Design tools for complex dynamic security systems.

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

Byrne, Raymond Harry; Rigdon, James Brian; Rohrer, Brandon Robinson

2007-01-01

The development of tools for complex dynamic security systems is not a straight forward engineering task but, rather, a scientific task where discovery of new scientific principles and math is necessary. For years, scientists have observed complex behavior but have had difficulty understanding it. Prominent examples include: insect colony organization, the stock market, molecular interactions, fractals, and emergent behavior. Engineering such systems will be an even greater challenge. This report explores four tools for engineered complex dynamic security systems: Partially Observable Markov Decision Process, Percolation Theory, Graph Theory, and Exergy/Entropy Theory. Additionally, enabling hardware technology for next generation security systemsmore » are described: a 100 node wireless sensor network, unmanned ground vehicle and unmanned aerial vehicle.« less

Disentangling the stochastic behavior of complex time series

NASA Astrophysics Data System (ADS)

Anvari, Mehrnaz; Tabar, M. Reza Rahimi; Peinke, Joachim; Lehnertz, Klaus

2016-10-01

Complex systems involving a large number of degrees of freedom, generally exhibit non-stationary dynamics, which can result in either continuous or discontinuous sample paths of the corresponding time series. The latter sample paths may be caused by discontinuous events - or jumps - with some distributed amplitudes, and disentangling effects caused by such jumps from effects caused by normal diffusion processes is a main problem for a detailed understanding of stochastic dynamics of complex systems. Here we introduce a non-parametric method to address this general problem. By means of a stochastic dynamical jump-diffusion modelling, we separate deterministic drift terms from different stochastic behaviors, namely diffusive and jumpy ones, and show that all of the unknown functions and coefficients of this modelling can be derived directly from measured time series. We demonstrate appli- cability of our method to empirical observations by a data-driven inference of the deterministic drift term and of the diffusive and jumpy behavior in brain dynamics from ten epilepsy patients. Particularly these different stochastic behaviors provide extra information that can be regarded valuable for diagnostic purposes.

Cyclic Game Dynamics Driven by Iterated Reasoning

PubMed Central

Frey, Seth; Goldstone, Robert L.

2013-01-01

Recent theories from complexity science argue that complex dynamics are ubiquitous in social and economic systems. These claims emerge from the analysis of individually simple agents whose collective behavior is surprisingly complicated. However, economists have argued that iterated reasoning–what you think I think you think–will suppress complex dynamics by stabilizing or accelerating convergence to Nash equilibrium. We report stable and efficient periodic behavior in human groups playing the Mod Game, a multi-player game similar to Rock-Paper-Scissors. The game rewards subjects for thinking exactly one step ahead of others in their group. Groups that play this game exhibit cycles that are inconsistent with any fixed-point solution concept. These cycles are driven by a “hopping” behavior that is consistent with other accounts of iterated reasoning: agents are constrained to about two steps of iterated reasoning and learn an additional one-half step with each session. If higher-order reasoning can be complicit in complex emergent dynamics, then cyclic and chaotic patterns may be endogenous features of real-world social and economic systems. PMID:23441191

Watching cellular machinery in action, one molecule at a time.

PubMed

Monachino, Enrico; Spenkelink, Lisanne M; van Oijen, Antoine M

2017-01-02

Single-molecule manipulation and imaging techniques have become important elements of the biologist's toolkit to gain mechanistic insights into cellular processes. By removing ensemble averaging, single-molecule methods provide unique access to the dynamic behavior of biomolecules. Recently, the use of these approaches has expanded to the study of complex multiprotein systems and has enabled detailed characterization of the behavior of individual molecules inside living cells. In this review, we provide an overview of the various force- and fluorescence-based single-molecule methods with applications both in vitro and in vivo, highlighting these advances by describing their applications in studies on cytoskeletal motors and DNA replication. We also discuss how single-molecule approaches have increased our understanding of the dynamic behavior of complex multiprotein systems. These methods have shown that the behavior of multicomponent protein complexes is highly stochastic and less linear and deterministic than previously thought. Further development of single-molecule tools will help to elucidate the molecular dynamics of these complex systems both inside the cell and in solutions with purified components. © 2017 Monachino et al.

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

Borreguero, Jose M.; Pincus, Philip A.; Sumpter, Bobby G.

Structure–property relationships of ionic block copolymer (BCP) surfactant complexes are critical toward the progress of favorable engineering design of efficient charge-transport materials. In this paper, molecular dynamics simulations are used to understand the dynamics of charged-neutral BCP and surfactant complexes. The dynamics are examined for two different systems: charged-neutral double-hydrophilic and hydrophobic–hydrophilic block copolymers with oppositely charged surfactant moieties. The dynamics of the surfactant head, tails, and charges are studied for five different BCP volume fractions. We observe that the dynamics of the different species solely depend on the balance between electrostatic and entropic interactions between the charged species andmore » the neutral monomers. The favorable hydrophobic–hydrophobic interactions and the unfavorable hydrophobic–hydrophilic interactions determine the mobilities of the monomers. The dynamical properties of the charge species influence complex formation. Structural relaxations exhibit length-scale dependent behavior, with slower relaxation at the radius of gyration length-scale and faster relaxation at the segmental length-scale, consistent with previous results. The dynamical analysis correlates ion-exchange kinetics to the self-assembly behavior of the complexes.« less

Time-spatial model on the dynamics of the proliferation of Aedes aegypti

NASA Astrophysics Data System (ADS)

Gouvêa, Maury Meirelles, Jr.

2017-03-01

Some complex physical systems, such as cellular regulation, ecosystems, and societies, can be represented by local interactions between agents. Then, complex behaviors may emerge. A cellular automaton is a discrete dynamic system with these features. Among the several complex systems, epidemic diseases are given special attention by researchers with respect to their dynamics. Understanding the behavior of an epidemic may well benefit a society. For instance, different proliferation scenarios may be produced and a prevention policy set. This paper presents a new simulation method of the time-spatial spread of the Dengue mosquito with a cellular automaton. Thus, it will be possible to create different dissemination scenarios and preventive policies for these in several regions. Simulations were performed with different initial conditions and parameters as a result of which the behavior of the proposed method was characterized.

Inclusion Complexes Behavior at the Air-Water Interface. Molecular Dynamic Simulation Study.

NASA Astrophysics Data System (ADS)

Gargallo, L.; Vargas, D.; Sandoval, C.; Saavedra, M.; Becerra, N.; Leiva, A.; Radić, D.

2008-08-01

The interfacial properties of the inclusion complexes (ICs), obtained from the threading of α-cyclodextrin (α-CD) onto poly(ethylene-oxide)(PEO), poly(ɛ-caprolactone)(PEC) and poly(tetrahydrofuran)(PTHF) and their precursor homopolymers (PHPoly), were studied at the air-water interface. The free surface energy was determined by wettability measurements. The experimental behavior of these systems was described by an atomistic molecular dynamics simulation (MDS).

Computational Models of Consumer Confidence from Large-Scale Online Attention Data: Crowd-Sourcing Econometrics

PubMed Central

2015-01-01

Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting. PMID:25826692

Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics.

PubMed

Dong, Xianlei; Bollen, Johan

2015-01-01

Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.

Complex social contagion makes networks more vulnerable to disease outbreaks.

PubMed

Campbell, Ellsworth; Salathé, Marcel

2013-01-01

Social network analysis is now widely used to investigate the dynamics of infectious disease spread. Vaccination dramatically disrupts disease transmission on a contact network, and indeed, high vaccination rates can potentially halt disease transmission altogether. Here, we build on mounting evidence that health behaviors - such as vaccination, and refusal thereof - can spread across social networks through a process of complex contagion that requires social reinforcement. Using network simulations that model health behavior and infectious disease spread, we find that under otherwise identical conditions, the process by which the health behavior spreads has a very strong effect on disease outbreak dynamics. This dynamic variability results from differences in the topology within susceptible communities that arise during the health behavior spreading process, which in turn depends on the topology of the overall social network. Our findings point to the importance of health behavior spread in predicting and controlling disease outbreaks.

Information driven self-organization of complex robotic behaviors.

PubMed

Martius, Georg; Der, Ralf; Ay, Nihat

2013-01-01

Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.

Mathematical Models to Determine Stable Behavior of Complex Systems

NASA Astrophysics Data System (ADS)

Sumin, V. I.; Dushkin, A. V.; Smolentseva, T. E.

2018-05-01

The paper analyzes a possibility to predict functioning of a complex dynamic system with a significant amount of circulating information and a large number of random factors impacting its functioning. Functioning of the complex dynamic system is described as a chaotic state, self-organized criticality and bifurcation. This problem may be resolved by modeling such systems as dynamic ones, without applying stochastic models and taking into account strange attractors.

Dynamics of nanoparticle-protein corona complex formation: analytical results from population balance equations.

PubMed

Darabi Sahneh, Faryad; Scoglio, Caterina; Riviere, Jim

2013-01-01

Nanoparticle-protein corona complex formation involves absorption of protein molecules onto nanoparticle surfaces in a physiological environment. Understanding the corona formation process is crucial in predicting nanoparticle behavior in biological systems, including applications of nanotoxicology and development of nano drug delivery platforms. This paper extends the modeling work in to derive a mathematical model describing the dynamics of nanoparticle corona complex formation from population balance equations. We apply nonlinear dynamics techniques to derive analytical results for the composition of nanoparticle-protein corona complex, and validate our results through numerical simulations. The model presented in this paper exhibits two phases of corona complex dynamics. In the first phase, proteins rapidly bind to the free surface of nanoparticles, leading to a metastable composition. During the second phase, continuous association and dissociation of protein molecules with nanoparticles slowly changes the composition of the corona complex. Given sufficient time, composition of the corona complex reaches an equilibrium state of stable composition. We find analytical approximate formulae for metastable and stable compositions of corona complex. Our formulae are very well-structured to clearly identify important parameters determining corona composition. The dynamics of biocorona formation constitute vital aspect of interactions between nanoparticles and living organisms. Our results further understanding of these dynamics through quantitation of experimental conditions, modeling results for in vitro systems to better predict behavior for in vivo systems. One potential application would involve a single cell culture medium related to a complex protein medium, such as blood or tissue fluid.

Archetypes for Organisational Safety

NASA Technical Reports Server (NTRS)

Marais, Karen; Leveson, Nancy G.

2003-01-01

We propose a framework using system dynamics to model the dynamic behavior of organizations in accident analysis. Most current accident analysis techniques are event-based and do not adequately capture the dynamic complexity and non-linear interactions that characterize accidents in complex systems. In this paper we propose a set of system safety archetypes that model common safety culture flaws in organizations, i.e., the dynamic behaviour of organizations that often leads to accidents. As accident analysis and investigation tools, the archetypes can be used to develop dynamic models that describe the systemic and organizational factors contributing to the accident. The archetypes help clarify why safety-related decisions do not always result in the desired behavior, and how independent decisions in different parts of the organization can combine to impact safety.

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.

Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics

NASA Astrophysics Data System (ADS)

Chen, Yu-Zhong; Lai, Ying-Cheng

2018-03-01

Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.

Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics.

PubMed

Chen, Yu-Zhong; Lai, Ying-Cheng

2018-03-01

Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.

Dynamic control and information processing in chemical reaction systems by tuning self-organization behavior

NASA Astrophysics Data System (ADS)

Lebiedz, Dirk; Brandt-Pollmann, Ulrich

2004-09-01

Specific external control of chemical reaction systems and both dynamic control and signal processing as central functions in biochemical reaction systems are important issues of modern nonlinear science. For example nonlinear input-output behavior and its regulation are crucial for the maintainance of the life process that requires extensive communication between cells and their environment. An important question is how the dynamical behavior of biochemical systems is controlled and how they process information transmitted by incoming signals. But also from a general point of view external forcing of complex chemical reaction processes is important in many application areas ranging from chemical engineering to biomedicine. In order to study such control issues numerically, here, we choose a well characterized chemical system, the CO oxidation on Pt(110), which is interesting per se as an externally forced chemical oscillator model. We show numerically that tuning of temporal self-organization by input signals in this simple nonlinear chemical reaction exhibiting oscillatory behavior can in principle be exploited for both specific external control of dynamical system behavior and processing of complex information.

Predictability and hierarchy in Drosophila behavior.

PubMed

Berman, Gordon J; Bialek, William; Shaevitz, Joshua W

2016-10-18

Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent among the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested subclusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal's entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly's future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states.

Investigating the Structural Impacts of I64T and P311S Mutations in APE1-DNA Complex: A Molecular Dynamics Approach

PubMed Central

Doss, C. George Priya; NagaSundaram, N.

2012-01-01

Background Elucidating the molecular dynamic behavior of Protein-DNA complex upon mutation is crucial in current genomics. Molecular dynamics approach reveals the changes on incorporation of variants that dictate the structure and function of Protein-DNA complexes. Deleterious mutations in APE1 protein modify the physicochemical property of amino acids that affect the protein stability and dynamic behavior. Further, these mutations disrupt the binding sites and prohibit the protein to form complexes with its interacting DNA. Principal Findings In this study, we developed a rapid and cost-effective method to analyze variants in APE1 gene that are associated with disease susceptibility and evaluated their impacts on APE1-DNA complex dynamic behavior. Initially, two different in silico approaches were used to identify deleterious variants in APE1 gene. Deleterious scores that overlap in these approaches were taken in concern and based on it, two nsSNPs with IDs rs61730854 (I64T) and rs1803120 (P311S) were taken further for structural analysis. Significance Different parameters such as RMSD, RMSF, salt bridge, H-bonds and SASA applied in Molecular dynamic study reveals that predicted deleterious variants I64T and P311S alters the structure as well as affect the stability of APE1-DNA interacting functions. This study addresses such new methods for validating functional polymorphisms of human APE1 which is critically involved in causing deficit in repair capacity, which in turn leads to genetic instability and carcinogenesis. PMID:22384055

Suppression of epidemic spreading in complex networks by local information based behavioral responses.

PubMed

Zhang, Hai-Feng; Xie, Jia-Rong; Tang, Ming; Lai, Ying-Cheng

2014-12-01

The interplay between individual behaviors and epidemic dynamics in complex networks is a topic of recent interest. In particular, individuals can obtain different types of information about the disease and respond by altering their behaviors, and this can affect the spreading dynamics, possibly in a significant way. We propose a model where individuals' behavioral response is based on a generic type of local information, i.e., the number of neighbors that has been infected with the disease. Mathematically, the response can be characterized by a reduction in the transmission rate by a factor that depends on the number of infected neighbors. Utilizing the standard susceptible-infected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Our analysis lays on a solid quantitative footing the intuition that individual behavioral response can in general suppress epidemic spreading. Furthermore, we find that the hub nodes play the role of "double-edged sword" in that they can either suppress or promote outbreak, depending on their responses to the epidemic, providing additional support for the idea that these nodes are key to controlling epidemic spreading in complex networks.

Suppression of epidemic spreading in complex networks by local information based behavioral responses

NASA Astrophysics Data System (ADS)

Zhang, Hai-Feng; Xie, Jia-Rong; Tang, Ming; Lai, Ying-Cheng

2014-12-01

The interplay between individual behaviors and epidemic dynamics in complex networks is a topic of recent interest. In particular, individuals can obtain different types of information about the disease and respond by altering their behaviors, and this can affect the spreading dynamics, possibly in a significant way. We propose a model where individuals' behavioral response is based on a generic type of local information, i.e., the number of neighbors that has been infected with the disease. Mathematically, the response can be characterized by a reduction in the transmission rate by a factor that depends on the number of infected neighbors. Utilizing the standard susceptible-infected-susceptible and susceptible-infected-recovery dynamical models for epidemic spreading, we derive a theoretical formula for the epidemic threshold and provide numerical verification. Our analysis lays on a solid quantitative footing the intuition that individual behavioral response can in general suppress epidemic spreading. Furthermore, we find that the hub nodes play the role of "double-edged sword" in that they can either suppress or promote outbreak, depending on their responses to the epidemic, providing additional support for the idea that these nodes are key to controlling epidemic spreading in complex networks.

MSC products for the simulation of tire behavior

NASA Technical Reports Server (NTRS)

Muskivitch, John C.

1995-01-01

The modeling of tires and the simulation of tire behavior are complex problems. The MacNeal-Schwendler Corporation (MSC) has a number of finite element analysis products that can be used to address the complexities of tire modeling and simulation. While there are many similarities between the products, each product has a number of capabilities that uniquely enable it to be used for a specific aspect of tire behavior. This paper discusses the following programs: (1) MSC/NASTRAN - general purpose finite element program for linear and nonlinear static and dynamic analysis; (2) MSC/ADAQUS - nonlinear statics and dynamics finite element program; (3) MSC/PATRAN AFEA (Advanced Finite Element Analysis) - general purpose finite element program with a subset of linear and nonlinear static and dynamic analysis capabilities with an integrated version of MSC/PATRAN for pre- and post-processing; and (4) MSC/DYTRAN - nonlinear explicit transient dynamics finite element program.

Dynamics of Nanoparticle-Protein Corona Complex Formation: Analytical Results from Population Balance Equations

PubMed Central

Darabi Sahneh, Faryad; Scoglio, Caterina; Riviere, Jim

2013-01-01

Background Nanoparticle-protein corona complex formation involves absorption of protein molecules onto nanoparticle surfaces in a physiological environment. Understanding the corona formation process is crucial in predicting nanoparticle behavior in biological systems, including applications of nanotoxicology and development of nano drug delivery platforms. Method This paper extends the modeling work in to derive a mathematical model describing the dynamics of nanoparticle corona complex formation from population balance equations. We apply nonlinear dynamics techniques to derive analytical results for the composition of nanoparticle-protein corona complex, and validate our results through numerical simulations. Results The model presented in this paper exhibits two phases of corona complex dynamics. In the first phase, proteins rapidly bind to the free surface of nanoparticles, leading to a metastable composition. During the second phase, continuous association and dissociation of protein molecules with nanoparticles slowly changes the composition of the corona complex. Given sufficient time, composition of the corona complex reaches an equilibrium state of stable composition. We find analytical approximate formulae for metastable and stable compositions of corona complex. Our formulae are very well-structured to clearly identify important parameters determining corona composition. Conclusion The dynamics of biocorona formation constitute vital aspect of interactions between nanoparticles and living organisms. Our results further understanding of these dynamics through quantitation of experimental conditions, modeling results for in vitro systems to better predict behavior for in vivo systems. One potential application would involve a single cell culture medium related to a complex protein medium, such as blood or tissue fluid. PMID:23741371

Modeling social crowds. Comment on "Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management" by Nicola Bellomo et al.

NASA Astrophysics Data System (ADS)

Poyato, David; Soler, Juan

2016-09-01

The study of human behavior is a complex task, but modeling some aspects of this behavior is an even more complicated and exciting idea. From crisis management to decision making in evacuation protocols, understanding the complexity of humans in stress situations is more and more demanded in our society by obvious reasons [5,6,8,12]. In this context, [4] deals with crowd dynamics with special attention to evacuation.

