Statistical mechanics of self-driven Carnot cycles.

PubMed

Smith, E

1999-10-01

The spontaneous generation and finite-amplitude saturation of sound, in a traveling-wave thermoacoustic engine, are derived as properties of a second-order phase transition. It has previously been argued that this dynamical phase transition, called "onset," has an equivalent equilibrium representation, but the saturation mechanism and scaling were not computed. In this work, the sound modes implementing the engine cycle are coarse-grained and statistically averaged, in a partition function derived from microscopic dynamics on criteria of scale invariance. Self-amplification performed by the engine cycle is introduced through higher-order modal interactions. Stationary points and fluctuations of the resulting phenomenological Lagrangian are analyzed and related to background dynamical currents. The scaling of the stable sound amplitude near the critical point is derived and shown to arise universally from the interaction of finite-temperature disorder, with the order induced by self-amplification.

Imprints of dynamical interactions on brown dwarf pairing statistics and kinematics

NASA Astrophysics Data System (ADS)

Sterzik, M. F.; Durisen, R. H.

2003-03-01

We present statistically robust predictions of brown dwarf properties arising from dynamical interactions during their early evolution in small clusters. Our conclusions are based on numerical calculations of the internal cluster dynamics as well as on Monte-Carlo models. Accounting for recent observational constraints on the sub-stellar mass function and initial properties in fragmenting star forming clumps, we derive multiplicity fractions, mass ratios, separation distributions, and velocity dispersions. We compare them with observations of brown dwarfs in the field and in young clusters. Observed brown dwarf companion fractions around 15 +/- 7% for very low-mass stars as reported recently by Close et al. (\\cite{CSFB03}) are consistent with certain dynamical decay models. A significantly smaller mean separation distribution for brown dwarf binaries than for binaries of late-type stars can be explained by similar specific energy at the time of cluster formation for all cluster masses. Due to their higher velocity dispersions, brown-dwarfs and low-mass single stars will undergo time-dependent spatial segregation from higher-mass stars and multiple systems. This will cause mass functions and binary statistics in star forming regions to vary with the age of the region and the volume sampled.

Polymer models of interphase chromosomes

PubMed Central

Vasquez, Paula A; Bloom, Kerry

2014-01-01

Clear organizational patterns on the genome have emerged from the statistics of population studies of fixed cells. However, how these results translate into the dynamics of individual living cells remains unexplored. We use statistical mechanics models derived from polymer physics to inquire into the effects that chromosome properties and dynamics have in the temporal and spatial behavior of the genome. Overall, changes in the properties of individual chains affect the behavior of all other chains in the domain. We explore two modifications of chain behavior: single chain motion and chain-chain interactions. We show that there is not a direct relation between these effects, as increase in motion, doesn’t necessarily translate into an increase on chain interaction. PMID:25482191

Interactions dominate the dynamics of visual cognition.

PubMed

Stephen, Damian G; Mirman, Daniel

2010-04-01

Many cognitive theories have described behavior as the summation of independent contributions from separate components. Contrasting views have emphasized the importance of multiplicative interactions and emergent structure. We describe a statistical approach to distinguishing additive and multiplicative processes and apply it to the dynamics of eye movements during classic visual cognitive tasks. The results reveal interaction-dominant dynamics in eye movements in each of the three tasks, and that fine-grained eye movements are modulated by task constraints. These findings reveal the interactive nature of cognitive processing and are consistent with theories that view cognition as an emergent property of processes that are broadly distributed over many scales of space and time rather than a componential assembly line. Copyright 2009 Elsevier B.V. All rights reserved.

STATWIZ - AN ELECTRONIC STATISTICAL TOOL (ABSTRACT)

EPA Science Inventory

StatWiz is a web-based, interactive, and dynamic statistical tool for researchers. It will allow researchers to input information and/or data and then receive experimental design options, or outputs from data analysis. StatWiz is envisioned as an expert system that will walk rese...

Nonlinear Relaxation in Population Dynamics

NASA Astrophysics Data System (ADS)

Cirone, Markus A.; de Pasquale, Ferdinando; Spagnolo, Bernardo

We analyze the nonlinear relaxation of a complex ecosystem composed of many interacting species. The ecological system is described by generalized Lotka-Volterra equations with a multiplicative noise. The transient dynamics is studied in the framework of the mean field theory and with random interaction between the species. We focus on the statistical properties of the asymptotic behaviour of the time integral of the ith population and on the distribution of the population and of the local field.

Two competing species in super-diffusive dynamical regimes

NASA Astrophysics Data System (ADS)

La Cognata, A.; Valenti, D.; Spagnolo, B.; Dubkov, A. A.

2010-09-01

The dynamics of two competing species within the framework of the generalized Lotka-Volterra equations, in the presence of multiplicative α-stable Lévy noise sources and a random time dependent interaction parameter, is studied. The species dynamics is characterized by two different dynamical regimes, exclusion of one species and coexistence of both, depending on the values of the interaction parameter, which obeys a Langevin equation with a periodically fluctuating bistable potential and an additive α-stable Lévy noise. The stochastic resonance phenomenon is analyzed for noise sources asymmetrically distributed. Finally, the effects of statistical dependence between multiplicative noise and additive noise on the dynamics of the two species are studied.

Visualizing and Understanding Probability and Statistics: Graphical Simulations Using Excel

ERIC Educational Resources Information Center

Gordon, Sheldon P.; Gordon, Florence S.

2009-01-01

The authors describe a collection of dynamic interactive simulations for teaching and learning most of the important ideas and techniques of introductory statistics and probability. The modules cover such topics as randomness, simulations of probability experiments such as coin flipping, dice rolling and general binomial experiments, a simulation…

Molecular dynamics simulations and statistical coupling analysis reveal functional coevolution network of oncogenic mutations in the CDKN2A-CDK6 complex.

PubMed

Wang, Jingwen; Zhao, Yuqi; Wang, Yanjie; Huang, Jingfei

2013-01-16

Coevolution between proteins is crucial for understanding protein-protein interaction. Simultaneous changes allow a protein complex to maintain its overall structural-functional integrity. In this study, we combined statistical coupling analysis (SCA) and molecular dynamics simulations on the CDK6-CDKN2A protein complex to evaluate coevolution between proteins. We reconstructed an inter-protein residue coevolution network, consisting of 37 residues and 37 interactions. It shows that most of the coevolved residue pairs are spatially proximal. When the mutations happened, the stable local structures were broken up and thus the protein interaction was decreased or inhibited, with a following increased risk of melanoma. The identification of inter-protein coevolved residues in the CDK6-CDKN2A complex can be helpful for designing protein engineering experiments. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Dynamic Displays of Data

ERIC Educational Resources Information Center

Angotti, Robin

2017-01-01

This article describes Gapminder, a dynamic time-series graph that can be found at http://www.gapminder.org. Gapminder was created by a team of developers (Rosling, Ronnlund, and Rosling 2005) to create beautiful, interactive graphs of otherwise lifeless numbers. Their goal is increased use and understanding of statistics and data that…

The introduction of hydrogen bond and hydrophobicity effects into the rotational isomeric states model for conformational analysis of unfolded peptides.

PubMed

Engin, Ozge; Sayar, Mehmet; Erman, Burak

2009-01-13

Relative contributions of local and non-local interactions to the unfolded conformations of peptides are examined by using the rotational isomeric states model which is a Markov model based on pairwise interactions of torsion angles. The isomeric states of a residue are well described by the Ramachandran map of backbone torsion angles. The statistical weight matrices for the states are determined by molecular dynamics simulations applied to monopeptides and dipeptides. Conformational properties of tripeptides formed from combinations of alanine, valine, tyrosine and tryptophan are investigated based on the Markov model. Comparison with molecular dynamics simulation results on these tripeptides identifies the sequence-distant long-range interactions that are missing in the Markov model. These are essentially the hydrogen bond and hydrophobic interactions that are obtained between the first and the third residue of a tripeptide. A systematic correction is proposed for incorporating these long-range interactions into the rotational isomeric states model. Preliminary results suggest that the Markov assumption can be improved significantly by renormalizing the statistical weight matrices to include the effects of the long-range correlations.

The introduction of hydrogen bond and hydrophobicity effects into the rotational isomeric states model for conformational analysis of unfolded peptides

NASA Astrophysics Data System (ADS)

Engin, Ozge; Sayar, Mehmet; Erman, Burak

2009-03-01

Relative contributions of local and non-local interactions to the unfolded conformations of peptides are examined by using the rotational isomeric states model which is a Markov model based on pairwise interactions of torsion angles. The isomeric states of a residue are well described by the Ramachandran map of backbone torsion angles. The statistical weight matrices for the states are determined by molecular dynamics simulations applied to monopeptides and dipeptides. Conformational properties of tripeptides formed from combinations of alanine, valine, tyrosine and tryptophan are investigated based on the Markov model. Comparison with molecular dynamics simulation results on these tripeptides identifies the sequence-distant long-range interactions that are missing in the Markov model. These are essentially the hydrogen bond and hydrophobic interactions that are obtained between the first and the third residue of a tripeptide. A systematic correction is proposed for incorporating these long-range interactions into the rotational isomeric states model. Preliminary results suggest that the Markov assumption can be improved significantly by renormalizing the statistical weight matrices to include the effects of the long-range correlations.

Experimental Determination of Dynamical Lee-Yang Zeros

NASA Astrophysics Data System (ADS)

Brandner, Kay; Maisi, Ville F.; Pekola, Jukka P.; Garrahan, Juan P.; Flindt, Christian

2017-05-01

Statistical physics provides the concepts and methods to explain the phase behavior of interacting many-body systems. Investigations of Lee-Yang zeros—complex singularities of the free energy in systems of finite size—have led to a unified understanding of equilibrium phase transitions. The ideas of Lee and Yang, however, are not restricted to equilibrium phenomena. Recently, Lee-Yang zeros have been used to characterize nonequilibrium processes such as dynamical phase transitions in quantum systems after a quench or dynamic order-disorder transitions in glasses. Here, we experimentally realize a scheme for determining Lee-Yang zeros in such nonequilibrium settings. We extract the dynamical Lee-Yang zeros of a stochastic process involving Andreev tunneling between a normal-state island and two superconducting leads from measurements of the dynamical activity along a trajectory. From the short-time behavior of the Lee-Yang zeros, we predict the large-deviation statistics of the activity which is typically difficult to measure. Our method paves the way for further experiments on the statistical mechanics of many-body systems out of equilibrium.

Machine Learning and Network Analysis of Molecular Dynamics Trajectories Reveal Two Chains of Red/Ox-specific Residue Interactions in Human Protein Disulfide Isomerase.

PubMed

Karamzadeh, Razieh; Karimi-Jafari, Mohammad Hossein; Sharifi-Zarchi, Ali; Chitsaz, Hamidreza; Salekdeh, Ghasem Hosseini; Moosavi-Movahedi, Ali Akbar

2017-06-16

The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are important for substrate binding and functional activities. Here, we address the redox-dependent conformational dynamics of hPDI through molecular dynamics (MD) simulations. Collective domain motions are identified by the principal component analysis of MD trajectories and redox-dependent opening-closing structure variations are highlighted on projected free energy landscapes. Then, important structural features that exhibit considerable differences in dynamics of redox states are extracted by statistical machine learning methods. Mapping the structural variations to time series of residue interaction networks also provides a holistic representation of the dynamical redox differences. With emphasizing on persistent long-lasting interactions, an approach is proposed that compiled these time series networks to a single dynamic residue interaction network (DRIN). Differential comparison of DRIN in oxidized and reduced states reveals chains of residue interactions that represent potential allosteric paths between catalytic and ligand binding sites of hPDI.

Coupling functions: Universal insights into dynamical interaction mechanisms

NASA Astrophysics Data System (ADS)

Stankovski, Tomislav; Pereira, Tiago; McClintock, Peter V. E.; Stefanovska, Aneta

2017-10-01

The dynamical systems found in nature are rarely isolated. Instead they interact and influence each other. The coupling functions that connect them contain detailed information about the functional mechanisms underlying the interactions and prescribe the physical rule specifying how an interaction occurs. A coherent and comprehensive review is presented encompassing the rapid progress made recently in the analysis, understanding, and applications of coupling functions. The basic concepts and characteristics of coupling functions are presented through demonstrative examples of different domains, revealing the mechanisms and emphasizing their multivariate nature. The theory of coupling functions is discussed through gradually increasing complexity from strong and weak interactions to globally coupled systems and networks. A variety of methods that have been developed for the detection and reconstruction of coupling functions from measured data is described. These methods are based on different statistical techniques for dynamical inference. Stemming from physics, such methods are being applied in diverse areas of science and technology, including chemistry, biology, physiology, neuroscience, social sciences, mechanics, and secure communications. This breadth of application illustrates the universality of coupling functions for studying the interaction mechanisms of coupled dynamical systems.

Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems.

PubMed

Sapsis, Themistoklis P; Majda, Andrew J

2013-08-20

A framework for low-order predictive statistical modeling and uncertainty quantification in turbulent dynamical systems is developed here. These reduced-order, modified quasilinear Gaussian (ROMQG) algorithms apply to turbulent dynamical systems in which there is significant linear instability or linear nonnormal dynamics in the unperturbed system and energy-conserving nonlinear interactions that transfer energy from the unstable modes to the stable modes where dissipation occurs, resulting in a statistical steady state; such turbulent dynamical systems are ubiquitous in geophysical and engineering turbulence. The ROMQG method involves constructing a low-order, nonlinear, dynamical system for the mean and covariance statistics in the reduced subspace that has the unperturbed statistics as a stable fixed point and optimally incorporates the indirect effect of non-Gaussian third-order statistics for the unperturbed system in a systematic calibration stage. This calibration procedure is achieved through information involving only the mean and covariance statistics for the unperturbed equilibrium. The performance of the ROMQG algorithm is assessed on two stringent test cases: the 40-mode Lorenz 96 model mimicking midlatitude atmospheric turbulence and two-layer baroclinic models for high-latitude ocean turbulence with over 125,000 degrees of freedom. In the Lorenz 96 model, the ROMQG algorithm with just a single mode captures the transient response to random or deterministic forcing. For the baroclinic ocean turbulence models, the inexpensive ROMQG algorithm with 252 modes, less than 0.2% of the total, captures the nonlinear response of the energy, the heat flux, and even the one-dimensional energy and heat flux spectra.

MOLSIM: A modular molecular simulation software

PubMed Central

Jurij, Reščič

2015-01-01

The modular software MOLSIM for all‐atom molecular and coarse‐grained simulations is presented with focus on the underlying concepts used. The software possesses four unique features: (1) it is an integrated software for molecular dynamic, Monte Carlo, and Brownian dynamics simulations; (2) simulated objects are constructed in a hierarchical fashion representing atoms, rigid molecules and colloids, flexible chains, hierarchical polymers, and cross‐linked networks; (3) long‐range interactions involving charges, dipoles and/or anisotropic dipole polarizabilities are handled either with the standard Ewald sum, the smooth particle mesh Ewald sum, or the reaction‐field technique; (4) statistical uncertainties are provided for all calculated observables. In addition, MOLSIM supports various statistical ensembles, and several types of simulation cells and boundary conditions are available. Intermolecular interactions comprise tabulated pairwise potentials for speed and uniformity and many‐body interactions involve anisotropic polarizabilities. Intramolecular interactions include bond, angle, and crosslink potentials. A very large set of analyses of static and dynamic properties is provided. The capability of MOLSIM can be extended by user‐providing routines controlling, for example, start conditions, intermolecular potentials, and analyses. An extensive set of case studies in the field of soft matter is presented covering colloids, polymers, and crosslinked networks. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:25994597

Modeling dynamic interactions and coherence between marine zooplankton and fishes linked to environmental variability

NASA Astrophysics Data System (ADS)

Liu, Hui; Fogarty, Michael J.; Hare, Jonathan A.; Hsieh, Chih-hao; Glaser, Sarah M.; Ye, Hao; Deyle, Ethan; Sugihara, George

2014-03-01

The dynamics of marine fishes are closely related to lower trophic levels and the environment. Quantitatively understanding ecosystem dynamics linking environmental variability and prey resources to exploited fishes is crucial for ecosystem-based management of marine living resources. However, standard statistical models typically grounded in the concept of linear system may fail to capture the complexity of ecological processes. We have attempted to model ecosystem dynamics using a flexible, nonparametric class of nonlinear forecasting models. We analyzed annual time series of four environmental indices, 22 marine copepod taxa, and four ecologically and commercially important fish species during 1977 to 2009 on Georges Bank, a highly productive and intensively studied area of the northeast U.S. continental shelf ecosystem. We examined the underlying dynamic features of environmental indices and copepods, quantified the dynamic interactions and coherence with fishes, and explored the potential control mechanisms of ecosystem dynamics from a nonlinear perspective. We found: (1) the dynamics of marine copepods and environmental indices exhibiting clear nonlinearity; (2) little evidence of complex dynamics across taxonomic levels of copepods; (3) strong dynamic interactions and coherence between copepods and fishes; and (4) the bottom-up forcing of fishes and top-down control of copepods coexisting as target trophic levels vary. These findings highlight the nonlinear interactions among ecosystem components and the importance of marine zooplankton to fish populations which point to two forcing mechanisms likely interactively regulating the ecosystem dynamics on Georges Bank under a changing environment.

Statistical similarities of pre-earthquake electromagnetic emissions to biological and economic extreme events

NASA Astrophysics Data System (ADS)

Potirakis, Stelios M.; Contoyiannis, Yiannis; Kopanas, John; Kalimeris, Anastasios; Antonopoulos, George; Peratzakis, Athanasios; Eftaxias, Konstantinos; Nomicos, Costantinos

2014-05-01

When one considers a phenomenon that is "complex" refers to a system whose phenomenological laws that describe the global behavior of the system, are not necessarily directly related to the "microscopic" laws that regulate the evolution of its elementary parts. The field of study of complex systems considers that the dynamics of complex systems are founded on universal principles that may be used to describe disparate problems ranging from particle physics to economies of societies. Several authors have suggested that earthquake (EQ) dynamics can be analyzed within similar mathematical frameworks with economy dynamics, and neurodynamics. A central property of the EQ preparation process is the occurrence of coherent large-scale collective behavior with a very rich structure, resulting from repeated nonlinear interactions among the constituents of the system. As a result, nonextensive statistics is an appropriate, physically meaningful, tool for the study of EQ dynamics. Since the fracture induced electromagnetic (EM) precursors are observable manifestations of the underlying EQ preparation process, the analysis of a fracture induced EM precursor observed prior to the occurrence of a large EQ can also be conducted within the nonextensive statistics framework. Within the frame of the investigation for universal principles that may hold for different dynamical systems that are related to the genesis of extreme events, we present here statistical similarities of the pre-earthquake EM emissions related to an EQ, with the pre-ictal electrical brain activity related to an epileptic seizure, and with the pre-crisis economic observables related to the collapse of a share. It is demonstrated the all three dynamical systems' observables can be analyzed in the frame of nonextensive statistical mechanics, while the frequency-size relations of appropriately defined "events" that precede the extreme event related to each one of these different systems present striking quantitative similarities. It is also demonstrated that, for the considered systems, the nonextensive parameter q increases as the extreme event approaches, which indicates that the strength of the long-memory / long-range interactions between the constituents of the system increases characterizing the dynamics of the system.

Chaotic Ising-like dynamics in traffic signals

PubMed Central

Suzuki, Hideyuki; Imura, Jun-ichi; Aihara, Kazuyuki

2013-01-01

The green and red lights of a traffic signal can be viewed as the up and down states of an Ising spin. Moreover, traffic signals in a city interact with each other, if they are controlled in a decentralised way. In this paper, a simple model of such interacting signals on a finite-size two-dimensional lattice is shown to have Ising-like dynamics that undergoes a ferromagnetic phase transition. Probabilistic behaviour of the model is realised by chaotic billiard dynamics that arises from coupled non-chaotic elements. This purely deterministic model is expected to serve as a starting point for considering statistical mechanics of traffic signals. PMID:23350034

Neocortical dynamics at multiple scales: EEG standing waves, statistical mechanics, and physical analogs.

PubMed

Ingber, Lester; Nunez, Paul L

2011-02-01

The dynamic behavior of scalp potentials (EEG) is apparently due to some combination of global and local processes with important top-down and bottom-up interactions across spatial scales. In treating global mechanisms, we stress the importance of myelinated axon propagation delays and periodic boundary conditions in the cortical-white matter system, which is topologically close to a spherical shell. By contrast, the proposed local mechanisms are multiscale interactions between cortical columns via short-ranged non-myelinated fibers. A mechanical model consisting of a stretched string with attached nonlinear springs demonstrates the general idea. The string produces standing waves analogous to large-scale coherent EEG observed in some brain states. The attached springs are analogous to the smaller (mesoscopic) scale columnar dynamics. Generally, we expect string displacement and EEG at all scales to result from both global and local phenomena. A statistical mechanics of neocortical interactions (SMNI) calculates oscillatory behavior consistent with typical EEG, within columns, between neighboring columns via short-ranged non-myelinated fibers, across cortical regions via myelinated fibers, and also derives a string equation consistent with the global EEG model. Copyright © 2010 Elsevier Inc. All rights reserved.

Punctuated equilibrium dynamics in human communications

NASA Astrophysics Data System (ADS)

Peng, Dan; Han, Xiao-Pu; Wei, Zong-Wen; Wang, Bing-Hong

2015-10-01

A minimal model based on network incorporating individual interactions is proposed to study the non-Poisson statistical properties of human behavior: individuals in system interact with their neighbors, the probability of an individual acting correlates to its activity, and all the individuals involved in action will change their activities randomly. The model reproduces varieties of spatial-temporal patterns observed in empirical studies of human daily communications, providing insight into various human activities and embracing a range of realistic social interacting systems, particularly, intriguing bimodal phenomenon. This model bridges priority queueing theory and punctuated equilibrium dynamics, and our modeling and analysis is likely to shed light on non-Poisson phenomena in many complex systems.

Assessing information content and interactive relationships of subgenomic DNA sequences of the MHC using complexity theory approaches based on the non-extensive statistical mechanics

NASA Astrophysics Data System (ADS)

Karakatsanis, L. P.; Pavlos, G. P.; Iliopoulos, A. C.; Pavlos, E. G.; Clark, P. M.; Duke, J. L.; Monos, D. S.

2018-09-01

This study combines two independent domains of science, the high throughput DNA sequencing capabilities of Genomics and complexity theory from Physics, to assess the information encoded by the different genomic segments of exonic, intronic and intergenic regions of the Major Histocompatibility Complex (MHC) and identify possible interactive relationships. The dynamic and non-extensive statistical characteristics of two well characterized MHC sequences from the homozygous cell lines, PGF and COX, in addition to two other genomic regions of comparable size, used as controls, have been studied using the reconstructed phase space theorem and the non-extensive statistical theory of Tsallis. The results reveal similar non-linear dynamical behavior as far as complexity and self-organization features. In particular, the low-dimensional deterministic nonlinear chaotic and non-extensive statistical character of the DNA sequences was verified with strong multifractal characteristics and long-range correlations. The nonlinear indices repeatedly verified that MHC sequences, whether exonic, intronic or intergenic include varying levels of information and reveal an interaction of the genes with intergenic regions, whereby the lower the number of genes in a region, the less the complexity and information content of the intergenic region. Finally we showed the significance of the intergenic region in the production of the DNA dynamics. The findings reveal interesting content information in all three genomic elements and interactive relationships of the genes with the intergenic regions. The results most likely are relevant to the whole genome and not only to the MHC. These findings are consistent with the ENCODE project, which has now established that the non-coding regions of the genome remain to be of relevance, as they are functionally important and play a significant role in the regulation of expression of genes and coordination of the many biological processes of the cell.

SOCR Motion Charts: An Efficient, Open-Source, Interactive and Dynamic Applet for Visualizing Longitudinal Multivariate Data

PubMed Central

Al-Aziz, Jameel; Christou, Nicolas; Dinov, Ivo D.

2011-01-01

The amount, complexity and provenance of data have dramatically increased in the past five years. Visualization of observed and simulated data is a critical component of any social, environmental, biomedical or scientific quest. Dynamic, exploratory and interactive visualization of multivariate data, without preprocessing by dimensionality reduction, remains a nearly insurmountable challenge. The Statistics Online Computational Resource (www.SOCR.ucla.edu) provides portable online aids for probability and statistics education, technology-based instruction and statistical computing. We have developed a new Java-based infrastructure, SOCR Motion Charts, for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. We validated this new visualization paradigm using several publicly available multivariate datasets including Ice-Thickness, Housing Prices, Consumer Price Index, and California Ozone Data. SOCR Motion Charts is designed using object-oriented programming, implemented as a Java Web-applet and is available to the entire community on the web at www.socr.ucla.edu/SOCR_MotionCharts. It can be used as an instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis. PMID:21479108

In-cell SHAPE uncovers dynamic interactions between the untranslated regions of the foot-and-mouth disease virus RNA.

PubMed

Diaz-Toledano, Rosa; Lozano, Gloria; Martinez-Salas, Encarnacion

2017-02-17

The genome of RNA viruses folds into 3D structures that include long-range RNA–RNA interactions relevant to control critical steps of the viral cycle. In particular, initiation of translation driven by the IRES element of foot-and-mouth disease virus is stimulated by the 3΄UTR. Here we sought to investigate the RNA local flexibility of the IRES element and the 3΄UTR in living cells. The SHAPE reactivity observed in vivo showed statistically significant differences compared to the free RNA, revealing protected or exposed positions within the IRES and the 3΄UTR. Importantly, the IRES local flexibility was modified in the presence of the 3΄UTR, showing significant protections at residues upstream from the functional start codon. Conversely, presence of the IRES element in cis altered the 3΄UTR local flexibility leading to an overall enhanced reactivity. Unlike the reactivity changes observed in the IRES element, the SHAPE differences of the 3΄UTR were large but not statistically significant, suggesting multiple dynamic RNA interactions. These results were supported by covariation analysis, which predicted IRES-3΄UTR conserved helices in agreement with the protections observed by SHAPE probing. Mutational analysis suggested that disruption of one of these interactions could be compensated by alternative base pairings, providing direct evidences for dynamic long-range interactions between these distant elements of the viral genome.

A fractal approach to dynamic inference and distribution analysis

PubMed Central

van Rooij, Marieke M. J. W.; Nash, Bertha A.; Rajaraman, Srinivasan; Holden, John G.

2013-01-01

Event-distributions inform scientists about the variability and dispersion of repeated measurements. This dispersion can be understood from a complex systems perspective, and quantified in terms of fractal geometry. The key premise is that a distribution's shape reveals information about the governing dynamics of the system that gave rise to the distribution. Two categories of characteristic dynamics are distinguished: additive systems governed by component-dominant dynamics and multiplicative or interdependent systems governed by interaction-dominant dynamics. A logic by which systems governed by interaction-dominant dynamics are expected to yield mixtures of lognormal and inverse power-law samples is discussed. These mixtures are described by a so-called cocktail model of response times derived from human cognitive performances. The overarching goals of this article are twofold: First, to offer readers an introduction to this theoretical perspective and second, to offer an overview of the related statistical methods. PMID:23372552

ONR Ocean Wave Dynamics Workshop

NASA Astrophysics Data System (ADS)

In anticipation of the start (in Fiscal Year 1988) of a new Office of Naval Research (ONR) Accelerated Research Initiative (ARI) on Ocean Surface Wave Dynamics, a workshop was held August 5-7, 1986, at Woods Hole, Mass., to discuss new ideas and directions of research. This new ARI on Ocean Surface Wave Dynamics is a 5-year effort that is organized by the ONR Physical Oceanography Program in cooperation with the ONR Fluid Mechanics Program and the Physical Oceanography Branch at the Naval Ocean Research and Development Activity (NORDA). The central theme is improvement of our understanding of the basic physics and dynamics of surface wave phenomena, with emphasis on the following areas: precise air-sea coupling mechanisms,dynamics of nonlinear wave-wave interaction under realistic environmental conditions,wave breaking and dissipation of energy,interaction between surface waves and upper ocean boundary layer dynamics, andsurface statistical and boundary layer coherent structures.

Warfighter Visualizations Compilations

DTIC Science & Technology

2013-05-01

list of the user’s favorite websites or other textual content, sub-categorized into types, such as blogs, social networking sites, comics , videos...available: The example in the prototype shows a random archived comic from the website. Other options include thumbnail strips of imagery or dynamic...varied, and range from serving as statistical benchmarks, for increasing social consciousness and interaction, for improving educational interactions

Exploring the Micro-Social Geography of Children's Interactions in Preschool: A Long-Term Observational Study and Analysis Using Geographic Information Technologies

ERIC Educational Resources Information Center

Torrens, Paul M.; Griffin, William A.

2013-01-01

The authors describe an observational and analytic methodology for recording and interpreting dynamic microprocesses that occur during social interaction, making use of space--time data collection techniques, spatial-statistical analysis, and visualization. The scheme has three investigative foci: Structure, Activity Composition, and Clustering.…

Entropy of dynamical social networks

NASA Astrophysics Data System (ADS)

Zhao, Kun; Karsai, Marton; Bianconi, Ginestra

2012-02-01

Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.

Marigold (Calendula officinalis L.): an evidence-based systematic review by the Natural Standard Research Collaboration.

PubMed

Basch, Ethan; Bent, Steve; Foppa, Ivo; Haskmi, Sadaf; Kroll, David; Mele, Michelle; Szapary, Philippe; Ulbricht, Catherine; Vora, Mamta; Yong, Sophanna

2006-01-01

An evidence-based systematic review including written and statistical analysis of scientific literature, expert opinion, folkloric precedent, history, pharmacology, kinetics/dynamics, interactions, adverse effects, toxicology and dosing.

An evidence-based systematic review of saffron (Crocus sativus) by the Natural Standard Research Collaboration.

PubMed

Ulbricht, Catherine; Conquer, Julie; Costa, Dawn; Hollands, Whitney; Iannuzzi, Carmen; Isaac, Richard; Jordan, Joseph K; Ledesma, Natalie; Ostroff, Cathy; Serrano, Jill M Grimes; Shaffer, Michael D; Varghese, Minney

2011-03-01

An evidence-based systematic review including written and statistical analysis of scientific literature, expert opinion, folkloric precedent, history, pharmacology, kinetics/dynamics, interactions, adverse effects, toxicology, and dosing.

Evidence-based systematic review of saw palmetto by the Natural Standard Research Collaboration.

PubMed

Ulbricht, Catherine; Basch, Ethan; Bent, Steve; Boon, Heather; Corrado, Michelle; Foppa, Ivo; Hashmi, Sadaf; Hammerness, Paul; Kingsbury, Eileen; Smith, Michael; Szapary, Philippe; Vora, Mamta; Weissner, Wendy

2006-01-01

Here presented is an evidence-based systematic review including written and statistical analysis of scientific literature, expert opinion, folkloric precedent, history, pharmacology, kinetics/dynamics, interactions, adverse effects, toxicology, and dosing.

Computationally efficient statistical differential equation modeling using homogenization

USGS Publications Warehouse

Hooten, Mevin B.; Garlick, Martha J.; Powell, James A.

2013-01-01

Statistical models using partial differential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scientific literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Specifically, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose computationally advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation using a form of PDE known as “ecological diffusion.” We also apply a statistical ecological diffusion model to a data set involving the spread of mountain pine beetle (Dendroctonus ponderosae) in Idaho, USA.

Nonequilibrium quantum field dynamics from the two-particle-irreducible effective action

NASA Astrophysics Data System (ADS)

Laurie, Nathan S.

The two-particle-irreducible effective action offers a powerful approach to the study of quantum field dynamics far from equilibrium. Recent and upcoming heavy ion collision experiments motivate the study of such nonequilibrium dynamics in an expanding space-time background. For the O(N) model I derive exact, causal evolution equations for the statistical and spectral functions in a longitudinally expanding system. It is followed by an investigation into how the expansion affects the prospect of the system reaching equilibrium. Results are obtained in 1+1 dimensions at next-to- leading order in loop- and 1/N-expansions of the 2PI effective action. I focus on the evolution of the statistical function from highly nonequilibrium initial conditions, presenting a detailed analysis of early, intermediate and late-time dynamics. It is found that dynamics at very early times is attracted by a nonthermal fixed point of the mean field equations, after which interactions attempt to drive the system to equilibrium. The competition between the interactions and the expansion is eventually won by the expansion, with so-called freeze-out emerging naturally in this description. In order to investigate the convergence of the 2PI-1/N expansion in the 0(N) model, I compare results obtained numerically in 1+1 dimensions at leading, next- to-leading and next-to-next-to-leading order in 1/N. Convergence with increasing N, and also with decreasing coupling are discussed. A comparison is also made in the classical statistical field theory limit, where exact numerical results are available. I focus on early-time dynamics and quasi-particle properties far from equilibrium and observe rapid effective convergence already for moderate values of 1/N or the coupling strength.

The role of fluctuations and interactions in pedestrian dynamics

NASA Astrophysics Data System (ADS)

Corbetta, Alessandro; Meeusen, Jasper; Benzi, Roberto; Lee, Chung-Min; Toschi, Federico

Understanding quantitatively the statistical behaviour of pedestrians walking in crowds is a major scientific challenge of paramount societal relevance. Walking humans exhibit a rich (stochastic) dynamics whose small and large deviations are driven, among others, by own will as well as by environmental conditions. Via 24/7 automatic pedestrian tracking from multiple overhead Microsoft Kinect depth sensors, we collected large ensembles of pedestrian trajectories (in the order of tens of millions) in different real-life scenarios. These scenarios include both narrow corridors and large urban hallways, enabling us to cover and compare a wide spectrum of typical pedestrian dynamics. We investigate the pedestrian motion measuring the PDFs, e.g. those of position, velocity and acceleration, and at unprecedentedly high statistical resolution. We consider the dependence of PDFs on flow conditions, focusing on diluted dynamics and pair-wise interactions (''collisions'') for mutual avoidance. By means of Langevin-like models we provide models for the measured data, inclusive typical fluctuations and rare events. This work is part of the JSTP research programme ``Vision driven visitor behaviour analysis and crowd management'' with Project Number 341-10-001, which is financed by the Netherlands Organisation for Scientific Research (NWO).

Breathing dynamics based parameter sensitivity analysis of hetero-polymeric DNA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Talukder, Srijeeta; Sen, Shrabani; Chaudhury, Pinaki, E-mail: pinakc@rediffmail.com

We study the parameter sensitivity of hetero-polymeric DNA within the purview of DNA breathing dynamics. The degree of correlation between the mean bubble size and the model parameters is estimated for this purpose for three different DNA sequences. The analysis leads us to a better understanding of the sequence dependent nature of the breathing dynamics of hetero-polymeric DNA. Out of the 14 model parameters for DNA stability in the statistical Poland-Scheraga approach, the hydrogen bond interaction ε{sub hb}(AT) for an AT base pair and the ring factor ξ turn out to be the most sensitive parameters. In addition, the stackingmore » interaction ε{sub st}(TA-TA) for an TA-TA nearest neighbor pair of base-pairs is found to be the most sensitive one among all stacking interactions. Moreover, we also establish that the nature of stacking interaction has a deciding effect on the DNA breathing dynamics, not the number of times a particular stacking interaction appears in a sequence. We show that the sensitivity analysis can be used as an effective measure to guide a stochastic optimization technique to find the kinetic rate constants related to the dynamics as opposed to the case where the rate constants are measured using the conventional unbiased way of optimization.« less

On the Effect of Dipole-Dipole Interactions on the Quantum Statistics of Surface Plasmons in Multiparticle Spaser Systems

NASA Astrophysics Data System (ADS)

Shesterikov, A. V.; Gubin, M. Yu.; Karpov, S. N.; Prokhorov, A. V.

2018-04-01

The problem of controlling the quantum dynamics of localized plasmons has been considered in the model of a four-particle spaser composed of metallic nanoparticles and semiconductor quantum dots. Conditions for the observation of stable steady-state regimes of the formation of surface plasmons in this model have been determined in the mean-field approximation. It has been shown that the presence of strong dipole-dipole interactions between metallic nanoparticles of the spaser system leads to a considerable change in the quantum statistics of plasmons generated on the nanoparticles.

Body size affects the strength of social interactions and spatial organization of a schooling fish (Pseudomugil signifer)

NASA Astrophysics Data System (ADS)

Romenskyy, Maksym; Herbert-Read, James E.; Ward, Ashley J. W.; Sumpter, David J. T.

2017-04-01

While a rich variety of self-propelled particle models propose to explain the collective motion of fish and other animals, rigorous statistical comparison between models and data remains a challenge. Plausible models should be flexible enough to capture changes in the collective behaviour of animal groups at their different developmental stages and group sizes. Here, we analyse the statistical properties of schooling fish (Pseudomugil signifer) through a combination of experiments and simulations. We make novel use of a Boltzmann inversion method, usually applied in molecular dynamics, to identify the effective potential of the mean force of fish interactions. Specifically, we show that larger fish have a larger repulsion zone, but stronger attraction, resulting in greater alignment in their collective motion. We model the collective dynamics of schools using a self-propelled particle model, modified to include varying particle speed and a local repulsion rule. We demonstrate that the statistical properties of the fish schools are reproduced by our model, thereby capturing a number of features of the behaviour and development of schooling fish.

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses

PubMed Central

Stephen, Emily P.; Lepage, Kyle Q.; Eden, Uri T.; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S.; Guenther, Frank H.; Kramer, Mark A.

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience. PMID:24678295

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.

PubMed

Stephen, Emily P; Lepage, Kyle Q; Eden, Uri T; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S; Guenther, Frank H; Kramer, Mark A

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty-both in the functional network edges and the corresponding aggregate measures of network topology-are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here-appropriate for static and dynamic network inference and different statistical measures of coupling-permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.

Statistical mechanics of neocortical interactions: Path-integral evolution of short-term memory

NASA Astrophysics Data System (ADS)

Ingber, Lester

1994-05-01

Previous papers in this series of statistical mechanics of neocortical interactions (SMNI) have detailed a development from the relatively microscopic scales of neurons up to the macroscopic scales as recorded by electroencephalography (EEG), requiring an intermediate mesocolumnar scale to be developed at the scale of minicolumns (~=102 neurons) and macrocolumns (~=105 neurons). Opportunity was taken to view SMNI as sets of statistical constraints, not necessarily describing specific synaptic or neuronal mechanisms, on neuronal interactions, on some aspects of short-term memory (STM), e.g., its capacity, stability, and duration. A recently developed c-language code, pathint, provides a non-Monte Carlo technique for calculating the dynamic evolution of arbitrary-dimension (subject to computer resources) nonlinear Lagrangians, such as derived for the two-variable SMNI problem. Here, pathint is used to explicitly detail the evolution of the SMNI constraints on STM.

A statistical state dynamics approach to wall turbulence.

PubMed

Farrell, B F; Gayme, D F; Ioannou, P J

2017-03-13

This paper reviews results obtained using statistical state dynamics (SSD) that demonstrate the benefits of adopting this perspective for understanding turbulence in wall-bounded shear flows. The SSD approach used in this work employs a second-order closure that retains only the interaction between the streamwise mean flow and the streamwise mean perturbation covariance. This closure restricts nonlinearity in the SSD to that explicitly retained in the streamwise constant mean flow together with nonlinear interactions between the mean flow and the perturbation covariance. This dynamical restriction, in which explicit perturbation-perturbation nonlinearity is removed from the perturbation equation, results in a simplified dynamics referred to as the restricted nonlinear (RNL) dynamics. RNL systems, in which a finite ensemble of realizations of the perturbation equation share the same mean flow, provide tractable approximations to the SSD, which is equivalent to an infinite ensemble RNL system. This infinite ensemble system, referred to as the stochastic structural stability theory system, introduces new analysis tools for studying turbulence. RNL systems provide computationally efficient means to approximate the SSD and produce self-sustaining turbulence exhibiting qualitative features similar to those observed in direct numerical simulations despite greatly simplified dynamics. The results presented show that RNL turbulence can be supported by as few as a single streamwise varying component interacting with the streamwise constant mean flow and that judicious selection of this truncated support or 'band-limiting' can be used to improve quantitative accuracy of RNL turbulence. These results suggest that the SSD approach provides new analytical and computational tools that allow new insights into wall turbulence.This article is part of the themed issue 'Toward the development of high-fidelity models of wall turbulence at large Reynolds number'. © 2017 The Author(s).

A statistical state dynamics approach to wall turbulence

PubMed Central

Gayme, D. F.; Ioannou, P. J.

2017-01-01

This paper reviews results obtained using statistical state dynamics (SSD) that demonstrate the benefits of adopting this perspective for understanding turbulence in wall-bounded shear flows. The SSD approach used in this work employs a second-order closure that retains only the interaction between the streamwise mean flow and the streamwise mean perturbation covariance. This closure restricts nonlinearity in the SSD to that explicitly retained in the streamwise constant mean flow together with nonlinear interactions between the mean flow and the perturbation covariance. This dynamical restriction, in which explicit perturbation–perturbation nonlinearity is removed from the perturbation equation, results in a simplified dynamics referred to as the restricted nonlinear (RNL) dynamics. RNL systems, in which a finite ensemble of realizations of the perturbation equation share the same mean flow, provide tractable approximations to the SSD, which is equivalent to an infinite ensemble RNL system. This infinite ensemble system, referred to as the stochastic structural stability theory system, introduces new analysis tools for studying turbulence. RNL systems provide computationally efficient means to approximate the SSD and produce self-sustaining turbulence exhibiting qualitative features similar to those observed in direct numerical simulations despite greatly simplified dynamics. The results presented show that RNL turbulence can be supported by as few as a single streamwise varying component interacting with the streamwise constant mean flow and that judicious selection of this truncated support or ‘band-limiting’ can be used to improve quantitative accuracy of RNL turbulence. These results suggest that the SSD approach provides new analytical and computational tools that allow new insights into wall turbulence. This article is part of the themed issue ‘Toward the development of high-fidelity models of wall turbulence at large Reynolds number’. PMID:28167577

Characteristics of level-spacing statistics in chaotic graphene billiards.

PubMed

Huang, Liang; Lai, Ying-Cheng; Grebogi, Celso

2011-03-01

A fundamental result in nonrelativistic quantum nonlinear dynamics is that the spectral statistics of quantum systems that possess no geometric symmetry, but whose classical dynamics are chaotic, are described by those of the Gaussian orthogonal ensemble (GOE) or the Gaussian unitary ensemble (GUE), in the presence or absence of time-reversal symmetry, respectively. For massless spin-half particles such as neutrinos in relativistic quantum mechanics in a chaotic billiard, the seminal work of Berry and Mondragon established the GUE nature of the level-spacing statistics, due to the combination of the chirality of Dirac particles and the confinement, which breaks the time-reversal symmetry. A question is whether the GOE or the GUE statistics can be observed in experimentally accessible, relativistic quantum systems. We demonstrate, using graphene confinements in which the quasiparticle motions are governed by the Dirac equation in the low-energy regime, that the level-spacing statistics are persistently those of GOE random matrices. We present extensive numerical evidence obtained from the tight-binding approach and a physical explanation for the GOE statistics. We also find that the presence of a weak magnetic field switches the statistics to those of GUE. For a strong magnetic field, Landau levels become influential, causing the level-spacing distribution to deviate markedly from the random-matrix predictions. Issues addressed also include the effects of a number of realistic factors on level-spacing statistics such as next nearest-neighbor interactions, different lattice orientations, enhanced hopping energy for atoms on the boundary, and staggered potential due to graphene-substrate interactions.

Nonlinear dynamics of global atmospheric and earth system processes

NASA Technical Reports Server (NTRS)

Zhang, Taiping; Verbitsky, Mikhail; Saltzman, Barry; Mann, Michael E.; Park, Jeffrey; Lall, Upmanu

1995-01-01

During the grant period, the authors continued ongoing studies aimed at enhancing their understanding of the operation of the atmosphere as a complex nonlinear system interacting with the hydrosphere, biosphere, and cryosphere in response to external radiative forcing. Five papers were completed with support from the grant, representing contributions in three main areas of study: (1) theoretical studies of the interactive atmospheric response to changed biospheric boundary conditions measurable from satellites; (2) statistical-observational studies of global-scale temperature variability on interannual to century time scales; and (3) dynamics of long-term earth system changes associated with ice sheet surges.

Real and financial market interactions in a multiplier-accelerator model: Nonlinear dynamics, multistability and stylized facts

NASA Astrophysics Data System (ADS)

Cavalli, F.; Naimzada, A.; Pecora, N.

2017-10-01

In the present paper, we investigate the dynamics of a model in which the real part of the economy, described within a multiplier-accelerator framework, interacts with a financial market with heterogeneous speculators, in order to study the channels through which the two sectors influence each other. Employing analytical and numerical tools, we investigate stability conditions as well as bifurcations and possible periodic, quasi-periodic, and chaotic dynamics, enlightening how the degree of market interaction, together with the accelerator parameter and the intervention of the fiscal authority, may affect the business cycle and the course of the financial market. In particular, we show that even if the steady state is locally stable, multistability phenomena can occur, with several and complex dynamic structures coexisting with the steady state. Finally, simulations reveal that the proposed model is able to explain several statistical properties and stylized facts observed in real financial markets, including persistent high volatility, fat-tailed return distributions, volatility clustering, and positive autocorrelation of absolute returns.

Real and financial market interactions in a multiplier-accelerator model: Nonlinear dynamics, multistability and stylized facts.

PubMed

Cavalli, F; Naimzada, A; Pecora, N

2017-10-01

In the present paper, we investigate the dynamics of a model in which the real part of the economy, described within a multiplier-accelerator framework, interacts with a financial market with heterogeneous speculators, in order to study the channels through which the two sectors influence each other. Employing analytical and numerical tools, we investigate stability conditions as well as bifurcations and possible periodic, quasi-periodic, and chaotic dynamics, enlightening how the degree of market interaction, together with the accelerator parameter and the intervention of the fiscal authority, may affect the business cycle and the course of the financial market. In particular, we show that even if the steady state is locally stable, multistability phenomena can occur, with several and complex dynamic structures coexisting with the steady state. Finally, simulations reveal that the proposed model is able to explain several statistical properties and stylized facts observed in real financial markets, including persistent high volatility, fat-tailed return distributions, volatility clustering, and positive autocorrelation of absolute returns.

Blended particle filters for large-dimensional chaotic dynamical systems

PubMed Central

Majda, Andrew J.; Qi, Di; Sapsis, Themistoklis P.

2014-01-01

A major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particle filters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particle filters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particle filter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below. PMID:24825886

Heterogeneous Structure of Stem Cells Dynamics: Statistical Models and Quantitative Predictions

PubMed Central

Bogdan, Paul; Deasy, Bridget M.; Gharaibeh, Burhan; Roehrs, Timo; Marculescu, Radu

2014-01-01

Understanding stem cell (SC) population dynamics is essential for developing models that can be used in basic science and medicine, to aid in predicting cells fate. These models can be used as tools e.g. in studying patho-physiological events at the cellular and tissue level, predicting (mal)functions along the developmental course, and personalized regenerative medicine. Using time-lapsed imaging and statistical tools, we show that the dynamics of SC populations involve a heterogeneous structure consisting of multiple sub-population behaviors. Using non-Gaussian statistical approaches, we identify the co-existence of fast and slow dividing subpopulations, and quiescent cells, in stem cells from three species. The mathematical analysis also shows that, instead of developing independently, SCs exhibit a time-dependent fractal behavior as they interact with each other through molecular and tactile signals. These findings suggest that more sophisticated models of SC dynamics should view SC populations as a collective and avoid the simplifying homogeneity assumption by accounting for the presence of more than one dividing sub-population, and their multi-fractal characteristics. PMID:24769917

Observation of prethermalization in long-range interacting spin chains

PubMed Central

Neyenhuis, Brian; Zhang, Jiehang; Hess, Paul W.; Smith, Jacob; Lee, Aaron C.; Richerme, Phil; Gong, Zhe-Xuan; Gorshkov, Alexey V.; Monroe, Christopher

2017-01-01

Although statistical mechanics describes thermal equilibrium states, these states may or may not emerge dynamically for a subsystem of an isolated quantum many-body system. For instance, quantum systems that are near-integrable usually fail to thermalize in an experimentally realistic time scale, and instead relax to quasi-stationary prethermal states that can be described by statistical mechanics, when approximately conserved quantities are included in a generalized Gibbs ensemble (GGE). We experimentally study the relaxation dynamics of a chain of up to 22 spins evolving under a long-range transverse-field Ising Hamiltonian following a sudden quench. For sufficiently long-range interactions, the system relaxes to a new type of prethermal state that retains a strong memory of the initial conditions. However, the prethermal state in this case cannot be described by a standard GGE; it rather arises from an emergent double-well potential felt by the spin excitations. This result shows that prethermalization occurs in a broader context than previously thought, and reveals new challenges for a generic understanding of the thermalization of quantum systems, particularly in the presence of long-range interactions. PMID:28875166

UniEnt: uniform entropy model for the dynamics of a neuronal population

NASA Astrophysics Data System (ADS)

Hernandez Lahme, Damian; Nemenman, Ilya

Sensory information and motor responses are encoded in the brain in a collective spiking activity of a large number of neurons. Understanding the neural code requires inferring statistical properties of such collective dynamics from multicellular neurophysiological recordings. Questions of whether synchronous activity or silence of multiple neurons carries information about the stimuli or the motor responses are especially interesting. Unfortunately, detection of such high order statistical interactions from data is especially challenging due to the exponentially large dimensionality of the state space of neural collectives. Here we present UniEnt, a method for the inference of strengths of multivariate neural interaction patterns. The method is based on the Bayesian prior that makes no assumptions (uniform a priori expectations) about the value of the entropy of the observed multivariate neural activity, in contrast to popular approaches that maximize this entropy. We then study previously published multi-electrode recordings data from salamander retina, exposing the relevance of higher order neural interaction patterns for information encoding in this system. This work was supported in part by Grants JSMF/220020321 and NSF/IOS/1208126.

Computational pathology: Exploring the spatial dimension of tumor ecology.

PubMed

Nawaz, Sidra; Yuan, Yinyin

2016-09-28

Tumors are evolving ecosystems where cancer subclones and the microenvironment interact. This is analogous to interaction dynamics between species in their natural habitats, which is a prime area of study in ecology. Spatial statistics are frequently used in ecological studies to infer complex relations including predator-prey, resource dependency and co-evolution. Recently, the emerging field of computational pathology has enabled high-throughput spatial analysis by using image processing to identify different cell types and their locations within histological tumor samples. We discuss how these data may be analyzed with spatial statistics used in ecology to reveal patterns and advance our understanding of ecological interactions occurring among cancer cells and their microenvironment. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Sampling Long- versus Short-Range Interactions Defines the Ability of Force Fields To Reproduce the Dynamics of Intrinsically Disordered Proteins.

PubMed

Mercadante, Davide; Wagner, Johannes A; Aramburu, Iker V; Lemke, Edward A; Gräter, Frauke

2017-09-12

Molecular dynamics (MD) simulations have valuably complemented experiments describing the dynamics of intrinsically disordered proteins (IDPs), particularly since the proposal of models to solve the artificial collapse of IDPs in silico. Such models suggest redefining nonbonded interactions, by either increasing water dispersion forces or adopting the Kirkwood-Buff force field. These approaches yield extended conformers that better comply with experiments, but it is unclear if they all sample the same intrachain dynamics of IDPs. We have tested this by employing MD simulations and single-molecule Förster resonance energy transfer spectroscopy to sample the dimensions of systems with different sequence compositions, namely strong and weak polyelectrolytes. For strong polyelectrolytes in which charge effects dominate, all the proposed solutions equally reproduce the expected ensemble's dimensions. For weak polyelectrolytes, at lower cutoffs, force fields abnormally alter intrachain dynamics, overestimating excluded volume over chain flexibility or reporting no difference between the dynamics of different chains. The TIP4PD water model alone can reproduce experimentally observed changes in extensions (dimensions), but not quantitatively and with only weak statistical significance. Force field limitations are reversed with increased interaction cutoffs, showing that chain dynamics are critically defined by the presence of long-range interactions. Force field analysis aside, our study provides the first insights into how long-range interactions critically define IDP dimensions and raises the question of which length range is crucial to correctly sample the overall dimensions and internal dynamics of the large group of weakly charged yet highly polar IDPs.

Langmuir waveforms at interplanetary shocks: STEREO statistical analysis

NASA Astrophysics Data System (ADS)

Briand, C.

2016-12-01

Wave-particle interactions and particle acceleration are the two main processes allowing energy dissipation at non collisional shocks. Ion acceleration has been deeply studied for many years, also for their central role in the shock front reformation. Electron dynamics is also important in the shock dynamics through the instabilities they can generate which may impact the ion dynamics.Particle measurements can be efficiently completed by wave measurements to determine the characteristics of the electron beams and study the turbulence of the medium. Electric waveforms obtained from the S/WAVES instrument of the STEREO mission between 2007 to 2014 are analyzed. Thus, clear signature of Langmuir waves are observed on 41 interplanetary shocks. These data enable a statistical analysis and to deduce some characteristics of the electron dynamics on different shocks sources (SIR or ICME) and types (quasi-perpendicular or quasi-parallel). The conversion process between electrostatic to electromagnetic waves has also been tested in several cases.

Polarization observables in few nucleon systems with CLAS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zachariou, Nicholas

The CEBAF Large Acceptance Spectrometer (CLAS), housed in Hall-B at the Thomas Jefferson National Accelerator Facility provides us with the experimental tools to study strongly-interacting matter and its dynamics in the transition from hadronic to partonic degrees of freedom in nuclear interactions. In this paper we discuss the progress made in understanding the relevant degrees of freedom using polarisation observables of deuteron photodisintegration in the few-GeV photon-energy region. We also address progress made in studying the interaction between Hyperons and Nucleons via polarisation observables, utilising high-statistics experiments that provided us with the large data samples needed to study final-state interactions,more » as well as perform detailed studies on initial-state effects. The polarisation observables presented here provide us with unique experimental tools to study the underlying dynamics of both initial and final-state interactions, as well as the information needed to disentangle signal from background contributions.« less

Polarization observables in few nucleon systems with CLAS

DOE PAGES

Zachariou, Nicholas

2017-12-01

The CEBAF Large Acceptance Spectrometer (CLAS), housed in Hall-B at the Thomas Jefferson National Accelerator Facility provides us with the experimental tools to study strongly-interacting matter and its dynamics in the transition from hadronic to partonic degrees of freedom in nuclear interactions. In this paper we discuss the progress made in understanding the relevant degrees of freedom using polarisation observables of deuteron photodisintegration in the few-GeV photon-energy region. We also address progress made in studying the interaction between Hyperons and Nucleons via polarisation observables, utilising high-statistics experiments that provided us with the large data samples needed to study final-state interactions,more » as well as perform detailed studies on initial-state effects. The polarisation observables presented here provide us with unique experimental tools to study the underlying dynamics of both initial and final-state interactions, as well as the information needed to disentangle signal from background contributions.« less

Accurate Structural Correlations from Maximum Likelihood Superpositions

PubMed Central

Theobald, Douglas L; Wuttke, Deborah S

2008-01-01

The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology. PMID:18282091

Nonthermal steady states after an interaction quench in the Falicov-Kimball model.

PubMed

Eckstein, Martin; Kollar, Marcus

2008-03-28

We present the exact solution of the Falicov-Kimball model after a sudden change of its interaction parameter using nonequilibrium dynamical mean-field theory. For different interaction quenches between the homogeneous metallic and insulating phases the system relaxes to a nonthermal steady state on time scales on the order of variant Planck's over 2pi/bandwidth, showing collapse and revival with an approximate period of h/interaction if the interaction is large. We discuss the reasons for this behavior and provide a statistical description of the final steady state by means of generalized Gibbs ensembles.

Exploring complex networks.

PubMed

Strogatz, S H

2001-03-08

The study of networks pervades all of science, from neurobiology to statistical physics. The most basic issues are structural: how does one characterize the wiring diagram of a food web or the Internet or the metabolic network of the bacterium Escherichia coli? Are there any unifying principles underlying their topology? From the perspective of nonlinear dynamics, we would also like to understand how an enormous network of interacting dynamical systems-be they neurons, power stations or lasers-will behave collectively, given their individual dynamics and coupling architecture. Researchers are only now beginning to unravel the structure and dynamics of complex networks.

Distinct polymer physics principles govern chromatin dynamics in mouse and Drosophila topological domains.

PubMed

Ea, Vuthy; Sexton, Tom; Gostan, Thierry; Herviou, Laurie; Baudement, Marie-Odile; Zhang, Yunzhe; Berlivet, Soizik; Le Lay-Taha, Marie-Noëlle; Cathala, Guy; Lesne, Annick; Victor, Jean-Marc; Fan, Yuhong; Cavalli, Giacomo; Forné, Thierry

2015-08-15

In higher eukaryotes, the genome is partitioned into large "Topologically Associating Domains" (TADs) in which the chromatin displays favoured long-range contacts. While a crumpled/fractal globule organization has received experimental supports at higher-order levels, the organization principles that govern chromatin dynamics within these TADs remain unclear. Using simple polymer models, we previously showed that, in mouse liver cells, gene-rich domains tend to adopt a statistical helix shape when no significant locus-specific interaction takes place. Here, we use data from diverse 3C-derived methods to explore chromatin dynamics within mouse and Drosophila TADs. In mouse Embryonic Stem Cells (mESC), that possess large TADs (median size of 840 kb), we show that the statistical helix model, but not globule models, is relevant not only in gene-rich TADs, but also in gene-poor and gene-desert TADs. Interestingly, this statistical helix organization is considerably relaxed in mESC compared to liver cells, indicating that the impact of the constraints responsible for this organization is weaker in pluripotent cells. Finally, depletion of histone H1 in mESC alters local chromatin flexibility but not the statistical helix organization. In Drosophila, which possesses TADs of smaller sizes (median size of 70 kb), we show that, while chromatin compaction and flexibility are finely tuned according to the epigenetic landscape, chromatin dynamics within TADs is generally compatible with an unconstrained polymer configuration. Models issued from polymer physics can accurately describe the organization principles governing chromatin dynamics in both mouse and Drosophila TADs. However, constraints applied on this dynamics within mammalian TADs have a peculiar impact resulting in a statistical helix organization.

An evidence-based systematic review of gymnema (Gymnema sylvestre R. Br.) by the Natural Standard Research Collaboration.

PubMed

Ulbricht, Catherine; Abrams, Tracee Rae; Basch, Ethan; Davies-Heerema, Theresa; Foppa, Ivo; Hammerness, Paul; Rusie, Erica; Tanguay-Colucci, Shaina; Taylor, Sarah; Ulbricht, Catherine; Varghese, Minney; Weissner, Wendy; Woods, Jen

2011-09-01

An evidence-based systematic review of gymnema (Gymnema sylvestre R. Br.), including written and statistical analysis of scientific literature, expert opinion, folkloric precedent, history, pharmacology, kinetics/dynamics, interactions, adverse effects, toxicology, and dosing.

Static and Dynamic Cognitive Reserve Proxy Measures: Interactions with Alzheimer’s Disease Neuropathology and Cognition

PubMed Central

Malek-Ahmadi, Michael; Lu, Sophie; Chan, YanYan; Perez, Sylvia E; Chen, Kewei; Mufson, Elliott J

2018-01-01

Objective Years of education are the most common proxy for measuring cognitive reserve (CR) when assessing the relationship between Alzheimer’s disease (AD) neuropathology and cognition. However, years of education may be limited as a CR proxy given that it represents a specific timeframe in early life and is static. Studies suggest that measures of intellectual function provide a dynamic estimate of CR that is superior to years of education since it captures the effect of continued learning over time. The present study determined whether dynamic measures of CR were better predictors of episodic memory and executive function in the presence of AD pathology than a static measure of CR. Methods Subjects examined died with a pre-mortem clinical diagnosis of no cognitive impaired, mild cognitive impairment and mild to moderate AD. CERAD and Braak stage were used to stratify the sample by AD pathology severity. Linear regression analyses using CR by CERAD and CR by Braak stage interaction terms were used to determine whether Extended Range Vocabulary Test (ERVT) scores or years of education were significantly associated with episodic memory composite (EMC) and executive function composite (EFC) performance. All models were adjusted for clinical diagnosis, age at death, gender, APOE e4 carrier status and Braak stage. Results For episodic memory, years of education by CERAD interaction were not statistically significant (β=-0.01, SE=0.01, p=0.53). By contrast, ERVT interaction with CERAD diagnosis was statistically significant (β=-0.03, SE=0.01, p=0.004). Among the models using Braak stages, none of the CR by pathology interactions were associated with EMC or EFC. Conclusion Results suggest that a dynamic rather than a static measure is a better indicator of CR and that the relationship between CR and cognition is dependent upon the severity of select AD criteria. PMID:29423338

The Peace Mediator effect: Heterogeneous agents can foster consensus in continuous opinion models

NASA Astrophysics Data System (ADS)

Vilone, Daniele; Carletti, Timoteo; Bagnoli, Franco; Guazzini, Andrea

2016-11-01

Statistical mechanics has proven to be able to capture the fundamental rules underlying phenomena of social aggregation and opinion dynamics, well studied in disciplines like sociology and psychology. This approach is based on the underlying paradigm that the interesting dynamics of multi-agent systems emerge from the correct definition of few parameters governing the evolution of each individual. In this context, we propose a particular model of opinion dynamics based on the psychological construct named ;cognitive dissonance;. Our system is made of interacting individuals, the agents, each bearing only two dynamical variables (respectively ;opinion; and ;affinity;) self-consistently adjusted during time evolution. We also define two special classes of interacting entities, both acting for a peace mediation process but via different course of action: ;diplomats; and ;auctoritates;. The behavior of the system with and without peace mediators (PMs) is investigated and discussed with reference to corresponding psychological and social implications.

Interacting opinion and disease dynamics in multiplex networks: Discontinuous phase transition and nonmonotonic consensus times

NASA Astrophysics Data System (ADS)

Velásquez-Rojas, Fátima; Vazquez, Federico

2017-05-01

Opinion formation and disease spreading are among the most studied dynamical processes on complex networks. In real societies, it is expected that these two processes depend on and affect each other. However, little is known about the effects of opinion dynamics over disease dynamics and vice versa, since most studies treat them separately. In this work we study the dynamics of the voter model for opinion formation intertwined with that of the contact process for disease spreading, in a population of agents that interact via two types of connections, social and contact. These two interacting dynamics take place on two layers of networks, coupled through a fraction q of links present in both networks. The probability that an agent updates its state depends on both the opinion and disease states of the interacting partner. We find that the opinion dynamics has striking consequences on the statistical properties of disease spreading. The most important is that the smooth (continuous) transition from a healthy to an endemic phase observed in the contact process, as the infection probability increases beyond a threshold, becomes abrupt (discontinuous) in the two-layer system. Therefore, disregarding the effects of social dynamics on epidemics propagation may lead to a misestimation of the real magnitude of the spreading. Also, an endemic-healthy discontinuous transition is found when the coupling q overcomes a threshold value. Furthermore, we show that the disease dynamics delays the opinion consensus, leading to a consensus time that varies nonmonotonically with q in a large range of the model's parameters. A mean-field approach reveals that the coupled dynamics of opinions and disease can be approximately described by the dynamics of the voter model decoupled from that of the contact process, with effective probabilities of opinion and disease transmission.

