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Displaying entries 31 through 45 of 105 found.

Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

Seidel, Benjamin, et al. Go with the Flow: The Self-Similar and Non-Linear Behaviour of Large-Scale In- and Outflows from Galaxies to Galaxy Clusters. arXiv:2503.19956. We cite this entry by Ludwig-Maximilians University, München and MPI Astrophysics cosmoscientists as an example of current Earthuman spacescape studies and their perceptive usage of the Magneticum software suite (www.magneticum.org/index, see also F. Dolaq herein). We recall this section from 2004 when such intrinsic, fractal-like structures were barely noticed. Two decades later a true universality is now confirmed as one more instance of our present 2025 discovery event.

From the scale-free nature of gravity, the structure in the universe is expected to be self-similar on large scales. In this work we investigate matter flows that connect to their cosmic environments for their agreement with self-similarity in various properties. For this purpose we use the hydrodynamical cosmological simulation suite Magneticum Pathfinder to calculate the in- and out- flow rates for haloes on a multitude of masses and redshifts and find a constant self-similarity across the whole mass range and cosmic epochs. (Excerpt)

Dai, Jian, et al. Versatile BiFeO3 shining in piezocatalysis: From materials engineering to diverse applications.. Journal of Advanced Ceramics. 14/3, 2025. We cite this work by nine scientists at the Harbin Institute of Technology, China, University of Macau, China, Universiti Sains Malaysia and Shenzhen Institute of Information Technology, China record how industrial production is turning to self-starter reactionary activities for ecological reasons.

The global demand to solve energy and environmental challenges has spurred significant interest in catalytic technologies. Piezocatalysis has emerged as a sustainable technology because its unique ability to harvest mechanical into electrochemical energy. Versatile BiFeO3 (BFO) stands out for its integration of piezoelectric, multiferroic, and optical properties. This review examines energy band theory to reveal the roles of internal charges, screening charges, and piezoelectric electrons in driving catalytic reactions. (Excerpts)

Harraz, Deiaa, et al. Homogeneous-heterogeneous bifunctionality in Pd-catalyzed vinyl acetate synthesis. Science. April 4, 2025. With regard to growing recognitions of nature’s self-initiating and sustaining material and functional propensities, we cite another instance as MIT and Harvard chemical biologists offer deeper insights to its pervasive presence and advantages. See also Catalysis at the crossroads by Cathy Tway and Sorin Filip in the same issue for an expert review. See also Proverbio, Daniele and Giulia Giordano. Resilience of the autocatalytic feedback loop for gene regulation by Daniele Proverbio and Giulia Giordano at arXiv:2504.03276 and Versatile BiFeO3 shining in piezocatalysis by Jian Dai, et al in the Journal of Advanced Ceramics, (14/3, 2025)

Mechanistic paradigms in catalysis posit that the active species remains either in a homogeneous or heterogeneous state during reactions. In this work, we show that a prominent industrial process, palladium (Pd)–catalyzed vinyl acetate synthesis, proceeds via interconversion of both heterogeneous and homogeneous during catalysis in a complementary manner. We found that heterogeneous, nanoparticulate Pd(0) serves as an active oxygen reduction electrocatalyst to form homogeneous Pd(II), which then catalyzes selective ethylene acetoxylation. (Harraz)

Approximately 90% of industrial chemical products use catalysts to speed up a reaction, minimize energy consumption, and reduce waste. They are classified into two main types. Homogeneous catalysts as the reactants decrease the activation energy by direct binding. while heterogeneous catalysts work by surface reactions. In this issue, Harraz et al. report an interconversion between homogeneous and heterogeneous catalysts within the same catalytic cycle of vinyl acetate synthesis. (Tway, Filip)

McGlothin, Connor, et al. Autocatalytic Nucleation and Self-Assembly of Inorganic Nanoparticles into Complex Biosimilar Networks.. Angewandte Chemie International Edition. 64/9, 2025. In this premier European chemistry journal, Center of Complex Particle Systems, University of Michigan science scholars including Paul Bogdan illuminate nature’s intrinsic usage of these creative reactivities in every instance and then describe how they spontaneously organize into active, viable network topologies. This whole scale scenario is taken further as it seen to apply to deeper material domains. The emergence of NanoParticle systems with quantifiable similarities to biological patterns may provide the missing link between inorganic and organic complex systems. So once more in the scientific periodicals, a common, phenomenal consistency from an ecosmic uniVerse all the way to ourselves becomes evident.