A theory of neural dimensionality, dynamics, and measurement

NASA Astrophysics Data System (ADS)

Ganguli, Surya

In many experiments, neuroscientists tightly control behavior, record many trials, and obtain trial-averaged firing rates from hundreds of neurons in circuits containing millions of behaviorally relevant neurons. Dimensionality reduction has often shown that such datasets are strikingly simple; they can be described using a much smaller number of dimensions than the number of recorded neurons, and the resulting projections onto these dimensions yield a remarkably insightful dynamical portrait of circuit computation. This ubiquitous simplicity raises several profound and timely conceptual questions. What is the origin of this simplicity and its implications for the complexity of brain dynamics? Would neuronal datasets become more complex if we recorded more neurons? How and when can we trust dynamical portraits obtained from only hundreds of neurons in circuits containing millions of neurons? We present a theory that answers these questions, and test it using neural data recorded from reaching monkeys. Overall, this theory yields a picture of the neural measurement process as a random projection of neural dynamics, conceptual insights into how we can reliably recover dynamical portraits in such under-sampled measurement regimes, and quantitative guidelines for the design of future experiments. Moreover, it reveals the existence of phase transition boundaries in our ability to successfully decode cognition and behavior as a function of the number of recorded neurons, the complexity of the task, and the smoothness of neural dynamics. membership pending.

Controlling Uncertainty: A Review of Human Behavior in Complex Dynamic Environments

ERIC Educational Resources Information Center

Osman, Magda

2010-01-01

Complex dynamic control (CDC) tasks are a type of problem-solving environment used for examining many cognitive activities (e.g., attention, control, decision making, hypothesis testing, implicit learning, memory, monitoring, planning, and problem solving). Because of their popularity, there have been many findings from diverse domains of research…

Special Educators as Intervention Specialists: Dynamic Systems and the Complexity of Intensifying Intervention for Students With Emotional and Behavioral Disorders

ERIC Educational Resources Information Center

Farmer, Thomas W.; Sutherland, Kevin S.; Talbott, Elizabeth; Brooks, Debbie S.; Norwalk, Kate; Huneke, Michelle

2016-01-01

We present a dynamic systems perspective for the intensification of interventions for students with emotional and behavioral disorders (EBD). With this framework, we suggest behavior involves the contributions of multiple factors and reflects the interplay between the characteristics of the student and the ecologies in which he or she is embedded.…

Crossover from antipersistent to persistent behavior in time series possessing the generalyzed dynamic scaling law

NASA Astrophysics Data System (ADS)

Balankin, Alexander S.; Morales Matamoros, Oswaldo; Gálvez M., Ernesto; Pérez A., Alfonso

2004-03-01

The behavior of crude oil price volatility is analyzed within a conceptual framework of kinetic roughening of growing interfaces. We find that the persistent long-horizon volatilities satisfy the Family-Viscek dynamic scaling ansatz, whereas the mean-reverting in time short horizon volatilities obey the generalized scaling law with continuously varying scaling exponents. Furthermore we find that the crossover from antipersistent to persistent behavior is accompanied by a change in the type of volatility distribution. These phenomena are attributed to the complex avalanche dynamics of crude oil markets and so a similar behavior may be observed in a wide variety of physical systems governed by avalanche dynamics.

Complexity, Robustness, and Network Thermodynamics in Large-Scale and Multiagent Systems: A Hybrid Control Approach

DTIC Science & Technology

2012-01-11

dynamic behavior , wherein a dissipative dynamical system can deliver only a fraction of its energy to its surroundings and can store only a fraction of the...collection of interacting subsystems. The behavior and properties of the aggregate large-scale system can then be deduced from the behaviors of the...uniqueness is established. This state space formalism of thermodynamics shows that the behavior of heat, as described by the conservation equations of

Dynamic Fracture Behavior of Plastic-Bonded Explosives

NASA Astrophysics Data System (ADS)

Fu, Hua; Li, Jun-Ling; Tan, Duo-Wang; Ifp, Caep Team

2011-06-01

Plastic-Bonded Explosives (PBX) are used as important energetic materials in nuclear or conventional weapons. Arms Warhead in the service process and the ballistic phase, may experience complex process such as long pulse and higher loading, compresson, tension and reciprocating compression - tension, friction with the projectile shell, which would lead to explosive deformation and fracture.And the dynamic deformation and fracture behavior of PBX subsequently affect reaction characteristics and initiation mechanism in explosives, then having influence on explosives safety. The dynamic fracure behavior of PBX are generally complex and not well studied or understood. In this paper, the dynamic fracture of explosives are conducted using a Kolsky bar. The Brazilian test, also known as a indirect tensile test or splitting test, is chosen as the test method. Tensile strength under different strain rates are obtained using quartz crystal embedded in rod end. The dynamic deformation and fracture process are captured in real-time by high-speed digital camera, and the displacement and strain fields distribution before specimen fracture are obtained by digital correlation method. Considering the non-uniform microstructure of explosives,the dynamic fracture behavior of explosive are simulated by discrete element method, the simulation results can reproduce the deformation and fracture process in Brazilian test using a maximum tensile strain criterion.

Evolutionary dynamics of the traveler's dilemma and minimum-effort coordination games on complex networks.

PubMed

Iyer, Swami; Killingback, Timothy

2014-10-01

The traveler's dilemma game and the minimum-effort coordination game are social dilemmas that have received significant attention resulting from the fact that the predictions of classical game theory are inconsistent with the results found when the games are studied experimentally. Moreover, both the traveler's dilemma and the minimum-effort coordination games have potentially important applications in evolutionary biology. Interestingly, standard deterministic evolutionary game theory, as represented by the replicator dynamics in a well-mixed population, is also inadequate to account for the behavior observed in these games. Here we study the evolutionary dynamics of both these games in populations with interaction patterns described by a variety of complex network topologies. We investigate the evolutionary dynamics of these games through agent-based simulations on both model and empirical networks. In particular, we study the effects of network clustering and assortativity on the evolutionary dynamics of both games. In general, we show that the evolutionary behavior of the traveler's dilemma and minimum-effort coordination games on complex networks is in good agreement with that observed experimentally. Thus, formulating the traveler's dilemma and the minimum-effort coordination games on complex networks neatly resolves the paradoxical aspects of these games.

Evolutionary dynamics of the traveler's dilemma and minimum-effort coordination games on complex networks

NASA Astrophysics Data System (ADS)

Iyer, Swami; Killingback, Timothy

2014-10-01

The traveler's dilemma game and the minimum-effort coordination game are social dilemmas that have received significant attention resulting from the fact that the predictions of classical game theory are inconsistent with the results found when the games are studied experimentally. Moreover, both the traveler's dilemma and the minimum-effort coordination games have potentially important applications in evolutionary biology. Interestingly, standard deterministic evolutionary game theory, as represented by the replicator dynamics in a well-mixed population, is also inadequate to account for the behavior observed in these games. Here we study the evolutionary dynamics of both these games in populations with interaction patterns described by a variety of complex network topologies. We investigate the evolutionary dynamics of these games through agent-based simulations on both model and empirical networks. In particular, we study the effects of network clustering and assortativity on the evolutionary dynamics of both games. In general, we show that the evolutionary behavior of the traveler's dilemma and minimum-effort coordination games on complex networks is in good agreement with that observed experimentally. Thus, formulating the traveler's dilemma and the minimum-effort coordination games on complex networks neatly resolves the paradoxical aspects of these games.

Dynamical singularities of glassy systems in a quantum quench.

PubMed

Obuchi, Tomoyuki; Takahashi, Kazutaka

2012-11-01

We present a prototype of behavior of glassy systems driven by quantum dynamics in a quenching protocol by analyzing the random energy model in a transverse field. We calculate several types of dynamical quantum amplitude and find a freezing transition at some critical time. The behavior is understood by the partition-function zeros in the complex temperature plane. We discuss the properties of the freezing phase as a dynamical chaotic phase, which are contrasted to those of the spin-glass phase in the static system.

Consequences of nonclassical measurement for the algorithmic description of continuous dynamical systems

NASA Technical Reports Server (NTRS)

Fields, Chris

1989-01-01

Continuous dynamical systems intuitively seem capable of more complex behavior than discrete systems. If analyzed in the framework of the traditional theory of computation, a continuous dynamical system with countably many quasistable states has at least the computational power of a universal Turing machine. Such an analysis assumes, however, the classical notion of measurement. If measurement is viewed nonclassically, a continuous dynamical system cannot, even in principle, exhibit behavior that cannot be simulated by a universal Turing machine.

Consequences of nonclassical measurement for the algorithmic description of continuous dynamical systems

NASA Technical Reports Server (NTRS)

Fields, Chris

1989-01-01

Continuous dynamical systems intuitively seem capable of more complex behavior than discrete systems. If analyzed in the framework of the traditional theory of computation, a continuous dynamical system with countablely many quasistable states has at least the computational power of a universal Turing machine. Such an analyses assumes, however, the classical notion of measurement. If measurement is viewed nonclassically, a continuous dynamical system cannot, even in principle, exhibit behavior that cannot be simulated by a universal Turing machine.

A Mathematical Framework for the Complex System Approach to Group Dynamics: The Case of Recovery House Social Integration.

PubMed

Light, John M; Jason, Leonard A; Stevens, Edward B; Callahan, Sarah; Stone, Ariel

2016-03-01

The complex system conception of group social dynamics often involves not only changing individual characteristics, but also changing within-group relationships. Recent advances in stochastic dynamic network modeling allow these interdependencies to be modeled from data. This methodology is discussed within a context of other mathematical and statistical approaches that have been or could be applied to study the temporal evolution of relationships and behaviors within small- to medium-sized groups. An example model is presented, based on a pilot study of five Oxford House recovery homes, sober living environments for individuals following release from acute substance abuse treatment. This model demonstrates how dynamic network modeling can be applied to such systems, examines and discusses several options for pooling, and shows how results are interpreted in line with complex system concepts. Results suggest that this approach (a) is a credible modeling framework for studying group dynamics even with limited data, (b) improves upon the most common alternatives, and (c) is especially well-suited to complex system conceptions. Continuing improvements in stochastic models and associated software may finally lead to mainstream use of these techniques for the study of group dynamics, a shift already occurring in related fields of behavioral science.

New approach of financial volatility duration dynamics by stochastic finite-range interacting voter system.

PubMed

Wang, Guochao; Wang, Jun

2017-01-01

We make an approach on investigating the fluctuation behaviors of financial volatility duration dynamics. A new concept of volatility two-component range intensity (VTRI) is developed, which constitutes the maximal variation range of volatility intensity and shortest passage time of duration, and can quantify the investment risk in financial markets. In an attempt to study and describe the nonlinear complex properties of VTRI, a random agent-based financial price model is developed by the finite-range interacting biased voter system. The autocorrelation behaviors and the power-law scaling behaviors of return time series and VTRI series are investigated. Then, the complexity of VTRI series of the real markets and the proposed model is analyzed by Fuzzy entropy (FuzzyEn) and Lempel-Ziv complexity. In this process, we apply the cross-Fuzzy entropy (C-FuzzyEn) to study the asynchrony of pairs of VTRI series. The empirical results reveal that the proposed model has the similar complex behaviors with the actual markets and indicate that the proposed stock VTRI series analysis and the financial model are meaningful and feasible to some extent.

New approach of financial volatility duration dynamics by stochastic finite-range interacting voter system

NASA Astrophysics Data System (ADS)

Wang, Guochao; Wang, Jun

2017-01-01

We make an approach on investigating the fluctuation behaviors of financial volatility duration dynamics. A new concept of volatility two-component range intensity (VTRI) is developed, which constitutes the maximal variation range of volatility intensity and shortest passage time of duration, and can quantify the investment risk in financial markets. In an attempt to study and describe the nonlinear complex properties of VTRI, a random agent-based financial price model is developed by the finite-range interacting biased voter system. The autocorrelation behaviors and the power-law scaling behaviors of return time series and VTRI series are investigated. Then, the complexity of VTRI series of the real markets and the proposed model is analyzed by Fuzzy entropy (FuzzyEn) and Lempel-Ziv complexity. In this process, we apply the cross-Fuzzy entropy (C-FuzzyEn) to study the asynchrony of pairs of VTRI series. The empirical results reveal that the proposed model has the similar complex behaviors with the actual markets and indicate that the proposed stock VTRI series analysis and the financial model are meaningful and feasible to some extent.

Collective Behavior of Camphor Floats Migrating on the Water Surface

NASA Astrophysics Data System (ADS)

Nishimori, Hiraku; Suematsu, Nobuhiko J.; Nakata, Satoshi

2017-10-01

As simple and easily controllable objects among various self-propelled particles, camphor floats on the water surface have been widely recognized. In this paper, we introduce characteristic behaviors and discuss the background mechanism of camphor floats on water, both in isolated and non-isolated conditions. In particular, we focus on: (i) the transition of dynamical characters through bifurcations exhibited by systems with small number of camphor floats and (ii) the emergence of a rich variety of complex dynamics observed in systems with large number camphor floats, and attempt to elucidate these phenomena through mathematical modeling as well as experimental analysis. Finally, we discuss the connection of the dynamics of camphor floats to that of a wider class of complex and sophisticated dynamics exhibited by various types of self-propelled particles.

How to analytically characterize the epidemic threshold within the coupled disease-behavior systems?. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Xia, Cheng-Yi; Ding, Shuai; Sun, Shi-Wen; Wang, Li; Gao, Zhong-Ke; Wang, Juan

2015-12-01

As is well known, outbreak of epidemics may drive the human population to take some necessary measures to protect themselves from not being infected by infective ones, these precautions in turn will also keep from the further spreading of infectious diseases among the population. Thus, to fully comprehend the epidemic spreading behavior within real-world systems, the interplay between disease dynamics and human behavioral and social dynamics needs to be considered simultaneously, such that some effective containment-measures can be successfully developed [1-3].

Nonlinear dynamics in the study of birdsong

NASA Astrophysics Data System (ADS)

Mindlin, Gabriel B.

2017-09-01

Birdsong, a rich and complex behavior, is a stellar model to understand a variety of biological problems, from motor control to learning. It also enables us to study how behavior emerges when a nervous system, a biomechanical device and the environment interact. In this review, I will show that many questions in the field can benefit from the approach of nonlinear dynamics, and how birdsong can inspire new directions for research in dynamics.

Synthetic microbial ecology and the dynamic interplay between microbial genotypes.

PubMed

Dolinšek, Jan; Goldschmidt, Felix; Johnson, David R

2016-11-01

Assemblages of microbial genotypes growing together can display surprisingly complex and unexpected dynamics and result in community-level functions and behaviors that are not readily expected from analyzing each genotype in isolation. This complexity has, at least in part, inspired a discipline of synthetic microbial ecology. Synthetic microbial ecology focuses on designing, building and analyzing the dynamic behavior of ‘ecological circuits’ (i.e. a set of interacting microbial genotypes) and understanding how community-level properties emerge as a consequence of those interactions. In this review, we discuss typical objectives of synthetic microbial ecology and the main advantages and rationales of using synthetic microbial assemblages. We then summarize recent findings of current synthetic microbial ecology investigations. In particular, we focus on the causes and consequences of the interplay between different microbial genotypes and illustrate how simple interactions can create complex dynamics and promote unexpected community-level properties. We finally propose that distinguishing between active and passive interactions and accounting for the pervasiveness of competition can improve existing frameworks for designing and predicting the dynamics of microbial assemblages.

Dynamics and Collapse in a Power System Model with Voltage Variation: The Damping Effect.

PubMed

Ma, Jinpeng; Sun, Yong; Yuan, Xiaoming; Kurths, Jürgen; Zhan, Meng

2016-01-01

Complex nonlinear phenomena are investigated in a basic power system model of the single-machine-infinite-bus (SMIB) with a synchronous generator modeled by a classical third-order differential equation including both angle dynamics and voltage dynamics, the so-called flux decay equation. In contrast, for the second-order differential equation considering the angle dynamics only, it is the classical swing equation. Similarities and differences of the dynamics generated between the third-order model and the second-order one are studied. We mainly find that, for positive damping, these two models show quite similar behavior, namely, stable fixed point, stable limit cycle, and their coexistence for different parameters. However, for negative damping, the second-order system can only collapse, whereas for the third-order model, more complicated behavior may happen, such as stable fixed point, limit cycle, quasi-periodicity, and chaos. Interesting partial collapse phenomena for angle instability only and not for voltage instability are also found here, including collapse from quasi-periodicity and from chaos etc. These findings not only provide a basic physical picture for power system dynamics in the third-order model incorporating voltage dynamics, but also enable us a deeper understanding of the complex dynamical behavior and even leading to a design of oscillation damping in electric power systems.

Energetic and flexibility properties captured by long molecular dynamics simulations of a membrane-embedded pMHCII-TCR complex.

PubMed

Bello, Martiniano; Correa-Basurto, José

2016-04-01

Although crystallographic data have provided important molecular insight into the interactions in the pMHC-TCR complex, the inherent features of this structural approach cause it to only provide a static picture of the interactions. While unbiased molecular dynamics simulations (UMDSs) have provided important information about the dynamic structural behavior of the pMHC-TCR complex, most of them have modeled the pMHC-TCR complex as soluble, when in physiological conditions, this complex is membrane bound; therefore, following this latter UMDS protocol might hamper important dynamic results. In this contribution, we performed three independent 300 ns-long UMDSs of the pMHCII-TCR complex anchored in two opposing membranes to explore the structural and energetic properties of the recognition of pMHCII by the TCR. The conformational ensemble generated through UMDSs was subjected to clustering and Cartesian principal component analyses (cPCA) to explore the dynamical behavior of the pMHCII-TCR association. Furthermore, based on the conformational population sampled through UMDSs, the effective binding free energy, per-residue free energy decomposition, and alanine scanning mutations were explored for the native pMHCII-TCR complex, as well as for 12 mutations (p1-p12MHCII-TCR) introduced in the native peptide. Clustering analyses and cPCA provide insight into the rocking motion of the TCR onto pMHCII, together with the presence of new electrostatic interactions not observed through crystallographic methods. Energetic results provide evidence of the main contributors to the pMHC-TCR complex formation as well as the key residues involved in this molecular recognition process.

Self-Organized Dynamic Flocking Behavior from a Simple Deterministic Map

NASA Astrophysics Data System (ADS)

Krueger, Wesley

2007-10-01

Coherent motion exhibiting large-scale order, such as flocking, swarming, and schooling behavior in animals, can arise from simple rules applied to an initial random array of self-driven particles. We present a completely deterministic dynamic map that exhibits emergent, collective, complex motion for a group of particles. Each individual particle is driven with a constant speed in two dimensions adopting the average direction of a fixed set of non-spatially related partners. In addition, the particle changes direction by π as it reaches a circular boundary. The dynamical patterns arising from these rules range from simple circular-type convective motion to highly sophisticated, complex, collective behavior which can be easily interpreted as flocking, schooling, or swarming depending on the chosen parameters. We present the results as a series of short movies and we also explore possible order parameters and correlation functions capable of quantifying the resulting coherence.