The emergence of collective phenomena in systems with random interactions

NASA Astrophysics Data System (ADS)

Abramkina, Volha

Emergent phenomena are one of the most profound topics in modern science, addressing the ways that collectivities and complex patterns appear due to multiplicity of components and simple interactions. Ensembles of random Hamiltonians allow one to explore emergent phenomena in a statistical way. In this work we adopt a shell model approach with a two-body interaction Hamiltonian. The sets of the two-body interaction strengths are selected at random, resulting in the two-body random ensemble (TBRE). Symmetries such as angular momentum, isospin, and parity entangled with complex many-body dynamics result in surprising order discovered in the spectrum of low-lying excitations. The statistical patterns exhibited in the TBRE are remarkably similar to those observed in real nuclei. Signs of almost every collective feature seen in nuclei, namely, pairing superconductivity, deformation, and vibration, have been observed in random ensembles [3, 4, 5, 6]. In what follows a systematic investigation of nuclear shape collectivities in random ensembles is conducted. The development of the mean field, its geometry, multipole collectivities and their dependence on the underlying two-body interaction are explored. Apart from the role of static symmetries such as SU(2) angular momentum and isospin groups, the emergence of dynamical symmetries including the seniority SU(2), rotational symmetry, as well as the Elliot SU(3) is shown to be an important precursor for the existence of geometric collectivities.

Dynamics of person-to-person interactions from distributed RFID sensor networks.

PubMed

Cattuto, Ciro; Van den Broeck, Wouter; Barrat, Alain; Colizza, Vittoria; Pinton, Jean-François; Vespignani, Alessandro

2010-07-15

Digital networks, mobile devices, and the possibility of mining the ever-increasing amount of digital traces that we leave behind in our daily activities are changing the way we can approach the study of human and social interactions. Large-scale datasets, however, are mostly available for collective and statistical behaviors, at coarse granularities, while high-resolution data on person-to-person interactions are generally limited to relatively small groups of individuals. Here we present a scalable experimental framework for gathering real-time data resolving face-to-face social interactions with tunable spatial and temporal granularities. We use active Radio Frequency Identification (RFID) devices that assess mutual proximity in a distributed fashion by exchanging low-power radio packets. We analyze the dynamics of person-to-person interaction networks obtained in three high-resolution experiments carried out at different orders of magnitude in community size. The data sets exhibit common statistical properties and lack of a characteristic time scale from 20 seconds to several hours. The association between the number of connections and their duration shows an interesting super-linear behavior, which indicates the possibility of defining super-connectors both in the number and intensity of connections. Taking advantage of scalability and resolution, this experimental framework allows the monitoring of social interactions, uncovering similarities in the way individuals interact in different contexts, and identifying patterns of super-connector behavior in the community. These results could impact our understanding of all phenomena driven by face-to-face interactions, such as the spreading of transmissible infectious diseases and information.

Quantum Quench Dynamics

NASA Astrophysics Data System (ADS)

Mitra, Aditi

2018-03-01

Quench dynamics is an active area of study encompassing condensed matter physics and quantum information, with applications to cold-atomic gases and pump-probe spectroscopy of materials. Recent theoretical progress in studying quantum quenches is reviewed. Quenches in interacting one-dimensional systems as well as systems in higher spatial dimensions are covered. The appearance of nontrivial steady states following a quench in exactly solvable models is discussed, and the stability of these states to perturbations is described. Proper conserving approximations needed to capture the onset of thermalization at long times are outlined. The appearance of universal scaling for quenches near critical points and the role of the renormalization group in capturing the transient regime are reviewed. Finally, the effect of quenches near critical points on the dynamics of entanglement entropy and entanglement statistics is discussed. The extraction of critical exponents from the entanglement statistics is outlined.

Learning and dynamics in social systems. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

NASA Astrophysics Data System (ADS)

Dolfin, Marina

2016-03-01

The interesting novelty of the paper by Burini et al. [1] is that the authors present a survey and a new approach of collective learning based on suitable development of methods of the kinetic theory [2] and theoretical tools of evolutionary game theory [3]. Methods of statistical dynamics and kinetic theory lead naturally to stochastic and collective dynamics. Indeed, the authors propose the use of games where the state of the interacting entities is delivered by probability distributions.

Can Facebook Reduce Perceived Anxiety Among College Students? Randomized Controlled Exercise Trial Using the Transtheoretical Model of Behavior Change

PubMed Central

Frith, Emily

2017-01-01

Background Recent studies suggest social media may be an attractive strategy to promote mental health and wellness. There remains a need to examine the utility for individually tailored wellness messages posted to social media sites such as Facebook to facilitate positive psychological outcomes. Objective Our aim was to extend the growing body of evidence supporting the potential for social media to enhance mental health. We evaluated the influence of an 8-week social media intervention on anxiety in college students and examined the impact of dynamic (active) versus static (passive) Facebook content on physical activity behaviors. Methods Participants in the static group (n=21) accessed a Facebook page featuring 96 statuses. Statuses were intended to engage cognitive processes followed by behavioral processes of change per the transtheoretical model of behavior change. Content posted on the static Facebook page was identical to the dynamic page; however, the static group viewed all 96 statuses on the first day of the study, while the dynamic group received only 1 to 2 of these status updates per day throughout the intervention. Anxiety was measured using the Overall Anxiety Severity and Impairment Scale (OASIS). Time spent engaging in physical activity was assessed using the International Physical Activity Questionnaire (IPAQ). Results The OASIS change score for the dynamic Facebook group was statistically significant (P=.003), whereas the change score for the static group was not (P=.48). A statistically significant group-by-time interaction was observed (P=.03). The total IPAQ group-by-time interaction was not statistically significant (P=.06). Conclusions We observed a decrease in anxiety and increase in total physical activity for the dynamic group only. Dynamic social networking sites, featuring regularly updated content, may be more advantageous than websites that retain static content over time. Trial Registration ClinicalTrials.gov NCT03363737; https://clinicaltrials.gov/ct2/show/NCT03363737 (Archived by WebCite at http://www.webcitation.org/6vXzNbOWJ) PMID:29222077

Statistical Analysis of Human Body Movement and Group Interactions in Response to Music

NASA Astrophysics Data System (ADS)

Desmet, Frank; Leman, Marc; Lesaffre, Micheline; de Bruyn, Leen

Quantification of time series that relate to physiological data is challenging for empirical music research. Up to now, most studies have focused on time-dependent responses of individual subjects in controlled environments. However, little is known about time-dependent responses of between-subject interactions in an ecological context. This paper provides new findings on the statistical analysis of group synchronicity in response to musical stimuli. Different statistical techniques were applied to time-dependent data obtained from an experiment on embodied listening in individual and group settings. Analysis of inter group synchronicity are described. Dynamic Time Warping (DTW) and Cross Correlation Function (CCF) were found to be valid methods to estimate group coherence of the resulting movements. It was found that synchronicity of movements between individuals (human-human interactions) increases significantly in the social context. Moreover, Analysis of Variance (ANOVA) revealed that the type of music is the predominant factor in both the individual and the social context.

Synchrony dynamics underlying effective connectivity reconstruction of neuronal circuits

NASA Astrophysics Data System (ADS)

Yu, Haitao; Guo, Xinmeng; Qin, Qing; Deng, Yun; Wang, Jiang; Liu, Jing; Cao, Yibin

2017-04-01

Reconstruction of effective connectivity between neurons is essential for neural systems with function-related significance, characterizing directionally causal influences among neurons. In this work, causal interactions between neurons in spinal dorsal root ganglion, activated by manual acupuncture at Zusanli acupoint of experimental rats, are estimated using Granger causality (GC) method. Different patterns of effective connectivity are obtained for different frequencies and types of acupuncture. Combined with synchrony analysis between neurons, we show a dependence of effective connection on the synchronization dynamics. Based on the experimental findings, a neuronal circuit model with synaptic connections is constructed. The variation of neuronal effective connectivity with respect to its structural connectivity and synchronization dynamics is further explored. Simulation results show that reciprocally causal interactions with statistically significant are formed between well-synchronized neurons. The effective connectivity may be not necessarily equivalent to synaptic connections, but rather depend on the synchrony relationship. Furthermore, transitions of effective interaction between neurons are observed following the synchronization transitions induced by conduction delay and synaptic conductance. These findings are helpful to further investigate the dynamical mechanisms underlying the reconstruction of effective connectivity of neuronal population.

Interface collisions

NASA Astrophysics Data System (ADS)

Aarão Reis, F. D. A.; Pierre-Louis, O.

2018-04-01

We provide a theoretical framework to analyze the properties of frontal collisions of two growing interfaces considering different short-range interactions between them. Due to their roughness, the collision events spread in time and form rough domain boundaries, which defines collision interfaces in time and space. We show that statistical properties of such interfaces depend on the kinetics of the growing interfaces before collision, but are independent of the details of their interaction and of their fluctuations during the collision. Those properties exhibit dynamic scaling with exponents related to the growth kinetics, but their distributions may be nonuniversal. Our results are supported by simulations of lattice models with irreversible dynamics and local interactions. Relations to first passage processes are discussed and a possible application to grain-boundary formation in two-dimensional materials is suggested.

Work distributions of one-dimensional fermions and bosons with dual contact interactions

NASA Astrophysics Data System (ADS)

Wang, Bin; Zhang, Jingning; Quan, H. T.

2018-05-01

We extend the well-known static duality [M. Girardeau, J. Math. Phys. 1, 516 (1960), 10.1063/1.1703687; T. Cheon and T. Shigehara, Phys. Rev. Lett. 82, 2536 (1999), 10.1103/PhysRevLett.82.2536] between one-dimensional (1D) bosons and 1D fermions to the dynamical version. By utilizing this dynamical duality, we find the duality of nonequilibrium work distributions between interacting 1D bosonic (Lieb-Liniger model) and 1D fermionic (Cheon-Shigehara model) systems with dual contact interactions. As a special case, the work distribution of the Tonks-Girardeau gas is identical to that of 1D noninteracting fermionic system even though their momentum distributions are significantly different. In the classical limit, the work distributions of Lieb-Liniger models (Cheon-Shigehara models) with arbitrary coupling strength converge to that of the 1D noninteracting distinguishable particles, although their elementary excitations (quasiparticles) obey different statistics, e.g., the Bose-Einstein, the Fermi-Dirac, and the fractional statistics. We also present numerical results of the work distributions of Lieb-Liniger model with various coupling strengths, which demonstrate the convergence of work distributions in the classical limit.

Potential energy surface fitting by a statistically localized, permutationally invariant, local interpolating moving least squares method for the many-body potential: Method and application to N{sub 4}

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bender, Jason D.; Doraiswamy, Sriram; Candler, Graham V., E-mail: truhlar@umn.edu, E-mail: candler@aem.umn.edu

2014-02-07

Fitting potential energy surfaces to analytic forms is an important first step for efficient molecular dynamics simulations. Here, we present an improved version of the local interpolating moving least squares method (L-IMLS) for such fitting. Our method has three key improvements. First, pairwise interactions are modeled separately from many-body interactions. Second, permutational invariance is incorporated in the basis functions, using permutationally invariant polynomials in Morse variables, and in the weight functions. Third, computational cost is reduced by statistical localization, in which we statistically correlate the cutoff radius with data point density. We motivate our discussion in this paper with amore » review of global and local least-squares-based fitting methods in one dimension. Then, we develop our method in six dimensions, and we note that it allows the analytic evaluation of gradients, a feature that is important for molecular dynamics. The approach, which we call statistically localized, permutationally invariant, local interpolating moving least squares fitting of the many-body potential (SL-PI-L-IMLS-MP, or, more simply, L-IMLS-G2), is used to fit a potential energy surface to an electronic structure dataset for N{sub 4}. We discuss its performance on the dataset and give directions for further research, including applications to trajectory calculations.« less

Isobaric first-principles molecular dynamics of liquid water with nonlocal van der Waals interactions

NASA Astrophysics Data System (ADS)

Miceli, Giacomo; de Gironcoli, Stefano; Pasquarello, Alfredo

2015-01-01

We investigate the structural properties of liquid water at near ambient conditions using first-principles molecular dynamics simulations based on a semilocal density functional augmented with nonlocal van der Waals interactions. The adopted scheme offers the advantage of simulating liquid water at essentially the same computational cost of standard semilocal functionals. Applied to the water dimer and to ice Ih, we find that the hydrogen-bond energy is only slightly enhanced compared to a standard semilocal functional. We simulate liquid water through molecular dynamics in the NpH statistical ensemble allowing for fluctuations of the system density. The structure of the liquid departs from that found with a semilocal functional leading to more compact structural arrangements. This indicates that the directionality of the hydrogen-bond interaction has a diminished role as compared to the overall attractions, as expected when dispersion interactions are accounted for. This is substantiated through a detailed analysis comprising the study of the partial radial distribution functions, various local order indices, the hydrogen-bond network, and the selfdiffusion coefficient. The explicit treatment of the van der Waals interactions leads to an overall improved description of liquid water.

Theoretical Chemistry Comes Alive: Full Partner with Experiment.

ERIC Educational Resources Information Center

Goddard, William A., III

1985-01-01

The expected thrust for theoretical chemistry in the next decade will be to combine knowledge of fundamental chemical steps/interactions with advances in chemical dynamics, irreversible statistical mechanics, and computer technology to produce simulations of chemical systems with reaction site competition. A sample simulation (using the enzyme…

On an aggregation in birth-and-death stochastic dynamics

NASA Astrophysics Data System (ADS)

Finkelshtein, Dmitri; Kondratiev, Yuri; Kutoviy, Oleksandr; Zhizhina, Elena

2014-06-01

We consider birth-and-death stochastic dynamics of particle systems with attractive interaction. The heuristic generator of the dynamics has a constant birth rate and density-dependent decreasing death rate. The corresponding statistical dynamics is constructed. Using the Vlasov-type scaling we derive the limiting mesoscopic evolution and prove that this evolution propagates chaos. We study a nonlinear non-local kinetic equation for the first correlation function (density of population). The existence of uniformly bounded solutions as well as solutions growing inside of a bounded domain and expanding in the space are shown. These solutions describe two regimes in the mesoscopic system: regulation and aggregation.

Non-Born-Oppenheimer molecular dynamics of the spin-forbidden reaction O(3P) + CO(X 1Σ+) → CO2(tilde X{}^1Σ _g^ +)

NASA Astrophysics Data System (ADS)

Jasper, Ahren W.; Dawes, Richard

2013-10-01

The lowest-energy singlet (1 1A') and two lowest-energy triplet (1 3A' and 1 3A″) electronic states of CO2 are characterized using dynamically weighted multireference configuration interaction (dw-MRCI+Q) electronic structure theory calculations extrapolated to the complete basis set (CBS) limit. Global analytic representations of the dw-MRCI+Q/CBS singlet and triplet surfaces and of their CASSCF/aug-cc-pVQZ spin-orbit coupling surfaces are obtained via the interpolated moving least squares (IMLS) semiautomated surface fitting method. The spin-forbidden kinetics of the title reaction is calculated using the coupled IMLS surfaces and coherent switches with decay of mixing non-Born-Oppenheimer molecular dynamics. The calculated spin-forbidden association rate coefficient (corresponding to the high pressure limit of the rate coefficient) is 7-35 times larger at 1000-5000 K than the rate coefficient used in many detailed chemical models of combustion. A dynamical analysis of the multistate trajectories is presented. The trajectory calculations reveal direct (nonstatistical) and indirect (statistical) spin-forbidden reaction mechanisms and may be used to test the suitability of transition-state-theory-like statistical methods for spin-forbidden kinetics. Specifically, we consider the appropriateness of the "double passage" approximation, of assuming statistical distributions of seam crossings, and of applications of the unified statistical model for spin-forbidden reactions.

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks

PubMed Central

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model. PMID:24634645

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks.

PubMed

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model.

Dynamics of traffic flow with real-time traffic information

NASA Astrophysics Data System (ADS)

Yokoya, Yasushi

2004-01-01

We studied dynamics of traffic flow with real-time information provided. Provision of the real-time traffic information based on advancements in telecommunication technology is expected to facilitate the efficient utilization of available road capacity. This system has a potentiality of not only engineering for road usage but also the science of complexity series. In the system, the information plays a role of feedback connecting microscopic and macroscopic phenomena beyond the hierarchical structure of statistical physics. In this paper, we tried to clarify how the information works in a network of traffic flow from the perspective of statistical physics. The dynamical feature of the traffic flow is abstracted by a contrastive study between the nonequilibrium statistical physics and a computer simulation based on cellular automaton. We found that the information disrupts the local equilibrium of traffic flow by a characteristic dissipation process due to interaction between the information and individual vehicles. The dissipative structure was observed in the time evolution of traffic flow driven far from equilibrium as a consequence of the breakdown of the local-equilibrium hypothesis.

Tuning structure and mobility of solvation shells surrounding tracer additives

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carmer, James; Jain, Avni; Bollinger, Jonathan A.

2015-03-28

Molecular dynamics simulations and a stochastic Fokker-Planck equation based approach are used to illuminate how position-dependent solvent mobility near one or more tracer particle(s) is affected when tracer-solvent interactions are rationally modified to affect corresponding solvation structure. For tracers in a dense hard-sphere fluid, we compare two types of tracer-solvent interactions: (1) a hard-sphere-like interaction, and (2) a soft repulsion extending beyond the hard core designed via statistical mechanical theory to enhance tracer mobility at infinite dilution by suppressing coordination-shell structure [Carmer et al., Soft Matter 8, 4083–4089 (2012)]. For the latter case, we show that the mobility of surroundingmore » solvent particles is also increased by addition of the soft repulsive interaction, which helps to rationalize the mechanism underlying the tracer’s enhanced diffusivity. However, if multiple tracer surfaces are in closer proximity (as at higher tracer concentrations), similar interactions that disrupt local solvation structure instead suppress the position-dependent solvent dynamics.« less

Tuning structure and mobility of solvation shells surrounding tracer additives.

PubMed

Carmer, James; Jain, Avni; Bollinger, Jonathan A; van Swol, Frank; Truskett, Thomas M

2015-03-28

Molecular dynamics simulations and a stochastic Fokker-Planck equation based approach are used to illuminate how position-dependent solvent mobility near one or more tracer particle(s) is affected when tracer-solvent interactions are rationally modified to affect corresponding solvation structure. For tracers in a dense hard-sphere fluid, we compare two types of tracer-solvent interactions: (1) a hard-sphere-like interaction, and (2) a soft repulsion extending beyond the hard core designed via statistical mechanical theory to enhance tracer mobility at infinite dilution by suppressing coordination-shell structure [Carmer et al., Soft Matter 8, 4083-4089 (2012)]. For the latter case, we show that the mobility of surrounding solvent particles is also increased by addition of the soft repulsive interaction, which helps to rationalize the mechanism underlying the tracer's enhanced diffusivity. However, if multiple tracer surfaces are in closer proximity (as at higher tracer concentrations), similar interactions that disrupt local solvation structure instead suppress the position-dependent solvent dynamics.

Entanglement and thermodynamics after a quantum quench in integrable systems.

PubMed

Alba, Vincenzo; Calabrese, Pasquale

2017-07-25

Entanglement and entropy are key concepts standing at the foundations of quantum and statistical mechanics. Recently, the study of quantum quenches revealed that these concepts are intricately intertwined. Although the unitary time evolution ensuing from a pure state maintains the system at zero entropy, local properties at long times are captured by a statistical ensemble with nonzero thermodynamic entropy, which is the entanglement accumulated during the dynamics. Therefore, understanding the entanglement evolution unveils how thermodynamics emerges in isolated systems. Alas, an exact computation of the entanglement dynamics was available so far only for noninteracting systems, whereas it was deemed unfeasible for interacting ones. Here, we show that the standard quasiparticle picture of the entanglement evolution, complemented with integrability-based knowledge of the steady state and its excitations, leads to a complete understanding of the entanglement dynamics in the space-time scaling limit. We thoroughly check our result for the paradigmatic Heisenberg chain.

A Molecular Dynamics Simulation of the Turbulent Couette Minimal Flow Unit

NASA Astrophysics Data System (ADS)

Smith, Edward

2016-11-01

What happens to turbulent motions below the Kolmogorov length scale? In order to explore this question, a 300 million molecule Molecular Dynamics (MD) simulation is presented for the minimal Couette channel in which turbulence can be sustained. The regeneration cycle and turbulent statistics show excellent agreement to continuum based computational fluid dynamics (CFD) at Re=400. As MD requires only Newton's laws and a form of inter-molecular potential, it captures a much greater range of phenomena without requiring the assumptions of Newton's law of viscosity, thermodynamic equilibrium, fluid isotropy or the limitation of grid resolution. The fundamental nature of MD means it is uniquely placed to explore the nature of turbulent transport. A number of unique insights from MD are presented, including energy budgets, sub-grid turbulent energy spectra, probability density functions, Lagrangian statistics and fluid wall interactions. EPSRC Post Doctoral Prize Fellowship.

Entanglement and thermodynamics after a quantum quench in integrable systems

NASA Astrophysics Data System (ADS)

Alba, Vincenzo; Calabrese, Pasquale

2017-07-01

Entanglement and entropy are key concepts standing at the foundations of quantum and statistical mechanics. Recently, the study of quantum quenches revealed that these concepts are intricately intertwined. Although the unitary time evolution ensuing from a pure state maintains the system at zero entropy, local properties at long times are captured by a statistical ensemble with nonzero thermodynamic entropy, which is the entanglement accumulated during the dynamics. Therefore, understanding the entanglement evolution unveils how thermodynamics emerges in isolated systems. Alas, an exact computation of the entanglement dynamics was available so far only for noninteracting systems, whereas it was deemed unfeasible for interacting ones. Here, we show that the standard quasiparticle picture of the entanglement evolution, complemented with integrability-based knowledge of the steady state and its excitations, leads to a complete understanding of the entanglement dynamics in the space-time scaling limit. We thoroughly check our result for the paradigmatic Heisenberg chain.

Entanglement and thermodynamics after a quantum quench in integrable systems

PubMed Central

Alba, Vincenzo; Calabrese, Pasquale

2017-01-01

Entanglement and entropy are key concepts standing at the foundations of quantum and statistical mechanics. Recently, the study of quantum quenches revealed that these concepts are intricately intertwined. Although the unitary time evolution ensuing from a pure state maintains the system at zero entropy, local properties at long times are captured by a statistical ensemble with nonzero thermodynamic entropy, which is the entanglement accumulated during the dynamics. Therefore, understanding the entanglement evolution unveils how thermodynamics emerges in isolated systems. Alas, an exact computation of the entanglement dynamics was available so far only for noninteracting systems, whereas it was deemed unfeasible for interacting ones. Here, we show that the standard quasiparticle picture of the entanglement evolution, complemented with integrability-based knowledge of the steady state and its excitations, leads to a complete understanding of the entanglement dynamics in the space–time scaling limit. We thoroughly check our result for the paradigmatic Heisenberg chain. PMID:28698379

Computationally Efficient Multiconfigurational Reactive Molecular Dynamics

PubMed Central

Yamashita, Takefumi; Peng, Yuxing; Knight, Chris; Voth, Gregory A.

2012-01-01

It is a computationally demanding task to explicitly simulate the electronic degrees of freedom in a system to observe the chemical transformations of interest, while at the same time sampling the time and length scales required to converge statistical properties and thus reduce artifacts due to initial conditions, finite-size effects, and limited sampling. One solution that significantly reduces the computational expense consists of molecular models in which effective interactions between particles govern the dynamics of the system. If the interaction potentials in these models are developed to reproduce calculated properties from electronic structure calculations and/or ab initio molecular dynamics simulations, then one can calculate accurate properties at a fraction of the computational cost. Multiconfigurational algorithms model the system as a linear combination of several chemical bonding topologies to simulate chemical reactions, also sometimes referred to as “multistate”. These algorithms typically utilize energy and force calculations already found in popular molecular dynamics software packages, thus facilitating their implementation without significant changes to the structure of the code. However, the evaluation of energies and forces for several bonding topologies per simulation step can lead to poor computational efficiency if redundancy is not efficiently removed, particularly with respect to the calculation of long-ranged Coulombic interactions. This paper presents accurate approximations (effective long-range interaction and resulting hybrid methods) and multiple-program parallelization strategies for the efficient calculation of electrostatic interactions in reactive molecular simulations. PMID:25100924

Particle-based simulations of self-motile suspensions

NASA Astrophysics Data System (ADS)

Hinz, Denis F.; Panchenko, Alexander; Kim, Tae-Yeon; Fried, Eliot

2015-11-01

A simple model for simulating flows of active suspensions is investigated. The approach is based on dissipative particle dynamics. While the model is potentially applicable to a wide range of self-propelled particle systems, the specific class of self-motile bacterial suspensions is considered as a modeling scenario. To mimic the rod-like geometry of a bacterium, two dissipative particle dynamics particles are connected by a stiff harmonic spring to form an aggregate dissipative particle dynamics molecule. Bacterial motility is modeled through a constant self-propulsion force applied along the axis of each such aggregate molecule. The model accounts for hydrodynamic interactions between self-propelled agents through the pairwise dissipative interactions conventional to dissipative particle dynamics. Numerical simulations are performed using a customized version of the open-source software package LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) software package. Detailed studies of the influence of agent concentration, pairwise dissipative interactions, and Stokes friction on the statistics of the system are provided. The simulations are used to explore the influence of hydrodynamic interactions in active suspensions. For high agent concentrations in combination with dominating pairwise dissipative forces, strongly correlated motion patterns and a fluid-like spectral distributions of kinetic energy are found. In contrast, systems dominated by Stokes friction exhibit weaker spatial correlations of the velocity field. These results indicate that hydrodynamic interactions may play an important role in the formation of spatially extended structures in active suspensions.

Simulating the Interactions Among Land Use, Transportation, and Economy to Inform Light Rail Transit Decisions

EPA Science Inventory

In most transportation studies, computer models that forecast travel behavior statistics for a future year use static projections of the spatial distribution of future population and employment growth as inputs. As a result, they are unable to account for the temporally dynamic a...

Simulating the Interactions Among Land Use, Transportation, and Economy to Inform Light Rail Transit Decisions (proceedings)

EPA Science Inventory

In most transportation studies, computer models that forecast travel behavior statistics for a future year use static projections of the spatial distribution of future population and employment growth as inputs. As a result, they are unable to account for the temporally dynamic a...

Making the Best of New Information Technologies at Eurostat.

ERIC Educational Resources Information Center

Guittet, Christian

Eurostat, the Statistical Office of the European Communities, is already making use of the new possibilities offered by the multimedia revolution and has started research to allow further progress in this direction. This paper defines interactive multimedia as the achievement of a dynamic symbiosis between several means of expression (several…

Robust hypothesis tests for detecting statistical evidence of two-dimensional and three-dimensional interactions in single-molecule measurements

NASA Astrophysics Data System (ADS)

Calderon, Christopher P.; Weiss, Lucien E.; Moerner, W. E.

2014-05-01

Experimental advances have improved the two- (2D) and three-dimensional (3D) spatial resolution that can be extracted from in vivo single-molecule measurements. This enables researchers to quantitatively infer the magnitude and directionality of forces experienced by biomolecules in their native environment. Situations where such force information is relevant range from mitosis to directed transport of protein cargo along cytoskeletal structures. Models commonly applied to quantify single-molecule dynamics assume that effective forces and velocity in the x ,y (or x ,y,z) directions are statistically independent, but this assumption is physically unrealistic in many situations. We present a hypothesis testing approach capable of determining if there is evidence of statistical dependence between positional coordinates in experimentally measured trajectories; if the hypothesis of independence between spatial coordinates is rejected, then a new model accounting for 2D (3D) interactions can and should be considered. Our hypothesis testing technique is robust, meaning it can detect interactions, even if the noise statistics are not well captured by the model. The approach is demonstrated on control simulations and on experimental data (directed transport of intraflagellar transport protein 88 homolog in the primary cilium).

Probing the Fluctuations of Optical Properties in Time-Resolved Spectroscopy

NASA Astrophysics Data System (ADS)

Randi, Francesco; Esposito, Martina; Giusti, Francesca; Misochko, Oleg; Parmigiani, Fulvio; Fausti, Daniele; Eckstein, Martin

2017-11-01

We show that, in optical pump-probe experiments on bulk samples, the statistical distribution of the intensity of ultrashort light pulses after interaction with a nonequilibrium complex material can be used to measure the time-dependent noise of the current in the system. We illustrate the general arguments for a photoexcited Peierls material. The transient noise spectroscopy allows us to measure to what extent electronic degrees of freedom dynamically obey the fluctuation-dissipation theorem, and how well they thermalize during the coherent lattice vibrations. The proposed statistical measurement developed here provides a new general framework to retrieve dynamical information on the excited distributions in nonequilibrium experiments, which could be extended to other degrees of freedom of magnetic or vibrational origin.

Pacific Decadal Variability and Central Pacific Warming El Niño in a Changing Climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di Lorenzo, Emanuele

This research aimed at understanding the dynamics controlling decadal variability in the Pacific Ocean and its interactions with global-scale climate change. The first goal was to assess how the dynamics and statistics of the El Niño Southern Oscillation and the modes of Pacific decadal variability are represented in global climate models used in the IPCC. The second goal was to quantify how decadal dynamics are projected to change under continued greenhouse forcing, and determine their significance in the context of paleo-proxy reconstruction of long-term climate.

Study of pre-seismic kHz EM emissions by means of complex systems

NASA Astrophysics Data System (ADS)

Balasis, Georgios; Papadimitriou, Constantinos; Eftaxias, Konstantinos

2010-05-01

The field of study of complex systems holds that the dynamics of complex systems are founded on universal principles that may used to describe disparate problems ranging from particle physics to economies of societies. A corollary is that transferring ideas and results from investigators in hitherto disparate areas will cross-fertilize and lead to important new results. It is well-known that the Boltzmann-Gibbs statistical mechanics works best in dealing with systems composed of either independent subsystems or interacting via short-range forces, and whose subsystems can access all the available phase space. For systems exhibiting long-range correlations, memory, or fractal properties, non-extensive Tsallis statistical mechanics becomes the most appropriate mathematical framework. As it was mentioned a central property of the magnetic storm, solar flare, and earthquake preparation process is the possible occurrence of coherent large-scale collective with a very rich structure, resulting from the repeated nonlinear interactions among collective with a very rich structure, resulting from the repeated nonlinear interactions among its constituents. Consequently, the non-extensive statistical mechanics is an appropriate regime to investigate universality, if any, in magnetic storm, solar flare, earthquake and pre-failure EM emission occurrence. A model for earthquake dynamics coming from a non-extensive Tsallis formulation, starting from first principles, has been recently introduced. This approach leads to a Gutenberg-Richter type law for the magnitude distribution of earthquakes which provides an excellent fit to seismicities generated in various large geographic areas usually identified as "seismic regions". We examine whether the Gutenberg-Richter law corresponding to a non-extensive Tsallis statistics is able to describe the distribution of amplitude of earthquakes, pre-seismic kHz EM emissions (electromagnetic earthquakes), solar flares, and magnetic storms. The analysis shows that the introduced non-extensive model provides an excellent fit to the experimental data, incorporating the characteristics of universality by means of non-extensive statistics into the extreme events under study.

Hierarchical relaxation dynamics in a tilted two-band Bose-Hubbard model

NASA Astrophysics Data System (ADS)

Cosme, Jayson G.

2018-04-01

We numerically examine slow and hierarchical relaxation dynamics of interacting bosons described by a tilted two-band Bose-Hubbard model. The system is found to exhibit signatures of quantum chaos within the spectrum and the validity of the eigenstate thermalization hypothesis for relevant physical observables is demonstrated for certain parameter regimes. Using the truncated Wigner representation in the semiclassical limit of the system, dynamics of relevant observables reveal hierarchical relaxation and the appearance of prethermalized states is studied from the perspective of statistics of the underlying mean-field trajectories. The observed prethermalization scenario can be attributed to different stages of glassy dynamics in the mode-time configuration space due to dynamical phase transition between ergodic and nonergodic trajectories.

Nonlinear stochastic interacting dynamics and complexity of financial gasket fractal-like lattice percolation

NASA Astrophysics Data System (ADS)

Zhang, Wei; Wang, Jun

2018-05-01

A novel nonlinear stochastic interacting price dynamics is proposed and investigated by the bond percolation on Sierpinski gasket fractal-like lattice, aim to make a new approach to reproduce and study the complexity dynamics of real security markets. Fractal-like lattices correspond to finite graphs with vertices and edges, which are similar to fractals, and Sierpinski gasket is a well-known example of fractals. Fractional ordinal array entropy and fractional ordinal array complexity are introduced to analyze the complexity behaviors of financial signals. To deeper comprehend the fluctuation characteristics of the stochastic price evolution, the complexity analysis of random logarithmic returns and volatility are preformed, including power-law distribution, fractional sample entropy and fractional ordinal array complexity. For further verifying the rationality and validity of the developed stochastic price evolution, the actual security market dataset are also studied with the same statistical methods for comparison. The empirical results show that this stochastic price dynamics can reconstruct complexity behaviors of the actual security markets to some extent.

Two-soliton interaction as an elementary act of soliton turbulence in integrable systems

NASA Astrophysics Data System (ADS)

Pelinovsky, E. N.; Shurgalina, E. G.; Sergeeva, A. V.; Talipova, T. G.; El, G. A.; Grimshaw, R. H. J.

2013-01-01

Two-soliton interactions play a definitive role in the formation of the structure of soliton turbulence in integrable systems. To quantify the contribution of these interactions to the dynamical and statistical characteristics of the nonlinear wave field of soliton turbulence we study properties of the spatial moments of the two-soliton solution of the Korteweg-de Vries (KdV) equation. While the first two moments are integrals of the KdV evolution, the 3rd and 4th moments undergo significant variations in the dominant interaction region, which could have strong effect on the values of the skewness and kurtosis in soliton turbulence.

The evolving cobweb of relations among partially rational investors

PubMed Central

DiMeglio, Anna; Garofalo, Franco; Lo Iudice, Francesco

2017-01-01

To overcome the limitations of neoclassical economics, researchers have leveraged tools of statistical physics to build novel theories. The idea was to elucidate the macroscopic features of financial markets from the interaction of its microscopic constituents, the investors. In this framework, the model of the financial agents has been kept separate from that of their interaction. Here, instead, we explore the possibility of letting the interaction topology emerge from the model of the agents’ behavior. Then, we investigate how the emerging cobweb of relationship affects the overall market dynamics. To this aim, we leverage tools from complex systems analysis and nonlinear dynamics, and model the network of mutual influence as the output of a dynamical system describing the edge evolution. In this work, the driver of the link evolution is the relative reputation between possibly coupled agents. The reputation is built differently depending on the extent of rationality of the investors. The continuous edge activation or deactivation induces the emergence of leaders and of peculiar network structures, typical of real influence networks. The subsequent impact on the market dynamics is investigated through extensive numerical simulations in selected scenarios populated by partially rational investors. PMID:28196144

The evolving cobweb of relations among partially rational investors.

PubMed

DeLellis, Pietro; DiMeglio, Anna; Garofalo, Franco; Lo Iudice, Francesco

2017-01-01

To overcome the limitations of neoclassical economics, researchers have leveraged tools of statistical physics to build novel theories. The idea was to elucidate the macroscopic features of financial markets from the interaction of its microscopic constituents, the investors. In this framework, the model of the financial agents has been kept separate from that of their interaction. Here, instead, we explore the possibility of letting the interaction topology emerge from the model of the agents' behavior. Then, we investigate how the emerging cobweb of relationship affects the overall market dynamics. To this aim, we leverage tools from complex systems analysis and nonlinear dynamics, and model the network of mutual influence as the output of a dynamical system describing the edge evolution. In this work, the driver of the link evolution is the relative reputation between possibly coupled agents. The reputation is built differently depending on the extent of rationality of the investors. The continuous edge activation or deactivation induces the emergence of leaders and of peculiar network structures, typical of real influence networks. The subsequent impact on the market dynamics is investigated through extensive numerical simulations in selected scenarios populated by partially rational investors.

Single Molecule Approaches in RNA-Protein Interactions.

PubMed

Serebrov, Victor; Moore, Melissa J

RNA-protein interactions govern every aspect of RNA metabolism, and aberrant RNA-binding proteins are the cause of hundreds of genetic diseases. Quantitative measurements of these interactions are necessary in order to understand mechanisms leading to diseases and to develop efficient therapies. Existing methods of RNA-protein interactome capture can afford a comprehensive snapshot of RNA-protein interaction networks but lack the ability to characterize the dynamics of these interactions. As all ensemble methods, their resolution is also limited by statistical averaging. Here we discuss recent advances in single molecule techniques that have the potential to tackle these challenges. We also provide a thorough overview of single molecule colocalization microscopy and the essential protein and RNA tagging and detection techniques.

Limitation of degree information for analyzing the interaction evolution in online social networks

NASA Astrophysics Data System (ADS)

Shang, Ke-Ke; Yan, Wei-Sheng; Xu, Xiao-Ke

2014-04-01

Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

Predicting oscillatory dynamics in the movement of territorial animals.

PubMed

Giuggioli, L; Potts, J R; Harris, S

2012-07-07

Understanding ecological processes relies upon the knowledge of the dynamics of each individual component. In the context of animal population ecology, the way animals move and interact is of fundamental importance in explaining a variety of observed patterns. Here, we present a theoretical investigation on the movement dynamics of interacting scent-marking animals. We study how the movement statistics of territorial animals is responsible for the appearance of damped oscillations in the mean square displacement (MSD) of the animals. This non-monotonicity is shown to depend on one dimensionless parameter, given by the ratio of the correlation distance between successive steps to the size of the territory. As that parameter increases, the time dependence of the animal's MSD displays a transition from monotonic, characteristic of Brownian walks, to non-monotonic, characteristic of highly correlated walks. The results presented here represent a novel way of determining the degree of persistence in animal movement processes within confined regions.

Predicting oscillatory dynamics in the movement of territorial animals

PubMed Central

Giuggioli, L.; Potts, J. R.; Harris, S.

2012-01-01

Understanding ecological processes relies upon the knowledge of the dynamics of each individual component. In the context of animal population ecology, the way animals move and interact is of fundamental importance in explaining a variety of observed patterns. Here, we present a theoretical investigation on the movement dynamics of interacting scent-marking animals. We study how the movement statistics of territorial animals is responsible for the appearance of damped oscillations in the mean square displacement (MSD) of the animals. This non-monotonicity is shown to depend on one dimensionless parameter, given by the ratio of the correlation distance between successive steps to the size of the territory. As that parameter increases, the time dependence of the animal's MSD displays a transition from monotonic, characteristic of Brownian walks, to non-monotonic, characteristic of highly correlated walks. The results presented here represent a novel way of determining the degree of persistence in animal movement processes within confined regions. PMID:22262814

Molecular-dynamics study on characteristics of energy and tangential momentum accommodation coefficients

NASA Astrophysics Data System (ADS)

Yamaguchi, Hiroki; Matsuda, Yu; Niimi, Tomohide

2017-07-01

Gas-surface interaction is studied by the molecular dynamics method to investigate qualitatively characteristics of accommodation coefficients. A large number of trajectories of gas molecules colliding to and scattering from a surface are statistically analyzed to calculate the energy (thermal) accommodation coefficient (EAC) and the tangential momentum accommodation coefficient (TMAC). Considering experimental measurements of the accommodation coefficients, the incident velocities are stochastically sampled to represent a bulk condition. The accommodation coefficients for noble gases show qualitative coincidence with experimental values. To investigate characteristics of these accommodation coefficients in detail, the gas-surface interaction is parametrically studied by varying the molecular mass of gas, the gas-surface interaction strength, and the molecular size of gas, one by one. EAC increases with increasing every parameter, while TMAC increases with increasing the interaction strength, but decreases with increasing the molecular mass and the molecular size. Thus, contradictory results in experimentally measured TMAC for noble gases could result from the difference between the surface conditions employed in the measurements in the balance among the effective parameters of molecular mass, interaction strength, and molecular size, due to surface roughness and/or adsorbed molecules. The accommodation coefficients for a thermo-fluid dynamics field with a temperature difference between gas and surface and a bulk flow at the same time are also investigated.

Zubarev's Nonequilibrium Statistical Operator Method in the Generalized Statistics of Multiparticle Systems

NASA Astrophysics Data System (ADS)

Glushak, P. A.; Markiv, B. B.; Tokarchuk, M. V.

2018-01-01

We present a generalization of Zubarev's nonequilibrium statistical operator method based on the principle of maximum Renyi entropy. In the framework of this approach, we obtain transport equations for the basic set of parameters of the reduced description of nonequilibrium processes in a classical system of interacting particles using Liouville equations with fractional derivatives. For a classical systems of particles in a medium with a fractal structure, we obtain a non-Markovian diffusion equation with fractional spatial derivatives. For a concrete model of the frequency dependence of a memory function, we obtain generalized Kettano-type diffusion equation with the spatial and temporal fractality taken into account. We present a generalization of nonequilibrium thermofield dynamics in Zubarev's nonequilibrium statistical operator method in the framework of Renyi statistics.

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS

PubMed Central

Almquist, Zack W.; Butts, Carter T.

2015-01-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach. PMID:26120218

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.

PubMed

Almquist, Zack W; Butts, Carter T

2014-08-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.

Model of mobile agents for sexual interactions networks

NASA Astrophysics Data System (ADS)

González, M. C.; Lind, P. G.; Herrmann, H. J.

2006-02-01

We present a novel model to simulate real social networks of complex interactions, based in a system of colliding particles (agents). The network is build by keeping track of the collisions and evolves in time with correlations which emerge due to the mobility of the agents. Therefore, statistical features are a consequence only of local collisions among its individual agents. Agent dynamics is realized by an event-driven algorithm of collisions where energy is gained as opposed to physical systems which have dissipation. The model reproduces empirical data from networks of sexual interactions, not previously obtained with other approaches.

Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells.

PubMed

Tan, Chris Soon Heng; Go, Ka Diam; Bisteau, Xavier; Dai, Lingyun; Yong, Chern Han; Prabhu, Nayana; Ozturk, Mert Burak; Lim, Yan Ting; Sreekumar, Lekshmy; Lengqvist, Johan; Tergaonkar, Vinay; Kaldis, Philipp; Sobota, Radoslaw M; Nordlund, Pär

2018-03-09

Proteins differentially interact with each other across cellular states and conditions, but an efficient proteome-wide strategy to monitor them is lacking. We report the application of thermal proximity coaggregation (TPCA) for high-throughput intracellular monitoring of protein complex dynamics. Significant TPCA signatures observed among well-validated protein-protein interactions correlate positively with interaction stoichiometry and are statistically observable in more than 350 annotated human protein complexes. Using TPCA, we identified many complexes without detectable differential protein expression, including chromatin-associated complexes, modulated in S phase of the cell cycle. Comparison of six cell lines by TPCA revealed cell-specific interactions even in fundamental cellular processes. TPCA constitutes an approach for system-wide studies of protein complexes in nonengineered cells and tissues and might be used to identify protein complexes that are modulated in diseases. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Attractive versus repulsive interactions in the Bose-Einstein condensation dynamics of relativistic field theories

NASA Astrophysics Data System (ADS)

Berges, J.; Boguslavski, K.; Chatrchyan, A.; Jaeckel, J.

2017-10-01

We study the impact of attractive self-interactions on the nonequilibrium dynamics of relativistic quantum fields with large occupancies at low momenta. Our primary focus is on Bose-Einstein condensation and nonthermal fixed points in such systems. For a model system, we consider O (N ) -symmetric scalar field theories. We use classical-statistical real-time simulations as well as a systematic 1 /N expansion of the quantum (two-particle-irreducible) effective action to next-to-leading order. When the mean self-interactions are repulsive, condensation occurs as a consequence of a universal inverse particle cascade to the zero-momentum mode with self-similar scaling behavior. For attractive mean self-interactions, the inverse cascade is absent, and the particle annihilation rate is enhanced compared to the repulsive case, which counteracts the formation of coherent field configurations. For N ≥2 , the presence of a nonvanishing conserved charge can suppress number-changing processes and lead to the formation of stable localized charge clumps, i.e., Q balls.

Dawn-dusk asymmetry induced by the Parker spiral angle in the plasma dynamics around comet 67P/Churyumov-Gerasimenko

NASA Astrophysics Data System (ADS)

Behar, E.; Tabone, B.; Nilsson, H.

2018-05-01

When interacting, the solar wind and the ionised atmosphere of a comet exchange energy and momentum. Our aim is to understand the influence of the average Parker spiral configuration of the solar wind magnetic field on this interaction. We compare the theoretical expectations of an analytical generalised gyromotion with Rosetta observations at comet 67P/Churyumov-Gerasimenko. A statistical approach allows one to overcome the lack of upstream solar wind measurement. We find that additionally to their acceleration along (for cometary pick-up ions) or against (for solar wind ions) the upstream electric field orientation and sense, the cometary pick-up ions are drifting towards the dawn side of the coma, while the solar wind ions are drifting towards the dusk side of the coma, independent of the heliocentric distance. The dynamics of the interaction is not taking place in a plane, as often assumed in previous works.

Synchronisation and stability in river metapopulation networks.

PubMed

Yeakel, J D; Moore, J W; Guimarães, P R; de Aguiar, M A M

2014-03-01

Spatial structure in landscapes impacts population stability. Two linked components of stability have large consequences for persistence: first, statistical stability as the lack of temporal fluctuations; second, synchronisation as an aspect of dynamic stability, which erodes metapopulation rescue effects. Here, we determine the influence of river network structure on the stability of riverine metapopulations. We introduce an approach that converts river networks to metapopulation networks, and analytically show how fluctuation magnitude is influenced by interaction structure. We show that river metapopulation complexity (in terms of branching prevalence) has nonlinear dampening effects on population fluctuations, and can also buffer against synchronisation. We conclude by showing that river transects generally increase synchronisation, while the spatial scale of interaction has nonlinear effects on synchronised dynamics. Our results indicate that this dual stability - conferred by fluctuation and synchronisation dampening - emerges from interaction structure in rivers, and this may strongly influence the persistence of river metapopulations. © 2013 John Wiley & Sons Ltd/CNRS.

Overbias light emission due to higher-order quantum noise in a tunnel junction.

PubMed

Xu, F; Holmqvist, C; Belzig, W

2014-08-08

Understanding tunneling from an atomically sharp tip to a metallic surface requires us to account for interactions on a nanoscopic scale. Inelastic tunneling of electrons generates emission of photons, whose energies intuitively should be limited by the applied bias voltage. However, experiments [G. Schull et al., Phys. Rev. Lett. 102, 057401 (2009) indicate that more complex processes involving the interaction of electrons with plasmon polaritons lead to photon emission characterized by overbias energies. We propose a model of this observation in analogy to the dynamical Coulomb blockade, originally developed for treating the electronic environment in mesoscopic circuits. We explain the experimental finding quantitatively by the correlated tunneling of two electrons interacting with a LRC circuit modeling the local plasmon-polariton mode. To explain the overbias emission, the non-Gaussian statistics of the tunneling dynamics of the electrons is essential.

Can Facebook Reduce Perceived Anxiety Among College Students? Randomized Controlled Exercise Trial Using the Transtheoretical Model of Behavior Change.

PubMed

Frith, Emily; Loprinzi, Paul

2017-12-08

Recent studies suggest social media may be an attractive strategy to promote mental health and wellness. There remains a need to examine the utility for individually tailored wellness messages posted to social media sites such as Facebook to facilitate positive psychological outcomes. Our aim was to extend the growing body of evidence supporting the potential for social media to enhance mental health. We evaluated the influence of an 8-week social media intervention on anxiety in college students and examined the impact of dynamic (active) versus static (passive) Facebook content on physical activity behaviors. Participants in the static group (n=21) accessed a Facebook page featuring 96 statuses. Statuses were intended to engage cognitive processes followed by behavioral processes of change per the transtheoretical model of behavior change. Content posted on the static Facebook page was identical to the dynamic page; however, the static group viewed all 96 statuses on the first day of the study, while the dynamic group received only 1 to 2 of these status updates per day throughout the intervention. Anxiety was measured using the Overall Anxiety Severity and Impairment Scale (OASIS). Time spent engaging in physical activity was assessed using the International Physical Activity Questionnaire (IPAQ). The OASIS change score for the dynamic Facebook group was statistically significant (P=.003), whereas the change score for the static group was not (P=.48). A statistically significant group-by-time interaction was observed (P=.03). The total IPAQ group-by-time interaction was not statistically significant (P=.06). We observed a decrease in anxiety and increase in total physical activity for the dynamic group only. Dynamic social networking sites, featuring regularly updated content, may be more advantageous than websites that retain static content over time. ClinicalTrials.gov NCT03363737; https://clinicaltrials.gov/ct2/show/NCT03363737 (Archived by WebCite at http://www.webcitation.org/6vXzNbOWJ). ©Emily Frith, Paul Loprinzi. Originally published in JMIR Mental Health (http://mental.jmir.org), 08.12.2017.

Flavor-singlet spectrum in multi-flavor QCD

NASA Astrophysics Data System (ADS)

Aoki, Yasumichi; Aoyama, Tatsumi; Bennett, Ed; Kurachi, Masafumi; Maskawa, Toshihide; Miura, Kohtaroh; Nagai, Kei-ichi; Ohki, Hiroshi; Rinaldi, Enrico; Shibata, Akihiro; Yamawaki, Koichi; Yamazaki, Takeshi

2018-03-01

Studying SU(3) gauge theories with increasing number of light fermions is relevant both for understanding the strong dynamics of QCD and for constructing strongly interacting extensions of the Standard Model (e.g. UV completions of composite Higgs models). In order to contrast these many-flavors strongly interacting theories with QCD, we study the flavor-singlet spectrum as an interesting probe. In fact, some composite Higgs models require the Higgs boson to be the lightest flavor-singlet scalar in the spectrum of a strongly interacting new sector with a well defined hierarchy with the rest of the states. Moreover, introducing many light flavors at fixed number of colors can influence the dynamics of the lightest flavor-singlet pseudoscalar. We present the on-going study of these flavor-singlet channels using multiple interpolating operators on high-statistics ensembles generated by the LatKMI collaboration and we compare results with available data obtained by the Lattice Strong Dynamics collaboration. For the theory with 8 flavors, the two collaborations have generated configurations that complement each others with the aim to tackle the massless limit using the largest possible volumes.

Flavor-singlet spectrum in multi-flavor QCD

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aoki, Yasamichi; Rinaldi, Enrico

2017-06-18

Studying SU(3) gauge theories with increasing number of light fermions is relevant both for understanding the strong dynamics of QCD and for constructing strongly interacting extensions of the Standard Model (e.g. UV completions of composite Higgs models). In order to contrast these many-flavors strongly interacting theories with QCD, we study the flavor-singlet spectrum as an interesting probe. In fact, some composite Higgs models require the Higgs boson to be the lightest flavor-singlet scalar in the spectrum of a strongly interacting new sector with a well defined hierarchy with the rest of the states. Moreover, introducing many light flavors at fixedmore » number of colors can influence the dynamics of the lightest flavor-singlet pseudoscalar. We present the on-going study of these flavor-singlet channels using multiple interpolating operators on high-statistics ensembles generated by the LatKMI collaboration and we compare results with available data obtained by the Lattice Strong Dynamics collaboration. For the theory with 8 flavors, the two collaborations have generated configurations that complement each others with the aim to tackle the massless limit using the largest possible volumes.« less

CollaborationViz: Interactive Visual Exploration of Biomedical Research Collaboration Networks

PubMed Central

Bian, Jiang; Xie, Mengjun; Hudson, Teresa J.; Eswaran, Hari; Brochhausen, Mathias; Hanna, Josh; Hogan, William R.

2014-01-01

Social network analysis (SNA) helps us understand patterns of interaction between social entities. A number of SNA studies have shed light on the characteristics of research collaboration networks (RCNs). Especially, in the Clinical Translational Science Award (CTSA) community, SNA provides us a set of effective tools to quantitatively assess research collaborations and the impact of CTSA. However, descriptive network statistics are difficult for non-experts to understand. In this article, we present our experiences of building meaningful network visualizations to facilitate a series of visual analysis tasks. The basis of our design is multidimensional, visual aggregation of network dynamics. The resulting visualizations can help uncover hidden structures in the networks, elicit new observations of the network dynamics, compare different investigators and investigator groups, determine critical factors to the network evolution, and help direct further analyses. We applied our visualization techniques to explore the biomedical RCNs at the University of Arkansas for Medical Sciences – a CTSA institution. And, we created CollaborationViz, an open-source visual analytical tool to help network researchers and administration apprehend the network dynamics of research collaborations through interactive visualization. PMID:25405477

Constant pressure and temperature discrete-time Langevin molecular dynamics

NASA Astrophysics Data System (ADS)

Grønbech-Jensen, Niels; Farago, Oded

2014-11-01

We present a new and improved method for simultaneous control of temperature and pressure in molecular dynamics simulations with periodic boundary conditions. The thermostat-barostat equations are built on our previously developed stochastic thermostat, which has been shown to provide correct statistical configurational sampling for any time step that yields stable trajectories. Here, we extend the method and develop a set of discrete-time equations of motion for both particle dynamics and system volume in order to seek pressure control that is insensitive to the choice of the numerical time step. The resulting method is simple, practical, and efficient. The method is demonstrated through direct numerical simulations of two characteristic model systems—a one-dimensional particle chain for which exact statistical results can be obtained and used as benchmarks, and a three-dimensional system of Lennard-Jones interacting particles simulated in both solid and liquid phases. The results, which are compared against the method of Kolb and Dünweg [J. Chem. Phys. 111, 4453 (1999)], show that the new method behaves according to the objective, namely that acquired statistical averages and fluctuations of configurational measures are accurate and robust against the chosen time step applied to the simulation.

On the Unsteadiness of a Transitional Shock Wave-Boundary Layer Interaction Using Fast-Response Pressure-Sensitive Paint

NASA Astrophysics Data System (ADS)

Lash, E. Lara; Schmisseur, John

2017-11-01

Pressure-sensitive paint has been used to evaluate the unsteady dynamics of transitional and turbulent shock wave-boundary layer interactions generated by a vertical cylinder on a flat plate in a Mach 2 freestream. The resulting shock structure consists of an inviscid bow shock that bifurcates into a separation shock and trailing shock. The primary features of interest are the separation shock and an upstream influence shock that is intermittently present in transitional boundary layer interactions, but not observed in turbulent interactions. The power spectral densities, frequency peaks, and normalized wall pressures are analyzed as the incoming boundary layer state changes from transitional to fully turbulent, comparing both centerline and outboard regions of the interaction. The present study compares the scales and frequencies of the dynamics of the separation shock structure in different boundary layer regimes. Synchronized high-speed Schlieren imaging provides quantitative statistical analyses as well as qualitative comparisons to the fast-response pressure sensitive paint measurements. Materials based on research supported by the U.S. Office of Naval Research under Award Number N00014-15-1-2269.

North American Extreme Temperature Events and Related Large Scale Meteorological Patterns: A Review of Statistical Methods, Dynamics, Modeling, and Trends

NASA Technical Reports Server (NTRS)

Grotjahn, Richard; Black, Robert; Leung, Ruby; Wehner, Michael F.; Barlow, Mathew; Bosilovich, Michael G.; Gershunov, Alexander; Gutowski, William J., Jr.; Gyakum, John R.; Katz, Richard W.;

Human dynamics scaling characteristics for aerial inbound logistics operation

NASA Astrophysics Data System (ADS)

Wang, Qing; Guo, Jin-Li

2010-05-01

In recent years, the study of power-law scaling characteristics of real-life networks has attracted much interest from scholars; it deviates from the Poisson process. In this paper, we take the whole process of aerial inbound operation in a logistics company as the empirical object. The main aim of this work is to study the statistical scaling characteristics of the task-restricted work patterns. We found that the statistical variables have the scaling characteristics of unimodal distribution with a power-law tail in five statistical distributions - that is to say, there obviously exists a peak in each distribution, the shape of the left part closes to a Poisson distribution, and the right part has a heavy-tailed scaling statistics. Furthermore, to our surprise, there is only one distribution where the right parts can be approximated by the power-law form with exponent α=1.50. Others are bigger than 1.50 (three of four are about 2.50, one of four is about 3.00). We then obtain two inferences based on these empirical results: first, the human behaviors probably both close to the Poisson statistics and power-law distributions on certain levels, and the human-computer interaction behaviors may be the most common in the logistics operational areas, even in the whole task-restricted work pattern areas. Second, the hypothesis in Vázquez et al. (2006) [A. Vázquez, J. G. Oliveira, Z. Dezsö, K.-I. Goh, I. Kondor, A.-L. Barabási. Modeling burst and heavy tails in human dynamics, Phys. Rev. E 73 (2006) 036127] is probably not sufficient; it claimed that human dynamics can be classified as two discrete university classes. There may be a new human dynamics mechanism that is different from the classical Barabási models.

Numerical solutions of ideal quantum gas dynamical flows governed by semiclassical ellipsoidal-statistical distribution.

PubMed

Yang, Jaw-Yen; Yan, Chih-Yuan; Diaz, Manuel; Huang, Juan-Chen; Li, Zhihui; Zhang, Hanxin

2014-01-08

The ideal quantum gas dynamics as manifested by the semiclassical ellipsoidal-statistical (ES) equilibrium distribution derived in Wu et al. (Wu et al . 2012 Proc. R. Soc. A 468 , 1799-1823 (doi:10.1098/rspa.2011.0673)) is numerically studied for particles of three statistics. This anisotropic ES equilibrium distribution was derived using the maximum entropy principle and conserves the mass, momentum and energy, but differs from the standard Fermi-Dirac or Bose-Einstein distribution. The present numerical method combines the discrete velocity (or momentum) ordinate method in momentum space and the high-resolution shock-capturing method in physical space. A decoding procedure to obtain the necessary parameters for determining the ES distribution is also devised. Computations of two-dimensional Riemann problems are presented, and various contours of the quantities unique to this ES model are illustrated. The main flow features, such as shock waves, expansion waves and slip lines and their complex nonlinear interactions, are depicted and found to be consistent with existing calculations for a classical gas.

Extreme-volatility dynamics in crude oil markets

NASA Astrophysics Data System (ADS)

Jiang, Xiong-Fei; Zheng, Bo; Qiu, Tian; Ren, Fei

2017-02-01

Based on concepts and methods from statistical physics, we investigate extreme-volatility dynamics in the crude oil markets, using the high-frequency data from 2006 to 2010 and the daily data from 1986 to 2016. The dynamic relaxation of extreme volatilities is described by a power law, whose exponents usually depend on the magnitude of extreme volatilities. In particular, the relaxation before and after extreme volatilities is time-reversal symmetric at the high-frequency time scale, but time-reversal asymmetric at the daily time scale. This time-reversal asymmetry is mainly induced by exogenous events. However, the dynamic relaxation after exogenous events exhibits the same characteristics as that after endogenous events. An interacting herding model both with and without exogenous driving forces could qualitatively describe the extreme-volatility dynamics.

From seconds to months: an overview of multi-scale dynamics of mobile telephone calls

NASA Astrophysics Data System (ADS)

Saramäki, Jari; Moro, Esteban

2015-06-01

Big Data on electronic records of social interactions allow approaching human behaviour and sociality from a quantitative point of view with unforeseen statistical power. Mobile telephone Call Detail Records (CDRs), automatically collected by telecom operators for billing purposes, have proven especially fruitful for understanding one-to-one communication patterns as well as the dynamics of social networks that are reflected in such patterns. We present an overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from mobile telephone calls. We begin with the shortest timescales and fastest dynamics, such as burstiness of call sequences between individuals, and "zoom out" towards longer temporal and larger structural scales, from temporal motifs formed by correlated calls between multiple individuals to long-term dynamics of social groups. We conclude this overview with a future outlook.

Characterizing complex networks through statistics of Möbius transformations

NASA Astrophysics Data System (ADS)

Jaćimović, Vladimir; Crnkić, Aladin

2017-04-01

It is well-known now that dynamics of large populations of globally (all-to-all) coupled oscillators can be reduced to low-dimensional submanifolds (WS transformation and OA ansatz). Marvel et al. (2009) described an intriguing algebraic structure standing behind this reduction: oscillators evolve by the action of the group of Möbius transformations. Of course, dynamics in complex networks of coupled oscillators is highly complex and not reducible. Still, closer look unveils that even in complex networks some (possibly overlapping) groups of oscillators evolve by Möbius transformations. In this paper, we study properties of the network by identifying Möbius transformations in the dynamics of oscillators. This enables us to introduce some new (statistical) concepts that characterize the network. In particular, the notion of coherence of the network (or subnetwork) is proposed. This conceptual approach is meaningful for the broad class of networks, including those with time-delayed, noisy or mixed interactions. In this paper, several simple (random) graphs are studied illustrating the meaning of the concepts introduced in the paper.

Statics and Dynamics of Selfish Interactions in Distributed Service Systems

PubMed Central

Altarelli, Fabrizio; Braunstein, Alfredo; Dall’Asta, Luca

2015-01-01

We study a class of games which models the competition among agents to access some service provided by distributed service units and which exhibits congestion and frustration phenomena when service units have limited capacity. We propose a technique, based on the cavity method of statistical physics, to characterize the full spectrum of Nash equilibria of the game. The analysis reveals a large variety of equilibria, with very different statistical properties. Natural selfish dynamics, such as best-response, usually tend to large-utility equilibria, even though those of smaller utility are exponentially more numerous. Interestingly, the latter actually can be reached by selecting the initial conditions of the best-response dynamics close to the saturation limit of the service unit capacities. We also study a more realistic stochastic variant of the game by means of a simple and effective approximation of the average over the random parameters, showing that the properties of the average-case Nash equilibria are qualitatively similar to the deterministic ones. PMID:26177449

Statistical physics of human cooperation

NASA Astrophysics Data System (ADS)

Perc, Matjaž; Jordan, Jillian J.; Rand, David G.; Wang, Zhen; Boccaletti, Stefano; Szolnoki, Attila

2017-05-01

Extensive cooperation among unrelated individuals is unique to humans, who often sacrifice personal benefits for the common good and work together to achieve what they are unable to execute alone. The evolutionary success of our species is indeed due, to a large degree, to our unparalleled other-regarding abilities. Yet, a comprehensive understanding of human cooperation remains a formidable challenge. Recent research in the social sciences indicates that it is important to focus on the collective behavior that emerges as the result of the interactions among individuals, groups, and even societies. Non-equilibrium statistical physics, in particular Monte Carlo methods and the theory of collective behavior of interacting particles near phase transition points, has proven to be very valuable for understanding counterintuitive evolutionary outcomes. By treating models of human cooperation as classical spin models, a physicist can draw on familiar settings from statistical physics. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among humans often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. The complexity of solutions therefore often surpasses that observed in physical systems. Here we review experimental and theoretical research that advances our understanding of human cooperation, focusing on spatial pattern formation, on the spatiotemporal dynamics of observed solutions, and on self-organization that may either promote or hinder socially favorable states.

Detailed Balance of Thermalization Dynamics in Rydberg-Atom Quantum Simulators.

PubMed

Kim, Hyosub; Park, YeJe; Kim, Kyungtae; Sim, H-S; Ahn, Jaewook

2018-05-04

Dynamics of large complex systems, such as relaxation towards equilibrium in classical statistical mechanics, often obeys a master equation that captures essential information from the complexities. Here, we find that thermalization of an isolated many-body quantum state can be described by a master equation. We observe sudden quench dynamics of quantum Ising-like models implemented in our quantum simulator, defect-free single-atom tweezers in conjunction with Rydberg-atom interaction. Saturation of their local observables, a thermalization signature, obeys a master equation experimentally constructed by monitoring the occupation probabilities of prequench states and imposing the principle of the detailed balance. Our experiment agrees with theories and demonstrates the detailed balance in a thermalization dynamics that does not require coupling to baths or postulated randomness.

Detailed Balance of Thermalization Dynamics in Rydberg-Atom Quantum Simulators

NASA Astrophysics Data System (ADS)

Kim, Hyosub; Park, YeJe; Kim, Kyungtae; Sim, H.-S.; Ahn, Jaewook

2018-05-01

Dynamics of large complex systems, such as relaxation towards equilibrium in classical statistical mechanics, often obeys a master equation that captures essential information from the complexities. Here, we find that thermalization of an isolated many-body quantum state can be described by a master equation. We observe sudden quench dynamics of quantum Ising-like models implemented in our quantum simulator, defect-free single-atom tweezers in conjunction with Rydberg-atom interaction. Saturation of their local observables, a thermalization signature, obeys a master equation experimentally constructed by monitoring the occupation probabilities of prequench states and imposing the principle of the detailed balance. Our experiment agrees with theories and demonstrates the detailed balance in a thermalization dynamics that does not require coupling to baths or postulated randomness.

