366 results match your criteria essentially dynamical

Chemo-hydrodynamic pulsations in simple batch A + B → C systems.

J Chem Phys 2021 Mar;154(11):114501

Nonlinear Physical Chemistry Unit, Service de Chimie Physique et Biologie Théorique, Université Libre de Bruxelles, CP 231 - Campus Plaine, 1050 Brussels, Belgium.

Spatio-temporal oscillations can be induced under batch conditions with ubiquitous bimolecular reactions in the absence of any nonlinear chemical feedback, thanks to an active interplay between the chemical process and chemically driven hydrodynamic flows. When two reactants A and B, initially separated in space, react upon diffusive contact, they can power convective flows by inducing a localized variation of surface tension and density at the mixing interface. These flows feedback with the reaction-diffusion dynamics, bearing damped or sustained spatio-temporal oscillations of the concentrations and flow field. Read More

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Non-Normalizable Quasi-Equilibrium Solution of the Fokker-Planck Equation for Nonconfining Fields.

Entropy (Basel) 2021 Jan 20;23(2). Epub 2021 Jan 20.

Department of Physics, Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel.

We investigate the overdamped Langevin motion for particles in a potential well that is asymptotically flat. When the potential well is deep as compared to the temperature, physical observables, like the mean square displacement, are essentially time-independent over a long time interval, the stagnation epoch. However, the standard Boltzmann-Gibbs (BG) distribution is non-normalizable, given that the usual partition function is divergent. Read More

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January 2021

Identification of effective spreaders in contact networks using dynamical influence.

Appl Netw Sci 2021 19;6(1). Epub 2021 Jan 19.

Department of Electronic and Electrical Engineering, University of Strathclyde, George Street, Glasgow, UK.

Contact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. Read More

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January 2021

Connecting relaxation time to a dynamical length scale in athermal active glass formers.

Phys Rev E 2020 Dec;102(6-1):062605

Department of Physics, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India.

Supercooled liquids display dynamics that are inherently heterogeneous in space. This essentially means that at temperatures below the melting point, particle dynamics in certain regions of the liquid can be orders of magnitude faster than other regions. Often dubbed dynamical heterogeneity, this behavior has fascinated researchers involved in the study of glass transition for over two decades. Read More

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December 2020

Dynamical magnetic behavior of anisotropic spinel-structured ferrite for GHz technologies.

Sci Rep 2021 Jan 12;11(1):614. Epub 2021 Jan 12.

Graduate School of Engineering, Tohoku University, Building No. 2, 6-6-05 Aoba Aza Aramaki, Aoba, Sendai, Miyagi, 980-8579, Japan.

We have fabricated a high quality magnetic NiZnFeO ferrite powder/polymer composite sheet consisting of common and environmentally friendly elements only. The sheet was then tested for its dynamic permeability by irradiating with electromagnetic waves with frequencies up to 50 GHz. Two different originally developed methods were used for the high-frequency permeability measurements, a short-circuited microstrip line method and a microstrip line-probe method. Read More

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January 2021

Quasi-stationary states of game-driven systems: A dynamical approach.

Chaos 2020 Dec;30(12):123145

Department of Applied Mathematics, Lobachevsky University, 603950 Nizhny Novgorod, Russia.

Evolutionary game theory is a framework to formalize the evolution of collectives ("populations") of competing agents that are playing a game and, after every round, update their strategies to maximize individual payoffs. There are two complementary approaches to modeling evolution of player populations. The first addresses essentially finite populations by implementing the apparatus of Markov chains. Read More

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December 2020

The End of a Classical Ontology for Quantum Mechanics?

Peter W Evans

Entropy (Basel) 2020 Dec 24;23(1). Epub 2020 Dec 24.

School of Historical and Philosophical Inquiry, University of Queensland, St Lucia, QLD 4072, Australia.