Nonlinear analysis of dynamic signature

NASA Astrophysics Data System (ADS)

Rashidi, S.; Fallah, A.; Towhidkhah, F.

2013-12-01

Signature is a long trained motor skill resulting in well combination of segments like strokes and loops. It is a physical manifestation of complex motor processes. The problem, generally stated, is that how relative simplicity in behavior emerges from considerable complexity of perception-action system that produces behavior within an infinitely variable biomechanical and environmental context. To solve this problem, we present evidences which indicate that motor control dynamic in signing process is a chaotic process. This chaotic dynamic may explain a richer array of time series behavior in motor skill of signature. Nonlinear analysis is a powerful approach and suitable tool which seeks for characterizing dynamical systems through concepts such as fractal dimension and Lyapunov exponent. As a result, they can be analyzed in both horizontal and vertical for time series of position and velocity. We observed from the results that noninteger values for the correlation dimension indicates low dimensional deterministic dynamics. This result could be confirmed by using surrogate data tests. We have also used time series to calculate the largest Lyapunov exponent and obtain a positive value. These results constitute significant evidence that signature data are outcome of chaos in a nonlinear dynamical system of motor control.

Impacts of complex behavioral responses on asymmetric interacting spreading dynamics in multiplex networks

PubMed Central

Liu, Quan-Hui; Wang, Wei; Tang, Ming; Zhang, Hai-Feng

2016-01-01

Information diffusion and disease spreading in communication-contact layered network are typically asymmetrically coupled with each other, in which disease spreading can be significantly affected by the way an individual being aware of disease responds to the disease. Many recent studies have demonstrated that human behavioral adoption is a complex and non-Markovian process, where the probability of behavior adoption is dependent on the cumulative times of information received and the social reinforcement effect of the cumulative information. In this paper, the impacts of such a non-Markovian vaccination adoption behavior on the epidemic dynamics and the control effects are explored. It is found that this complex adoption behavior in the communication layer can significantly enhance the epidemic threshold and reduce the final infection rate. By defining the social cost as the total cost of vaccination and treatment, it can be seen that there exists an optimal social reinforcement effect and optimal information transmission rate allowing the minimal social cost. Moreover, a mean-field theory is developed to verify the correctness of simulation results. PMID:27156574

Impacts of complex behavioral responses on asymmetric interacting spreading dynamics in multiplex networks.

PubMed

Liu, Quan-Hui; Wang, Wei; Tang, Ming; Zhang, Hai-Feng

2016-05-09

Information diffusion and disease spreading in communication-contact layered network are typically asymmetrically coupled with each other, in which disease spreading can be significantly affected by the way an individual being aware of disease responds to the disease. Many recent studies have demonstrated that human behavioral adoption is a complex and non-Markovian process, where the probability of behavior adoption is dependent on the cumulative times of information received and the social reinforcement effect of the cumulative information. In this paper, the impacts of such a non-Markovian vaccination adoption behavior on the epidemic dynamics and the control effects are explored. It is found that this complex adoption behavior in the communication layer can significantly enhance the epidemic threshold and reduce the final infection rate. By defining the social cost as the total cost of vaccination and treatment, it can be seen that there exists an optimal social reinforcement effect and optimal information transmission rate allowing the minimal social cost. Moreover, a mean-field theory is developed to verify the correctness of simulation results.

Double dynamic scaling in human communication dynamics

NASA Astrophysics Data System (ADS)

Wang, Shengfeng; Feng, Xin; Wu, Ye; Xiao, Jinhua

2017-05-01

In the last decades, human behavior has been deeply understanding owing to the huge quantities data of human behavior available for study. The main finding in human dynamics shows that temporal processes consist of high-activity bursty intervals alternating with long low-activity periods. A model, assuming the initiator of bursty follow a Poisson process, is widely used in the modeling of human behavior. Here, we provide further evidence for the hypothesis that different bursty intervals are independent. Furthermore, we introduce a special threshold to quantitatively distinguish the time scales of complex dynamics based on the hypothesis. Our results suggest that human communication behavior is a composite process of double dynamics with midrange memory length. The method for calculating memory length would enhance the performance of many sequence-dependent systems, such as server operation and topic identification.

Nonlinear complexity of random visibility graph and Lempel-Ziv on multitype range-intensity interacting financial dynamics

NASA Astrophysics Data System (ADS)

Zhang, Yali; Wang, Jun

2017-09-01

In an attempt to investigate the nonlinear complex evolution of financial dynamics, a new financial price model - the multitype range-intensity contact (MRIC) financial model, is developed based on the multitype range-intensity interacting contact system, in which the interaction and transmission of different types of investment attitudes in a stock market are simulated by viruses spreading. Two new random visibility graph (VG) based analyses and Lempel-Ziv complexity (LZC) are applied to study the complex behaviors of return time series and the corresponding random sorted series. The VG method is the complex network theory, and the LZC is a non-parametric measure of complexity reflecting the rate of new pattern generation of a series. In this work, the real stock market indices are considered to be comparatively studied with the simulation data of the proposed model. Further, the numerical empirical study shows the similar complexity behaviors between the model and the real markets, the research confirms that the financial model is reasonable to some extent.

An overview of the crash dynamics failure behavior of metal and composite aircraft structures

NASA Technical Reports Server (NTRS)

Carden, Huey D.; Boitnott, Richard L.; Fasanella, Edwin L.; Jones, Lisa E.

1991-01-01

An overview of failure behavior results is presented from some of the crash dynamics research conducted with concepts of aircraft elements and substructure not necessarily designed or optimized for energy absorption or crash loading considerations. Experimental and analytical data are presented that indicate some general trends in the failure behavior of a class of composite structures that includes fuselage panels, individual fuselage sections, fuselage frames, skeleton subfloors with stringers and floor beams without skin covering, and subfloors with skin added to the frame stringer structure. Although the behavior is complex, a strong similarity in the static/dynamic failure behavior among these structures is illustrated through photographs of the experimental results and through analytical data of generic composite structural models.

Capturing complexity in work disability research: application of system dynamics modeling methodology.

PubMed

Jetha, Arif; Pransky, Glenn; Hettinger, Lawrence J

2016-01-01

Work disability (WD) is characterized by variable and occasionally undesirable outcomes. The underlying determinants of WD outcomes include patterns of dynamic relationships among health, personal, organizational and regulatory factors that have been challenging to characterize, and inadequately represented by contemporary WD models. System dynamics modeling (SDM) methodology applies a sociotechnical systems thinking lens to view WD systems as comprising a range of influential factors linked by feedback relationships. SDM can potentially overcome limitations in contemporary WD models by uncovering causal feedback relationships, and conceptualizing dynamic system behaviors. It employs a collaborative and stakeholder-based model building methodology to create a visual depiction of the system as a whole. SDM can also enable researchers to run dynamic simulations to provide evidence of anticipated or unanticipated outcomes that could result from policy and programmatic intervention. SDM may advance rehabilitation research by providing greater insights into the structure and dynamics of WD systems while helping to understand inherent complexity. Challenges related to data availability, determining validity, and the extensive time and technical skill requirements for model building may limit SDM's use in the field and should be considered. Contemporary work disability (WD) models provide limited insight into complexity associated with WD processes. System dynamics modeling (SDM) has the potential to capture complexity through a stakeholder-based approach that generates a simulation model consisting of multiple feedback loops. SDM may enable WD researchers and practitioners to understand the structure and behavior of the WD system as a whole, and inform development of improved strategies to manage straightforward and complex WD cases.

A nonlinear dynamical system for combustion instability in a pulse model combustor

NASA Astrophysics Data System (ADS)

Takagi, Kazushi; Gotoda, Hiroshi

2016-11-01

We theoretically and numerically study the bifurcation phenomena of nonlinear dynamical system describing combustion instability in a pulse model combustor on the basis of dynamical system theory and complex network theory. The dynamical behavior of pressure fluctuations undergoes a significant transition from steady-state to deterministic chaos via the period-doubling cascade process known as Feigenbaum scenario with decreasing the characteristic flow time. Recurrence plots and recurrence networks analysis we adopted in this study can quantify the significant changes in dynamic behavior of combustion instability that cannot be captured in the bifurcation diagram.

Oscillations and Multiple Equilibria in Microvascular Blood Flow.

PubMed

Karst, Nathaniel J; Storey, Brian D; Geddes, John B

2015-07-01

We investigate the existence of oscillatory dynamics and multiple steady-state flow rates in a network with a simple topology and in vivo microvascular blood flow constitutive laws. Unlike many previous analytic studies, we employ the most biologically relevant models of the physical properties of whole blood. Through a combination of analytic and numeric techniques, we predict in a series of two-parameter bifurcation diagrams a range of dynamical behaviors, including multiple equilibria flow configurations, simple oscillations in volumetric flow rate, and multiple coexistent limit cycles at physically realizable parameters. We show that complexity in network topology is not necessary for complex behaviors to arise and that nonlinear rheology, in particular the plasma skimming effect, is sufficient to support oscillatory dynamics similar to those observed in vivo.

Editorial: Cognitive Architectures, Model Comparison and AGI

NASA Astrophysics Data System (ADS)

Lebiere, Christian; Gonzalez, Cleotilde; Warwick, Walter

2010-12-01

Cognitive Science and Artificial Intelligence share compatible goals of understanding and possibly generating broadly intelligent behavior. In order to determine if progress is made, it is essential to be able to evaluate the behavior of complex computational models, especially those built on general cognitive architectures, and compare it to benchmarks of intelligent behavior such as human performance. Significant methodological challenges arise, however, when trying to extend approaches used to compare model and human performance from tightly controlled laboratory tasks to complex tasks involving more open-ended behavior. This paper describes a model comparison challenge built around a dynamic control task, the Dynamic Stocks and Flows. We present and discuss distinct approaches to evaluating performance and comparing models. Lessons drawn from this challenge are discussed in light of the challenge of using cognitive architectures to achieve Artificial General Intelligence.

Complex collective dynamics of active torque-driven colloids at interfaces

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

Snezhko, Alexey

Modern self-assembly techniques aiming to produce complex structural order or functional diversity often rely on non-equilibrium conditions in the system. Light, electric, or magnetic fields are predominantly used to modify interaction profiles of colloidal particles during self-assembly or induce complex out-of-equilibrium dynamic ordering. The energy injection rate, properties of the environment are important control parameters that influence the outcome of active (dynamic) self-assembly. The current review is focused on a case of collective dynamics and self-assembly of particles with externally driven torques coupled to a liquid or solid interface. The complexity of interactions in such systems is further enriched bymore » strong hydrodynamic coupling between particles. Unconventionally ordered dynamic self-assembled patterns, spontaneous symmetry breaking phenomena, self-propulsion, and collective transport have been reported in torque-driven colloids. Some of the features of the complex collective behavior and dynamic pattern formation in those active systems have been successfully captured in simulations.« less

Probing Teachers' Lesson Planning: Promoting Metacognition

ERIC Educational Resources Information Center

Eilam, Billie

2017-01-01

Classrooms are complex systems, with dynamic interactions of different kinds among their composing varied elements. Such complex interactions lead to the system's unpredictable emergent learning behaviors. To support teachers' lesson planning and monitoring in the complex environment of classrooms, the present article examines the core…

Scaling behavior of online human activity

NASA Astrophysics Data System (ADS)

Zhao, Zhi-Dan; Cai, Shi-Min; Huang, Junming; Fu, Yan; Zhou, Tao

2012-11-01

The rapid development of the Internet technology enables humans to explore the web and record the traces of online activities. From the analysis of these large-scale data sets (i.e., traces), we can get insights about the dynamic behavior of human activity. In this letter, the scaling behavior and complexity of human activity in the e-commerce, such as music, books, and movies rating, are comprehensively investigated by using the detrended fluctuation analysis technique and the multiscale entropy method. Firstly, the interevent time series of rating behaviors of these three types of media show similar scaling properties with exponents ranging from 0.53 to 0.58, which implies that the collective behaviors of rating media follow a process embodying self-similarity and long-range correlation. Meanwhile, by dividing the users into three groups based on their activities (i.e., rating per unit time), we find that the scaling exponents of the interevent time series in the three groups are different. Hence, these results suggest that a stronger long-range correlations exist in these collective behaviors. Furthermore, their information complexities vary in the three groups. To explain the differences of the collective behaviors restricted to the three groups, we study the dynamic behavior of human activity at the individual level, and find that the dynamic behaviors of a few users have extremely small scaling exponents associated with long-range anticorrelations. By comparing the interevent time distributions of four representative users, we can find that the bimodal distributions may bring forth the extraordinary scaling behaviors. These results of the analysis of the online human activity in the e-commerce may not only provide insight into its dynamic behaviors but may also be applied to acquire potential economic interest.

Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware

PubMed Central

Srinivasa, Narayan; Stepp, Nigel D.; Cruz-Albrecht, Jose

2015-01-01

Neuromorphic hardware are designed by drawing inspiration from biology to overcome limitations of current computer architectures while forging the development of a new class of autonomous systems that can exhibit adaptive behaviors. Several designs in the recent past are capable of emulating large scale networks but avoid complexity in network dynamics by minimizing the number of dynamic variables that are supported and tunable in hardware. We believe that this is due to the lack of a clear understanding of how to design self-tuning complex systems. It has been widely demonstrated that criticality appears to be the default state of the brain and manifests in the form of spontaneous scale-invariant cascades of neural activity. Experiment, theory and recent models have shown that neuronal networks at criticality demonstrate optimal information transfer, learning and information processing capabilities that affect behavior. In this perspective article, we argue that understanding how large scale neuromorphic electronics can be designed to enable emergent adaptive behavior will require an understanding of how networks emulated by such hardware can self-tune local parameters to maintain criticality as a set-point. We believe that such capability will enable the design of truly scalable intelligent systems using neuromorphic hardware that embrace complexity in network dynamics rather than avoiding it. PMID:26648839

Discrimination of Complex Human Behavior by Pigeons (Columba livia) and Humans

PubMed Central

Qadri, Muhammad A. J.; Sayde, Justin M.; Cook, Robert G.

2014-01-01

The cognitive and neural mechanisms for recognizing and categorizing behavior are not well understood in non-human animals. In the current experiments, pigeons and humans learned to categorize two non-repeating, complex human behaviors (“martial arts” vs. “Indian dance”). Using multiple video exemplars of a digital human model, pigeons discriminated these behaviors in a go/no-go task and humans in a choice task. Experiment 1 found that pigeons already experienced with discriminating the locomotive actions of digital animals acquired the discrimination more rapidly when action information was available than when only pose information was available. Experiments 2 and 3 found this same dynamic superiority effect with naïve pigeons and human participants. Both species used the same combination of immediately available static pose information and more slowly perceived dynamic action cues to discriminate the behavioral categories. Theories based on generalized visual mechanisms, as opposed to embodied, species-specific action networks, offer a parsimonious account of how these different animals recognize behavior across and within species. PMID:25379777

A mixed integer program to model spatial wildfire behavior and suppression placement decisions

Treesearch

Erin J. Belval; Yu Wei; Michael Bevers

2015-01-01

Wildfire suppression combines multiple objectives and dynamic fire behavior to form a complex problem for decision makers. This paper presents a mixed integer program designed to explore integrating spatial fire behavior and suppression placement decisions into a mathematical programming framework. Fire behavior and suppression placement decisions are modeled using...

Assessing the Extent of Influence Subglacial Hydrology Has on Dynamic Ice Sheet Behavior

NASA Astrophysics Data System (ADS)

Babonis, G. S.; Csatho, B. M.

2012-12-01

Numerous recent studies have done an excellent job capturing and quantifying the complex pattern of dynamic changes of the Greenland Ice Sheet (GrIS) over the past several decades. The timing of changes in ice velocities and mass balance indicate that the mechanisms controlling these behaviors, both external and internal, act over variable spatial and temporal regimes, can change in rapid and complex fashion, and have significant effect on ice sheet behavior as well as sea level rise. With roughly half of the estimated ice loss from the GrIS attributed to dynamic processes, these changes account for about 250 Gt/yr (2003-2008), equivalence to 0.6 mm/yr sea level rise. One of the primary influences of dynamic ice behavior is ice sheet hydrology, including the storage and transport of water from the supraglacial to subglacial environment, and the subsequent development of water transport pathways, thus demonstrating the need for further characterization of the subglacial environment. Enhanced dynamic flow of ice due to the influence of meltwater distribution on the subglacial environment has been reported, including In-SAR observations of large velocity increases over short periods of time, suggesting regions where dynamic changes are likely being caused by changes in hydrology. Additionally, building upon the 1993-2011 laser altimetry record, analyzed by our Surface Elevation Reconstruction And Change detection (SERAC) procedure, we have detected complex patterns of rapid thickening and thinning patterns over several outlet glaciers. This study presents a comprehensive investigation of hydrologic control on dynamic glacier behavior for several key sites in Greenland. We combine a high resolution surface digital elevation model (DEM) derived by fusing space- and airborne laser altimetry observations and SPIRIT SPOT DEMs, with a high resolution, hydrologically-corrected bedrock DEM derived from a combination of CResIS and Operation Icebridge ice penetrating radar data for generating potentiometric maps for each region of interest. Using these potentiometric maps, along with surficial DEMs, supra- and subglacial routing paths, as well as potential sites for discrete supraglacial hydrologic input sources are identified. Comparison of hydrologic drainage networks with the spatial distribution of recent rapid dynamic changes detected by altimetry allows for the assessment of the extent of influence that subglacial hydrology has on ice sheet behavior.

Transition Manifolds of Complex Metastable Systems: Theory and Data-Driven Computation of Effective Dynamics.

PubMed

Bittracher, Andreas; Koltai, Péter; Klus, Stefan; Banisch, Ralf; Dellnitz, Michael; Schütte, Christof

2018-01-01

We consider complex dynamical systems showing metastable behavior, but no local separation of fast and slow time scales. The article raises the question of whether such systems exhibit a low-dimensional manifold supporting its effective dynamics. For answering this question, we aim at finding nonlinear coordinates, called reaction coordinates, such that the projection of the dynamics onto these coordinates preserves the dominant time scales of the dynamics. We show that, based on a specific reducibility property, the existence of good low-dimensional reaction coordinates preserving the dominant time scales is guaranteed. Based on this theoretical framework, we develop and test a novel numerical approach for computing good reaction coordinates. The proposed algorithmic approach is fully local and thus not prone to the curse of dimension with respect to the state space of the dynamics. Hence, it is a promising method for data-based model reduction of complex dynamical systems such as molecular dynamics.