Dynamics versus thermodynamics

NASA Astrophysics Data System (ADS)

Berdichevsky, V. L.

1991-05-01

An effort is made to characterize the ways in which the approaches of statistical mechanics and thermodynamics can be useful in the study of the dynamic behavior of structures. This meditation proceeds through consideration of such wide-ranging and deliberately provocative questions as: 'What are to be considered values in a stress-distribution function?' and 'How many degrees-of-freedom has a beam?'; it then gives attention to the hierarchy of vibrations, the interaction of the mechanism of dissipation with invisible degrees of freedom, and a plausible view of vibrations for the case of small dissipation.

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems.

PubMed

Williams, Richard A; Timmis, Jon; Qwarnstrom, Eva E

2016-01-01

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model.

Statistical Techniques Complement UML When Developing Domain Models of Complex Dynamical Biosystems

PubMed Central

Timmis, Jon; Qwarnstrom, Eva E.

2016-01-01

Computational modelling and simulation is increasingly being used to complement traditional wet-lab techniques when investigating the mechanistic behaviours of complex biological systems. In order to ensure computational models are fit for purpose, it is essential that the abstracted view of biology captured in the computational model, is clearly and unambiguously defined within a conceptual model of the biological domain (a domain model), that acts to accurately represent the biological system and to document the functional requirements for the resultant computational model. We present a domain model of the IL-1 stimulated NF-κB signalling pathway, which unambiguously defines the spatial, temporal and stochastic requirements for our future computational model. Through the development of this model, we observe that, in isolation, UML is not sufficient for the purpose of creating a domain model, and that a number of descriptive and multivariate statistical techniques provide complementary perspectives, in particular when modelling the heterogeneity of dynamics at the single-cell level. We believe this approach of using UML to define the structure and interactions within a complex system, along with statistics to define the stochastic and dynamic nature of complex systems, is crucial for ensuring that conceptual models of complex dynamical biosystems, which are developed using UML, are fit for purpose, and unambiguously define the functional requirements for the resultant computational model. PMID:27571414

Dynamics of interacting fermions under spin-orbit coupling in an optical lattice clock

NASA Astrophysics Data System (ADS)

Bromley, S. L.; Kolkowitz, S.; Bothwell, T.; Kedar, D.; Safavi-Naini, A.; Wall, M. L.; Salomon, C.; Rey, A. M.; Ye, J.

2018-04-01

Quantum statistics and symmetrization dictate that identical fermions do not interact via s-wave collisions. However, in the presence of spin-orbit coupling (SOC), fermions prepared in identical internal states with distinct momenta become distinguishable. The resulting strongly interacting system can exhibit exotic topological and pairing behaviours, many of which are yet to be observed in condensed matter systems. Ultracold atomic gases offer a promising pathway for simulating these rich phenomena, but until recently have been hindered by heating and losses. Here we enter a new regime of many-body interacting SOC in a fermionic optical lattice clock (OLC), where the long-lived electronic clock states mitigate unwanted dissipation. Using clock spectroscopy, we observe the precession of the collective magnetization and the emergence of spin-locking effects arising from an interplay between p-wave and SOC-induced exchange interactions. The many-body dynamics are well captured by a collective XXZ spin model, which describes a broad class of condensed matter systems ranging from superconductors to quantum magnets. Furthermore, our work will aid in the design of next-generation OLCs by offering a route for avoiding the observed large density shifts caused by SOC-induced exchange interactions.

Dynamics of Sleep Stage Transitions in Health and Disease

NASA Astrophysics Data System (ADS)

Kishi, Akifumi; Struzik, Zbigniew R.; Natelson, Benjamin H.; Togo, Fumiharu; Yamamoto, Yoshiharu

2007-07-01

Sleep dynamics emerges from complex interactions between neuronal populations in many brain regions. Annotated sleep stages from electroencephalography (EEG) recordings could potentially provide a non-invasive way to obtain valuable insights into the mechanisms of these interactions, and ultimately into the very nature of sleep regulation. However, to date, sleep stage analysis has been restricted, only very recently expanding the scope of the traditional descriptive statistics to more dynamical concepts of the duration of and transitions between vigilance states and temporal evaluation of transition probabilities among different stages. Physiological and/or pathological implications of the dynamics of sleep stage transitions have, to date, not been investigated. Here, we study detailed duration and transition statistics among sleep stages in healthy humans and patients with chronic fatigue syndrome, known to be associated with disturbed sleep. We find that the durations of waking and non-REM sleep, in particular deep sleep (Stages III and IV), during the nighttime, follow a power-law probability distribution function, while REM sleep durations follow an exponential function, suggestive of complex underlying mechanisms governing the onset of light sleep. We also find a substantial number of REM to non-REM transitions in humans, while this transition is reported to be virtually non-existent in rats. Interestingly, the probability of this REM to non-REM transition is significantly lower in the patients than in controls, resulting in a significantly greater REM to awake, together with Stage I to awake, transition probability. This might potentially account for the reported poor sleep quality in the patients because the normal continuation of sleep after either the lightest or REM sleep is disrupted. We conclude that the dynamical transition analysis of sleep stages is useful for elucidating yet-to-be-determined human sleep regulation mechanisms with a pathophysiological implication.

Strategies for Reduced-Order Models in Uncertainty Quantification of Complex Turbulent Dynamical Systems

NASA Astrophysics Data System (ADS)

Qi, Di

Turbulent dynamical systems are ubiquitous in science and engineering. Uncertainty quantification (UQ) in turbulent dynamical systems is a grand challenge where the goal is to obtain statistical estimates for key physical quantities. In the development of a proper UQ scheme for systems characterized by both a high-dimensional phase space and a large number of instabilities, significant model errors compared with the true natural signal are always unavoidable due to both the imperfect understanding of the underlying physical processes and the limited computational resources available. One central issue in contemporary research is the development of a systematic methodology for reduced order models that can recover the crucial features both with model fidelity in statistical equilibrium and with model sensitivity in response to perturbations. In the first part, we discuss a general mathematical framework to construct statistically accurate reduced-order models that have skill in capturing the statistical variability in the principal directions of a general class of complex systems with quadratic nonlinearity. A systematic hierarchy of simple statistical closure schemes, which are built through new global statistical energy conservation principles combined with statistical equilibrium fidelity, are designed and tested for UQ of these problems. Second, the capacity of imperfect low-order stochastic approximations to model extreme events in a passive scalar field advected by turbulent flows is investigated. The effects in complicated flow systems are considered including strong nonlinear and non-Gaussian interactions, and much simpler and cheaper imperfect models with model error are constructed to capture the crucial statistical features in the stationary tracer field. Several mathematical ideas are introduced to improve the prediction skill of the imperfect reduced-order models. Most importantly, empirical information theory and statistical linear response theory are applied in the training phase for calibrating model errors to achieve optimal imperfect model parameters; and total statistical energy dynamics are introduced to improve the model sensitivity in the prediction phase especially when strong external perturbations are exerted. The validity of reduced-order models for predicting statistical responses and intermittency is demonstrated on a series of instructive models with increasing complexity, including the stochastic triad model, the Lorenz '96 model, and models for barotropic and baroclinic turbulence. The skillful low-order modeling methods developed here should also be useful for other applications such as efficient algorithms for data assimilation.

Beyond Classical Information Theory: Advancing the Fundamentals for Improved Geophysical Prediction

NASA Astrophysics Data System (ADS)

Perdigão, R. A. P.; Pires, C. L.; Hall, J.; Bloeschl, G.

2016-12-01

Information Theory, in its original and quantum forms, has gradually made its way into various fields of science and engineering. From the very basic concepts of Information Entropy and Mutual Information to Transit Information, Interaction Information and respective partitioning into statistical synergy, redundancy and exclusivity, the overall theoretical foundations have matured as early as the mid XX century. In the Earth Sciences various interesting applications have been devised over the last few decades, such as the design of complex process networks of descriptive and/or inferential nature, wherein earth system processes are "nodes" and statistical relationships between them designed as information-theoretical "interactions". However, most applications still take the very early concepts along with their many caveats, especially in heavily non-Normal, non-linear and structurally changing scenarios. In order to overcome the traditional limitations of information theory and tackle elusive Earth System phenomena, we introduce a new suite of information dynamic methodologies towards a more physically consistent and information comprehensive framework. The methodological developments are then illustrated on a set of practical examples from geophysical fluid dynamics, where high-order nonlinear relationships elusive to the current non-linear information measures are aptly captured. In doing so, these advances increase the predictability of critical events such as the emergence of hyper-chaotic regimes in ocean-atmospheric dynamics and the occurrence of hydro-meteorological extremes.

Relaxation dynamics of ultracold bosons in a double-well potential: Thermalization and prethermalization in a nearly integrable model

NASA Astrophysics Data System (ADS)

Cosme, Jayson G.

2015-09-01

We numerically investigate the relaxation dynamics in an isolated quantum system of interacting bosons trapped in a double-well potential after an integrability breaking quench. Using the statistics of the spectrum, we identify the postquench Hamiltonian as nonchaotic and close to integrability over a wide range of interaction parameters. We demonstrate that the system exhibits thermalization in the context of the eigenstate thermalization hypothesis (ETH). We also explore the possibility of an initial state to delocalize with respect to the eigenstates of the postquench Hamiltonian even for energies away from the middle of the spectrum. We observe distinct regimes of equilibration process depending on the initial energy. For low energies, the system rapidly relaxes in a single step to a thermal state. As the energy increases towards the middle of the spectrum, the relaxation dynamics exhibits prethermalization and the lifetime of the metastable states grows. Time evolution of the occupation numbers and the von Neumann entropy in the mode-partitioned system underpins the analyses of the relaxation dynamics.

Force Dynamics During T Cell Activation

NASA Astrophysics Data System (ADS)

Garcia, David A.; Upadhyaya, Arpita

T cell activation is an essential step in the adaptive immune response. The binding of the T cell receptor (TCR) with antigen triggers signaling cascades and cell spreading. Physical forces exerted on the TCR by the cytoskeleton have been shown to induce signaling events. While cellular forces are known to depend on the mechanical properties of the cytoskeleton, the biophysical mechanisms underlying force induced activation of TCR-antigen interactions unknown. Here, we use traction force microscopy to measure the force dynamics of activated Jurkat T cells. The movements of beads embedded in an elastic gel serve as a non-invasive reporter of cytoskeletal and molecular motor dynamics. We examined the statistical structure of the force profiles throughout the cell during signaling activation. We found two spatially distinct active regimes of force generation characterized by different time scales. Typically, the interior of the cells was found to be more active than the periphery. Inhibition of myosin motor activity altered the correlation time of the bead displacements indicating additional sources of stochastic force generation. Our results indicate a complex interaction between myosin activity and actin polymerization dynamics in producing cellular forces in immune cells.

Computational Models for Nanoscale Fluid Dynamics and Transport Inspired by Nonequilibrium Thermodynamics1

PubMed Central

Radhakrishnan, Ravi; Yu, Hsiu-Yu; Eckmann, David M.; Ayyaswamy, Portonovo S.

2017-01-01

Traditionally, the numerical computation of particle motion in a fluid is resolved through computational fluid dynamics (CFD). However, resolving the motion of nanoparticles poses additional challenges due to the coupling between the Brownian and hydrodynamic forces. Here, we focus on the Brownian motion of a nanoparticle coupled to adhesive interactions and confining-wall-mediated hydrodynamic interactions. We discuss several techniques that are founded on the basis of combining CFD methods with the theory of nonequilibrium statistical mechanics in order to simultaneously conserve thermal equipartition and to show correct hydrodynamic correlations. These include the fluctuating hydrodynamics (FHD) method, the generalized Langevin method, the hybrid method, and the deterministic method. Through the examples discussed, we also show a top-down multiscale progression of temporal dynamics from the colloidal scales to the molecular scales, and the associated fluctuations, hydrodynamic correlations. While the motivation and the examples discussed here pertain to nanoscale fluid dynamics and mass transport, the methodologies presented are rather general and can be easily adopted to applications in convective heat transfer. PMID:28035168

Stochasticity and Spatial Interaction Govern Stem Cell Differentiation Dynamics

NASA Astrophysics Data System (ADS)

Smith, Quinton; Stukalin, Evgeny; Kusuma, Sravanti; Gerecht, Sharon; Sun, Sean X.

2015-07-01

Stem cell differentiation underlies many fundamental processes such as development, tissue growth and regeneration, as well as disease progression. Understanding how stem cell differentiation is controlled in mixed cell populations is an important step in developing quantitative models of cell population dynamics. Here we focus on quantifying the role of cell-cell interactions in determining stem cell fate. Toward this, we monitor stem cell differentiation in adherent cultures on micropatterns and collect statistical cell fate data. Results show high cell fate variability and a bimodal probability distribution of stem cell fraction on small (80-140 μm diameter) micropatterns. On larger (225-500 μm diameter) micropatterns, the variability is also high but the distribution of the stem cell fraction becomes unimodal. Using a stochastic model, we analyze the differentiation dynamics and quantitatively determine the differentiation probability as a function of stem cell fraction. Results indicate that stem cells can interact and sense cellular composition in their immediate neighborhood and adjust their differentiation probability accordingly. Blocking epithelial cadherin (E-cadherin) can diminish this cell-cell contact mediated sensing. For larger micropatterns, cell motility adds a spatial dimension to the picture. Taken together, we find stochasticity and cell-cell interactions are important factors in determining cell fate in mixed cell populations.

Dynamics in terahertz semiconductor microcavity: quantum noise spectra

NASA Astrophysics Data System (ADS)

Jabri, H.; Eleuch, H.

2018-05-01

We investigate the physics of an optical semiconductor microcavity containing a coupled double quantum well interacting with cavity photons. The photon statistics of the transmitted light by the cavity is explored. We show that the nonlinear interactions in the direct and indirect excitonic modes generate an important squeezing despite the weak nonlinearities. When the strong coupling regime is achieved, the noise spectra of the system is dominated by the indirect exciton distribution. At the opposite, in the weak regime, direct excitons contribute much larger in the noise spectra.

Random Evolution of Idiotypic Networks: Dynamics and Architecture

NASA Astrophysics Data System (ADS)

Brede, Markus; Behn, Ulrich

The paper deals with modelling a subsystem of the immune system, the so-called idiotypic network (INW). INWs, conceived by N.K. Jerne in 1974, are functional networks of interacting antibodies and B cells. In principle, Jernes' framework provides solutions to many issues in immunology, such as immunological memory, mechanisms for antigen recognition and self/non-self discrimination. Explaining the interconnection between the elementary components, local dynamics, network formation and architecture, and possible modes of global system function appears to be an ideal playground of statistical mechanics. We present a simple cellular automaton model, based on a graph representation of the system. From a simplified description of idiotypic interactions, rules for the random evolution of networks of occupied and empty sites on these graphs are derived. In certain biologically relevant parameter ranges the resultant dynamics leads to stationary states. A stationary state is found to correspond to a specific pattern of network organization. It turns out that even these very simple rules give rise to a multitude of different kinds of patterns. We characterize these networks by classifying `static' and `dynamic' network-patterns. A type of `dynamic' network is found to display many features of real INWs.

A standardized tritrophic small-scale system (TriCosm) for the assessment of stressor-induced effects on aquatic community dynamics.

PubMed

Riedl, Verena; Agatz, Annika; Benstead, Rachel; Ashauer, Roman

2018-04-01

Chemical impacts on the environment are routinely assessed in single-species tests. They are employed to measure direct effects on nontarget organisms, but indirect effects on ecological interactions can only be detected in multispecies tests. Micro- and mesocosms are more complex and environmentally realistic, yet they are less frequently used for environmental risk assessment because resource demand is high, whereas repeatability and statistical power are often low. Test systems fulfilling regulatory needs (i.e., standardization, repeatability, and replication) and the assessment of impacts on species interactions and indirect effects are lacking. In the present study we describe the development of the TriCosm, a repeatable aquatic multispecies test with 3 trophic levels and increased statistical power. High repeatability of community dynamics of 3 interacting aquatic populations (algae, Ceriodaphnia, and Hydra) was found with an average coefficient of variation of 19.5% and the ability to determine small effect sizes. The TriCosm combines benefits of both single-species tests (fulfillment of regulatory requirements) and complex multispecies tests (ecological relevance) and can be used, for instance, at an intermediate tier in environmental risk assessment. Furthermore, comparatively quickly generated population and community toxicity data can be useful for the development and testing of mechanistic effect models. Environ Toxicol Chem 2018;37:1051-1060. © 2017 SETAC. © 2017 SETAC.

Complexity and dynamics of topological and community structure in complex networks

NASA Astrophysics Data System (ADS)

Berec, Vesna

2017-07-01

Complexity is highly susceptible to variations in the network dynamics, reflected on its underlying architecture where topological organization of cohesive subsets into clusters, system's modular structure and resulting hierarchical patterns, are cross-linked with functional dynamics of the system. Here we study connection between hierarchical topological scales of the simplicial complexes and the organization of functional clusters - communities in complex networks. The analysis reveals the full dynamics of different combinatorial structures of q-th-dimensional simplicial complexes and their Laplacian spectra, presenting spectral properties of resulting symmetric and positive semidefinite matrices. The emergence of system's collective behavior from inhomogeneous statistical distribution is induced by hierarchically ordered topological structure, which is mapped to simplicial complex where local interactions between the nodes clustered into subcomplexes generate flow of information that characterizes complexity and dynamics of the full system.

Thermodynamics and Dynamics of Bose condensation in a quasi-homogeneous gas

NASA Astrophysics Data System (ADS)

Navon, Nir; Schmidutz, Tobias; Gotlibovych, Igor; Gaunt, Alexander; Robert-de-Saint-Vincent, Martin; Smith, Robert; Hadzibabic, Zoran

2014-05-01

We present an experimental study of the thermodynamics and dynamics of Bose-Einstein condensation (BEC) in an optical-box trap. We first characterize the critical point for BEC, and observe saturation of the thermal component in a partially condensed cloud, in agreement with Einstein's textbook picture of a purely statistical phase transition. We also observed the quantum Joule-Thomson effect, namely isoenthalpic cooling of a non-interacting gas. We then investigate the dynamics of Bose condensation in the box potential following a rapid temperature quench through the phase transition, and focus on the time-evolution of the condensed fraction, the coherence length and the mean-field shift, that we probe via Bragg spectroscopy.

Bringing Data to Life into an Introductory Statistics Course with Gapminder

ERIC Educational Resources Information Center

Le, Dai-Trang

2013-01-01

"Gapminder" is a free and easy to use software for visualising real-world data in multiple dimensions. The simple format of the Cartesian coordinate system is used in a dynamic and interactive way to convey a great deal of information. This tool can be readily used to arouse students' natural curiosity regarding world events and to…

Atomic Bose-Hubbard Systems with Single-Particle Control

NASA Astrophysics Data System (ADS)

Preiss, Philipp Moritz

Experiments with ultracold atoms in optical lattices provide outstanding opportunities to realize exotic quantum states due to a high degree of tunability and control. In this thesis, I present experiments that extend this control from global parameters to the level of individual particles. Using a quantum gas microscope for 87Rb, we have developed a single-site addressing scheme based on digital amplitude holograms. The system self-corrects for aberrations in the imaging setup and creates arbitrary beam profiles. We are thus able to shape optical potentials on the scale of single lattice sites and control the dynamics of individual atoms. We study the role of quantum statistics and interactions in the Bose-Hubbard model on the fundamental level of two particles. Bosonic quantum statistics are apparent in the Hong-Ou-Mandel interference of massive particles, which we observe in tailored double-well potentials. These underlying statistics, in combination with tunable repulsive interactions, dominate the dynamics in single- and two-particle quantum walks. We observe highly coherent position-space Bloch oscillations, bosonic bunching in Hanbury Brown-Twiss interference and the fermionization of strongly interacting bosons. Many-body states of indistinguishable quantum particles are characterized by large-scale spatial entanglement, which is difficult to detect in itinerant systems. Here, we extend the concept of Hong-Ou-Mandel interference from individual particles to many-body states to directly quantify entanglement entropy. We perform collective measurements on two copies of a quantum state and detect entanglement entropy through many-body interference. We measure the second order Renyi entropy in small Bose-Hubbard systems and detect the buildup of spatial entanglement across the superfluid-insulator transition. Our experiments open new opportunities for the single-particle-resolved preparation and characterization of many-body quantum states.

Finding equilibrium in the spatiotemporal chaos of the complex Ginzburg-Landau equation

NASA Astrophysics Data System (ADS)

Ballard, Christopher C.; Esty, C. Clark; Egolf, David A.

2016-11-01

Equilibrium statistical mechanics allows the prediction of collective behaviors of large numbers of interacting objects from just a few system-wide properties; however, a similar theory does not exist for far-from-equilibrium systems exhibiting complex spatial and temporal behavior. We propose a method for predicting behaviors in a broad class of such systems and apply these ideas to an archetypal example, the spatiotemporal chaotic 1D complex Ginzburg-Landau equation in the defect chaos regime. Building on the ideas of Ruelle and of Cross and Hohenberg that a spatiotemporal chaotic system can be considered a collection of weakly interacting dynamical units of a characteristic size, the chaotic length scale, we identify underlying, mesoscale, chaotic units and effective interaction potentials between them. We find that the resulting equilibrium Takahashi model accurately predicts distributions of particle numbers. These results suggest the intriguing possibility that a class of far-from-equilibrium systems may be well described at coarse-grained scales by the well-established theory of equilibrium statistical mechanics.

Finding equilibrium in the spatiotemporal chaos of the complex Ginzburg-Landau equation.

PubMed

Ballard, Christopher C; Esty, C Clark; Egolf, David A

2016-11-01

Equilibrium statistical mechanics allows the prediction of collective behaviors of large numbers of interacting objects from just a few system-wide properties; however, a similar theory does not exist for far-from-equilibrium systems exhibiting complex spatial and temporal behavior. We propose a method for predicting behaviors in a broad class of such systems and apply these ideas to an archetypal example, the spatiotemporal chaotic 1D complex Ginzburg-Landau equation in the defect chaos regime. Building on the ideas of Ruelle and of Cross and Hohenberg that a spatiotemporal chaotic system can be considered a collection of weakly interacting dynamical units of a characteristic size, the chaotic length scale, we identify underlying, mesoscale, chaotic units and effective interaction potentials between them. We find that the resulting equilibrium Takahashi model accurately predicts distributions of particle numbers. These results suggest the intriguing possibility that a class of far-from-equilibrium systems may be well described at coarse-grained scales by the well-established theory of equilibrium statistical mechanics.

Universal Power Law Governing Pedestrian Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karamouzas, Ioannis; Skinner, Brian; Guy, Stephen J.

2014-12-01

Human crowds often bear a striking resemblance to interacting particle systems, and this has prompted many researchers to describe pedestrian dynamics in terms of interaction forces and potential energies. The correct quantitative form of this interaction, however, has remained an open question. Here, we introduce a novel statistical-mechanical approach to directly measure the interaction energy between pedestrians. This analysis, when applied to a large collection of human motion data, reveals a simple power-law interaction that is based not on the physical separation between pedestrians but on their projected time to a potential future collision, and is therefore fundamentally anticipatory inmore » nature. Remarkably, this simple law is able to describe human interactions across a wide variety of situations, speeds, and densities. We further show, through simulations, that the interaction law we identify is sufficient to reproduce many known crowd phenomena.« less

Velocity statistics for interacting edge dislocations in one dimension from Dyson's Coulomb gas model.

PubMed

Jafarpour, Farshid; Angheluta, Luiza; Goldenfeld, Nigel

2013-10-01

The dynamics of edge dislocations with parallel Burgers vectors, moving in the same slip plane, is mapped onto Dyson's model of a two-dimensional Coulomb gas confined in one dimension. We show that the tail distribution of the velocity of dislocations is power law in form, as a consequence of the pair interaction of nearest neighbors in one dimension. In two dimensions, we show the presence of a pairing phase transition in a system of interacting dislocations with parallel Burgers vectors. The scaling exponent of the velocity distribution at effective temperatures well below this pairing transition temperature can be derived from the nearest-neighbor interaction, while near the transition temperature, the distribution deviates from the form predicted by the nearest-neighbor interaction, suggesting the presence of collective effects.

How Osmolytes Counteract Pressure Denaturation on a Molecular Scale.

PubMed

Shimizu, Seishi; Smith, Paul E

2017-08-18

Life in the deep sea exposes enzymes to high hydrostatic pressure, which decreases their stability. For survival, deep sea organisms tend to accumulate various osmolytes, most notably trimethylamine N-oxide used by fish, to counteract pressure denaturation. However, exactly how these osmolytes work remains unclear. Here, a rigorous statistical thermodynamics approach is used to clarify the mechanism of osmoprotection. It is shown that the weak, nonspecific, and dynamic interactions of water and osmolytes with proteins can be characterized only statistically, and that the competition between protein-osmolyte and protein-water interactions is crucial in determining conformational stability. Osmoprotection is driven by a stronger exclusion of osmolytes from the denatured protein than from the native conformation, and water distribution has no significant effect on these changes at low osmolyte concentrations. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Chemodetection in fluctuating environments: receptor coupling, buffering, and antagonism.

PubMed

Lalanne, Jean-Benoît; François, Paul

2015-02-10

Variability in the chemical composition of the extracellular environment can significantly degrade the ability of cells to detect rare cognate ligands. Using concepts from statistical detection theory, we formalize the generic problem of detection of small concentrations of ligands in a fluctuating background of biochemically similar ligands binding to the same receptors. We discover that in contrast with expectations arising from considerations of signal amplification, inhibitory interactions between receptors can improve detection performance in the presence of substantial environmental variability, providing an adaptive interpretation to the phenomenon of ligand antagonism. Our results suggest that the structure of signaling pathways responsible for chemodetection in fluctuating and heterogeneous environments might be optimized with respect to the statistics and dynamics of environmental composition. The developed formalism stresses the importance of characterizing nonspecific interactions to understand function in signaling pathways.

Relationship between the Macroscopic and Quantum Characteristics of Dynamic Viscosity for Hydrocarbons upon the Compensation Effect

NASA Astrophysics Data System (ADS)

Dolomatov, M. Yu.; Kovaleva, E. A.; Khamidullina, D. A.

2018-05-01

An approach that allows the calculation of dynamic viscosity for liquid hydrocarbons from quantum (ionization energies) and molecular (Wiener topological indices) parameters is proposed. A physical relationship is revealed between ionization and the energies of viscous flow activation. This relationship is due to the contribution from the dispersion component of Van der Waals forces to intermolecular interaction. A two-parameter dependence of the energy of viscous flow activation, energy of ionization, and Wiener topological indices is obtained. The dynamic viscosities of liquid hydrocarbons can be calculated from the kinetic compensation effect of dynamic viscosity, which indicates a relationship between the energy of activation and the Arrhenius pre-exponental factor of the Frenkel-Eyring hole model. Calculation results are confirmed through statistical processing of the experimental data.

Dynamic model of the force driving kinesin to move along microtubule-Simulation with a model system

NASA Astrophysics Data System (ADS)

Chou, Y. C.; Hsiao, Yi-Feng; To, Kiwing

2015-09-01

A dynamic model for the motility of kinesin, including stochastic-force generation and step formation is proposed. The force driving the motion of kinesin motor is generated by the impulse from the collision between the randomly moving long-chain stalk and the ratchet-shaped outer surface of microtubule. Most of the dynamical and statistical features of the motility of kinesin are reproduced in a simulation system, with (a) ratchet structures similar to the outer surface of microtubule, (b) a bead chain connected to two heads, similarly to the stalk of the real kinesin motor, and (c) the interaction between the heads of the simulated kinesin and microtubule. We also propose an experiment to discriminate between the conventional hand-over-hand model and the dynamic model.

Continuous measurement of two spatially separated superconducting qubits: quantum trajectories and statistics

NASA Astrophysics Data System (ADS)

Roch, Nicolas

2015-03-01

Measurement can be harnessed to probabilistically generate entanglement in the absence of local interactions, for example between spatially separated quantum objects. Continuous weak measurement allows us to observe the dynamics associated with this process. In particular, we perform joint dispersive readout of two superconducting transmon qubits separated by one meter of coaxial cable. We track the evolution of a joint quantum state under the influence of measurement, both as an ensemble and as a set of individual quantum trajectories. Analyzing the statistics of such quantum trajectories can shed new light on the underlying entangling mechanism.

Experimental and Numerical Investigation of Vortical Structures in Lean Premixed Swirl-Stabilized Combustion

NASA Astrophysics Data System (ADS)

Taamallah, Soufien; Chakroun, Nadim; Shanbhogue, Santosh; Kewlani, Gaurav; Ghoniem, Ahmed

2015-11-01

A combined experimental and LES investigation is performed to identify the origin of major flow dynamics and vortical structures in a model gas turbine's swirl-stabilized turbulent combustor. Swirling flows in combustion lead to the formation of complex flow dynamics and vortical structures that can interact with flames and influence its stabilization. Our experimental results for non-reacting flow show the existence of large scale precession motion. The precessing vortex core (PVC) dynamics disappears with combustion but only above a threshold of equivalence ratio. In addition, large scale vortices along the inner shear layer (ISL) are observed. These structures interact with the ISL stabilized flame and contribute to its wrinkling. Next, the LES setup is validated against the flow field's low-order statistics and point temperature measurement in relevant areas of the chamber. Finally, we show that LES is capable of predicting the precession motion as well as the ISL vortices in the reacting case: we find that ISL vortices originate from a vortex core that is formed right downstream of the swirler's centerbody. The vortex core has a conical spiral shape resembling a corkscrew that interacts - as it winds out - with the flame when it reaches the ISL.

Dynamic Simulation of Random Packing of Polydispersive Fine Particles

NASA Astrophysics Data System (ADS)

Ferraz, Carlos Handrey Araujo; Marques, Samuel Apolinário

2018-02-01

In this paper, we perform molecular dynamic (MD) simulations to study the two-dimensional packing process of both monosized and random size particles with radii ranging from 1.0 to 7.0 μm. The initial positions as well as the radii of five thousand fine particles were defined inside a rectangular box by using a random number generator. Both the translational and rotational movements of each particle were considered in the simulations. In order to deal with interacting fine particles, we take into account both the contact forces and the long-range dispersive forces. We account for normal and static/sliding tangential friction forces between particles and between particle and wall by means of a linear model approach, while the long-range dispersive forces are computed by using a Lennard-Jones-like potential. The packing processes were studied assuming different long-range interaction strengths. We carry out statistical calculations of the different quantities studied such as packing density, mean coordination number, kinetic energy, and radial distribution function as the system evolves over time. We find that the long-range dispersive forces can strongly influence the packing process dynamics as they might form large particle clusters, depending on the intensity of the long-range interaction strength.

Finite-size scaling with respect to interaction and disorder strength at the many-body localization transition

NASA Astrophysics Data System (ADS)

Kudo, Kazue; Deguchi, Tetsuo

2018-06-01

We present a finite-size scaling for both interaction and disorder strengths in the critical regime of the many-body localization (MBL) transition for a spin-1/2 X X Z spin chain with a random field by studying level statistics. We show how the dynamical transition from the thermal to MBL phase depends on interaction together with disorder by evaluating the ratio of adjacent level spacings, and thus, extend previous studies in which interaction coupling is fixed. We introduce an extra critical exponent in order to describe the nontrivial interaction dependence of the MBL transition. It is characterized by the ratio of the disorder strength to the power of the interaction coupling with respect to the extra critical exponent and not by the simple ratio between them.

Identification of redundant and synergetic circuits in triplets of electrophysiological data

NASA Astrophysics Data System (ADS)

Erramuzpe, Asier; Ortega, Guillermo J.; Pastor, Jesus; de Sola, Rafael G.; Marinazzo, Daniele; Stramaglia, Sebastiano; Cortes, Jesus M.

2015-12-01

Objective. Neural systems are comprised of interacting units, and relevant information regarding their function or malfunction can be inferred by analyzing the statistical dependencies between the activity of each unit. While correlations and mutual information are commonly used to characterize these dependencies, our objective here is to extend interactions to triplets of variables to better detect and characterize dynamic information transfer. Approach. Our approach relies on the measure of interaction information (II). The sign of II provides information as to the extent to which the interaction of variables in triplets is redundant (R) or synergetic (S). Three variables are said to be redundant when a third variable, say Z, added to a pair of variables (X, Y), diminishes the information shared between X and Y. Similarly, the interaction in the triplet is said to be synergetic when conditioning on Z enhances the information shared between X and Y with respect to the unconditioned state. Here, based on this approach, we calculated the R and S status for triplets of electrophysiological data recorded from drug-resistant patients with mesial temporal lobe epilepsy in order to study the spatial organization and dynamics of R and S close to the epileptogenic zone (the area responsible for seizure propagation). Main results. In terms of spatial organization, our results show that R matched the epileptogenic zone while S was distributed more in the surrounding area. In relation to dynamics, R made the largest contribution to high frequency bands (14-100 Hz), while S was expressed more strongly at lower frequencies (1-7 Hz). Thus, applying II to such clinical data reveals new aspects of epileptogenic structure in terms of the nature (redundancy versus synergy) and dynamics (fast versus slow rhythms) of the interactions. Significance. We expect this methodology, robust and simple, can reveal new aspects beyond pair-interactions in networks of interacting units in other setups with multi-recording data sets (and thus, not necessarily in epilepsy, the pathology we have approached here).

Molecular Dynamics of Hot Dense Plasmas: New Horizons

NASA Astrophysics Data System (ADS)

Graziani, Frank

2011-10-01

We describe the status of a new time-dependent simulation capability for hot dense plasmas. The backbone of this multi-institutional computational and experimental effort--the Cimarron Project--is the massively parallel molecular dynamics (MD) code ``ddcMD''. The project's focus is material conditions such as exist in inertial confinement fusion experiments, and in many stellar interiors: high temperatures, high densities, significant electromagnetic fields, mixtures of high- and low- Zelements, and non-Maxwellian particle distributions. Of particular importance is our ability to incorporate into this classical MD code key atomic, radiative, and nuclear processes, so that their interacting effects under non-ideal plasma conditions can be investigated. This talk summarizes progress in computational methodology, discusses strengths and weaknesses of quantum statistical potentials as effective interactions for MD, explains the model used for quantum events possibly occurring in a collision and highlights some significant results obtained to date. We describe the status of a new time-dependent simulation capability for hot dense plasmas. The backbone of this multi-institutional computational and experimental effort--the Cimarron Project--is the massively parallel molecular dynamics (MD) code ``ddcMD''. The project's focus is material conditions such as exist in inertial confinement fusion experiments, and in many stellar interiors: high temperatures, high densities, significant electromagnetic fields, mixtures of high- and low- Zelements, and non-Maxwellian particle distributions. Of particular importance is our ability to incorporate into this classical MD code key atomic, radiative, and nuclear processes, so that their interacting effects under non-ideal plasma conditions can be investigated. This talk summarizes progress in computational methodology, discusses strengths and weaknesses of quantum statistical potentials as effective interactions for MD, explains the model used for quantum events possibly occurring in a collision and highlights some significant results obtained to date. This work is performed under the auspices of the U. S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Feedback Controlled Colloidal Assembly at Fluid Interfaces

NASA Astrophysics Data System (ADS)

Bevan, Michael

The autonomous and reversible assembly of colloidal nano- and micro- scale components into ordered configurations is often suggested as a scalable process capable of manufacturing meta-materials with exotic electromagnetic properties. As a result, there is strong interest in understanding how thermal motion, particle interactions, patterned surfaces, and external fields can be optimally coupled to robustly control the assembly of colloidal components into hierarchically structured functional meta-materials. We approach this problem by directly relating equilibrium and dynamic colloidal microstructures to kT-scale energy landscapes mediated by colloidal forces, physically and chemically patterned surfaces, multiphase fluid interfaces, and electromagnetic fields. 3D colloidal trajectories are measured in real-space and real-time with nanometer resolution using an integrated suite of evanescent wave, video, and confocal microscopy methods. Equilibrium structures are connected to energy landscapes via statistical mechanical models. The dynamic evolution of initially disordered colloidal fluid configurations into colloidal crystals in the presence of tunable interactions (electromagnetic field mediated interactions, particle-interface interactions) is modeled using a novel approach based on fitting the Fokker-Planck equation to experimental microscopy and computer simulated assembly trajectories. This approach is based on the use of reaction coordinates that capture important microstructural features of crystallization processes and quantify both statistical mechanical (free energy) and fluid mechanical (hydrodynamic) contributions. Ultimately, we demonstrate real-time control of assembly, disassembly, and repair of colloidal crystals using both open loop and closed loop control to produce perfectly ordered colloidal microstructures. This approach is demonstrated for close packed colloidal crystals of spherical particles at fluid-solid interfaces and is being extended to anisotropic particles and multiphase fluid interfaces.

Classical Electrodynamics: Lecture notes

NASA Astrophysics Data System (ADS)

Likharev, Konstantin K.

2018-06-01

Essential Advanced Physics is a series comprising four parts: Classical Mechanics, Classical Electrodynamics, Quantum Mechanics and Statistical Mechanics. Each part consists of two volumes, Lecture notes and Problems with solutions, further supplemented by an additional collection of test problems and solutions available to qualifying university instructors. This volume, Classical Electrodynamics: Lecture notes is intended to be the basis for a two-semester graduate-level course on electricity and magnetism, including not only the interaction and dynamics charged point particles, but also properties of dielectric, conducting, and magnetic media. The course also covers special relativity, including its kinematics and particle-dynamics aspects, and electromagnetic radiation by relativistic particles.

Auditory Power-Law Activation Avalanches Exhibit a Fundamental Computational Ground State

NASA Astrophysics Data System (ADS)

Stoop, Ruedi; Gomez, Florian

2016-07-01

The cochlea provides a biological information-processing paradigm that we are only beginning to understand in its full complexity. Our work reveals an interacting network of strongly nonlinear dynamical nodes, on which even a simple sound input triggers subnetworks of activated elements that follow power-law size statistics ("avalanches"). From dynamical systems theory, power-law size distributions relate to a fundamental ground state of biological information processing. Learning destroys these power laws. These results strongly modify the models of mammalian sound processing and provide a novel methodological perspective for understanding how the brain processes information.

Coupled disease-behavior dynamics on complex networks: A review

NASA Astrophysics Data System (ADS)

Wang, Zhen; Andrews, Michael A.; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T.

2015-12-01

It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

Recent advances in mathematical criminology. Comment on "Statistical physics of crime: A review" by M.R. D'Orsogna and M. Perc

NASA Astrophysics Data System (ADS)

Rodríguez, Nancy

2015-03-01

The use of mathematical tools has long proved to be useful in gaining understanding of complex systems in physics [1]. Recently, many researchers have realized that there is an analogy between emerging phenomena in complex social systems and complex physical or biological systems [4,5,12]. This realization has particularly benefited the modeling and understanding of crime, a ubiquitous phenomena that is far from being understood. In fact, when one is interested in the bulk behavior of patterns that emerge from small and seemingly unrelated interactions as well as decisions that occur at the individual level, the mathematical tools that have been developed in statistical physics, game theory, network theory, dynamical systems, and partial differential equations can be useful in shedding light into the dynamics of these patterns [2-4,6,12].

Complex patterns of abnormal heartbeats

NASA Technical Reports Server (NTRS)

Schulte-Frohlinde, Verena; Ashkenazy, Yosef; Goldberger, Ary L.; Ivanov, Plamen Ch; Costa, Madalena; Morley-Davies, Adrian; Stanley, H. Eugene; Glass, Leon

2002-01-01

Individuals having frequent abnormal heartbeats interspersed with normal heartbeats may be at an increased risk of sudden cardiac death. However, mechanistic understanding of such cardiac arrhythmias is limited. We present a visual and qualitative method to display statistical properties of abnormal heartbeats. We introduce dynamical "heartprints" which reveal characteristic patterns in long clinical records encompassing approximately 10(5) heartbeats and may provide information about underlying mechanisms. We test if these dynamics can be reproduced by model simulations in which abnormal heartbeats are generated (i) randomly, (ii) at a fixed time interval following a preceding normal heartbeat, or (iii) by an independent oscillator that may or may not interact with the normal heartbeat. We compare the results of these three models and test their limitations to comprehensively simulate the statistical features of selected clinical records. This work introduces methods that can be used to test mathematical models of arrhythmogenesis and to develop a new understanding of underlying electrophysiologic mechanisms of cardiac arrhythmia.

Colloquium: Hierarchy of scales in language dynamics

NASA Astrophysics Data System (ADS)

Blythe, Richard A.

2015-11-01

Methods and insights from statistical physics are finding an increasing variety of applications where one seeks to understand the emergent properties of a complex interacting system. One such area concerns the dynamics of language at a variety of levels of description, from the behaviour of individual agents learning simple artificial languages from each other, up to changes in the structure of languages shared by large groups of speakers over historical timescales. In this Colloquium, we survey a hierarchy of scales at which language and linguistic behaviour can be described, along with the main progress in understanding that has been made at each of them - much of which has come from the statistical physics community. We argue that future developments may arise by linking the different levels of the hierarchy together in a more coherent fashion, in particular where this allows more effective use of rich empirical data sets.

deltaGseg: macrostate estimation via molecular dynamics simulations and multiscale time series analysis.

PubMed

Low, Diana H P; Motakis, Efthymios

2013-10-01

Binding free energy calculations obtained through molecular dynamics simulations reflect intermolecular interaction states through a series of independent snapshots. Typically, the free energies of multiple simulated series (each with slightly different starting conditions) need to be estimated. Previous approaches carry out this task by moving averages at certain decorrelation times, assuming that the system comes from a single conformation description of binding events. Here, we discuss a more general approach that uses statistical modeling, wavelets denoising and hierarchical clustering to estimate the significance of multiple statistically distinct subpopulations, reflecting potential macrostates of the system. We present the deltaGseg R package that performs macrostate estimation from multiple replicated series and allows molecular biologists/chemists to gain physical insight into the molecular details that are not easily accessible by experimental techniques. deltaGseg is a Bioconductor R package available at http://bioconductor.org/packages/release/bioc/html/deltaGseg.html.

Perspective: chemical dynamics simulations of non-statistical reaction dynamics

PubMed Central

Ma, Xinyou; Hase, William L.

2017-01-01

Non-statistical chemical dynamics are exemplified by disagreements with the transition state (TS), RRKM and phase space theories of chemical kinetics and dynamics. The intrinsic reaction coordinate (IRC) is often used for the former two theories, and non-statistical dynamics arising from non-IRC dynamics are often important. In this perspective, non-statistical dynamics are discussed for chemical reactions, with results primarily obtained from chemical dynamics simulations and to a lesser extent from experiment. The non-statistical dynamical properties discussed are: post-TS dynamics, including potential energy surface bifurcations, product energy partitioning in unimolecular dissociation and avoiding exit-channel potential energy minima; non-RRKM unimolecular decomposition; non-IRC dynamics; direct mechanisms for bimolecular reactions with pre- and/or post-reaction potential energy minima; non-TS theory barrier recrossings; and roaming dynamics. This article is part of the themed issue ‘Theoretical and computational studies of non-equilibrium and non-statistical dynamics in the gas phase, in the condensed phase and at interfaces’. PMID:28320906

Internet dynamics

NASA Astrophysics Data System (ADS)

Lukose, Rajan Mathew

The World Wide Web and the Internet are rapidly expanding spaces, of great economic and social significance, which offer an opportunity to study many phenomena, often previously inaccessible, on an unprecedented scale and resolution with relative ease. These phenomena are measurable on the scale of tens of millions of users and hundreds of millions of pages. By virtue of nearly complete electronic mediation, it is possible in principle to observe the time and ``spatial'' evolution of nearly all choices and interactions. This cyber-space therefore provides a view into a number of traditional research questions (from many academic disciplines) and creates its own new phenomena accessible for study. Despite its largely self-organized and dynamic nature, a number of robust quantitative regularities are found in the aggregate statistics of interesting and useful quantities. These regularities can be understood with the help of models that draw on ideas from statistical physics as well as other fields such as economics, psychology and decision theory. This thesis develops models that can account for regularities found in the statistics of Internet congestion and user surfing patterns and discusses some practical consequences. practical consequences.

Numerical solutions of ideal quantum gas dynamical flows governed by semiclassical ellipsoidal-statistical distribution

PubMed Central

Yang, Jaw-Yen; Yan, Chih-Yuan; Diaz, Manuel; Huang, Juan-Chen; Li, Zhihui; Zhang, Hanxin

2014-01-01

The ideal quantum gas dynamics as manifested by the semiclassical ellipsoidal-statistical (ES) equilibrium distribution derived in Wu et al. (Wu et al. 2012 Proc. R. Soc. A 468, 1799–1823 (doi:10.1098/rspa.2011.0673)) is numerically studied for particles of three statistics. This anisotropic ES equilibrium distribution was derived using the maximum entropy principle and conserves the mass, momentum and energy, but differs from the standard Fermi–Dirac or Bose–Einstein distribution. The present numerical method combines the discrete velocity (or momentum) ordinate method in momentum space and the high-resolution shock-capturing method in physical space. A decoding procedure to obtain the necessary parameters for determining the ES distribution is also devised. Computations of two-dimensional Riemann problems are presented, and various contours of the quantities unique to this ES model are illustrated. The main flow features, such as shock waves, expansion waves and slip lines and their complex nonlinear interactions, are depicted and found to be consistent with existing calculations for a classical gas. PMID:24399919

Emerging Concepts of Data Integration in Pathogen Phylodynamics.

PubMed

Baele, Guy; Suchard, Marc A; Rambaut, Andrew; Lemey, Philippe

2017-01-01

Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics.

Emerging Concepts of Data Integration in Pathogen Phylodynamics

PubMed Central

Baele, Guy; Suchard, Marc A.; Rambaut, Andrew; Lemey, Philippe

2017-01-01

Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics. PMID:28173504

Interspecific and intraspecific competition as causes of direct and delayed density dependence in a fluctuating vole population

PubMed Central

Hansen, Thomas F.; Stenseth, Nils C.; Henttonen, Heikki; Tast, Johan

1999-01-01

A 3- to 5-year cycle of vole abundances is a characteristic phenomenon in the ecology of northern regions, and their explanation stands as a central theoretical challenge in population ecology. Although many species of voles usually coexist and are in severe competition for food and breeding space, the role of interspecific competition in vole cycles has never been evaluated statistically. After studying community effects on the population dynamics of the gray-sided vole (Clethrionomys rufocanus) in the subarctic birch forest at Kilpisjärvi, Finland, we report statistical results showing that both interspecific and intraspecific effects are important in the direct year-to-year density dependence. However, interspecific effects are not detectable in the 2-year delayed density dependence that is crucial for generating the characteristic cycles. Furthermore, we show that most of the competition takes place during the winter. The results are evaluated against two models of community dynamics. One assumes that the delayed effects are caused by an interaction with a specialist predator, and the other assumes that they are caused by overgrazing food plants. These statistical results show that vole cycles may be generated by a species-specific trophic interaction. The results also suggest that the gray-sided vole may be the focal species in the birch-forest community, as field voles may be in the taiga and as lemmings may be on the tundra. PMID:9927680

Climate and dengue transmission: evidence and implications.

PubMed

Morin, Cory W; Comrie, Andrew C; Ernst, Kacey

2013-01-01

Climate influences dengue ecology by affecting vector dynamics, agent development, and mosquito/human interactions. Although these relationships are known, the impact climate change will have on transmission is unclear. Climate-driven statistical and process-based models are being used to refine our knowledge of these relationships and predict the effects of projected climate change on dengue fever occurrence, but results have been inconsistent. We sought to identify major climatic influences on dengue virus ecology and to evaluate the ability of climate-based dengue models to describe associations between climate and dengue, simulate outbreaks, and project the impacts of climate change. We reviewed the evidence for direct and indirect relationships between climate and dengue generated from laboratory studies, field studies, and statistical analyses of associations between vectors, dengue fever incidence, and climate conditions. We assessed the potential contribution of climate-driven, process-based dengue models and provide suggestions to improve their performance. Relationships between climate variables and factors that influence dengue transmission are complex. A climate variable may increase dengue transmission potential through one aspect of the system while simultaneously decreasing transmission potential through another. This complexity may at least partly explain inconsistencies in statistical associations between dengue and climate. Process-based models can account for the complex dynamics but often omit important aspects of dengue ecology, notably virus development and host-species interactions. Synthesizing and applying current knowledge of climatic effects on all aspects of dengue virus ecology will help direct future research and enable better projections of climate change effects on dengue incidence.

Tropical and Monsoonal Studies.

DTIC Science & Technology

1988-01-01

School. The reearch was partially sup- Fleasle. IL G ., 1947: The fieds of temperattur, psre and three ported by the National Scaenc Foundation under...the three-dimensional structure of the observed -. adK. G . Lum, 1985: Tropical-midlatitude interactions over transient eddy statistics of the...ERL GFDL,6, Newell. R. E., J. W. Kidson, D. G . Vincent and G . J. Boer, 1974: Geophysical Fuid Dynamics Laboratory, Princeton, NJ. The General

Noise Response Data Reveal Novel Controllability Gramian for Nonlinear Network Dynamics

PubMed Central

Kashima, Kenji

2016-01-01

Control of nonlinear large-scale dynamical networks, e.g., collective behavior of agents interacting via a scale-free connection topology, is a central problem in many scientific and engineering fields. For the linear version of this problem, the so-called controllability Gramian has played an important role to quantify how effectively the dynamical states are reachable by a suitable driving input. In this paper, we first extend the notion of the controllability Gramian to nonlinear dynamics in terms of the Gibbs distribution. Next, we show that, when the networks are open to environmental noise, the newly defined Gramian is equal to the covariance matrix associated with randomly excited, but uncontrolled, dynamical state trajectories. This fact theoretically justifies a simple Monte Carlo simulation that can extract effectively controllable subdynamics in nonlinear complex networks. In addition, the result provides a novel insight into the relationship between controllability and statistical mechanics. PMID:27264780

Dynamics of Granular Materials

NASA Technical Reports Server (NTRS)

Behringer, Robert P.

1996-01-01

Granular materials exhibit a rich variety of dynamical behavior, much of which is poorly understood. Fractal-like stress chains, convection, a variety of wave dynamics, including waves which resemble capillary waves, l/f noise, and fractional Brownian motion provide examples. Work beginning at Duke will focus on gravity driven convection, mixing and gravitational collapse. Although granular materials consist of collections of interacting particles, there are important differences between the dynamics of a collections of grains and the dynamics of a collections of molecules. In particular, the ergodic hypothesis is generally invalid for granular materials, so that ordinary statistical physics does not apply. In the absence of a steady energy input, granular materials undergo a rapid collapse which is strongly influenced by the presence of gravity. Fluctuations on laboratory scales in such quantities as the stress can be very large-as much as an order of magnitude greater than the mean.

Combining super-ensembles and statistical emulation to improve a regional climate and vegetation model

NASA Astrophysics Data System (ADS)

Hawkins, L. R.; Rupp, D. E.; Li, S.; Sarah, S.; McNeall, D. J.; Mote, P.; Betts, R. A.; Wallom, D.

2017-12-01

Changing regional patterns of surface temperature, precipitation, and humidity may cause ecosystem-scale changes in vegetation, altering the distribution of trees, shrubs, and grasses. A changing vegetation distribution, in turn, alters the albedo, latent heat flux, and carbon exchanged with the atmosphere with resulting feedbacks onto the regional climate. However, a wide range of earth-system processes that affect the carbon, energy, and hydrologic cycles occur at sub grid scales in climate models and must be parameterized. The appropriate parameter values in such parameterizations are often poorly constrained, leading to uncertainty in predictions of how the ecosystem will respond to changes in forcing. To better understand the sensitivity of regional climate to parameter selection and to improve regional climate and vegetation simulations, we used a large perturbed physics ensemble and a suite of statistical emulators. We dynamically downscaled a super-ensemble (multiple parameter sets and multiple initial conditions) of global climate simulations using a 25-km resolution regional climate model HadRM3p with the land-surface scheme MOSES2 and dynamic vegetation module TRIFFID. We simultaneously perturbed land surface parameters relating to the exchange of carbon, water, and energy between the land surface and atmosphere in a large super-ensemble of regional climate simulations over the western US. Statistical emulation was used as a computationally cost-effective tool to explore uncertainties in interactions. Regions of parameter space that did not satisfy observational constraints were eliminated and an ensemble of parameter sets that reduce regional biases and span a range of plausible interactions among earth system processes were selected. This study demonstrated that by combining super-ensemble simulations with statistical emulation, simulations of regional climate could be improved while simultaneously accounting for a range of plausible land-atmosphere feedback strengths.

Theoretical approaches to the steady-state statistical physics of interacting dissipative units

NASA Astrophysics Data System (ADS)

Bertin, Eric

2017-02-01

The aim of this review is to provide a concise overview of some of the generic approaches that have been developed to deal with the statistical description of large systems of interacting dissipative ‘units’. The latter notion includes, e.g. inelastic grains, active or self-propelled particles, bubbles in a foam, low-dimensional dynamical systems like driven oscillators, or even spatially extended modes like Fourier modes of the velocity field in a fluid. We first review methods based on the statistical properties of a single unit, starting with elementary mean-field approximations, either static or dynamic, that describe a unit embedded in a ‘self-consistent’ environment. We then discuss how this basic mean-field approach can be extended to account for spatial dependences, in the form of space-dependent mean-field Fokker-Planck equations, for example. We also briefly review the use of kinetic theory in the framework of the Boltzmann equation, which is an appropriate description for dilute systems. We then turn to descriptions in terms of the full N-body distribution, starting from exact solutions of one-dimensional models, using a matrix-product ansatz method when correlations are present. Since exactly solvable models are scarce, we also present some approximation methods which can be used to determine the N-body distribution in a large system of dissipative units. These methods include the Edwards approach for dense granular matter and the approximate treatment of multiparticle Langevin equations with colored noise, which models systems of self-propelled particles. Throughout this review, emphasis is put on methodological aspects of the statistical modeling and on formal similarities between different physical problems, rather than on the specific behavior of a given system.

Pairwise contact energy statistical potentials can help to find probability of point mutations.

PubMed

Saravanan, K M; Suvaithenamudhan, S; Parthasarathy, S; Selvaraj, S

2017-01-01

To adopt a particular fold, a protein requires several interactions between its amino acid residues. The energetic contribution of these residue-residue interactions can be approximated by extracting statistical potentials from known high resolution structures. Several methods based on statistical potentials extracted from unrelated proteins are found to make a better prediction of probability of point mutations. We postulate that the statistical potentials extracted from known structures of similar folds with varying sequence identity can be a powerful tool to examine probability of point mutation. By keeping this in mind, we have derived pairwise residue and atomic contact energy potentials for the different functional families that adopt the (α/β) 8 TIM-Barrel fold. We carried out computational point mutations at various conserved residue positions in yeast Triose phosphate isomerase enzyme for which experimental results are already reported. We have also performed molecular dynamics simulations on a subset of point mutants to make a comparative study. The difference in pairwise residue and atomic contact energy of wildtype and various point mutations reveals probability of mutations at a particular position. Interestingly, we found that our computational prediction agrees with the experimental studies of Silverman et al. (Proc Natl Acad Sci 2001;98:3092-3097) and perform better prediction than i Mutant and Cologne University Protein Stability Analysis Tool. The present work thus suggests deriving pairwise contact energy potentials and molecular dynamics simulations of functionally important folds could help us to predict probability of point mutations which may ultimately reduce the time and cost of mutation experiments. Proteins 2016; 85:54-64. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Understanding quantum measurement from the solution of dynamical models

NASA Astrophysics Data System (ADS)

Allahverdyan, Armen E.; Balian, Roger; Nieuwenhuizen, Theo M.

2013-04-01

The quantum measurement problem, to wit, understanding why a unique outcome is obtained in each individual experiment, is currently tackled by solving models. After an introduction we review the many dynamical models proposed over the years for elucidating quantum measurements. The approaches range from standard quantum theory, relying for instance on quantum statistical mechanics or on decoherence, to quantum-classical methods, to consistent histories and to modifications of the theory. Next, a flexible and rather realistic quantum model is introduced, describing the measurement of the z-component of a spin through interaction with a magnetic memory simulated by a Curie-Weiss magnet, including N≫1 spins weakly coupled to a phonon bath. Initially prepared in a metastable paramagnetic state, it may transit to its up or down ferromagnetic state, triggered by its coupling with the tested spin, so that its magnetization acts as a pointer. A detailed solution of the dynamical equations is worked out, exhibiting several time scales. Conditions on the parameters of the model are found, which ensure that the process satisfies all the features of ideal measurements. Various imperfections of the measurement are discussed, as well as attempts of incompatible measurements. The first steps consist in the solution of the Hamiltonian dynamics for the spin-apparatus density matrix Dˆ(t). Its off-diagonal blocks in a basis selected by the spin-pointer coupling, rapidly decay owing to the many degrees of freedom of the pointer. Recurrences are ruled out either by some randomness of that coupling, or by the interaction with the bath. On a longer time scale, the trend towards equilibrium of the magnet produces a final state Dˆ(t) that involves correlations between the system and the indications of the pointer, thus ensuring registration. Although Dˆ(t) has the form expected for ideal measurements, it only describes a large set of runs. Individual runs are approached by analyzing the final states associated with all possible subensembles of runs, within a specified version of the statistical interpretation. There the difficulty lies in a quantum ambiguity: There exist many incompatible decompositions of the density matrix Dˆ(t) into a sum of sub-matrices, so that one cannot infer from its sole determination the states that would describe small subsets of runs. This difficulty is overcome by dynamics due to suitable interactions within the apparatus, which produce a special combination of relaxation and decoherence associated with the broken invariance of the pointer. Any subset of runs thus reaches over a brief delay a stable state which satisfies the same hierarchic property as in classical probability theory; the reduction of the state for each individual run follows. Standard quantum statistical mechanics alone appears sufficient to explain the occurrence of a unique answer in each run and the emergence of classicality in a measurement process. Finally, pedagogical exercises are proposed and lessons for future works on models are suggested, while the statistical interpretation is promoted for teaching.

A Statistical Description of Neural Ensemble Dynamics

PubMed Central

Long, John D.; Carmena, Jose M.

2011-01-01

The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an ensemble. Inferring these connections is often intractable because the set of possible interactions grows exponentially with ensemble size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing changes in the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility. PMID:22319486

Genomic analysis of regulatory network dynamics reveals large topological changes

NASA Astrophysics Data System (ADS)

Luscombe, Nicholas M.; Madan Babu, M.; Yu, Haiyuan; Snyder, Michael; Teichmann, Sarah A.; Gerstein, Mark

2004-09-01

Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here-particularly the large-scale topological changes and hub transience-will apply to other biological networks, including complex sub-systems in higher eukaryotes.

Collective Behaviors in Spatially Extended Systems with Local Interactions and Synchronous Updating

NASA Astrophysics Data System (ADS)

ChatÉ, H.; Manneville, P.

1992-01-01

Assessing the extent to which dynamical systems with many degrees of freedom can be described within a thermodynamics formalism is a problem that currently attracts much attention. In this context, synchronously updated regular lattices of identical, chaotic elements with local interactions are promising models for which statistical mechanics may be hoped to provide some insights. This article presents a large class of cellular automata rules and coupled map lattices of the above type in space dimensions d = 2 to 6.Such simple models can be approached by a mean-field approximation which usually reduces the dynamics to that of a map governing the evolution of some extensive density. While this approximation is exact in the d = infty limit, where macroscopic variables must display the time-dependent behavior of the mean-field map, basic intuition from equilibrium statistical mechanics rules out any such behavior in a low-dimensional systems, since it would involve the collective motion of locally disordered elements.The models studied are chosen to be as close as possible to mean-field conditions, i.e., rather high space dimension, large connectivity, and equal-weight coupling between sites. While the mean-field evolution is never observed, a new type of non-trivial collective behavior is found, at odds with the predictions of equilibrium statistical mechanics. Both in the cellular automata models and in the coupled map lattices, macroscopic variables frequently display a non-transient, time-dependent, low-dimensional dynamics emerging out of local disorder. Striking examples are period 3 cycles in two-state cellular automata and a Hopf bifurcation for a d = 5 lattice of coupled logistic maps. An extensive account of the phenomenology is given, including a catalog of behaviors, classification tables for the celular automata rules, and bifurcation diagrams for the coupled map lattices.The observed underlying dynamics is accompanied by an intrinsic quasi-Gaussian noise (stemming from the local disorder) which disappears in the infinite-size limit. The collective behaviors constitute a robust phenomenon, resisting external noise, small changes in the local dynamics, and modifications of the initial and boundary conditions. Synchronous updating, high space dimension and the regularity of connections are shown to be crucial ingredients in the subtle build-up of correlations giving rise to the collective motion. The discussion stresses the need for a theoretical understanding that neither equilibrium statistical mechanics nor higher-order mean-field approximations are able to provide.

NASA Marshall Space Flight Center Controls Systems Design and Analysis Branch

NASA Technical Reports Server (NTRS)

Gilligan, Eric

2014-01-01

Marshall Space Flight Center maintains a critical national capability in the analysis of launch vehicle flight dynamics and flight certification of GN&C algorithms. MSFC analysts are domain experts in the areas of flexible-body dynamics and control-structure interaction, thrust vector control, sloshing propellant dynamics, and advanced statistical methods. Marshall's modeling and simulation expertise has supported manned spaceflight for over 50 years. Marshall's unparalleled capability in launch vehicle guidance, navigation, and control technology stems from its rich heritage in developing, integrating, and testing launch vehicle GN&C systems dating to the early Mercury-Redstone and Saturn vehicles. The Marshall team is continuously developing novel methods for design, including advanced techniques for large-scale optimization and analysis.

Agent-Based Model Approach to Complex Phenomena in Real Economy

NASA Astrophysics Data System (ADS)

Iyetomi, H.; Aoyama, H.; Fujiwara, Y.; Ikeda, Y.; Souma, W.

An agent-based model for firms' dynamics is developed. The model consists of firm agents with identical characteristic parameters and a bank agent. Dynamics of those agents are described by their balance sheets. Each firm tries to maximize its expected profit with possible risks in market. Infinite growth of a firm directed by the ``profit maximization" principle is suppressed by a concept of ``going concern". Possibility of bankruptcy of firms is also introduced by incorporating a retardation effect of information on firms' decision. The firms, mutually interacting through the monopolistic bank, become heterogeneous in the course of temporal evolution. Statistical properties of firms' dynamics obtained by simulations based on the model are discussed in light of observations in the real economy.

Analysis of brain patterns using temporal measures

DOEpatents

Georgopoulos, Apostolos

2015-08-11

A set of brain data representing a time series of neurophysiologic activity acquired by spatially distributed sensors arranged to detect neural signaling of a brain (such as by the use of magnetoencephalography) is obtained. The set of brain data is processed to obtain a dynamic brain model based on a set of statistically-independent temporal measures, such as partial cross correlations, among groupings of different time series within the set of brain data. The dynamic brain model represents interactions between neural populations of the brain occurring close in time, such as with zero lag, for example. The dynamic brain model can be analyzed to obtain the neurophysiologic assessment of the brain. Data processing techniques may be used to assess structural or neurochemical brain pathologies.

Long-Range Self-Assembly via the Mutual Lorentz Force of Plasmon Radiation.

