becomes dominated by the stationary infinite memory. We illustrate some conjectures ranging from stress induced annihilation of crucial events to the emergence of quantum coherence.

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          Permutation entropy: a natural complexity measure for time series.

          We introduce complexity parameters for time series based on comparison of neighboring values. The definition directly applies to arbitrary real-world data. For some well-known chaotic dynamical systems it is shown that our complexity behaves similar to Lyapunov exponents, and is particularly useful in the presence of dynamical or observational noise. The advantages of our method are its simplicity, extremely fast calculation, robustness, and invariance with respect to nonlinear monotonous transformations.
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            Long-Term Storage Capacity of Reservoirs

            H E Hurst (1951)
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              • Article: not found

              Transport, Collective Motion, and Brownian Motion

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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Entropy (Basel)
                Entropy (Basel)
                entropy
                Entropy
                MDPI
                1099-4300
                29 April 2021
                May 2021
                : 23
                : 5
                : 554
                Affiliations
                [1 ]Laboratori Nazionali di Frascati, Istituto Nazionale di Fisica Nucleare, Via E. Fermi 40, 00044 Frascati, Italy; elisabetta.pace@ 123456lnf.infn.it (E.P.); Catalina.Curceanu@ 123456lnf.infn.it (C.C.); alessandro.scordo@ 123456lnf.infn.it (A.S.); alberto.clozza@ 123456lnf.infn.it (A.C.)
                [2 ]Dipartimento di Fisica, Università di Roma “Tor Vergata”, Via della Ricerca Scientifica, 00133 Roma, Italy; Ivan.Davoli@ 123456roma2.infn.it (I.D.); massimiliano.lucci@ 123456roma2.infn.it (M.L.)
                [3 ]Dipartimento di Ingegneria Industriale, Università di Roma “Tor Vergata”, Via del Politecnico, 00133 Roma, Italy; roberto.francini@ 123456roma2.infn.it (R.F.); dematteis@ 123456roma2.infn.it (F.D.M.)
                [4 ]Istituto La Torre, Via M. Ponzio 10, 10141 Torino, Italy; mauriziograndi@ 123456mauriziograndi.it
                [5 ]Department of Biology, University of Texas at San Antonio, San Antonio, TX 78249, USA; raisha.t@ 123456gmail.com
                [6 ]Center for Nonlinear Science, University of North Texas, Denton, TX 76203-5017, USA
                Author notes
                Author information
                https://orcid.org/0000-0002-2493-8939
                https://orcid.org/0000-0002-3374-8555
                https://orcid.org/0000-0002-7703-7050
                https://orcid.org/0000-0003-2110-9104
                https://orcid.org/0000-0003-2771-9341
                https://orcid.org/0000-0001-7860-9754
                https://orcid.org/0000-0003-2762-9856
                Article
                entropy-23-00554
                10.3390/e23050554
                8146849
                33947077
                bea83427-36f5-4f99-b223-16f42bacd486
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 20 February 2021
                : 28 April 2021
                Categories
                Article

                biophotons,diffusion entropy analysis,complexity,cognition

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