Nonlinear complexity behaviors of agent-based 3D Potts financial dynamics with random environments

NASA Astrophysics Data System (ADS)

Xing, Yani; Wang, Jun

2018-02-01

A new microscopic 3D Potts interaction financial price model is established in this work, to investigate the nonlinear complexity behaviors of stock markets. 3D Potts model, which extends the 2D Potts model to three-dimensional, is a cubic lattice model to explain the interaction behavior among the agents. In order to explore the complexity of real financial markets and the 3D Potts financial model, a new random coarse-grained Lempel-Ziv complexity is proposed to certain series, such as the price returns, the price volatilities, and the random time d-returns. Then the composite multiscale entropy (CMSE) method is applied to the intrinsic mode functions (IMFs) and the corresponding shuffled data to study the complexity behaviors. The empirical results indicate that the 3D financial model is feasible.

On learning navigation behaviors for small mobile robots with reservoir computing architectures.

PubMed

Antonelo, Eric Aislan; Schrauwen, Benjamin

2015-04-01

This paper proposes a general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) be fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior that can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on the examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using a hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors toward the goal.

Critical Behaviors in Contagion Dynamics.

PubMed

Böttcher, L; Nagler, J; Herrmann, H J

2017-02-24

We study the critical behavior of a general contagion model where nodes are either active (e.g., with opinion A, or functioning) or inactive (e.g., with opinion B, or damaged). The transitions between these two states are determined by (i) spontaneous transitions independent of the neighborhood, (ii) transitions induced by neighboring nodes, and (iii) spontaneous reverse transitions. The resulting dynamics is extremely rich including limit cycles and random phase switching. We derive a unifying mean-field theory. Specifically, we analytically show that the critical behavior of systems whose dynamics is governed by processes (i)-(iii) can only exhibit three distinct regimes: (a) uncorrelated spontaneous transition dynamics, (b) contact process dynamics, and (c) cusp catastrophes. This ends a long-standing debate on the universality classes of complex contagion dynamics in mean field and substantially deepens its mathematical understanding.

Altruism: A natural strategy for enhancing survival

NASA Astrophysics Data System (ADS)

Rozenfeld, Alejandro F.; Luis Gruver, José; Albano, Ezequiel V.; Havlin, Shlomo

2006-09-01

We study the influence of altruistic behavior in a prey-predator model permitting the preys to commit suicide by confronting the predators instead of escaping. Surprising, altruistic behavior at microscopic (local) scale, leads to the emergence of new complex macroscopic (global) phenomena characterized by dramatic changes in the dynamic topology of the prey-predator spatiotemporal distribution, yielding spiral patterns. We show that such dynamics enhances the prey's survivability.

TASI: A software tool for spatial-temporal quantification of tumor spheroid dynamics.

PubMed

Hou, Yue; Konen, Jessica; Brat, Daniel J; Marcus, Adam I; Cooper, Lee A D

2018-05-08

Spheroid cultures derived from explanted cancer specimens are an increasingly utilized resource for studying complex biological processes like tumor cell invasion and metastasis, representing an important bridge between the simplicity and practicality of 2-dimensional monolayer cultures and the complexity and realism of in vivo animal models. Temporal imaging of spheroids can capture the dynamics of cell behaviors and microenvironments, and when combined with quantitative image analysis methods, enables deep interrogation of biological mechanisms. This paper presents a comprehensive open-source software framework for Temporal Analysis of Spheroid Imaging (TASI) that allows investigators to objectively characterize spheroid growth and invasion dynamics. TASI performs spatiotemporal segmentation of spheroid cultures, extraction of features describing spheroid morpho-phenotypes, mathematical modeling of spheroid dynamics, and statistical comparisons of experimental conditions. We demonstrate the utility of this tool in an analysis of non-small cell lung cancer spheroids that exhibit variability in metastatic and proliferative behaviors.

Dynamic Primitives of Motor Behavior

PubMed Central

Hogan, Neville; Sternad, Dagmar

2013-01-01

We present in outline a theory of sensorimotor control based on dynamic primitives, which we define as attractors. To account for the broad class of human interactive behaviors—especially tool use—we propose three distinct primitives: submovements, oscillations and mechanical impedances, the latter necessary for interaction with objects. Due to fundamental features of the neuromuscular system, most notably its slow response, we argue that encoding in terms of parameterized primitives may be an essential simplification required for learning, performance, and retention of complex skills. Primitives may simultaneously and sequentially be combined to produce observable forces and motions. This may be achieved by defining a virtual trajectory composed of submovements and/or oscillations interacting with impedances. Identifying primitives requires care: in principle, overlapping submovements would be sufficient to compose all observed movements but biological evidence shows that oscillations are a distinct primitive. Conversely, we suggest that kinematic synergies, frequently discussed as primitives of complex actions, may be an emergent consequence of neuromuscular impedance. To illustrate how these dynamic primitives may account for complex actions, we briefly review three types of interactive behaviors: constrained motion, impact tasks, and manipulation of dynamic objects. PMID:23124919

Using a Complexity Approach to Study the Interpersonal Dynamics in Teacher-­Student Interactions: A Case Study of Two Teachers

ERIC Educational Resources Information Center

Pennings, Helena J. M.

2017-01-01

In the present study, complex dynamic systems theory and interpersonal theory are combined to describe the teacher-student interactions of two teachers with different interpersonal styles. The aim was to show and explain the added value of looking at different steps in the analysis of behavioral time-series data (i.e., observations of teacher and…

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

Rivera-Durón, R. R., E-mail: roberto.rivera@ipicyt.edu.mx; Campos-Cantón, E., E-mail: eric.campos@ipicyt.edu.mx; Campos-Cantón, I.

We present the design of an autonomous time-delay Boolean network realized with readily available electronic components. Through simulations and experiments that account for the detailed nonlinear response of each circuit element, we demonstrate that a network with five Boolean nodes displays complex behavior. Furthermore, we show that the dynamics of two identical networks display near-instantaneous synchronization to a periodic state when forced by a common periodic Boolean signal. A theoretical analysis of the network reveals the conditions under which complex behavior is expected in an individual network and the occurrence of synchronization in the forced networks. This research will enablemore » future experiments on autonomous time-delay networks using readily available electronic components with dynamics on a slow enough time-scale so that inexpensive data collection systems can faithfully record the dynamics.« less

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.

Epidemic dynamics and endemic states in complex networks

NASA Astrophysics Data System (ADS)

Pastor-Satorras, Romualdo; Vespignani, Alessandro

2001-06-01

We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks.

Transition Manifolds of Complex Metastable Systems

NASA Astrophysics Data System (ADS)

Bittracher, Andreas; Koltai, Péter; Klus, Stefan; Banisch, Ralf; Dellnitz, Michael; Schütte, Christof

2018-04-01

We consider complex dynamical systems showing metastable behavior, but no local separation of fast and slow time scales. The article raises the question of whether such systems exhibit a low-dimensional manifold supporting its effective dynamics. For answering this question, we aim at finding nonlinear coordinates, called reaction coordinates, such that the projection of the dynamics onto these coordinates preserves the dominant time scales of the dynamics. We show that, based on a specific reducibility property, the existence of good low-dimensional reaction coordinates preserving the dominant time scales is guaranteed. Based on this theoretical framework, we develop and test a novel numerical approach for computing good reaction coordinates. The proposed algorithmic approach is fully local and thus not prone to the curse of dimension with respect to the state space of the dynamics. Hence, it is a promising method for data-based model reduction of complex dynamical systems such as molecular dynamics.

Linking brain, mind and behavior.

PubMed

Makeig, Scott; Gramann, Klaus; Jung, Tzyy-Ping; Sejnowski, Terrence J; Poizner, Howard

2009-08-01

Cortical brain areas and dynamics evolved to organize motor behavior in our three-dimensional environment also support more general human cognitive processes. Yet traditional brain imaging paradigms typically allow and record only minimal participant behavior, then reduce the recorded data to single map features of averaged responses. To more fully investigate the complex links between distributed brain dynamics and motivated natural behavior, we propose the development of wearable mobile brain/body imaging (MoBI) systems that continuously capture the wearer's high-density electrical brain and muscle signals, three-dimensional body movements, audiovisual scene and point of regard, plus new data-driven analysis methods to model their interrelationships. The new imaging modality should allow new insights into how spatially distributed brain dynamics support natural human cognition and agency.

Family Complexity, Siblings, and Children's Aggressive Behavior at School Entry.

PubMed

Fomby, Paula; Goode, Joshua A; Mollborn, Stefanie

2016-02-01

As family structure in the United States has become increasingly dynamic and complex, children have become more likely to reside with step- or half-siblings through a variety of pathways. When these pathways are accounted for, more than one in six U.S. children live with a step- or half-sibling at age 4. We use data from the Early Childhood Longitudinal Study-Birth Cohort (N ~ 6,550) to assess the independent and joint influences of residing with a single parent or stepparent and with step- or half-siblings on children's aggressive behavior at school entry. The influences of parents' union status and complex sibship status on aggressive behavior are independent. Family resources partially explain the association between residing with an unpartnered mother and aggressive behavior regardless of sibship status. However, the resource hypothesis does not explain the association of complex sibship with aggressive behavior.

Family Complexity, Siblings, and Children's Aggressive Behavior at School Entry

PubMed Central

Fomby, Paula; Goode, Joshua A.; Mollborn, Stefanie

2016-01-01

As family structure in the United States has become increasingly dynamic and complex, children have become more likely to reside with step- or half-siblings through a variety of pathways. When these pathways are accounted for, more than one in six children in the United States lives with a half- or step-sibling at age 4. We use data from the Early Childhood Longitudinal Study-Birth Cohort (N~6,550) to assess the independent and joint influences of residing with a single parent or stepparent and with step or half-siblings on children's aggressive behavior at school entry. The influences of parents’ union status and complex sibship status on aggressive behavior are independent. Family resources partially explain the association between residing with an unpartnered mother and aggressive behavior regardless of sibship status. However, the resource hypothesis does not explain the association of complex sibship with aggressive behavior. PMID:26608795

Complex Sequencing Rules of Birdsong Can be Explained by Simple Hidden Markov Processes

PubMed Central

Katahira, Kentaro; Suzuki, Kenta; Okanoya, Kazuo; Okada, Masato

2011-01-01

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors such as human speech and musical performance, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical properties of the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable labeles, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model; GMM), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex behavioral sequences with higher-order dependencies. PMID:21915345

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.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns

PubMed Central

Cruchet, Steeve; Gustafson, Kyle; Benton, Richard; Floreano, Dario

2015-01-01

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs—locomotor bouts—matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior. PMID:26600381

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks

PubMed Central

Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

2017-01-01

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the “information networks” different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed. PMID:28796797

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks.

PubMed

Park, Jihoon; Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

2017-01-01

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the "information networks" different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.

Crowd motion segmentation and behavior recognition fusing streak flow and collectiveness

NASA Astrophysics Data System (ADS)

Gao, Mingliang; Jiang, Jun; Shen, Jin; Zou, Guofeng; Fu, Guixia

2018-04-01

Crowd motion segmentation and crowd behavior recognition are two hot issues in computer vision. A number of methods have been proposed to tackle these two problems. Among the methods, flow dynamics is utilized to model the crowd motion, with little consideration of collective property. Moreover, the traditional crowd behavior recognition methods treat the local feature and dynamic feature separately and overlook the interconnection of topological and dynamical heterogeneity in complex crowd processes. A crowd motion segmentation method and a crowd behavior recognition method are proposed based on streak flow and crowd collectiveness. The streak flow is adopted to reveal the dynamical property of crowd motion, and the collectiveness is incorporated to reveal the structure property. Experimental results show that the proposed methods improve the crowd motion segmentation accuracy and the crowd recognition rates compared with the state-of-the-art methods.

Oculometric Assessment of Dynamic Visual Processing

NASA Technical Reports Server (NTRS)

Liston, Dorion Bryce; Stone, Lee

2014-01-01

Eye movements are the most frequent (3 per second), shortest-latency (150-250 ms), and biomechanically simplest (1 joint, no inertial complexities) voluntary motor behavior in primates, providing a model system to assess sensorimotor disturbances arising from trauma, fatigue, aging, or disease states (e.g., Diefendorf and Dodge, 1908). We developed a 15-minute behavioral tracking protocol consisting of randomized stepramp radial target motion to assess several aspects of the behavioral response to dynamic visual motion, including pursuit initiation, steadystate tracking, direction-tuning, and speed-tuning thresholds. This set of oculomotor metrics provide valid and reliable measures of dynamic visual performance (Stone and Krauzlis, 2003; Krukowski and Stone, 2005; Stone et al, 2009; Liston and Stone, 2014), and may prove to be a useful assessment tool for functional impairments of dynamic visual processing.

Coupled binding-bending-folding: The complex conformational dynamics of protein-DNA binding studied by atomistic molecular dynamics simulations.

PubMed

van der Vaart, Arjan

2015-05-01

Protein-DNA binding often involves dramatic conformational changes such as protein folding and DNA bending. While thermodynamic aspects of this behavior are understood, and its biological function is often known, the mechanism by which the conformational changes occur is generally unclear. By providing detailed structural and energetic data, molecular dynamics simulations have been helpful in elucidating and rationalizing protein-DNA binding. This review will summarize recent atomistic molecular dynamics simulations of the conformational dynamics of DNA and protein-DNA binding. A brief overview of recent developments in DNA force fields is given as well. Simulations have been crucial in rationalizing the intrinsic flexibility of DNA, and have been instrumental in identifying the sequence of binding events, the triggers for the conformational motion, and the mechanism of binding for a number of important DNA-binding proteins. Molecular dynamics simulations are an important tool for understanding the complex binding behavior of DNA-binding proteins. With recent advances in force fields and rapid increases in simulation time scales, simulations will become even more important for future studies. This article is part of a Special Issue entitled Recent developments of molecular dynamics. Copyright © 2014. Published by Elsevier B.V.

Single Molecule Cluster Analysis Identifies Signature Dynamic Conformations along the Splicing Pathway

PubMed Central

Blanco, Mario R.; Martin, Joshua S.; Kahlscheuer, Matthew L.; Krishnan, Ramya; Abelson, John; Laederach, Alain; Walter, Nils G.

2016-01-01

The spliceosome is the dynamic RNA-protein machine responsible for faithfully splicing introns from precursor messenger RNAs (pre-mRNAs). Many of the dynamic processes required for the proper assembly, catalytic activation, and disassembly of the spliceosome as it acts on its pre-mRNA substrate remain poorly understood, a challenge that persists for many biomolecular machines. Here, we developed a fluorescence-based Single Molecule Cluster Analysis (SiMCAn) tool to dissect the manifold conformational dynamics of a pre-mRNA through the splicing cycle. By clustering common dynamic behaviors derived from selectively blocked splicing reactions, SiMCAn was able to identify signature conformations and dynamic behaviors of multiple ATP-dependent intermediates. In addition, it identified a conformation adopted late in splicing by a 3′ splice site mutant, invoking a mechanism for substrate proofreading. SiMCAn presents a novel framework for interpreting complex single molecule behaviors that should prove widely useful for the comprehensive analysis of a plethora of dynamic cellular machines. PMID:26414013

On Chaotic and Hyperchaotic Complex Nonlinear Dynamical Systems

NASA Astrophysics Data System (ADS)

Mahmoud, Gamal M.

Dynamical systems described by real and complex variables are currently one of the most popular areas of scientific research. These systems play an important role in several fields of physics, engineering, and computer sciences, for example, laser systems, control (or chaos suppression), secure communications, and information science. Dynamical basic properties, chaos (hyperchaos) synchronization, chaos control, and generating hyperchaotic behavior of these systems are briefly summarized. The main advantage of introducing complex variables is the reduction of phase space dimensions by a half. They are also used to describe and simulate the physics of detuned laser and thermal convection of liquid flows, where the electric field and the atomic polarization amplitudes are both complex. Clearly, if the variables of the system are complex the equations involve twice as many variables and control parameters, thus making it that much harder for a hostile agent to intercept and decipher the coded message. Chaotic and hyperchaotic complex systems are stated as examples. Finally there are many open problems in the study of chaotic and hyperchaotic complex nonlinear dynamical systems, which need further investigations. Some of these open problems are given.

Creating Time: Social Collaboration in Music Improvisation.

PubMed

Walton, Ashley E; Washburn, Auriel; Langland-Hassan, Peter; Chemero, Anthony; Kloos, Heidi; Richardson, Michael J

2018-01-01

Musical collaboration emerges from the complex interaction of environmental and informational constraints, including those of the instruments and the performance context. Music improvisation in particular is more like everyday interaction in that dynamics emerge spontaneously without a rehearsed score or script. We examined how the structure of the musical context affords and shapes interactions between improvising musicians. Six pairs of professional piano players improvised with two different backing tracks while we recorded both the music produced and the movements of their heads, left arms, and right arms. The backing tracks varied in rhythmic and harmonic information, from a chord progression to a continuous drone. Differences in movement coordination and playing behavior were evaluated using the mathematical tools of complex dynamical systems, with the aim of uncovering the multiscale dynamics that characterize musical collaboration. Collectively, the findings indicated that each backing track afforded the emergence of different patterns of coordination with respect to how the musicians played together, how they moved together, as well as their experience collaborating with each other. Additionally, listeners' experiences of the music when rating audio recordings of the improvised performances were related to the way the musicians coordinated both their playing behavior and their bodily movements. Accordingly, the study revealed how complex dynamical systems methods (namely recurrence analysis) can capture the turn-taking dynamics that characterized both the social exchange of the music improvisation and the sounds of collaboration more generally. The study also demonstrated how musical improvisation provides a way of understanding how social interaction emerges from the structure of the behavioral task context. Copyright © 2017 Cognitive Science Society, Inc.

Complex Nonlinear Dynamic System of Oligopolies Price Game with Heterogeneous Players Under Noise

NASA Astrophysics Data System (ADS)

Liu, Feng; Li, Yaguang

A nonlinear four oligopolies price game with heterogeneous players, that are boundedly rational and adaptive, is built using two different special demand costs. Based on the theory of complex discrete dynamical system, the stability and the existing equilibrium point are investigated. The complex dynamic behavior is presented via bifurcation diagrams, the Lyapunov exponents to show equilibrium state, bifurcation and chaos with the variation in parameters. As disturbance is ubiquitous in economic systems, this paper focuses on the analysis of delay feedback control method under noise circumstances. Stable dynamics is confirmed to depend mainly on the low price adjustment speed, and if all four players have limited opportunities to stabilize the market, the new adaptive player facing profits of scale are found to be higher than the incumbents of bounded rational.

Chaos on the conveyor belt.