PubMed

Ji, Haojie; Trevino, Jacob; Tu, Raymond; Knapp, Ellen; McQuade, James; Yurkiv, Vitaliy; Mashayek, Farzad; Vuong, Luat T

2018-04-11

Long-range interactions often proceed as a sequence of hopping through intermediate, statistically favored events. Here, we demonstrate predictable mechanical dynamics of particles that arise from the Lorentz force between plasmons. Even if the radiation is weak, the nonconservative Lorentz force produces stable locations perpendicular to the plasmon oscillation; over time, distinct patterns emerge. Experimentally, linearly polarized light illumination leads to the formation of 80 nm diameter Au nanoparticle chains, perpendicularly aligned, with lengths that are orders of magnitude greater than their plasmon near-field interaction. There is a critical intensity threshold and optimal concentration for observing self-assembly.

Measurements of the interaction of wave groups with shorter wind-generated waves

NASA Technical Reports Server (NTRS)

Chu, Jacob S.; Long, Steven R.; Phillips, O. M.

1992-01-01

Fields of statistically steady wind-generated waves produced in a wind wave facility were perturbed by the injection of groups of longer, mechanically generated waves with various slopes. The time histories of the surface displacements were measured at four fetches in ensembles consisting of 100 realizations of each set of experimental conditions; the data were stored and analyzed digitally. Four distinct stages in the overall interaction are identified and characterized. The properties of the wave energy front are documented, and a preliminary discussion is given of the dynamic processes involved in its formation.

A Large Scale Dynamical System Immune Network Modelwith Finite Connectivity

NASA Astrophysics Data System (ADS)

Uezu, T.; Kadono, C.; Hatchett, J.; Coolen, A. C. C.

We study a model of an idiotypic immune network which was introduced by N. K. Jerne. It is known that in immune systems there generally exist several kinds of immune cells which can recognize any particular antigen. Taking this fact into account and assuming that each cell interacts with only a finite number of other cells, we analyze a large scale immune network via both numerical simulations and statistical mechanical methods, and show that the distribution of the concentrations of antibodies becomes non-trivial for a range of values of the strength of the interaction and the connectivity.

Spectra of KeV Protons Related to Ion-Cyclotron Wave Packets

NASA Technical Reports Server (NTRS)

Khazanov, G. V.; Sibeck, D. G.; Tel'Nikhin, A. A.; Kronberg, T. K.

2017-01-01

We use the Fokker-Planck-Kolmogorov equation to study the statistical aspects of stochastic dynamics of the radiation belt (RB) protons driven by nonlinear electromagnetic ion-cyclotron (EMIC) wave packets. We obtain the spectra of keV protons scattered by these waves that showsteeping near the gyroresonance, the signature of resonant wave-particle interaction that cannot be described by a simple power law. The most likely mechanism for proton precipitation events in RBs is shown to be nonlinear wave-particle interaction, namely, the scattering of RB protons into the loss cone by EMIC waves.

Long Range Earthquake Interaction in Iceland

NASA Astrophysics Data System (ADS)

Goltz, C.

2003-12-01

It has been observed that earthquakes can be triggered by similarly sized events at large distances. The phenomenon has recently been shown to be statistically significant at a range up to several source dimensions in global earthquake data. The most appropriate explanation of the phenomenon seems to be criticality of the Earth's crust as e.g. changes in static and dynamic stresses would otherwise be too small to trigger remote events. I present results for a regional (as opposed to global) study of seismicity in Iceland which is based on a high quality reprocessed catalogue. Results include the time-dependent determination of the maximum range of interaction and the correlation length and also address the question whether small events can trigger larger ones. Pitfalls such as data accuracy and geometry as well as boundary effects are thoroughly discussed. A comparison with surrogate data helps to assess the statistical significance of the results.

Attitude determination using an adaptive multiple model filtering Scheme

NASA Technical Reports Server (NTRS)

Lam, Quang; Ray, Surendra N.

1995-01-01

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

Attitude determination using an adaptive multiple model filtering Scheme

NASA Astrophysics Data System (ADS)

Lam, Quang; Ray, Surendra N.

1995-05-01

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

Thermostatistically approaching living systems: Boltzmann Gibbs or nonextensive statistical mechanics?

NASA Astrophysics Data System (ADS)

Tsallis, Constantino

2006-03-01

Boltzmann-Gibbs ( BG) statistical mechanics is, since well over one century, successfully used for many nonlinear dynamical systems which, in one way or another, exhibit strong chaos. A typical case is a classical many-body short-range-interacting Hamiltonian system (e.g., the Lennard-Jones model for a real gas at moderately high temperature). Its Lyapunov spectrum (which characterizes the sensitivity to initial conditions) includes positive values. This leads to ergodicity, the stationary state being thermal equilibrium, hence standard applicability of the BG theory is verified. The situation appears to be of a different nature for various phenomena occurring in living organisms. Indeed, such systems exhibit a complexity which does not really accommodate with this standard dynamical behavior. Life appears to emerge and evolve in a kind of delicate situation, at the frontier between large order (low adaptability and long memory; typically characterized by regular dynamics, hence only nonpositive Lyapunov exponents) and large disorder (high adaptability and short memory; typically characterized by strong chaos, hence at least one positive Lyapunov exponent). Along this frontier, the maximal relevant Lyapunov exponents are either zero or close to that, characterizing what is currently referred to as weak chaos. This type of situation is shared by a great variety of similar complex phenomena in economics, linguistics, to cite but a few. BG statistical mechanics is built upon the entropy S=-k∑plnp. A generalization of this form, S=k(1-∑piq)/(q-1) (with S=S), has been proposed in 1988 as a basis for formulating what is nowadays currently called nonextensive statistical mechanics. This theory appears to be particularly adapted for nonlinear dynamical systems exhibiting, precisely, weak chaos. Here, we briefly review the theory, its dynamical foundation, its applications in a variety of disciplines (with special emphasis to living systems), and its connections with the ubiquitous scale-free networks.

Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

PubMed

Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

2016-01-01

This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

How Stuttering Develops: The Multifactorial Dynamic Pathways Theory

PubMed Central

Weber, Christine

2017-01-01

Purpose We advanced a multifactorial, dynamic account of the complex, nonlinear interactions of motor, linguistic, and emotional factors contributing to the development of stuttering. Our purpose here is to update our account as the multifactorial dynamic pathways theory. Method We review evidence related to how stuttering develops, including genetic/epigenetic factors; motor, linguistic, and emotional features; and advances in neuroimaging studies. We update evidence for our earlier claim: Although stuttering ultimately reflects impairment in speech sensorimotor processes, its course over the life span is strongly conditioned by linguistic and emotional factors. Results Our current account places primary emphasis on the dynamic developmental context in which stuttering emerges and follows its course during the preschool years. Rapid changes in many neurobehavioral systems are ongoing, and critical interactions among these systems likely play a major role in determining persistence of or recovery from stuttering. Conclusion Stuttering, or childhood onset fluency disorder (Diagnostic and Statistical Manual of Mental Disorders, 5th edition; American Psychiatric Association [APA], 2013), is a neurodevelopmental disorder that begins when neural networks supporting speech, language, and emotional functions are rapidly developing. The multifactorial dynamic pathways theory motivates experimental and clinical work to determine the specific factors that contribute to each child's pathway to the diagnosis of stuttering and those most likely to promote recovery. PMID:28837728

How Stuttering Develops: The Multifactorial Dynamic Pathways Theory.

PubMed

Smith, Anne; Weber, Christine

2017-09-18

We advanced a multifactorial, dynamic account of the complex, nonlinear interactions of motor, linguistic, and emotional factors contributing to the development of stuttering. Our purpose here is to update our account as the multifactorial dynamic pathways theory. We review evidence related to how stuttering develops, including genetic/epigenetic factors; motor, linguistic, and emotional features; and advances in neuroimaging studies. We update evidence for our earlier claim: Although stuttering ultimately reflects impairment in speech sensorimotor processes, its course over the life span is strongly conditioned by linguistic and emotional factors. Our current account places primary emphasis on the dynamic developmental context in which stuttering emerges and follows its course during the preschool years. Rapid changes in many neurobehavioral systems are ongoing, and critical interactions among these systems likely play a major role in determining persistence of or recovery from stuttering. Stuttering, or childhood onset fluency disorder (Diagnostic and Statistical Manual of Mental Disorders, 5th edition; American Psychiatric Association [APA], 2013), is a neurodevelopmental disorder that begins when neural networks supporting speech, language, and emotional functions are rapidly developing. The multifactorial dynamic pathways theory motivates experimental and clinical work to determine the specific factors that contribute to each child's pathway to the diagnosis of stuttering and those most likely to promote recovery.

Predicting Physical Interactions between Protein Complexes*

PubMed Central

Clancy, Trevor; Rødland, Einar Andreas; Nygard, Ståle; Hovig, Eivind

2013-01-01

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships. PMID:23438732

Molecular modelling of protein-protein/protein-solvent interactions

NASA Astrophysics Data System (ADS)

Luchko, Tyler

The inner workings of individual cells are based on intricate networks of protein-protein interactions. However, each of these individual protein interactions requires a complex physical interaction between proteins and their aqueous environment at the atomic scale. In this thesis, molecular dynamics simulations are used in three theoretical studies to gain insight at the atomic scale about protein hydration, protein structure and tubulin-tubulin (protein-protein) interactions, as found in microtubules. Also presented, in a fourth project, is a molecular model of solvation coupled with the Amber molecular modelling package, to facilitate further studies without the need of explicitly modelled water. Basic properties of a minimally solvated protein were calculated through an extended study of myoglobin hydration with explicit solvent, directly investigating water and protein polarization. Results indicate a close correlation between polarization of both water and protein and the onset of protein function. The methodology of explicit solvent molecular dynamics was further used to study tubulin and microtubules. Extensive conformational sampling of the carboxy-terminal tails of 8-tubulin was performed via replica exchange molecular dynamics, allowing the characterisation of the flexibility, secondary structure and binding domains of the C-terminal tails through statistical analysis methods. Mechanical properties of tubulin and microtubules were calculated with adaptive biasing force molecular dynamics. The function of the M-loop in microtubule stability was demonstrated in these simulations. The flexibility of this loop allowed constant contacts between the protofilaments to be maintained during simulations while the smooth deformation provided a spring-like restoring force. Additionally, calculating the free energy profile between the straight and bent tubulin configurations was used to test the proposed conformational change in tubulin, thought to cause microtubule destabilization. No conformational change was observed but a nucleotide dependent 'softening' of the interaction was found instead, suggesting that an entropic force in a microtubule configuration could be the mechanism of microtubule collapse. Finally, to overcome much of the computational costs associated with explicit soIvent calculations, a new combination of molecular dynamics with the 3D-reference interaction site model (3D-RISM) of solvation was integrated into the Amber molecular dynamics package. Our implementation of 3D-RISM shows excellent agreement with explicit solvent free energy calculations. Several optimisation techniques, including a new multiple time step method, provide a nearly 100 fold performance increase, giving similar computational performance to explicit solvent.

Dynamics and Emergent Structures in Active Fluids

NASA Astrophysics Data System (ADS)

Baskaran, Aparna

2014-03-01

In this talk, we consider an active fluid of colloidal sized particles, with the primary manifestation of activity being a self-replenishing velocity along one body axis of the particle. This is a minimal model for varied systems such as bacterial colonies, cytoskeletal filament motility assays vibrated granular particles and self propelled diffusophoretic colloids, depending on the nature of interaction among the particles. Using microscopic Brownian dynamics simulations, coarse-graining using the tools of non-equilibrium statistical mechanics and analysis of macroscopic hydrodynamic theories, we characterize emergent structures seen in these systems, which are determined by the symmetry of the interactions among the active units, such as propagating density waves, dense stationary bands, asters and phase separated isotropic clusters. We identify a universal mechanism, termed ``self-regulation,'' as the underlying physics that leads to these structures in diverse systems. Support from NSF through DMR-1149266 and DMR-0820492.

Complexity multiscale asynchrony measure and behavior for interacting financial dynamics

NASA Astrophysics Data System (ADS)

Yang, Ge; Wang, Jun; Niu, Hongli

2016-08-01

A stochastic financial price process is proposed and investigated by the finite-range multitype contact dynamical system, in an attempt to study the nonlinear behaviors of real asset markets. The viruses spreading process in a finite-range multitype system is used to imitate the interacting behaviors of diverse investment attitudes in a financial market, and the empirical research on descriptive statistics and autocorrelation behaviors of return time series is performed for different values of propagation rates. Then the multiscale entropy analysis is adopted to study several different shuffled return series, including the original return series, the corresponding reversal series, the random shuffled series, the volatility shuffled series and the Zipf-type shuffled series. Furthermore, we propose and compare the multiscale cross-sample entropy and its modification algorithm called composite multiscale cross-sample entropy. We apply them to study the asynchrony of pairs of time series under different time scales.

Hydrologic drivers and controls of stream biofilm-grazer interactions

NASA Astrophysics Data System (ADS)

Ceola, S.; Bertuzzo, E.; Mari, L.; Botter, G.; Hödl, I.; Battin, T.; Rinaldo, A.

2012-04-01

Understanding the dynamics of fluvial ecosystems linked to hydrology is one of the most important challenges of ecohydrology. In fact, streamflow, which chiefly relies on rainfall, climate, land use and geomorphologic properties, plays a fundamental role in sustaining and regulating fluvial ecosystem integrity. To analyze possible implications of hydrological fluctuations on the biofilm-grazer interaction, an experimental campaign has been conducted between June and September 2011 at the Wasser Cluster Lunz, in Lunz am See (AU). 36 flumes have been used to perform biofilm growth and grazing activity under two distinct discharge conditions (i.e., constant and stochastic discharge regimes) and four different light regimes (from natural light conditions to nearly 70% attenuation). Experimental results concerning (i) dynamics of biofilm growth, (ii) grazing effect, and (iii) grazing rate will be presented. Results of performed statistical analysis for testing the effects of discharge treatment and light regime on the grazing rate will be also discussed.

Chimeras and clusters in networks of hyperbolic chaotic oscillators

NASA Astrophysics Data System (ADS)

Cano, A. V.; Cosenza, M. G.

2017-03-01

We show that chimera states, where differentiated subsets of synchronized and desynchronized dynamical elements coexist, can emerge in networks of hyperbolic chaotic oscillators subject to global interactions. As local dynamics we employ Lozi maps, which possess hyperbolic chaotic attractors. We consider a globally coupled system of these maps and use two statistical quantities to describe its collective behavior: the average fraction of elements belonging to clusters and the average standard deviation of state variables. Chimera states, clusters, complete synchronization, and incoherence are thus characterized on the space of parameters of the system. We find that chimera states are related to the formation of clusters in the system. In addition, we show that chimera states arise for a sufficiently long range of interactions in nonlocally coupled networks of these maps. Our results reveal that, under some circumstances, hyperbolicity does not impede the formation of chimera states in networks of coupled chaotic systems, as it had been previously hypothesized.

A consensus-based dynamics for market volumes

NASA Astrophysics Data System (ADS)

Sabatelli, Lorenzo; Richmond, Peter

2004-12-01

We develop a model of trading orders based on opinion dynamics. The agents may be thought as the share holders of a major mutual fund rather than as direct traders. The balance between their buy and sell orders determines the size of the fund order (volume) and has an impact on prices and indexes. We assume agents interact simultaneously to each other through a Sznajd-like interaction. Their degree of connection is determined by the probability of changing opinion independently of what their neighbours are doing. We assume that such a probability may change randomly, after each transaction, of an amount proportional to the relative difference between the volatility then measured and a benchmark that we assume to be an exponential moving average of the past volume values. We show how this simple model is compatible with some of the main statistical features observed for the asset volumes in financial markets.

Modeling Endoplasmic Reticulum Network Maintenance in a Plant Cell.

PubMed

Lin, Congping; White, Rhiannon R; Sparkes, Imogen; Ashwin, Peter

2017-07-11

The endoplasmic reticulum (ER) in plant cells forms a highly dynamic network of complex geometry. ER network morphology and dynamics are influenced by a number of biophysical processes, including filament/tubule tension, viscous forces, Brownian diffusion, and interactions with many other organelles and cytoskeletal elements. Previous studies have indicated that ER networks can be thought of as constrained minimal-length networks acted on by a variety of forces that perturb and/or remodel the network. Here, we study two specific biophysical processes involved in remodeling. One is the dynamic relaxation process involving a combination of tubule tension and viscous forces. The other is the rapid creation of cross-connection tubules by direct or indirect interactions with cytoskeletal elements. These processes are able to remodel the ER network: the first reduces network length and complexity whereas the second increases both. Using live cell imaging of ER network dynamics in tobacco leaf epidermal cells, we examine these processes on ER network dynamics. Away from regions of cytoplasmic streaming, we suggest that the dynamic network structure is a balance between the two processes, and we build an integrative model of the two processes for network remodeling. This model produces quantitatively similar ER networks to those observed in experiments. We use the model to explore the effect of parameter variation on statistical properties of the ER network. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Oscillatory dynamics of investment and capacity utilization

NASA Astrophysics Data System (ADS)

Greenblatt, R. E.

2017-01-01

Capitalist economic systems display a wide variety of oscillatory phenomena whose underlying causes are often not well understood. In this paper, I consider a very simple model of the reciprocal interaction between investment, capacity utilization, and their time derivatives. The model, which gives rise periodic oscillations, predicts qualitatively the phase relations between these variables. These predictions are observed to be consistent in a statistical sense with econometric data from the US economy.

An evidence-based systematic review of elderberry and elderflower (Sambucus nigra) by the Natural Standard Research Collaboration.

PubMed

Ulbricht, Catherine; Basch, Ethan; Cheung, Lisa; Goldberg, Harley; Hammerness, Paul; Isaac, Richard; Khalsa, Karta Purkh Singh; Romm, Aviva; Rychlik, Idalia; Varghese, Minney; Weissner, Wendy; Windsor, Regina C; Wortley, Jayme

2014-03-01

An evidence-based systematic review of elderberry and elderflower (Sambucus nigra) by the Natural Standard Research Collaboration consolidates the safety and efficacy data available in the scientific literature using a validated, reproducible grading rationale. This article includes written and statistical analysis of clinical trials, plus a compilation of expert opinion, folkloric precedent, history, pharmacology, kinetics/dynamics, interactions, adverse effects, toxicology, and dosing.

Characterizing interactions in online social networks during exceptional events

NASA Astrophysics Data System (ADS)

Omodei, Elisa; De Domenico, Manlio; Arenas, Alex

2015-08-01

Nowadays, millions of people interact on a daily basis on online social media like Facebook and Twitter, where they share and discuss information about a wide variety of topics. In this paper, we focus on a specific online social network, Twitter, and we analyze multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered. We consider important events that occurred in different arenas that range from policy to culture or science. For each dataset, the users' online activities are modeled by a multilayer network in which each layer conveys a different kind of interaction, specifically: retweeting, mentioning and replying. This representation allows us to unveil that these distinct types of interaction produce networks with different statistical properties, in particular concerning the degree distribution and the clustering structure. These results suggests that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions. Secondly, our analysis unveils the presence of statistical regularities among the different events, suggesting that the non-trivial topological patterns that we observe may represent universal features of the social dynamics on online social networks during exceptional events.

Stability estimation of autoregulated genes under Michaelis-Menten-type kinetics

NASA Astrophysics Data System (ADS)

Arani, Babak M. S.; Mahmoudi, Mahdi; Lahti, Leo; González, Javier; Wit, Ernst C.

2018-06-01

Feedback loops are typical motifs appearing in gene regulatory networks. In some well-studied model organisms, including Escherichia coli, autoregulated genes, i.e., genes that activate or repress themselves through their protein products, are the only feedback interactions. For these types of interactions, the Michaelis-Menten (MM) formulation is a suitable and widely used approach, which always leads to stable steady-state solutions representative of homeostatic regulation. However, in many other biological phenomena, such as cell differentiation, cancer progression, and catastrophes in ecosystems, one might expect to observe bistable switchlike dynamics in the case of strong positive autoregulation. To capture this complex behavior we use the generalized family of MM kinetic models. We give a full analysis regarding the stability of autoregulated genes. We show that the autoregulation mechanism has the capability to exhibit diverse cellular dynamics including hysteresis, a typical characteristic of bistable systems, as well as irreversible transitions between bistable states. We also introduce a statistical framework to estimate the kinetics parameters and probability of different stability regimes given observational data. Empirical data for the autoregulated gene SCO3217 in the SOS system in Streptomyces coelicolor are analyzed. The coupling of a statistical framework and the mathematical model can give further insight into understanding the evolutionary mechanisms toward different cell fates in various systems.

WASP (Write a Scientific Paper) using Excel - 2: Pivot tables.

PubMed

Grech, Victor

2018-02-01

Data analysis at the descriptive stage and the eventual presentation of results requires the tabulation and summarisation of data. This exercise should always precede inferential statistics. Pivot tables and pivot charts are one of Excel's most powerful and underutilised features, with tabulation functions that immensely facilitate descriptive statistics. Pivot tables permit users to dynamically summarise and cross-tabulate data, create tables in several dimensions, offer a range of summary statistics and can be modified interactively with instant outputs. Large and detailed datasets are thereby easily manipulated making pivot tables arguably the best way to explore, summarise and present data from many different angles. This second paper in the WASP series in Early Human Development provides pointers for pivot table manipulation in Excel™. Copyright © 2018 Elsevier B.V. All rights reserved.

Active Brownian Particles. From Individual to Collective Stochastic Dynamics

NASA Astrophysics Data System (ADS)

Romanczuk, P.; Bär, M.; Ebeling, W.; Lindner, B.; Schimansky-Geier, L.

2012-03-01

We review theoretical models of individual motility as well as collective dynamics and pattern formation of active particles. We focus on simple models of active dynamics with a particular emphasis on nonlinear and stochastic dynamics of such self-propelled entities in the framework of statistical mechanics. Examples of such active units in complex physico-chemical and biological systems are chemically powered nano-rods, localized patterns in reaction-diffusion system, motile cells or macroscopic animals. Based on the description of individual motion of point-like active particles by stochastic differential equations, we discuss different velocity-dependent friction functions, the impact of various types of fluctuations and calculate characteristic observables such as stationary velocity distributions or diffusion coefficients. Finally, we consider not only the free and confined individual active dynamics but also different types of interaction between active particles. The resulting collective dynamical behavior of large assemblies and aggregates of active units is discussed and an overview over some recent results on spatiotemporal pattern formation in such systems is given.

Addressing the statistical mechanics of planet orbits in the solar system

NASA Astrophysics Data System (ADS)

Mogavero, Federico

2017-10-01

The chaotic nature of planet dynamics in the solar system suggests the relevance of a statistical approach to planetary orbits. In such a statistical description, the time-dependent position and velocity of the planets are replaced by the probability density function (PDF) of their orbital elements. It is natural to set up this kind of approach in the framework of statistical mechanics. In the present paper, I focus on the collisionless excitation of eccentricities and inclinations via gravitational interactions in a planetary system. The future planet trajectories in the solar system constitute the prototype of this kind of dynamics. I thus address the statistical mechanics of the solar system planet orbits and try to reproduce the PDFs numerically constructed by Laskar (2008, Icarus, 196, 1). I show that the microcanonical ensemble of the Laplace-Lagrange theory accurately reproduces the statistics of the giant planet orbits. To model the inner planets I then investigate the ansatz of equiprobability in the phase space constrained by the secular integrals of motion. The eccentricity and inclination PDFs of Earth and Venus are reproduced with no free parameters. Within the limitations of a stationary model, the predictions also show a reasonable agreement with Mars PDFs and that of Mercury inclination. The eccentricity of Mercury demands in contrast a deeper analysis. I finally revisit the random walk approach of Laskar to the time dependence of the inner planet PDFs. Such a statistical theory could be combined with direct numerical simulations of planet trajectories in the context of planet formation, which is likely to be a chaotic process.

Simulating the Interactions Among Land Use, Transportation ...

EPA Pesticide Factsheets

In most transportation studies, computer models that forecast travel behavior statistics for a future year use static projections of the spatial distribution of future population and employment growth as inputs. As a result, they are unable to account for the temporally dynamic and non-linear interactions among transportation, land use, and socioeconomic systems. System dynamics (SD) provides a common framework for modeling the complex interactions among transportation and other related systems. This study uses a SD model to simulate the cascading impacts of a proposed light rail transit (LRT) system in central North Carolina, USA. The Durham-Orange Light Rail Project (D-O LRP) SD model incorporates relationships among the land use, transportation, and economy sectors to simulate the complex feedbacks that give rise to the travel behavior changes forecasted by the region’s transportation model. This paper demonstrates the sensitivity of changes in travel behavior to the proposed LRT system and the assumptions that went into the transportation modeling, and compares those results to the impacts of an alternative fare-free transit system. SD models such as the D-O LRP SD model can complement transportation studies by providing valuable insight into the interdependent community systems that collectively contribute to travel behavior changes. Presented at the 35th International Conference of the System Dynamics Society in Cambridge, MA, July 18th, 2017

Earthquake nucleation in a stochastic fault model of globally coupled units with interaction delays

NASA Astrophysics Data System (ADS)

Vasović, Nebojša; Kostić, Srđan; Franović, Igor; Todorović, Kristina

2016-09-01

In present paper we analyze dynamics of fault motion by considering delayed interaction of 100 all-to-all coupled blocks with rate-dependent friction law in presence of random seismic noise. Such a model sufficiently well describes a real fault motion, whose prevailing stochastic nature is implied by surrogate data analysis of available GPS measurements of active fault movement. Interaction of blocks in an analyzed model is studied as a function of time delay, observed both for dynamics of individual faults and phenomenological models. Analyzed model is examined as a system of all-to-all coupled blocks according to typical assumption of compound faults as complex of globally coupled segments. We apply numerical methods to show that there are local bifurcations from equilibrium state to periodic oscillations, with an occurrence of irregular aperiodic behavior when initial conditions are set away from the equilibrium point. Such a behavior indicates a possible existence of a bi-stable dynamical regime, due to effect of the introduced seismic noise or the existence of global attractor. The latter assumption is additionally confirmed by analyzing the corresponding mean-field approximated model. In this bi-stable regime, distribution of event magnitudes follows Gutenberg-Richter power law with satisfying statistical accuracy, including the b-value within the real observed range.

Classical Electrodynamics: Problems with solutions; Problems with solutions

NASA Astrophysics Data System (ADS)

Likharev, Konstantin K.

2018-06-01

l Advanced Physics is a series comprising four parts: Classical Mechanics, Classical Electrodynamics, Quantum Mechanics and Statistical Mechanics. Each part consists of two volumes, Lecture notes and Problems with solutions, further supplemented by an additional collection of test problems and solutions available to qualifying university instructors. This volume, Classical Electrodynamics: Lecture notes is intended to be the basis for a two-semester graduate-level course on electricity and magnetism, including not only the interaction and dynamics charged point particles, but also properties of dielectric, conducting, and magnetic media. The course also covers special relativity, including its kinematics and particle-dynamics aspects, and electromagnetic radiation by relativistic particles.

Nonequilibrium localization and the interplay between disorder and interactions.

PubMed

Mascarenhas, Eduardo; Bragança, Helena; Drumond, R; Aguiar, M C O; França Santos, M

2016-05-18

We study the nonequilibrium interplay between disorder and interactions in a closed quantum system. We base our analysis on the notion of dynamical state-space localization, calculated via the Loschmidt echo. Although real-space and state-space localization are independent concepts in general, we show that both perspectives may be directly connected through a specific choice of initial states, namely, maximally localized states (ML-states). We show numerically that in the noninteracting case the average echo is found to be monotonically increasing with increasing disorder; these results are in agreement with an analytical evaluation in the single particle case in which the echo is found to be inversely proportional to the localization length. We also show that for interacting systems, the length scale under which equilibration may occur is upper bounded and such bound is smaller the greater the average echo of ML-states. When disorder and interactions, both being localization mechanisms, are simultaneously at play the echo features a non-monotonic behaviour indicating a non-trivial interplay of the two processes. This interplay induces delocalization of the dynamics which is accompanied by delocalization in real-space. This non-monotonic behaviour is also present in the effective integrability which we show by evaluating the gap statistics.

How Structured Is the Entangled Bank? The Surprisingly Simple Organization of Multiplex Ecological Networks Leads to Increased Persistence and Resilience

PubMed Central

Wieters, Evie A.; Navarrete, Sergio A.

2016-01-01

Species are linked to each other by a myriad of positive and negative interactions. This complex spectrum of interactions constitutes a network of links that mediates ecological communities’ response to perturbations, such as exploitation and climate change. In the last decades, there have been great advances in the study of intricate ecological networks. We have, nonetheless, lacked both the data and the tools to more rigorously understand the patterning of multiple interaction types between species (i.e., “multiplex networks”), as well as their consequences for community dynamics. Using network statistical modeling applied to a comprehensive ecological network, which includes trophic and diverse non-trophic links, we provide a first glimpse at what the full “entangled bank” of species looks like. The community exhibits clear multidimensional structure, which is taxonomically coherent and broadly predictable from species traits. Moreover, dynamic simulations suggest that this non-random patterning of how diverse non-trophic interactions map onto the food web could allow for higher species persistence and higher total biomass than expected by chance and tends to promote a higher robustness to extinctions. PMID:27487303

Potential for the dynamics of pedestrians in a socially interacting group

NASA Astrophysics Data System (ADS)

Zanlungo, Francesco; Ikeda, Tetsushi; Kanda, Takayuki

2014-01-01

We introduce a simple potential to describe the dynamics of the relative motion of two pedestrians socially interacting in a walking group. We show that the proposed potential, based on basic empirical observations and theoretical considerations, can qualitatively describe the statistical properties of pedestrian behavior. In detail, we show that the two-dimensional probability distribution of the relative distance is determined by the proposed potential through a Boltzmann distribution. After calibrating the parameters of the model on the two-pedestrian group data, we apply the model to three-pedestrian groups, showing that it describes qualitatively and quantitatively well their behavior. In particular, the model predicts that three-pedestrian groups walk in a V-shaped formation and provides accurate values for the position of the three pedestrians. Furthermore, the model correctly predicts the average walking velocity of three-person groups based on the velocity of two-person ones. Possible extensions to larger groups, along with alternative explanations of the social dynamics that may be implied by our model, are discussed at the end of the paper.

Temporal switching of homo-FRET pathways in single-chromophore dimer models of π-conjugated polymers.

PubMed

Stangl, Thomas; Bange, Sebastian; Schmitz, Daniela; Würsch, Dominik; Höger, Sigurd; Vogelsang, Jan; Lupton, John M

2013-01-09

A set of π-conjugated oligomer dimers templated in molecular scaffolds is presented as a model system for studying the interactions between chromophores in conjugated polymers (CPs). Single-molecule spectroscopy was used to reveal energy transfer dynamics between two oligomers in either a parallel or oblique-angle geometry. In particular, the conformation of single molecules embedded in a host matrix was investigated via polarized excitation and emission fluorescence microscopy in combination with fluorescence correlation spectroscopy. While the intramolecular interchromophore conformation was found to have no impact on the fluorescence quantum yield, lifetime, or photon statistics (antibunching), the long-term nonequilibrium dynamics of energy transfer within these bichromophoric systems was accessible by studying the linear dichroism in emission at the single-molecule level, which revealed reversible switching of the emission between the two oligomers. In bulk polymer films, interchromophore coupling promotes the migration of excitation energy to quenching sites. Realizing the presence and dynamics of such interactions is crucial for understanding limitations on the quantum efficiency of larger CP materials.

Observing Consistency in Online Communication Patterns for User Re-Identification.

PubMed

Adeyemi, Ikuesan Richard; Razak, Shukor Abd; Salleh, Mazleena; Venter, Hein S

2016-01-01

Comprehension of the statistical and structural mechanisms governing human dynamics in online interaction plays a pivotal role in online user identification, online profile development, and recommender systems. However, building a characteristic model of human dynamics on the Internet involves a complete analysis of the variations in human activity patterns, which is a complex process. This complexity is inherent in human dynamics and has not been extensively studied to reveal the structural composition of human behavior. A typical method of anatomizing such a complex system is viewing all independent interconnectivity that constitutes the complexity. An examination of the various dimensions of human communication pattern in online interactions is presented in this paper. The study employed reliable server-side web data from 31 known users to explore characteristics of human-driven communications. Various machine-learning techniques were explored. The results revealed that each individual exhibited a relatively consistent, unique behavioral signature and that the logistic regression model and model tree can be used to accurately distinguish online users. These results are applicable to one-to-one online user identification processes, insider misuse investigation processes, and online profiling in various areas.

Coupled disease-behavior dynamics on complex networks: A review.

PubMed

Wang, Zhen; Andrews, Michael A; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T

2015-12-01

It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years. Copyright © 2015 Elsevier B.V. All rights reserved.

A Diagnostics Tool to detect ensemble forecast system anomaly and guide operational decisions

NASA Astrophysics Data System (ADS)

Park, G. H.; Srivastava, A.; Shrestha, E.; Thiemann, M.; Day, G. N.; Draijer, S.

2017-12-01

The hydrologic community is moving toward using ensemble forecasts to take uncertainty into account during the decision-making process. The New York City Department of Environmental Protection (DEP) implements several types of ensemble forecasts in their decision-making process: ensemble products for a statistical model (Hirsch and enhanced Hirsch); the National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) forecasts based on the classical Ensemble Streamflow Prediction (ESP) technique; and the new NWS Hydrologic Ensemble Forecasting Service (HEFS) forecasts. To remove structural error and apply the forecasts to additional forecast points, the DEP post processes both the AHPS and the HEFS forecasts. These ensemble forecasts provide mass quantities of complex data, and drawing conclusions from these forecasts is time-consuming and difficult. The complexity of these forecasts also makes it difficult to identify system failures resulting from poor data, missing forecasts, and server breakdowns. To address these issues, we developed a diagnostic tool that summarizes ensemble forecasts and provides additional information such as historical forecast statistics, forecast skill, and model forcing statistics. This additional information highlights the key information that enables operators to evaluate the forecast in real-time, dynamically interact with the data, and review additional statistics, if needed, to make better decisions. We used Bokeh, a Python interactive visualization library, and a multi-database management system to create this interactive tool. This tool compiles and stores data into HTML pages that allows operators to readily analyze the data with built-in user interaction features. This paper will present a brief description of the ensemble forecasts, forecast verification results, and the intended applications for the diagnostic tool.

Visualizing statistical significance of disease clusters using cartograms.

PubMed

Kronenfeld, Barry J; Wong, David W S

2017-05-15

Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological mapping. However, we do not have existing guidelines for visual assessment of statistical uncertainty. To address this shortcoming, we develop techniques for visual determination of statistical significance of clusters spanning one or more districts on a cartogram. We developed the techniques within a geovisual analytics framework that does not rely on automated significance testing, and can therefore facilitate visual analysis to detect clusters that automated techniques might miss. On a cartogram of the at-risk population, the statistical significance of a disease cluster is determinate from the rate, area and shape of the cluster under standard hypothesis testing scenarios. We develop formulae to determine, for a given rate, the area required for statistical significance of a priori and a posteriori designated regions under certain test assumptions. Uniquely, our approach enables dynamic inference of aggregate regions formed by combining individual districts. The method is implemented in interactive tools that provide choropleth mapping, automated legend construction and dynamic search tools to facilitate cluster detection and assessment of the validity of tested assumptions. A case study of leukemia incidence analysis in California demonstrates the ability to visually distinguish between statistically significant and insignificant regions. The proposed geovisual analytics approach enables intuitive visual assessment of statistical significance of arbitrarily defined regions on a cartogram. Our research prompts a broader discussion of the role of geovisual exploratory analyses in disease mapping and the appropriate framework for visually assessing the statistical significance of spatial clusters.

Effects of the finite particle size in turbulent wall-bounded flows of dense suspensions

NASA Astrophysics Data System (ADS)

Costa, Pedro; Picano, Francesco; Brandt, Luca; Breugem, Wim-Paul

2018-05-01

We use interface-resolved simulations to study finite-size effects in turbulent channel flow of neutrally-buoyant spheres. Two cases with particle sizes differing by a factor of 2, at the same solid volume fraction of 20% and bulk Reynolds number are considered. These are complemented with two reference single-phase flows: the unladen case, and the flow of a Newtonian fluid with the effective suspension viscosity of the same mixture in the laminar regime. As recently highlighted in Costa et al. (PRL 117, 134501), a particle-wall layer is responsible for deviations of the statistics from what is observed in the continuum limit where the suspension is modeled as a Newtonian fluid with an effective viscosity. Here we investigate the fluid and particle dynamics in this layer and in the bulk. In the particle-wall layer, the near wall inhomogeneity has an influence on the suspension micro-structure over a distance proportional to the particle size. In this layer, particles have a significant (apparent) slip velocity that is reflected in the distribution of wall shear stresses. This is characterized by extreme events (both much higher and much lower than the mean). Based on these observations we provide a scaling for the particle-to-fluid apparent slip velocity as a function of the flow parameters. We also extend the flow scaling laws in to second-order Eulerian statistics in the homogeneous suspension region away from the wall. Finite-size effects in the bulk of the channel become important for larger particles, while negligible for lower-order statistics and smaller particles. Finally, we study the particle dynamics along the wall-normal direction. Our results suggest that 1-point dispersion is dominated by particle-turbulence (and not particle-particle) interactions, while differences in 2-point dispersion and collisional dynamics are consistent with a picture of shear-driven interactions.

Cross-immunity between strains explains the dynamical pattern of paramyxoviruses

PubMed Central

Bhattacharyya, Samit; Gesteland, Per H.; Korgenski, Kent; Bjørnstad, Ottar N.; Adler, Frederick R.

2015-01-01

Viral respiratory tract diseases pose serious public health problems. Our ability to predict and thus, be able to prepare for outbreaks is strained by the complex factors driving the prevalence and severity of these diseases. The abundance of diseases and transmission dynamics of strains are not only affected by external factors, such as weather, but also driven by interactions among viruses mediated by human behavior and immunity. To untangle the complex out-of-phase annual and biennial pattern of three common paramyxoviruses, Respiratory Syncytial Virus (RSV), Human Parainfluenza Virus (HPIV), and Human Metapneumovirus (hMPV), we adopt a theoretical approach that integrates ecological and immunological mechanisms of disease interactions. By estimating parameters from multiyear time series of laboratory-confirmed cases from the intermountain west region of the United States and using statistical inference, we show that models of immune-mediated interactions better explain the data than those based on ecological competition by convalescence. The strength of cross-protective immunity among viruses is correlated with their genetic distance in the phylogenetic tree of the paramyxovirus family. PMID:26460003

Towards a Web-Enabled Geovisualization and Analytics Platform for the Energy and Water Nexus

NASA Astrophysics Data System (ADS)

Sanyal, J.; Chandola, V.; Sorokine, A.; Allen, M.; Berres, A.; Pang, H.; Karthik, R.; Nugent, P.; McManamay, R.; Stewart, R.; Bhaduri, B. L.

2017-12-01

Interactive data analytics are playing an increasingly vital role in the generation of new, critical insights regarding the complex dynamics of the energy/water nexus (EWN) and its interactions with climate variability and change. Integration of impacts, adaptation, and vulnerability (IAV) science with emerging, and increasingly critical, data science capabilities offers a promising potential to meet the needs of the EWN community. To enable the exploration of pertinent research questions, a web-based geospatial visualization platform is being built that integrates a data analysis toolbox with advanced data fusion and data visualization capabilities to create a knowledge discovery framework for the EWN. The system, when fully built out, will offer several geospatial visualization capabilities including statistical visual analytics, clustering, principal-component analysis, dynamic time warping, support uncertainty visualization and the exploration of data provenance, as well as support machine learning discoveries to render diverse types of geospatial data and facilitate interactive analysis. Key components in the system architecture includes NASA's WebWorldWind, the Globus toolkit, postgresql, as well as other custom built software modules.

Weakly Nonergodic Dynamics in the Gross-Pitaevskii Lattice

NASA Astrophysics Data System (ADS)

Mithun, Thudiyangal; Kati, Yagmur; Danieli, Carlo; Flach, Sergej

2018-05-01

The microcanonical Gross-Pitaevskii (also known as the semiclassical Bose-Hubbard) lattice model dynamics is characterized by a pair of energy and norm densities. The grand canonical Gibbs distribution fails to describe a part of the density space, due to the boundedness of its kinetic energy spectrum. We define Poincaré equilibrium manifolds and compute the statistics of microcanonical excursion times off them. The tails of the distribution functions quantify the proximity of the many-body dynamics to a weakly nonergodic phase, which occurs when the average excursion time is infinite. We find that a crossover to weakly nonergodic dynamics takes place inside the non-Gibbs phase, being unnoticed by the largest Lyapunov exponent. In the ergodic part of the non-Gibbs phase, the Gibbs distribution should be replaced by an unknown modified one. We relate our findings to the corresponding integrable limit, close to which the actions are interacting through a short range coupling network.

Numerical analysis for finite-range multitype stochastic contact financial market dynamic systems

NASA Astrophysics Data System (ADS)

Yang, Ge; Wang, Jun; Fang, Wen

2015-04-01

In an attempt to reproduce and study the dynamics of financial markets, a random agent-based financial price model is developed and investigated by the finite-range multitype contact dynamic system, in which the interaction and dispersal of different types of investment attitudes in a stock market are imitated by viruses spreading. With different parameters of birth rates and finite-range, the normalized return series are simulated by Monte Carlo simulation method and numerical studied by power-law distribution analysis and autocorrelation analysis. To better understand the nonlinear dynamics of the return series, a q-order autocorrelation function and a multi-autocorrelation function are also defined in this work. The comparisons of statistical behaviors of return series from the agent-based model and the daily historical market returns of Shanghai Composite Index and Shenzhen Component Index indicate that the proposed model is a reasonable qualitative explanation for the price formation process of stock market systems.

Radical chiral Floquet phases in a periodically driven Kitaev model and beyond

NASA Astrophysics Data System (ADS)

Po, Hoi Chun; Fidkowski, Lukasz; Vishwanath, Ashvin; Potter, Andrew C.

2017-12-01

We theoretically discover a family of nonequilibrium fractional topological phases in which time-periodic driving of a 2D system produces excitations with fractional statistics, and produces chiral quantum channels that propagate a quantized fractional number of qubits along the sample edge during each driving period. These phases share some common features with fractional quantum Hall states, but are sharply distinct dynamical phenomena. Unlike the integer-valued invariant characterizing the equilibrium quantum Hall conductance, these phases are characterized by a dynamical topological invariant that is a square root of a rational number, inspiring the label: radical chiral Floquet phases. We construct solvable models of driven and interacting spin systems with these properties, and identify an unusual bulk-boundary correspondence between the chiral edge dynamics and bulk "anyon time-crystal" order characterized by dynamical transmutation of electric-charge into magnetic-flux excitations in the bulk.

A novel energy conversion based method for velocity correction in molecular dynamics simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Hanhui; Collaborative Innovation Center of Advanced Aero-Engine, Hangzhou 310027; Liu, Ningning

2017-05-01

Molecular dynamics (MD) simulation has become an important tool for studying micro- or nano-scale dynamics and the statistical properties of fluids and solids. In MD simulations, there are mainly two approaches: equilibrium and non-equilibrium molecular dynamics (EMD and NEMD). In this paper, a new energy conversion based correction (ECBC) method for MD is developed. Unlike the traditional systematic correction based on macroscopic parameters, the ECBC method is developed strictly based on the physical interaction processes between the pair of molecules or atoms. The developed ECBC method can apply to EMD and NEMD directly. While using MD with this method, themore » difference between the EMD and NEMD is eliminated, and no macroscopic parameters such as external imposed potentials or coefficients are needed. With this method, many limits of using MD are lifted. The application scope of MD is greatly extended.« less

Single-Molecule Probing the Energy Landscape of Enzymatic Reaction and Non-Covalent Interactions

NASA Astrophysics Data System (ADS)

Lu, H. Peter; Hu, Dehong; Chen, Yu; Vorpagel, Erich R.

2002-03-01

We have applied single-molecule spectroscopy under physiological conditions to study the mechanisms and dynamics of T4 lysozyme enzymatic reactions, characterizing mode-specific protein conformational dynamics. Enzymatic reaction turnovers and the associated structure changes of individual protein molecules were observed simultaneously in real-time. The overall reaction rates were found to vary widely from molecule-to-molecule, and the initial non-specific binding of the enzyme to the substrate was seen to dominate this inhomogeneity. The reaction steps subsequent to the initial binding were found to have homogeneous rates. Molecular dynamics simulation has been applied to elucidate the mechanism and intermediate states of the single-molecule enzymatic reaction. Combining the analysis of single-molecule experimental trajectories, MD simulation trajectories, and statistical modeling, we have revealed the nature of multiple intermediate states involved in the active enzyme-substrate complex formation and the associated conformational change mechanism and dynamics.

Renormalization of Collective Modes in Large-Scale Neural Dynamics

NASA Astrophysics Data System (ADS)

Moirogiannis, Dimitrios; Piro, Oreste; Magnasco, Marcelo O.

2017-05-01

The bulk of studies of coupled oscillators use, as is appropriate in Physics, a global coupling constant controlling all individual interactions. However, because as the coupling is increased, the number of relevant degrees of freedom also increases, this setting conflates the strength of the coupling with the effective dimensionality of the resulting dynamics. We propose a coupling more appropriate to neural circuitry, where synaptic strengths are under biological, activity-dependent control and where the coupling strength and the dimensionality can be controlled separately. Here we study a set of N→ ∞ strongly- and nonsymmetrically-coupled, dissipative, powered, rotational dynamical systems, and derive the equations of motion of the reduced system for dimensions 2 and 4. Our setting highlights the statistical structure of the eigenvectors of the connectivity matrix as the fundamental determinant of collective behavior, inheriting from this structure symmetries and singularities absent from the original microscopic dynamics.

Molecular dynamics simulation of melting of 2D glassy monatomic system

NASA Astrophysics Data System (ADS)

Nhu Tranh, Duong Thi; Van Hoang, Vo; Thu Hanh, Tran Thi

2018-01-01

The melting of two-dimensional (2D) glassy monatomic systems is studied using the molecular dynamics simulation with Lennard-Jones-Gauss interaction potential. The temperature dependence of various structural and dynamical properties of the systems during heating is analyzed and discussed via the radial distribution functions, the coordination number distributions, the ring statistics, the mobility of atoms and their clustering. Atomic mechanism of melting is also analyzed via tendency to increase mobility and breaking clusters of atoms upon heating. We found that melting of a 2D glass does not follow any theory of the melting of 2D crystals proposed in the past. The melting exhibits a homogeneous nature, i.e. liquid-like atoms occur homogeneously throughout the system and melting proceeds further leading to the formation of an entire liquid phase. In addition, we found a defined transition temperature region in which structural and dynamical properties of systems strongly change with increasing temperature.

Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series

PubMed Central

Ferguson, Jake M; Ponciano, José M

2014-01-01

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series. PMID:24304946

A Nonlinear Interactions Approximation Model for Large-Eddy Simulation

NASA Astrophysics Data System (ADS)

Haliloglu, Mehmet U.; Akhavan, Rayhaneh

2003-11-01

A new approach to LES modelling is proposed based on direct approximation of the nonlinear terms \\overlineu_iuj in the filtered Navier-Stokes equations, instead of the subgrid-scale stress, τ_ij. The proposed model, which we call the Nonlinear Interactions Approximation (NIA) model, uses graded filters and deconvolution to parameterize the local interactions across the LES cutoff, and a Smagorinsky eddy viscosity term to parameterize the distant interactions. A dynamic procedure is used to determine the unknown eddy viscosity coefficient, rendering the model free of adjustable parameters. The proposed NIA model has been applied to LES of turbulent channel flows at Re_τ ≈ 210 and Re_τ ≈ 570. The results show good agreement with DNS not only for the mean and resolved second-order turbulence statistics but also for the full (resolved plus subgrid) Reynolds stress and turbulence intensities.

Far-from-Equilibrium Route to Superthermal Light in Bimodal Nanolasers

NASA Astrophysics Data System (ADS)

Marconi, Mathias; Javaloyes, Julien; Hamel, Philippe; Raineri, Fabrice; Levenson, Ariel; Yacomotti, Alejandro M.

2018-02-01

Microscale and nanoscale lasers inherently exhibit rich photon statistics due to complex light-matter interaction in a strong spontaneous emission noise background. It is well known that they may display superthermal fluctuations—photon superbunching—in specific situations due to either gain competition, leading to mode-switching instabilities, or carrier-carrier coupling in superradiant microcavities. Here we show a generic route to superbunching in bimodal nanolasers by preparing the system far from equilibrium through a parameter quench. We demonstrate, both theoretically and experimentally, that transient dynamics after a short-pump-pulse-induced quench leads to heavy-tailed superthermal statistics when projected onto the weak mode. We implement a simple experimental technique to access the probability density functions that further enables quantifying the distance from thermal equilibrium via the thermodynamic entropy. The universality of this mechanism relies on the far-from-equilibrium dynamical scenario, which can be mapped to a fast cooling process of a suspension of Brownian particles in a liquid. Our results open up new avenues to mold photon statistics in multimode optical systems and may constitute a test bed to investigate out-of-equilibrium thermodynamics using micro or nanocavity arrays.

The self-assembly of particles with isotropic interactions: Using DNA coated colloids to create designer nanomaterials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thompson, R. B.; Dion, S.; Konigslow, K. von

Self-consistent field theory equations are presented that are suitable for use as a coarse-grained model for DNA coated colloids, polymer-grafted nanoparticles and other systems with approximately isotropic interactions. The equations are generalized for arbitrary numbers of chemically distinct colloids. The advantages and limitations of such a coarse-grained approach for DNA coated colloids are discussed, as are similarities with block copolymer self-assembly. In particular, preliminary results for three species self-assembly are presented that parallel results from a two dimensional ABC triblock copolymer phase. The possibility of incorporating crystallization, dynamics, inverse statistical mechanics and multiscale modelling techniques are discussed.

Statistics and Dynamics of Aircraft Encounters of Turbulence over Greenland

DTIC Science & Technology

2009-08-01

America and Europe , and turbulence above Greenland is the fo- cus of this study. Turbulence derived from interactions with terrain and mountain waves can...Seasonal variations in the large- scale circulation (viz., storm tracks) will modify the frequency of occurrence of cyclones. Such variations coupled with...Greenland’s southern tip is from the southeast quadrant. The passage of extratropical cyclones to the south of the turbulent regions is one source of low

A Study of Particle Beam Spin Dynamics for High Precision Experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fiedler, Andrew J.

In the search for physics beyond the Standard Model, high precision experiments to measure fundamental properties of particles are an important frontier. One group of such measurements involves magnetic dipole moment (MDM) values as well as searching for an electric dipole moment (EDM), both of which could provide insights about how particles interact with their environment at the quantum level and if there are undiscovered new particles. For these types of high precision experiments, minimizing statistical uncertainties in the measurements plays a critical role. \\\\ \\indent This work leverages computer simulations to quantify the effects of statistical uncertainty for experimentsmore » investigating spin dynamics. In it, analysis of beam properties and lattice design effects on the polarization of the beam is performed. As a case study, the beam lines that will provide polarized muon beams to the Fermilab Muon \\emph{g}-2 experiment are analyzed to determine the effects of correlations between the phase space variables and the overall polarization of the muon beam.« less

Statistical against dynamical PLF fission as seen by the IMF-IMF correlation functions and comparisons with CoMD model

NASA Astrophysics Data System (ADS)

Pagano, E. V.; Acosta, L.; Auditore, L.; Cap, T.; Cardella, G.; Colonna, M.; De Filippo, E.; Geraci, E.; Gnoffo, B.; Lanzalone, G.; Maiolino, C.; Martorana, N.; Pagano, A.; Papa, M.; Piasecki, E.; Pirrone, S.; Politi, G.; Porto, F.; Quattrocchi, L.; Rizzo, F.; Russotto, P.; Trifiro’, A.; Trimarchi, M.; Siwek-Wilczynska, K.

2018-05-01

In nuclear reactions at Fermi energies two and multi particles intensity interferometry correlation methods are powerful tools in order to pin down the characteristic time scale of the emission processes. In this paper we summarize an improved application of the fragment-fragment correlation function in the specific physics case of heavy projectile-like (PLF) binary massive splitting in two fragments of intermediate mass(IMF). Results are shown for the reverse kinematics reaction 124 Sn+64 Ni at 35 AMeV that has been investigated by using the forward part of CHIMERA multi-detector. The analysis was performed as a function of the charge asymmetry of the observed couples of IMF. We show a coexistence of dynamical and statistical components as a function of the charge asymmetry. Transport CoMD simulations are compared with the data in order to pin down the timescale of the fragments production and the relevant ingredients of the in medium effective interaction used in the transport calculations.

Multidimensional Modeling of Coronal Rain Dynamics

NASA Astrophysics Data System (ADS)

Fang, X.; Xia, C.; Keppens, R.

2013-07-01

We present the first multidimensional, magnetohydrodynamic simulations that capture the initial formation and long-term sustainment of the enigmatic coronal rain phenomenon. We demonstrate how thermal instability can induce a spectacular display of in situ forming blob-like condensations which then start their intimate ballet on top of initially linear force-free arcades. Our magnetic arcades host a chromospheric, transition region, and coronal plasma. Following coronal rain dynamics for over 80 minutes of physical time, we collect enough statistics to quantify blob widths, lengths, velocity distributions, and other characteristics which directly match modern observational knowledge. Our virtual coronal rain displays the deformation of blobs into V-shaped features, interactions of blobs due to mostly pressure-mediated levitations, and gives the first views of blobs that evaporate in situ or are siphoned over the apex of the background arcade. Our simulations pave the way for systematic surveys of coronal rain showers in true multidimensional settings to connect parameterized heating prescriptions with rain statistics, ultimately allowing us to quantify the coronal heating input.

Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making

PubMed Central

Seamans, Jeremy K.; Durstewitz, Daniel

2011-01-01

A common theoretical view is that attractor-like properties of neuronal dynamics underlie cognitive processing. However, although often proposed theoretically, direct experimental support for the convergence of neural activity to stable population patterns as a signature of attracting states has been sparse so far, especially in higher cortical areas. Combining state space reconstruction theorems and statistical learning techniques, we were able to resolve details of anterior cingulate cortex (ACC) multiple single-unit activity (MSUA) ensemble dynamics during a higher cognitive task which were not accessible previously. The approach worked by constructing high-dimensional state spaces from delays of the original single-unit firing rate variables and the interactions among them, which were then statistically analyzed using kernel methods. We observed cognitive-epoch-specific neural ensemble states in ACC which were stable across many trials (in the sense of being predictive) and depended on behavioral performance. More interestingly, attracting properties of these cognitively defined ensemble states became apparent in high-dimensional expansions of the MSUA spaces due to a proper unfolding of the neural activity flow, with properties common across different animals. These results therefore suggest that ACC networks may process different subcomponents of higher cognitive tasks by transiting among different attracting states. PMID:21625577

Self-organization in irregular landscapes: Detecting autogenic interactions from field data using descriptive statistics and dynamical systems theory

NASA Astrophysics Data System (ADS)

Larsen, L.; Watts, D.; Khurana, A.; Anderson, J. L.; Xu, C.; Merritts, D. J.

2015-12-01

The classic signal of self-organization in nature is pattern formation. However, the interactions and feedbacks that organize depositional landscapes do not always result in regular or fractal patterns. How might we detect their existence and effects in these "irregular" landscapes? Emergent landscapes such as newly forming deltaic marshes or some restoration sites provide opportunities to study the autogenic processes that organize landscapes and their physical signatures. Here we describe a quest to understand autogenic vs. allogenic controls on landscape evolution in Big Spring Run, PA, a landscape undergoing restoration from bare-soil conditions to a target wet meadow landscape. The contemporary motivation for asking questions about autogenic vs. allogenic controls is to evaluate how important initial conditions or environmental controls may be for the attainment of management objectives. However, these questions can also inform interpretation of the sedimentary record by enabling researchers to separate signals that may have arisen through self-organization processes from those resulting from environmental perturbations. Over three years at Big Spring Run, we mapped the dynamic evolution of floodplain vegetation communities and distributions of abiotic variables and topography. We used principal component analysis and transition probability analysis to detect associative interactions between vegetation and geomorphic variables and convergent cross-mapping on lidar data to detect causal interactions between biomass and topography. Exploratory statistics revealed that plant communities with distinct morphologies exerted control on landscape evolution through stress divergence (i.e., channel initiation) and promoting the accumulation of fine sediment in channels. Together, these communities participated in a negative feedback that maintains low energy and multiple channels. Because of the spatially explicit nature of this feedback, causal interactions could not be uncovered from convergent cross-mapping with this limited dataset, serving as a reminder that spatially explicit approaches for revealing causality are needed to reconstruct self-organizing mechanisms from data.

Joint estimation of habitat dynamics and species interactions: Disturbance reduces co-occurrence of non-native predators with an endangered toad

USGS Publications Warehouse

Miller, David A.W.; Brehme, Cheryl S.; Hines, James E.; Nichols, James D.; Fisher, Robert N.

2012-01-01

1. Ecologists have long been interested in the processes that determine patterns of species occurrence and co-occurrence. Potential short-comings of many existing empirical approaches that address these questions include a reliance on patterns of occurrence at a single time point, failure to account properly for imperfect detection and treating the environment as a static variable.2. We fit detection and non-detection data collected from repeat visits using a dynamic site occupancy model that simultaneously accounts for the temporal dynamics of a focal prey species, its predators and its habitat. Our objective was to determine how disturbance and species interactions affect the co-occurrence probabilities of an endangered toad and recently introduced non-native predators in stream breeding habitats. For this, we determined statistical support for alternative processes that could affect co-occurrence frequency in the system.3. We collected occurrence data at stream segments in two watersheds where streams were largely ephemeral and one watershed dominated by perennial streams. Co-occurrence probabilities of toads with non-native predators were related to disturbance frequency, with low co-occurrence in the ephemeral watershed and high co-occurrence in the perennial watershed. This occurred because once predators were established at a site, they were rarely lost from the site except in cases when the site dried out. Once dry sites became suitable again, toads colonized them much more rapidly than predators, creating a period of predator-free space.4. We attribute the dynamics to a storage effect, where toads persisting outside the stream environment during periods of drought rapidly colonized sites when they become suitable again. Our results support that even in highly connected stream networks, temporal disturbance can structure frequencies with which breeding amphibians encounter non-native predators.5. Dynamic multi-state occupancy models are a powerful tool for rigorously examining hypotheses about inter-species and species–habitat interactions. In contrast to previous methods that infer dynamic processes based on static patterns in occupancy, the approach we took allows the dynamic processes that determine species–species and species–habitat interactions to be directly estimated.

Drivers of forest cover dynamics in smallholder farming systems: the case of northwestern Vietnam.

PubMed

Jadin, Isaline; Vanacker, Veerle; Hoang, Huong Thi Thu

2013-04-01

The national-scale forest recovery of Vietnam started in the early 1990s and is associated with a shift from net deforestation to net reforestation. Large disparities in forest cover dynamics are, however, observed at the local scale. This study aims to unravel the mechanisms driving forest cover change for a mountainous region located in northwest Vietnam. Statistical analyses were used to explore the association between forest cover change and household characteristics. In Sa Pa district, deforestation rates are decreasing, but forest degradation continues at similar rates. Deforestation is not necessarily associated with impoverished ethnic communities or high levels of subsistence farming, and the largest forest cover dynamics are found in villages with the best socio-economic conditions. Our empirical study does not provide strong evidence of a dominant role of agriculture in forest cover dynamics. It shows that empirical studies on local-scale forest dynamics remain important to unravel the complexity of human-environment interactions.

Using Bayes' theorem for free energy calculations

NASA Astrophysics Data System (ADS)

Rogers, David M.

Statistical mechanics is fundamentally based on calculating the probabilities of molecular-scale events. Although Bayes' theorem has generally been recognized as providing key guiding principals for setup and analysis of statistical experiments [83], classical frequentist models still predominate in the world of computational experimentation. As a starting point for widespread application of Bayesian methods in statistical mechanics, we investigate the central quantity of free energies from this perspective. This dissertation thus reviews the basics of Bayes' view of probability theory, and the maximum entropy formulation of statistical mechanics before providing examples of its application to several advanced research areas. We first apply Bayes' theorem to a multinomial counting problem in order to determine inner shell and hard sphere solvation free energy components of Quasi-Chemical Theory [140]. We proceed to consider the general problem of free energy calculations from samples of interaction energy distributions. From there, we turn to spline-based estimation of the potential of mean force [142], and empirical modeling of observed dynamics using integrator matching. The results of this research are expected to advance the state of the art in coarse-graining methods, as they allow a systematic connection from high-resolution (atomic) to low-resolution (coarse) structure and dynamics. In total, our work on these problems constitutes a critical starting point for further application of Bayes' theorem in all areas of statistical mechanics. It is hoped that the understanding so gained will allow for improvements in comparisons between theory and experiment.

Statistical Mechanics of Temporal and Interacting Networks

NASA Astrophysics Data System (ADS)

Zhao, Kun

In the last ten years important breakthroughs in the understanding of the topology of complexity have been made in the framework of network science. Indeed it has been found that many networks belong to the universality classes called small-world networks or scale-free networks. Moreover it was found that the complex architecture of real world networks strongly affects the critical phenomena defined on these structures. Nevertheless the main focus of the research has been the characterization of single and static networks. Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system. In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement. Second, we investigate the entropy of temporal networks and growing networks , to provide a new framework to quantify the information encoded in these networks and to answer a fundamental problem in network science: how complex are temporal and growing networks. Finally, we consider two examples of critical phenomena in interacting networks. In particular, on one side we investigate the percolation of interacting networks by introducing antagonistic interactions. On the other side, we investigate a model of political election based on the percolation of antagonistic networks. The aim of this research is to show how antagonistic interactions change the physics of critical phenomena on interacting networks. We believe that the work presented in these thesis offers the possibility to appreciate the large variability of problems that can be addressed in the new framework of temporal and interacting networks.

Multiple time-scales and the developmental dynamics of social systems

PubMed Central

Flack, Jessica C.

2012-01-01

To build a theory of social complexity, we need to understand how aggregate social properties arise from individual interaction rules. Here, I review a body of work on the developmental dynamics of pigtailed macaque social organization and conflict management that provides insight into the mechanistic causes of multi-scale social systems. In this model system coarse-grained, statistical representations of collective dynamics are more predictive of the future state of the system than the constantly in-flux behavioural patterns at the individual level. The data suggest that individuals can perceive and use these representations for strategical decision-making. As an interaction history accumulates the coarse-grained representations consolidate. This constrains individual behaviour and provides the foundations for new levels of organization. The time-scales on which these representations change impact whether the consolidating higher-levels can be modified by individuals and collectively. The time-scales appear to be a function of the ‘coarseness’ of the representations and the character of the collective dynamics over which they are averages. The data suggest that an advantage of multiple timescales is that they allow social systems to balance tradeoffs between predictability and adaptability. I briefly discuss the implications of these findings for cognition, social niche construction and the evolution of new levels of organization in biological systems. PMID:22641819

Classification of Dynamical Diffusion States in Single Molecule Tracking Microscopy

PubMed Central

Bosch, Peter J.; Kanger, Johannes S.; Subramaniam, Vinod

2014-01-01

Single molecule tracking of membrane proteins by fluorescence microscopy is a promising method to investigate dynamic processes in live cells. Translating the trajectories of proteins to biological implications, such as protein interactions, requires the classification of protein motion within the trajectories. Spatial information of protein motion may reveal where the protein interacts with cellular structures, because binding of proteins to such structures often alters their diffusion speed. For dynamic diffusion systems, we provide an analytical framework to determine in which diffusion state a molecule is residing during the course of its trajectory. We compare different methods for the quantification of motion to utilize this framework for the classification of two diffusion states (two populations with different diffusion speed). We found that a gyration quantification method and a Bayesian statistics-based method are the most accurate in diffusion-state classification for realistic experimentally obtained datasets, of which the gyration method is much less computationally demanding. After classification of the diffusion, the lifetime of the states can be determined, and images of the diffusion states can be reconstructed at high resolution. Simulations validate these applications. We apply the classification and its applications to experimental data to demonstrate the potential of this approach to obtain further insights into the dynamics of cell membrane proteins. PMID:25099798

Multiple time-scales and the developmental dynamics of social systems.

