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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Generative Code-Script Source

5. Common Code: A Further Report of Reliable, Invariant Principles

Balasis, Georgios, et al. Universality in Solar Flare, Magnetic Storm and Earthquake Dynamics using Tsallis Statistical Mechanics. Physica A. In Press, October, 2010. Astrophysicists from Athens and Paris contribute to a rush of recognitions and explanations from experiment and theory of a constant form and flux in every stellar and geospheric corner, which surely augurs for a common mathematical origin.

The new field of complex system studies holds that the dynamics of various complex systems are founded on universal principles, which can be used to describe disparate problems. A basic reason for our interest in complexity is the striking similarity in behavior near the global instability among systems that are otherwise quite different in nature. The observed similarity suggests a common approach to the interpretation of these diverse phenomena in terms of driving physical mechanisms that have the same character.

Mounting empirical evidence has been supporting the possibility that a number of systems arising in disciplines as diverse as physics, biology, engineering, and economics may have certain quantitative features that are intriguingly similar. These properties can be conveniently grouped under the headings of scale invariance and universality.

Banavar, Jayanth, et al. Form, Function, and Evolution of Living Organisms. Proceedings of the National Academy of Sciences. 111/3332, 2014. The international team of Banavar, Todd Cooke, Andrea Rinaldo, and Amos Maritan (search each) broach a synthesis after years of research by many scientists upon the persistent, mathematical topologies of plants and animals. From innate natural principles, a universal self-similarity is found to be expressed by flora and fauna which serves to optimize energetic efficiencies. Once again a constant pattern recurs everywhere, which quite implies an intrinsic animate source.

Despite the vast diversity of sizes and shapes of living organisms, life’s organization across scales exhibits remarkable commonalities, most notably through the approximate validity of Kleiber’s law, the power law scaling of metabolic rates with the mass of an organism. Here, we present a derivation of Kleiber’s law that is independent of the specificity of the myriads of organism species. Specifically, we account for the distinct geometries of trees and mammals as well as deviations from the pure power law behavior of Kleiber’s law, and predict the possibility of life forms with geometries intermediate between trees and mammals. We also make several predictions in excellent accord with empirical data. Our theory relates the separate evolutionary histories of plants and animals through the fundamental physics underlying their distinct overall forms and physiologies. (Abstract)

Barrat, Alain, et al. Complex Networks: From Biology to Information Technology. Journal of Physics A: Mathematical and Theoretical. 41/22, 2008. An introduction to the proceedings of a July 2007 STATPHYS23 meeting on the subject which then divides into two main categories – their common Structure and Dynamics, and ubiquitous Biological, Social, and Technological applications. See Porter, et al below for more on this genesis nature.

Baruchi, Itay, et al. Functional Holography of Complex Networks Activity – From Cultures to the Human Brain. Complexity. 10/3, 2005. In a similar way to holographic universe theories (see Quantum Cosmology) Baruchi, along with Vernon Towle and Eshel Ben-Jacob, find that biological and neural networks, in their algorithmic processes, take on the typical properties of a hologram. Here is still another approach which finds nature to be distinguished by the same pattern and process at each scale and instance.

In a similar way to holographic universe theories (see Quantum Cosmology) Baruchi, along with Vernon Towle and Eshel Ben-Jacob, find that biological and neural networks, in their algorithmic processes, take on the typical properties of a hologram. Here is still another approach which finds nature to be distinguished by the same pattern and process at each scale and instance.

Bhattacharyya, Pratip, et al. A Common Mode of Origin of Power Laws in Models of Market and Earthquake. Physica A. 381/377, 2007. From the Theoretical Condensed Matter Physics Division and Centre for Applied Mathematics and Computational Science, Saha Instute of Nuclear Physics, and the Physics Department, Gurudas College, Kolkata, India, comes this report of widely separated natural and social domains which are yet seen hold to the same dynamic topologies.