Self-replication of bioorganic molecules and oil microdroplets have been explored as models in prebiotic chemistry. An analogous process for inorganic nanomaterials would involve the autocatalytic nucleation of metal, semiconductor, or ceramic nanoparticles-an area that remains largely uncharted. A demonstration of such systems would be especially relevant if they were seen to self-assemble into complex structures. Here, we show that an autocatalytic nucleation of nanoparticles yields conformal networks with hierarchical organization, including “colonies.” This work establishes mathematical and structural parallels between biotic and abiotic matter, integrating self-organization, autocatalytic nucleation, and theoretical description of complex systems. (Excerpt)

Lawzer, Arun-Libertsen, et al. Isomerisation of phosphabutyne and a photochemical route to phosphabutadiyne (HC3P), a phosphorus analogue of cyanoacetylene. Physical Chemistry Chemical Physics. April 21, 2025. Polish Academy of Sciences, Warsaw and University of Rennes, France astrochemists describe a latest, detailed ISM detection and spectrographic analysis of an array of vital complex phosphorus-based precursors. As these endeavors proceed via a sophisticated depth and detail, these sequential, biospecific formations altogether well imply and attest to an innate, oriented process toward organisms and evolution. From simple signs in the 1970s to an instrumental acumen today, this natural phenomena is not accidental nor pointless but an quite indicative of a fertile, organic milieu on its way to our notice, record, amazement and continuance.

The photochemistry of phosphabut-1-yne, CH3CH2CP, was investigated by means of infrared spectroscopy assisted by theoretical (DFT) predictions. The UV-irradiated compound, isolated in a cryogenic argon matrix, undergoes isomerization and dissociation. Several isomers of phosphabutyne, in addition to phosphabutadiyne (HC3P), ethynylphosphinidene (HCCP), and phoshaethyne (HCP) are formed as the main photoproducts. Vibrational spectra of astrochemically relevant molecules HC3P and CH2CHCP (vinylphosphaethyne), have been detected and analyzed here for the first time. (Abstract)

Ecosmomics: Independent Complex Network Systems, Computational Programs, Genetic Ecode Scripts

Andersen, Benjamin, et al. Evidence of universal conformal invariance in living biological matter. Nature Reviews Physics.. March, 2025. Eight computational physicists posted at the University of Copenhagen, University of Lisbon, University of Lausanne and University of Sheffield press on with mid 2020s complexity studies so to provide another theoretical perspective upon nature’s universal, recurrent lawfulness. A notable difference is that they begin with physical inorganic materials which are seen to likewise express organized structures known as conformal invariance. The project continues to biological phases where the view prompts a further way to perceive a constant viable criticality. Our planatural philoSophia take is to then suggest that these many current integral insights we record seem just now coming to their phenomenal realization and factual discovery.

The emergent dynamics of collective cellular movement depend on how cells interact and move across biological systems. Here we report experimental evidence of a universal feature in the patterns of flow that spontaneously emerge in cellular groups. Specifically, we show that the flows generated by dog kidney cells, human breast cancer cells and pathogenic bacteria exhibit robust conformal invariance. This constant recurrence reveals that the macroscopic features of living biological matter exemplify universal translational, rotational and scale symmetries that are independent of their microscopic constituents. (Abstract)

Although many attempts have been made to model the patterns of collective movement made by organisms, we still lack a general unifying theory. In contrast, the study of complex interactions between the components that make up inanimate materials has led to common behaviours near critical regimes. The principles that give rise to this universality have been described using the framework of conformal field theory, which predicts how shapes and angles of structures are locally conserved across different systems. (1)

In this paper, we experimentally demonstrate that the patterns of collective movement observed in different types of living matter exhibit common characteristics that transcend the properties of the cells from which they are composed. We show that many instances including colonies of pathogenic bacteria, groups of collective kidney cells and breast cancer cells, spontaneously generate flows that exhibit a universal conformal invariance described by the percolation universality class. (2)

These results suggest that the theories used to describe conformally invariant structures might have a much broader range of applications. Although our results do not necessarily indicate that the collective movement we observe is operating at the critical point of a phase transition, many different biological systems are thought to be poised near criticality which allows them to easily switch between different states. Our findings, thus, imply that the mathematics to study conformally invariant structures could also lead to new methods to detect and understand critical phenomena in biology. (5)

Knona, Mikail, et a. Global modules robustly emerge from local interactions and smooth gradients. Nature. February 19, 2025. MIT neuroscientists including Ila Fiete provide another, novel explanation for nature’s apparent spontaneous, oriented propensity to organize itself into ascendant entity/ensemble vitalities.