PubMed

Sándor, Bulcsú; Járai-Szabó, Ferenc; Tél, Tamás; Néda, Zoltán

2013-04-01

The dynamics of a spring-block train placed on a moving conveyor belt is investigated both by simple experiments and computer simulations. The first block is connected by a spring to an external static point and, due to the dragging effect of the belt, the blocks undergo complex stick-slip dynamics. A qualitative agreement with the experimental results can be achieved only by taking into account the spatial inhomogeneity of the friction force on the belt's surface, modeled as noise. As a function of the velocity of the conveyor belt and the noise strength, the system exhibits complex, self-organized critical, sometimes chaotic, dynamics and phase transition-like behavior. Noise-induced chaos and intermittency is also observed. Simulations suggest that the maximum complexity of the dynamical states is achieved for a relatively small number of blocks (around five).

Unraveling complex nonlinear elastic behaviors in rocks using dynamic acousto-elasticity

NASA Astrophysics Data System (ADS)

Riviere, J.; Guyer, R.; Renaud, G.; TenCate, J. A.; Johnson, P. A.

2012-12-01

In comparison with standard nonlinear ultrasonic methods like frequency mixing or resonance based measurements that allow one to extract average, bulk variations of modulus and attenuation versus strain level, dynamic acousto-elasticity (DAE) allows to obtain the elastic behavior over the entire dynamic cycle, detailing the full nonlinear behavior under tension and compression, including hysteresis and memory effects. This method consists of exciting a sample in Bulk-mode resonance at strains of 10-7 to 10-5 and simultaneously probing with a sequence of high frequency, low amplitude pulses. Time of flight and amplitudes of these pulses, respectively related to nonlinear elastic and dissipative parameters, can be plotted versus vibration strain level. Despite complex nonlinear signatures obtained for most rocks, it can be shown that for low strain amplitude (< 10-6), the nonlinear classical theory issued from a Taylor decomposition can explain the harmonic content. For higher strain, harmonic content becomes richer and the material exhibits more hysteretic behaviors, i.e. strain rate dependencies. Such observations have been made in the past (e.g., Pasqualini et al., JGR 2007), but not with the extreme detail of elasticity provided by DAE. Previous quasi-static measurements made in Berea sandstone (Claytor et al, GRL 2009), show that the hysteretic behavior disappears when the protocol is performed at a very low strain-rate (static limit). Therefore, future work will aim at linking quasi-static and dynamic observations, i.e. the frequency or strain-rate dependence, in order to understand underlying physical phenomena.

Selective predation and productivity jointly drive complex behavior in host-parasite systems.

PubMed

Hall, Spencer R; Duffy, Meghan A; Cáceres, Carla E

2005-01-01

Successful invasion of a parasite into a host population and resulting host-parasite dynamics can depend crucially on other members of a host's community such as predators. We do not fully understand how predation intensity and selectivity shape host-parasite dynamics because the interplay between predator density, predator foraging behavior, and ecosystem productivity remains incompletely explored. By modifying a standard susceptible-infected model, we show how productivity can modulate complex behavior induced by saturating and selective foraging behavior of predators in an otherwise stable host-parasite system. When predators strongly prefer parasitized hosts, the host-parasite system can oscillate, but predators can also create alternative stable states, Allee effects, and catastrophic extinction of parasites. In the latter three cases, parasites have difficulty invading and/or persisting in ecosystems. When predators are intermediately selective, these more complex behaviors become less important, but the host-parasite system can switch from stable to oscillating and then back to stable states along a gradient of predator control. Surprisingly, at higher productivity, predators that neutrally select or avoid parasitized hosts can catalyze extinction of both hosts and parasites. Thus, synergy between two enemies can end disastrously for the host. Such diverse outcomes underscore the crucial importance of the community and ecosystem context in which host-parasite interactions occur.

Resource Recovery-based Sustainable Water Systems - the City of Tomorrow

EPA Science Inventory

Urban water systems are an example of complex, dynamic human-environment coupled systems which exhibit emergent behaviors that transcends individual scientific disciplines. To address the complexities associated with municipal water issues there is a need to shift from our tradi...

Structure-Activity Relationship in TLR4 Mutations: Atomistic Molecular Dynamics Simulations and Residue Interaction Network Analysis

NASA Astrophysics Data System (ADS)

Anwar, Muhammad Ayaz; Choi, Sangdun

2017-03-01

Toll-like receptor 4 (TLR4), a vital innate immune receptor present on cell surfaces, initiates a signaling cascade during danger and bacterial intrusion. TLR4 needs to form a stable hexamer complex, which is necessary to dimerize the cytoplasmic domain. However, D299G and T399I polymorphism may abrogate the stability of the complex, leading to compromised TLR4 signaling. Crystallography provides valuable insights into the structural aspects of the TLR4 ectodomain; however, the dynamic behavior of polymorphic TLR4 is still unclear. Here, we employed molecular dynamics simulations (MDS), as well as principal component and residue network analyses, to decipher the structural aspects and signaling propagation associated with mutations in TLR4. The mutated complexes were less cohesive, displayed local and global variation in the secondary structure, and anomalous decay in rotational correlation function. Principal component analysis indicated that the mutated complexes also exhibited distinct low-frequency motions, which may be correlated to the differential behaviors of these TLR4 variants. Moreover, residue interaction networks (RIN) revealed that the mutated TLR4/myeloid differentiation factor (MD) 2 complex may perpetuate abnormal signaling pathways. Cumulatively, the MDS and RIN analyses elucidated the mutant-specific conformational alterations, which may help in deciphering the mechanism of loss-of-function mutations.

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

Learning predictive statistics from temporal sequences: Dynamics and strategies

PubMed Central

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E.; Kourtzi, Zoe

2017-01-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics—that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments. PMID:28973111

Learning predictive statistics from temporal sequences: Dynamics and strategies.

PubMed

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

A novel simulation methodology merging source-sink dynamics and landscape connectivity

EPA Science Inventory

Source-sink dynamics are an emergent property of complex species-landscape interactions. This study explores the patterns of source and sink behavior that become established across a large landscape, using a simulation model for the northern spotted owl (Strix occidentalis cauri...

Natural neural projection dynamics underlying social behavior

PubMed Central

Gunaydin, Lisa A.; Grosenick, Logan; Finkelstein, Joel C.; Kauvar, Isaac V.; Fenno, Lief E.; Adhikari, Avishek; Lammel, Stephan; Mirzabekov, Julie J.; Airan, Raag D.; Zalocusky, Kelly A.; Tye, Kay M.; Anikeeva, Polina; Malenka, Robert C.; Deisseroth, Karl

2014-01-01

Social interaction is a complex behavior essential for many species, and is impaired in major neuropsychiatric disorders. Pharmacological studies have implicated certain neurotransmitter systems in social behavior, but circuit-level understanding of endogenous neural activity during social interaction is lacking. We therefore developed and applied a new methodology, termed fiber photometry, to optically record natural neural activity in genetically- and connectivity-defined projections to elucidate the real-time role of specified pathways in mammalian behavior. Fiber photometry revealed that activity dynamics of a ventral tegmental area (VTA)-to-nucleus accumbens (NAc) projection could encode and predict key features of social but not novel-object interaction. Consistent with this observation, optogenetic control of cells specifically contributing to this projection was sufficient to modulate social behavior, which was mediated by type-1 dopamine receptor signaling downstream in the NAc. Direct observation of projection-specific activity in this way captures a fundamental and previously inaccessible dimension of circuit dynamics. PMID:24949967

SEARCH: Spatially Explicit Animal Response to Composition of Habitat.

PubMed

Pauli, Benjamin P; McCann, Nicholas P; Zollner, Patrick A; Cummings, Robert; Gilbert, Jonathan H; Gustafson, Eric J

2013-01-01

Complex decisions dramatically affect animal dispersal and space use. Dispersing individuals respond to a combination of fine-scale environmental stimuli and internal attributes. Individual-based modeling offers a valuable approach for the investigation of such interactions because it combines the heterogeneity of animal behaviors with spatial detail. Most individual-based models (IBMs), however, vastly oversimplify animal behavior and such behavioral minimalism diminishes the value of these models. We present program SEARCH (Spatially Explicit Animal Response to Composition of Habitat), a spatially explicit, individual-based, population model of animal dispersal through realistic landscapes. SEARCH uses values in Geographic Information System (GIS) maps to apply rules that animals follow during dispersal, thus allowing virtual animals to respond to fine-scale features of the landscape and maintain a detailed memory of areas sensed during movement. SEARCH also incorporates temporally dynamic landscapes so that the environment to which virtual animals respond can change during the course of a simulation. Animals in SEARCH are behaviorally dynamic and able to respond to stimuli based upon their individual experiences. Therefore, SEARCH is able to model behavioral traits of dispersing animals at fine scales and with many dynamic aspects. Such added complexity allows investigation of unique ecological questions. To illustrate SEARCH's capabilities, we simulated case studies using three mammals. We examined the impact of seasonally variable food resources on the weight distribution of dispersing raccoons (Procyon lotor), the effect of temporally dynamic mortality pressure in combination with various levels of behavioral responsiveness in eastern chipmunks (Tamias striatus), and the impact of behavioral plasticity and home range selection on disperser mortality and weight change in virtual American martens (Martes americana). These simulations highlight the relevance of SEARCH for a variety of applications and illustrate benefits it can provide for conservation planning.

Can Multilayer Networks Advance Animal Behavior Research?

PubMed

Silk, Matthew J; Finn, Kelly R; Porter, Mason A; Pinter-Wollman, Noa

2018-06-01

Interactions among individual animals - and between these individuals and their environment - yield complex, multifaceted systems. The development of multilayer network analysis offers a promising new approach for studying animal social behavior and its relation to eco-evolutionary dynamics. Copyright © 2018 Elsevier Ltd. All rights reserved.

Optogenetic interrogation of neural circuits: technology for probing mammalian brain structures

PubMed Central

Zhang, Feng; Gradinaru, Viviana; Adamantidis, Antoine R; Durand, Remy; Airan, Raag D; de Lecea, Luis; Deisseroth, Karl

2015-01-01

Elucidation of the neural substrates underlying complex animal behaviors depends on precise activity control tools, as well as compatible readout methods. Recent developments in optogenetics have addressed this need, opening up new possibilities for systems neuroscience. Interrogation of even deep neural circuits can be conducted by directly probing the necessity and sufficiency of defined circuit elements with millisecond-scale, cell type-specific optical perturbations, coupled with suitable readouts such as electrophysiology, optical circuit dynamics measures and freely moving behavior in mammals. Here we collect in detail our strategies for delivering microbial opsin genes to deep mammalian brain structures in vivo, along with protocols for integrating the resulting optical control with compatible readouts (electrophysiological, optical and behavioral). The procedures described here, from initial virus preparation to systems-level functional readout, can be completed within 4–5 weeks. Together, these methods may help in providing circuit-level insight into the dynamics underlying complex mammalian behaviors in health and disease. PMID:20203662

Adaptive control of structural balance for complex dynamical networks based on dynamic coupling of nodes

NASA Astrophysics Data System (ADS)

Gao, Zilin; Wang, Yinhe; Zhang, Lili

2018-02-01

In the existing research results of the complex dynamical networks controlled, the controllers are mainly used to guarantee the synchronization or stabilization of the nodes’ state, and the terms coupled with connection relationships may affect the behaviors of nodes, this obviously ignores the dynamic common behavior of the connection relationships between the nodes. In fact, from the point of view of large-scale system, a complex dynamical network can be regarded to be composed of two time-varying dynamic subsystems, which can be called the nodes subsystem and the connection relationships subsystem, respectively. Similar to the synchronization or stabilization of the nodes subsystem, some characteristic phenomena can be also emerged in the connection relationships subsystem. For example, the structural balance in the social networks and the synaptic facilitation in the biological neural networks. This paper focuses on the structural balance in dynamic complex networks. Generally speaking, the state of the connection relationships subsystem is difficult to be measured accurately in practical applications, and thus it is not easy to implant the controller directly into the connection relationships subsystem. It is noted that the nodes subsystem and the relationships subsystem are mutually coupled, which implies that the state of the connection relationships subsystem can be affected by the controllable state of nodes subsystem. Inspired by this observation, by using the structural balance theory of triad, the controller with the parameter adaptive law is proposed for the nodes subsystem in this paper, which may ensure the connection relationship matrix to approximate a given structural balance matrix in the sense of the uniformly ultimately bounded (UUB). That is, the structural balance may be obtained by employing the controlling state of the nodes subsystem. Finally, the simulations are used to show the validity of the method in this paper.

Quantifying the statistical complexity of low-frequency fluctuations in semiconductor lasers with optical feedback

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

Tiana-Alsina, J.; Torrent, M. C.; Masoller, C.

Low-frequency fluctuations (LFFs) represent a dynamical instability that occurs in semiconductor lasers when they are operated near the lasing threshold and subject to moderate optical feedback. LFFs consist of sudden power dropouts followed by gradual, stepwise recoveries. We analyze experimental time series of intensity dropouts and quantify the complexity of the underlying dynamics employing two tools from information theory, namely, Shannon's entropy and the Martin, Plastino, and Rosso statistical complexity measure. These measures are computed using a method based on ordinal patterns, by which the relative length and ordering of consecutive interdropout intervals (i.e., the time intervals between consecutive intensitymore » dropouts) are analyzed, disregarding the precise timing of the dropouts and the absolute durations of the interdropout intervals. We show that this methodology is suitable for quantifying subtle characteristics of the LFFs, and in particular the transition to fully developed chaos that takes place when the laser's pump current is increased. Our method shows that the statistical complexity of the laser does not increase continuously with the pump current, but levels off before reaching the coherence collapse regime. This behavior coincides with that of the first- and second-order correlations of the interdropout intervals, suggesting that these correlations, and not the chaotic behavior, are what determine the level of complexity of the laser's dynamics. These results hold for two different dynamical regimes, namely, sustained LFFs and coexistence between LFFs and steady-state emission.« less

Sleeping of a Complex Brain Networks with Hierarchical Organization

NASA Astrophysics Data System (ADS)

Zhang, Ying-Yue; Yang, Qiu-Ying; Chen, Tian-Lun

2009-01-01

The dynamical behavior in the cortical brain network of macaque is studied by modeling each cortical area with a subnetwork of interacting excitable neurons. We characterize the system by studying how to perform the transition, which is now topology-dependent, from the active state to that with no activity. This could be a naive model for the wakening and sleeping of a brain-like system, i.e., a multi-component system with two different dynamical behavior.

Ionic liquid structure, dynamics, and electrosorption in carbon electrodes with bimodal pores and heterogeneous surfaces

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

Dyatkin, Boris; Osti, Naresh C.; Zhang, Yu

In this paper, we investigate the aggregation, diffusion, and resulting electrochemical behavior of ionic liquids inside carbon electrodes with complex pore architectures and surface chemistries. Carbide-derived carbons (CDCs) with bimodal porosities and defunctionalized or oxidized electrode surfaces served as model electrode materials. Our goal was to obtain a fundamental understanding of room-temperature ionic liquid ion orientation, mobility, and electrosorption behavior. Neat 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide confined in CDCs was studied using an integrated experimental and modeling approach, consisting of quasielastic neutron scattering, small-angle neutron scattering, X-ray pair distribution function analysis, and electrochemical measurements, which were combined with molecular dynamics simulations. Our analysismore » shows that surface oxygen groups increase the diffusion of confined electrolytes. Consequently, the ions become more than twice as mobile in oxygen-rich pores. Although greater self-diffusion of ions translates into higher electrochemical mobilities in oxidized pores, bulk-like behavior of ions dominates in the larger mesopores and increases the overall capacitance in defunctionalized pores. Experimental results highlight strong confinement and surface effects of carbon electrodes on electrolyte behavior, and molecular dynamics simulations yield insight into diffusion and capacitance differences in specific pore regions. Finally, we demonstrate the significance of surface defects on electrosorption dynamics of complex electrolytes in hierarchical pore architectures of supercapacitor electrodes.« less

Ionic liquid structure, dynamics, and electrosorption in carbon electrodes with bimodal pores and heterogeneous surfaces

DOE PAGES

Dyatkin, Boris; Osti, Naresh C.; Zhang, Yu; ...

2017-12-05

In this paper, we investigate the aggregation, diffusion, and resulting electrochemical behavior of ionic liquids inside carbon electrodes with complex pore architectures and surface chemistries. Carbide-derived carbons (CDCs) with bimodal porosities and defunctionalized or oxidized electrode surfaces served as model electrode materials. Our goal was to obtain a fundamental understanding of room-temperature ionic liquid ion orientation, mobility, and electrosorption behavior. Neat 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide confined in CDCs was studied using an integrated experimental and modeling approach, consisting of quasielastic neutron scattering, small-angle neutron scattering, X-ray pair distribution function analysis, and electrochemical measurements, which were combined with molecular dynamics simulations. Our analysismore » shows that surface oxygen groups increase the diffusion of confined electrolytes. Consequently, the ions become more than twice as mobile in oxygen-rich pores. Although greater self-diffusion of ions translates into higher electrochemical mobilities in oxidized pores, bulk-like behavior of ions dominates in the larger mesopores and increases the overall capacitance in defunctionalized pores. Experimental results highlight strong confinement and surface effects of carbon electrodes on electrolyte behavior, and molecular dynamics simulations yield insight into diffusion and capacitance differences in specific pore regions. Finally, we demonstrate the significance of surface defects on electrosorption dynamics of complex electrolytes in hierarchical pore architectures of supercapacitor electrodes.« less

Complexity in neuronal noise depends on network interconnectivity.

PubMed

Serletis, Demitre; Zalay, Osbert C; Valiante, Taufik A; Bardakjian, Berj L; Carlen, Peter L

2011-06-01

"Noise," or noise-like activity (NLA), defines background electrical membrane potential fluctuations at the cellular level of the nervous system, comprising an important aspect of brain dynamics. Using whole-cell voltage recordings from fast-spiking stratum oriens interneurons and stratum pyramidale neurons located in the CA3 region of the intact mouse hippocampus, we applied complexity measures from dynamical systems theory (i.e., 1/f(γ) noise and correlation dimension) and found evidence for complexity in neuronal NLA, ranging from high- to low-complexity dynamics. Importantly, these high- and low-complexity signal features were largely dependent on gap junction and chemical synaptic transmission. Progressive neuronal isolation from the surrounding local network via gap junction blockade (abolishing gap junction-dependent spikelets) and then chemical synaptic blockade (abolishing excitatory and inhibitory post-synaptic potentials), or the reverse order of these treatments, resulted in emergence of high-complexity NLA dynamics. Restoring local network interconnectivity via blockade washout resulted in resolution to low-complexity behavior. These results suggest that the observed increase in background NLA complexity is the result of reduced network interconnectivity, thereby highlighting the potential importance of the NLA signal to the study of network state transitions arising in normal and abnormal brain dynamics (such as in epilepsy, for example).