We show that there is a common mode of origin for the power laws observed in two different models: (i) the Pareto law for the distribution of money among the agents with random-saving propensities in an ideal gas-like market model and (ii) the Gutenberg–Richter law for the distribution of overlaps in a fractal-overlap model for earthquakes. The identification of the generic origin of the power laws helps in better understanding and in developing generalized views of phenomena in such diverse areas as economics and geophysics. (377)

Bickhard, Mark. Interactivism: Introduction to the Special Issue. Synthese. 166/449, 2009. This citation could also apply to “Does Process Matter? An Introduction to the Special Issue on Interactivism” of Aximathes (21/1-2, 2011). For each edition, the Lehigh University philosopher views the scene and articles with regard to the title turn from preoccupations with particulate matter to give equal worth to the real presence of dynamical interrelations between elements. In both issues, Australian systems scholar Cliff Hooker pens a large chapter on a bio-cognitive rationality. Another sample could be “Physicalism, Emergence and Downward Causation.”

Blagus, Neli, et al. Self-Similar Scaling of Density in Complex Real-World Networks. Physica A. 391/8, 2011. As an example of our instant, worldwide science collaboration, as if a global brain/mind learning on her/his own, University of Ljubljana, Slovenia, Computer and Information scientists contribute to the current distillation and discovery (search herein, Systems Physics, and throughout) from a decade of theory and evidence of a genesis nature innately distinguished by such a dynamic network anatomy and physiology. With total online access, the authors could drawn on a vast array of social, citation, web graph, commerce, communication, information, software, technological, biological, microbial, protein web, modular, hierarchical living systems to make their case. Compare, e.g., with Kim, J., et al. “Fractality and Self-Similarity in Scale-Free Networks” in New Journal of Physics. (9/6, 2007).

Despite their diverse origin, networks of large real-world systems reveal a number of common properties including small-world phenomena, scale-free degree distributions and modularity. Recently, network self-similarity as a natural outcome of the evolution of real-world systems has also attracted much attention within the physics literature. Here we investigate the scaling of density in complex networks under two classical box-covering renormalizations - network coarse-graining - and also different community-based renormalizations. The analysis on over 50 real-world networks reveals a power-law scaling of network density and size under adequate renormalization technique, yet irrespective of network type and origin. The results thus advance a recent discovery of a universal scaling of density among different real-world networks (search Laurienti) and imply an existence of a scale-free density also within - among different self-similar scales of - complex real-world networks. (Abstract, 1)

Self-similarity is primarily studied under the framework of network renormalization. As already discussed, renormalization is an iterative coarse-graining technique, where the original network is covered with boxes, thus each node belongs to exactly one box. Boxes are then replaced by super-nodes that are linked when a corresponding link also exists in the (original) network. The entire process repeats until no links remain and the number of nodes equals to the number of connected components. (3)

Blasone, Massimo. A Physicist’s View on Chopin’s Etudes. European Physical Journal Special Topics. 226/12, 2017. In an issue of papers from a 2016 Quantum Gases and Quantum Coherence, a University of Salerno researcher offers that classical music compositions can readily be seen as expressions of cosmic principles. Apropos, see also Complexity Measures of Music by April Pease, et al at arXiv:1708.08041 which reports the same inverse power-law index for melodies and brains, along with Statistical Evolutionary Laws in Music Styles by Eita Nakamura and Kunihiko Kaneko in Nature Scientific Reports (9/15993, 2019).

We propose the use of specific dynamical processes and more in general of ideas from Physics to model the evolution in time of musical structures. We apply this approach to two Études by F. Chopin, namely Op.10 n.3 and Op.25 n.1, proposing some original description based on concepts of symmetry breaking/restoration and quantum coherence, which could be useful for interpretation. In this analysis, we take advantage of colored musical scores, obtained by implementing Scriabin’s color code for sounds to musical notation. (Abstract)

Boettiger, Alistair and George Oster. Emergent Complexity in Simple Neural Systems. Communicative & Integrative Biology. 2/6, 2009. University of California, Berkeley, biophysicists find seashell patternings and our cortical neural activities to spring from and express the same fractal-like geometries and dynamics.

