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IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic Code-Script Source4. Universality Affirmations: A Critical Complementarity Mandal, Abhijit, et al. Phase Transition and Critical Phenomena of Black Holes. arXiv:1608.04176. As our worldwise sapiensphere proceeds to explore any breadth and depth of a self-realizing creative cosmos, Jadavpur University, Narasinha Dutt College, and Indian Institute of Technology astrophysicists quantify the presence of thermodynamic transformations in these extreme gravitational fields. And we wish to reflect how fantastic is it that we human beings can altogether achieve such knowledge, surely there must be some grand cosmic reason and purpose for it. We present a general framework to study the phase transition of a black hole. Assuming that there is a phase transition, it is shown that without invoking any specific black hole, the critical exponents and the scaling powers can be obtained. We find that the values are exactly same which were calculated by taking explicit forms of different black hole spacetimes. The reason for this universality is also explained. The implication of the analysis is -- one does not need to investigate this problem case by case, what the people are doing right now. We also observe that these, except two such quantities, are independent of the details of the spacetime dimensions. (Abstract) Manicka, Santosh, et al. Effective Connectivity Determines the Critical Dynamics of Biochemical Networks. arXiv:2101.08111. Into 2021, Indiana University, Center for Social and Biomedical Complexity theorists including Luis Rocha (see his website at IU) advance understandings of the phenomena of self-organized criticality which is seen to naturally mediate life’s need to conserve and preserve, along with an openness to creative change. And if this golden middle way might ever at be confirmed and comprehended, it could revise our awful politics which now pits one mode vs. the other. Living systems operate in a critical dynamical regime between order and chaos where they are both resilient to perturbation, and flexible enough to evolve. To characterize such critical dynamics, the present method uses automata network connectivity and node bias (to be on or off) as tuning parameters, which can lead to uncertain predictions. We derive a more accurate approach by way of canalization, a redundancy that buffers response to inputs and traits keeping them close to optimal states despite genetic and environmental perturbations. The new 'canalization theory' of criticality is based on a measure of effective connectivity, which resolves how to find precise ways to design or control network models of biochemical regulation. (Abstract excerpt) Markovic, Dimitrije and Claudius Gros. Power Laws and Self-Organized Criticality in Theory and Nature. Physics Reports. 536/2, 2014. In a 21st century tutorial that would please Johann Wolfgang, Goethe University, Frankfurt, physicists can quantify via sophisticated mathematics the presence of a natural vitality that repeats in constant sequential and structural kind everywhere. Its essence is a dynamic balance of order and disorder, form and fracture, separate entities and integral wholes, as found via a scale-invariance from solar flares to neural cognition and Internet sites. By these propensities, it is proposed that a good way to understand life’s evolution is an on-going endeavor toward states of “highly optimized tolerance,” i.e. an optimizing process. In regard, a true “universality” of the same pattern and process from cosmos to civilization can be now affirmed. We cite its long Abstract. Power laws and distributions with heavy tails are common features of many complex systems. Examples are the distribution of earthquake magnitudes, solar flare intensities and the sizes of neuronal avalanches. Previously, researchers surmised that a single general concept may act as an underlying generative mechanism, with the theory of self organized criticality being a weighty contender. The power-law scaling observed in the primary statistical analysis is an important, but by far not the only feature characterizing experimental data. The scaling function, the distribution of energy fluctuations, the distribution of inter-event waiting times, and other higher order spatial and temporal correlations, have seen increased consideration over the last years. Leading to realization that basic models, like the original sandpile model, are often insufficient to adequately describe the complexity of real-world systems with power-law distribution. Munoz, Miguel. Colloquium: Criticality and Dynamical Scaling in Living Systems. Reviews of Modern Physics. 