Complex groundwater flow systems as traveling agent models

PubMed Central

Padilla, Pablo; Escolero, Oscar; González, Tomas; Morales-Casique, Eric; Osorio-Olvera, Luis

2014-01-01

Analyzing field data from pumping tests, we show that as with many other natural phenomena, groundwater flow exhibits complex dynamics described by 1/f power spectrum. This result is theoretically studied within an agent perspective. Using a traveling agent model, we prove that this statistical behavior emerges when the medium is complex. Some heuristic reasoning is provided to justify both spatial and dynamic complexity, as the result of the superposition of an infinite number of stochastic processes. Even more, we show that this implies that non-Kolmogorovian probability is needed for its study, and provide a set of new partial differential equations for groundwater flow. PMID:25337455

Heterogeneity and Self-Organization of Complex Systems Through an Application to Financial Market with Multiagent Systems

NASA Astrophysics Data System (ADS)

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2017-12-01

Multiagent systems (MAS) provide a useful tool for exploring the complex dynamics and behavior of financial markets and now MAS approach has been widely implemented and documented in the empirical literature. This paper introduces the implementation of an innovative multi-scale mathematical model for a computational agent-based financial market. The paper develops a method to quantify the degree of self-organization which emerges in the system and shows that the capacity of self-organization is maximized when the agent behaviors are heterogeneous. Numerical results are presented and analyzed, showing how the global market behavior emerges from specific individual behavior interactions.

Enzyme-free nucleic acid dynamical systems.

PubMed

Srinivas, Niranjan; Parkin, James; Seelig, Georg; Winfree, Erik; Soloveichik, David

2017-12-15

Chemistries exhibiting complex dynamics-from inorganic oscillators to gene regulatory networks-have been long known but either cannot be reprogrammed at will or rely on the sophisticated enzyme chemistry underlying the central dogma. Can simpler molecular mechanisms, designed from scratch, exhibit the same range of behaviors? Abstract chemical reaction networks have been proposed as a programming language for complex dynamics, along with their systematic implementation using short synthetic DNA molecules. We developed this technology for dynamical systems by identifying critical design principles and codifying them into a compiler automating the design process. Using this approach, we built an oscillator containing only DNA components, establishing that Watson-Crick base-pairing interactions alone suffice for complex chemical dynamics and that autonomous molecular systems can be designed via molecular programming languages. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Evolutionary fields can explain patterns of high-dimensional complexity in ecology

NASA Astrophysics Data System (ADS)

Wilsenach, James; Landi, Pietro; Hui, Cang

2017-04-01

One of the properties that make ecological systems so unique is the range of complex behavioral patterns that can be exhibited by even the simplest communities with only a few species. Much of this complexity is commonly attributed to stochastic factors that have very high-degrees of freedom. Orthodox study of the evolution of these simple networks has generally been limited in its ability to explain complexity, since it restricts evolutionary adaptation to an inertia-free process with few degrees of freedom in which only gradual, moderately complex behaviors are possible. We propose a model inspired by particle-mediated field phenomena in classical physics in combination with fundamental concepts in adaptation, which suggests that small but high-dimensional chaotic dynamics near to the adaptive trait optimum could help explain complex properties shared by most ecological datasets, such as aperiodicity and pink, fractal noise spectra. By examining a simple predator-prey model and appealing to real ecological data, we show that this type of complexity could be easily confused for or confounded by stochasticity, especially when spurred on or amplified by stochastic factors that share variational and spectral properties with the underlying dynamics.

Synchronization in Random Pulse Oscillator Networks

NASA Astrophysics Data System (ADS)

Brown, Kevin; Hermundstad, Ann

Motivated by synchronization phenomena in neural systems, we study synchronization of random networks of coupled pulse oscillators. We begin by considering binomial random networks whose nodes have intrinsic linear dynamics. We quantify order in the network spiking dynamics using a new measure: the normalized Lev-Zimpel complexity (LZC) of the nodes' spike trains. Starting from a globally-synchronized state, we see two broad classes of behaviors. In one (''temporally random''), the LZC is high and nodes spike independently with no coherent pattern. In another (''temporally regular''), the network does not globally synchronize but instead forms coherent, repeating population firing patterns with low LZC. No topological feature of the network reliably predicts whether an individual network will show temporally random or regular behavior; however, we find evidence that degree heterogeneity in binomial networks has a strong effect on the resulting state. To confirm these findings, we generate random networks with independently-adjustable degree mean and variance. We find that the likelihood of temporally-random behavior increases as degree variance increases. Our results indicate the subtle and complex relationship between network structure and dynamics.

A Qualitative Model of Human Interaction with Complex Dynamic Systems

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1987-01-01

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

A qualitative model of human interaction with complex dynamic systems

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1987-01-01

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

Short-Range Temporal Interactions in Sleep; Hippocampal Spike Avalanches Support a Large Milieu of Sequential Activity Including Replay

PubMed Central

Mahoney, J. Matthew; Titiz, Ali S.; Hernan, Amanda E.; Scott, Rod C.

2016-01-01

Hippocampal neural systems consolidate multiple complex behaviors into memory. However, the temporal structure of neural firing supporting complex memory consolidation is unknown. Replay of hippocampal place cells during sleep supports the view that a simple repetitive behavior modifies sleep firing dynamics, but does not explain how multiple episodes could be integrated into associative networks for recollection during future cognition. Here we decode sequential firing structure within spike avalanches of all pyramidal cells recorded in sleeping rats after running in a circular track. We find that short sequences that combine into multiple long sequences capture the majority of the sequential structure during sleep, including replay of hippocampal place cells. The ensemble, however, is not optimized for maximally producing the behavior-enriched episode. Thus behavioral programming of sequential correlations occurs at the level of short-range interactions, not whole behavioral sequences and these short sequences are assembled into a large and complex milieu that could support complex memory consolidation. PMID:26866597

Evaluating fuel complexes for fire hazard mitigation planning in the southeastern United States

Treesearch

Anne G. Andreu; Dan Shea; Bernard R. Parresol; Roger D. Ottmar

2012-01-01

Fire hazard mitigation planning requires an accurate accounting of fuel complexes to predict potential fire behavior and effects of treatment alternatives. In the southeastern United States, rapid vegetation growth coupled with complex land use history and forest management options requires a dynamic approach to fuel characterization. In this study we assessed...

Evaluating Effects of Localized Habitat Manipulations on Landscape-level Dynamics of White-footed Mouse Populations

EPA Science Inventory

Due to complex population dynamics and migration behaviors, the well-being of animal populations that host human diseases sometimes varies across landscapes in ways that cannot be deduced from geographic abundance patterns alone. In such cases, efficient management of ecological...

Insights into dynamic processes of cations in pyrochlores and other complex oxides

DOE PAGES

Uberuaga, Blas Pedro; Perriot, Romain

2015-08-26

Complex oxides are critical components of many key technologies, from solid oxide fuel cells and superionics to inert matrix fuels and nuclear waste forms. In many cases, understanding mass transport is important for predicting performance and, thus, extensive effort has been devoted to understanding mass transport in these materials. However, most work has focused on the behavior of oxygen while cation transport has received relatively little attention, even though cation diffusion is responsible for many phenomena, including sintering, radiation damage evolution, and deformation processes. Here, we use accelerated molecular dynamics simulations to examine the kinetics of cation defects in onemore » class of complex oxides, A₂B₂O₇ pyrochlore. In some pyrochlore chemistries, B cation defects are kinetically unstable, transforming to A cation defects and antisites at rates faster than they can diffuse. When this occurs, transport of B cations occurs through defect processes on the A sublattice. Further, these A cation defects, either interstitials or vacancies, can interact with antisite disorder, reordering the material locally, though this process is much more efficient for interstitials than vacancies. Whether this behavior occurs in a given pyrochlore depends on the A and B chemistry. Pyrochlores with a smaller ratio of cation radii exhibit this complex behavior, while those with larger ratios exhibit direct migration of B interstitials. Similar behavior has been reported in other complex oxides such as spinels and perovskites, suggesting that this coupling of transport between the A and B cation sublattices, while not universal, occurs in many complex oxide.« less

Agent-Based Models in Social Physics

NASA Astrophysics Data System (ADS)

Quang, Le Anh; Jung, Nam; Cho, Eun Sung; Choi, Jae Han; Lee, Jae Woo

2018-06-01

We review the agent-based models (ABM) on social physics including econophysics. The ABM consists of agent, system space, and external environment. The agent is autonomous and decides his/her behavior by interacting with the neighbors or the external environment with the rules of behavior. Agents are irrational because they have only limited information when they make decisions. They adapt using learning from past memories. Agents have various attributes and are heterogeneous. ABM is a non-equilibrium complex system that exhibits various emergence phenomena. The social complexity ABM describes human behavioral characteristics. In ABMs of econophysics, we introduce the Sugarscape model and the artificial market models. We review minority games and majority games in ABMs of game theory. Social flow ABM introduces crowding, evacuation, traffic congestion, and pedestrian dynamics. We also review ABM for opinion dynamics and voter model. We discuss features and advantages and disadvantages of Netlogo, Repast, Swarm, and Mason, which are representative platforms for implementing ABM.

Complexity analysis of dual-channel game model with different managers' business objectives

NASA Astrophysics Data System (ADS)

Li, Ting; Ma, Junhai

2015-01-01

This paper considers dual-channel game model with bounded rationality, using the theory of bifurcations of dynamical system. The business objectives of retailers are assumed to be different, which is closer to reality than previous studies. We study the local stable region of Nash equilibrium point and find that business objectives can expand the stable region and play an important role in price strategy. One interesting finding is that a fiercer competition tends to stabilize the Nash equilibrium. Simulation shows the complex behavior of two dimensional dynamic system, we find period doubling bifurcation and chaos phenomenon. We measure performances of the model in different period by using the index of average profit. The results show that unstable behavior in economic system is often an unfavorable outcome. So this paper discusses the application of adaptive adjustment mechanism when the model exhibits chaotic behavior and then allows the retailers to eliminate the negative effects.

Nonlinear dynamics behavior analysis of the spatial configuration of a tendril-bearing plant

NASA Astrophysics Data System (ADS)

Feng, Jingjing; Zhang, Qichang; Wang, Wei; Hao, Shuying

2017-03-01

Tendril-bearing plants appear to have a spiraling shape when tendrils climb along a support during growth. The growth characteristics of a tendril-bearer can be simplified to a model of a thin elastic rod with a cylindrical constraint. In this paper, the connection between some typical configuration characteristics of tendrils and complex nonlinear dynamic behavior are qualitatively analyzed. The space configuration problem of tendrils can be explained through the study of the nonlinear dynamic behavior of the thin elastic rod system equation. In this study, the complex non-Z2 symmetric critical orbits in the system equation under critical parameters were presented. A new function transformation method that can effectively maintain the critical orbit properties was proposed, and a new nonlinear differential equations system containing complex nonlinear terms can been obtained to describe the cross section position and direction of a rod during climbing. Numerical simulation revealed that the new system can describe the configuration of a rod with reasonable accuracy. To adequately explain the growing regulation of the rod shape, the critical orbit and configuration of rod are connected in a direct way. The high precision analytical expressions of these complex non-Z2 symmetric critical orbits are obtained by introducing a suitable analytical method, and then these expressions are used to draw the corresponding three-dimensional configuration figures of an elastic thin rod. Combined with actual tendrils on a live plant, the space configuration of the winding knots of tendril is explained by the concept of heteroclinic orbit from the perspective of nonlinear dynamics, and correctness of the theoretical analysis was verified. This theoretical analysis method could also be effectively applied to other similar slender structures.

Organization of excitable dynamics in hierarchical biological networks.

PubMed

Müller-Linow, Mark; Hilgetag, Claus C; Hütt, Marc-Thorsten

2008-09-26

This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

Local orientational mobility in regular hyperbranched polymers.

PubMed

Dolgushev, Maxim; Markelov, Denis A; Fürstenberg, Florian; Guérin, Thomas

2016-07-01

We study the dynamics of local bond orientation in regular hyperbranched polymers modeled by Vicsek fractals. The local dynamics is investigated through the temporal autocorrelation functions of single bonds and the corresponding relaxation forms of the complex dielectric susceptibility. We show that the dynamic behavior of single segments depends on their remoteness from the periphery rather than on the size of the whole macromolecule. Remarkably, the dynamics of the core segments (which are most remote from the periphery) shows a scaling behavior that differs from the dynamics obtained after structural average. We analyze the most relevant processes of single segment motion and provide an analytic approximation for the corresponding relaxation times. Furthermore, we describe an iterative method to calculate the orientational dynamics in the case of very large macromolecular sizes.

Nonlinear dynamics of the magnetosphere and space weather

NASA Technical Reports Server (NTRS)

Sharma, A. Surjalal

1996-01-01

The solar wind-magnetosphere system exhibits coherence on the global scale and such behavior can arise from nonlinearity on the dynamics. The observational time series data were used together with phase space reconstruction techniques to analyze the magnetospheric dynamics. Analysis of the solar wind, auroral electrojet and Dst indices showed low dimensionality of the dynamics and accurate prediction can be made with an input/output model. The predictability of the magnetosphere in spite of the apparent complexity arises from its dynamical synchronism with the solar wind. The electrodynamic coupling between different regions of the magnetosphere yields its coherent, low dimensional behavior. The data from multiple satellites and ground stations can be used to develop a spatio-temporal model that identifies the coupling between different regions. These nonlinear dynamical models provide space weather forecasting capabilities.

Continuous monitoring the vehicle dynamics and driver behavior using navigation systems

NASA Astrophysics Data System (ADS)

Ene, George

2017-10-01

In all fields cost is very important and the increasing amount of data that are needed for active safety systems, like ADAS, lead to implementation of some complex and powerful unit for processing raw data. In this manner is necessary a cost-effective method to estimate the maximum available tire road friction during acceleration and braking by continuous monitoring the vehicle dynamics and driver behavior. The method is based on the hypothesis that short acceleration and braking periods can indicate vehicle dynamics, and thus the available tire road friction coefficient, and also human behavior and his limits. Support for this hypothesis is found in the literature and is supported by the result of experiments conducted under different conditions and seasons.

Logical Interactions in AN Expanded Space

NASA Astrophysics Data System (ADS)

Tadić, Bosiljka

Understanding the emergent behavior in many complex systems in the physical world and society requires a detailed study of dynamical phenomena occurring and mutually coupled at different scales. The brain processes underlying the social conduct of each, and the emergent social behavior of interacting individuals on a larger scale, represent striking examples of the multiscale complexity. Studies of the human brain, a paradigm of a complex functional system, are enabled by a wealth of brain imaging data that provide clues of how we comprehend space, time, languages, numbers, and differentiate normal from diseased individuals, for example. The social brain, a neural basis for social cognition, represents a dynamically organized part of the brain which is involved in the inference of thoughts, feelings, and intentions going on in the brains of others. Research in this currently unexplored area opens a new perspective on the genesis of the societal organization at different levels and the associated social values...

Modeling fluctuations in default-mode brain network using a spiking neural network.

PubMed

Yamanishi, Teruya; Liu, Jian-Qin; Nishimura, Haruhiko

2012-08-01

Recently, numerous attempts have been made to understand the dynamic behavior of complex brain systems using neural network models. The fluctuations in blood-oxygen-level-dependent (BOLD) brain signals at less than 0.1 Hz have been observed by functional magnetic resonance imaging (fMRI) for subjects in a resting state. This phenomenon is referred to as a "default-mode brain network." In this study, we model the default-mode brain network by functionally connecting neural communities composed of spiking neurons in a complex network. Through computational simulations of the model, including transmission delays and complex connectivity, the network dynamics of the neural system and its behavior are discussed. The results show that the power spectrum of the modeled fluctuations in the neuron firing patterns is consistent with the default-mode brain network's BOLD signals when transmission delays, a characteristic property of the brain, have finite values in a given range.

Hsc70 chaperone activity is required for the cytosolic slow axonal transport of synapsin

PubMed Central

Ganguly, Archan; Han, Xuemei; Das, Utpal; Caillol, Ghislaine

2017-01-01

Soluble cytosolic proteins vital to axonal and presynaptic function are synthesized in the neuronal soma and conveyed via slow axonal transport. Our previous studies suggest that the overall slow transport of synapsin is mediated by dynamic assembly/disassembly of cargo complexes followed by short-range vectorial transit (the “dynamic recruitment” model). However, neither the composition of these complexes nor the mechanistic basis for the dynamic behavior is understood. In this study, we first examined putative cargo complexes associated with synapsin using coimmunoprecipitation and multidimensional protein identification technology mass spectrometry (MS). MS data indicate that synapsin is part of a multiprotein complex enriched in chaperones/cochaperones including Hsc70. Axonal synapsin–Hsc70 coclusters are also visualized by two-color superresolution microscopy. Inhibition of Hsc70 ATPase activity blocked the slow transport of synapsin, disrupted axonal synapsin organization, and attenuated Hsc70–synapsin associations, advocating a model where Hsc70 activity dynamically clusters cytosolic proteins into cargo complexes, allowing transport. Collectively, our study offers insight into the molecular organization of cytosolic transport complexes and identifies a novel regulator of slow transport. PMID:28559423

Surfactant mediated polyelectrolyte self-assembly

DOE PAGES

Goswami, Monojoy; Borreguero Calvo, Jose M.; Pincus, Phillip A.; ...

2015-11-25

Self-assembly and dynamics of polyelectrolyte (PE) surfactant complex (PES) is investigated using molecular dynamics simulations. The complexation is systematically studied for five different PE backbone charge densities. At a fixed surfactant concentration the PES complexation exhibits pearl-necklace to agglomerated double spherical structures with a PE chain decorating the surfactant micelles. The counterions do not condense on the complex, but are released in the medium with a random distribution. The relaxation dynamics for three different length scales, polymer chain, segmental and monomer, show distinct features of the charge and neutral species; the counterions are fastest followed by the PE chain andmore » surfactants. The surfactant heads and tails have the slowest relaxation due to their restricted movement inside the agglomerated structure. At the shortest length scale, all the charge and neutral species show similar relaxation dynamics confirming Rouse behavior at monomer length scales. Overall, the present study highlights the structure-property relationship for polymer-surfactant complexation. These results will help improve the understanding of PES complex and should aid in the design of better materials for future applications.« less

Patterns of Negotiation

NASA Astrophysics Data System (ADS)

Sood, Suresh; Pattinson, Hugh

Traditionally, face-to-face negotiations in the real world have not been looked at as a complex systems interaction of actors resulting in a dynamic and potentially emergent system. If indeed negotiations are an outcome of a dynamic interaction of simpler behavior just as with a complex system, we should be able to see the patterns contributing to the complexities of a negotiation under study. This paper and the supporting research sets out to show B2B (business-to-business) negotiations as complex systems of interacting actors exhibiting dynamic and emergent behavior. This paper discusses the exploratory research based on negotiation simulations in which a large number of business students participate as buyers and sellers. The student interactions are captured on video and a purpose built research method attempts to look for patterns of interactions between actors using visualization techniques traditionally reserved to observe the algorithmic complexity of complex systems. Students are videoed negotiating with partners. Each video is tagged according to a recognized classification and coding scheme for negotiations. The classification relates to the phases through which any particular negotiation might pass, such as laughter, aggression, compromise, and so forth — through some 30 possible categories. Were negotiations more or less successful if they progressed through the categories in different ways? Furthermore, does the data depict emergent pathway segments considered to be more or less successful? This focus on emergence within the data provides further strong support for face-to-face (F2F) negotiations to be construed as complex systems.