The ornate and diverse patterns of seashells testify to the complexity of living systems. Provocative computational explorations have shown that similarly complex patterns may arise from the collective interaction of a small number of rules. This suggests that, although a system may appear complex, it may still be understood in terms of simple principles. (467)

Boldini, Alain, et al. Application of Symbolic Recurrence to Experimental Data from Firearm Prevalence to Fish Swimming. Chaos. 29/113128, 2019. NYU and Technical University of Cartagena, Spain bioengineers finesse mathematical techniques in search of better ways to parse and compare complex interactive phenomena across wide scales and instances. And coincidently we log in on the December 14 date of the 2012 Newton school shooting, which is mentioned in the paper. However then might a breadth and depth of credible, sufficient, phenomenal proof be achieved so we peoples could realize and implement an independent, universal naturome code? See also Symbolic Recurrence Plots to Analyze Dynamical Systems by Victoria Caballero-Pintado, et al in Chaos (28/063112, 2018).

Recurrence plots and recurrence quantification analysis are powerful tools to study the behavior of nonlinear dynamical systems. Previous usages, however, have led to arbitrary definitions of recurrence. Here we describe a symbolic recurrence to overcome this issue, and to better book-keep recurrent portions of the phase space and real time series. We illustrate by examining a wide range of experimental datasets from firearm prevalence and media coverage to the sexual interaction of swimming fish. These results demonstrate the potential of symbolic recurrence in real-world applications across research fields. (Abstract excerpt)

Bransburg-Zabary, Sharron, et al. Individual and Meta-Immune Networks. Physical Biology. 10/2, 2013. In this 21st century journal, an international team from Tel Aviv University, Boston University, Harvard Medical School, Weismann Institute, and Rice University, including Alfred Tauber and Eshel Ben-Jacob, provide a strong affirmation of the similarly nonlinear essence of immune systems. Indeed, it is noted that the 1970s network theories of Danish immunologist Niels Jerne, for which he received the 1984 Nobel Prize in Medicine, are now well explicated and quite proven.

In recent years, network theory has become one of the central theoretical frameworks that can be applied to the description, analysis and understanding of complex systems and in particular in strongly coupled multi-level complex systems. Complex networks can be found everywhere, in man-made systems and in human social systems, in organic and non-organic matter, in natural and anthropogenic structures as well as in biological systems. Examples include linked molecular or cellular structures, climate networks, communication and infrastructure networks, social and economic networks, gene networks, neuron networks and immune networks. The understanding of the growth, structure, dynamics and functioning of these networks, and their mutual interrelationships, is critical. (1)

Physical Biology is a new peer-reviewed publication from Institute of Physics Publishing. Launched in 2004, the journal will foster the integration of biology with the traditionally more quantitative fields of physics, chemistry, computer science and other math-based disciplines. Its primary aim is to further the understanding of biological systems at all levels of complexity, ranging from the role of structure and dynamics of a single molecule to cellular networks and organisms. The journal encourages the development of a new biology-driven physics based on the extraordinary and increasingly rich data arising in biology, and provides research directions for those involved in the creation of novel bio-engineered systems.

Brummer, Alexander, et al. A General Model for Metabolic Scaling in Self-Similar Asymmetric Networks. PLoS Computational Biology. 13/3, 2017. Brummer, University of Arizona physics, Van Savage, UCLA biomathematics, and Brian Enquist, UA ecology expand and finesse the WBE (West, Geoffrey, Brown, James H. and Enquist) theory of life’s nested recurrences. Since its inception in 1997 (search), this well researched and tested explanation has become basically verified and established. The new National Geographic TV program Xray Earth, available on You Tube, has segments by Enguist and West about these luminous findings.

We have derived a more general form of the WBE model. It incorporates different branching geometries reflected in differences in branching asymmetry. We believe that our approach can offer a more general theory that can better relate variation in organismal form and function than the original WBE model. Our definition of asymmetry in a strictly bifurcating network allows for a more accurate analysis of biological branching networks. In addition, the theory makes a set of novel predictions for the type of branching asymmetry favored under different fluid flow types/transfer regimes. (19)

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