PubMed

Flack, Jessica C

2012-07-05

To build a theory of social complexity, we need to understand how aggregate social properties arise from individual interaction rules. Here, I review a body of work on the developmental dynamics of pigtailed macaque social organization and conflict management that provides insight into the mechanistic causes of multi-scale social systems. In this model system coarse-grained, statistical representations of collective dynamics are more predictive of the future state of the system than the constantly in-flux behavioural patterns at the individual level. The data suggest that individuals can perceive and use these representations for strategical decision-making. As an interaction history accumulates the coarse-grained representations consolidate. This constrains individual behaviour and provides the foundations for new levels of organization. The time-scales on which these representations change impact whether the consolidating higher-levels can be modified by individuals and collectively. The time-scales appear to be a function of the 'coarseness' of the representations and the character of the collective dynamics over which they are averages. The data suggest that an advantage of multiple timescales is that they allow social systems to balance tradeoffs between predictability and adaptability. I briefly discuss the implications of these findings for cognition, social niche construction and the evolution of new levels of organization in biological systems.

Mapping the dynamical organization of the cell nucleus through fluorescence correlation spectroscopy.

PubMed

Stortz, Martin; Angiolini, Juan; Mocskos, Esteban; Wolosiuk, Alejandro; Pecci, Adali; Levi, Valeria

2018-05-01

The hierarchical organization of the cell nucleus into specialized open reservoirs and the nucleoplasm overcrowding impose restrictions to the mobility of biomolecules and their interactions with nuclear targets. These properties determine that many nuclear functions such as transcription, replication, splicing or DNA repair are regulated by complex, dynamical processes that do not follow simple rules. Advanced fluorescence microscopy tools and, in particular, fluorescence correlation spectroscopy (FCS) provide complementary and exquisite information on the dynamics of fluorescent labeled molecules moving through the nuclear space and are helping us to comprehend the complexity of the nuclear structure. Here, we describe how FCS methods can be applied to reveal the dynamical organization of the nucleus in live cells. Specifically, we provide instructions for the preparation of cellular samples with fluorescent tagged proteins and detail how FCS can be easily instrumented in commercial confocal microscopes. In addition, we describe general rules to set the parameters for one and two-color experiments and the required controls for these experiments. Finally, we review the statistical analysis of the FCS data and summarize the use of numerical simulations as a complementary approach that helps us to understand the complex matrix of molecular interactions network within the nucleus. Copyright © 2017 Elsevier Inc. All rights reserved.

Time-Series Analysis of Embodied Interaction: Movement Variability and Complexity Matching As Dyadic Properties

PubMed Central

Zapata-Fonseca, Leonardo; Dotov, Dobromir; Fossion, Ruben; Froese, Tom

2016-01-01

There is a growing consensus that a fuller understanding of social cognition depends on more systematic studies of real-time social interaction. Such studies require methods that can deal with the complex dynamics taking place at multiple interdependent temporal and spatial scales, spanning sub-personal, personal, and dyadic levels of analysis. We demonstrate the value of adopting an extended multi-scale approach by re-analyzing movement time-series generated in a study of embodied dyadic interaction in a minimal virtual reality environment (a perceptual crossing experiment). Reduced movement variability revealed an interdependence between social awareness and social coordination that cannot be accounted for by either subjective or objective factors alone: it picks out interactions in which subjective and objective conditions are convergent (i.e., elevated coordination is perceived as clearly social, and impaired coordination is perceived as socially ambiguous). This finding is consistent with the claim that interpersonal interaction can be partially constitutive of direct social perception. Clustering statistics (Allan Factor) of salient events revealed fractal scaling. Complexity matching defined as the similarity between these scaling laws was significantly more pronounced in pairs of participants as compared to surrogate dyads. This further highlights the multi-scale and distributed character of social interaction and extends previous complexity matching results from dyadic conversation to non-verbal social interaction dynamics. Trials with successful joint interaction were also associated with an increase in local coordination. Consequently, a local coordination pattern emerges on the background of complex dyadic interactions in the PCE task and makes joint successful performance possible. PMID:28018274

Non-local Second Order Closure Scheme for Boundary Layer Turbulence and Convection

NASA Astrophysics Data System (ADS)

Meyer, Bettina; Schneider, Tapio

2017-04-01

There has been scientific consensus that the uncertainty in the cloud feedback remains the largest source of uncertainty in the prediction of climate parameters like climate sensitivity. To narrow down this uncertainty, not only a better physical understanding of cloud and boundary layer processes is required, but specifically the representation of boundary layer processes in models has to be improved. General climate models use separate parameterisation schemes to model the different boundary layer processes like small-scale turbulence, shallow and deep convection. Small scale turbulence is usually modelled by local diffusive parameterisation schemes, which truncate the hierarchy of moment equations at first order and use second-order equations only to estimate closure parameters. In contrast, the representation of convection requires higher order statistical moments to capture their more complex structure, such as narrow updrafts in a quasi-steady environment. Truncations of moment equations at second order may lead to more accurate parameterizations. At the same time, they offer an opportunity to take spatially correlated structures (e.g., plumes) into account, which are known to be important for convective dynamics. In this project, we study the potential and limits of local and non-local second order closure schemes. A truncation of the momentum equations at second order represents the same dynamics as a quasi-linear version of the equations of motion. We study the three-dimensional quasi-linear dynamics in dry and moist convection by implementing it in a LES model (PyCLES) and compare it to a fully non-linear LES. In the quasi-linear LES, interactions among turbulent eddies are suppressed but nonlinear eddy—mean flow interactions are retained, as they are in the second order closure. In physical terms, suppressing eddy—eddy interactions amounts to suppressing, e.g., interactions among convective plumes, while retaining interactions between plumes and the environment (e.g., entrainment and detrainment). In a second part, we employ the possibility to include non-local statistical correlations in a second-order closure scheme. Such non-local correlations allow to directly incorporate the spatially coherent structures that occur in the form of convective updrafts penetrating the boundary layer. This allows us to extend the work that has been done using assumed-PDF schemes for parameterising boundary layer turbulence and shallow convection in a non-local sense.

Quantum Field Theory Approach to Condensed Matter Physics

NASA Astrophysics Data System (ADS)

Marino, Eduardo C.

2017-09-01

Preface; Part I. Condensed Matter Physics: 1. Independent electrons and static crystals; 2. Vibrating crystals; 3. Interacting electrons; 4. Interactions in action; Part II. Quantum Field Theory: 5. Functional formulation of quantum field theory; 6. Quantum fields in action; 7. Symmetries: explicit or secret; 8. Classical topological excitations; 9. Quantum topological excitations; 10. Duality, bosonization and generalized statistics; 11. Statistical transmutation; 12. Pseudo quantum electrodynamics; Part III. Quantum Field Theory Approach to Condensed Matter Systems: 13. Quantum field theory methods in condensed matter; 14. Metals, Fermi liquids, Mott and Anderson insulators; 15. The dynamics of polarons; 16. Polyacetylene; 17. The Kondo effect; 18. Quantum magnets in 1D: Fermionization, bosonization, Coulomb gases and 'all that'; 19. Quantum magnets in 2D: nonlinear sigma model, CP1 and 'all that'; 20. The spin-fermion system: a quantum field theory approach; 21. The spin glass; 22. Quantum field theory approach to superfluidity; 23. Quantum field theory approach to superconductivity; 24. The cuprate high-temperature superconductors; 25. The pnictides: iron based superconductors; 26. The quantum Hall effect; 27. Graphene; 28. Silicene and transition metal dichalcogenides; 29. Topological insulators; 30. Non-abelian statistics and quantum computation; References; Index.

A fractional factorial probabilistic collocation method for uncertainty propagation of hydrologic model parameters in a reduced dimensional space

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Huang, W.; Fan, Y. R.; Li, Z.

2015-10-01

In this study, a fractional factorial probabilistic collocation method is proposed to reveal statistical significance of hydrologic model parameters and their multi-level interactions affecting model outputs, facilitating uncertainty propagation in a reduced dimensional space. The proposed methodology is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability, as well as its capability of revealing complex and dynamic parameter interactions. A set of reduced polynomial chaos expansions (PCEs) only with statistically significant terms can be obtained based on the results of factorial analysis of variance (ANOVA), achieving a reduction of uncertainty in hydrologic predictions. The predictive performance of reduced PCEs is verified by comparing against standard PCEs and the Monte Carlo with Latin hypercube sampling (MC-LHS) method in terms of reliability, sharpness, and Nash-Sutcliffe efficiency (NSE). Results reveal that the reduced PCEs are able to capture hydrologic behaviors of the Xiangxi River watershed, and they are efficient functional representations for propagating uncertainties in hydrologic predictions.

On Theoretical Broadband Shock-Associated Noise Near-Field Cross-Spectra

NASA Technical Reports Server (NTRS)

Miller, Steven A. E.

2015-01-01

The cross-spectral acoustic analogy is used to predict auto-spectra and cross-spectra of broadband shock-associated noise in the near-field and far-field from a range of heated and unheated supersonic off-design jets. A single equivalent source model is proposed for the near-field, mid-field, and far-field terms, that contains flow-field statistics of the shock wave shear layer interactions. Flow-field statistics are modeled based upon experimental observation and computational fluid dynamics solutions. An axisymmetric assumption is used to reduce the model to a closed-form equation involving a double summation over the equivalent source at each shock wave shear layer interaction. Predictions are compared with a wide variety of measurements at numerous jet Mach numbers and temperature ratios from multiple facilities. Auto-spectral predictions of broadband shock-associated noise in the near-field and far-field capture trends observed in measurement and other prediction theories. Predictions of spatial coherence of broadband shock-associated noise accurately capture the peak coherent intensity, frequency, and spectral width.

Brownian dynamics simulations of sequence-dependent duplex denaturation in dynamically superhelical DNA

NASA Astrophysics Data System (ADS)

Mielke, Steven P.; Grønbech-Jensen, Niels; Krishnan, V. V.; Fink, William H.; Benham, Craig J.

2005-09-01

The topological state of DNA in vivo is dynamically regulated by a number of processes that involve interactions with bound proteins. In one such process, the tracking of RNA polymerase along the double helix during transcription, restriction of rotational motion of the polymerase and associated structures, generates waves of overtwist downstream and undertwist upstream from the site of transcription. The resulting superhelical stress is often sufficient to drive double-stranded DNA into a denatured state at locations such as promoters and origins of replication, where sequence-specific duplex opening is a prerequisite for biological function. In this way, transcription and other events that actively supercoil the DNA provide a mechanism for dynamically coupling genetic activity with regulatory and other cellular processes. Although computer modeling has provided insight into the equilibrium dynamics of DNA supercoiling, to date no model has appeared for simulating sequence-dependent DNA strand separation under the nonequilibrium conditions imposed by the dynamic introduction of torsional stress. Here, we introduce such a model and present results from an initial set of computer simulations in which the sequences of dynamically superhelical, 147 base pair DNA circles were systematically altered in order to probe the accuracy with which the model can predict location, extent, and time of stress-induced duplex denaturation. The results agree both with well-tested statistical mechanical calculations and with available experimental information. Additionally, we find that sites susceptible to denaturation show a propensity for localizing to supercoil apices, suggesting that base sequence determines locations of strand separation not only through the energetics of interstrand interactions, but also by influencing the geometry of supercoiling.

Brownian dynamics simulations of sequence-dependent duplex denaturation in dynamically superhelical DNA.

PubMed

Mielke, Steven P; Grønbech-Jensen, Niels; Krishnan, V V; Fink, William H; Benham, Craig J

2005-09-22

The topological state of DNA in vivo is dynamically regulated by a number of processes that involve interactions with bound proteins. In one such process, the tracking of RNA polymerase along the double helix during transcription, restriction of rotational motion of the polymerase and associated structures, generates waves of overtwist downstream and undertwist upstream from the site of transcription. The resulting superhelical stress is often sufficient to drive double-stranded DNA into a denatured state at locations such as promoters and origins of replication, where sequence-specific duplex opening is a prerequisite for biological function. In this way, transcription and other events that actively supercoil the DNA provide a mechanism for dynamically coupling genetic activity with regulatory and other cellular processes. Although computer modeling has provided insight into the equilibrium dynamics of DNA supercoiling, to date no model has appeared for simulating sequence-dependent DNA strand separation under the nonequilibrium conditions imposed by the dynamic introduction of torsional stress. Here, we introduce such a model and present results from an initial set of computer simulations in which the sequences of dynamically superhelical, 147 base pair DNA circles were systematically altered in order to probe the accuracy with which the model can predict location, extent, and time of stress-induced duplex denaturation. The results agree both with well-tested statistical mechanical calculations and with available experimental information. Additionally, we find that sites susceptible to denaturation show a propensity for localizing to supercoil apices, suggesting that base sequence determines locations of strand separation not only through the energetics of interstrand interactions, but also by influencing the geometry of supercoiling.

Statistical Analyses of Marine Mammal Occurrence, Habitat Associations and Interactions with Ocean Dynamic Features

DTIC Science & Technology

2006-03-30

Sirena campaigns) have been successfully conducted in the northwestern Mediterranean Sea since 1999. Six sea trials have been conducted in the...trials ( Sirena campaigns) have been successfully conducted in the northwestern Mediterranean Sea since 1999. Six sea trials have been conducted in the...oceanographic measurements in the canyon region. 3-13 Aug 22 Aug- 6 Sep󈧏 23 Sep - 7 Oct ,- Figure 1: Sirena Sea Trials: yearly multi-platform at-sea

An Evidence-Based Systematic Review of Beta-Sitosterol, Sitosterol (22,23- dihydrostigmasterol, 24-ethylcholesterol) by the Natural Standard Research Collaboration.

PubMed

Ulbricht, Catherine E

2016-01-01

An evidence-based systematic review of beta-sitosterol, sitosterol (22,23-dihydrostigmasterol, 24-ethylcholesterol) by the Natural Standard Research Collaboration consolidates the safety and efficacy data available in the scientific literature using a validated, reproducible grading rationale. This article includes written and statistical analysis of clinical trials, plus a compilation of expert opinion, folkloric precedent, history, pharmacology, kinetics/dynamics, interactions, adverse effects, toxicology, and dosing.

Particle statistics and lossy dynamics of ultracold atoms in optical lattices

NASA Astrophysics Data System (ADS)

Yago Malo, J.; van Nieuwenburg, E. P. L.; Fischer, M. H.; Daley, A. J.

2018-05-01

Experimental control over ultracold quantum gases has made it possible to investigate low-dimensional systems of both bosonic and fermionic atoms. In closed one-dimensional systems there are many similarities in the dynamics of local quantities for spinless fermions and strongly interacting "hard-core" bosons, which on a lattice can be formalized via a Jordan-Wigner transformation. In this study, we analyze the similarities and differences for spinless fermions and hard-core bosons on a lattice in the presence of particle loss. The removal of a single fermion causes differences in local quantities compared with the bosonic case because of the different particle exchange symmetry in the two cases. We identify deterministic and probabilistic signatures of these dynamics in terms of local particle density, which could be measured in ongoing experiments with quantum gas microscopes.

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

PubMed

Schwalger, Tilo; Deger, Moritz; Gerstner, Wulfram

2017-04-01

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.

Kappa Distribution in a Homogeneous Medium: Adiabatic Limit of a Super-diffusive Process?

NASA Astrophysics Data System (ADS)

Roth, I.

2015-12-01

The classical statistical theory predicts that an ergodic, weakly interacting system like charged particles in the presence of electromagnetic fields, performing Brownian motions (characterized by small range deviations in phase space and short-term microscopic memory), converges into the Gibbs-Boltzmann statistics. Observation of distributions with a kappa-power-law tails in homogeneous systems contradicts this prediction and necessitates a renewed analysis of the basic axioms of the diffusion process: characteristics of the transition probability density function (pdf) for a single interaction, with a possibility of non-Markovian process and non-local interaction. The non-local, Levy walk deviation is related to the non-extensive statistical framework. Particles bouncing along (solar) magnetic field with evolving pitch angles, phases and velocities, as they interact resonantly with waves, undergo energy changes at undetermined time intervals, satisfying these postulates. The dynamic evolution of a general continuous time random walk is determined by pdf of jumps and waiting times resulting in a fractional Fokker-Planck equation with non-integer derivatives whose solution is given by a Fox H-function. The resulting procedure involves the known, although not frequently used in physics fractional calculus, while the local, Markovian process recasts the evolution into the standard Fokker-Planck equation. Solution of the fractional Fokker-Planck equation with the help of Mellin transform and evaluation of its residues at the poles of its Gamma functions results in a slowly converging sum with power laws. It is suggested that these tails form the Kappa function. Gradual vs impulsive solar electron distributions serve as prototypes of this description.

Predicting the process of extinction in experimental microcosms and accounting for interspecific interactions in single-species time series.

PubMed

Ferguson, Jake M; Ponciano, José M

2014-02-01

Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series. © 2013 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

Influence of homology and node age on the growth of protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Bottinelli, Arianna; Bassetti, Bruno; Lagomarsino, Marco Cosentino; Gherardi, Marco

2012-10-01

Proteins participating in a protein-protein interaction network can be grouped into homology classes following their common ancestry. Proteins added to the network correspond to genes added to the classes, so the dynamics of the two objects are intrinsically linked. Here we first introduce a statistical model describing the joint growth of the network and the partitioning of nodes into classes, which is studied through a combined mean-field and simulation approach. We then employ this unified framework to address the specific issue of the age dependence of protein interactions through the definition of three different node wiring or divergence schemes. A comparison with empirical data indicates that an age-dependent divergence move is necessary in order to reproduce the basic topological observables together with the age correlation between interacting nodes visible in empirical data. We also discuss the possibility of nontrivial joint partition and topology observables.

Population dynamics of minimally cognitive individuals. Part I: Introducing knowledge into the dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schmieder, R.W.

The author presents a new approach for modeling the dynamics of collections of objects with internal structure. Based on the fact that the behavior of an individual in a population is modified by its knowledge of other individuals, a procedure for accounting for knowledge in a population of interacting objects is presented. It is assumed that each object has partial (or complete) knowledge of some (or all) other objects in the population. The dynamical equations for the objects are then modified to include the effects of this pairwise knowledge. This procedure has the effect of projecting out what the populationmore » will do from the much larger space of what it could do, i.e., filtering or smoothing the dynamics by replacing the complex detailed physical model with an effective model that produces the behavior of interest. The procedure therefore provides a minimalist approach for obtaining emergent collective behavior. The use of knowledge as a dynamical quantity, and its relationship to statistical mechanics, thermodynamics, information theory, and cognition microstructure are discussed.« less

A model for cross-cultural reciprocal interactions through mass media.

PubMed

González-Avella, Juan Carlos; Cosenza, Mario G; San Miguel, Maxi

2012-01-01

We investigate the problem of cross-cultural interactions through mass media in a model where two populations of social agents, each with its own internal dynamics, get information about each other through reciprocal global interactions. As the agent dynamics, we employ Axelrod's model for social influence. The global interaction fields correspond to the statistical mode of the states of the agents and represent mass media messages on the cultural trend originating in each population. Several phases are found in the collective behavior of either population depending on parameter values: two homogeneous phases, one having the state of the global field acting on that population, and the other consisting of a state different from that reached by the applied global field; and a disordered phase. In addition, the system displays nontrivial effects: (i) the emergence of a largest minority group of appreciable size sharing a state different from that of the applied global field; (ii) the appearance of localized ordered states for some values of parameters when the entire system is observed, consisting of one population in a homogeneous state and the other in a disordered state. This last situation can be considered as a social analogue to a chimera state arising in globally coupled populations of oscillators.

Sex differences in the oxygen delivery, extraction, and uptake during moderate-walking exercise transition.

PubMed

Beltrame, Thomas; Villar, Rodrigo; Hughson, Richard L

2017-09-01

Previous studies in children and older adults demonstrated faster oxygen uptake (V̇O 2 ) kinetics in males compared with females, but young healthy adults have not been studied. We hypothesized that young men would have faster aerobic system dynamics in response to the onset of exercise than women. Interactions between oxygen supply and utilization were characterized by the dynamics of V̇O 2 , deoxyhemoglobin (HHb), tissue saturation index (TSI), cardiac output (Q̇), and calculated arteriovenous O 2 difference (a-vO 2 diff ) in women and men. Eighteen healthy active young women and men (9 of each sex) with similar aerobic fitness levels volunteered for this study. Participants performed an incremental cardiopulmonary treadmill exercise test and 3 moderate-intensity treadmill exercise tests (at 80% V̇O 2 of gas exchange threshold). Data related to the moderate exercise were submitted to exponential data modelling to obtain parameters related to the aerobic system dynamics. The time constants of V̇O 2 , a-vO 2 diff , HHb, and TSI (30 ± 6, 29 ± 1, 16 ± 1, and 15 ± 2 s, respectively) in women were statistically (p < 0.05) faster than the time constants in men (42 ± 10, 49 ± 21, 19 ± 3, and 20 ± 4 s, respectively). Although Q̇ dynamics were not statistically different (p = 0.06) between groups, there was a trend to slower Q̇ dynamics in men corresponding with the slower V̇O 2 kinetics. These results indicated that the peripheral and pulmonary oxygen extraction dynamics were remarkably faster in women. Thus, contrary to the hypothesis, V̇O 2 dynamics measured at the mouth at the onset of submaximal treadmill walking were faster in women compared with men.

Interacting particle systems on graphs

NASA Astrophysics Data System (ADS)

Sood, Vishal

In this dissertation, the dynamics of socially or biologically interacting populations are investigated. The individual members of the population are treated as particles that interact via links on a social or biological network represented as a graph. The effect of the structure of the graph on the properties of the interacting particle system is studied using statistical physics techniques. In the first chapter, the central concepts of graph theory and social and biological networks are presented. Next, interacting particle systems that are drawn from physics, mathematics and biology are discussed in the second chapter. In the third chapter, the random walk on a graph is studied. The mean time for a random walk to traverse between two arbitrary sites of a random graph is evaluated. Using an effective medium approximation it is found that the mean first-passage time between pairs of sites, as well as all moments of this first-passage time, are insensitive to the density of links in the graph. The inverse of the mean-first passage time varies non-monotonically with the density of links near the percolation transition of the random graph. Much of the behavior can be understood by simple heuristic arguments. Evolutionary dynamics, by which mutants overspread an otherwise uniform population on heterogeneous graphs, are studied in the fourth chapter. Such a process underlies' epidemic propagation, emergence of fads, social cooperation or invasion of an ecological niche by a new species. The first part of this chapter is devoted to neutral dynamics, in which the mutant genotype does not have a selective advantage over the resident genotype. The time to extinction of one of the two genotypes is derived. In the second part of this chapter, selective advantage or fitness is introduced such that the mutant genotype has a higher birth rate or a lower death rate. This selective advantage leads to a dynamical competition in which selection dominates for large populations, while for small populations the dynamics are similar to the neutral case. The likelihood for the fitter mutants to drive the resident genotype to extinction is calculated.

Perceptual precision of passive body tilt is consistent with statistically optimal cue integration

PubMed Central

Karmali, Faisal; Nicoucar, Keyvan; Merfeld, Daniel M.

2017-01-01

When making perceptual decisions, humans have been shown to optimally integrate independent noisy multisensory information, matching maximum-likelihood (ML) limits. Such ML estimators provide a theoretic limit to perceptual precision (i.e., minimal thresholds). However, how the brain combines two interacting (i.e., not independent) sensory cues remains an open question. To study the precision achieved when combining interacting sensory signals, we measured perceptual roll tilt and roll rotation thresholds between 0 and 5 Hz in six normal human subjects. Primary results show that roll tilt thresholds between 0.2 and 0.5 Hz were significantly lower than predicted by a ML estimator that includes only vestibular contributions that do not interact. In this paper, we show how other cues (e.g., somatosensation) and an internal representation of sensory and body dynamics might independently contribute to the observed performance enhancement. In short, a Kalman filter was combined with an ML estimator to match human performance, whereas the potential contribution of nonvestibular cues was assessed using published bilateral loss patient data. Our results show that a Kalman filter model including previously proven canal-otolith interactions alone (without nonvestibular cues) can explain the observed performance enhancements as can a model that includes nonvestibular contributions. NEW & NOTEWORTHY We found that human whole body self-motion direction-recognition thresholds measured during dynamic roll tilts were significantly lower than those predicted by a conventional maximum-likelihood weighting of the roll angular velocity and quasistatic roll tilt cues. Here, we show that two models can each match this “apparent” better-than-optimal performance: 1) inclusion of a somatosensory contribution and 2) inclusion of a dynamic sensory interaction between canal and otolith cues via a Kalman filter model. PMID:28179477

Mapping temporal dynamics in social interactions with unified structural equation modeling: A description and demonstration revealing time-dependent sex differences in play behavior

PubMed Central

Beltz, Adriene M.; Beekman, Charles; Molenaar, Peter C. M.; Buss, Kristin A.

2013-01-01

Developmental science is rich with observations of social interactions, but few available methodological and statistical approaches take full advantage of the information provided by these data. The authors propose implementation of the unified structural equation model (uSEM), a network analysis technique, for observational data coded repeatedly across time; uSEM captures the temporal dynamics underlying changes in behavior at the individual level by revealing the ways in which a single person influences – concurrently and in the future – other people. To demonstrate the utility of uSEM, the authors applied it to ratings of positive affect and vigor of activity during children’s unstructured laboratory play with unfamiliar, same-sex peers. Results revealed the time-dependent nature of sex differences in play behavior. For girls more than boys, positive affect was dependent upon peers’ prior positive affect. For boys more than girls, vigor of activity was dependent upon peers’ current vigor of activity. PMID:24039386

Cardiorespiratory and cardiovascular interactions in cardiomyopathy patients using joint symbolic dynamic analysis.

PubMed

Giraldo, Beatriz F; Rodriguez, Javier; Caminal, Pere; Bayes-Genis, Antonio; Voss, Andreas

2015-01-01

Cardiovascular diseases are the first cause of death in developed countries. Using electrocardiographic (ECG), blood pressure (BP) and respiratory flow signals, we obtained parameters for classifying cardiomyopathy patients. 42 patients with ischemic (ICM) and dilated (DCM) cardiomyopathies were studied. The left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF>35%, 14 patients) and high risk (HR: LVEF≤ 35%, 28 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, BP and respiratory flow signals, respectively. The time series were transformed to a binary space and then analyzed using Joint Symbolic Dynamic with a word length of three, characterizing them by the probability of occurrence of the words. Extracted parameters were then reduced using correlation and statistical analysis. Principal component analysis and support vector machines methods were applied to characterize the cardiorespiratory and cardiovascular interactions in ICM and DCM cardiomyopathies, obtaining an accuracy of 85.7%.

Observing Consistency in Online Communication Patterns for User Re-Identification

PubMed Central

Venter, Hein S.

2016-01-01

Comprehension of the statistical and structural mechanisms governing human dynamics in online interaction plays a pivotal role in online user identification, online profile development, and recommender systems. However, building a characteristic model of human dynamics on the Internet involves a complete analysis of the variations in human activity patterns, which is a complex process. This complexity is inherent in human dynamics and has not been extensively studied to reveal the structural composition of human behavior. A typical method of anatomizing such a complex system is viewing all independent interconnectivity that constitutes the complexity. An examination of the various dimensions of human communication pattern in online interactions is presented in this paper. The study employed reliable server-side web data from 31 known users to explore characteristics of human-driven communications. Various machine-learning techniques were explored. The results revealed that each individual exhibited a relatively consistent, unique behavioral signature and that the logistic regression model and model tree can be used to accurately distinguish online users. These results are applicable to one-to-one online user identification processes, insider misuse investigation processes, and online profiling in various areas. PMID:27918593

The role of ecological dynamics in analysing performance in team sports.

PubMed

Vilar, Luís; Araújo, Duarte; Davids, Keith; Button, Chris

2012-01-01

Performance analysis is a subdiscipline of sports sciences and one-approach, notational analysis, has been used to objectively audit and describe behaviours of performers during different subphases of play, providing additional information for practitioners to improve future sports performance. Recent criticisms of these methods have suggested the need for a sound theoretical rationale to explain performance behaviours, not just describe them. The aim of this article was to show how ecological dynamics provides a valid theoretical explanation of performance in team sports by explaining the formation of successful and unsuccessful patterns of play, based on symmetry-breaking processes emerging from functional interactions between players and the performance environment. We offer the view that ecological dynamics is an upgrade to more operational methods of performance analysis that merely document statistics of competitive performance. In support of our arguments, we refer to exemplar data on competitive performance in team sports that have revealed functional interpersonal interactions between attackers and defenders, based on variations in the spatial positioning of performers relative to each other in critical performance areas, such as the scoring zones. Implications of this perspective are also considered for practice task design and sport development programmes.

Quantifying economic fluctuations by adapting methods of statistical physics

NASA Astrophysics Data System (ADS)

Plerou, Vasiliki

2001-09-01

The first focus of this thesis is the investigation of cross-correlations between the price fluctuations of different stocks using the conceptual framework of random matrix theory (RMT), developed in physics to describe the statistical properties of energy-level spectra of complex nuclei. RMT makes predictions for the statistical properties of matrices that are universal, i.e., do not depend on the interactions between the elements comprising the system. In physical systems, deviations from the predictions of RMT provide clues regarding the mechanisms controlling the dynamics of a given system so this framework is of potential value if applied to economic systems. This thesis compares the statistics of cross-correlation matrix C-whose elements Cij are the correlation coefficients of price fluctuations of stock i and j-against the ``null hypothesis'' of a random matrix having the same symmetry properties. It is shown that comparison of the eigenvalue statistics of C with RMT results can be used to distinguish random and non-random parts of C. The non-random part of C which deviates from RMT results, provides information regarding genuine cross-correlations between stocks. The interpretations and potential practical utility of these deviations are also investigated. The second focus is the characterization of the dynamics of stock price fluctuations. The statistical properties of the changes G Δt in price over a time interval Δ t are quantified and the statistical relation between G Δt and the trading activity-measured by the number of transactions NΔ t in the interval Δt is investigated. The statistical properties of the volatility, i.e., the time dependent standard deviation of price fluctuations, is related to two microscopic quantities: NΔt and the variance W2Dt of the price changes for all transactions in the interval Δ t. In addition, the statistical relationship between G Δt and the number of shares QΔt traded in Δ t is investigated.

Microswimmers near surfaces

NASA Astrophysics Data System (ADS)

Elgeti, Jens; Gompper, Gerhard

2016-11-01

Both, in their natural environment and in a controlled experimental setup, microswimmers regularly interact with surfaces. These surfaces provide a steric boundary, both for the swimming motion and the hydrodynamic flow pattern. These effects typically imply a strong accumulation of microswimmers near surfaces. While some generic features can be derived, details of the swimmer shape and propulsion mechanism matter, which give rise to a broad range of adhesion phenomena and have to be taken into account to predict the surface accumulation for a given swimmer. We show in this minireview how numerical simulations and analytic theory can be used to predict the accumulation statistics for different systems, with an emphasis on swimmer shape, hydrodynamics interactions, and type of noisy dynamics.

Linear and non-linear interdependence of EEG and HRV frequency bands in human sleep.

PubMed

Chaparro-Vargas, Ramiro; Dissanayaka, P Chamila; Patti, Chanakya Reddy; Schilling, Claudia; Schredl, Michael; Cvetkovic, Dean

2014-01-01

The characterisation of functional interdependencies of the autonomic nervous system (ANS) stands an evergrowing interest to unveil electroencephalographic (EEG) and Heart Rate Variability (HRV) interactions. This paper presents a biosignal processing approach as a supportive computational resource in the estimation of sleep dynamics. The application of linear, non-linear methods and statistical tests upon 10 overnight polysomnographic (PSG) recordings, allowed the computation of wavelet coherence and phase locking values, in order to identify discerning features amongst the clinical healthy subjects. Our findings showed that neuronal oscillations θ, α and σ interact with cardiac power bands at mid-to-high rank of coherence and phase locking, particularly during NREM sleep stages.

Intracultural diversity in a model of social dynamics

NASA Astrophysics Data System (ADS)

Parravano, A.; Rivera-Ramirez, H.; Cosenza, M. G.

2007-06-01

We study the consequences of introducing individual nonconformity in social interactions, based on Axelrod's model for the dissemination of culture. A constraint on the number of situations in which interaction may take place is introduced in order to lift the unavoidable homogeneity present in the final configurations arising in Axelrod's related models. The inclusion of this constraint leads to the occurrence of complex patterns of intracultural diversity whose statistical properties and spatial distribution are characterized by means of the concepts of cultural affinity and cultural cline. It is found that the relevant quantity that determines the properties of intracultural diversity is given by the fraction of cultural features that characterizes the cultural nonconformity of individuals.

Graceful Failure and Societal Resilience Analysis Via Agent-Based Modeling and Simulation

NASA Astrophysics Data System (ADS)

Schopf, P. S.; Cioffi-Revilla, C.; Rogers, J. D.; Bassett, J.; Hailegiorgis, A. B.

2014-12-01

Agent-based social modeling is opening up new methodologies for the study of societal response to weather and climate hazards, and providing measures of resiliency that can be studied in many contexts, particularly in coupled human and natural-technological systems (CHANTS). Since CHANTS are complex adaptive systems, societal resiliency may or may not occur, depending on dynamics that lack closed form solutions. Agent-based modeling has been shown to provide a viable theoretical and methodological approach for analyzing and understanding disasters and societal resiliency in CHANTS. Our approach advances the science of societal resilience through computational modeling and simulation methods that complement earlier statistical and mathematical approaches. We present three case studies of social dynamics modeling that demonstrate the use of these agent based models. In Central Asia, we exmaine mutltiple ensemble simulations with varying climate statistics to see how droughts and zuds affect populations, transmission of wealth across generations, and the overall structure of the social system. In Eastern Africa, we explore how successive episodes of drought events affect the adaptive capacity of rural households. Human displacement, mainly, rural to urban migration, and livelihood transition particularly from pastoral to farming are observed as rural households interacting dynamically with the biophysical environment and continually adjust their behavior to accommodate changes in climate. In the far north case we demonstrate one of the first successful attempts to model the complete climate-permafrost-infrastructure-societal interaction network as a complex adaptive system/CHANTS implemented as a ``federated'' agent-based model using evolutionary computation. Analysis of population changes resulting from extreme weather across these and other cases provides evidence for the emergence of new steady states and shifting patterns of resilience.

Communication Dynamics of Blog Networks

NASA Astrophysics Data System (ADS)

Goldberg, Mark; Kelley, Stephen; Magdon-Ismail, Malik; Mertsalov, Konstantin; Wallace, William (Al)

We study the communication dynamics of Blog networks, focusing on the Russian section of LiveJournal as a case study. Communication (blogger-to-blogger links) in such online communication networks is very dynamic: over 60% of the links in the network are new from one week to the next, though the set of bloggers remains approximately constant. Two fundamental questions are: (i) what models adequately describe such dynamic communication behavior; and (ii) how does one detect the phase transitions, i.e. the changes that go beyond the standard high-level dynamics? We approach these questions through the notion of stable statistics. We give strong experimental evidence to the fact that, despite the extreme amount of communication dynamics, several aggregate statistics are remarkably stable. We use stable statistics to test our models of communication dynamics postulating that any good model should produce values for these statistics which are both stable and close to the observed ones. Stable statistics can also be used to identify phase transitions, since any change in a normally stable statistic indicates a substantial change in the nature of the communication dynamics. We describe models of the communication dynamics in large social networks based on the principle of locality of communication: a node's communication energy is spent mostly within its own "social area," the locality of the node.

Utah Virtual Lab: JAVA interactivity for teaching science and statistics on line.

PubMed

Malloy, T E; Jensen, G C

2001-05-01

The Utah on-line Virtual Lab is a JAVA program run dynamically off a database. It is embedded in StatCenter (www.psych.utah.edu/learn/statsampler.html), an on-line collection of tools and text for teaching and learning statistics. Instructors author a statistical virtual reality that simulates theories and data in a specific research focus area by defining independent, predictor, and dependent variables and the relations among them. Students work in an on-line virtual environment to discover the principles of this simulated reality: They go to a library, read theoretical overviews and scientific puzzles, and then go to a lab, design a study, collect and analyze data, and write a report. Each student's design and data analysis decisions are computer-graded and recorded in a database; the written research report can be read by the instructor or by other students in peer groups simulating scientific conventions.

Interactions of social, terrestrial, and marine sub-systems in the Galapagos Islands, Ecuador.

PubMed

Walsh, Stephen J; Mena, Carlos F

2016-12-20

Galapagos is often cited as an example of the conflicts that are emerging between resource conservation and economic development in island ecosystems, as the pressures associated with tourism threaten nature, including the iconic and emblematic species, unique terrestrial landscapes, and special marine environments. In this paper, two projects are described that rely upon dynamic systems models and agent-based models to examine human-environment interactions. We use a theoretical context rooted in complexity theory to guide the development of our models that are linked to social-ecological dynamics. The goal of this paper is to describe key elements, relationships, and processes to inform and enhance our understanding of human-environment interactions in the Galapagos Islands of Ecuador. By formalizing our knowledge of how systems operate and the manner in which key elements are linked in coupled human-natural systems, we specify rules, relationships, and rates of exchange between social and ecological features derived through statistical functions and/or functions specified in theory or practice. The processes described in our models also have practical applications in that they emphasize how political policies generate different human responses and model outcomes, many detrimental to the social-ecological sustainability of the Galapagos Islands.

Chern-Simons improved Hamiltonians for strings in three space dimensions

NASA Astrophysics Data System (ADS)

Gordeli, Ivan; Melnikov, Dmitry; Niemi, Antti J.; Sedrakyan, Ara

2016-07-01

In the case of a structureless string the extrinsic curvature and torsion determine uniquely its shape in three-dimensional ambient space, by way of solution of the Frenet equation. In many physical scenarios there are in addition symmetries that constrain the functional form of the ensuing energy function. For example, the energy of a structureless string should be independent of the way the string is framed in the Frenet equation. Thus the energy should only involve the curvature and torsion as dynamical variables, in a manner that resembles the Hamiltonian of the Abelian Higgs model. Here we investigate the effect of symmetry principles in the construction of Hamiltonians for structureless strings. We deduce from the concept of frame independence that in addition to extrinsic curvature and torsion, the string can also engage a three-dimensional Abelian bulk gauge field as a dynamical variable. We find that the presence of a bulk gauge field gives rise to a long-range interaction between different strings. Moreover, when this gauge field is subject to Chern-Simons self-interaction, it becomes plausible that interacting strings are subject to fractional statistics in three space dimensions.

On deciphering the book of nature: human communication in psychotherapy.

PubMed

Goodheart, W B

1992-10-01

The tools of contemporary applied mathematics reveal important hidden regularities amidst the ongoing interactive feedback phenomena occurring in interactional or dynamical systems in nature where everything affects everything else. Badalamenti and Langs investigate each therapy session as a continuous sequential emergence of interrelated communicative events (or communicative states) which meet the criteria of a dynamical system. Applying mathematical modeling the authors demonstrate how otherwise hidden regularities occurring between patients and therapists become accessible to us that are unavailable to our unaided powers of observation, intuition, and thought. This is a systems or population investigation of clinical interaction that begins in a qualitative or domain mode, but which opens immediately toward statistical and formal modes of discussion. It can lead to statements of properties and laws that meet the criteria of scientific dialogue and validity. It provides the clinician with guidelines for making interpretations and for assessing their immediate subsequent effect. It is distinguished from the essentialist approach at the foundation of traditional clinical thought which provides no access to such feedback phenomena and their properties. Communicative Psychoanalysts have adopted the systems perspective and are evolving a clinical language and treatment based upon its principles and discoveries.

Dynamic assessment of microbial ecology (DAME): a web app for interactive analysis and visualization of microbial sequencing data.

PubMed

Piccolo, Brian D; Wankhade, Umesh D; Chintapalli, Sree V; Bhattacharyya, Sudeepa; Chunqiao, Luo; Shankar, Kartik

2018-03-15

Dynamic assessment of microbial ecology (DAME) is a Shiny-based web application for interactive analysis and visualization of microbial sequencing data. DAME provides researchers not familiar with R programming the ability to access the most current R functions utilized for ecology and gene sequencing data analyses. Currently, DAME supports group comparisons of several ecological estimates of α-diversity and β-diversity, along with differential abundance analysis of individual taxa. Using the Shiny framework, the user has complete control of all aspects of the data analysis, including sample/experimental group selection and filtering, estimate selection, statistical methods and visualization parameters. Furthermore, graphical and tabular outputs are supported by R packages using D3.js and are fully interactive. DAME was implemented in R but can be modified by Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. It is freely available on the web at https://acnc-shinyapps.shinyapps.io/DAME/. Local installation and source code are available through Github (https://github.com/bdpiccolo/ACNC-DAME). Any system with R can launch DAME locally provided the shiny package is installed. bdpiccolo@uams.edu.

Interactions of social, terrestrial, and marine sub-systems in the Galapagos Islands, Ecuador

PubMed Central

Walsh, Stephen J.; Mena, Carlos F.

2016-01-01

Galapagos is often cited as an example of the conflicts that are emerging between resource conservation and economic development in island ecosystems, as the pressures associated with tourism threaten nature, including the iconic and emblematic species, unique terrestrial landscapes, and special marine environments. In this paper, two projects are described that rely upon dynamic systems models and agent-based models to examine human–environment interactions. We use a theoretical context rooted in complexity theory to guide the development of our models that are linked to social–ecological dynamics. The goal of this paper is to describe key elements, relationships, and processes to inform and enhance our understanding of human–environment interactions in the Galapagos Islands of Ecuador. By formalizing our knowledge of how systems operate and the manner in which key elements are linked in coupled human–natural systems, we specify rules, relationships, and rates of exchange between social and ecological features derived through statistical functions and/or functions specified in theory or practice. The processes described in our models also have practical applications in that they emphasize how political policies generate different human responses and model outcomes, many detrimental to the social–ecological sustainability of the Galapagos Islands. PMID:27791072

Capturing RNA Folding Free Energy with Coarse-Grained Molecular Dynamics Simulations

PubMed Central

Bell, David R.; Cheng, Sara Y.; Salazar, Heber; Ren, Pengyu

2017-01-01

We introduce a coarse-grained RNA model for molecular dynamics simulations, RACER (RnA CoarsE-gRained). RACER achieves accurate native structure prediction for a number of RNAs (average RMSD of 2.93 Å) and the sequence-specific variation of free energy is in excellent agreement with experimentally measured stabilities (R2 = 0.93). Using RACER, we identified hydrogen-bonding (or base pairing), base stacking, and electrostatic interactions as essential driving forces for RNA folding. Also, we found that separating pairing vs. stacking interactions allowed RACER to distinguish folded vs. unfolded states. In RACER, base pairing and stacking interactions each provide an approximate stability of 3–4 kcal/mol for an A-form helix. RACER was developed based on PDB structural statistics and experimental thermodynamic data. In contrast with previous work, RACER implements a novel effective vdW potential energy function, which led us to re-parameterize hydrogen bond and electrostatic potential energy functions. Further, RACER is validated and optimized using a simulated annealing protocol to generate potential energy vs. RMSD landscapes. Finally, RACER is tested using extensive equilibrium pulling simulations (0.86 ms total) on eleven RNA sequences (hairpins and duplexes). PMID:28393861

Dynamical density functional theory for microswimmers

NASA Astrophysics Data System (ADS)

Menzel, Andreas M.; Saha, Arnab; Hoell, Christian; Löwen, Hartmut

2016-01-01

Dynamical density functional theory (DDFT) has been successfully derived and applied to describe on one hand passive colloidal suspensions, including hydrodynamic interactions between individual particles. On the other hand, active "dry" crowds of self-propelled particles have been characterized using DDFT. Here, we go one essential step further and combine these two approaches. We establish a DDFT for active microswimmer suspensions. For this purpose, simple minimal model microswimmers are introduced. These microswimmers self-propel by setting the surrounding fluid into motion. They hydrodynamically interact with each other through their actively self-induced fluid flows and via the common "passive" hydrodynamic interactions. An effective soft steric repulsion is also taken into account. We derive the DDFT starting from common statistical approaches. Our DDFT is then tested and applied by characterizing a suspension of microswimmers, the motion of which is restricted to a plane within a three-dimensional bulk fluid. Moreover, the swimmers are confined by a radially symmetric trapping potential. In certain parameter ranges, we find rotational symmetry breaking in combination with the formation of a "hydrodynamic pumping state," which has previously been observed in the literature as a result of particle-based simulations. An additional instability of this pumping state is revealed.

Localized coherence in two interacting populations of social agents

NASA Astrophysics Data System (ADS)

González-Avella, J. C.; Cosenza, M. G.; San Miguel, M.

2014-04-01

We investigate the emergence of localized coherent behavior in systems consisting of two populations of social agents possessing a condition for non-interacting states, mutually coupled through global interaction fields. We employ two examples of such dynamics: (i) Axelrod’s model for social influence, and (ii) a discrete version of a bounded confidence model for opinion formation. In each case, the global interaction fields correspond to the statistical mode of the states of the agents in each population. In both systems we find localized coherent states for some values of parameters, consisting of one population in a homogeneous state and the other in a disordered state. This situation can be considered as a social analogue to a chimera state arising in two interacting populations of oscillators. In addition, other asymptotic collective behaviors appear in both systems depending on parameter values: a common homogeneous state, where both populations reach the same state; different homogeneous states, where both population reach homogeneous states different from each other; and a disordered state, where both populations reach inhomogeneous states.

Emergence of a new pair-coherent phase in many-body quenches of repulsive bosons

NASA Astrophysics Data System (ADS)

Fischer, Uwe R.; Lee, Kang-Soo; Xiong, Bo

2011-07-01

We investigate the dynamical mode population statistics and associated first- and second-order coherence of an interacting bosonic two-mode model when the pair-exchange coupling is quenched from negative to positive values. It is shown that for moderately rapid second-order transitions, a new pair-coherent phase emerges on the positive coupling side in an excited state, which is not fragmented as the ground-state single-particle density matrix would prescribe it to be.

The response of an airplane to random atmospheric disturbances

NASA Technical Reports Server (NTRS)

Diederich, Franklin W

1957-01-01

The statistical approach to the gust-load problem which consists in considering flight through turbulent air to be a stationary random process is extended by including the effect of lateral variation of the instantaneous gust intensity on the aerodynamic forces. The forces obtained in this manner are used in dynamic analyses of rigid and flexible airplanes free to move vertically, in pitch, and in roll. The effect of the interaction of longitudinal, vertical, and lateral gusts on the wing stresses is also considered.

Maternal Cocaine Use: Estimated Effects on Mother-Child Play Interactions in the Preschool Period

PubMed Central

Johnson, Arnise L.; Morrow, Connie E.; Accornero, Veronica H.; Xue, Lihua; Anthony, James C.; Bandstra, Emmalee S.

2009-01-01

The study objective was to evaluate the quality of parent-child interactions in preschool-aged children exposed prenatally to cocaine. African-American mothers and their full-term newborns (n = 343) were enrolled prospectively at birth and classified as either prenatally cocaine-exposed (n = 157) or non–cocaine-exposed (n = 186) on the basis of maternal self-report and bioassays. Follow-up evaluations at 3 years of age (mean age, 40 mo) included a videotaped dyadic play session and maternal interviews to assess ongoing drug use and maternal psychological distress. Play interactions were coded using a modified version of Egeland et al’s Teaching Task coding scheme. Regression analyses indicated cocaine-associated deficits in mother-child interaction, even with statistical adjustment for multiple suspected influences on interaction dynamics. Mother-child interactions were most impaired in cocaine-exposed dyads when the mother continued to report cocaine use at the 3-year follow-up. Multivariate profile analysis of the Egeland interaction subscales indicated greater maternal intrusiveness and hostility, poorer quality of instruction, lower maternal confidence, and diminished child persistence in the cocaine-exposed dyads. PMID:12177564

Are lemmings prey or predators?

NASA Astrophysics Data System (ADS)

Turchin, P.; Oksanen, L.; Ekerholm, P.; Oksanen, T.; Henttonen, H.

2000-06-01

Large oscillations in the populations of Norwegian lemmings have mystified both professional ecologists and lay public. Ecologists suspect that these oscillations are driven by a trophic mechanism: either an interaction between lemmings and their food supply, or an interaction between lemmings and their predators. If lemming cycles are indeed driven by a trophic interaction, can we tell whether lemmings act as the resource (`prey') or the consumer (`predator')? In trophic interaction models, peaks of resource density generally have a blunt, rounded shape, whereas peaks of consumer density are sharp and angular. Here we have applied several statistical tests to three lemming datasets and contrasted them with comparable data for cyclic voles. We find that vole peaks are blunt, consistent with their cycles being driven by the interaction with predators. In contrast, the shape of lemming peaks is consistent with the hypothesis that lemmings are functional predators, that is, their cycles are driven by their interaction with food plants. Our findings suggest that a single mechanism, such as interaction between rodents and predators, is unlikely to provide the `universal' explanation of all cyclic rodent dynamics.

Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression

PubMed Central

Fisher, Charles K.; Mehta, Pankaj

2014-01-01

Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is now possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the ecological interactions between species directly from sequence data. Any algorithm for inferring ecological interactions must overcome three major obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions due to a statistical problem called “errors-in-variables”. Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct “keystone species”, Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome. PMID:25054627

Statistical mechanics of complex neural systems and high dimensional data

NASA Astrophysics Data System (ADS)

Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

2013-03-01

Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks.

Dynamic Encoding of Speech Sequence Probability in Human Temporal Cortex

PubMed Central

Leonard, Matthew K.; Bouchard, Kristofer E.; Tang, Claire

2015-01-01

Sensory processing involves identification of stimulus features, but also integration with the surrounding sensory and cognitive context. Previous work in animals and humans has shown fine-scale sensitivity to context in the form of learned knowledge about the statistics of the sensory environment, including relative probabilities of discrete units in a stream of sequential auditory input. These statistics are a defining characteristic of one of the most important sequential signals humans encounter: speech. For speech, extensive exposure to a language tunes listeners to the statistics of sound sequences. To address how speech sequence statistics are neurally encoded, we used high-resolution direct cortical recordings from human lateral superior temporal cortex as subjects listened to words and nonwords with varying transition probabilities between sound segments. In addition to their sensitivity to acoustic features (including contextual features, such as coarticulation), we found that neural responses dynamically encoded the language-level probability of both preceding and upcoming speech sounds. Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge. These results demonstrate that sensory processing of deeply learned stimuli involves integrating physical stimulus features with their contextual sequential structure. Despite not being consciously aware of phoneme sequence statistics, listeners use this information to process spoken input and to link low-level acoustic representations with linguistic information about word identity and meaning. PMID:25948269

Quenched dynamics of classical isolated systems: the spherical spin model with two-body random interactions or the Neumann integrable model

NASA Astrophysics Data System (ADS)

Cugliandolo, Leticia F.; Lozano, Gustavo S.; Nessi, Nicolás; Picco, Marco; Tartaglia, Alessandro

2018-06-01

We study the Hamiltonian dynamics of the spherical spin model with fully-connected two-body random interactions. In the statistical physics framework, the potential energy is of the so-called p  =  2 kind, closely linked to the scalar field theory. Most importantly for our setting, the energy conserving dynamics are equivalent to the ones of the Neumann integrable model. We take initial conditions from the Boltzmann equilibrium measure at a temperature that can be above or below the static phase transition, typical of a disordered (paramagnetic) or of an ordered (disguised ferromagnetic) equilibrium phase. We subsequently evolve the configurations with Newton dynamics dictated by a different Hamiltonian, obtained from an instantaneous global rescaling of the elements in the interaction random matrix. In the limit of infinitely many degrees of freedom, , we identify three dynamical phases depending on the parameters that characterise the initial state and the final Hamiltonian. We next set the analysis of the system with finite number of degrees of freedom in terms of N non-linearly coupled modes. We argue that in the limit the modes decouple at long times. We evaluate the mode temperatures and we relate them to the frequency-dependent effective temperature measured with the fluctuation-dissipation relation in the frequency domain, similarly to what was recently proposed for quantum integrable cases. Finally, we analyse the N  ‑  1 integrals of motion, notably, their scaling with N, and we use them to show that the system is out of equilibrium in all phases, even for parameters that show an apparent Gibbs–Boltzmann behaviour of the global observables. We elaborate on the role played by these constants of motion after the quench and we briefly discuss the possible description of the asymptotic dynamics in terms of a generalised Gibbs ensemble.

Numerical analysis for finite-range multitype stochastic contact financial market dynamic systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Ge; Wang, Jun; Fang, Wen, E-mail: fangwen@bjtu.edu.cn

In an attempt to reproduce and study the dynamics of financial markets, a random agent-based financial price model is developed and investigated by the finite-range multitype contact dynamic system, in which the interaction and dispersal of different types of investment attitudes in a stock market are imitated by viruses spreading. With different parameters of birth rates and finite-range, the normalized return series are simulated by Monte Carlo simulation method and numerical studied by power-law distribution analysis and autocorrelation analysis. To better understand the nonlinear dynamics of the return series, a q-order autocorrelation function and a multi-autocorrelation function are also definedmore » in this work. The comparisons of statistical behaviors of return series from the agent-based model and the daily historical market returns of Shanghai Composite Index and Shenzhen Component Index indicate that the proposed model is a reasonable qualitative explanation for the price formation process of stock market systems.« less

Crossing the threshold

NASA Astrophysics Data System (ADS)

Bush, John; Tambasco, Lucas

2017-11-01

First, we summarize the circumstances in which chaotic pilot-wave dynamics gives rise to quantum-like statistical behavior. For ``closed'' systems, in which the droplet is confined to a finite domain either by boundaries or applied forces, quantum-like features arise when the persistence time of the waves exceeds the time required for the droplet to cross its domain. Second, motivated by the similarities between this hydrodynamic system and stochastic electrodynamics, we examine the behavior of a bouncing droplet above the Faraday threshold, where a stochastic element is introduced into the drop dynamics by virtue of its interaction with a background Faraday wave field. With a view to extending the dynamical range of pilot-wave systems to capture more quantum-like features, we consider a generalized theoretical framework for stochastic pilot-wave dynamics in which the relative magnitudes of the drop-generated pilot-wave field and a stochastic background field may be varied continuously. We gratefully acknowledge the financial support of the NSF through their CMMI and DMS divisions.

Investigation of stickiness influence in the anomalous transport and diffusion for a non-dissipative Fermi-Ulam model

NASA Astrophysics Data System (ADS)

Livorati, André L. P.; Palmero, Matheus S.; Díaz-I, Gabriel; Dettmann, Carl P.; Caldas, Iberê L.; Leonel, Edson D.

2018-02-01

We study the dynamics of an ensemble of non interacting particles constrained by two infinitely heavy walls, where one of them is moving periodically in time, while the other is fixed. The system presents mixed dynamics, where the accessible region for the particle to diffuse chaotically is bordered by an invariant spanning curve. Statistical analysis for the root mean square velocity, considering high and low velocity ensembles, leads the dynamics to the same steady state plateau for long times. A transport investigation of the dynamics via escape basins reveals that depending of the initial velocity ensemble, the decay rates of the survival probability present different shapes and bumps, in a mix of exponential, power law and stretched exponential decays. After an analysis of step-size averages, we found that the stable manifolds play the role of a preferential path for faster escape, being responsible for the bumps and different shapes of the survival probability.

Collective effects in models for interacting molecular motors and motor-microtubule mixtures

NASA Astrophysics Data System (ADS)

Menon, Gautam I.

2006-12-01

Three problems in the statistical mechanics of models for an assembly of molecular motors interacting with cytoskeletal filaments are reviewed. First, a description of the hydrodynamical behaviour of density-density correlations in fluctuating ratchet models for interacting molecular motors is outlined. Numerical evidence indicates that the scaling properties of dynamical behaviour in such models belong to the KPZ universality class. Second, the generalization of such models to include boundary injection and removal of motors is provided. In common with known results for the asymmetric exclusion processes, simulations indicate that such models exhibit sharp boundary driven phase transitions in the thermodynamic limit. In the third part of this paper, recent progress towards a continuum description of pattern formation in mixtures of motors and microtubules is described, and a non-equilibrium “phase-diagram” for such systems discussed.

Effect of Charge Localization on the Effective Hyperfine Interaction in Organic Semiconducting Polymers

NASA Astrophysics Data System (ADS)

Geng, Rugang; Subedi, Ram C.; Luong, Hoang M.; Pham, Minh T.; Huang, Weichuan; Li, Xiaoguang; Hong, Kunlun; Shao, Ming; Xiao, Kai; Hornak, Lawrence A.; Nguyen, Tho D.

2018-02-01

Hyperfine interaction (HFI), originating from the coupling between spins of charge carriers and nuclei, has been demonstrated to strongly influence the spin dynamics of localized charges in organic semiconductors. Nevertheless, the role of charge localization on the HFI strength in organic thin films has not yet been experimentally investigated. In this study, the statistical relation hypothesis that the effective HFI of holes in regioregular poly(3-hexylthiophene) (P3HT) is proportional to 1 /N0.5 has been examined, where N is the number of the random nuclear spins within the envelope of the hole wave function. First, by studying magnetoconductance in hole-only devices made by isotope-labeled P3HT we verify that HFI is indeed the dominant spin interaction in P3HT. Second, assuming that holes delocalize fully over the P3HT polycrystalline domain, the strength of HFI is experimentally demonstrated to be proportional to 1 /N0.52 in excellent agreement with the statistical relation. Third, the HFI of electrons in P3HT is about 3 times stronger than that of holes due to the stronger localization of the electrons. Finally, the effective HFI in organic light emitting diodes is found to be a superposition of effective electron and hole HFI. Such a statistical relation may be generally applied to other semiconducting polymers. This Letter may provide great benefits for organic optoelectronics, chemical reaction kinetics, and magnetoreception in biology.

Effect of Charge Localization on the Effective Hyperfine Interaction in Organic Semiconducting Polymers.

PubMed

Geng, Rugang; Subedi, Ram C; Luong, Hoang M; Pham, Minh T; Huang, Weichuan; Li, Xiaoguang; Hong, Kunlun; Shao, Ming; Xiao, Kai; Hornak, Lawrence A; Nguyen, Tho D

2018-02-23

Hyperfine interaction (HFI), originating from the coupling between spins of charge carriers and nuclei, has been demonstrated to strongly influence the spin dynamics of localized charges in organic semiconductors. Nevertheless, the role of charge localization on the HFI strength in organic thin films has not yet been experimentally investigated. In this study, the statistical relation hypothesis that the effective HFI of holes in regioregular poly(3-hexylthiophene) (P3HT) is proportional to 1/N^{0.5} has been examined, where N is the number of the random nuclear spins within the envelope of the hole wave function. First, by studying magnetoconductance in hole-only devices made by isotope-labeled P3HT we verify that HFI is indeed the dominant spin interaction in P3HT. Second, assuming that holes delocalize fully over the P3HT polycrystalline domain, the strength of HFI is experimentally demonstrated to be proportional to 1/N^{0.52} in excellent agreement with the statistical relation. Third, the HFI of electrons in P3HT is about 3 times stronger than that of holes due to the stronger localization of the electrons. Finally, the effective HFI in organic light emitting diodes is found to be a superposition of effective electron and hole HFI. Such a statistical relation may be generally applied to other semiconducting polymers. This Letter may provide great benefits for organic optoelectronics, chemical reaction kinetics, and magnetoreception in biology.

Fault Detection and Diagnosis In Hall-Héroult Cells Based on Individual Anode Current Measurements Using Dynamic Kernel PCA

NASA Astrophysics Data System (ADS)

Yao, Yuchen; Bao, Jie; Skyllas-Kazacos, Maria; Welch, Barry J.; Akhmetov, Sergey

2018-04-01

Individual anode current signals in aluminum reduction cells provide localized cell conditions in the vicinity of each anode, which contain more information than the conventionally measured cell voltage and line current. One common use of this measurement is to identify process faults that can cause significant changes in the anode current signals. While this method is simple and direct, it ignores the interactions between anode currents and other important process variables. This paper presents an approach that applies multivariate statistical analysis techniques to individual anode currents and other process operating data, for the detection and diagnosis of local process abnormalities in aluminum reduction cells. Specifically, since the Hall-Héroult process is time-varying with its process variables dynamically and nonlinearly correlated, dynamic kernel principal component analysis with moving windows is used. The cell is discretized into a number of subsystems, with each subsystem representing one anode and cell conditions in its vicinity. The fault associated with each subsystem is identified based on multivariate statistical control charts. The results show that the proposed approach is able to not only effectively pinpoint the problematic areas in the cell, but also assess the effect of the fault on different parts of the cell.

Interaction of feel system and flight control system dynamics on lateral flying qualities

NASA Technical Reports Server (NTRS)

Bailey, Randall E.; Powers, Bruce G.; Shafer, Mary F.

1988-01-01

An investigation of feel system and flight control system dynamics on lateral flying qualities was conducted using the variable stability USAF NT-33 aircraft. Experimental variations in feel system natural frequency, force-deflection gradient, control system command architecture type, flight control system filter frequency, and control system delay were made. The experiment data include pilot ratings using the Cooper-Harper (1969) rating scale, pilot comments, and tracking performance statistic. Three test pilots served as evaluators. The data indicate that as the feel system natural frequency is reduced lateral flying qualities degrade. At the slowest feel system frequency, the closed-loop response becomes nonlinear with a 'bobweight' effect apparent in the feel system. Feel system influences were essentially independent of the control system architecture. The flying qualities influence due to the feel system was different than when the identical dynamic systenm was used as a flight control system element.

Structural aspects of the solvation shell of lysine and acetylated lysine: A Car-Parrinello and classical molecular dynamics investigation

NASA Astrophysics Data System (ADS)

Carnevale, V.; Raugei, S.

2009-12-01

Lysine acetylation is a post-translational modification, which modulates the affinity of protein-protein and/or protein-DNA complexes. Its crucial role as a switch in signaling pathways highlights the relevance of charged chemical groups in determining the interactions between water and biomolecules. A great effort has been recently devoted to assess the reliability of classical molecular dynamics simulations in describing the solvation properties of charged moieties. In the spirit of these investigations, we performed classical and Car-Parrinello molecular dynamics simulations on lysine and acetylated-lysine in aqueous solution. A comparative analysis between the two computational schemes is presented with a focus on the first solvation shell of the charged groups. An accurate structural analysis unveils subtle, yet statistically significant, differences which are discussed in connection to the significant electronic density charge transfer occurring between the solute and the surrounding water molecules.

Optimal region of latching activity in an adaptive Potts model for networks of neurons

NASA Astrophysics Data System (ADS)

Abdollah-nia, Mohammad-Farshad; Saeedghalati, Mohammadkarim; Abbassian, Abdolhossein

2012-02-01

In statistical mechanics, the Potts model is a model for interacting spins with more than two discrete states. Neural networks which exhibit features of learning and associative memory can also be modeled by a system of Potts spins. A spontaneous behavior of hopping from one discrete attractor state to another (referred to as latching) has been proposed to be associated with higher cognitive functions. Here we propose a model in which both the stochastic dynamics of Potts models and an adaptive potential function are present. A latching dynamics is observed in a limited region of the noise(temperature)-adaptation parameter space. We hence suggest noise as a fundamental factor in such alternations alongside adaptation. From a dynamical systems point of view, the noise-adaptation alternations may be the underlying mechanism for multi-stability in attractor-based models. An optimality criterion for realistic models is finally inferred.