90/031001, 2018. This entry by the veteran University of Granada complexity theorist is reviewed more in Chap. IV Ecosmomics. It has since become considered as a premier exposition of the 21st century vivifying revolution. Nagata, Shintaro and Macoto Kikuchi. Emergence of Cooperative Bistability and Robustness of Gene Regulatory Networks. . An Osaka University biochemist and a biophysicist report that the common bistability state (Wikipedia) of dynamical systems can likewise be recognized in this genomic mode, whence GRNs reside in two coordinated, genes on and off, positions at once. See also a slide presentation Simultaneous emergence of Cooperative Response and Mutational Robustness in Gene Regulatory Networks by the authors at www.cp.cmc.osaka-u.ac.jp/~kikuchi/presentation/CCS2018. Gene regulatory networks (GRNs) are complex systems in which many genes mutually regulate their expressions for changing the cell state adaptively to environmental conditions. The GRNs utilized by living systems possess several kinds of robustness which here means that they do not lose their functions when exposed to mutation or noises. In this study, we explore the fitness landscape of GRNs and investigate how the robust feature emerges in the "well-fitted" GRNs. Thus the more sensitively a GRN responds to the input, the fitter it is. To do this, they exhibit bistability, which necessarily emerges as the fitness becomes high. These properties are universal irrespective of the evolutionary pathway, because we did not perform evolutionary simulations. (Abstract excerpt) Nicolaides, Christos, et al. Self-Organization of Network Dynamics into Local Quantized States. Nature Scientific Reports. 6/21360, 2016. In a contribution that typifies how such papers can be written nowadays, MIT engineers distill and describe a general, mechanism by which an interaction of many nodes, entities, or components in heterogeneous networks results in a spontaneous, emergent self-organization. We cite the Abstract, and the first two paragraphs where many references are listed that describe this same, archetypal phenomena from biology and brains to ecologies and societies.
Nicolas-Carlock, J., et al. Universal Fractality of Morphological Transitions in Stochastic Growth Processes. Nature Scientific Reports. 7/3523, 2017. Benemérita Universidad Autónoma de Puebla, Mexico theorists cleverly quantify the presence of common, ever repetitive dynamic forms across organismic nature. We note that over the course of this website chronicle since 2000 and before, it was not possible until just now to assert and mathematically prove such a whole scale recurrence. See also the cited paper Global Optimization, Local Adaptation, and the Role of Growth in Distribution Networks in Physical Review Letters (117/138301, 2016) and Angular and Radial Correlation Scaling in Stochastic Growth Morphodynamics at arXiv:1803.03715. Stochastic growth processes give rise to diverse and intricate structures everywhere in nature, often referred to as fractals. In general, these complex structures reflect the non-trivial competition among the interactions that generate them. In particular, the paradigmatic Laplacian-growth model exhibits a characteristic fractal to non-fractal morphological transition as the non-linear effects of its growth dynamics increase. So far, a complete scaling theory for this type of transitions, as well as a general analytical description for their fractal dimensions has been lacking. In this work, we show that despite the enormous variety of shapes, these morphological transitions have clear universal scaling characteristics. Using a statistical approach to fundamental particle-cluster aggregation, we introduce two non-trivial fractal to non-fractal transitions that capture all the main features of fractal growth. (Abstract excerpt) Norrman, Andreas and Lukasz Rudnicki. Quantum Correlations and Complementarity of Vectorial Light Fields. arXiv:1904.07533. We review this entry by MPI Science of Light researchers much more in Quantum Organics, especially for its introduction of a “triality” concept to join and unite complements. Nosonovsky, Michael and Prosun Roy. Scaling in Collodial and Biological Networks. Entropy. 22/6, 2020. We cite this contribution by University of Wisconsin bioengineers as another good example of how worldwide collaborations are finding a consistency of active topologies which form into similar nested recurrences across material, biochemical, cellular, metabolic to neural and communicative domains. By a philoSophia 2020 vision, a revolutionary organic genesis ecosmos seems well underway to being quantified. Scaling and dimensional analysis is applied to networks that describe various physical systems. Some of these networks possess fractal, scale-free, and small-world properties. First, we consider networks arising from granular and colloidal systems due to pairwise interaction between the particles. Many networks found in colloidal science possess self-organizing properties and/or self-organized criticality. Then, we discuss the allometric laws in branching vascular networks, artificial neural networks, cortical neural networks, as well as immune networks. Scaling relationships in complex networks of neurons, which are organized in the neocortex in a hierarchical manner, suggest that the characteristic time constant is independent of brain size when interspecies comparison is conducted. The information content, scaling, dimensional, and topological properties of these networks are discussed. (Abstract excerpt) Ossandon, Sebastian, et al. Neural Network Approach for the Calculation of Potential Coefficients in Quantum Mechanics. Computer Physics Communications. 