COMPARISON OF CHAOTIC AND FRACTAL PROPERTIES OF POLAR FACULAE WITH SUNSPOT ACTIVITY

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

Deng, L. H.; Xiang, Y. Y.; Dun, G. T.

The solar magnetic activity is governed by a complex dynamo mechanism and exhibits a nonlinear dissipation behavior in nature. The chaotic and fractal properties of solar time series are of great importance to understanding the solar dynamo actions, especially with regard to the nonlinear dynamo theories. In the present work, several nonlinear analysis approaches are proposed to investigate the nonlinear dynamical behavior of the polar faculae and sunspot activity for the time interval from 1951 August to 1998 December. The following prominent results are found: (1) both the high- and the low-latitude solar activity are governed by a three-dimensional chaoticmore » attractor, and the chaotic behavior of polar faculae is the most complex, followed by that of the sunspot areas, and then the sunspot numbers; (2) both the high- and low-latitude solar activity exhibit a high degree of persistent behavior, and their fractal nature is due to such long-range correlation; (3) the solar magnetic activity cycle is predictable in nature, but the high-accuracy prediction should only be done for short- to mid-term due to its intrinsically dynamical complexity. With the help of the Babcock–Leighton dynamo model, we suggest that the nonlinear coupling of the polar magnetic fields with strong active-region fields exhibits a complex manner, causing the statistical similarities and differences between the polar faculae and the sunspot-related indicators.« less

Interaction between Liénard and Ikeda dynamics in a nonlinear electro-optical oscillator with delayed bandpass feedback.

PubMed

Marquez, Bicky A; Larger, Laurent; Brunner, Daniel; Chembo, Yanne K; Jacquot, Maxime

2016-12-01

We report on experimental and theoretical analysis of the complex dynamics generated by a nonlinear time-delayed electro-optic bandpass oscillator. We investigate the interaction between the slow- and fast-scale dynamics of autonomous oscillations in the breather regime. We analyze in detail the coupling between the fast-scale behavior associated to a characteristic low-pass Ikeda behavior and the slow-scale dynamics associated to a Liénard limit-cycle. Finally, we show that when projected onto a two-dimensional phase space, the attractors corresponding to periodic and chaotic breathers display a spiral-like pattern, which strongly depends on the shape of the nonlinear function.

Chaos and insect ecology

Treesearch

Jesse A. Logan; Fred P. Hain

1990-01-01

Recent advances in applied mathematical analysis have uncovered a fascinating and unexpected dynamical richness that underlies behavior of even the simplest non-linear mathematical models. Due to the complexity of solutions to these non-linear equations, a new mathematical term, chaos, has been coined to describe the resulting dynamics. This term captures the notion...

Complex Dynamics of the Power Transmission Grid (and other Critical Infrastructures)

NASA Astrophysics Data System (ADS)

Newman, David

2015-03-01

Our modern societies depend crucially on a web of complex critical infrastructures such as power transmission networks, communication systems, transportation networks and many others. These infrastructure systems display a great number of the characteristic properties of complex systems. Important among these characteristics, they exhibit infrequent large cascading failures that often obey a power law distribution in their probability versus size. This power law behavior suggests that conventional risk analysis does not apply to these systems. It is thought that much of this behavior comes from the dynamical evolution of the system as it ages, is repaired, upgraded, and as the operational rules evolve with human decision making playing an important role in the dynamics. In this talk, infrastructure systems as complex dynamical systems will be introduced and some of their properties explored. The majority of the talk will then be focused on the electric power transmission grid though many of the results can be easily applied to other infrastructures. General properties of the grid will be discussed and results from a dynamical complex systems power transmission model will be compared with real world data. Then we will look at a variety of uses of this type of model. As examples, we will discuss the impact of size and network homogeneity on the grid robustness, the change in risk of failure as generation mix (more distributed vs centralized for example) changes, as well as the effect of operational changes such as the changing the operational risk aversion or grid upgrade strategies. One of the important outcomes from this work is the realization that ``improvements'' in the system components and operational efficiency do not always improve the system robustness, and can in fact greatly increase the risk, when measured as a risk of large failure.

Spontaneous mode switching in coupled oscillators competing for constant amounts of resources

NASA Astrophysics Data System (ADS)

Hirata, Yoshito; Aono, Masashi; Hara, Masahiko; Aihara, Kazuyuki

2010-03-01

We propose a widely applicable scheme of coupling that models competitions among dynamical systems for fixed amounts of resources. Two oscillators coupled in this way synchronize in antiphase. Three oscillators coupled circularly show a number of oscillation modes such as rotation and partially in-phase synchronization. Intriguingly, simple oscillators in the model also produce complex behavior such as spontaneous switching among different modes. The dynamics reproduces well the spatiotemporal oscillatory behavior of a true slime mold Physarum, which is capable of computational optimization.

Dynamically Tunable Cell Culture Platforms for Tissue Engineering and Mechanobiology

PubMed Central

Uto, Koichiro; Tsui, Jonathan H.; DeForest, Cole A.; Kim, Deok-Ho

2016-01-01

Human tissues are sophisticated ensembles of many distinct cell types embedded in the complex, but well-defined, structures of the extracellular matrix (ECM). Dynamic biochemical, physicochemical, and mechano-structural changes in the ECM define and regulate tissue-specific cell behaviors. To recapitulate this complex environment in vitro, dynamic polymer-based biomaterials have emerged as powerful tools to probe and direct active changes in cell function. The rapid evolution of polymerization chemistries, structural modulation, and processing technologies, as well as the incorporation of stimuli-responsiveness, now permit synthetic microenvironments to capture much of the dynamic complexity of native tissue. These platforms are comprised not only of natural polymers chemically and molecularly similar to ECM, but those fully synthetic in origin. Here, we review recent in vitro efforts to mimic the dynamic microenvironment comprising native tissue ECM from the viewpoint of material design. We also discuss how these dynamic polymer-based biomaterials are being used in fundamental cell mechanobiology studies, as well as towards efforts in tissue engineering and regenerative medicine. PMID:28522885

Digit replacement: A generic map for nonlinear dynamical systems.

PubMed

García-Morales, Vladimir

2016-09-01

A simple discontinuous map is proposed as a generic model for nonlinear dynamical systems. The orbit of the map admits exact solutions for wide regions in parameter space and the method employed (digit manipulation) allows the mathematical design of useful signals, such as regular or aperiodic oscillations with specific waveforms, the construction of complex attractors with nontrivial properties as well as the coexistence of different basins of attraction in phase space with different qualitative properties. A detailed analysis of the dynamical behavior of the map suggests how the latter can be used in the modeling of complex nonlinear dynamics including, e.g., aperiodic nonchaotic attractors and the hierarchical deposition of grains of different sizes on a surface.

Snowflakes, Living Systems, and the Mystery of Giftedness

ERIC Educational Resources Information Center

Dai, David Yun; Renzulli, Joseph S.

2008-01-01

The main argument of this article is that human living systems are open, dynamic, intentional systems and, therefore, are capable of building ever more complex behaviors through self-organization and self-direction. This principle underlying general human development is also applicable to the development of gifted and talented behaviors. These…

Nanoscopic Dynamic Mechanical Properties of Intertubular and Peritubular Dentin

PubMed Central

Ryou, Heon; Romberg, Elaine; Pashley, David H.; Tay, Franklin R.; Arola, Dwayne

2011-01-01

An experimental evaluation of intertubular and peritubular dentin was performed using nanoindentation and Dynamic Mechanical Analysis (DMA). The objective of the investigation was to evaluate the differences in dynamic mechanical behavior of these two constituents and to assess if their response is frequency dependent. Specimens of hydrated coronal dentin were evaluated by DMA using single indents over a range in parametric conditions and using scanning probe microscopy. The complex (E*), storage (E’) and loss moduli (E”) of the intertubular and peritubular dentin were evaluated as a function of the dynamic loading frequency and static load in the fully hydrated condition. The mean complex E* (19.6 GPa) and storage E’ (19.2 GPa) moduli of the intertubular dentin were significantly lower than those quantities of peritubular dentin (E* = 31.1 GPa, p< 0.05; E’ = 30.3 GPa, p< 0.05). There was no significant influence of dynamic loading frequency on these measures. Though there was no significant difference in the loss modulus (E”) between the two materials (p> 0.05), both constituents exhibited a significant increase in E” with dynamic load frequency and reduction in the quasi-static component of indentation load. The largest difference in dynamic behavior of the two tissues was noted at small quasi-static indentation loads and the highest frequency. PMID:22340680

Dynamics of Numerics & Spurious Behaviors in CFD Computations. Revised

NASA Technical Reports Server (NTRS)

Yee, Helen C.; Sweby, Peter K.

1997-01-01

The global nonlinear behavior of finite discretizations for constant time steps and fixed or adaptive grid spacings is studied using tools from dynamical systems theory. Detailed analysis of commonly used temporal and spatial discretizations for simple model problems is presented. The role of dynamics in the understanding of long time behavior of numerical integration and the nonlinear stability, convergence, and reliability of using time-marching approaches for obtaining steady-state numerical solutions in computational fluid dynamics (CFD) is explored. The study is complemented with examples of spurious behavior observed in steady and unsteady CFD computations. The CFD examples were chosen to illustrate non-apparent spurious behavior that was difficult to detect without extensive grid and temporal refinement studies and some knowledge from dynamical systems theory. Studies revealed the various possible dangers of misinterpreting numerical simulation of realistic complex flows that are constrained by available computing power. In large scale computations where the physics of the problem under study is not well understood and numerical simulations are the only viable means of solution, extreme care must be taken in both computation and interpretation of the numerical data. The goal of this paper is to explore the important role that dynamical systems theory can play in the understanding of the global nonlinear behavior of numerical algorithms and to aid the identification of the sources of numerical uncertainties in CFD.

Self-spreading of the wetting ridge during stick-slip on a viscoelastic surface

DOE PAGES

Park, S. J.; Bostwick, J. B.; De Andrade, V.; ...

2017-10-23

Dynamic wetting behaviors on soft solids are important to interpret complex biological processes from cell–substrate interactions. Despite intensive research studies over the past half-century, the underlying mechanisms of spreading behaviors are not clearly understood. The most interesting feature of wetting on soft matter is the formation of a “wetting ridge”, a surface deformation by a competition between elasticity and capillarity. Dynamics of the wetting ridge formed at the three-phase contact line underlies the dynamic wetting behaviors, but remains largely unexplored mostly due to limitations in indirect observation. Here, we directly visualize wetting ridge dynamics during continuous- and stick-slip motions onmore » a viscoelastic surface using X-ray microscopy. Strikingly, we discover that the ridge spreads spontaneously during stick and triggers contact line depinning (stick-to-slip transition) by changing the ridge geometry which weakens the contact line pinning. Finally, we clarify ‘viscoelastic-braking’, ‘stick-slipping’, and ‘stick-breaking’ spreading behaviors through the ridge dynamics. In stick-breaking, no ridge-spreading occurs and contact line pinning (hysteresis) is enhanced by cusp-bending while preserving a microscopic equilibrium at the ridge tip. We have furthered the understanding of spreading behaviors on soft solids and demonstrated the value of X-ray microscopy in elucidating various dynamic wetting behaviors on soft solids as well as puzzling biological issues.« less

The Speech Community in Evolutionary Language Dynamics

ERIC Educational Resources Information Center

Blythe, Richard A.; Croft, William A.

2009-01-01

Language is a complex adaptive system: Speakers are agents who interact with each other, and their past and current interactions feed into speakers' future behavior in complex ways. In this article, we describe the social cognitive linguistic basis for this analysis of language and a mathematical model developed in collaboration between…

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

Study nonlinear dynamics of stratospheric ozone concentration at Pakistan Terrestrial region

NASA Astrophysics Data System (ADS)

Jan, Bulbul; Zai, Muhammad Ayub Khan Yousuf; Afradi, Faisal Khan; Aziz, Zohaib

2018-03-01

This study investigates the nonlinear dynamics of the stratospheric ozone layer at Pakistan atmospheric region. Ozone considered now the most important issue in the world because of its diverse effects on earth biosphere, including human health, ecosystem, marine life, agriculture yield and climate change. Therefore, this paper deals with total monthly time series data of stratospheric ozone over the Pakistan atmospheric region from 1970 to 2013. Two approaches, basic statistical analysis and Fractal dimension (D) have adapted to study the nature of nonlinear dynamics of stratospheric ozone level. Results obtained from this research have shown that the Hurst exponent values of both methods of fractal dimension revealed an anti-persistent behavior (negatively correlated), i.e. decreasing trend for all lags and Rescaled range analysis is more appropriate as compared to Detrended fluctuation analysis. For seasonal time series all month follows an anti-persistent behavior except in the month of November which shown persistence behavior i.e. time series is an independent and increasing trend. The normality test statistics also confirmed the nonlinear behavior of ozone and the rejection of hypothesis indicates the strong evidence of the complexity of data. This study will be useful to the researchers working in the same field in the future to verify the complex nature of stratospheric ozone.

Numerical Study of the Complex Temporal Pattern of Spontaneous Oscillation in Bullfrog Saccular Hair Cells

NASA Astrophysics Data System (ADS)

Roongthumskul, Yuttana; Fredrickson-Hemsing, Lea; Kao, Albert; Bozovic, Dolores

2011-11-01

Hair bundles of the bullfrog sacculus display spontaneous oscillations that show complex temporal profiles. Quiescent intervals are typically interspersed with oscillations, analogous to bursting behavior observed in neural systems. By introducing slow calcium dynamics into the theoretical model of bundle mechanics, we reproduce numerically the multi-mode oscillations and explore the effects of internal parameters on the temporal profiles and the frequency tuning of their linear response functions. We also study the effects of mechanical overstimulation on the oscillatory behavior.

Organizational Adaptative Behavior: The Complex Perspective of Individuals-Tasks Interaction

NASA Astrophysics Data System (ADS)

Wu, Jiang; Sun, Duoyong; Hu, Bin; Zhang, Yu

Organizations with different organizational structures have different organizational behaviors when responding environmental changes. In this paper, we use a computational model to examine organizational adaptation on four dimensions: Agility, Robustness, Resilience, and Survivability. We analyze the dynamics of organizational adaptation by a simulation study from a complex perspective of the interaction between tasks and individuals in a sales enterprise. The simulation studies in different scenarios show that more flexible communication between employees and less hierarchy level with the suitable centralization can improve organizational adaptation.

Controlling collective dynamics in complex minority-game resource-allocation systems

NASA Astrophysics Data System (ADS)

Zhang, Ji-Qiang; Huang, Zi-Gang; Dong, Jia-Qi; Huang, Liang; Lai, Ying-Cheng

2013-05-01

Resource allocation takes place in various kinds of real-world complex systems, such as traffic systems, social services institutions or organizations, or even ecosystems. The fundamental principle underlying complex resource-allocation dynamics is Boolean interactions associated with minority games, as resources are generally limited and agents tend to choose the least used resource based on available information. A common but harmful dynamical behavior in resource-allocation systems is herding, where there are time intervals during which a large majority of the agents compete for a few resources, leaving many other resources unused. Accompanying the herd behavior is thus strong fluctuations with time in the number of resources being used. In this paper, we articulate and establish that an intuitive control strategy, namely pinning control, is effective at harnessing the herding dynamics. In particular, by fixing the choices of resources for a few agents while leaving the majority of the agents free, herding can be eliminated completely. Our investigation is systematic in that we consider random and targeted pinning and a variety of network topologies, and we carry out a comprehensive analysis in the framework of mean-field theory to understand the working of control. The basic philosophy is then that, when a few agents waive their freedom to choose resources by receiving sufficient incentives, the majority of the agents benefit in that they will make fair, efficient, and effective use of the available resources. Our work represents a basic and general framework to address the fundamental issue of fluctuations in complex dynamical systems with significant applications to social, economical, and political systems.

Effect of Rho-kinase inhibition on complexity of breathing pattern in a guinea pig model of asthma

PubMed Central

Pazhoohan, Saeed; Javan, Mohammad; Hajizadeh, Sohrab

2017-01-01

Asthma represents an episodic and fluctuating behavior characterized with decreased complexity of respiratory dynamics. Several evidence indicate that asthma severity or control is associated with alteration in variability of lung function. The pathophysiological basis of alteration in complexity of breathing pattern in asthma has remained poorly understood. Regarding the point that Rho-kinase is involved in pathophysiology of asthma, in present study we investigated the effect of Rho-kinase inhibition on complexity of respiratory dynamics in a guinea pig model of asthma. Male Dunkin Hartley guinea pigs were exposed to 12 series of inhalations with ovalbumin or saline. Animals were treated by the Rho-kinase inhibitor Y-27632 (1mM aerosols) prior to each allergen challenge. We recorded respiration of conscious animals using whole-body plethysmography. Exposure to ovalbumin induced lung inflammation, airway hyperresponsiveness and remodeling including goblet cell hyperplasia, increase in the thickness of airways smooth muscles and subepithelial collagen deposition. Complexity analysis of respiratory dynamics revealed a dramatic decrease in irregularity of respiratory rhythm representing less complexity in asthmatic guinea pigs. Inhibition of Rho-kinase reduced the airway remodeling and hyperreponsiveness, but had no significant effect on lung inflammation and complexity of respiratory dynamics in asthmatic animals. It seems that airway hyperresponsiveness and remodeling do not significantly affect the complexity of respiratory dynamics. Our results suggest that inflammation might be the probable cause of shift in the respiratory dynamics away from the normal fluctuation in asthma. PMID:29088265

Creating virtual humans for simulation-based training and planning

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

Stansfield, S.; Sobel, A.

1998-05-12

Sandia National Laboratories has developed a distributed, high fidelity simulation system for training and planning small team Operations. The system provides an immersive environment populated by virtual objects and humans capable of displaying complex behaviors. The work has focused on developing the behaviors required to carry out complex tasks and decision making under stress. Central to this work are techniques for creating behaviors for virtual humans and for dynamically assigning behaviors to CGF to allow scenarios without fixed outcomes. Two prototype systems have been developed that illustrate these capabilities: MediSim, a trainer for battlefield medics and VRaptor, a system formore » planning, rehearsing and training assault operations.« less

Engine dynamic analysis with general nonlinear finite element codes

NASA Technical Reports Server (NTRS)

Adams, M. L.; Padovan, J.; Fertis, D. G.