In this paper, I argue that the Shrapnel-Costa no-go theorem undermines the last remaining viability of the view that the fundamental ontology of quantum mechanics is essentially classical: that is, the view that physical reality is underpinned by objectively real, counterfactually definite, uniquely spatiotemporally defined, local, dynamical entities with determinate valued properties, and where typically 'quantum' behaviour emerges as a function of our own in-principle ignorance of such entities. Call this view Einstein-Bell realism. One can show that the causally symmetric local hidden variable approach to interpreting quantum theory is the most natural interpretation that follows from Einstein-Bell realism, where causal symmetry plays a significant role in circumventing the nonclassical consequences of the traditional no-go theorems. Read More

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December 2020

Measuring Information Coupling between the Solar Wind and the Magnetosphere-Ionosphere System.

Entropy (Basel) 2020 Feb 28;22(3). Epub 2020 Feb 28.

INAF-Istituto di Astrofisica e Planetologia Spaziali, via del Fosso del Cavaliere 100, 00133 Roma, Italy.

The interaction between the solar wind and the Earth's magnetosphere-ionosphere system is very complex, being essentially the result of the interplay between an external driver, the solar wind, and internal processes to the magnetosphere-ionosphere system. In this framework, modelling the Earth's magnetosphere-ionosphere response to the changes of the solar wind conditions requires a correct identification of the causality relations between the different parameters/quantities used to monitor this coupling. Nowadays, in the framework of complex dynamical systems, both linear statistical tools and Granger causality models drastically fail to detect causal relationships between time series. Read More

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February 2020

Combination of Hybrid Particle-Field Molecular Dynamics and Slip-Springs for the Efficient Simulation of Coarse-Grained Polymer Models: Static and Dynamic Properties of Polystyrene Melts.

J Chem Theory Comput 2021 Jan 4;17(1):474-487. Epub 2020 Dec 4.

Eduard-Zintl-Institut für Anorganische und Physikalische Chemie, Technische Universität Darmstadt, Alarich-Weiss-Str. 8, 64287 Darmstadt, Germany.

A quantitative prediction of polymer-entangled dynamics based on molecular simulation is a grand challenge in contemporary computational material science. The drastic increase of relaxation time and viscosity in high-molecular-weight polymeric fluids essentially limits the usage of classic molecular dynamics simulation. Here, we demonstrate a systematic coarse-graining approach for modeling entangled polymers under the slip-spring particle-field scheme. Read More

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January 2021

Is spread of COVID-19 a chaotic epidemic?

Chaos Solitons Fractals 2021 Jan 20;142:110376. Epub 2020 Oct 20.

Washington State University, Vancouver, WA, USA.

The COVID-19 epidemic challenges humanity in 2020. It has already taken an enormous number of human lives and had a substantial negative economic impact. Traditional compartmental epidemiological models demonstrated limited ability to predict the scale and dynamics of COVID-19 epidemic in different countries. Read More

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January 2021

Mathematical modeling and cellular automata simulation of infectious disease dynamics: Applications to the understanding of herd immunity.

J Chem Phys 2020 Sep;153(11):114119

Solid State and Structural Chemistry Unit, Indian Institute of Science, Bengaluru, India.

The complexity associated with an epidemic defies any quantitatively reliable predictive theoretical scheme. Here, we pursue a generalized mathematical model and cellular automata simulations to study the dynamics of infectious diseases and apply it in the context of the COVID-19 spread. Our model is inspired by the theory of coupled chemical reactions to treat multiple parallel reaction pathways. Read More

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September 2020

Incorporating the Connectivity Timescale in Metapopulation Partitioning.

Am Nat 2020 08 6;196(2):145-156. Epub 2020 Jul 6.

The often complex spatial patterns of propagule dispersal across a metapopulation present a challenge for species management, motivating efforts to represent the connectivity in simpler but meaningful ways. The reduction of complexity may be achieved by partitioning the metapopulation into groups of highly connected patches called "subpopulations." To have relevance for management, these subunits must be defined from ecological or evolutionary principles. Read More

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Efficient and exact sampling of transition path ensembles on Markovian networks.

J Chem Phys 2020 Jul;153(2):024121

Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.