Modeling Selection and Extinction Mechanisms of Biological Systems

NASA Astrophysics Data System (ADS)

Amirjanov, Adil

In this paper, the behavior of a genetic algorithm is modeled to enhance its applicability as a modeling tool of biological systems. A new description model for selection mechanism is introduced which operates on a portion of individuals of population. The extinction and recolonization mechanism is modeled, and solving the dynamics analytically shows that the genetic drift in the population with extinction/recolonization is doubled. The mathematical analysis of the interaction between selection and extinction/recolonization processes is carried out to assess the dynamics of motion of the macroscopic statistical properties of population. Computer simulations confirm that the theoretical predictions of described models are in good approximations. A mathematical model of GA dynamics was also examined, which describes the anti-predator vigilance in an animal group with respect to a known analytical solution of the problem, and showed a good agreement between them to find the evolutionarily stable strategies.

Molecular Dynamic Simulation of Water Vapor and Determination of Diffusion Characteristics in the Pore

NASA Astrophysics Data System (ADS)

Nikonov, Eduard G.; Pavluš, Miron; Popovičová, Mária

2018-02-01

One of the varieties of pores, often found in natural or artificial building materials, are the so-called blind pores of dead-end or saccate type. Three-dimensional model of such kind of pore has been developed in this work. This model has been used for simulation of water vapor interaction with individual pore by molecular dynamics in combination with the diffusion equation method. Special investigations have been done to find dependencies between thermostats implementations and conservation of thermodynamic and statistical values of water vapor - pore system. The two types of evolution of water - pore system have been investigated: drying and wetting of the pore. Full research of diffusion coefficient, diffusion velocity and other diffusion parameters has been made.

Dynamics of a coherently driven micromaser by the Monte Carlo wavefunction approach

NASA Astrophysics Data System (ADS)

Bonacina, L.; Casagrande, F.; Lulli, A.

2000-08-01

Using a Monte Carlo wavefunction approach we investigate the dynamics of a micromaser driven by a resonant coherent field. At steady state, for increasing interaction times, the system exhibits driven Rabi oscillations, followed by collapse as the range of micromaser trapping states is approached. The system operates in regimes ranging from a strong to a weak amplifier. In the strong-amplifier regime the cavity mode shows a preferred phase and can exhibit quadrature squeezing and sub-Poissonian photon statistics. In the weak-amplifier regime the cavity mode has no preferred phase, is super-Poissonian and is influenced by trapping effects; no revival of Rabi oscillations occurs. The main predictions can be compared with experimental measurements on the populations of atoms leaving the cavity.

A novel Kinetic Monte Carlo algorithm for Non-Equilibrium Simulations

NASA Astrophysics Data System (ADS)

Jha, Prateek; Kuzovkov, Vladimir; Grzybowski, Bartosz; Olvera de La Cruz, Monica

2012-02-01

We have developed an off-lattice kinetic Monte Carlo simulation scheme for reaction-diffusion problems in soft matter systems. The definition of transition probabilities in the Monte Carlo scheme are taken identical to the transition rates in a renormalized master equation of the diffusion process and match that of the Glauber dynamics of Ising model. Our scheme provides several advantages over the Brownian dynamics technique for non-equilibrium simulations. Since particle displacements are accepted/rejected in a Monte Carlo fashion as opposed to moving particles following a stochastic equation of motion, nonphysical movements (e.g., violation of a hard core assumption) are not possible (these moves have zero acceptance). Further, the absence of a stochastic ``noise'' term resolves the computational difficulties associated with generating statistically independent trajectories with definitive mean properties. Finally, since the timestep is independent of the magnitude of the interaction forces, much longer time-steps can be employed than Brownian dynamics. We discuss the applications of this scheme for dynamic self-assembly of photo-switchable nanoparticles and dynamical problems in polymeric systems.

Quantum regression theorem and non-Markovianity of quantum dynamics

NASA Astrophysics Data System (ADS)

Guarnieri, Giacomo; Smirne, Andrea; Vacchini, Bassano

2014-08-01

We explore the connection between two recently introduced notions of non-Markovian quantum dynamics and the validity of the so-called quantum regression theorem. While non-Markovianity of a quantum dynamics has been defined looking at the behavior in time of the statistical operator, which determines the evolution of mean values, the quantum regression theorem makes statements about the behavior of system correlation functions of order two and higher. The comparison relies on an estimate of the validity of the quantum regression hypothesis, which can be obtained exactly evaluating two-point correlation functions. To this aim we consider a qubit undergoing dephasing due to interaction with a bosonic bath, comparing the exact evaluation of the non-Markovianity measures with the violation of the quantum regression theorem for a class of spectral densities. We further study a photonic dephasing model, recently exploited for the experimental measurement of non-Markovianity. It appears that while a non-Markovian dynamics according to either definition brings with itself violation of the regression hypothesis, even Markovian dynamics can lead to a failure of the regression relation.

Statistical Mechanical Theory of Coupled Slow Dynamics in Glassy Polymer-Molecule Mixtures

NASA Astrophysics Data System (ADS)

Zhang, Rui; Schweizer, Kenneth

The microscopic Elastically Collective Nonlinear Langevin Equation theory of activated relaxation in one-component supercooled liquids and glasses is generalized to polymer-molecule mixtures. The key idea is to account for dynamic coupling between molecule and polymer segment motion. For describing the molecule hopping event, a temporal casuality condition is formulated to self-consistently determine a dimensionless degree of matrix distortion relative to the molecule jump distance based on the concept of coupled dynamic free energies. Implementation for real materials employs an established Kuhn sphere model of the polymer liquid and a quantitative mapping to a hard particle reference system guided by the experimental equation-of-state. The theory makes predictions for the mixture dynamic shear modulus, activated relaxation time and diffusivity of both species, and mixture glass transition temperature as a function of molecule-Kuhn segment size ratio and attraction strength, composition and temperature. Model calculations illustrate the dynamical behavior in three distinct mixture regimes (fully miscible, bridging, clustering) controlled by the molecule-polymer interaction or chi-parameter. Applications to specific experimental systems will be discussed.

Stochastic Lagrangian dynamics for charged flows in the E-F regions of ionosphere

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang Wenbo; Mahalov, Alex

2013-03-15

We develop a three-dimensional numerical model for the E-F region ionosphere and study the Lagrangian dynamics for plasma flows in this region. Our interest rests on the charge-neutral interactions and the statistics associated with stochastic Lagrangian motion. In particular, we examine the organizing mixing patterns for plasma flows due to polarized gravity wave excitations in the neutral field, using Lagrangian coherent structures (LCS). LCS objectively depict the flow topology-the extracted attractors indicate generation of ionospheric density gradients, due to accumulation of plasma. Using Lagrangian measures such as the finite-time Lyapunov exponents, we locate the Lagrangian skeletons for mixing in plasma,more » hence where charged fronts are expected to appear. With polarized neutral wind, we find that the corresponding plasma velocity is also polarized. Moreover, the polarized velocity alone, coupled with stochastic Lagrangian motion, may give rise to polarized density fronts in plasma. Statistics of these trajectories indicate high level of non-Gaussianity. This includes clear signatures of variance, skewness, and kurtosis of displacements taking polarized structures aligned with the gravity waves, and being anisotropic.« less

Direct numerical simulations of flow-chemistry interactions in statistically turbulent premixed flames

NASA Astrophysics Data System (ADS)

Arias, Paul; Uranakar, Harshavardhana; Chaudhuri, Swetaprovo; Im, Hong

2015-11-01

The effects of Damköhler number and Karlovitz number on the flame dynamics of three-dimensional statistically planar turbulent premixed flames are investigated by direct numerical simulation incorporating detailed chemistry and transport for a hydrogen-air mixture. The mean inlet velocity was dynamically adjusted to ensure a stable flame within the computational domain, allowing the investigation of time-averaged quantities of interest. A particular interest was on understanding the effects of turbulence on the displacement speed of the flame relative to the local fluid flow. Results show a linear dependence on the displacement speed as a function of total strain, consistent with earlier work on premixed-laminar flames. Additional analysis on the local flame thickness reveals that the effect of turbulence is twofold: (1) the increase in mixing results in flame thinning due to the enhancement of combustion at early onset of the flame, and (2) for large Reynolds number flows, the penetration of the turbulence far into the preheat zone and into the reaction zone results in localized flame broadening.

Joint estimation of habitat dynamics and species interactions: disturbance reduces co-occurrence of non-native predators with an endangered toad.

PubMed

Miller, David A W; Brehme, Cheryl S; Hines, James E; Nichols, James D; Fisher, Robert N

2012-11-01

1. Ecologists have long been interested in the processes that determine patterns of species occurrence and co-occurrence. Potential short-comings of many existing empirical approaches that address these questions include a reliance on patterns of occurrence at a single time point, failure to account properly for imperfect detection and treating the environment as a static variable. 2. We fit detection and non-detection data collected from repeat visits using a dynamic site occupancy model that simultaneously accounts for the temporal dynamics of a focal prey species, its predators and its habitat. Our objective was to determine how disturbance and species interactions affect the co-occurrence probabilities of an endangered toad and recently introduced non-native predators in stream breeding habitats. For this, we determined statistical support for alternative processes that could affect co-occurrence frequency in the system. 3. We collected occurrence data at stream segments in two watersheds where streams were largely ephemeral and one watershed dominated by perennial streams. Co-occurrence probabilities of toads with non-native predators were related to disturbance frequency, with low co-occurrence in the ephemeral watershed and high co-occurrence in the perennial watershed. This occurred because once predators were established at a site, they were rarely lost from the site except in cases when the site dried out. Once dry sites became suitable again, toads colonized them much more rapidly than predators, creating a period of predator-free space. 4. We attribute the dynamics to a storage effect, where toads persisting outside the stream environment during periods of drought rapidly colonized sites when they become suitable again. Our results support that even in highly connected stream networks, temporal disturbance can structure frequencies with which breeding amphibians encounter non-native predators. 5. Dynamic multi-state occupancy models are a powerful tool for rigorously examining hypotheses about inter-species and species-habitat interactions. In contrast to previous methods that infer dynamic processes based on static patterns in occupancy, the approach we took allows the dynamic processes that determine species-species and species-habitat interactions to be directly estimated. © 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.

Order statistics inference for describing topological coupling and mechanical symmetry breaking in multidomain proteins

NASA Astrophysics Data System (ADS)

Kononova, Olga; Jones, Lee; Barsegov, V.

2013-09-01

Cooperativity is a hallmark of proteins, many of which show a modular architecture comprising discrete structural domains. Detecting and describing dynamic couplings between structural regions is difficult in view of the many-body nature of protein-protein interactions. By utilizing the GPU-based computational acceleration, we carried out simulations of the protein forced unfolding for the dimer WW - WW of the all-β-sheet WW domains used as a model multidomain protein. We found that while the physically non-interacting identical protein domains (WW) show nearly symmetric mechanical properties at low tension, reflected, e.g., in the similarity of their distributions of unfolding times, these properties become distinctly different when tension is increased. Moreover, the uncorrelated unfolding transitions at a low pulling force become increasingly more correlated (dependent) at higher forces. Hence, the applied force not only breaks "the mechanical symmetry" but also couples the physically non-interacting protein domains forming a multi-domain protein. We call this effect "the topological coupling." We developed a new theory, inspired by order statistics, to characterize protein-protein interactions in multi-domain proteins. The method utilizes the squared-Gaussian model, but it can also be used in conjunction with other parametric models for the distribution of unfolding times. The formalism can be taken to the single-molecule experimental lab to probe mechanical cooperativity and domain communication in multi-domain proteins.

Influence of van der Waals forces on increasing the strength and toughness in dynamic fracture of nanofibre networks: a peridynamic approach

NASA Astrophysics Data System (ADS)

Bobaru, F.

2007-07-01

The peridynamic method is used here to analyse the effect of van der Waals forces on the mechanical behaviour and strength and toughness properties of three-dimensional nanofibre networks under imposed stretch deformation. The peridynamic formulation allows for a natural inclusion of long-range forces (such as van der Waals forces) by considering all interactions as 'long-range'. We use van der Waals interactions only between different fibres and do not need to model individual atoms. Fracture is introduced at the microstructural (peridynamic bond) level for the microelastic type bonds, while van der Waals bonds can reform at any time. We conduct statistical studies to determine a certain volume element for which the network of randomly oriented fibres becomes quasi-isotropic and insensitive to statistical variations. This qualitative study shows that the presence of van der Waals interactions and of heterogeneities (sacrificial bonds) in the strength of the bonds at the crosslinks between fibres can help in increasing the strength and toughness of the nanofibre network. Two main mechanisms appear to control the deformation of nanofibre networks: fibre reorientation (caused by deformation and breakage) and fibre accretion (due to van der Waals interaction). Similarities to the observed toughness of polymer adhesive in the abalone shell composition are explained. The author would like to dedicate this work to the 60th anniversary of Professor Subrata Mukherjee.

Editorial: Focus on Dynamics and Thermalization in Isolated Quantum Many-Body Systems

NASA Astrophysics Data System (ADS)

Cazalilla, M. A.; Rigol, M.

2010-05-01

The dynamics and thermalization of classical systems have been extensively studied in the past. However, the corresponding quantum phenomena remain, to a large extent, uncharted territory. Recent experiments with ultracold quantum gases have at last allowed exploration of the coherent dynamics of isolated quantum systems, as well as observation of non-equilibrium phenomena that challenge our current understanding of the dynamics of quantum many-body systems. These experiments have also posed many new questions. How can we control the dynamics to engineer new states of matter? Given that quantum dynamics is unitary, under which conditions can we expect observables of the system to reach equilibrium values that can be predicted by conventional statistical mechanics? And, how do the observables dynamically approach their statistical equilibrium values? Could the approach to equilibrium be hampered if the system is trapped in long-lived metastable states characterized, for example, by a certain distribution of topological defects? How does the dynamics depend on the way the system is perturbed, such as changing, as a function of time and at a given rate, a parameter across a quantum critical point? What if, conversely, after relaxing to a steady state, the observables cannot be described by the standard equilibrium ensembles of statistical mechanics? How would they depend on the initial conditions in addition to the other properties of the system, such as the existence of conserved quantities? The search for answers to questions like these is fundamental to a new research field that is only beginning to be explored, and to which researchers with different backgrounds, such as nuclear, atomic, and condensed-matter physics, as well as quantum optics, can make, and are making, important contributions. This body of knowledge has an immediate application to experiments in the field of ultracold atomic gases, but can also fundamentally change the way we approach and understand many-body quantum systems. This focus issue of New Journal Physics brings together both experimentalists and theoreticians working on these problems to provide a comprehensive picture of the state of the field. Focus on Dynamics and Thermalization in Isolated Quantum Many-Body Systems Contents Spin squeezing of high-spin, spatially extended quantum fields Jay D Sau, Sabrina R Leslie, Marvin L Cohen and Dan M Stamper-Kurn Thermodynamic entropy of a many-body energy eigenstate J M Deutsch Ground states and dynamics of population-imbalanced Fermi condensates in one dimension Masaki Tezuka and Masahito Ueda Relaxation dynamics in the gapped XXZ spin-1/2 chain Jorn Mossel and Jean-Sébastien Caux Canonical thermalization Peter Reimann Minimally entangled typical thermal state algorithms E M Stoudenmire and Steven R White Manipulation of the dynamics of many-body systems via quantum control methods Julie Dinerman and Lea F Santos Multimode analysis of non-classical correlations in double-well Bose-Einstein condensates Andrew J Ferris and Matthew J Davis Thermalization in a quasi-one-dimensional ultracold bosonic gas I E Mazets and J Schmiedmayer Two simple systems with cold atoms: quantum chaos tests and non-equilibrium dynamics Cavan Stone, Yassine Ait El Aoud, Vladimir A Yurovsky and Maxim Olshanii On the speed of fluctuations around thermodynamic equilibrium Noah Linden, Sandu Popescu, Anthony J Short and Andreas Winter A quantum central limit theorem for non-equilibrium systems: exact local relaxation of correlated states M Cramer and J Eisert Quantum quench dynamics of the sine-Gordon model in some solvable limits A Iucci and M A Cazalilla Nonequilibrium quantum dynamics of atomic dark solitons A D Martin and J Ruostekoski Quantum quenches in the anisotropic spin-1⁄2 Heisenberg chain: different approaches to many-body dynamics far from equilibrium Peter Barmettler, Matthias Punk, Vladimir Gritsev, Eugene Demler and Ehud Altman Crossover from adiabatic to sudden interaction quenches in the Hubbard model: prethermalization and non-equilibrium dynamics Michael Moeckel and Stefan Kehrein Quantum quenches in integrable field theories Davide Fioretto and Giuseppe Mussardo Dynamical delocalization of Majorana edge states by sweeping across a quantum critical point A Bermudez, L Amico and M A Martin-Delgado Thermometry with spin-dependent lattices D McKay and B DeMarco Near-adiabatic parameter changes in correlated systems: influence of the ramp protocol on the excitation energy Martin Eckstein and Marcus Kollar Sudden change of the thermal contact between two quantum systems J Restrepo and S Camalet Reflection of a Lieb-Liniger wave packet from the hard-wall potential D Jukić and H Buljan Probing interaction-induced ferromagnetism in optical superlattices J von Stecher, E Demler, M D Lukin and A M Rey Sudden interaction quench in the quantum sine-Gordon model Javier Sabio and Stefan Kehrein Dynamics of an inhomogeneous quantum phase transition Jacek Dziarmaga and Marek M Rams

Brownian Dynamics Simulation of Nucleocytoplasmic Transport: A Coarse-Grained Model for the Functional State of the Nuclear Pore Complex

PubMed Central

Moussavi-Baygi, Ruhollah; Jamali, Yousef; Karimi, Reza; Mofrad, Mohammad R. K.

2011-01-01

The nuclear pore complex (NPC) regulates molecular traffic across the nuclear envelope (NE). Selective transport happens on the order of milliseconds and the length scale of tens of nanometers; however, the transport mechanism remains elusive. Central to the transport process is the hydrophobic interactions between karyopherins (kaps) and Phe-Gly (FG) repeat domains. Taking into account the polymeric nature of FG-repeats grafted on the elastic structure of the NPC, and the kap-FG hydrophobic affinity, we have established a coarse-grained model of the NPC structure that mimics nucleocytoplasmic transport. To establish a foundation for future works, the methodology and biophysical rationale behind the model is explained in details. The model predicts that the first-passage time of a 15 nm cargo-complex is about 2.6±0.13 ms with an inverse Gaussian distribution for statistically adequate number of independent Brownian dynamics simulations. Moreover, the cargo-complex is primarily attached to the channel wall where it interacts with the FG-layer as it passes through the central channel. The kap-FG hydrophobic interaction is highly dynamic and fast, which ensures an efficient translocation through the NPC. Further, almost all eight hydrophobic binding spots on kap-β are occupied simultaneously during transport. Finally, as opposed to intact NPCs, cytoplasmic filaments-deficient NPCs show a high degree of permeability to inert cargos, implying the defining role of cytoplasmic filaments in the selectivity barrier. PMID:21673865

A web-based relational database for monitoring and analyzing mosquito population dynamics.

PubMed

Sucaet, Yves; Van Hemert, John; Tucker, Brad; Bartholomay, Lyric

2008-07-01

Mosquito population dynamics have been monitored on an annual basis in the state of Iowa since 1969. The primary goal of this project was to integrate light trap data from these efforts into a centralized back-end database and interactive website that is available through the internet at http://iowa-mosquito.ent.iastate.edu. For comparative purposes, all data were categorized according to the week of the year and normalized according to the number of traps running. Users can readily view current, weekly mosquito abundance compared with data from previous years. Additional interactive capabilities facilitate analyses of the data based on mosquito species, distribution, or a time frame of interest. All data can be viewed in graphical and tabular format and can be downloaded to a comma separated value (CSV) file for import into a spreadsheet or more specialized statistical software package. Having this long-term dataset in a centralized database/website is useful for informing mosquito and mosquito-borne disease control and for exploring the ecology of the species represented therein. In addition to mosquito population dynamics, this database is available as a standardized platform that could be modified and applied to a multitude of projects that involve repeated collection of observational data. The development and implementation of this tool provides capacity for the user to mine data from standard spreadsheets into a relational database and then view and query the data in an interactive website.

The hydration structure of the heavy-alkalines Rb+ and Cs+ through molecular dynamics and X-ray absorption spectroscopy: surface clusters and eccentricity.

PubMed

Caralampio, Daniel Z; Martínez, José M; Pappalardo, Rafael R; Marcos, Enrique Sánchez

2017-11-01

Physicochemical properties of the two heaviest stable alkaline cations, Rb + and Cs + , in water have been examined from classical molecular dynamics (MD) simulations. Alkaline cation-water intermolecular potentials have been built from ab initio interaction energies of [M(H 2 O) n ] + clusters. Unlike in the case of other monatomic metal cations, the sampling needed the inclusion of surface clusters to properly describe the interactions. The first coordination shell is found at an average M-O distance of 2.87 Å and 3.12 Å for Rb + and Cs + , respectively, with coordination numbers of 8 and 10. Structural, dynamical and energetic properties are discussed on the basis of the delicate compromise among the ion-water and water-water interactions which contribute almost on the same foot to the definition of the solvent structure around the ions. A significant asymmetry is detected in the Rb + and Cs + first hydration shell. Reorientational times of first-shell water molecules for Cs + support a clear structure-breaking nature for this cation, whereas the Rb + values do not differ from pure water behavior. Experimental EXAFS and XANES spectra have been compared to simulated ones, obtained by means of application of the FEFF code to a set of statistically significant structures taken from the MD simulations. Due to the presence of multi-excitations in the absorption spectra, theoretical-experimental agreement for the EXAFS spectra is reached when the multi-excitations are removed from the experimental spectra.

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

PubMed Central

Gerstner, Wulfram

2017-01-01

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations. PMID:28422957

Cutoff size need not strongly influence molecular dynamics results for solvated polypeptides.

PubMed

Beck, David A C; Armen, Roger S; Daggett, Valerie

2005-01-18

The correct treatment of van der Waals and electrostatic nonbonded interactions in molecular force fields is essential for performing realistic molecular dynamics (MD) simulations of solvated polypeptides. The most computationally tractable treatment of nonbonded interactions in MD utilizes a spherical distance cutoff (typically, 8-12 A) to reduce the number of pairwise interactions. In this work, we assess three spherical atom-based cutoff approaches for use with all-atom explicit solvent MD: abrupt truncation, a CHARMM-style electrostatic shift truncation, and our own force-shifted truncation. The chosen system for this study is an end-capped 17-residue alanine-based alpha-helical peptide, selected because of its use in previous computational and experimental studies. We compare the time-averaged helical content calculated from these MD trajectories with experiment. We also examine the effect of varying the cutoff treatment and distance on energy conservation. We find that the abrupt truncation approach is pathological in its inability to conserve energy. The CHARMM-style shift truncation performs quite well but suffers from energetic instability. On the other hand, the force-shifted spherical cutoff method conserves energy, correctly predicts the experimental helical content, and shows convergence in simulation statistics as the cutoff is increased. This work demonstrates that by using proper and rigorous techniques, it is possible to correctly model polypeptide dynamics in solution with a spherical cutoff. The inherent computational advantage of spherical cutoffs over Ewald summation (and related) techniques is essential in accessing longer MD time scales.

Analysis of the Duration of Rising Tone Chorus Elements

NASA Astrophysics Data System (ADS)

Teng, S.; Tao, X.; Xie, Y.; Zonca, F.; Chen, L.; Fang, W. B.; Wang, S.

2017-12-01

The duration of chorus elements is an important parameter to understand chorus excitation and to quantify the effects of nonlinear wave-particle interactions on energetic electron dynamics. In this work, we analyze the duration of rising tone chorus elements statistically using Van Allen Probes data. We present the distribution of chorus element duration (τ) as a function of magnetic local time (MLT) and the geomagnetic activity level characterized by auroral electrojet (AE) index. We show that the typical value of τ for nightside and dawnside is about 0.12 s, smaller than that for dayside and duskside by about a factor of 2 to 4. Using a previously developed hybrid code, DAWN, we suggest that the background magnetic field inhomogeneity might be an important factor in controlling the chorus element duration. We also report that τ is larger during quiet times and shorter during moderate and active periods; this result is consistent with the MLT dependence of τ and the occurrence pattern of chorus waves at different levels of geomagnetic activity. We then investigate the correlation between τ and the frequency chirping rate (Γ). We show that, from observation, τ scales with Γ as τ∝Γ-1.1, suggesting that statistically the frequency range of chorus elements (τΓ) should be roughly the same for different elements. These findings should be useful to the further development of a theoretical model of chorus excitation and to the quantification of nonlinear wave-particle interactions on energetic electron dynamics.

Coherent structure dynamics and identification during the multistage transitions of polymeric turbulent channel flow

NASA Astrophysics Data System (ADS)

Zhu, Lu; Xi, Li

2018-04-01

Drag reduction induced by polymer additives in wall-bounded turbulence has been studied for decades. A small dosage of polymer additives can drastically reduce the energy dissipation in turbulent flows and alter the flow structures at the same time. As the polymer-induced fluid elasticity increases, drag reduction goes through several stages of transition with drastically different flow statistics. While much attention in the area of polymer-turbulence interactions has been focused on the onset and the asymptotic stage of maximum drag reduction, the transition between the two intermediate stages – low-extent drag reduction (LDR) and high-extent drag reduction (HDR) – likely reflects a qualitative change in the underlying vortex dynamics according to our recent study [1]. In particular, we proposed that polymers start to suppress the lift-up and bursting of vortices at HDR, leading to the localization of turbulent structures. To test our hypothesis, a statistically robust conditional sampling algorithm, based on Jenong and Hussain [2]’s work, was adopted in this study. The comparison of conditional eddies between the Newtonian and the highly elastic turbulence shows that (i) the lifting “strength” of vortices is suppressed by polymers as reflected by the decreasing lifting angle of the conditional eddy and (ii) the curvature of vortices is also eliminated as the orientation of the head of the conditional eddy changes. In summary, the results of conditional sampling support our hypothesis of polymer-turbulence interactions during the LDR-HDR transition.

What Fraction of Global Fire Activity Can Be Forecast Using Sea Surface Temperatures?

NASA Astrophysics Data System (ADS)

Chen, Y.; Randerson, J. T.; Morton, D. C.; Andela, N.; Giglio, L.

2015-12-01

Variations in sea surface temperatures (SSTs) can influence climate dynamics in local and remote land areas, and thus influence fire-climate interactions that govern burned area. SST information has been recently used in statistical models to create seasonal outlooks of fire season severity in South America and as the initial condition for dynamical model predictions of fire activity in Indonesia. However, the degree to which large-scale ocean-atmosphere interactions can influence burned area in other continental regions has not been systematically explored. Here we quantified the amount of global burned area that can be predicted using SSTs in 14 different oceans regions as statistical predictors. We first examined lagged correlations between GFED4s burned area and the 14 ocean climate indices (OCIs) individually. The maximum correlations from different OCIs were used to construct a global map of fire predictability. About half of the global burned area can be forecast by this approach 3 months before the peak burning month (with a Pearson's r of 0.5 or higher), with the highest levels of predictability in Central America and Equatorial Asia. Several hotspots of predictability were identified using k-means cluster analysis. Within these regions, we tested the improvements of the forecast by using two OCIs from different oceans. Our forecast models were based on near-real-time SST data and may therefore support the development of new seasonal outlooks for fire activity that can aid the sustainable management of these fire-prone ecosystems.

Examining dynamic interactions among experimental factors influencing hydrologic data assimilation with the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Wang, S.; Huang, G. H.; Baetz, B. W.; Cai, X. M.; Ancell, B. C.; Fan, Y. R.

2017-11-01

The ensemble Kalman filter (EnKF) is recognized as a powerful data assimilation technique that generates an ensemble of model variables through stochastic perturbations of forcing data and observations. However, relatively little guidance exists with regard to the proper specification of the magnitude of the perturbation and the ensemble size, posing a significant challenge in optimally implementing the EnKF. This paper presents a robust data assimilation system (RDAS), in which a multi-factorial design of the EnKF experiments is first proposed for hydrologic ensemble predictions. A multi-way analysis of variance is then used to examine potential interactions among factors affecting the EnKF experiments, achieving optimality of the RDAS with maximized performance of hydrologic predictions. The RDAS is applied to the Xiangxi River watershed which is the most representative watershed in China's Three Gorges Reservoir region to demonstrate its validity and applicability. Results reveal that the pairwise interaction between perturbed precipitation and streamflow observations has the most significant impact on the performance of the EnKF system, and their interactions vary dynamically across different settings of the ensemble size and the evapotranspiration perturbation. In addition, the interactions among experimental factors vary greatly in magnitude and direction depending on different statistical metrics for model evaluation including the Nash-Sutcliffe efficiency and the Box-Cox transformed root-mean-square error. It is thus necessary to test various evaluation metrics in order to enhance the robustness of hydrologic prediction systems.

Simultaneously exciting two atoms with photon-mediated Raman interactions

NASA Astrophysics Data System (ADS)

Zhao, Peng; Tan, Xinsheng; Yu, Haifeng; Zhu, Shi-Liang; Yu, Yang

2017-06-01

We propose an approach to simultaneously excite two atoms by using a cavity-assisted Raman process in combination with a cavity-photon-mediated interaction. The system consists of a two-level atom and a Λ -type or V -type three-level atom, which are coupled together with a cavity mode. Having derived the effective Hamiltonian, we find that under certain circumstances a single photon can simultaneously excite two atoms. In addition, multiple photons and even a classical field can also simultaneously excite two atoms. As an example, we show a scheme to realize our proposal in a circuit QED setup, which is artificial atoms coupled with a cavity. The dynamics and the quantum-statistical properties of the process are investigated with experimentally feasible parameters.

Superslow relaxation in identical phase oscillators with random and frustrated interactions

NASA Astrophysics Data System (ADS)

Daido, H.

2018-04-01

This paper is concerned with the relaxation dynamics of a large population of identical phase oscillators, each of which interacts with all the others through random couplings whose parameters obey the same Gaussian distribution with the average equal to zero and are mutually independent. The results obtained by numerical simulation suggest that for the infinite-size system, the absolute value of Kuramoto's order parameter exhibits superslow relaxation, i.e., 1/ln t as time t increases. Moreover, the statistics on both the transient time T for the system to reach a fixed point and the absolute value of Kuramoto's order parameter at t = T are also presented together with their distribution densities over many realizations of the coupling parameters.

The North Pacific as a Regulator of Summertime Climate Over North America and the Asian Monsoon

NASA Technical Reports Server (NTRS)

Lau, William K. M.; Wang, H.

2004-01-01

The interannual variability of summertime rainfall over the U.S. may be linked to climate anomalies over Pacific and East Asia through teleconnection patterns that may be components of recurring global climate modes in boreal summer (Lau and Weng 2002). In this study, maintenance of the boreal summer teleconnection patterns is investigated. The particular focus is on the potential effects of North Pacific air-sea interaction on climate anomalies over the U.S. Observational data, reanalysis and outputs of a series of NASA NSIPP AGCM and AGCM coupled to NASA GSFC MLO model experiments are used. Statistical analysis of observations and NSIPP AMIP type simulations indicates that, the interannual variability of observed warm season precipitation over the U.S. is related to SST variation in both tropical and North Pacific, whereas the NSIPP AMIP simulated summertime US. precipitation variation mainly reflects impact of ENS0 in tropical Pacific. This implies the potential importance of air-sea interaction in North Pacific in contributing to the interannual variability of observed summer climate over the U.S. The anomalous atmospheric circulation associated with the dominant summertime teleconnection modes in both observations and NSIPP AMIP simulations are further diagnosed, using stationary wave modeling approach. In observations, for the two dominant modes, both anomalous diabatic heating and anomalous transients significantly contribute to the anomalous circulation. The distributions of the anomalous diabatic heating and transient forcing are quadrature configured over North Pacific and North America, so that both forcings act constructively to maintain the teleconnection patterns. The contrast between observations and NSIPP AMIP simulations from stationary wave modeling diagnosis confirms the previous conclusion based on statistical analysis. To better appreciate the role of extra-tropical air-sea interaction in maintaining the summertime teleconnection pattern, various dynamical and physical fields and their inter- linkage in the series of NSIPP AGCM and AGCM coupled to MLO model experiments are examined in-depth. Based on comparison between different model experiments, we will discuss the physical and dynamical mechanisms through which the air-sea interaction in extratropics, and transient mean flow interactions over the North Pacific, affects interannual variation of U.S. climate during boreal summer.

Statistical inference for noisy nonlinear ecological dynamic systems.

PubMed

Wood, Simon N

2010-08-26

Chaotic ecological dynamic systems defy conventional statistical analysis. Systems with near-chaotic dynamics are little better. Such systems are almost invariably driven by endogenous dynamic processes plus demographic and environmental process noise, and are only observable with error. Their sensitivity to history means that minute changes in the driving noise realization, or the system parameters, will cause drastic changes in the system trajectory. This sensitivity is inherited and amplified by the joint probability density of the observable data and the process noise, rendering it useless as the basis for obtaining measures of statistical fit. Because the joint density is the basis for the fit measures used by all conventional statistical methods, this is a major theoretical shortcoming. The inability to make well-founded statistical inferences about biological dynamic models in the chaotic and near-chaotic regimes, other than on an ad hoc basis, leaves dynamic theory without the methods of quantitative validation that are essential tools in the rest of biological science. Here I show that this impasse can be resolved in a simple and general manner, using a method that requires only the ability to simulate the observed data on a system from the dynamic model about which inferences are required. The raw data series are reduced to phase-insensitive summary statistics, quantifying local dynamic structure and the distribution of observations. Simulation is used to obtain the mean and the covariance matrix of the statistics, given model parameters, allowing the construction of a 'synthetic likelihood' that assesses model fit. This likelihood can be explored using a straightforward Markov chain Monte Carlo sampler, but one further post-processing step returns pure likelihood-based inference. I apply the method to establish the dynamic nature of the fluctuations in Nicholson's classic blowfly experiments.

Self-Organization: Complex Dynamical Systems in the Evolution of Speech

NASA Astrophysics Data System (ADS)

Oudeyer, Pierre-Yves

Human vocalization systems are characterized by complex structural properties. They are combinatorial, based on the systematic reuse of phonemes, and the set of repertoires in human languages is characterized by both strong statistical regularities—universals—and a great diversity. Besides, they are conventional codes culturally shared in each community of speakers. What are the origins of the forms of speech? What are the mechanisms that permitted their evolution in the course of phylogenesis and cultural evolution? How can a shared speech code be formed in a community of individuals? This chapter focuses on the way the concept of self-organization, and its interaction with natural selection, can throw light on these three questions. In particular, a computational model is presented which shows that a basic neural equipment for adaptive holistic vocal imitation, coupling directly motor and perceptual representations in the brain, can generate spontaneously shared combinatorial systems of vocalizations in a society of babbling individuals. Furthermore, we show how morphological and physiological innate constraints can interact with these self-organized mechanisms to account for both the formation of statistical regularities and diversity in vocalization systems.

Studies of protein-protein and protein-water interactions by small angle x-ray scattering, terahertz spectroscopy, ASMOS, and computer simulation

NASA Astrophysics Data System (ADS)

Kim, Seung Joong

The protein folding problem has been one of the most challenging subjects in biological physics due to its complexity. Energy landscape theory based on statistical mechanics provides a thermodynamic interpretation of the protein folding process. We have been working to answer fundamental questions about protein-protein and protein-water interactions, which are very important for describing the energy landscape surface of proteins correctly. At first, we present a new method for computing protein-protein interaction potentials of solvated proteins directly from SAXS data. An ensemble of proteins was modeled by Metropolis Monte Carlo and Molecular Dynamics simulations, and the global X-ray scattering of the whole model ensemble was computed at each snapshot of the simulation. The interaction potential model was optimized and iterated by a Levenberg-Marquardt algorithm. Secondly, we report that terahertz spectroscopy directly probes hydration dynamics around proteins and determines the size of the dynamical hydration shell. We also present the sequence and pH-dependence of the hydration shell and the effect of the hydrophobicity. On the other hand, kinetic terahertz absorption (KITA) spectroscopy is introduced to study the refolding kinetics of ubiquitin and its mutants. KITA results are compared to small angle X-ray scattering, tryptophan fluorescence, and circular dichroism results. We propose that KITA monitors the rearrangement of hydrogen bonding during secondary structure formation. Finally, we present development of the automated single molecule operating system (ASMOS) for a high throughput single molecule detector, which levitates a single protein molecule in a 10 microm diameter droplet by the laser guidance. I also have performed supporting calculations and simulations with my own program codes.

Time-dependent spectral analysis of interactions within groups of walking pedestrians and vertical structural motion using wavelets

NASA Astrophysics Data System (ADS)

Bocian, M.; Brownjohn, J. M. W.; Racic, V.; Hester, D.; Quattrone, A.; Gilbert, L.; Beasley, R.

2018-05-01

A multi-scale and multi-object interaction phenomena can arise when a group of walking pedestrians crosses a structure capable of exhibiting dynamic response. This is because each pedestrian is an autonomous dynamic system capable of displaying intricate behaviour affected by social, psychological, biomechanical and environmental factors, including adaptations to the structural motion. Despite a wealth of mathematical models attempting to describe and simulate coupled crowd-structure system, their applicability can generally be considered uncertain. This can be assigned to a number of assumptions made in their development and the scarcity or unavailability of data suitable for their validation, in particular those associated with pedestrian-pedestrian and pedestrian-structure interaction. To alleviate this problem, data on behaviour of individual pedestrians within groups of six walkers with different spatial arrangements are gathered simultaneously with data on dynamic structural response of a footbridge, from a series of measurements utilising wireless motion monitors. Unlike in previous studies on coordination of pedestrian behaviour, the collected data can serve as a proxy for pedestrian vertical force, which is of critical importance from the point of view of structural stability. A bivariate analysis framework is proposed and applied to these data, encompassing wavelet transform, synchronisation measures based on Shannon entropy and circular statistics. A topological pedestrian map is contrived showing the strength and directionality of between-subjects interactions. It is found that the coordination in pedestrians' vertical force depends on the spatial collocation within a group, but it is generally weak. The relationship between the bridge and pedestrian behaviour is also analysed, revealing stronger propensity for pedestrians to coordinate their force with the structural motion rather than with each other.

Identifying consumer-resource population dynamics using paleoecological data.

PubMed

Einarsson, Árni; Hauptfleisch, Ulf; Leavitt, Peter R; Ives, Anthony R

2016-02-01

Ecologists have long been fascinated by cyclic population fluctuations, because they suggest strong interactions between exploiter and victim species. Nonetheless, even for populations showing high-amplitude fluctuations, it is often hard to identify which species are the key drivers of the dynamics, because data are generally only available for a single species. Here, we use a paleoecological approach to investigate fluctuations in the midge population in Lake Mývatn, Iceland, which ranges over several orders of magnitude in irregular, multigeneration cycles. Previous circumstantial evidence points to consumer-resource interactions between midges and their primary food, diatoms, as the cause of these high-amplitude fluctuations. Using a pair of sediment cores from the lake, we reconstructed 26 years of dynamics of midges using egg remains and of algal groups using diagnostic pigments. We analyzed these data using statistical methods that account for both the autocorrelated nature of paleoecological data and measurement error caused by the mixing of sediment layers. The analyses revealed a signature of consumer-resource interactions in the fluctuations of midges and diatoms: diatom abundance (as inferred from biomarker pigment diatoxanthin) increased when midge abundance was low, and midge abundance (inferred from egg capsules) decreased when diatom abundance was low. Similar patterns were not found for pigments characterizing the other dominant primary producer group in the lake (cyanobacteria), subdominant algae (cryptophytes), or ubiquitous but chemically unstable biomarkers of total algal abundance (chlorophyll a); however, a significant but weaker pattern was found for the chemically stable indicator of total algal populations (β-carotene) to which diatoms are the dominant contributor. These analyses provide the first paleoecological evaluation of specific trophic interactions underlying high amplitude population fluctuations in lakes.

A RESEARCH DATABASE FOR IMPROVED DATA MANAGEMENT AND ANALYSIS IN LONGITUDINAL STUDIES

PubMed Central

BIELEFELD, ROGER A.; YAMASHITA, TOYOKO S.; KEREKES, EDWARD F.; ERCANLI, EHAT; SINGER, LYNN T.

2014-01-01

We developed a research database for a five-year prospective investigation of the medical, social, and developmental correlates of chronic lung disease during the first three years of life. We used the Ingres database management system and the Statit statistical software package. The database includes records containing 1300 variables each, the results of 35 psychological tests, each repeated five times (providing longitudinal data on the child, the parents, and behavioral interactions), both raw and calculated variables, and both missing and deferred values. The four-layer menu-driven user interface incorporates automatic activation of complex functions to handle data verification, missing and deferred values, static and dynamic backup, determination of calculated values, display of database status, reports, bulk data extraction, and statistical analysis. PMID:7596250

Colloquium: Statistical mechanics of money, wealth, and income

NASA Astrophysics Data System (ADS)

Yakovenko, Victor M.; Rosser, J. Barkley, Jr.

2009-10-01

This Colloquium reviews statistical models for money, wealth, and income distributions developed in the econophysics literature since the late 1990s. By analogy with the Boltzmann-Gibbs distribution of energy in physics, it is shown that the probability distribution of money is exponential for certain classes of models with interacting economic agents. Alternative scenarios are also reviewed. Data analysis of the empirical distributions of wealth and income reveals a two-class distribution. The majority of the population belongs to the lower class, characterized by the exponential (“thermal”) distribution, whereas a small fraction of the population in the upper class is characterized by the power-law (“superthermal”) distribution. The lower part is very stable, stationary in time, whereas the upper part is highly dynamical and out of equilibrium.

Statistical properties of Charney-Hasegawa-Mima zonal flows

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, Johan, E-mail: anderson.johan@gmail.com; Botha, G. J. J.

2015-05-15

A theoretical interpretation of numerically generated probability density functions (PDFs) of intermittent plasma transport events in unforced zonal flows is provided within the Charney-Hasegawa-Mima (CHM) model. The governing equation is solved numerically with various prescribed density gradients that are designed to produce different configurations of parallel and anti-parallel streams. Long-lasting vortices form whose flow is governed by the zonal streams. It is found that the numerically generated PDFs can be matched with analytical predictions of PDFs based on the instanton method by removing the autocorrelations from the time series. In many instances, the statistics generated by the CHM dynamics relaxesmore » to Gaussian distributions for both the electrostatic and vorticity perturbations, whereas in areas with strong nonlinear interactions it is found that the PDFs are exponentially distributed.« less

The Shock and Vibration Bulletin. Part 2. Invited Papers, Structural Dynamics

DTIC Science & Technology

1974-08-01

VIKING LANDER DYNAMICS 41 Mr. Joseph C. Pohlen, Martin Marietta Aerospace, Denver, Colorado Structural Dynamics PERFORMANCE OF STATISTICAL ENERGY ANALYSIS 47...aerospace structures. Analytical prediction of these environments is beyond the current scope of classical modal techniques. Statistical energy analysis methods...have been developed that circumvent the difficulties of high-frequency nodal analysis. These statistical energy analysis methods are evaluated

An agent-based approach for modeling dynamics of contagious disease spread

PubMed Central

Perez, Liliana; Dragicevic, Suzana

2009-01-01

Background The propagation of communicable diseases through a population is an inherent spatial and temporal process of great importance for modern society. For this reason a spatially explicit epidemiologic model of infectious disease is proposed for a greater understanding of the disease's spatial diffusion through a network of human contacts. Objective The objective of this study is to develop an agent-based modelling approach the integrates geographic information systems (GIS) to simulate the spread of a communicable disease in an urban environment, as a result of individuals' interactions in a geospatial context. Methods The methodology for simulating spatiotemporal dynamics of communicable disease propagation is presented and the model is implemented using measles outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by agents associated to places where they interact with other agents. They are endowed with mobility, through a transportation network allowing them to move between places within the urban environment, in order to represent the spatial heterogeneity and the complexity involved in infectious diseases diffusion. The model is implemented on georeferenced land use dataset from Metro Vancouver and makes use of census data sets from Statistics Canada for the municipality of Burnaby, BC, Canada study site. Results The results provide insights into the application of the model to calculate ratios of susceptible/infected in specific time frames and urban environments, due to its ability to depict the disease progression based on individuals' interactions. It is demonstrated that the dynamic spatial interactions within the population lead to high numbers of exposed individuals who perform stationary activities in areas after they have finished commuting. As a result, the sick individuals are concentrated in geographical locations like schools and universities. Conclusion The GIS-agent based model designed for this study can be easily customized to study the disease spread dynamics of any other communicable disease by simply adjusting the modeled disease timeline and/or the infection model and modifying the transmission process. This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an epidemic outbreak. PMID:19656403

Time tracking and interaction of energy-eddies at different scales

NASA Astrophysics Data System (ADS)

Cardesa, Jose I.; Vela-Martin, Alberto; Jimenez, Javier

2016-11-01

We study the energy cascade through coherent structures obtained in time-resolved simulations of incompressible, statistically steady isotropic turbulence. The structures are defined as geometrically connected regions of the flow with high kinetic energy. We compute the latter by band-pass filtering the velocity field around a scale r. We analyse the dynamics of structures extracted with different r, which are a proxy for eddies containing energy at those r. We find that the size of these "energy-eddies" scales with r, while their lifetime scales with the local eddy-turnover r 2 / 3ɛ - 1 / 3 , where ɛ is the energy dissipation averaged over all space and time. Furthermore, a statistical analysis over the lives of the eddies shows a slight predominance of the splitting over the merging process. When we isolate the eddies which do not interact with other eddies of the same scale, we observe a parent-child dependence by which, on average, structures are born at scale r during the decaying part of the life of a structure at scale r' > r . The energy-eddy at r' lives in the same region of space as that at r. Finally, we investigate how interactions between eddies at the same scale are echoed across other scales. Funded by the ERC project Coturb.

Role-separating ordering in social dilemmas controlled by topological frustration

NASA Astrophysics Data System (ADS)

Amaral, Marco A.; Perc, Matjaž; Wardil, Lucas; Szolnoki, Attila; da Silva Júnior, Elton J.; da Silva, Jafferson K. L.

2017-03-01

``Three is a crowd" is an old proverb that applies as much to social interactions as it does to frustrated configurations in statistical physics models. Accordingly, social relations within a triangle deserve special attention. With this motivation, we explore the impact of topological frustration on the evolutionary dynamics of the snowdrift game on a triangular lattice. This topology provides an irreconcilable frustration, which prevents anticoordination of competing strategies that would be needed for an optimal outcome of the game. By using different strategy updating protocols, we observe complex spatial patterns in dependence on payoff values that are reminiscent to a honeycomb-like organization, which helps to minimize the negative consequence of the topological frustration. We relate the emergence of these patterns to the microscopic dynamics of the evolutionary process, both by means of mean-field approximations and Monte Carlo simulations. For comparison, we also consider the same evolutionary dynamics on the square lattice, where of course the topological frustration is absent. However, with the deletion of diagonal links of the triangular lattice, we can gradually bridge the gap to the square lattice. Interestingly, in this case the level of cooperation in the system is a direct indicator of the level of topological frustration, thus providing a method to determine frustration levels in an arbitrary interaction network.

Mean Field Analysis of Large-Scale Interacting Populations of Stochastic Conductance-Based Spiking Neurons Using the Klimontovich Method

NASA Astrophysics Data System (ADS)

Gandolfo, Daniel; Rodriguez, Roger; Tuckwell, Henry C.

2017-03-01

We investigate the dynamics of large-scale interacting neural populations, composed of conductance based, spiking model neurons with modifiable synaptic connection strengths, which are possibly also subjected to external noisy currents. The network dynamics is controlled by a set of neural population probability distributions (PPD) which are constructed along the same lines as in the Klimontovich approach to the kinetic theory of plasmas. An exact non-closed, nonlinear, system of integro-partial differential equations is derived for the PPDs. As is customary, a closing procedure leads to a mean field limit. The equations we have obtained are of the same type as those which have been recently derived using rigorous techniques of probability theory. The numerical solutions of these so called McKean-Vlasov-Fokker-Planck equations, which are only valid in the limit of infinite size networks, actually shows that the statistical measures as obtained from PPDs are in good agreement with those obtained through direct integration of the stochastic dynamical system for large but finite size networks. Although numerical solutions have been obtained for networks of Fitzhugh-Nagumo model neurons, which are often used to approximate Hodgkin-Huxley model neurons, the theory can be readily applied to networks of general conductance-based model neurons of arbitrary dimension.

Role-separating ordering in social dilemmas controlled by topological frustration.

PubMed

Amaral, Marco A; Perc, Matjaž; Wardil, Lucas; Szolnoki, Attila; da Silva Júnior, Elton J; da Silva, Jafferson K L

2017-03-01

''Three is a crowd" is an old proverb that applies as much to social interactions as it does to frustrated configurations in statistical physics models. Accordingly, social relations within a triangle deserve special attention. With this motivation, we explore the impact of topological frustration on the evolutionary dynamics of the snowdrift game on a triangular lattice. This topology provides an irreconcilable frustration, which prevents anticoordination of competing strategies that would be needed for an optimal outcome of the game. By using different strategy updating protocols, we observe complex spatial patterns in dependence on payoff values that are reminiscent to a honeycomb-like organization, which helps to minimize the negative consequence of the topological frustration. We relate the emergence of these patterns to the microscopic dynamics of the evolutionary process, both by means of mean-field approximations and Monte Carlo simulations. For comparison, we also consider the same evolutionary dynamics on the square lattice, where of course the topological frustration is absent. However, with the deletion of diagonal links of the triangular lattice, we can gradually bridge the gap to the square lattice. Interestingly, in this case the level of cooperation in the system is a direct indicator of the level of topological frustration, thus providing a method to determine frustration levels in an arbitrary interaction network.

Exploring the mechanism of how tvMyb2 recognizes and binds ap65-1 by molecular dynamics simulations and free energy calculations.

PubMed

Li, Wei-Kang; Zheng, Qing-Chuan; Zhang, Hong-Xing

2016-01-01

TvMyb2, one of the Myb-like transcriptional factors in Trichomonas vaginalis, binds to two closely spaced promoter sites, MRE-1/MRE-2r and MRE-2f, on the ap65-1 gene. However, detailed dynamical structural characteristics of the tvMyb2-ap65-1 complex and a detailed study of the protein in the complex have not been done. Focused on a specific tvMyb2-MRE-2-13 complex (PDB code: ) and a series of mutants K51A, R84A and R87A, we applied molecular dynamics (MD) simulation and molecular mechanics generalized Born surface area (MM-GBSA) free energy calculations to examine the role of the tvMyb2 protein in recognition interaction. The simulation results indicate that tvMyb2 becomes stable when it binds the DNA duplex. A series of mutants, K51A, R84A and R87A, have been followed, and the results of statistical analyses of the H-bond and hydrophobic contacts show that some residues have significant influence on recognition and binding to ap65-1 DNA. Our work gives important information to understand the interactions of tvMyb2 with ap65-1.

Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

NASA Astrophysics Data System (ADS)

Charakopoulos, A. K.; Katsouli, G. A.; Karakasidis, T. E.

2018-04-01

Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

Economic dynamics with financial fragility and mean-field interaction: A model

NASA Astrophysics Data System (ADS)

Di Guilmi, C.; Gallegati, M.; Landini, S.

2008-06-01

Following Aoki’s statistical mechanics methodology [Masanao Aoki, New Approaches to Macroeconomic Modeling, Cambridge University Press, 1996; Masanao Aoki, Modeling Aggregate Behaviour and Fluctuations in Economics, Cambridge University Press, 2002; Masanao Aoki, and Hiroshi Yoshikawa, Reconstructing Macroeconomics, Cambridge University Press, 2006], we provide some insights into the well-known works of [Bruce Greenwald, Joseph Stiglitz, Macroeconomic models with equity and credit rationing, in: R. Hubbard (Ed.), Information, Capital Markets and Investment, Chicago University Press, Chicago, 1990; Bruce Greenwald, Joseph Stiglitz, Financial markets imperfections and business cycles, Quarterly journal of Economics (1993)]. Specifically, we reach analytically a closed form solution of their models overcoming the aggregation problem. The key idea is to represent the economy as an evolving complex system, composed by heterogeneous interacting agents, that can be partitioned into a space of macroscopic states. This meso level of aggregation permits to adopt mean-field interaction modeling and master equation techniques.

Non-renewal statistics for electron transport in a molecular junction with electron-vibration interaction

NASA Astrophysics Data System (ADS)

Kosov, Daniel S.

2017-09-01

Quantum transport of electrons through a molecule is a series of individual electron tunneling events separated by stochastic waiting time intervals. We study the emergence of temporal correlations between successive waiting times for the electron transport in a vibrating molecular junction. Using the master equation approach, we compute the joint probability distribution for waiting times of two successive tunneling events. We show that the probability distribution is completely reset after each tunneling event if molecular vibrations are thermally equilibrated. If we treat vibrational dynamics exactly without imposing the equilibration constraint, the statistics of electron tunneling events become non-renewal. Non-renewal statistics between two waiting times τ1 and τ2 means that the density matrix of the molecule is not fully renewed after time τ1 and the probability of observing waiting time τ2 for the second electron transfer depends on the previous electron waiting time τ1. The strong electron-vibration coupling is required for the emergence of the non-renewal statistics. We show that in the Franck-Condon blockade regime, extremely rare tunneling events become positively correlated.

Model-free inference of direct network interactions from nonlinear collective dynamics.

PubMed

Casadiego, Jose; Nitzan, Mor; Hallerberg, Sarah; Timme, Marc

2017-12-19

The topology of interactions in network dynamical systems fundamentally underlies their function. Accelerating technological progress creates massively available data about collective nonlinear dynamics in physical, biological, and technological systems. Detecting direct interaction patterns from those dynamics still constitutes a major open problem. In particular, current nonlinear dynamics approaches mostly require to know a priori a model of the (often high dimensional) system dynamics. Here we develop a model-independent framework for inferring direct interactions solely from recording the nonlinear collective dynamics generated. Introducing an explicit dependency matrix in combination with a block-orthogonal regression algorithm, the approach works reliably across many dynamical regimes, including transient dynamics toward steady states, periodic and non-periodic dynamics, and chaos. Together with its capabilities to reveal network (two point) as well as hypernetwork (e.g., three point) interactions, this framework may thus open up nonlinear dynamics options of inferring direct interaction patterns across systems where no model is known.

Single-Molecule Resolution of Antimicrobial Peptide Interactions with Supported Lipid A Bilayers.

PubMed

Nelson, Nathaniel; Schwartz, Daniel K

2018-06-05

The molecular interactions between antimicrobial peptides (AMPs) and lipid A-containing supported lipid bilayers were probed using single-molecule total internal reflection fluorescence microscopy. Hybrid supported lipid bilayers with lipid A outer leaflets and phospholipid (1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE)) inner leaflets were prepared and characterized, and the spatiotemporal trajectories of individual fluorescently labeled LL37 and Melittin AMPs were determined as they interacted with the bilayer surfaces comprising either monophosphoryl or diphosphoryl lipid A (from Escherichia coli) to determine the impact of electrostatic interactions. Large numbers of trajectories were obtained and analyzed to obtain the distributions of surface residence times and the statistics of the spatial trajectories. Interestingly, the AMP species were sensitive to subtle differences in the charge of the lipid, with both peptides diffusing more slowly and residing longer on the diphosphoryl lipid A. Furthermore, the single-molecule dynamics indicated a qualitative difference between the behavior of AMPs on hybrid Lipid A bilayers and on those composed entirely of DOPE. Whereas AMPs interacting with a DOPE bilayer exhibited two-dimensional Brownian diffusion with a diffusion coefficient of ∼1.7 μm 2 /s, AMPs adsorbed to the lipid A surface exhibited much slower apparent diffusion (on the order of ∼0.1 μm 2 /s) and executed intermittent trajectories that alternated between two-dimensional Brownian diffusion and desorption-mediated three-dimensional flights. Overall, these findings suggested that bilayers with lipid A in the outer leaflet, as it is in bacterial outer membranes, are valuable model systems for the study of the initial stage of AMP-bacterium interactions. Furthermore, single-molecule dynamics was sensitive to subtle differences in electrostatic interactions between cationic AMPs and monovalent or divalent anionic lipid A moieties. Copyright © 2018 Biophysical Society. All rights reserved.

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering.

PubMed

Guo, Xuan; Meng, Yu; Yu, Ning; Pan, Yi

2014-04-10

Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS.

Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins.

PubMed

May, Ali; Pool, René; van Dijk, Erik; Bijlard, Jochem; Abeln, Sanne; Heringa, Jaap; Feenstra, K Anton

2014-02-01

To assess whether two proteins will interact under physiological conditions, information on the interaction free energy is needed. Statistical learning techniques and docking methods for predicting protein-protein interactions cannot quantitatively estimate binding free energies. Full atomistic molecular simulation methods do have this potential, but are completely unfeasible for large-scale applications in terms of computational cost required. Here we investigate whether applying coarse-grained (CG) molecular dynamics simulations is a viable alternative for complexes of known structure. We calculate the free energy barrier with respect to the bound state based on molecular dynamics simulations using both a full atomistic and a CG force field for the TCR-pMHC complex and the MP1-p14 scaffolding complex. We find that the free energy barriers from the CG simulations are of similar accuracy as those from the full atomistic ones, while achieving a speedup of >500-fold. We also observe that extensive sampling is extremely important to obtain accurate free energy barriers, which is only within reach for the CG models. Finally, we show that the CG model preserves biological relevance of the interactions: (i) we observe a strong correlation between evolutionary likelihood of mutations and the impact on the free energy barrier with respect to the bound state; and (ii) we confirm the dominant role of the interface core in these interactions. Therefore, our results suggest that CG molecular simulations can realistically be used for the accurate prediction of protein-protein interaction strength. The python analysis framework and data files are available for download at http://www.ibi.vu.nl/downloads/bioinformatics-2013-btt675.tgz.

Cloud computing for detecting high-order genome-wide epistatic interaction via dynamic clustering

PubMed Central

2014-01-01

Backgroud Taking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. Results In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Conclusions Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS. PMID:24717145

Monogenean parasites from fishes of the Vaal Dam, Gauteng Province, South Africa. I. Winter survey versus summer survey comparison from Labeo capensis (Smith, 1841) and Labeo umbratus (Smith, 1841) hosts.

PubMed

Crafford, Dionne; Luus-Powell, Wilmien; Avenant-Oldewage, Annemariè

2014-03-01

Indigenous South African Labeo spp. show promise with regard to development of semi-intensive aquaculture, yet little research on their monogenean fauna has been conducted. Ecological aspects of monogenean fauna of the moggel Labeo umbratus (Smith 1841) and the Orange River mudfish Labeo capensis (Smith 1841), as recorded during both winter and summer sampling surveys, are reported here. Fish were collected using gill nets, euthanized and gills removed and examined to both quantify parasite numbers and distribution on the gills. Results obtained support the hypothesis that gill site preference is not due to active choice for a particular attachment site, but rather a result of water flow over gills during respiration in conjunction with fish behaviour and habitat use. Interaction between individual elements investigated (temperature effects, parasite population dynamics and host population dynamics) may be largely responsible for seasonal differences in infection statistics of monogenean parasites. Such interactions should be investigated in future large scale ecological studies, in combination with experimental studies, to further elucidate these effects.

Bayesian Inference for Functional Dynamics Exploring in fMRI Data.

PubMed

Guo, Xuan; Liu, Bing; Chen, Le; Chen, Guantao; Pan, Yi; Zhang, Jing

2016-01-01

This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM), Bayesian Connectivity Change Point Model (BCCPM), and Dynamic Bayesian Variable Partition Model (DBVPM), and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

Polarization chaos and random bit generation in nonlinear fiber optics induced by a time-delayed counter-propagating feedback loop.

PubMed

Morosi, J; Berti, N; Akrout, A; Picozzi, A; Guasoni, M; Fatome, J

2018-01-22

In this manuscript, we experimentally and numerically investigate the chaotic dynamics of the state-of-polarization in a nonlinear optical fiber due to the cross-interaction between an incident signal and its intense backward replica generated at the fiber-end through an amplified reflective delayed loop. Thanks to the cross-polarization interaction between the two-delayed counter-propagating waves, the output polarization exhibits fast temporal chaotic dynamics, which enable a powerful scrambling process with moving speeds up to 600-krad/s. The performance of this all-optical scrambler was then evaluated on a 10-Gbit/s On/Off Keying telecom signal achieving an error-free transmission. We also describe how these temporal and chaotic polarization fluctuations can be exploited as an all-optical random number generator. To this aim, a billion-bit sequence was experimentally generated and successfully confronted to the dieharder benchmarking statistic tools. Our experimental analysis are supported by numerical simulations based on the resolution of counter-propagating coupled nonlinear propagation equations that confirm the observed behaviors.

Advances in single-cell experimental design made possible by automated imaging platforms with feedback through segmentation.

PubMed

Crick, Alex J; Cammarota, Eugenia; Moulang, Katie; Kotar, Jurij; Cicuta, Pietro

2015-01-01

Live optical microscopy has become an essential tool for studying the dynamical behaviors and variability of single cells, and cell-cell interactions. However, experiments and data analysis in this area are often extremely labor intensive, and it has often not been achievable or practical to perform properly standardized experiments on a statistically viable scale. We have addressed this challenge by developing automated live imaging platforms, to help standardize experiments, increasing throughput, and unlocking previously impossible ones. Our real-time cell tracking programs communicate in feedback with microscope and camera control software, and they are highly customizable, flexible, and efficient. As examples of our current research which utilize these automated platforms, we describe two quite different applications: egress-invasion interactions of malaria parasites and red blood cells, and imaging of immune cells which possess high motility and internal dynamics. The automated imaging platforms are able to track a large number of motile cells simultaneously, over hours or even days at a time, greatly increasing data throughput and opening up new experimental possibilities. Copyright © 2015 Elsevier Inc. All rights reserved.

Steric interactions lead to collective tilting motion in the ribosome during mRNA-tRNA translocation

NASA Astrophysics Data System (ADS)

Nguyen, Kien; Whitford, Paul C.

2016-02-01

Translocation of mRNA and tRNA through the ribosome is associated with large-scale rearrangements of the head domain in the 30S ribosomal subunit. To elucidate the relationship between 30S head dynamics and mRNA-tRNA displacement, we apply molecular dynamics simulations using an all-atom structure-based model. Here we provide a statistical analysis of 250 spontaneous transitions between the A/P-P/E and P/P-E/E ensembles. Consistent with structural studies, the ribosome samples a chimeric ap/P-pe/E intermediate, where the 30S head is rotated ~18°. It then transiently populates a previously unreported intermediate ensemble, which is characterized by a ~10° tilt of the head. To identify the origins of head tilting, we analyse 781 additional simulations in which specific steric features are perturbed. These calculations show that head tilting may be attributed to specific steric interactions between tRNA and the 30S subunit (PE loop and protein S13). Taken together, this study demonstrates how molecular structure can give rise to large-scale collective rearrangements.

Quasilinear models through the lens of resolvent analysis

NASA Astrophysics Data System (ADS)

McKeon, Beverley; Chini, Greg

2017-11-01

Quasilinear (QL) and generalized quasilinear (GQL) analyses, e.g. Marston et al., also variously described as statistical state dynamics models, e.g., Farrell et al., restricted nonlinear models, e.g. Thomas et al., or 2D/3C models, e.g. Gayme et al., have achieved considerable success in recovering the mean velocity profile for a range of turbulent flows. In QL approaches, the portion of the velocity field that can be represented as streamwise constant, i.e. with streamwise wavenumber kx = 0 , is fully resolved, while the streamwise-varying dynamics are linearized about the streamwise-constant field; that is, only those nonlinear interactions that drive the streamwise-constant field are retained, and the non-streamwise constant ``fluctuation-fluctuation'' interactions are ignored. Here, we show how these QL approaches can be reformulated in terms of the closed-loop resolvent analysis of McKeon & Sharma (2010), which enables us to identify reasons for their evident success as well as algorithms for their efficient computation. The support of ONR through Grant No. N00014-17-2307 is gratefully acknowledged.