Modular structure and function are ubiquitous in biology from the organization of animal brains and bodies to the scale of ecosystems. However, the way modularity emerges from non-modular precursors remain unclear. Here we introduce the principle of peak selection, a process by which purely local interactions and smooth gradients can drive the self-organization of discrete global modules. The process combines the positional and Turing pattern-formation mechanisms into a model for morphogenesis. (Excerpt)

Nichele, Stefano, et al. Cellular Automata, Distributed Dynamical Systems, and Their Applications to Intelligence. Artificial Life. 31/1, 2025. SN, Hiroki Sayama and Chrystopher Nehaniv introduce a special issue on these title aspects with regard to how they serve and embellish nature’s vital, complex spontaneities across the ecomos. The four included papers are from a workshop at a 2023 Artificial Life conference in Sapporo, Japan, to bridge the gap between the ALife community and the artificial intelligence (AI) researchers interested in exploring concepts from nonlinear phenomena.

Distributed dynamical systems like cellular automata (CAs) and random boolean networks (RBNs) have long been used to understand computation and self-replication in biology, morphogenesis, gene regulation, life-as-it-could-be, and the Universe. Recent advances, such as continuous CAs, Lenia, and neural-based CAs have been proposed to study the emergence of a more general intelligence based on their support properties like self-organization, emergence, and open-endedness. (Abstract)

In “Cell-Cell Interactions: How Coupled Boolean Networks Tend to Criticality,” Braccini and coauthors investigate interacting RBNs as a theoretical model of multicellular biological systems with cell–cell interactions. They find not only that the interacting versions of RBNs show the same general trends of dynamical properties as their individual counterparts but also that the networks in ordered or chaotic regimes tend toward a critical regime when turned into interacting networks.

Barzon, Giacomo, et al. Excitation-Inhibition Balance Controls Information Encoding in Neural Populations. Physics Review Letters. 134/068403, 2025. University of Padova, MPI Physics of Complex Systems, and École Polytechnique de Lausanne contribute more evidential proof of life’s ubiquitous preference to balance beam these coincident opposites for best behaviors. See also Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics by Guillermo Morales, et al in PNAS (120/9, 2023).

Understanding how the complex connectivity structure of the brain shapes its information-processing capabilities is a work in process. Here we focus on a paradigmatic architecture to study how the neural activity of excitatory and inhibitory populations encodes information from external signals. We show that informative content is maximized at the edge of stability as inhibition balances excitation. Along with other recent findings, our results portend a deeper information-theoretic understanding of how the balance between excitation and inhibition controls optimal information-processing in neural populations. IAbstract)

Deco, Gustavo, et al. Complex harmonics reveal low-dimensional manifolds of critical brain dynamics.. Physical Review E. 111/014410, 2025. Universitat Pompeu Fabra, Barcelona and Oxford University open another window to view a neural nature which attains a twintelligence (herein a reciprocal poise) and effective cognizance by way of this complementarity and familiarity. See also Emergence of Power-Law Avalanches from Collective Stochastic Dynamics of Adaptive Neurons by Lik-Chun Chan, et al in PRX Life (3/013013, 2025).

The brain needs to perform time-critical computations to ensure survival, for which nonlocal, distributed computation at the whole-brain level make possible by self-organized criticality. These responses accord with Schrödinger's wave equation, so as to form a complex harmonics decomposition (CHARM) framework to express the complex network dynamics that are the key computational engines of critical brain dynamics. (Excerpt)

Hurtado-Gutiérrez, Hurtado-Gutiérrez. Programmable time crystals from higher-order packing fields. Physical Review E. 111/934119, 2025. We cite these findings by Electromagnetismo y Física de la Materia, Universidad de Granada researchers as still another window on the ubiquitous presence of critically poised, transitional phenomena in any manner of the curious geometric formations that an animated nature can take.