The problem of flickering trajectories in standard kinetic Monte Carlo (kMC) simulations prohibits sampling of the transition path ensembles (TPEs) on Markovian networks representing many slow dynamical processes of interest. In the present contribution, we overcome this problem using knowledge of the metastable macrostates, determined by an unsupervised community detection algorithm, to perform enhanced sampling kMC simulations. We implement two accelerated kMC methods to simulate the nonequilibrium stochastic dynamics on arbitrary Markovian networks, namely, weighted ensemble (WE) sampling and kinetic path sampling (kPS). Read More

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Invertible generalized synchronization: A putative mechanism for implicit learning in neural systems.

Chaos 2020 Jun;30(6):063133

Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

Regardless of the marked differences between biological and artificial neural systems, one fundamental similarity is that they are essentially dynamical systems that can learn to imitate other dynamical systems whose governing equations are unknown. The brain is able to learn the dynamic nature of the physical world via experience; analogously, artificial neural systems such as reservoir computing networks (RCNs) can learn the long-term behavior of complex dynamical systems from data. Recent work has shown that the mechanism of such learning in RCNs is invertible generalized synchronization (IGS). Read More

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On the microscopic origins of relaxation processes in aqueous peptide solutions undergoing a glass transition.

J Chem Phys 2020 Jun;152(23):234503

Institut für Festkörperphysik, Technische Universität Darmstadt, Hochschulstr. 6, 64289 Darmstadt, Germany.

We combine broadband dielectric spectroscopy (BDS) with H and H nuclear magnetic resonance (NMR) to study molecular dynamics in mixtures of ε-polylysine with HO or DO. In BDS, four relaxation processes can be attributed to molecular dynamics. While the fastest process P1 obeys the Arrhenius law, the slowest process P4 shows prominent non-Arrhenius behavior typical of structural α relaxation. Read More

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Tetrahedrality Dictates Dynamics in Hard Sphere Mixtures.

Phys Rev Lett 2020 May;124(20):208005

Université Paris-Saclay, CNRS, Laboratoire de Physique des Solides, 91405 Orsay, France.

The link between local structure and dynamical slowdown in glassy fluids has been the focus of intense debate for the better part of a century. Nonetheless, a simple method to predict the dynamical behavior of a fluid purely from its local structural features is still missing. Here, we demonstrate that the diffusivity of perhaps the most fundamental family of glass formers-hard sphere mixtures-can be accurately predicted based on just the packing fraction and a simple order parameter measuring the tetrahedrality of the local structure. Read More

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Evolutionary dynamics from deterministic microscopic ecological processes.

Vaibhav Madhok

Phys Rev E 2020 Mar;101(3-1):032411

Department of Physics, Indian Institute of Technology Madras, Chennai 600036, India.

The central goal of a dynamical theory of evolution is to abstract the mean evolutionary trajectory in the trait space by considering ecological processes at the level of the individual. In this work we develop such a theory for a class of deterministic individual-based models describing individual births and deaths, which captures the essential features of standard stochastic individual-based models and becomes identical to the latter under maximal competition. The key motivation is to derive the canonical equation of adaptive dynamics from this microscopic ecological model, which can be regarded as a paradigm to study evolution in a simple way and give it an intuitive geometric interpretation. Read More

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Separation of chaotic signals by reservoir computing.

Chaos 2020 Feb;30(2):023123

University of Maryland, College Park, Maryland 20742, USA.

We demonstrate the utility of machine learning in the separation of superimposed chaotic signals using a technique called reservoir computing. We assume no knowledge of the dynamical equations that produce the signals and require only training data consisting of finite-time samples of the component signals. We test our method on signals that are formed as linear combinations of signals from two Lorenz systems with different parameters. Read More

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February 2020

Experimental and simulation study of the high-pressure behavior of squalane and poly-α-olefins.

J Chem Phys 2020 Feb;152(7):074504

School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, Scotland.