Dynamics of entropy and nonclassical properties of the state of a Λ-type three-level atom interacting with a single-mode cavity field with intensity-dependent coupling in a Kerr medium

NASA Astrophysics Data System (ADS)

Faghihi, M. J.; Tavassoly, M. K.

2012-02-01

In this paper, we study the interaction between a three-level atom and a quantized single-mode field with ‘intensity-dependent coupling’ in a ‘Kerr medium’. The three-level atom is considered to be in a Λ-type configuration. Under particular initial conditions, which may be prepared for the atom and the field, the dynamical state vector of the entire system will be explicitly obtained, for the arbitrary nonlinearity function f(n) associated with any physical system. Then, after evaluating the variation of the field entropy against time, we will investigate the quantum statistics as well as some of the nonclassical properties of the introduced state. During our calculations we investigate the effects of intensity-dependent coupling, Kerr medium and detuning parameters on the depth and domain of the nonclassicality features of the atom-field state vector. Finally, we compare our obtained results with those of V-type three-level atoms.

Stochastic population dynamics in spatially extended predator-prey systems

NASA Astrophysics Data System (ADS)

Dobramysl, Ulrich; Mobilia, Mauro; Pleimling, Michel; Täuber, Uwe C.

2018-02-01

Spatially extended population dynamics models that incorporate demographic noise serve as case studies for the crucial role of fluctuations and correlations in biological systems. Numerical and analytic tools from non-equilibrium statistical physics capture the stochastic kinetics of these complex interacting many-particle systems beyond rate equation approximations. Including spatial structure and stochastic noise in models for predator-prey competition invalidates the neutral Lotka-Volterra population cycles. Stochastic models yield long-lived erratic oscillations stemming from a resonant amplification mechanism. Spatially extended predator-prey systems display noise-stabilized activity fronts that generate persistent correlations. Fluctuation-induced renormalizations of the oscillation parameters can be analyzed perturbatively via a Doi-Peliti field theory mapping of the master equation; related tools allow detailed characterization of extinction pathways. The critical steady-state and non-equilibrium relaxation dynamics at the predator extinction threshold are governed by the directed percolation universality class. Spatial predation rate variability results in more localized clusters, enhancing both competing species’ population densities. Affixing variable interaction rates to individual particles and allowing for trait inheritance subject to mutations induces fast evolutionary dynamics for the rate distributions. Stochastic spatial variants of three-species competition with ‘rock-paper-scissors’ interactions metaphorically describe cyclic dominance. These models illustrate intimate connections between population dynamics and evolutionary game theory, underscore the role of fluctuations to drive populations toward extinction, and demonstrate how space can support species diversity. Two-dimensional cyclic three-species May-Leonard models are characterized by the emergence of spiraling patterns whose properties are elucidated by a mapping onto a complex Ginzburg-Landau equation. Multiple-species extensions to general ‘food networks’ can be classified on the mean-field level, providing both fundamental understanding of ensuing cooperativity and profound insight into the rich spatio-temporal features and coarsening kinetics in the corresponding spatially extended systems. Novel space-time patterns emerge as a result of the formation of competing alliances; e.g. coarsening domains that each incorporate rock-paper-scissors competition games.

Satellite disintegration dynamics

NASA Technical Reports Server (NTRS)

Dasenbrock, R. R.; Kaufman, B.; Heard, W. B.

1975-01-01

The subject of satellite disintegration is examined in detail. Elements of the orbits of individual fragments, determined by DOD space surveillance systems, are used to accurately predict the time and place of fragmentation. Dual time independent and time dependent analyses are performed for simulated and real breakups. Methods of statistical mechanics are used to study the evolution of the fragment clouds. The fragments are treated as an ensemble of non-interacting particles. A solution of Liouville's equation is obtained which enables the spatial density to be calculated as a function of position, time and initial velocity distribution.

Local conformity induced global oscillation

NASA Astrophysics Data System (ADS)

Li, Dong; Li, Wei; Hu, Gang; Zheng, Zhigang

2009-04-01

The game ‘rock-paper-scissors’ model, with the consideration of the effect of the psychology of conformity, is investigated. The interaction between each two agents is global, but the strategy of the conformity is local for individuals. In the statistical opinion, the probability of the appearance of each strategy is uniform. The dynamical analysis of this model indicates that the equilibrium state may lose its stability at a threshold and is replaced by a globally oscillating state. The global oscillation is induced by the local conformity, which is originated from the synchronization of individual strategies.

Ensemble inequivalence and Maxwell construction in the self-gravitating ring model

NASA Astrophysics Data System (ADS)

Rocha Filho, T. M.; Silvestre, C. H.; Amato, M. A.

2018-06-01

The statement that Gibbs equilibrium ensembles are equivalent is a base line in many approaches in the context of equilibrium statistical mechanics. However, as a known fact, for some physical systems this equivalence may not be true. In this paper we illustrate from first principles the inequivalence between the canonical and microcanonical ensembles for a system with long range interactions. We make use of molecular dynamics simulations and Monte Carlo simulations to explore the thermodynamics properties of the self-gravitating ring model and discuss on what conditions the Maxwell construction is applicable.

Predicting and downscaling ENSO impacts on intraseasonal precipitation statistics in California: The 1997/98 event

USGS Publications Warehouse

Gershunov, A.; Barnett, T.P.; Cayan, D.R.; Tubbs, T.; Goddard, L.

2000-01-01

Three long-range forecasting methods have been evaluated for prediction and downscaling of seasonal and intraseasonal precipitation statistics in California. Full-statistical, hybrid-dynamical - statistical and full-dynamical approaches have been used to forecast El Nin??o - Southern Oscillation (ENSO) - related total precipitation, daily precipitation frequency, and average intensity anomalies during the January - March season. For El Nin??o winters, the hybrid approach emerges as the best performer, while La Nin??a forecasting skill is poor. The full-statistical forecasting method features reasonable forecasting skill for both La Nin??a and El Nin??o winters. The performance of the full-dynamical approach could not be evaluated as rigorously as that of the other two forecasting schemes. Although the full-dynamical forecasting approach is expected to outperform simpler forecasting schemes in the long run, evidence is presented to conclude that, at present, the full-dynamical forecasting approach is the least viable of the three, at least in California. The authors suggest that operational forecasting of any intraseasonal temperature, precipitation, or streamflow statistic derivable from the available records is possible now for ENSO-extreme years.

Quantifying the Dynamic Interactions Between a Clathrin-Coated Pit and Cargo Molecules

NASA Astrophysics Data System (ADS)

Weigel, Aubrey; Tamkun, Michael; Krapf, Diego

2014-03-01

Clathrin-mediated endocytosis is a major pathway of internalization of cargo in eukaryotic cells. This process involves the recruitment of cargo molecules into a growing clathrin-coated pit (CCP). However, cargo-CCP interactions are difficult to study because CCPs display a large degree of lifetime heterogeneity and the interactions with cargo molecules evolve over time. We use single-molecule total internal reflection fluorescence (TIRF) microscopy, in combination with automatic detection and tracking algorithms, to directly visualize the recruitment of individual voltage-gated potassium channels into forming CCPs in living cells. Contrary to widespread ideas, cargo often escapes from a pit before abortive CCP termination or endocytic vesicle production. By measuring tens of thousands of capturing events, we build the distribution of capture times and the times that cargo remains confined to a CCP. An analytical stochastic model is developed and compared to the measured distributions. Due to the dynamic nature of the pit, the model is non-Markovian and it displays long-tail power law statistics. Our findings identify one source of the large heterogeneities observed in CCP maturation and provide a mechanism for the anomalous diffusion of proteins in the plasma membrane. This work was supported by National Science Foundation Grant PHY-0956714.

Phase Transitions in Living Neural Networks

NASA Astrophysics Data System (ADS)

Williams-Garcia, Rashid Vladimir

Our nervous systems are composed of intricate webs of interconnected neurons interacting in complex ways. These complex interactions result in a wide range of collective behaviors with implications for features of brain function, e.g., information processing. Under certain conditions, such interactions can drive neural network dynamics towards critical phase transitions, where power-law scaling is conjectured to allow optimal behavior. Recent experimental evidence is consistent with this idea and it seems plausible that healthy neural networks would tend towards optimality. This hypothesis, however, is based on two problematic assumptions, which I describe and for which I present alternatives in this thesis. First, critical transitions may vanish due to the influence of an environment, e.g., a sensory stimulus, and so living neural networks may be incapable of achieving "critical" optimality. I develop a framework known as quasicriticality, in which a relative optimality can be achieved depending on the strength of the environmental influence. Second, the power-law scaling supporting this hypothesis is based on statistical analysis of cascades of activity known as neuronal avalanches, which conflate causal and non-causal activity, thus confounding important dynamical information. In this thesis, I present a new method to unveil causal links, known as causal webs, between neuronal activations, thus allowing for experimental tests of the quasicriticality hypothesis and other practical applications.

Complexity of life via collective mind

NASA Technical Reports Server (NTRS)

Zak, Michail

2004-01-01

e mind is introduced as a set of simple intelligent units (say, neurons, or interacting agents), which can communicate by exchange of information without explicit global control. Incomplete information is compensated by a sequence of random guesses symmetrically distributed around expectations with prescribed variances. Both the expectations and variances are the invariants characterizing the whole class of agents. These invariants are stored as parameters of the collective mind, while they contribute into dynamical formalism of the agents' evolution, and in particular, into the reflective chains of their nested abstract images of the selves and non-selves. The proposed model consists of the system of stochastic differential equations in the Langevin form representing the motor dynamics, and the corresponding Fokker-Planck equation representing the mental dynamics (Motor dynamics describes the motion in physical space, while mental dynamics simulates the evolution of initial errors in terms of the probability density). The main departure of this model from Newtonian and statistical physics is due to a feedback from the mental to the motor dynamics which makes the Fokker-Planck equation nonlinear. Interpretation of this model from mathematical and physical viewpoints, as well as possible interpretation from biological, psychological, and social viewpoints are discussed. The model is illustrated by the dynamics of a dialog.

Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.

PubMed

Shen, Xianjun; Yi, Li; Jiang, Xingpeng; He, Tingting; Yang, Jincai; Xie, Wei; Hu, Po; Hu, Xiaohua

2017-01-01

How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI) data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment). It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.

Dynamically biased statistical model for the ortho/para conversion in the H2 + H3+ → H3+ + H2 reaction.

PubMed

Gómez-Carrasco, Susana; González-Sánchez, Lola; Aguado, Alfredo; Sanz-Sanz, Cristina; Zanchet, Alexandre; Roncero, Octavio

2012-09-07

In this work we present a dynamically biased statistical model to describe the evolution of the title reaction from statistical to a more direct mechanism, using quasi-classical trajectories (QCT). The method is based on the one previously proposed by Park and Light [J. Chem. Phys. 126, 044305 (2007)]. A recent global potential energy surface is used here to calculate the capture probabilities, instead of the long-range ion-induced dipole interactions. The dynamical constraints are introduced by considering a scrambling matrix which depends on energy and determine the probability of the identity/hop/exchange mechanisms. These probabilities are calculated using QCT. It is found that the high zero-point energy of the fragments is transferred to the rest of the degrees of freedom, what shortens the lifetime of H(5)(+) complexes and, as a consequence, the exchange mechanism is produced with lower proportion. The zero-point energy (ZPE) is not properly described in quasi-classical trajectory calculations and an approximation is done in which the initial ZPE of the reactants is reduced in QCT calculations to obtain a new ZPE-biased scrambling matrix. This reduction of the ZPE is explained by the need of correcting the pure classical level number of the H(5)(+) complex, as done in classical simulations of unimolecular processes and to get equivalent quantum and classical rate constants using Rice-Ramsperger-Kassel-Marcus theory. This matrix allows to obtain a ratio of hop/exchange mechanisms, α(T), in rather good agreement with recent experimental results by Crabtree et al. [J. Chem. Phys. 134, 194311 (2011)] at room temperature. At lower temperatures, however, the present simulations predict too high ratios because the biased scrambling matrix is not statistical enough. This demonstrates the importance of applying quantum methods to simulate this reaction at the low temperatures of astrophysical interest.

Dynamically biased statistical model for the ortho/para conversion in the H2+H3+ --> H3++ H2 reaction

NASA Astrophysics Data System (ADS)

Gómez-Carrasco, Susana; González-Sánchez, Lola; Aguado, Alfredo; Sanz-Sanz, Cristina; Zanchet, Alexandre; Roncero, Octavio

2012-09-01

In this work we present a dynamically biased statistical model to describe the evolution of the title reaction from statistical to a more direct mechanism, using quasi-classical trajectories (QCT). The method is based on the one previously proposed by Park and Light [J. Chem. Phys. 126, 044305 (2007), 10.1063/1.2430711]. A recent global potential energy surface is used here to calculate the capture probabilities, instead of the long-range ion-induced dipole interactions. The dynamical constraints are introduced by considering a scrambling matrix which depends on energy and determine the probability of the identity/hop/exchange mechanisms. These probabilities are calculated using QCT. It is found that the high zero-point energy of the fragments is transferred to the rest of the degrees of freedom, what shortens the lifetime of H_5^+ complexes and, as a consequence, the exchange mechanism is produced with lower proportion. The zero-point energy (ZPE) is not properly described in quasi-classical trajectory calculations and an approximation is done in which the initial ZPE of the reactants is reduced in QCT calculations to obtain a new ZPE-biased scrambling matrix. This reduction of the ZPE is explained by the need of correcting the pure classical level number of the H_5^+ complex, as done in classical simulations of unimolecular processes and to get equivalent quantum and classical rate constants using Rice-Ramsperger-Kassel-Marcus theory. This matrix allows to obtain a ratio of hop/exchange mechanisms, α(T), in rather good agreement with recent experimental results by Crabtree et al. [J. Chem. Phys. 134, 194311 (2011), 10.1063/1.3587246] at room temperature. At lower temperatures, however, the present simulations predict too high ratios because the biased scrambling matrix is not statistical enough. This demonstrates the importance of applying quantum methods to simulate this reaction at the low temperatures of astrophysical interest.

North American extreme temperature events and related large scale meteorological patterns: A review of statistical methods, dynamics, modeling, and trends

DOE PAGES

Grotjahn, Richard; Black, Robert; Leung, Ruby; ...

2015-05-22

This paper reviews research approaches and open questions regarding data, statistical analyses, dynamics, modeling efforts, and trends in relation to temperature extremes. Our specific focus is upon extreme events of short duration (roughly less than 5 days) that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). Methods used to define extreme events statistics and to identify and connect LSMPs to extreme temperatures are presented. Recent advances in statistical techniques can connect LSMPs to extreme temperatures through appropriately defined covariates that supplements more straightforward analyses. A wide array of LSMPs, ranging from synoptic tomore » planetary scale phenomena, have been implicated as contributors to extreme temperature events. Current knowledge about the physical nature of these contributions and the dynamical mechanisms leading to the implicated LSMPs is incomplete. There is a pressing need for (a) systematic study of the physics of LSMPs life cycles and (b) comprehensive model assessment of LSMP-extreme temperature event linkages and LSMP behavior. Generally, climate models capture the observed heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreaks frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Climate models have been used to investigate past changes and project future trends in extreme temperatures. Overall, modeling studies have identified important mechanisms such as the effects of large-scale circulation anomalies and land-atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs more specifically to understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated so more research is needed to understand the limitations of climate models and improve model skill in simulating extreme temperatures and their associated LSMPs. Furthermore, the paper concludes with unresolved issues and research questions.« less

A Flexible Approach for the Statistical Visualization of Ensemble Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Potter, K.; Wilson, A.; Bremer, P.

2009-09-29

Scientists are increasingly moving towards ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. We present a collection of overview and statistical displays linked through a high level of interactivity to provide a framework for gaining key scientific insight into the distribution of the simulation results as well as the uncertainty associated with the data. In contrast to methodsmore » that present large amounts of diverse information in a single display, we argue that combining multiple linked statistical displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate this approach using driving problems from climate modeling and meteorology and discuss generalizations to other fields.« less

Superstatistical Energy Distributions of an Ion in an Ultracold Buffer Gas

NASA Astrophysics Data System (ADS)

Rouse, I.; Willitsch, S.

2017-04-01

An ion in a radio frequency ion trap interacting with a buffer gas of ultracold neutral atoms is a driven dynamical system which has been found to develop a nonthermal energy distribution with a power law tail. The exact analytical form of this distribution is unknown, but has often been represented empirically by q -exponential (Tsallis) functions. Based on the concepts of superstatistics, we introduce a framework for the statistical mechanics of an ion trapped in an rf field subject to collisions with a buffer gas. We derive analytic ion secular energy distributions from first principles both neglecting and including the effects of the thermal energy of the buffer gas. For a buffer gas with a finite temperature, we prove that Tsallis statistics emerges from the combination of a constant heating term and multiplicative energy fluctuations. We show that the resulting distributions essentially depend on experimentally controllable parameters paving the way for an accurate control of the statistical properties of ion-atom hybrid systems.

Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models.

PubMed

Sourty, Marion; Thoraval, Laurent; Roquet, Daniel; Armspach, Jean-Paul; Foucher, Jack; Blanc, Frédéric

2016-01-01

Exploring time-varying connectivity networks in neurodegenerative disorders is a recent field of research in functional MRI. Dementia with Lewy bodies (DLB) represents 20% of the neurodegenerative forms of dementia. Fluctuations of cognition and vigilance are the key symptoms of DLB. To date, no dynamic functional connectivity (DFC) investigations of this disorder have been performed. In this paper, we refer to the concept of connectivity state as a piecewise stationary configuration of functional connectivity between brain networks. From this concept, we propose a new method for group-level as well as for subject-level studies to compare and characterize connectivity state changes between a set of resting-state networks (RSNs). Dynamic Bayesian networks, statistical and graph theory-based models, enable one to learn dependencies between interacting state-based processes. Product hidden Markov models (PHMM), an instance of dynamic Bayesian networks, are introduced here to capture both statistical and temporal aspects of DFC of a set of RSNs. This analysis was based on sliding-window cross-correlations between seven RSNs extracted from a group independent component analysis performed on 20 healthy elderly subjects and 16 patients with DLB. Statistical models of DFC differed in patients compared to healthy subjects for the occipito-parieto-frontal network, the medial occipital network and the right fronto-parietal network. In addition, pairwise comparisons of DFC of RSNs revealed a decrease of dependency between these two visual networks (occipito-parieto-frontal and medial occipital networks) and the right fronto-parietal control network. The analysis of DFC state changes thus pointed out networks related to the cognitive functions that are known to be impaired in DLB: visual processing as well as attentional and executive functions. Besides this context, product HMM applied to RSNs cross-correlations offers a promising new approach to investigate structural and temporal aspects of brain DFC.

Geometry of behavioral spaces: A computational approach to analysis and understanding of agent based models and agent behaviors

NASA Astrophysics Data System (ADS)

Cenek, Martin; Dahl, Spencer K.

2016-11-01

Systems with non-linear dynamics frequently exhibit emergent system behavior, which is important to find and specify rigorously to understand the nature of the modeled phenomena. Through this analysis, it is possible to characterize phenomena such as how systems assemble or dissipate and what behaviors lead to specific final system configurations. Agent Based Modeling (ABM) is one of the modeling techniques used to study the interaction dynamics between a system's agents and its environment. Although the methodology of ABM construction is well understood and practiced, there are no computational, statistically rigorous, comprehensive tools to evaluate an ABM's execution. Often, a human has to observe an ABM's execution in order to analyze how the ABM functions, identify the emergent processes in the agent's behavior, or study a parameter's effect on the system-wide behavior. This paper introduces a new statistically based framework to automatically analyze agents' behavior, identify common system-wide patterns, and record the probability of agents changing their behavior from one pattern of behavior to another. We use network based techniques to analyze the landscape of common behaviors in an ABM's execution. Finally, we test the proposed framework with a series of experiments featuring increasingly emergent behavior. The proposed framework will allow computational comparison of ABM executions, exploration of a model's parameter configuration space, and identification of the behavioral building blocks in a model's dynamics.

Geometry of behavioral spaces: A computational approach to analysis and understanding of agent based models and agent behaviors.

PubMed

Cenek, Martin; Dahl, Spencer K

2016-11-01

Systems with non-linear dynamics frequently exhibit emergent system behavior, which is important to find and specify rigorously to understand the nature of the modeled phenomena. Through this analysis, it is possible to characterize phenomena such as how systems assemble or dissipate and what behaviors lead to specific final system configurations. Agent Based Modeling (ABM) is one of the modeling techniques used to study the interaction dynamics between a system's agents and its environment. Although the methodology of ABM construction is well understood and practiced, there are no computational, statistically rigorous, comprehensive tools to evaluate an ABM's execution. Often, a human has to observe an ABM's execution in order to analyze how the ABM functions, identify the emergent processes in the agent's behavior, or study a parameter's effect on the system-wide behavior. This paper introduces a new statistically based framework to automatically analyze agents' behavior, identify common system-wide patterns, and record the probability of agents changing their behavior from one pattern of behavior to another. We use network based techniques to analyze the landscape of common behaviors in an ABM's execution. Finally, we test the proposed framework with a series of experiments featuring increasingly emergent behavior. The proposed framework will allow computational comparison of ABM executions, exploration of a model's parameter configuration space, and identification of the behavioral building blocks in a model's dynamics.

Models for inference in dynamic metacommunity systems

USGS Publications Warehouse

Dorazio, Robert M.; Kery, Marc; Royle, J. Andrew; Plattner, Matthias

2010-01-01

A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species- and location-specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species-specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity.

Revealing giant internal magnetic fields due to spin fluctuations in magnetically doped colloidal nanocrystals

DOE PAGES

Rice, William D.; Liu, Wenyong; Baker, Thomas A.; ...

2015-11-23

Strong quantum confinement in semiconductors can compress the wavefunctions of band electrons and holes to nanometre-scale volumes, significantly enhancing interactions between themselves and individual dopants. In magnetically doped semiconductors, where paramagnetic dopants (such as Mn 2+, Co 2+ and so on) couple to band carriers via strong sp–d spin exchange, giant magneto-optical effects can therefore be realized in confined geometries using few or even single impurity spins. Importantly, however, thermodynamic spin fluctuations become increasingly relevant in this few-spin limit. In nanoscale volumes, the statistical √N fluctuations of N spins are expected to generate giant effective magnetic fields B eff, whichmore » should dramatically impact carrier spin dynamics, even in the absence of any applied field. In this paper, we directly and unambiguously reveal the large B eff that exist in Mn 2+-doped CdSe colloidal nanocrystals using ultrafast optical spectroscopy. At zero applied magnetic field, extremely rapid (300–600 GHz) spin precession of photoinjected electrons is observed, indicating B eff ~ 15-30 T for electrons. Precession frequencies exceed 2 THz in applied magnetic fields. Finally, these signals arise from electron precession about the random fields due to statistically incomplete cancellation of the embedded Mn 2+ moments, thereby revealing the initial coherent dynamics of magnetic polaron formation, and highlighting the importance of magnetization fluctuations on carrier spin dynamics in nanomaterials.« less

Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues.

PubMed

Perišić, Ognjen

2018-03-16

Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM's weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol's ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner's interacting scaffold is more flexible and adaptable.

Dynamics of essential collective motions in proteins: Theory

NASA Astrophysics Data System (ADS)

Stepanova, Maria

2007-11-01

A general theoretical background is introduced for characterization of conformational motions in protein molecules, and for building reduced coarse-grained models of proteins, based on the statistical analysis of their phase trajectories. Using the projection operator technique, a system of coupled generalized Langevin equations is derived for essential collective coordinates, which are generated by principal component analysis of molecular dynamic trajectories. The number of essential degrees of freedom is not limited in the theory. An explicit analytic relation is established between the generalized Langevin equation for essential collective coordinates and that for the all-atom phase trajectory projected onto the subspace of essential collective degrees of freedom. The theory introduced is applied to identify correlated dynamic domains in a macromolecule and to construct coarse-grained models representing the conformational motions in a protein through a few interacting domains embedded in a dissipative medium. A rigorous theoretical background is provided for identification of dynamic correlated domains in a macromolecule. Examples of domain identification in protein G are given and employed to interpret NMR experiments. Challenges and potential outcomes of the theory are discussed.

Noise-driven switching and chaotic itinerancy among dynamic states in a three-mode intracavity second-harmonic generation laser operating on a Λ transition

NASA Astrophysics Data System (ADS)

Otsuka, Kenju; Ohtomo, Takayuki; Maniwa, Tsuyoshi; Kawasaki, Hazumi; Ko, Jing-Yuan

2003-09-01

We studied the antiphase self-pulsation in a globally coupled three-mode laser operating in different optical spectrum configurations. We observed locking of modal pulsation frequencies, quasiperiodicity, clustering behaviors, and chaos, resulting from the nonlinear interaction among modes. The robustness of [p:q:r] three-frequency locking states and quasiperiodic oscillations against residual noise has been examined by using joint time-frequency analysis of long-term experimental time series. Two sharply antithetical types of switching behaviors among different dynamic states were observed during temporal evolutions; noise-driven switching and self-induced switching, which manifests itself in chaotic itinerancy. The modal interplay behind observed behaviors was studied by using the statistical dynamic quantity of the information circulation. Well-organized information flows among modes, which correspond to the number of degeneracies of modal pulsation frequencies, were found to be established in accordance with the inherent antiphase dynamics. Observed locking behaviors, quasiperiodic motions, and chaotic itinerancy were reproduced by numerical simulation of the model equations.

Results of an Analysis of Field Studies of the Intrinsic Dynamic Characteristics Important for the Safety of Nuclear Power Plant Equipment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaznovsky, A. P., E-mail: kaznovskyap@atech.ru; Kasiyanov, K. G.; Ryasnyj, S. I.

2015-01-15

A classification of the equipment important for the safety of nuclear power plants is proposed in terms of its dynamic behavior under seismic loading. An extended bank of data from dynamic tests over the entire range of thermal and mechanical equipment in generating units with VVER-1000 and RBMK-1000 reactors is analyzed. Results are presented from a study of the statistical behavior of the distribution of vibrational frequencies and damping decrements with the “small perturbation” factor that affects the measured damping decrements taken into account. A need to adjust the regulatory specifications for choosing the values of the damping decrements withmore » specified inertial loads on equipment owing to seismic effects during design calculations is identified. Minimum values of the decrements are determined and proposed for all types of equipment as functions of the directions and natural vibration frequencies of the dynamic interactions to be adopted as conservative standard values in the absence of actual experimental data in the course of design studies of seismic resistance.« less

Quantum-like model of unconscious–conscious dynamics

PubMed Central

Khrennikov, Andrei

2015-01-01

We present a quantum-like model of sensation–perception dynamics (originated in Helmholtz theory of unconscious inference) based on the theory of quantum apparatuses and instruments. We illustrate our approach with the model of bistable perception of a particular ambiguous figure, the Schröder stair. This is a concrete model for unconscious and conscious processing of information and their interaction. The starting point of our quantum-like journey was the observation that perception dynamics is essentially contextual which implies impossibility of (straightforward) embedding of experimental statistical data in the classical (Kolmogorov, 1933) framework of probability theory. This motivates application of nonclassical probabilistic schemes. And the quantum formalism provides a variety of the well-approved and mathematically elegant probabilistic schemes to handle results of measurements. The theory of quantum apparatuses and instruments is the most general quantum scheme describing measurements and it is natural to explore it to model the sensation–perception dynamics. In particular, this theory provides the scheme of indirect quantum measurements which we apply to model unconscious inference leading to transition from sensations to perceptions. PMID:26283979

Structural, electronic, and dynamical properties of liquid water by ab initio molecular dynamics based on SCAN functional within the canonical ensemble

NASA Astrophysics Data System (ADS)

Zheng, Lixin; Chen, Mohan; Sun, Zhaoru; Ko, Hsin-Yu; Santra, Biswajit; Dhuvad, Pratikkumar; Wu, Xifan

2018-04-01

We perform ab initio molecular dynamics (AIMD) simulation of liquid water in the canonical ensemble at ambient conditions using the strongly constrained and appropriately normed (SCAN) meta-generalized-gradient approximation (GGA) functional approximation and carry out systematic comparisons with the results obtained from the GGA-level Perdew-Burke-Ernzerhof (PBE) functional and Tkatchenko-Scheffler van der Waals (vdW) dispersion correction inclusive PBE functional. We analyze various properties of liquid water including radial distribution functions, oxygen-oxygen-oxygen triplet angular distribution, tetrahedrality, hydrogen bonds, diffusion coefficients, ring statistics, density of states, band gaps, and dipole moments. We find that the SCAN functional is generally more accurate than the other two functionals for liquid water by not only capturing the intermediate-range vdW interactions but also mitigating the overly strong hydrogen bonds prescribed in PBE simulations. We also compare the results of SCAN-based AIMD simulations in the canonical and isothermal-isobaric ensembles. Our results suggest that SCAN provides a reliable description for most structural, electronic, and dynamical properties in liquid water.

Training a Constitutional Dynamic Network for Effector Recognition: Storage, Recall, and Erasing of Information.

PubMed

Holub, Jan; Vantomme, Ghislaine; Lehn, Jean-Marie

2016-09-14

Constitutional dynamic libraries (CDLs) of hydrazones, acylhydrazones, and imines undergo reorganization and adaptation in response to chemical effectors (herein metal cations) via component exchange and selection. Such CDLs can be subjected to training by exposition to given effectors and keep memory of the information stored by interaction with a specific metal ion. The long-term storage of the acquired information into the set of constituents of the system allows for fast recognition on subsequent contacts with the same effector(s). Dynamic networks of constituents were designed to adapt orthogonally to different metal cations by up- and down-regulation of specific constituents in the final distribution. The memory may be erased by component exchange between the constituents so as to regenerate the initial (statistical) distribution. The libraries described represent constitutional dynamic systems capable of acting as information storage molecular devices, in which the presence of components linked by reversible covalent bonds in slow exchange and bearing adequate coordination sites allows for the adaptation to different metal ions by constitutional variation. The system thus performs information storage, recall, and erase processes.

Uniform strongly interacting soliton gas in the frame of the Nonlinear Schrodinger Equation

NASA Astrophysics Data System (ADS)

Gelash, Andrey; Agafontsev, Dmitry

2017-04-01

The statistical properties of many soliton systems play the key role in the fundamental studies of integrable turbulence and extreme sea wave formation. It is well known that separated solitons are stable nonlinear coherent structures moving with constant velocity. After collisions with each other they restore the original shape and only acquire an additional phase shift. However, at the moment of strong nonlinear soliton interaction (i.e. when solitons are located close) the wave field are highly complicated and should be described by the theory of inverse scattering transform (IST), which allows to integrate the KdV equation, the NLSE and many other important nonlinear models. The usual approach of studying the dynamics and statistics of soliton wave field is based on relatively rarefied gas of solitons [1,2] or restricted by only two-soliton interactions [3]. From the other hand, the exceptional role of interacting solitons and similar coherent structures - breathers in the formation of rogue waves statistics was reported in several recent papers [4,5]. In this work we study the NLSE and use the most straightforward and general way to create many soliton initial condition - the exact N-soliton formulas obtained in the theory of the IST [6]. We propose the recursive numerical scheme for Zakharov-Mikhailov variant of the dressing method [7,8] and discuss its stability with respect to increasing the number of solitons. We show that the pivoting, i.e. the finding of an appropriate order for recursive operations, has a significant impact on the numerical accuracy. We use the developed scheme to generate statistical ensembles of 32 strongly interacting solitons, i.e. solve the inverse scattering problem for the high number of discrete eigenvalues. Then we use this ensembles as initial conditions for numerical simulations in the box with periodic boundary conditions and study statics of obtained uniform strongly interacting gas of NLSE solitons. Author thanks the support of the Russian Science Foundation (Grand No. 14-22-00174) [1] D. Dutykh, E. Pelinovsky, Numerical simulation of a solitonic gas in kdv and kdv-bbm equations, Physics Letters A 378 (42) (2014) 3102-3110. [2] E. Shurgalina, E. Pelinovsky, Nonlinear dynamics of a soliton gas: Modified korteweg-de vries equation framework, Physics Letters A 380 (24) (2016) 2049-2053. [3] E. N. Pelinovsky, E. Shurgalina, A. Sergeeva, T. G. Talipova, G. El, R. H. Grimshaw, Two-soliton interaction as an elementary act of soliton turbulence in integrable systems, Physics Letters A 377 (3) (2013) 272-275 [4] J. Soto-Crespo, N. Devine, N. Akhmediev, Integrable turbulence and rogue waves: Breathers or solitons?, Physical review letters 116 (10) (2016) 103901. [5] D. S. Agafontsev, V. E. Zakharov, Integrable turbulence and formation of rogue waves, Nonlinearity 28 (8) (2015) 2791. [6] V. E. Zakharov, A. B. Shabat, Exact theory of two-dimensional self-focusing and one-dimensional self-modulation of waves in nonlinear media, Soviet Physics JETP 34 (1) (1972) 62. [7] V. Zakharov, A. Mikhailov, Relativistically invariant two-dimensional models of field theory which are integrable by means of the inverse scattering problem method, Sov. Phys.-JETP (Engl. Transl.) 47 (6) (1978). [8] A. A. Gelash, V. E. Zakharov, Superregular solitonic solutions: a novel scenario for the nonlinear stage of modulation instability, Nonlinearity 27 (4) (2014) R1.

Dynamic Interactive Learning Systems

ERIC Educational Resources Information Center

Sabry, Khaled; Barker, Jeff

2009-01-01

This paper reviews and discusses the notions of interactivity and dynamicity of learning systems in relation to information technologies and design principles that can contribute to interactive and dynamic learning. It explores the concept of dynamic interactive learning systems based on the emerging generation of information as part of a…

Dynamic structural disorder in supported nanoscale catalysts

NASA Astrophysics Data System (ADS)

Rehr, J. J.; Vila, F. D.

2014-04-01

We investigate the origin and physical effects of "dynamic structural disorder" (DSD) in supported nano-scale catalysts. DSD refers to the intrinsic fluctuating, inhomogeneous structure of such nano-scale systems. In contrast to bulk materials, nano-scale systems exhibit substantial fluctuations in structure, charge, temperature, and other quantities, as well as large surface effects. The DSD is driven largely by the stochastic librational motion of the center of mass and fluxional bonding at the nanoparticle surface due to thermal coupling with the substrate. Our approach for calculating and understanding DSD is based on a combination of real-time density functional theory/molecular dynamics simulations, transient coupled-oscillator models, and statistical mechanics. This approach treats thermal and dynamic effects over multiple time-scales, and includes bond-stretching and -bending vibrations, and transient tethering to the substrate at longer ps time-scales. Potential effects on the catalytic properties of these clusters are briefly explored. Model calculations of molecule-cluster interactions and molecular dissociation reaction paths are presented in which the reactant molecules are adsorbed on the surface of dynamically sampled clusters. This model suggests that DSD can affect both the prefactors and distribution of energy barriers in reaction rates, and thus can significantly affect catalytic activity at the nano-scale.

Dynamics of Cell Ensembles on Adhesive Micropatterns: Bridging the Gap between Single Cell Spreading and Collective Cell Migration

PubMed Central

Albert, Philipp J.; Schwarz, Ulrich S.

2016-01-01

The collective dynamics of multicellular systems arise from the interplay of a few fundamental elements: growth, division and apoptosis of single cells; their mechanical and adhesive interactions with neighboring cells and the extracellular matrix; and the tendency of polarized cells to move. Micropatterned substrates are increasingly used to dissect the relative roles of these fundamental processes and to control the resulting dynamics. Here we show that a unifying computational framework based on the cellular Potts model can describe the experimentally observed cell dynamics over all relevant length scales. For single cells, the model correctly predicts the statistical distribution of the orientation of the cell division axis as well as the final organisation of the two daughters on a large range of micropatterns, including those situations in which a stable configuration is not achieved and rotation ensues. Large ensembles migrating in heterogeneous environments form non-adhesive regions of inward-curved arcs like in epithelial bridge formation. Collective migration leads to swirl formation with variations in cell area as observed experimentally. In each case, we also use our model to predict cell dynamics on patterns that have not been studied before. PMID:27054883

Coevolution of game and network structure with adjustable linking

NASA Astrophysics Data System (ADS)

Qin, Shao-Meng; Zhang, Guo-Yong; Chen, Yong

2009-12-01

Most papers about the evolutionary game on graph assume the statistic network structure. However, in the real world, social interaction could change the relationship among people. And the change of social structure will also affect people’s strategies. We build a coevolution model of prisoner’s dilemma game and network structure to study the dynamic interaction in the real world. Differing from other coevolution models, players rewire their network connections according to the density of cooperation and other players’ payoffs. We use a parameter α to control the effect of payoff in the process of rewiring. Based on the asynchronous update rule and Monte Carlo simulation, we find that, when players prefer to rewire their links to those who are richer, the temptation can increase the cooperation density.

Trust and Fertility Dynamics

PubMed Central

Billari, Francesco C.; Pessin, Léa

2016-01-01

We argue that the divergence in fertility trends in advanced societies is influenced by the interaction of long-standing differences in generalized trust with the increase in women’s educational attainment. Our argument builds on the idea that trust enhances individuals’ and couples’ willingness to outsource childcare to outside their extended family. This becomes critically important as women’s increased education enhances the demand for combining work and family life. We test our hypothesis using data from the World Values Survey and European Values Study on 36 industrialized countries between the years 1981 and 2009. Multilevel statistical analyses reveal that the interaction between national-level generalized trust and cohort-level women’s education is positively associated with completed fertility. As education among women expands, high levels of generalized trust moderate fertility decline. PMID:28003707

The Validation of Macro and Micro Observations of Parent–Child Dynamics Using the Relationship Affect Coding System in Early Childhood

PubMed Central

Mun, Chung Jung; Tein, Jenn-Yun; Kim, Hanjoe; Shaw, Daniel S.; Gardner, Frances; Wilson, Melvin N.; Peterson, Jenene

2018-01-01

This study examined the validity of micro social observations and macro ratings of parent–child interaction in early to middle childhood. Seven hundred and thirty-one families representing multiple ethnic groups were recruited and screened as at risk in the context of Women, Infant, and Children (WIC) Nutritional Supplement service settings. Families were randomly assigned to the Family Checkup (FCU) intervention or the control condition at age 2 and videotaped in structured interactions in the home at ages 2, 3, 4, and 5. Parent–child interaction videotapes were microcoded using the Relationship Affect Coding System (RACS) that captures the duration of two mutual dyadic states: positive engagement and coercion. Macro ratings of parenting skills were collected after coding the videotapes to assess parent use of positive behavior support and limit setting skills (or lack thereof). Confirmatory factor analyses revealed that the measurement model of macro ratings of limit setting and positive behavior support was not supported by the data, and thus, were excluded from further analyses. However, there was moderate stability in the families’ micro social dynamics across early childhood and it showed significant improvements as a function of random assignment to the FCU. Moreover, parent–child dynamics were predictive of chronic behavior problems as rated by parents in middle childhood, but not emotional problems. We conclude with a discussion of the validity of the RACS and on methodological advantages of micro social coding over the statistical limitations of macro rating observations. Future directions are discussed for observation research in prevention science. PMID:27620623

[On the relation between encounter rate and population density: Are classical models of population dynamics justified?].

PubMed

Nedorezov, L V

2015-01-01

A stochastic model of migrations on a lattice and with discrete time is considered. It is assumed that space is homogenous with respect to its properties and during one time step every individual (independently of local population numbers) can migrate to nearest nodes of lattice with equal probabilities. It is also assumed that population size remains constant during certain time interval of computer experiments. The following variants of estimation of encounter rate between individuals are considered: when for the fixed time moments every individual in every node of lattice interacts with all other individuals in the node; when individuals can stay in nodes independently, or can be involved in groups in two, three or four individuals. For each variant of interactions between individuals, average value (with respect to space and time) is computed for various values of population size. The samples obtained were compared with respective functions of classic models of isolated population dynamics: Verhulst model, Gompertz model, Svirezhev model, and theta-logistic model. Parameters of functions were calculated with least square method. Analyses of deviations were performed using Kolmogorov-Smirnov test, Lilliefors test, Shapiro-Wilk test, and other statistical tests. It is shown that from traditional point of view there are no correspondence between the encounter rate and functions describing effects of self-regulatory mechanisms on population dynamics. Best fitting of samples was obtained with Verhulst and theta-logistic models when using the dataset resulted from the situation when every individual in the node interacts with all other individuals.

The Validation of Macro and Micro Observations of Parent-Child Dynamics Using the Relationship Affect Coding System in Early Childhood.

PubMed

Dishion, Thomas J; Mun, Chung Jung; Tein, Jenn-Yun; Kim, Hanjoe; Shaw, Daniel S; Gardner, Frances; Wilson, Melvin N; Peterson, Jenene

2017-04-01

This study examined the validity of micro social observations and macro ratings of parent-child interaction in early to middle childhood. Seven hundred and thirty-one families representing multiple ethnic groups were recruited and screened as at risk in the context of Women, Infant, and Children (WIC) Nutritional Supplement service settings. Families were randomly assigned to the Family Checkup (FCU) intervention or the control condition at age 2 and videotaped in structured interactions in the home at ages 2, 3, 4, and 5. Parent-child interaction videotapes were micro-coded using the Relationship Affect Coding System (RACS) that captures the duration of two mutual dyadic states: positive engagement and coercion. Macro ratings of parenting skills were collected after coding the videotapes to assess parent use of positive behavior support and limit setting skills (or lack thereof). Confirmatory factor analyses revealed that the measurement model of macro ratings of limit setting and positive behavior support was not supported by the data, and thus, were excluded from further analyses. However, there was moderate stability in the families' micro social dynamics across early childhood and it showed significant improvements as a function of random assignment to the FCU. Moreover, parent-child dynamics were predictive of chronic behavior problems as rated by parents in middle childhood, but not emotional problems. We conclude with a discussion of the validity of the RACS and on methodological advantages of micro social coding over the statistical limitations of macro rating observations. Future directions are discussed for observation research in prevention science.

Applying dynamic Bayesian networks to perturbed gene expression data.

PubMed

Dojer, Norbert; Gambin, Anna; Mizera, Andrzej; Wilczyński, Bartek; Tiuryn, Jerzy

2006-05-08

A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments. We extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed. We show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough.

Coherent structures in wall-bounded turbulence

NASA Astrophysics Data System (ADS)

Jiménez, Javier

2018-05-01

This article discusses the description of wall-bounded turbulence as a deterministic high-dimensional dynamical system of interacting coherent structures, defined as eddies with enough internal dynamics to behave relatively autonomously from any remaining incoherent part of the flow. The guiding principle is that randomness is not a property, but a methodological choice of what to ignore in the flow, and that a complete understanding of turbulence, including the possibility of control, requires that it be kept to a minimum. After briefly reviewing the underlying low-order statistics of flows at moderate Reynolds numbers, the article examines what two-point statistics imply for the decomposition of the flow into individual eddies. Intense eddies are examined next, including their temporal evolution, and shown to satisfy many of the properties required for coherence. In particular, it is shown that coherent structures larger than the Corrsin scale are a natural consequence of the shear. In wall-bounded turbulence, they can be classified into coherent dispersive waves and transient bursts. The former are found in the viscous layer near the wall and as very-large structures spanning the boundary layer thickness. Although they are shear-driven, these waves have enough internal structure to maintain a uniform advection velocity. Conversely, bursts exist at all scales, are characteristic of the logarithmic layer, and interact almost linearly with the shear. While the waves require a wall to determine their length scale, the bursts are essentially independent from it. The article concludes with a brief review of our present theoretical understanding of turbulent structures, and with a list of open problems and future perspectives.

Estimating HIV-1 Fitness Characteristics from Cross-Sectional Genotype Data

PubMed Central

Gopalakrishnan, Sathej; Montazeri, Hesam; Menz, Stephan; Beerenwinkel, Niko; Huisinga, Wilhelm

2014-01-01

Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure. PMID:25375675

Networks and the Epidemiology of Infectious Disease

PubMed Central

Danon, Leon; Ford, Ashley P.; House, Thomas; Jewell, Chris P.; Keeling, Matt J.; Roberts, Gareth O.; Ross, Joshua V.; Vernon, Matthew C.

2011-01-01

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues. PMID:21437001

Recurrent network dynamics reconciles visual motion segmentation and integration.

PubMed

Medathati, N V Kartheek; Rankin, James; Meso, Andrew I; Kornprobst, Pierre; Masson, Guillaume S

2017-09-12

In sensory systems, a range of computational rules are presumed to be implemented by neuronal subpopulations with different tuning functions. For instance, in primate cortical area MT, different classes of direction-selective cells have been identified and related either to motion integration, segmentation or transparency. Still, how such different tuning properties are constructed is unclear. The dominant theoretical viewpoint based on a linear-nonlinear feed-forward cascade does not account for their complex temporal dynamics and their versatility when facing different input statistics. Here, we demonstrate that a recurrent network model of visual motion processing can reconcile these different properties. Using a ring network, we show how excitatory and inhibitory interactions can implement different computational rules such as vector averaging, winner-take-all or superposition. The model also captures ordered temporal transitions between these behaviors. In particular, depending on the inhibition regime the network can switch from motion integration to segmentation, thus being able to compute either a single pattern motion or to superpose multiple inputs as in motion transparency. We thus demonstrate that recurrent architectures can adaptively give rise to different cortical computational regimes depending upon the input statistics, from sensory flow integration to segmentation.

Dynamical and statistical behavior of discrete combustion waves: a theoretical and numerical study.

PubMed

Bharath, Naine Tarun; Rashkovskiy, Sergey A; Tewari, Surya P; Gundawar, Manoj Kumar

2013-04-01

We present a detailed theoretical and numerical study of combustion waves in a discrete one-dimensional disordered system. The distances between neighboring reaction cells were modeled with a gamma distribution. The results show that the random structure of the microheterogeneous system plays a crucial role in the dynamical and statistical behavior of the system. This is a consequence of the nonlinear interaction of the random structure of the system with the thermal wave. An analysis of the experimental data on the combustion of a gasless system (Ti + xSi) and a wide range of thermite systems was performed in view of the developed model. We have shown that the burning rate of the powder system sensitively depends on its internal structure. The present model allows for reproducing theoretically the experimental data for a wide range of pyrotechnic mixtures. We show that Arrhenius' macrokinetics at combustion of disperse systems can take place even in the absence of Arrhenius' microkinetics; it can have a purely thermal nature and be related to their heterogeneity and to the existence of threshold temperature. It is also observed that the combustion of disperse systems always occurs in the microheterogeneous mode according to the relay-race mechanism.

Dynamical and statistical behavior of discrete combustion waves: A theoretical and numerical study

NASA Astrophysics Data System (ADS)

Bharath, Naine Tarun; Rashkovskiy, Sergey A.; Tewari, Surya P.; Gundawar, Manoj Kumar

2013-04-01

We present a detailed theoretical and numerical study of combustion waves in a discrete one-dimensional disordered system. The distances between neighboring reaction cells were modeled with a gamma distribution. The results show that the random structure of the microheterogeneous system plays a crucial role in the dynamical and statistical behavior of the system. This is a consequence of the nonlinear interaction of the random structure of the system with the thermal wave. An analysis of the experimental data on the combustion of a gasless system (Ti + xSi) and a wide range of thermite systems was performed in view of the developed model. We have shown that the burning rate of the powder system sensitively depends on its internal structure. The present model allows for reproducing theoretically the experimental data for a wide range of pyrotechnic mixtures. We show that Arrhenius’ macrokinetics at combustion of disperse systems can take place even in the absence of Arrhenius’ microkinetics; it can have a purely thermal nature and be related to their heterogeneity and to the existence of threshold temperature. It is also observed that the combustion of disperse systems always occurs in the microheterogeneous mode according to the relay-race mechanism.

Universal self-similar dynamics of relativistic and nonrelativistic field theories near nonthermal fixed points

NASA Astrophysics Data System (ADS)

Piñeiro Orioli, Asier; Boguslavski, Kirill; Berges, Jürgen

2015-07-01

We investigate universal behavior of isolated many-body systems far from equilibrium, which is relevant for a wide range of applications from ultracold quantum gases to high-energy particle physics. The universality is based on the existence of nonthermal fixed points, which represent nonequilibrium attractor solutions with self-similar scaling behavior. The corresponding dynamic universality classes turn out to be remarkably large, encompassing both relativistic as well as nonrelativistic quantum and classical systems. For the examples of nonrelativistic (Gross-Pitaevskii) and relativistic scalar field theory with quartic self-interactions, we demonstrate that infrared scaling exponents as well as scaling functions agree. We perform two independent nonperturbative calculations, first by using classical-statistical lattice simulation techniques and second by applying a vertex-resummed kinetic theory. The latter extends kinetic descriptions to the nonperturbative regime of overoccupied modes. Our results open new perspectives to learn from experiments with cold atoms aspects about the dynamics during the early stages of our universe.

A parallel direct-forcing fictitious domain method for simulating microswimmers

NASA Astrophysics Data System (ADS)

Gao, Tong; Lin, Zhaowu

2017-11-01

We present a 3D parallel direct-forcing fictitious domain method for simulating swimming micro-organisms at small Reynolds numbers. We treat the motile micro-swimmers as spherical rigid particles using the ``Squirmer'' model. The particle dynamics are solved on the moving Larangian meshes that overlay upon a fixed Eulerian mesh for solving the fluid motion, and the momentum exchange between the two phases is resolved by distributing pseudo body-forces over the particle interior regions which constrain the background fictitious fluids to follow the particle movement. While the solid and fluid subproblems are solved separately, no inner-iterations are required to enforce numerical convergence. We demonstrate the accuracy and robustness of the method by comparing our results with the existing analytical and numerical studies for various cases of single particle dynamics and particle-particle interactions. We also perform a series of numerical explorations to obtain statistical and rheological measurements to characterize the dynamics and structures of Squirmer suspensions. NSF DMS 1619960.

Pilot-Wave Hydrodynamics

NASA Astrophysics Data System (ADS)

Bush, John W. M.

2015-01-01

Yves Couder, Emmanuel Fort, and coworkers recently discovered that a millimetric droplet sustained on the surface of a vibrating fluid bath may self-propel through a resonant interaction with its own wave field. This article reviews experimental evidence indicating that the walking droplets exhibit certain features previously thought to be exclusive to the microscopic, quantum realm. It then reviews theoretical descriptions of this hydrodynamic pilot-wave system that yield insight into the origins of its quantum-like behavior. Quantization arises from the dynamic constraint imposed on the droplet by its pilot-wave field, and multimodal statistics appear to be a feature of chaotic pilot-wave dynamics. I attempt to assess the potential and limitations of this hydrodynamic system as a quantum analog. This fluid system is compared to quantum pilot-wave theories, shown to be markedly different from Bohmian mechanics and more closely related to de Broglie's original conception of quantum dynamics, his double-solution theory, and its relatively recent extensions through researchers in stochastic electrodynamics.

Local alignment vectors reveal cancer cell-induced ECM fiber remodeling dynamics

PubMed Central

Lee, Byoungkoo; Konen, Jessica; Wilkinson, Scott; Marcus, Adam I.; Jiang, Yi

2017-01-01

Invasive cancer cells interact with the surrounding extracellular matrix (ECM), remodeling ECM fiber network structure by condensing, degrading, and aligning these fibers. We developed a novel local alignment vector analysis method to quantitatively measure collagen fiber alignment as a vector field using Circular Statistics. This method was applied to human non-small cell lung carcinoma (NSCLC) cell lines, embedded as spheroids in a collagen gel. Collagen remodeling was monitored using second harmonic generation imaging under normal conditions and when the LKB1-MARK1 pathway was disrupted through RNAi-based approaches. The results showed that inhibiting LKB1 or MARK1 in NSCLC increases the collagen fiber alignment and captures outward alignment vectors from the tumor spheroid, corresponding to high invasiveness of LKB1 mutant cancer cells. With time-lapse imaging of ECM micro-fiber morphology, the local alignment vector can measure the dynamic signature of invasive cancer cell activity and cell-migration-induced ECM and collagen remodeling and realigning dynamics. PMID:28045069

Robust variable selection method for nonparametric differential equation models with application to nonlinear dynamic gene regulatory network analysis.

PubMed

Lu, Tao

2016-01-01

The gene regulation network (GRN) evaluates the interactions between genes and look for models to describe the gene expression behavior. These models have many applications; for instance, by characterizing the gene expression mechanisms that cause certain disorders, it would be possible to target those genes to block the progress of the disease. Many biological processes are driven by nonlinear dynamic GRN. In this article, we propose a nonparametric differential equation (ODE) to model the nonlinear dynamic GRN. Specially, we address following questions simultaneously: (i) extract information from noisy time course gene expression data; (ii) model the nonlinear ODE through a nonparametric smoothing function; (iii) identify the important regulatory gene(s) through a group smoothly clipped absolute deviation (SCAD) approach; (iv) test the robustness of the model against possible shortening of experimental duration. We illustrate the usefulness of the model and associated statistical methods through a simulation and a real application examples.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sule, Nishant; Yifat, Yuval; Gray, Stephen K.

We examine the formation and concomitant rotation of electrodynamically bound dimers (EBD) of 150nm diameter Ag nanoparticles trapped in circularly polarized focused Gaussian beams. The rotation frequency of an EBD increases linearly with the incident beam power, reaching high mean values of ~ 4kHz for a relatively low incident power of 14mW. Using a coupled-dipole/effective polarizability model, we reveal that retardation of the scattered fields and electrodynamic interactions can lead to a “negative torque” causing rotation of the EBD in the direction opposite to that of the circular polarization. This intriguing opposite-handed rotation due to negative torque is clearly demonstratedmore » using electrodynamics-Langevin dynamics simulations by changing particle separations and thus varying the retardation effects. Finally, negative torque is also demonstrated in experiments from statistical analysis of the EBD trajectories. These results demonstrate novel rotational dynamics of nanoparticles in optical matter using circular polarization and open a new avenue to control orientational dynamics through coupling to interparticle separation.« less

Simulations for designing and interpreting intervention trials in infectious diseases.

PubMed

Halloran, M Elizabeth; Auranen, Kari; Baird, Sarah; Basta, Nicole E; Bellan, Steven E; Brookmeyer, Ron; Cooper, Ben S; DeGruttola, Victor; Hughes, James P; Lessler, Justin; Lofgren, Eric T; Longini, Ira M; Onnela, Jukka-Pekka; Özler, Berk; Seage, George R; Smith, Thomas A; Vespignani, Alessandro; Vynnycky, Emilia; Lipsitch, Marc

2017-12-29

Interventions in infectious diseases can have both direct effects on individuals who receive the intervention as well as indirect effects in the population. In addition, intervention combinations can have complex interactions at the population level, which are often difficult to adequately assess with standard study designs and analytical methods. Herein, we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly with regard to emerging infectious diseases, one that more accurately reflects the dynamics of the transmission process. In an increasingly complex world, simulations can explicitly represent transmission dynamics, which are critical for proper trial design and interpretation. Certain ethical aspects of a trial can also be quantified using simulations. Further, after a trial has been conducted, simulations can be used to explore the possible explanations for the observed effects. Much is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods, and the conduct of clinical trials.

Modeling Stochastic Complexity in Complex Adaptive Systems: Non-Kolmogorov Probability and the Process Algebra Approach.

PubMed

Sulis, William H

2017-10-01

Walter Freeman III pioneered the application of nonlinear dynamical systems theories and methodologies in his work on mesoscopic brain dynamics.Sadly, mainstream psychology and psychiatry still cling to linear correlation based data analysis techniques, which threaten to subvert the process of experimentation and theory building. In order to progress, it is necessary to develop tools capable of managing the stochastic complexity of complex biopsychosocial systems, which includes multilevel feedback relationships, nonlinear interactions, chaotic dynamics and adaptability. In addition, however, these systems exhibit intrinsic randomness, non-Gaussian probability distributions, non-stationarity, contextuality, and non-Kolmogorov probabilities, as well as the absence of mean and/or variance and conditional probabilities. These properties and their implications for statistical analysis are discussed. An alternative approach, the Process Algebra approach, is described. It is a generative model, capable of generating non-Kolmogorov probabilities. It has proven useful in addressing fundamental problems in quantum mechanics and in the modeling of developing psychosocial systems.

Dynamics of Large-Scale Fluctuations in Native Proteins.

NASA Astrophysics Data System (ADS)

Erman, Burak; Erkip, Albert

2003-03-01

The fluctuations of residues of proteins about their equilibrium configurations are analyzed by Langevin dynamics. Residue pairs that are within a given cutoff distance of each other are assumed to be connected by linear springs. The action of the solvent and intramolecular interactions on each residue are treated as random noise. The correlations of fluctuations resulting from the solution of the Langevin equation are observed to be identical to those obtained by the Gaussian Network Model based on equilibrium statistical mechanics. The time delayed correlations of fluctuations, and the response of the protein to a given frequency and to a window of frequencies are determined. The fluctuations of the residues resulting from a given fixed externally applied frequency are evaluated for different modes of the system. Synchronous and asynchronous components of correlations for different modes are formulated. The results of the present study are applied to study the fluctuation dynamics of the 241 residue protein S. marcescens endonuclease (1QL0).

Advanced functional network analysis in the geosciences: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Runge, Jakob; Schultz, Hanna C. H.; Wiedermann, Marc; Zech, Alraune; Feldhoff, Jan; Rheinwalt, Aljoscha; Kutza, Hannes; Radebach, Alexander; Marwan, Norbert; Kurths, Jürgen

2013-04-01

Functional networks are a powerful tool for analyzing large geoscientific datasets such as global fields of climate time series originating from observations or model simulations. pyunicorn (pythonic unified complex network and recurrence analysis toolbox) is an open-source, fully object-oriented and easily parallelizable package written in the language Python. It allows for constructing functional networks (aka climate networks) representing the structure of statistical interrelationships in large datasets and, subsequently, investigating this structure using advanced methods of complex network theory such as measures for networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn allows to study the complex dynamics of geoscientific systems as recorded by time series by means of recurrence networks and visibility graphs. The range of possible applications of the package is outlined drawing on several examples from climatology.

Volatility behavior of visibility graph EMD financial time series from Ising interacting system

NASA Astrophysics Data System (ADS)

Zhang, Bo; Wang, Jun; Fang, Wen

2015-08-01

A financial market dynamics model is developed and investigated by stochastic Ising system, where the Ising model is the most popular ferromagnetic model in statistical physics systems. Applying two graph based analysis and multiscale entropy method, we investigate and compare the statistical volatility behavior of return time series and the corresponding IMF series derived from the empirical mode decomposition (EMD) method. And the real stock market indices are considered to be comparatively studied with the simulation data of the proposed model. Further, we find that the degree distribution of visibility graph for the simulation series has the power law tails, and the assortative network exhibits the mixing pattern property. All these features are in agreement with the real market data, the research confirms that the financial model established by the Ising system is reasonable.

Propagation of monochromatic light in a hot and dense medium

NASA Astrophysics Data System (ADS)

Masood, Samina S.

2017-12-01

Photons, as quanta of electromagnetic fields, determine the electromagnetic properties of an extremely hot and dense medium. Considering the properties of the photons in the interacting medium of charged particles, we explicitly calculate the electromagnetic properties such as the electric permittivity, magnetic permeability, refractive index and the propagation speed of electromagnetic signals in an extremely hot and dense background. Photons acquire a dynamically generated mass in such a medium. The screening mass of the photon, the Debye shielding length and the plasma frequency are calculated as functions of the statistical parameters of the medium. We study the properties of the propagating particles in astrophysical systems of distinct statistical conditions. The modifications in the properties of the medium lead to the equation of state of the system. We mainly calculate all these parameters for extremely high temperatures of the early universe.

A mechanistic approach to explore novel HDAC1 inhibitor using pharmacophore modeling, 3D- QSAR analysis, molecular docking, density functional and molecular dynamics simulation study.

PubMed

Choubey, Sanjay K; Jeyaraman, Jeyakanthan

2016-11-01

Deregulated epigenetic activity of Histone deacetylase 1 (HDAC1) in tumor development and carcinogenesis pronounces it as promising therapeutic target for cancer treatment. HDAC1 has recently captured the attention of researchers owing to its decisive role in multiple types of cancer. In the present study a multistep framework combining ligand based 3D-QSAR, molecular docking and Molecular Dynamics (MD) simulation studies were performed to explore potential compound with good HDAC1 binding affinity. Four different pharmacophore hypotheses Hypo1 (AADR), Hypo2 (AAAH), Hypo3 (AAAR) and Hypo4 (ADDR) were obtained. The hypothesis Hypo1 (AADR) with two hydrogen bond acceptors (A), one hydrogen bond donor (D) and one aromatics ring (R) was selected to build 3D-QSAR model on the basis of statistical parameter. The pharmacophore hypothesis produced a statistically significant QSAR model, with co-efficient of correlation r 2 =0.82 and cross validation correlation co-efficient q 2 =0.70. External validation result displays high predictive power with r 2 (o) value of 0.88 and r 2 (m) value of 0.58 to carry out further in silico studies. Virtual screening result shows ZINC70450932 as the most promising lead where HDAC1 interacts with residues Asp99, His178, Tyr204, Phe205 and Leu271 forming seven hydrogen bonds. A high docking score (-11.17kcal/mol) and lower docking energy -37.84kcal/mol) displays the binding efficiency of the ligand. Binding free energy calculation was done using MM/GBSA to access affinity of ligands towards protein. Density Functional Theory was employed to explore electronic features of the ligands describing intramolcular charge transfer reaction. Molecular dynamics simulation studies at 50ns display metal ion (Zn)-ligand interaction which is vital to inhibit the enzymatic activity of the protein. Copyright © 2016 Elsevier Inc. All rights reserved.

The physics of flocking: Correlation as a compass from experiments to theory

NASA Astrophysics Data System (ADS)

Cavagna, Andrea; Giardina, Irene; Grigera, Tomás S.

2018-01-01

Collective behavior in biological systems is a complex topic, to say the least. It runs wildly across scales in both space and time, involving taxonomically vastly different organisms, from bacteria and cell clusters, to insect swarms and up to vertebrate groups. It entails concepts as diverse as coordination, emergence, interaction, information, cooperation, decision-making, and synchronization. Amid this jumble, however, we cannot help noting many similarities between collective behavior in biological systems and collective behavior in statistical physics, even though none of these organisms remotely looks like an Ising spin. Such similarities, though somewhat qualitative, are startling, and regard mostly the emergence of global dynamical patterns qualitatively different from individual behavior, and the development of system-level order from local interactions. It is therefore tempting to describe collective behavior in biology within the conceptual framework of statistical physics, in the hope to extend to this new fascinating field at least part of the great predictive power of theoretical physics. In this review we propose that the conceptual cornerstone of this ambitious program be that of correlation. To illustrate this idea we address the case of collective behavior in bird flocks. Two key threads emerge, as two sides of one single story: the presence of scale-free correlations and the dynamical mechanism of information transfer. We discuss first static correlations in starling flocks, in particular the experimental finding of their scale-free nature, the formulation of models that account for this fact using maximum entropy, and the relation of scale-free correlations to information transfer. This is followed by a dynamic treatment of information propagation (propagation of turns across a flock), starting with a discussion of experimental results and following with possible theoretical explanations of those, which require the addition of behavioral inertia to existing theories of flocking. We finish with the definition and analysis of space-time correlations and their relevance to the detection of inertial behavior in the absence of external perturbations.