214/31, 2017. We note this paper by Chilean scientists in Valparaiso, Santiago, and Chillan as an instance of 2017 integrative frontiers as cerebral dynamics gain an iconic utility and application from human to universe. In this instance, they serve to quantify quantum phenomena, as also for chemical, genetic, behavioral, and evolutionary realms. What might all this infer and portend, we wonder in April 2017, with everything so amenable to a neural, brainy essence? Who are we phenomenal interlocutors as if a procreative cosmos trying to accomplish its own self-cognizance? A numerical method based on artificial neural networks is used to solve the inverse Schrödinger equation for a multi-parameter class of potentials. First, the finite element method was used to solve repeatedly the direct problem for different parametrizations of the chosen potential function. Then, using the attainable eigenvalues as a training set of the direct radial basis neural network a map of new eigenvalues was obtained. This relationship was later inverted and refined by training an inverse radial basis neural network, allowing the calculation of the unknown parameters and therefore estimating the potential function. Three numerical examples are presented in order to prove the effectiveness of the method. The results show that the method proposed has the advantage to use less computational resources without a significant accuracy loss. (Abstract) Parastesh, Fatemeh, et al. Chimeras. Physics Reports. October, 2020. Amirkabir University of Technology, University of Western Australia, Northwestern Polytechnical University, Xi’an, China. CNR Institute of Complex Systems, Fiorentino, Italy (Stefano Boccaletti), and University of Maribor, Slovenia (Matjaz Perc) system theorists post a major 80 page, 324 reference review about this recently recognized condition. While its name is taken from organisms with a double genome, here it stands for a complex dynamism which actively resides in more or less orderly or coherent modes at the same time. The paper covers various occasions such as chemical or neural, their network topologies, and more. The import in later 2020 is to add a major explication that natural phenomena everywhere seeks and prefers a “middle way golden balance” optimum poise. Chimeras are this year coming of age since they were first observed by Kuramoto and Battogtokh in 2002. What started as an observation of a coexistence of synchronized and desynchronized states turned out to be an important new paradigm of nonlinear dynamics at the interface of physical and life sciences. Here we present a major review of chimeras, dedicated to all aspects of their theoretical and practical existence. We cover different dynamical systems in which they have been observed along with network structure for the emergence of chimeras. (Abstract) Pavithran, Induja, et al. Universality in Spectral Condensation. Nature Scientific Reports. 10/17405, 2020. As the Abstract says, by an advanced technical finesse nine scientists from the Indian Institute of Technology, Madras, UC San Diego, and the Potsdam Institute for Climate Impact Research including Jurgen Kurths uncover a constant presence of this manifest physical phenomena. The article number means that it is amongst thousands each year, millions more if eprint sites are added. Whenever might we be able to perceive our worldwise endeavor as a vital work of ecosmic self-quantification and ultimate discovery? Self-organization is the spontaneous formation of spatial, temporal, or spatiotemporal patterns in complex systems far from equilibrium. During such self-organization, energy distributed in a broadband of frequencies gets condensed into a dominant mode, analogous to a condensation phenomenon. We call this phenomenon spectral condensation and study its occurrence in fluid mechanical, optical and electronic systems. We define a set of spectral measures to quantify this condensation spanning several dynamical systems. Further, we uncover an inverse power law behaviour of spectral measures with the power corresponding to the dominant peak in the power spectrum in all the aforementioned systems. (Abstract)
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