The equation of state, dynamical properties, and molecular-scale structure of squalane and mixtures of poly-α-olefins at room temperature are studied with a combination of state-of-the-art, high-pressure experiments and molecular-dynamics simulations. Diamond-anvil cell experiments indicate that both materials are non-hydrostatic media at pressures above ∼1 GPa. The equation of state does not exhibit any sign of a first-order phase transition. Read More

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February 2020

Functionability in complex networks: Leading nodes for the transition from structural to functional networks through remote asynchronization.

Chaos 2020 Jan;30(1):013105

Departament de Física de la Matèria Condensada, Universitat de Barcelona, 08028 Barcelona, Spain.

Complex networks are essentially heterogeneous not only in the basic properties of the constituent nodes, such as their degree, but also in the effects that these have on the global dynamical properties of the network. Networks of coupled identical phase oscillators are good examples for analyzing these effects, since an overall synchronized state can be considered a reference state. A small variation of intrinsic node parameters may cause the system to move away from synchronization, and a new phase-locked stationary state can be achieved. Read More

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January 2020

Dynamical Scaling of Charge and Spin Responses at a Kondo Destruction Quantum Critical Point.

Phys Rev Lett 2020 Jan;124(2):027205

Department of Physics and Astronomy, Rice Center for Quantum Materials, Rice University, Houston, Texas 77005, USA.

Quantum critical points often arise in metals perched at the border of an antiferromagnetic order. The recent observation of singular and dynamically scaling charge conductivity in an antiferromagnetic quantum critical heavy fermion metal implicates beyond-Landau quantum criticality. Here we study the charge and spin dynamics of a Kondo destruction quantum critical point (QCP), as realized in an SU(2)-symmetric Bose-Fermi Kondo model. Read More

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January 2020

Bridging the Physics and Chemistry of Graphene(s): From Hückel's Aromaticity to Dirac's Cones and Topological Insulators.

J Phys Chem A 2020 Feb 23;124(5):976-986. Epub 2020 Jan 23.

Molecular Engineering Laboratory, Department of Physics , University of Patras , Patras 26500 , Greece.

By bridging graphene and benzene through a well-defined sequence of polycyclic aromatic hydrocarbons and their inherent shell structure, it is shown that graphene is actually a coherent arrangement of interwoven benzene molecules, coordinated by aromaticity, shell structure, and topology, all interrelated and microscopically realized through dynamical flipping of the atomic p-orbitals, playing the role of pseudospins or "qubits". This renders graphene resonance structure, "resonating" between two complementary aromaticity patterns, involving 2, → ∞ Kekulé type of resonances, resulting in "robust electronic coherence", with a dual "molecular crystalline" nature, and two valence-conduction bands of opposite parity, driven by inversion symmetry competition, which is essentially a "molecule-versus-crystal" competition, in accordance with topological insulators and many-body theory. The "average picture" converges to the usual band structure with two aromatic π-electrons per ring, and with the fingerprints of inversion competition at the -symmetric Dirac points, which for rectangular nanographene(s) appear as gapless topological edge states without real spin polarization, in contrast to opposite claims. Read More

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February 2020

Localized versus itinerant character of 4f-states in cerium oxides.

J Phys Condens Matter 2020 May;32(21):215502

Department of Physics and Astronomy, Uppsala University, Box 516, 751 20 Uppsala, Sweden.

The electronic structure of cerium oxide is investigated here using a combination of ab initio one-electron theory and elements from many-body physics, with emphasis on the nature of the 4f electron shell of cerium ions. We propose to use the hybridization function as a convenient measure for the degree of localization of the 4f shell of this material, and observe that changing the oxidation state is related to distinct changes in the hybridization between the 4f shell and ligand states. The theory reveals that CeO has essentially itinerant 4f states, and that in the least oxidized form of ceria, CeO, the 4f states are almost (but not fully) localized. Read More

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Specific Heat and Transport Functions ofWater.

Int J Mol Sci 2020 Jan 17;21(2). Epub 2020 Jan 17.