Statistical physics, seismogenesis, and seismic hazard

NASA Astrophysics Data System (ADS)

Main, Ian

1996-11-01

The scaling properties of earthquake populations show remarkable similarities to those observed at or near the critical point of other composite systems in statistical physics. This has led to the development of a variety of different physical models of seismogenesis as a critical phenomenon, involving locally nonlinear dynamics, with simplified rheologies exhibiting instability or avalanche-type behavior, in a material composed of a large number of discrete elements. In particular, it has been suggested that earthquakes are an example of a "self-organized critical phenomenon" analogous to a sandpile that spontaneously evolves to a critical angle of repose in response to the steady supply of new grains at the summit. In this stationary state of marginal stability the distribution of avalanche energies is a power law, equivalent to the Gutenberg-Richter frequency-magnitude law, and the behavior is relatively insensitive to the details of the dynamics. Here we review the results of some of the composite physical models that have been developed to simulate seismogenesis on different scales during (1) dynamic slip on a preexisting fault, (2) fault growth, and (3) fault nucleation. The individual physical models share some generic features, such as a dynamic energy flux applied by tectonic loading at a constant strain rate, strong local interactions, and fluctuations generated either dynamically or by fixed material heterogeneity, but they differ significantly in the details of the assumed dynamics and in the methods of numerical solution. However, all exhibit critical or near-critical behavior, with behavior quantitatively consistent with many of the observed fractal or multifractal scaling laws of brittle faulting and earthquakes, including the Gutenberg-Richter law. Some of the results are sensitive to the details of the dynamics and hence are not strict examples of self-organized criticality. Nevertheless, the results of these different physical models share some generic statistical properties similar to the "universal" behavior seen in a wide variety of critical phenomena, with significant implications for practical problems in probabilistic seismic hazard evaluation. In particular, the notion of self-organized criticality (or near-criticality) gives a scientific rationale for the a priori assumption of "stationarity" used as a first step in the prediction of the future level of hazard. The Gutenberg-Richter law (a power law in energy or seismic moment) is found to apply only within a finite scale range, both in model and natural seismicity. Accordingly, the frequency-magnitude distribution can be generalized to a gamma distribution in energy or seismic moment (a power law, with an exponential tail). This allows extrapolations of the frequency-magnitude distribution and the maximum credible magnitude to be constrained by observed seismic or tectonic moment release rates. The answers to other questions raised are less clear, for example, the effect of the a priori assumption of a Poisson process in a system with strong local interactions, and the impact of zoning a potentially multifractal distribution of epicentres with smooth polygons. The results of some models show premonitory patterns of seismicity which could in principle be used as mainshock precursors. However, there remains no consensus, on both theoretical and practical grounds, on the possibility or otherwise of reliable intermediate-term earthquake prediction.

System theoretic models for high density VLSI structures

NASA Astrophysics Data System (ADS)

Dickinson, Bradley W.; Hopkins, William E., Jr.

This research project involved the development of mathematical models for analysis, synthesis, and simulation of large systems of interacting devices. The work was motivated by problems that may become important in high density VLSI chips with characteristic feature sizes less than 1 micron: it is anticipated that interactions of neighboring devices will play an important role in the determination of circuit properties. It is hoped that the combination of high device densities and such local interactions can somehow be exploited to increase circuit speed and to reduce power consumption. To address these issues from the point of view of system theory, research was pursued in the areas of nonlinear and stochastic systems and into neural network models. Statistical models were developed to characterize various features of the dynamic behavior of interacting systems. Random process models for studying the resulting asynchronous modes of operation were investigated. The local interactions themselves may be modeled as stochastic effects. The resulting behavior was investigated through the use of various scaling limits, and by a combination of other analytical and simulation techniques. Techniques arising in a variety of disciplines where models of interaction were formulated and explored were considered and adapted for use.

Lags in the response of mountain plant communities to climate change

PubMed Central

Alexander, Jake M.; Chalmandrier, Loïc; Lenoir, Jonathan; Burgess, Treena I.; Essl, Franz; Haider, Sylvia; Kueffer, Christoph; McDougall, Keith; Milbau, Ann; Nuñez, Martin A.; Pauchard, Aníbal; Rabitsch, Wolfgang; Rew, Lisa J.; Sanders, Nathan J.; Pellissier, Loïc

2018-01-01

Rapid climatic changes and increasing human influence at high elevations around the world will have profound impacts on mountain biodiversity. However, forecasts from statistical models (e.g. species distribution models) rarely consider that plant community changes could substantially lag behind climatic changes, hindering our ability to make temporally realistic projections for the coming century. Indeed, the magnitudes of lags, and the relative importance of the different factors giving rise to them, remain poorly understood. We review evidence for three types of lag: “dispersal lags” affecting plant species’ spread along elevational gradients, “establishment lags” following their arrival in recipient communities, and “extinction lags” of resident species. Variation in lags is explained by variation among species in physiological and demographic responses, by effects of altered biotic interactions, and by aspects of the physical environment. Of these, altered biotic interactions could contribute substantially to establishment and extinction lags, yet impacts of biotic interactions on range dynamics are poorly understood. We develop a mechanistic community model to illustrate how species turnover in future communities might lag behind simple expectations based on species’ range shifts with unlimited dispersal. The model shows a combined contribution of altered biotic interactions and dispersal lags to plant community turnover along an elevational gradient following climate warming. Our review and simulation support the view that accounting for disequilibrium range dynamics will be essential for realistic forecasts of patterns of biodiversity under climate change, with implications for the conservation of mountain species and the ecosystem functions they provide. PMID:29112781

Forward-backward multiplicity correlation in high-energy nucleus-nucleus interactions at a few AGeV/c

NASA Astrophysics Data System (ADS)

Bhattacharyya, Swarnapratim; Haiduc, Maria; Neagu, Alina Tania; Firu, Elena

2014-07-01

We have presented a systematic study of two-particle rapidity correlations in terms of investigating the dynamical fluctuation observable \\sigma _c^2 in the forward-backward pseudo-rapidity windows by analyzing the experimental data of {}_{}^{16} O{--}AgBr interactions at 4.5 AGeV/c, {}_{}^{22} Ne{--}AgBr interactions at 4.1 AGeV/c, {}_{}^{28} Si{--}AgBr and {}_{}^{32} S{--}AgBr interactions at 4.5 AGeV/c. The experimental results have been compared with the results obtained from the analysis of event sample simulated (MC-RAND) by generating random numbers and also with the analysis of events generated by the UrQMD and AMPT model. Our study confirms the presence of strong short-range correlations among the produced particles in the forward and the backward pseudo-rapidity region. The analysis of the simple Monte Carlo-simulated (MC-RAND) events signifies that the observed correlations are not due to mere statistics only; explanation of such correlations can be attributed to the presence of dynamical fluctuations during the production of charged pions. Comparisons of the experimental results with the results obtained from analyzing the UrQMD data sample indicate that the UrQMD model cannot reproduce the experimental findings. The AMPT model also cannot explain the experimental results satisfactorily. Comparisons of our experimental results with the results obtained from the analysis of higher energy emulsion data and with the results of the RHIC data have also been presented.

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers

PubMed Central

Hsiao, Tzu-Hung; Chiu, Yu-Chiao; Hsu, Pei-Yin; Lu, Tzu-Pin; Lai, Liang-Chuan; Tsai, Mong-Hsun; Huang, Tim H.-M.; Chuang, Eric Y.; Chen, Yidong

2016-01-01

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC. PMID:26972162

Exploring Explanations of Subglacial Bedform Sizes Using Statistical Models.

PubMed

Hillier, John K; Kougioumtzoglou, Ioannis A; Stokes, Chris R; Smith, Michael J; Clark, Chris D; Spagnolo, Matteo S

2016-01-01

Sediments beneath modern ice sheets exert a key control on their flow, but are largely inaccessible except through geophysics or boreholes. In contrast, palaeo-ice sheet beds are accessible, and typically characterised by numerous bedforms. However, the interaction between bedforms and ice flow is poorly constrained and it is not clear how bedform sizes might reflect ice flow conditions. To better understand this link we present a first exploration of a variety of statistical models to explain the size distribution of some common subglacial bedforms (i.e., drumlins, ribbed moraine, MSGL). By considering a range of models, constructed to reflect key aspects of the physical processes, it is possible to infer that the size distributions are most effectively explained when the dynamics of ice-water-sediment interaction associated with bedform growth is fundamentally random. A 'stochastic instability' (SI) model, which integrates random bedform growth and shrinking through time with exponential growth, is preferred and is consistent with other observations of palaeo-bedforms and geophysical surveys of active ice sheets. Furthermore, we give a proof-of-concept demonstration that our statistical approach can bridge the gap between geomorphological observations and physical models, directly linking measurable size-frequency parameters to properties of ice sheet flow (e.g., ice velocity). Moreover, statistically developing existing models as proposed allows quantitative predictions to be made about sizes, making the models testable; a first illustration of this is given for a hypothesised repeat geophysical survey of bedforms under active ice. Thus, we further demonstrate the potential of size-frequency distributions of subglacial bedforms to assist the elucidation of subglacial processes and better constrain ice sheet models.

Fermi-Pasta-Ulam-Tsingou problems: Passage from Boltzmann to q-statistics

NASA Astrophysics Data System (ADS)

Bagchi, Debarshee; Tsallis, Constantino

2018-02-01

The Fermi-Pasta-Ulam (FPU) one-dimensional Hamiltonian includes a quartic term which guarantees ergodicity of the system in the thermodynamic limit. Consistently, the Boltzmann factor P(ε) ∼e-βε describes its equilibrium distribution of one-body energies, and its velocity distribution is Maxwellian, i.e., P(v) ∼e - βv2 /2. We consider here a generalized system where the quartic coupling constant between sites decays as 1 / dijα (α ≥ 0 ;dij = 1 , 2 , …) . Through first-principle molecular dynamics we demonstrate that, for large α (above α ≃ 1), i.e., short-range interactions, Boltzmann statistics (based on the additive entropic functional SB [ P(z) ] = - k ∫ dzP(z) ln P(z)) is verified. However, for small values of α (below α ≃ 1), i.e., long-range interactions, Boltzmann statistics dramatically fails and is replaced by q-statistics (based on the nonadditive entropic functional Sq [ P(z) ] = k(1 - ∫ dz[ P(z) ]q) /(q - 1) , with S1 =SB). Indeed, the one-body energy distribution is q-exponential, P(ε) ∼ eqε-βε ε ≡[ 1 +(qε - 1) βε ε ]-1 /(qε - 1) with qε > 1, and its velocity distribution is given by P(v) ∼ eqv-βvv2 / 2 with qv > 1. Moreover, within small error bars, we verify qε =qv = q, which decreases from an extrapolated value q ≃ 5 / 3 to q = 1 when α increases from zero to α ≃ 1, and remains q = 1 thereafter.

Temporal scaling and spatial statistical analyses of groundwater level fluctuations

NASA Astrophysics Data System (ADS)

Sun, H.; Yuan, L., Sr.; Zhang, Y.

2017-12-01

Natural dynamics such as groundwater level fluctuations can exhibit multifractionality and/or multifractality due likely to multi-scale aquifer heterogeneity and controlling factors, whose statistics requires efficient quantification methods. This study explores multifractionality and non-Gaussian properties in groundwater dynamics expressed by time series of daily level fluctuation at three wells located in the lower Mississippi valley, after removing the seasonal cycle in the temporal scaling and spatial statistical analysis. First, using the time-scale multifractional analysis, a systematic statistical method is developed to analyze groundwater level fluctuations quantified by the time-scale local Hurst exponent (TS-LHE). Results show that the TS-LHE does not remain constant, implying the fractal-scaling behavior changing with time and location. Hence, we can distinguish the potentially location-dependent scaling feature, which may characterize the hydrology dynamic system. Second, spatial statistical analysis shows that the increment of groundwater level fluctuations exhibits a heavy tailed, non-Gaussian distribution, which can be better quantified by a Lévy stable distribution. Monte Carlo simulations of the fluctuation process also show that the linear fractional stable motion model can well depict the transient dynamics (i.e., fractal non-Gaussian property) of groundwater level, while fractional Brownian motion is inadequate to describe natural processes with anomalous dynamics. Analysis of temporal scaling and spatial statistics therefore may provide useful information and quantification to understand further the nature of complex dynamics in hydrology.

An Examination of Statistical Power in Multigroup Dynamic Structural Equation Models

ERIC Educational Resources Information Center

Prindle, John J.; McArdle, John J.

2012-01-01

This study used statistical simulation to calculate differential statistical power in dynamic structural equation models with groups (as in McArdle & Prindle, 2008). Patterns of between-group differences were simulated to provide insight into how model parameters influence power approximations. Chi-square and root mean square error of…

A mini-review on econophysics: Comparative study of Chinese and western financial markets

NASA Astrophysics Data System (ADS)

Zheng, Bo; Jiang, Xiong-Fei; Ni, Peng-Yun

2014-07-01

We present a review of our recent research in econophysics, and focus on the comparative study of Chinese and western financial markets. By virtue of concepts and methods in statistical physics, we investigate the time correlations and spatial structure of financial markets based on empirical high-frequency data. We discover that the Chinese stock market shares common basic properties with the western stock markets, such as the fat-tail probability distribution of price returns, the long-range auto-correlation of volatilities, and the persistence probability of volatilities, while it exhibits very different higher-order time correlations of price returns and volatilities, spatial correlations of individual stock prices, and large-fluctuation dynamic behaviors. Furthermore, multi-agent-based models are developed to simulate the microscopic interaction and dynamic evolution of the stock markets.

Quantitative Description of Crystal Nucleation and Growth from in Situ Liquid Scanning Transmission Electron Microscopy.

PubMed

Ievlev, Anton V; Jesse, Stephen; Cochell, Thomas J; Unocic, Raymond R; Protopopescu, Vladimir A; Kalinin, Sergei V

2015-12-22

Recent advances in liquid cell (scanning) transmission electron microscopy (S)TEM has enabled in situ nanoscale investigations of controlled nanocrystal growth mechanisms. Here, we experimentally and quantitatively investigated the nucleation and growth mechanisms of Pt nanostructures from an aqueous solution of K2PtCl6. Averaged statistical, network, and local approaches have been used for the data analysis and the description of both collective particles dynamics and local growth features. In particular, interaction between neighboring particles has been revealed and attributed to reduction of the platinum concentration in the vicinity of the particle boundary. The local approach for solving the inverse problem showed that particles dynamics can be simulated by a stationary diffusional model. The obtained results are important for understanding nanocrystal formation and growth processes and for optimization of synthesis conditions.

[EEG-correlates of pilots' functional condition in simulated flight dynamics].

PubMed

Kiroy, V N; Aslanyan, E V; Bakhtin, O M; Minyaeva, N R; Lazurenko, D M

2015-01-01

The spectral characteristics of the EEG recorded on two professional pilots in the simulator TU-154 aircraft in flight dynamics, including takeoff, landing and horizontal flight (in particular during difficult conditions) were analyzed. EEG recording was made with frequency band 0.1-70 Hz continuously from 15 electrodes. The EEG recordings were evaluated using analysis of variance and discriminant analysis. Statistical significant of the identified differences and the influence of the main factors and their interactions were evaluated using Greenhouse - Gaiser corrections. It was shown that the spectral characteristics of the EEG are highly informative features of the state of the pilots, reflecting the different flight phases. High validity ofthe differences including individual characteristic, indicates their non-random nature and the possibility of constructing a system of pilots' state control during all phases of flight, based on EEG features.

Thermodynamics of inequalities: From precariousness to economic stratification

NASA Astrophysics Data System (ADS)

Smerlak, Matteo

2016-01-01

Growing economic inequalities are observed in several countries throughout the world. Following Pareto, the power-law structure of these inequalities has been the subject of much theoretical and empirical work. But their nonequilibrium dynamics, e.g. after a policy change, remains incompletely understood. Here we introduce a thermodynamical theory of inequalities based on the analogy between economic stratification and statistical entropy. Within this framework we identify the combination of upward mobility with precariousness as a fundamental driver of inequality. We formalize this statement by a "second-law" inequality displaying upward mobility and precariousness as thermodynamic conjugate variables. We estimate the time scale for the "relaxation" of the wealth distribution after a sudden change of the after-tax return on capital. Our method can be generalized to gain insight into the dynamics of inequalities in any Markovian model of socioeconomic interactions.

A Markov model for the temporal dynamics of balanced random networks of finite size

PubMed Central

Lagzi, Fereshteh; Rotter, Stefan

2014-01-01

The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between neuronal populations also opens new doors to analyze the joint dynamics of multiple interacting networks. PMID:25520644

Dynamic programming re-ranking for PPI interactor and pair extraction in full-text articles

PubMed Central

2011-01-01

Background Experimentally verified protein-protein interactions (PPIs) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be facilitated by employing text-mining systems to identify genes which play the interactor role in PPIs and to map these genes to unique database identifiers (interactor normalization task or INT) and then to return a list of interaction pairs for each article (interaction pair task or IPT). These two tasks are evaluated in terms of the area under curve of the interpolated precision/recall (AUC iP/R) score because the order of identifiers in the output list is important for ease of curation. Results Our INT system developed for the BioCreAtIvE II.5 INT challenge achieved a promising AUC iP/R of 43.5% by using a support vector machine (SVM)-based ranking procedure. Using our new re-ranking algorithm, we have been able to improve system performance (AUC iP/R) by 1.84%. Our experimental results also show that with the re-ranked INT results, our unsupervised IPT system can achieve a competitive AUC iP/R of 23.86%, which outperforms the best BC II.5 INT system by 1.64%. Compared to using only SVM ranked INT results, using re-ranked INT results boosts AUC iP/R by 7.84%. Statistical significance t-test results show that our INT/IPT system with re-ranking outperforms that without re-ranking by a statistically significant difference. Conclusions In this paper, we present a new re-ranking algorithm that considers co-occurrence among identifiers in an article to improve INT and IPT ranking results. Combining the re-ranked INT results with an unsupervised approach to find associations among interactors, the proposed method can boost the IPT performance. We also implement score computation using dynamic programming, which is faster and more efficient than traditional approaches. PMID:21342534

Dynamic programming re-ranking for PPI interactor and pair extraction in full-text articles.

PubMed

Tsai, Richard Tzong-Han; Lai, Po-Ting

2011-02-23

Experimentally verified protein-protein interactions (PPIs) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be facilitated by employing text-mining systems to identify genes which play the interactor role in PPIs and to map these genes to unique database identifiers (interactor normalization task or INT) and then to return a list of interaction pairs for each article (interaction pair task or IPT). These two tasks are evaluated in terms of the area under curve of the interpolated precision/recall (AUC iP/R) score because the order of identifiers in the output list is important for ease of curation. Our INT system developed for the BioCreAtIvE II.5 INT challenge achieved a promising AUC iP/R of 43.5% by using a support vector machine (SVM)-based ranking procedure. Using our new re-ranking algorithm, we have been able to improve system performance (AUC iP/R) by 1.84%. Our experimental results also show that with the re-ranked INT results, our unsupervised IPT system can achieve a competitive AUC iP/R of 23.86%, which outperforms the best BC II.5 INT system by 1.64%. Compared to using only SVM ranked INT results, using re-ranked INT results boosts AUC iP/R by 7.84%. Statistical significance t-test results show that our INT/IPT system with re-ranking outperforms that without re-ranking by a statistically significant difference. In this paper, we present a new re-ranking algorithm that considers co-occurrence among identifiers in an article to improve INT and IPT ranking results. Combining the re-ranked INT results with an unsupervised approach to find associations among interactors, the proposed method can boost the IPT performance. We also implement score computation using dynamic programming, which is faster and more efficient than traditional approaches.

Understanding past, contemporary, and future dynamics of plants, populations, and communities using Sonoran Desert winter annuals.

PubMed

Huxman, Travis E; Kimball, Sarah; Angert, Amy L; Gremer, Jennifer R; Barron-Gafford, Greg A; Venable, D Lawrence

2013-07-01

Global change requires plant ecologists to predict future states of biological diversity to aid the management of natural communities, thus introducing a number of significant challenges. One major challenge is considering how the many interacting features of biological systems, including ecophysiological processes, plant life histories, and species interactions, relate to performance in the face of a changing environment. We have employed a functional trait approach to understand the individual, population, and community dynamics of a model system of Sonoran Desert winter annual plants. We have used a comprehensive approach that connects physiological ecology and comparative biology to population and community dynamics, while emphasizing both ecological and evolutionary processes. This approach has led to a fairly robust understanding of past and contemporary dynamics in response to changes in climate. In this community, there is striking variation in physiological and demographic responses to both precipitation and temperature that is described by a trade-off between water-use efficiency (WUE) and relative growth rate (RGR). This community-wide trade-off predicts both the demographic and life history variation that contribute to species coexistence. Our framework has provided a mechanistic explanation to the recent warming, drying, and climate variability that has driven a surprising shift in these communities: cold-adapted species with more buffered population dynamics have increased in relative abundance. These types of comprehensive approaches that acknowledge the hierarchical nature of biology may be especially useful in aiding prediction. The emerging, novel and nonstationary climate constrains our use of simplistic statistical representations of past plant behavior in predicting the future, without understanding the mechanistic basis of change.

Coevolutionary dynamics of aspiration and strategy in spatial repeated public goods games

NASA Astrophysics Data System (ADS)

Wu, Te; Fu, Feng; Wang, Long

2018-06-01

The evolutionary dynamics remain largely unknown for spatial populations where individuals are more likely to interact repeatedly. Under this settings, individuals can make their decisions to cooperate or not based on the decisions previously adopted by others in their neighborhoods. Using repeated public goods game, we construct a spatial model and use a statistical physics approach to study the coevolutionary dynamics of aspiration and strategy. Individuals each have an aspiration towards the groups they are involved. According to the outcome of each group, individuals have assessment of whether their aspirations are satisfied. If satisfied, they cooperate next round. Otherwise, they switch to defecting. Results show threshold phenomenon for harsh collective dilemma: cooperators sticking to high levels of aspiration can prevail over defectors, while cooperators with other levels are invariably wiped out. When the collective dilemma is relaxed, cooperation is greatly facilitated by inducing a high level of diversity of aspiration. Snapshots further show the spatial patterns of how this coevolutionary process leads to the emergence of an optimal solution associated with aspiration level, whose corresponding strategy are most prevalent. This optimal solution lies in one and the highest aspiration level allowed, and depends on the intensity of the social dilemma. By removing the memory effect, our results also confirm that repeated interactions can promote cooperation, but to a limited degree.

Proline Restricts Loop I Conformation of the High Affinity WW Domain from Human Nedd4-1 to a Ligand Binding-Competent Type I β-Turn.

PubMed

Schulte, Marianne; Panwalkar, Vineet; Freischem, Stefan; Willbold, Dieter; Dingley, Andrew J

2018-04-19

Sequence alignment of the four WW domains from human Nedd4-1 (neuronal precursor cell expressed developmentally down-regulated gene 4-1) reveals that the highest sequence diversity exists in loop I. Three residues in this type I β-turn interact with the PPxY motif of the human epithelial Na + channel (hENaC) subunits, indicating that peptide affinity is defined by the loop I sequence. The third WW domain (WW3*) has the highest ligand affinity and unlike the other three hNedd4-1 WW domains or other WW domains studied contains the highly statistically preferred proline at the ( i + 1) position found in β-turns. In this report, molecular dynamics simulations and experimental data were combined to characterize loop I stability and dynamics. Exchange of the proline to the equivalent residue in WW4 (Thr) results in the presence of a predominantly open seven residue Ω loop rather than the type I β-turn conformation for the wild-type apo-WW3*. In the presence of the ligand, the structure of the mutated loop I is locked into a type I β-turn. Thus, proline in loop I ensures a stable peptide binding-competent β-turn conformation, indicating that amino acid sequence modulates local flexibility to tune binding preferences and stability of dynamic interaction motifs.

Towards representing human behavior and decision making in Earth system models - an overview of techniques and approaches

NASA Astrophysics Data System (ADS)

Müller-Hansen, Finn; Schlüter, Maja; Mäs, Michael; Donges, Jonathan F.; Kolb, Jakob J.; Thonicke, Kirsten; Heitzig, Jobst

2017-11-01

Today, humans have a critical impact on the Earth system and vice versa, which can generate complex feedback processes between social and ecological dynamics. Integrating human behavior into formal Earth system models (ESMs), however, requires crucial modeling assumptions about actors and their goals, behavioral options, and decision rules, as well as modeling decisions regarding human social interactions and the aggregation of individuals' behavior. Here, we review existing modeling approaches and techniques from various disciplines and schools of thought dealing with human behavior at different levels of decision making. We demonstrate modelers' often vast degrees of freedom but also seek to make modelers aware of the often crucial consequences of seemingly innocent modeling assumptions. After discussing which socioeconomic units are potentially important for ESMs, we compare models of individual decision making that correspond to alternative behavioral theories and that make diverse modeling assumptions about individuals' preferences, beliefs, decision rules, and foresight. We review approaches to model social interaction, covering game theoretic frameworks, models of social influence, and network models. Finally, we discuss approaches to studying how the behavior of individuals, groups, and organizations can aggregate to complex collective phenomena, discussing agent-based, statistical, and representative-agent modeling and economic macro-dynamics. We illustrate the main ingredients of modeling techniques with examples from land-use dynamics as one of the main drivers of environmental change bridging local to global scales.

Constructing networks from a dynamical system perspective for multivariate nonlinear time series.

PubMed

Nakamura, Tomomichi; Tanizawa, Toshihiro; Small, Michael

2016-03-01

We describe a method for constructing networks for multivariate nonlinear time series. We approach the interaction between the various scalar time series from a deterministic dynamical system perspective and provide a generic and algorithmic test for whether the interaction between two measured time series is statistically significant. The method can be applied even when the data exhibit no obvious qualitative similarity: a situation in which the naive method utilizing the cross correlation function directly cannot correctly identify connectivity. To establish the connectivity between nodes we apply the previously proposed small-shuffle surrogate (SSS) method, which can investigate whether there are correlation structures in short-term variabilities (irregular fluctuations) between two data sets from the viewpoint of deterministic dynamical systems. The procedure to construct networks based on this idea is composed of three steps: (i) each time series is considered as a basic node of a network, (ii) the SSS method is applied to verify the connectivity between each pair of time series taken from the whole multivariate time series, and (iii) the pair of nodes is connected with an undirected edge when the null hypothesis cannot be rejected. The network constructed by the proposed method indicates the intrinsic (essential) connectivity of the elements included in the system or the underlying (assumed) system. The method is demonstrated for numerical data sets generated by known systems and applied to several experimental time series.

Molecular Dynamics of Hot Dense Plasmas: New Horizons

NASA Astrophysics Data System (ADS)

Graziani, Frank

2011-06-01

We describe the status of a new time-dependent simulation capability for hot dense plasmas. The backbone of this multi-institutional computational and experimental effort--the Cimarron Project--is the massively parallel molecular dynamics (MD) code ``ddcMD''. The project's focus is material conditions such as exist in inertial confinement fusion experiments, and in many stellar interiors: high temperatures, high densities, significant electromagnetic fields, mixtures of high- and low- Z elements, and non-Maxwellian particle distributions. Of particular importance is our ability to incorporate into this classical MD code key atomic, radiative, and nuclear processes, so that their interacting effects under non-ideal plasma conditions can be investigated. This talk summarizes progress in computational methodology, discusses strengths and weaknesses of quantum statistical potentials as effective interactions for MD, explains the model used for quantum events possibly occurring in a collision and highlights some significant results obtained to date. We will also discuss a new idea called kinetic theory MD which now being explored to deal more efficiently with the very disparate dynamical timescales that arise in fusion plasmas. We discuss how this approach can be derived rigorously from the n-body quantum Wigner equation and illustrate the approach with an example. This work is performed under the auspices of the U. S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Quantum-mechanical analysis of low-gain free-electron laser oscillators

NASA Astrophysics Data System (ADS)

Fares, H.; Yamada, M.; Chiadroni, E.; Ferrario, M.

2018-05-01

In the previous classical theory of the low-gain free-electron laser (FEL) oscillators, the electron is described as a point-like particle, a delta function in the spatial space. On the other hand, in the previous quantum treatments, the electron is described as a plane wave with a single momentum state, a delta function in the momentum space. In reality, an electron must have statistical uncertainties in the position and momentum domains. Then, the electron is neither a point-like charge nor a plane wave of a single momentum. In this paper, we rephrase the theory of the low-gain FEL where the interacting electron is represented quantum mechanically by a plane wave with a finite spreading length (i.e., a wave packet). Using the concepts of the transformation of reference frames and the statistical quantum mechanics, an expression for the single-pass radiation gain is derived. The spectral broadening of the radiation is expressed in terms of the spreading length of an electron, the relaxation time characterizing the energy spread of electrons, and the interaction time. We introduce a comparison between our results and those obtained in the already known classical analyses where a good agreement between both results is shown. While the correspondence between our results and the classical results are shown, novel insights into the electron dynamics and the interaction mechanism are presented.

Non-Gaussian statistics and nanosecond dynamics of electrostatic fluctuations affecting optical transitions in proteins.

PubMed

Martin, Daniel R; Matyushov, Dmitry V

2012-08-30

We show that electrostatic fluctuations of the protein-water interface are globally non-Gaussian. The electrostatic component of the optical transition energy (energy gap) in a hydrated green fluorescent protein is studied here by classical molecular dynamics simulations. The distribution of the energy gap displays a high excess in the breadth of electrostatic fluctuations over the prediction of the Gaussian statistics. The energy gap dynamics include a nanosecond component. When simulations are repeated with frozen protein motions, the statistics shifts to the expectations of linear response and the slow dynamics disappear. We therefore suggest that both the non-Gaussian statistics and the nanosecond dynamics originate largely from global, low-frequency motions of the protein coupled to the interfacial water. The non-Gaussian statistics can be experimentally verified from the temperature dependence of the first two spectral moments measured at constant-volume conditions. Simulations at different temperatures are consistent with other indicators of the non-Gaussian statistics. In particular, the high-temperature part of the energy gap variance (second spectral moment) scales linearly with temperature and extrapolates to zero at a temperature characteristic of the protein glass transition. This result, violating the classical limit of the fluctuation-dissipation theorem, leads to a non-Boltzmann statistics of the energy gap and corresponding non-Arrhenius kinetics of radiationless electronic transitions, empirically described by the Vogel-Fulcher-Tammann law.

Statistical prediction of dynamic distortion of inlet flow using minimum dynamic measurement. An application to the Melick statistical method and inlet flow dynamic distortion prediction without RMS measurements

NASA Technical Reports Server (NTRS)

Schweikhard, W. G.; Chen, Y. S.

1986-01-01

The Melick method of inlet flow dynamic distortion prediction by statistical means is outlined. A hypothetic vortex model is used as the basis for the mathematical formulations. The main variables are identified by matching the theoretical total pressure rms ratio with the measured total pressure rms ratio. Data comparisons, using the HiMAT inlet test data set, indicate satisfactory prediction of the dynamic peak distortion for cases with boundary layer control device vortex generators. A method for the dynamic probe selection was developed. Validity of the probe selection criteria is demonstrated by comparing the reduced-probe predictions with the 40-probe predictions. It is indicated that the the number of dynamic probes can be reduced to as few as two and still retain good accuracy.

Using Interactive Simulations in Assessment: The Use of Computer-Based Interactive Simulations in the Assessment of Statistical Concepts

ERIC Educational Resources Information Center

Neumann, David L.

2010-01-01

Interactive computer-based simulations have been applied in several contexts to teach statistical concepts in university level courses. In this report, the use of interactive simulations as part of summative assessment in a statistics course is described. Students accessed the simulations via the web and completed questions relating to the…

Vehicle systems: coupled and interactive dynamics analysis

NASA Astrophysics Data System (ADS)

Vantsevich, Vladimir V.

2014-11-01

This article formulates a new direction in vehicle dynamics, described as coupled and interactive vehicle system dynamics. Formalised procedures and analysis of case studies are presented. An analytical consideration, which explains the physics of coupled system dynamics and its consequences for dynamics of a vehicle, is given for several sets of systems including: (i) driveline and suspension of a 6×6 truck, (ii) a brake mechanism and a limited slip differential of a drive axle and (iii) a 4×4 vehicle steering system and driveline system. The article introduces a formal procedure to turn coupled system dynamics into interactive dynamics of systems. A new research direction in interactive dynamics of an active steering and a hybrid-electric power transmitting unit is presented and analysed to control power distribution between the drive axles of a 4×4 vehicle. A control strategy integrates energy efficiency and lateral dynamics by decoupling dynamics of the two systems thus forming their interactive dynamics.

Density profiles in the Scrape-Off Layer interpreted through filament dynamics

NASA Astrophysics Data System (ADS)

Militello, Fulvio

2017-10-01

We developed a new theoretical framework to clarify the relation between radial Scrape-Off Layer density profiles and the fluctuations that generate them. The framework provides an interpretation of the experimental features of the profiles and of the turbulence statistics on the basis of simple properties of the filaments, such as their radial motion and their draining towards the divertor. L-mode and inter-ELM filaments are described as a Poisson process in which each event is independent and modelled with a wave function of amplitude and width statistically distributed according to experimental observations and evolving according to fluid equations. We will rigorously show that radially accelerating filaments, less efficient parallel exhaust and also a statistical distribution of their radial velocity can contribute to induce flatter profiles in the far SOL and therefore enhance plasma-wall interactions. A quite general result of our analysis is the resiliency of this non-exponential nature of the profiles and the increase of the relative fluctuation amplitude towards the wall, as experimentally observed. According to the framework, profile broadening at high fueling rates can be caused by interactions with neutrals (e.g. charge exchange) in the divertor or by a significant radial acceleration of the filaments. The framework assumptions were tested with 3D numerical simulations of seeded SOL filaments based on a two fluid model. In particular, filaments interact through the electrostatic field they generate only when they are in close proximity (separation comparable to their width in the drift plane), thus justifying our independence hypothesis. In addition, we will discuss how isolated filament motion responds to variations in the plasma conditions, and specifically divertor conditions. Finally, using the theoretical framework we will reproduce and interpret experimental results obtained on JET, MAST and HL-2A.

A Survey of Probabilistic Methods for Dynamical Systems with Uncertain Parameters.

DTIC Science & Technology

1986-05-01

J., "An Approach to the Theoretical Background of Statistical Energy Analysis Applied to Structural Vibration," Journ. Acoust. Soc. Amer., Vol. 69...1973, Sect. 8.3. 80. Lyon, R.H., " Statistical Energy Analysis of Dynamical Systems," M.I.T. Press, 1975. e) Late References added in Proofreading !! 81...Dowell, E.H., and Kubota, Y., "Asymptotic Modal Analysis and ’~ y C-" -165- Statistical Energy Analysis of Dynamical Systems," Journ. Appi. - Mech

Statistical-Dynamical Seasonal Forecasts of Central-Southwest Asian Winter Precipitation.

NASA Astrophysics Data System (ADS)

Tippett, Michael K.; Goddard, Lisa; Barnston, Anthony G.

2005-06-01

Interannual precipitation variability in central-southwest (CSW) Asia has been associated with East Asian jet stream variability and western Pacific tropical convection. However, atmospheric general circulation models (AGCMs) forced by observed sea surface temperature (SST) poorly simulate the region's interannual precipitation variability. The statistical-dynamical approach uses statistical methods to correct systematic deficiencies in the response of AGCMs to SST forcing. Statistical correction methods linking model-simulated Indo-west Pacific precipitation and observed CSW Asia precipitation result in modest, but statistically significant, cross-validated simulation skill in the northeast part of the domain for the period from 1951 to 1998. The statistical-dynamical method is also applied to recent (winter 1998/99 to 2002/03) multimodel, two-tier December-March precipitation forecasts initiated in October. This period includes 4 yr (winter of 1998/99 to 2001/02) of severe drought. Tercile probability forecasts are produced using ensemble-mean forecasts and forecast error estimates. The statistical-dynamical forecasts show enhanced probability of below-normal precipitation for the four drought years and capture the return to normal conditions in part of the region during the winter of 2002/03.May Kabul be without gold, but not without snow.—Traditional Afghan proverb

Governing Laws of Complex System Predictability under Co-evolving Uncertainty Sources: Theory and Nonlinear Geophysical Applications

NASA Astrophysics Data System (ADS)

Perdigão, R. A. P.

2017-12-01

Predictability assessments are traditionally made on a case-by-case basis, often by running the particular model of interest with randomly perturbed initial/boundary conditions and parameters, producing computationally expensive ensembles. These approaches provide a lumped statistical view of uncertainty evolution, without eliciting the fundamental processes and interactions at play in the uncertainty dynamics. In order to address these limitations, we introduce a systematic dynamical framework for predictability assessment and forecast, by analytically deriving governing equations of predictability in terms of the fundamental architecture of dynamical systems, independent of any particular problem under consideration. The framework further relates multiple uncertainty sources along with their coevolutionary interplay, enabling a comprehensive and explicit treatment of uncertainty dynamics along time, without requiring the actual model to be run. In doing so, computational resources are freed and a quick and effective a-priori systematic dynamic evaluation is made of predictability evolution and its challenges, including aspects in the model architecture and intervening variables that may require optimization ahead of initiating any model runs. It further brings out universal dynamic features in the error dynamics elusive to any case specific treatment, ultimately shedding fundamental light on the challenging issue of predictability. The formulated approach, framed with broad mathematical physics generality in mind, is then implemented in dynamic models of nonlinear geophysical systems with various degrees of complexity, in order to evaluate their limitations and provide informed assistance on how to optimize their design and improve their predictability in fundamental dynamical terms.

Visualizing biological reaction intermediates with DNA curtains

NASA Astrophysics Data System (ADS)

Zhao, Yiling; Jiang, Yanzhou; Qi, Zhi

2017-04-01

Single-molecule approaches have tremendous potential analyzing dynamic biological reaction with heterogeneity that cannot be effectively accessed via traditional ensemble-level biochemical approaches. The approach of deoxyribonucleic acid (DNA) curtains developed by Dr Eric Greene and his research team at Columbia University is a high-throughput single-molecule technique that utilizes fluorescent imaging to visualize protein-DNA interactions directly and allows the acquisition of statistically relevant information from hundreds or even thousands of individual reactions. This review aims to summarize the past, present, and future of DNA curtains, with an emphasis on its applications to solve important biological questions.

Dynamic and programmable self-assembly of micro-rafts at the air-water interface

PubMed Central

Wang, Wendong; Giltinan, Joshua; Zakharchenko, Svetlana; Sitti, Metin

2017-01-01

Dynamic self-assembled material systems constantly consume energy to maintain their spatiotemporal structures and functions. Programmable self-assembly translates information from individual parts to the collective whole. Combining dynamic and programmable self-assembly in a single platform opens up the possibilities to investigate both types of self-assembly simultaneously and to explore their synergy. This task is challenging because of the difficulty in finding suitable interactions that are both dissipative and programmable. We present a dynamic and programmable self-assembling material system consisting of spinning at the air-water interface circular magnetic micro-rafts of radius 50 μm and with cosinusoidal edge-height profiles. The cosinusoidal edge-height profiles not only create a net dissipative capillary repulsion that is sustained by continuous torque input but also enable directional assembly of micro-rafts. We uncover the layered arrangement of micro-rafts in the patterns formed by dynamic self-assembly and offer mechanistic insights through a physical model and geometric analysis. Furthermore, we demonstrate programmable self-assembly and show that a 4-fold rotational symmetry encoded in individual micro-rafts translates into 90° bending angles and square-based tiling in the assembled structures of micro-rafts. We anticipate that our dynamic and programmable material system will serve as a model system for studying nonequilibrium dynamics and statistical mechanics in the future. PMID:28560332

Dynamic and programmable self-assembly of micro-rafts at the air-water interface.

PubMed

Wang, Wendong; Giltinan, Joshua; Zakharchenko, Svetlana; Sitti, Metin

2017-05-01

Dynamic self-assembled material systems constantly consume energy to maintain their spatiotemporal structures and functions. Programmable self-assembly translates information from individual parts to the collective whole. Combining dynamic and programmable self-assembly in a single platform opens up the possibilities to investigate both types of self-assembly simultaneously and to explore their synergy. This task is challenging because of the difficulty in finding suitable interactions that are both dissipative and programmable. We present a dynamic and programmable self-assembling material system consisting of spinning at the air-water interface circular magnetic micro-rafts of radius 50 μm and with cosinusoidal edge-height profiles. The cosinusoidal edge-height profiles not only create a net dissipative capillary repulsion that is sustained by continuous torque input but also enable directional assembly of micro-rafts. We uncover the layered arrangement of micro-rafts in the patterns formed by dynamic self-assembly and offer mechanistic insights through a physical model and geometric analysis. Furthermore, we demonstrate programmable self-assembly and show that a 4-fold rotational symmetry encoded in individual micro-rafts translates into 90° bending angles and square-based tiling in the assembled structures of micro-rafts. We anticipate that our dynamic and programmable material system will serve as a model system for studying nonequilibrium dynamics and statistical mechanics in the future.

Statistical physics of vehicular traffic and some related systems

NASA Astrophysics Data System (ADS)

Chowdhury, Debashish; Santen, Ludger; Schadschneider, Andreas

2000-05-01

In the so-called “microscopic” models of vehicular traffic, attention is paid explicitly to each individual vehicle each of which is represented by a “particle”; the nature of the “interactions” among these particles is determined by the way the vehicles influence each others’ movement. Therefore, vehicular traffic, modeled as a system of interacting “particles” driven far from equilibrium, offers the possibility to study various fundamental aspects of truly nonequilibrium systems which are of current interest in statistical physics. Analytical as well as numerical techniques of statistical physics are being used to study these models to understand rich variety of physical phenomena exhibited by vehicular traffic. Some of these phenomena, observed in vehicular traffic under different circumstances, include transitions from one dynamical phase to another, criticality and self-organized criticality, metastability and hysteresis, phase-segregation, etc. In this critical review, written from the perspective of statistical physics, we explain the guiding principles behind all the main theoretical approaches. But we present detailed discussions on the results obtained mainly from the so-called “particle-hopping” models, particularly emphasizing those which have been formulated in recent years using the language of cellular automata.

Effective interactions and dynamics of small passive particles in an active bacterial medium

NASA Astrophysics Data System (ADS)

Semeraro, Enrico F.; Devos, Juliette M.; Narayanan, Theyencheri

2018-05-01

This article presents an investigation of the interparticle interactions and dynamics of submicron silica colloids suspended in a bath of motile Escherichia coli bacteria. The colloidal microstructure and dynamics were probed by ultra-small-angle x-ray scattering and multi-speckles x-ray photon correlation spectroscopy, respectively. Both static and hydrodynamic interactions were obtained for different colloid volume fractions and bacteria concentrations as well as when the interparticle interaction potential was modified by the motility buffer. Results suggest that motile bacteria reduce the effective attractive interactions between passive colloids and enhance their dynamics at high colloid volume fractions. The enhanced dynamics under different static interparticle interactions can be rationalized in terms of an effective viscosity of the medium and unified by means of an empirical effective temperature of the system. While the influence of swimming bacteria on the colloid dynamics is significantly lower for small particles, the role of motility buffer on the static and dynamic interactions becomes more pronounced.

A physical-based gas-surface interaction model for rarefied gas flow simulation

NASA Astrophysics Data System (ADS)

Liang, Tengfei; Li, Qi; Ye, Wenjing

2018-01-01

Empirical gas-surface interaction models, such as the Maxwell model and the Cercignani-Lampis model, are widely used as the boundary condition in rarefied gas flow simulations. The accuracy of these models in the prediction of macroscopic behavior of rarefied gas flows is less satisfactory in some cases especially the highly non-equilibrium ones. Molecular dynamics simulation can accurately resolve the gas-surface interaction process at atomic scale, and hence can predict accurate macroscopic behavior. They are however too computationally expensive to be applied in real problems. In this work, a statistical physical-based gas-surface interaction model, which complies with the basic relations of boundary condition, is developed based on the framework of the washboard model. In virtue of its physical basis, this new model is capable of capturing some important relations/trends for which the classic empirical models fail to model correctly. As such, the new model is much more accurate than the classic models, and in the meantime is more efficient than MD simulations. Therefore, it can serve as a more accurate and efficient boundary condition for rarefied gas flow simulations.

Statistical physics of vaccination

NASA Astrophysics Data System (ADS)

Wang, Zhen; Bauch, Chris T.; Bhattacharyya, Samit; d'Onofrio, Alberto; Manfredi, Piero; Perc, Matjaž; Perra, Nicola; Salathé, Marcel; Zhao, Dawei

2016-12-01

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues

PubMed Central

Perišić, Ognjen

2018-01-01

Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM’s weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol’s ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner’s interacting scaffold is more flexible and adaptable. PMID:29547506

Kinetic theory for strongly coupled Coulomb systems

NASA Astrophysics Data System (ADS)

Dufty, James; Wrighton, Jeffrey

2018-01-01

The calculation of dynamical properties for matter under extreme conditions is a challenging task. The popular Kubo-Greenwood model exploits elements from equilibrium density-functional theory (DFT) that allow a detailed treatment of electron correlations, but its origin is largely phenomenological; traditional kinetic theories have a more secure foundation but are limited to weak ion-electron interactions. The objective here is to show how a combination of the two evolves naturally from the short-time limit for the generator of the effective single-electron dynamics governing time correlation functions without such limitations. This provides a theoretical context for the current DFT-related approach, the Kubo-Greenwood model, while showing the nature of its corrections. The method is to calculate the short-time dynamics in the single-electron subspace for a given configuration of the ions. This differs from the usual kinetic theory approach in which an average over the ions is performed as well. In this way the effective ion-electron interaction includes strong Coulomb coupling and is shown to be determined from DFT. The correlation functions have the form of the random-phase approximation for an inhomogeneous system but with renormalized ion-electron and electron-electron potentials. The dynamic structure function, density response function, and electrical conductivity are calculated as examples. The static local field corrections in the dielectric function are identified in this way. The current analysis is limited to semiclassical electrons (quantum statistical potentials), so important quantum conditions are excluded. However, a quantization of the kinetic theory is identified for broader application while awaiting its detailed derivation.

Advanced Response Surface Modeling of Ares I Roll Control Jet Aerodynamic Interactions

NASA Technical Reports Server (NTRS)

Favaregh, Noah M.

2010-01-01

The Ares I rocket uses roll control jets. These jets have aerodynamic implications as they impinge on the surface and protuberances of the vehicle. The jet interaction on the body can cause an amplification or a reduction of the rolling moment produced by the jet itself, either increasing the jet effectiveness or creating an adverse effect. A design of experiments test was planned and carried out using computation fluid dynamics, and a subsequent response surface analysis ensued on the available data to characterize the jet interaction across the ascent portion of the Ares I flight envelope. Four response surface schemes were compared including a single response surface covering the entire design space, separate sector responses that did not overlap, continuously overlapping surfaces, and recursive weighted response surfaces. These surfaces were evaluated on traditional statistical metrics as well as visual inspection. Validation of the recursive weighted response surface was performed using additionally available data at off-design point locations.

Spin-Orbit Interactions and Quantum Spin Dynamics in Cold Ion-Atom Collisions

NASA Astrophysics Data System (ADS)

Tscherbul, Timur V.; Brumer, Paul; Buchachenko, Alexei A.

2016-09-01

We present accurate ab initio and quantum scattering calculations on a prototypical hybrid ion-atom system Yb+ -Rb, recently suggested as a promising candidate for the experimental study of open quantum systems, quantum information processing, and quantum simulation. We identify the second-order spin-orbit (SO) interaction as the dominant source of hyperfine relaxation in cold Yb+ -Rb collisions. Our results are in good agreement with recent experimental observations [L. Ratschbacher et al., Phys. Rev. Lett. 110, 160402 (2013)] of hyperfine relaxation rates of trapped Yb+ immersed in an ultracold Rb gas. The calculated rates are 4 times smaller than is predicted by the Langevin capture theory and display a weak T-0.3 temperature dependence, indicating significant deviations from statistical behavior. Our analysis underscores the deleterious nature of the SO interaction and implies that light ion-atom combinations such as Yb+ -Li should be used to minimize hyperfine relaxation and decoherence of trapped ions in ultracold atomic gases.

Spectra, current flow, and wave-function morphology in a model PT -symmetric quantum dot with external interactions

NASA Astrophysics Data System (ADS)

Tellander, Felix; Berggren, Karl-Fredrik

2017-04-01

In this paper we use numerical simulations to study a two-dimensional (2D) quantum dot (cavity) with two leads for passing currents (electrons, photons, etc.) through the system. By introducing an imaginary potential in each lead the system is made symmetric under parity-time inversion (PT symmetric). This system is experimentally realizable in the form of, e.g., quantum dots in low-dimensional semiconductors, optical and electromagnetic cavities, and other classical wave analogs. The computational model introduced here for studying spectra, exceptional points (EPs), wave-function symmetries and morphology, and current flow includes thousands of interacting states. This supplements previous analytic studies of few interacting states by providing more detail and higher resolution. The Hamiltonian describing the system is non-Hermitian; thus, the eigenvalues are, in general, complex. The structure of the wave functions and probability current densities are studied in detail at and in between EPs. The statistics for EPs is evaluated, and reasons for a gradual dynamical crossover are identified.

Differential principal component analysis of ChIP-seq.

PubMed

Ji, Hongkai; Li, Xia; Wang, Qian-fei; Ning, Yang

2013-04-23

We propose differential principal component analysis (dPCA) for analyzing multiple ChIP-sequencing datasets to identify differential protein-DNA interactions between two biological conditions. dPCA integrates unsupervised pattern discovery, dimension reduction, and statistical inference into a single framework. It uses a small number of principal components to summarize concisely the major multiprotein synergistic differential patterns between the two conditions. For each pattern, it detects and prioritizes differential genomic loci by comparing the between-condition differences with the within-condition variation among replicate samples. dPCA provides a unique tool for efficiently analyzing large amounts of ChIP-sequencing data to study dynamic changes of gene regulation across different biological conditions. We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein-DNA interactions.

Vanderbilt free-electron-laser project in biomedical and materials research. Annual report, 1 February 1987-31 January 1988

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haglund, R.F.; Tolk, N.H.

The Medical Free Electron Laser Program was awarded to develop, construct and operate a free-electron laser facility dedicated to biomedical and materials studies, with particular emphases on: fundamental studies of absorption and localization of electromagnetic energy on and near material surfaces, especially through electronic and other selective, non-statistical processes; non-thermal photon-materials interactions (e.g., electronic bond-breaking or vibrational energy transfer) in physical and biological materials as well as in long-wavelength biopolymer dynamics; development of FEL-based methods to study drug action and to characterize biomolecular properties and metabolic processes in biomembranes; clinical applications in otolaryngology, neurosurgery, ophthalmology and radiology stressing the usemore » of the laser for selective laser-tissue, laser-cellular and laser-molecule interactions in both therapeutic and diagnostic modalities.« less

Sleep electroencephalography and heart rate variability interdependence amongst healthy subjects and insomnia/schizophrenia patients.

PubMed

Chaparro-Vargas, Ramiro; Schilling, Claudia; Schredl, Michael; Cvetkovic, Dean

2016-01-01

The quantification of interdependencies within autonomic nervous system has gained increasing importance to characterise healthy and psychiatric disordered subjects. The present work introduces a biosignal processing approach, suggesting a computational resource to estimate coherent or synchronised interactions as an eventual supportive aid in the diagnosis of primary insomnia and schizophrenia pathologies. By deploying linear, nonlinear and statistical methods upon 25 electroencephalographic and electrocardiographic overnight sleep recordings, the assessment of cross-correlation, wavelet coherence and [Formula: see text]:[Formula: see text] phase synchronisation is focused on tracking discerning features amongst the clinical cohorts. Our results indicate that certain neuronal oscillations interact with cardiac power bands in distinctive ways responding to standardised sleep stages and patient groups, which promotes the hypothesis of subtle functional dynamics between neuronal assembles and (para)sympathetic activity subject to pathophysiological conditions.

Gro2mat: a package to efficiently read gromacs output in MATLAB.

PubMed

Dien, Hung; Deane, Charlotte M; Knapp, Bernhard

2014-07-30

Molecular dynamics (MD) simulations are a state-of-the-art computational method used to investigate molecular interactions at atomic scale. Interaction processes out of experimental reach can be monitored using MD software, such as Gromacs. Here, we present the gro2mat package that allows fast and easy access to Gromacs output files from Matlab. Gro2mat enables direct parsing of the most common Gromacs output formats including the binary xtc-format. No openly available Matlab parser currently exists for this format. The xtc reader is orders of magnitudes faster than other available pdb/ascii workarounds. Gro2mat is especially useful for scientists with an interest in quick prototyping of new mathematical and statistical approaches for Gromacs trajectory analyses. © 2014 Wiley Periodicals, Inc. Copyright © 2014 Wiley Periodicals, Inc.

Compression Algorithm Analysis of In-Situ (S)TEM Video: Towards Automatic Event Detection and Characterization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teuton, Jeremy R.; Griswold, Richard L.; Mehdi, Beata L.

Precise analysis of both (S)TEM images and video are time and labor intensive processes. As an example, determining when crystal growth and shrinkage occurs during the dynamic process of Li dendrite deposition and stripping involves manually scanning through each frame in the video to extract a specific set of frames/images. For large numbers of images, this process can be very time consuming, so a fast and accurate automated method is desirable. Given this need, we developed software that uses analysis of video compression statistics for detecting and characterizing events in large data sets. This software works by converting the datamore » into a series of images which it compresses into an MPEG-2 video using the open source “avconv” utility [1]. The software does not use the video itself, but rather analyzes the video statistics from the first pass of the video encoding that avconv records in the log file. This file contains statistics for each frame of the video including the frame quality, intra-texture and predicted texture bits, forward and backward motion vector resolution, among others. In all, avconv records 15 statistics for each frame. By combining different statistics, we have been able to detect events in various types of data. We have developed an interactive tool for exploring the data and the statistics that aids the analyst in selecting useful statistics for each analysis. Going forward, an algorithm for detecting and possibly describing events automatically can be written based on statistic(s) for each data type.« less

The Problem of Auto-Correlation in Parasitology

PubMed Central

Pollitt, Laura C.; Reece, Sarah E.; Mideo, Nicole; Nussey, Daniel H.; Colegrave, Nick

2012-01-01

Explaining the contribution of host and pathogen factors in driving infection dynamics is a major ambition in parasitology. There is increasing recognition that analyses based on single summary measures of an infection (e.g., peak parasitaemia) do not adequately capture infection dynamics and so, the appropriate use of statistical techniques to analyse dynamics is necessary to understand infections and, ultimately, control parasites. However, the complexities of within-host environments mean that tracking and analysing pathogen dynamics within infections and among hosts poses considerable statistical challenges. Simple statistical models make assumptions that will rarely be satisfied in data collected on host and parasite parameters. In particular, model residuals (unexplained variance in the data) should not be correlated in time or space. Here we demonstrate how failure to account for such correlations can result in incorrect biological inference from statistical analysis. We then show how mixed effects models can be used as a powerful tool to analyse such repeated measures data in the hope that this will encourage better statistical practices in parasitology. PMID:22511865

Filtering Meteoroid Flights Using Multiple Unscented Kalman Filters

NASA Astrophysics Data System (ADS)

Sansom, E. K.; Bland, P. A.; Rutten, M. G.; Paxman, J.; Towner, M. C.

2016-11-01

Estimator algorithms are immensely versatile and powerful tools that can be applied to any problem where a dynamic system can be modeled by a set of equations and where observations are available. A well designed estimator enables system states to be optimally predicted and errors to be rigorously quantified. Unscented Kalman filters (UKFs) and interactive multiple models can be found in methods from satellite tracking to self-driving cars. The luminous trajectory of the Bunburra Rockhole fireball was observed by the Desert Fireball Network in mid-2007. The recorded data set is used in this paper to examine the application of these two techniques as a viable approach to characterizing fireball dynamics. The nonlinear, single-body system of equations, used to model meteoroid entry through the atmosphere, is challenged by gross fragmentation events that may occur. The incorporation of the UKF within an interactive multiple model smoother provides a likely solution for when fragmentation events may occur as well as providing a statistical analysis of the state uncertainties. In addition to these benefits, another advantage of this approach is its automatability for use within an image processing pipeline to facilitate large fireball data analyses and meteorite recoveries.

Chaotic oscillation and random-number generation based on nanoscale optical-energy transfer.

PubMed

Naruse, Makoto; Kim, Song-Ju; Aono, Masashi; Hori, Hirokazu; Ohtsu, Motoichi

2014-08-12

By using nanoscale energy-transfer dynamics and density matrix formalism, we demonstrate theoretically and numerically that chaotic oscillation and random-number generation occur in a nanoscale system. The physical system consists of a pair of quantum dots (QDs), with one QD smaller than the other, between which energy transfers via optical near-field interactions. When the system is pumped by continuous-wave radiation and incorporates a timing delay between two energy transfers within the system, it emits optical pulses. We refer to such QD pairs as nano-optical pulsers (NOPs). Irradiating an NOP with external periodic optical pulses causes the oscillating frequency of the NOP to synchronize with the external stimulus. We find that chaotic oscillation occurs in the NOP population when they are connected by an external time delay. Moreover, by evaluating the time-domain signals by statistical-test suites, we confirm that the signals are sufficiently random to qualify the system as a random-number generator (RNG). This study reveals that even relatively simple nanodevices that interact locally with each other through optical energy transfer at scales far below the wavelength of irradiating light can exhibit complex oscillatory dynamics. These findings are significant for applications such as ultrasmall RNGs.

Emergent structures and dynamics in suspensions of self-phoretic colloids

NASA Astrophysics Data System (ADS)

Scagliarini, Andrea; Pagonabarraga, Ignacio

2013-11-01

Active fluids, such as suspensions of self-propelled particles , are a fascinating example of Soft Matter displaying complex collective behaviours which provide challenges in non-equilibrium Statistical Physics. The recent development of techniques to assemble miniaturized devices has led to a growing interest for micro and nanoscale engines that can perform autonomous motion (``microrobots''), as, for instance, self-phoretic colloids, for which the propulsion is induced by the generation of a chemical species in a reaction catalyzed at the particle surface. We perform a mesoscopic numerical study of suspensions of self-phoretic colloids. We show that, at changing the sign of the phoretic mobility (which accounts for the colloid-solute interactions), the system switches from a cluster phase to a state with slowed dynamics. We find that the cluster size distribution follows an exponential behaviour, with a characteristic size growing linearly with the colloid activity, while the density fluctuations grow as a power-law with an exponent depending on the cluster fractal dimension.We single out hydrodynamic interactions, showing that their effect is to work against cluster formation. For positive μ, we observe that colloids tend to reach an ordered state on a triangular lattice.

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics.

PubMed

Sridharan, Gautham Vivek; Bruinsma, Bote Gosse; Bale, Shyam Sundhar; Swaminathan, Anandh; Saeidi, Nima; Yarmush, Martin L; Uygun, Korkut

2017-11-13

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

Emulating Many-Body Localization with a Superconducting Quantum Processor

NASA Astrophysics Data System (ADS)

Xu, Kai; Chen, Jin-Jun; Zeng, Yu; Zhang, Yu-Ran; Song, Chao; Liu, Wuxin; Guo, Qiujiang; Zhang, Pengfei; Xu, Da; Deng, Hui; Huang, Keqiang; Wang, H.; Zhu, Xiaobo; Zheng, Dongning; Fan, Heng

2018-02-01

The law of statistical physics dictates that generic closed quantum many-body systems initialized in nonequilibrium will thermalize under their own dynamics. However, the emergence of many-body localization (MBL) owing to the interplay between interaction and disorder, which is in stark contrast to Anderson localization, which only addresses noninteracting particles in the presence of disorder, greatly challenges this concept, because it prevents the systems from evolving to the ergodic thermalized state. One critical evidence of MBL is the long-time logarithmic growth of entanglement entropy, and a direct observation of it is still elusive due to the experimental challenges in multiqubit single-shot measurement and quantum state tomography. Here we present an experiment fully emulating the MBL dynamics with a 10-qubit superconducting quantum processor, which represents a spin-1 /2 X Y model featuring programmable disorder and long-range spin-spin interactions. We provide essential signatures of MBL, such as the imbalance due to the initial nonequilibrium, the violation of eigenstate thermalization hypothesis, and, more importantly, the direct evidence of the long-time logarithmic growth of entanglement entropy. Our results lay solid foundations for precisely simulating the intriguing physics of quantum many-body systems on the platform of large-scale multiqubit superconducting quantum processors.

Shapes, spectra and new methods in nonlinear spatial optics

NASA Astrophysics Data System (ADS)

Sun, Can

For a myriad of optical applications, the quality of the light source is poor and the beam is inherently spatially partially-coherent. For this broad class of systems, wave dynamics depends not only on the wave intensity, but also on its distribution of spatial frequencies. Unfortunately, this entire spectrum of problems has often been overlooked - for reasons of theoretical ease or experimental difficulties. Here, we remedy this by demonstrating a novel experimental setup which, for the first time, allows arbitrarily modulation of the spatial spectra of light to obtain any distribution of interest. Using modulation instability as an example, we isolate the effect of different spectral shapes and observe distinct beam dynamics. Next, we turn to a thermodynamic description of the long-term evolution of statistical fields. For quantum systems, a major consequence is Bose-Einstein Condensation. However, recent theoretical studies have suggested that quantum mechanics is not necessary for the condensation process: classical waves with random phases can also self-organize into a coherent state. Starting from a random ensemble, nonlinear interactions can lead to a turbulent energy cascade towards longer spatial scales. In complete analogy with the kinetics of a gas system, there is a statistical dynamics of waves in which particle velocities map to wavepacket k-vectors while collisions are mimicked by four-wave mixing. As with collisions, each wave interaction is formally reversible, yet entropy principles mandate that the ensemble evolves towards an equilibrium state of maximum disorder. The result is an equipartition of energy, in the form of a Rayleigh-Jeans spectrum, with information about the condensation process recorded in small-scale fluctuations. Here, we give the first experimental observation of the condensation of classical waves in any media. Using classical light in a self-defocusing photorefractive, we observe all aspects of the condensation process, including the population of a coherent state, spectral redistribution towards the Rayleigh-Jeans spectrum, and formal reversibility of the interactions. The latter is proved experimentally by introducing a digital "Maxwell's Demon" to reverse (phase-conjugate) the momentum of each wavepacket and recover the original "thermal cloud". The results integrate digital and physical methods of nonlinear processing, confirm fundamental ideas in wave turbulence, and greatly extend the range of Bose-Einstein theory.

An Empirical Validation of a Dynamic Systems Model of Interaction: Do Children of Different Sociometric Statuses Differ in Their Dyadic Play?

ERIC Educational Resources Information Center

Steenbeek, Henderien; van Geert, Paul

2008-01-01

Studying short-term dynamic processes and change mechanisms in interaction yields important knowledge that contributes to understanding long-term social development of children. In order to get a grip on this short-term dynamics of interaction processes, the authors made a dynamic systems model of dyadic interaction of children during one play…

Network approach towards understanding the crazing in glassy amorphous polymers

NASA Astrophysics Data System (ADS)

Venkatesan, Sudarkodi; Vivek-Ananth, R. P.; Sreejith, R. P.; Mangalapandi, Pattulingam; Hassanali, Ali A.; Samal, Areejit

2018-04-01

We have used molecular dynamics to simulate an amorphous glassy polymer with long chains to study the deformation mechanism of crazing and associated void statistics. The Van der Waals interactions and the entanglements between chains constituting the polymer play a crucial role in crazing. Thus, we have reconstructed two underlying weighted networks, namely, the Van der Waals network and the entanglement network from polymer configurations extracted from the molecular dynamics simulation. Subsequently, we have performed graph-theoretic analysis of the two reconstructed networks to reveal the role played by them in the crazing of polymers. Our analysis captured various stages of crazing through specific trends in the network measures for Van der Waals networks and entanglement networks. To further corroborate the effectiveness of network analysis in unraveling the underlying physics of crazing in polymers, we have contrasted the trends in network measures for Van der Waals networks and entanglement networks in the light of stress-strain behaviour and voids statistics during deformation. We find that the Van der Waals network plays a crucial role in craze initiation and growth. Although, the entanglement network was found to maintain its structure during craze initiation stage, it was found to progressively weaken and undergo dynamic changes during the hardening and failure stages of crazing phenomena. Our work demonstrates the utility of network theory in quantifying the underlying physics of polymer crazing and widens the scope of applications of network science to characterization of deformation mechanisms in diverse polymers.