Time crystals are many-body systems that break time-translation symmetry, exhibit spatiotemporal order and periodic motion. Recent results have shown that coupling an external packing field to density fluctuations can trigger a transition to a time-crystal phase. Here, we exploit this mechanism to create on-demand programmable time crystals and elucidate the underlying critical point. Overall, these results demonstrate the versatility and broad possibilities of this promising route to time crystals. (Excerpt)
A scaling analysis of the results allows us to determine critical points which characterize this class of time-crystal phase transitions. Their exponents are compatible with the Kuramoto universality class that characterizes the synchronization of oscillators, independently of the packing order. We also define the condensates density profiles predicted for the higher-order shapes in terms of first-order ones. (10)

Gabrielli, Andrea, et al. Network Renormalization. arXiv:2412.12988. Enrico Fermi Research Center, Rome, IMT School for Advanced Studies, Lucca, University of Leiden and Universitat de Barcelona physicists including Ángeles Serrano begin to methodically scope out how this reliable physical approach can now be effectively applied to life’s many complex network vitalities, which has mostly eluded prior success. Their contribution so far involves a new informational content and the presence of chimeras and criticalities.

Renormalization group (RG) theories were developed to describe system configurations with many degrees of freedom, along with the associated model parameters and coupling constants. They also can identify critical points of phase transitions. Usually, the RG builds on the notions of homogeneity, symmetry, geometry and locality to define metric distances, scale transformations and self-similar coarse-graining. However, the strong heterogeneity of real-world networks complicates renormalization procedures. In this review, we discuss past attempts, the important advances, and the ochallenges on the road to network renormalization. (Excerpt)

Zhang, Zhang et al. Coarse-graining network flow through statistical physics and machine learning.. Nature Communications. 16/1605, 2025. We cite this entry by Beijing Normal University, Indiana University and University of Padua theorists including Manlio De Domenico as an example of new abilities to root complex system phenomena in deep physical substrates by way of an AI assistance.

Information dynamics plays a crucial role in complex systems from cells to societies. Recent advances in statistical physics have been able to find key network properties but large system sizes have computational issues. We use graph neural networks to identify coarse-graining groups to achieve a low computational complexity for practical applications. Our method offers multiscale compression perspective that preserves information flow in biological, social, and technological networks better than other methods mostly focused on network structure. (Excerpt)

, . Stepney, Susan. Physical reservoir computing: a tutorial. Natural Computing. November 2024.. Natural Computing.. November, 2024. The University of York computer scientist (search) provides a latest succinct explanation of this increasingly popular procedure especially as quantum versions become available. See, for example, A Reservoir-based Model for Human-like Perception of Complex Rhythm Pattern by Zhongju Yuan, et al at arXiv:2503.12509.

This tutorial covers physical reservoir computing which first defines what it means for a physical system to compute, rather than evolve under the laws of physics. It describes the underlying computational Echo State Network (ESN) model, and explains why the it is suitable for direct physical implementation. The entry then describes how to characterise a physical reservoir in terms of benchmark tasks, and task-independent measures, along with optimising configuration parameters, and exploring the space of potential configurations. (Excerpt)

Reservoir computing is derived from recurrent neural network theory that maps input signals into dimensional spaces through a non-linear system called a reservoir The first key benefit is that training is performed only at the readout stage. The second is that the computational power of natural systems, both classical and quantum, can reduce the relative cost.

Flamm, Christoph, et al. Computation in chemical graph rewriting networks. Journal of Physics: Complexity. 6/1, 2025. CF and Peter Stadler, University of Vienna and Daniel Merkle, Algorithmic Cheminformatics Group, Bielefeld University discuss perceptive ways to investigate and identify the computational capabilities of ‘constructive’ chemistry.

transformations underlying the turn-over of their molecular components. In chemical reaction networks, computation may refer to two main aspects: concentrations of molecules, and molecular structures. The latter can be modeled by a chemical rewriting system acting on structural formulae, i.e. labeled graphs. We investigate graph rewriting and show that it can emulate Turing machines. and the computational capabilities of ‘constructive’ chemistry. (Excerpt)

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