Dipartimento di Fisica e Chimica, Università di Palermo, 90128 Palermo, Italy.

Numerous water characteristics are essentially ascribed to its peculiarity to form stronghydrogen bonds that become progressively more stable on decreasing the temperature. However, thestructural and dynamical implications of the molecular rearrangement are still subject of debate andintense studies. In this work, we observe that the thermodynamic characteristics of liquid water arestrictly connected to its dynamic characteristics. Read More

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January 2020

Enhancement of Many-Body Quantum Interference in Chaotic Bosonic Systems: The Role of Symmetry and Dynamics.

Phys Rev Lett 2019 Nov;123(21):215302

Institut für Theoretische Physik, Universität Regensburg, D-93040 Regensburg, Germany.

Although highly successful, the truncated Wigner approximation (TWA) leaves out many-body quantum interference between mean-field Gross-Pitaevskii solutions as well as other quantum effects, and is therefore essentially classical. Turned around, if a system's quantum properties deviate from TWA, they must be exhibiting some quantum phenomenon, such as localization, diffraction, or tunneling. Here, we examine a particular interference effect arising from discrete symmetries, which can significantly enhance quantum observables with respect to the TWA prediction, and derive an augmented TWA in order to incorporate them. Read More

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November 2019

Phase transition in time-reversible Navier-Stokes equations.

Phys Rev E 2019 Oct;100(4-1):043104

Instituto Nacional de Matemática Pura e Aplicada, IMPA, 22460-320 Rio de Janeiro, Brazil.

We present a comprehensive study of the statistical features of a three-dimensional (3D) time-reversible truncated Navier-Stokes (RNS) system, wherein the standard viscosity ν is replaced by a fluctuating thermostat that dynamically compensates for fluctuations in the total energy. We analyze the statistical features of the RNS steady states in terms of a non-negative dimensionless control parameter R_{r}, which quantifies the balance between the fluctuations of kinetic energy at the forcing length scale ℓ_{f} and the total energy E_{0}. For small R_{r}, the RNS equations are found to produce "warm" stationary statistics, e. Read More

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October 2019

Assessing the properties of supercritical water in terms of structural dynamics and electronic polarization effects.

Phys Chem Chem Phys 2020 May 13;22(19):10462-10479. Epub 2019 Nov 13.

Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany.

Supercritical water features fascinating physical properties which are fundamentally different compared to ambient liquid water. Importantly, it can gradually be compressed from gas-like to liquid-like densities while avoiding any thermodynamic phase transition. Although the interest in supercritical water has recently increased, many microscopic characteristics still remain unknown. Read More

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Reconstructing dynamical networks via feature ranking.

Chaos 2019 Sep;29(9):093107

Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia.

Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the structure of networks from time-resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Read More

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September 2019

A Novel Recurrent Neural Network-Based Ultra-Fast, Robust, and Scalable Solver for Inverting a "Time-Varying Matrix".

Sensors (Basel) 2019 Sep 16;19(18). Epub 2019 Sep 16.

Institute for Smart Systems Technologies, University Klagenfurt, A9020 Klagenfurt, Austria.

The concept presented in this paper is based on previous dynamical methods to realize a time-varying matrix inversion. It is essentially a set of coupled ordinary differential equations (ODEs) which does indeed constitute a recurrent neural network (RNN) model. The coupled ODEs constitute a universal modeling framework for realizing a matrix inversion provided the matrix is invertible. Read More

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September 2019

Modelling Bacteria-Inspired Dynamics with Networks of Interacting Chemicals.

Life (Basel) 2019 Jul 29;9(3). Epub 2019 Jul 29.

Department of Chemical and Biological Engineering, University of Sheffield, Sheffield S1 3JD, UK.

One approach to understanding how life-like properties emerge involves building synthetic cellular systems that mimic certain dynamical features of living cells such as bacteria. Here, we developed a model of a reaction network in a cellular system inspired by the ability of bacteria to form a biofilm in response to increasing cell density. Our aim was to determine the role of chemical feedback in the dynamics. Read More

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