1991-01-01

A general engine dynamic analysis as a standard design study computational tool is described for the prediction and understanding of complex engine dynamic behavior. Improved definition of engine dynamic response provides valuable information and insights leading to reduced maintenance and overhaul costs on existing engine configurations. Application of advanced engine dynamic simulation methods provides a considerable cost reduction in the development of new engine designs by eliminating some of the trial and error process done with engine hardware development.

Modeling mechanical interactions in growing populations of rod-shaped bacteria

NASA Astrophysics Data System (ADS)

Winkle, James J.; Igoshin, Oleg A.; Bennett, Matthew R.; Josić, Krešimir; Ott, William

2017-10-01

Advances in synthetic biology allow us to engineer bacterial collectives with pre-specified characteristics. However, the behavior of these collectives is difficult to understand, as cellular growth and division as well as extra-cellular fluid flow lead to complex, changing arrangements of cells within the population. To rationally engineer and control the behavior of cell collectives we need theoretical and computational tools to understand their emergent spatiotemporal dynamics. Here, we present an agent-based model that allows growing cells to detect and respond to mechanical interactions. Crucially, our model couples the dynamics of cell growth to the cell’s environment: Mechanical constraints can affect cellular growth rate and a cell may alter its behavior in response to these constraints. This coupling links the mechanical forces that influence cell growth and emergent behaviors in cell assemblies. We illustrate our approach by showing how mechanical interactions can impact the dynamics of bacterial collectives growing in microfluidic traps.

Dynamical singularities for complex initial conditions and the motion at a real separatrix.

PubMed

Shnerb, Tamar; Kay, K G

2006-04-01

This work investigates singularities occurring at finite real times in the classical dynamics of one-dimensional double-well systems with complex initial conditions. The objective is to understand the relationship between these singularities and the behavior of the systems for real initial conditions. An analytical treatment establishes that the dynamics of a quartic double well system possesses a doubly infinite sequence of singularities. These are associated with initial conditions that converge to those for the real separatrix as the singularity time becomes infinite. This confluence of singularities is shown to lead to the unstable behavior that characterizes the real motion at the separatrix. Numerical calculations confirm the existence of a large number of singularities converging to the separatrix for this and two additional double-well systems. The approach of singularities to the real axis is of particular interest since such behavior has been related to the formation of chaos in nonintegrable systems. The properties of the singular trajectories which cause this convergence to the separatrix are identified. The hyperbolic fixed point corresponding to the potential energy maximum, responsible for the characteristic motion at a separatrix, also plays a critical role in the formation of the complex singularities by delaying trajectories and then deflecting them into asymptotic regions of space from where they are directly repelled to infinity in a finite time.

The coordination dynamics of social neuromarkers.

PubMed

Tognoli, Emmanuelle; Kelso, J A Scott

2015-01-01

Social behavior is a complex integrative function that entails many aspects of the brain's sensory, cognitive, emotional and movement capacities. Its neural processes are seldom simultaneous but occur according to precise spatiotemporal choreographies, manifested by the coordination of their oscillations within and between brains. Methods with good temporal resolution can help to identify so-called "neuromarkers" of social function and aid in disentangling the dynamical architecture of social brains. In our ongoing research, we have used dual-electroencephalography (EEG) to study neuromarker dynamics during synchronic interactions in which pairs of subjects coordinate behavior spontaneously and intentionally (social coordination) and during diachronic transactions that require subjects to perceive or behave in turn (action observation, delayed imitation). In this paper, after outlining our dynamical approach to the neurophysiological basis of social behavior, we examine commonalities and differences in the neuromarkers that are recruited for both kinds of tasks. We find the neuromarker landscape to be task-specific: synchronic paradigms of social coordination reveal medial mu, alpha and the phi complex as contributing neuromarkers. Diachronic tasks recruit alpha as well, in addition to lateral mu rhythms and the newly discovered nu and kappa rhythms whose functional significance is still unclear. Social coordination, observation, and delayed imitation share commonality of context: in each of our experiments, subjects exchanged information through visual perception and moved in similar ways. Nonetheless, there was little overlap between their neuromarkers, a result that hints strongly of task-specific neural mechanisms for social behavior. The only neuromarker that transcended both synchronic and diachronic social behaviors was the ubiquitous alpha rhythm, which appears to be a key signature of visually-mediated social behaviors. The present paper is both an entry point and a challenge: much work remains to determine the nature and scope of recruitment of other neuromarkers, and to create theoretical models of their within- and between-brain dynamics during social interaction.

The coordination dynamics of social neuromarkers

PubMed Central

Tognoli, Emmanuelle; Kelso, J. A. Scott

2015-01-01

Social behavior is a complex integrative function that entails many aspects of the brain’s sensory, cognitive, emotional and movement capacities. Its neural processes are seldom simultaneous but occur according to precise spatiotemporal choreographies, manifested by the coordination of their oscillations within and between brains. Methods with good temporal resolution can help to identify so-called “neuromarkers” of social function and aid in disentangling the dynamical architecture of social brains. In our ongoing research, we have used dual-electroencephalography (EEG) to study neuromarker dynamics during synchronic interactions in which pairs of subjects coordinate behavior spontaneously and intentionally (social coordination) and during diachronic transactions that require subjects to perceive or behave in turn (action observation, delayed imitation). In this paper, after outlining our dynamical approach to the neurophysiological basis of social behavior, we examine commonalities and differences in the neuromarkers that are recruited for both kinds of tasks. We find the neuromarker landscape to be task-specific: synchronic paradigms of social coordination reveal medial mu, alpha and the phi complex as contributing neuromarkers. Diachronic tasks recruit alpha as well, in addition to lateral mu rhythms and the newly discovered nu and kappa rhythms whose functional significance is still unclear. Social coordination, observation, and delayed imitation share commonality of context: in each of our experiments, subjects exchanged information through visual perception and moved in similar ways. Nonetheless, there was little overlap between their neuromarkers, a result that hints strongly of task-specific neural mechanisms for social behavior. The only neuromarker that transcended both synchronic and diachronic social behaviors was the ubiquitous alpha rhythm, which appears to be a key signature of visually-mediated social behaviors. The present paper is both an entry point and a challenge: much work remains to determine the nature and scope of recruitment of other neuromarkers, and to create theoretical models of their within- and between-brain dynamics during social interaction. PMID:26557067

Parameterization and Sensitivity Analysis of a Complex Simulation Model for Mosquito Population Dynamics, Dengue Transmission, and Their Control

PubMed Central

Ellis, Alicia M.; Garcia, Andres J.; Focks, Dana A.; Morrison, Amy C.; Scott, Thomas W.

2011-01-01

Models can be useful tools for understanding the dynamics and control of mosquito-borne disease. More detailed models may be more realistic and better suited for understanding local disease dynamics; however, evaluating model suitability, accuracy, and performance becomes increasingly difficult with greater model complexity. Sensitivity analysis is a technique that permits exploration of complex models by evaluating the sensitivity of the model to changes in parameters. Here, we present results of sensitivity analyses of two interrelated complex simulation models of mosquito population dynamics and dengue transmission. We found that dengue transmission may be influenced most by survival in each life stage of the mosquito, mosquito biting behavior, and duration of the infectious period in humans. The importance of these biological processes for vector-borne disease models and the overwhelming lack of knowledge about them make acquisition of relevant field data on these biological processes a top research priority. PMID:21813844

Complex and unexpected dynamics in simple genetic regulatory networks

NASA Astrophysics Data System (ADS)

Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey

2014-03-01

One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

Evolution of weighted complex bus transit networks with flow

NASA Astrophysics Data System (ADS)

Huang, Ailing; Xiong, Jie; Shen, Jinsheng; Guan, Wei

2016-02-01

Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.

Failure behavior of generic metallic and composite aircraft structural components under crash loads

NASA Technical Reports Server (NTRS)

Carden, Huey D.; Robinson, Martha P.

1990-01-01

Failure behavior results are presented from crash dynamics research using concepts of aircraft elements and substructure not necessarily designed or optimized for energy absorption or crash loading considerations. To achieve desired new designs incorporating improved energy absorption capabilities often requires an understanding of how more conventional designs behave under crash loadings. Experimental and analytical data are presented which indicate some general trends in the failure behavior of a class of composite structures including individual fuselage frames, skeleton subfloors with stringers and floor beams without skin covering, and subfloors with skin added to the frame-stringer arrangement. Although the behavior is complex, a strong similarity in the static/dynamic failure behavior among these structures is illustrated through photographs of the experimental results and through analytical data of generic composite structural models.

Dynamics in Complex Coacervates

NASA Astrophysics Data System (ADS)

Perry, Sarah

Understanding the dynamics of a material provides detailed information about the self-assembly, structure, and intermolecular interactions present in a material. While rheological methods have long been used for the characterization of complex coacervate-based materials, it remains a challenge to predict the dynamics for a new system of materials. Furthermore, most work reports only qualitative trends exist as to how parameters such as charge stoichiometry, ionic strength, and polymer chain length impact self-assembly and material dynamics, and there is little information on the effects of polymer architecture or the organization of charges within a polymer. We seek to link thermodynamic studies of coacervation phase behavior with material dynamics through a carefully-controlled, systematic study of coacervate linear viscoelasticity for different polymer chemistries. We couple various methods of characterizing the dynamics of polymer-based complex coacervates, including the time-salt superposition methods developed first by Spruijt and coworkers to establish a more mechanistic strategy for comparing the material dynamics and linear viscoelasticity of different systems. Acknowledgment is made to the Donors of the American Chemical Society Petroleum Research Fund for support of this research.

Dynamic model of time-dependent complex networks.

PubMed

Hill, Scott A; Braha, Dan

2010-10-01

The characterization of the "most connected" nodes in static or slowly evolving complex networks has helped in understanding and predicting the behavior of social, biological, and technological networked systems, including their robustness against failures, vulnerability to deliberate attacks, and diffusion properties. However, recent empirical research of large dynamic networks (characterized by irregular connections that evolve rapidly) has demonstrated that there is little continuity in degree centrality of nodes over time, even when their degree distributions follow a power law. This unexpected dynamic centrality suggests that the connections in these systems are not driven by preferential attachment or other known mechanisms. We present an approach to explain real-world dynamic networks and qualitatively reproduce these dynamic centrality phenomena. This approach is based on a dynamic preferential attachment mechanism, which exhibits a sharp transition from a base pure random walk scheme.

Learning to Manage Intergroup Dynamics in Changing Task Environments: An Experiential Exercise

ERIC Educational Resources Information Center

Hunsaker, Phillip L.

2004-01-01

This article describes an exercise that allows participants to experience the challenges of managing intergroup behavior as an organization's task environment grows and becomes more complex. The article begins with a brief review of models and concepts relating to intergroup dynamics, intergroup conflict, and interventions for effectively managing…

Multifractal Approach to the Analysis of Crime Dynamics: Results for Burglary in San Francisco

NASA Astrophysics Data System (ADS)

Melgarejo, Miguel; Obregon, Nelson

This paper provides evidence of fractal, multifractal and chaotic behaviors in urban crime by computing key statistical attributes over a long data register of criminal activity. Fractal and multifractal analyses based on power spectrum, Hurst exponent computation, hierarchical power law detection and multifractal spectrum are considered ways to characterize and quantify the footprint of complexity of criminal activity. Moreover, observed chaos analysis is considered a second step to pinpoint the nature of the underlying crime dynamics. This approach is carried out on a long database of burglary activity reported by 10 police districts of San Francisco city. In general, interarrival time processes of criminal activity in San Francisco exhibit fractal and multifractal patterns. The behavior of some of these processes is close to 1/f noise. Therefore, a characterization as deterministic, high-dimensional, chaotic phenomena is viable. Thus, the nature of crime dynamics can be studied from geometric and chaotic perspectives. Our findings support that crime dynamics may be understood from complex systems theories like self-organized criticality or highly optimized tolerance.

Multi-source micro-friction identification for a class of cable-driven robots with passive backbone

NASA Astrophysics Data System (ADS)

Tjahjowidodo, Tegoeh; Zhu, Ke; Dailey, Wayne; Burdet, Etienne; Campolo, Domenico

2016-12-01

This paper analyses the dynamics of cable-driven robots with a passive backbone and develops techniques for their dynamic identification, which are tested on the H-Man, a planar cabled differential transmission robot for haptic interaction. The mechanism is optimized for human-robot interaction by accounting for the cost-benefit-ratio of the system, specifically by eliminating the necessity of an external force sensor to reduce the overall cost. As a consequence, this requires an effective dynamic model for accurate force feedback applications which include friction behavior in the system. We first consider the significance of friction in both the actuator and backbone spaces. Subsequently, we study the required complexity of the stiction model for the application. Different models representing different levels of complexity are investigated, ranging from the conventional approach of Coulomb to an advanced model which includes hysteresis. The results demonstrate each model's ability to capture the dynamic behavior of the system. In general, it is concluded that there is a trade-off between model accuracy and the model cost.

Evolving dynamics of trading behavior based on coordination game in complex networks

NASA Astrophysics Data System (ADS)

Bian, Yue-tang; Xu, Lu; Li, Jin-sheng

2016-05-01

This work concerns the modeling of evolvement of trading behavior in stock markets. Based on the assumption of the investors' limited rationality, the evolution mechanism of trading behavior is modeled according to the investment strategy of coordination game in network, that investors are prone to imitate their neighbors' activity through comprehensive analysis on the risk dominance degree of certain investment behavior, the network topology of their relationship and its heterogeneity. We investigate by mean-field analysis and extensive simulations the evolution of investors' trading behavior in various typical networks under different risk dominance degree of investment behavior. Our results indicate that the evolution of investors' behavior is affected by the network structure of stock market and the effect of risk dominance degree of investment behavior; the stability of equilibrium states of investors' behavior dynamics is directly related with the risk dominance degree of some behavior; connectivity and heterogeneity of the network plays an important role in the evolution of the investment behavior in stock market.

Regional differences in hyoid muscle activity and length-dynamics during mammalian head-shaking

PubMed Central

Wentzel, Sarah E.; Konow, Nicolai; German, Rebecca Z.

2010-01-01

The sternohyoid (SH) and geniohyoid (GH) are antagonist strap-muscles that are active during a number of different behaviors, including sucking, intraoral transport, swallowing, breathing, and extension/flexion of the neck. Because these muscles have served different functions through the evolutionary history of vertebrates, it is quite likely they will have complex patterns of electrical activity and muscle fiber contraction. Different regions of the sternohyoid exhibit different contraction and activity patterns during a swallow. We examined the dynamics of the sternohyoid and geniohyoid muscles during an unrestrained, and vigorous head-shake behavior in an animal model of human head, neck and hyolingual movement. A gentle touch to infant pig ears elicited a head shake of several head revolutions. Using sonomicrometry and intramuscular EMG we measured regional (within) muscle strain and activity in SH and GH. We found that EMG was consistent across three regions (anterior, belly and posterior) of each muscle. Changes in muscle length however, were more complex. In the SH, mid-belly length-change occurred out of phase with the anterior and posterior end-regions, but with a zero-lag timing; the anterior region shortened prior to the posterior. In the GH, the anterior region shortened prior to, and out of phase with the mid-belly and posterior regions. Head-shaking is a relatively simple reflex behavior, yet the underlying patterns of muscle length-dynamics and EMG activity are not. The regional complexity in SH and GH, similar to regionalization of SH during swallowing, suggests that these ‘simple hyoid strap muscles’ are more complex than textbooks often suggest. PMID:21370479

Dynamic Modeling of ALS Systems

NASA Technical Reports Server (NTRS)

Jones, Harry

2002-01-01

The purpose of dynamic modeling and simulation of Advanced Life Support (ALS) systems is to help design them. Static steady state systems analysis provides basic information and is necessary to guide dynamic modeling, but static analysis is not sufficient to design and compare systems. ALS systems must respond to external input variations and internal off-nominal behavior. Buffer sizing, resupply scheduling, failure response, and control system design are aspects of dynamic system design. We develop two dynamic mass flow models and use them in simulations to evaluate systems issues, optimize designs, and make system design trades. One model is of nitrogen leakage in the space station, the other is of a waste processor failure in a regenerative life support system. Most systems analyses are concerned with optimizing the cost/benefit of a system at its nominal steady-state operating point. ALS analysis must go beyond the static steady state to include dynamic system design. All life support systems exhibit behavior that varies over time. ALS systems must respond to equipment operating cycles, repair schedules, and occasional off-nominal behavior or malfunctions. Biological components, such as bioreactors, composters, and food plant growth chambers, usually have operating cycles or other complex time behavior. Buffer sizes, material stocks, and resupply rates determine dynamic system behavior and directly affect system mass and cost. Dynamic simulation is needed to avoid the extremes of costly over-design of buffers and material reserves or system failure due to insufficient buffers and lack of stored material.

Aging, memory, and nonhierarchical energy landscape of spin jam

NASA Astrophysics Data System (ADS)

Samarakoon, Anjana; Sato, Taku J.; Chen, Tianran; Chern, Gai-Wei; Yang, Junjie; Klich, Israel; Sinclair, Ryan; Zhou, Haidong; Lee, Seung-Hun

2016-10-01

The notion of complex energy landscape underpins the intriguing dynamical behaviors in many complex systems ranging from polymers, to brain activity, to social networks and glass transitions. The spin glass state found in dilute magnetic alloys has been an exceptionally convenient laboratory frame for studying complex dynamics resulting from a hierarchical energy landscape with rugged funnels. Here, we show, by a bulk susceptibility and Monte Carlo simulation study, that densely populated frustrated magnets in a spin jam state exhibit much weaker memory effects than spin glasses, and the characteristic properties can be reproduced by a nonhierarchical landscape with a wide and nearly flat but rough bottom. Our results illustrate that the memory effects can be used to probe different slow dynamics of glassy materials, hence opening a window to explore their distinct energy landscapes.

Non-stationary dynamics in the bouncing ball: A wavelet perspective

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

Behera, Abhinna K., E-mail: abhinna@iiserkol.ac.in; Panigrahi, Prasanta K., E-mail: pprasanta@iiserkol.ac.in; Sekar Iyengar, A. N., E-mail: ansekar.iyengar@saha.ac.in

2014-12-01

The non-stationary dynamics of a bouncing ball, comprising both periodic as well as chaotic behavior, is studied through wavelet transform. The multi-scale characterization of the time series displays clear signatures of self-similarity, complex scaling behavior, and periodicity. Self-similar behavior is quantified by the generalized Hurst exponent, obtained through both wavelet based multi-fractal detrended fluctuation analysis and Fourier methods. The scale dependent variable window size of the wavelets aptly captures both the transients and non-stationary periodic behavior, including the phase synchronization of different modes. The optimal time-frequency localization of the continuous Morlet wavelet is found to delineate the scales corresponding tomore » neutral turbulence, viscous dissipation regions, and different time varying periodic modulations.« less

Phase Transition Behavior in a Neutral Evolution Model

NASA Astrophysics Data System (ADS)

King, Dawn; Scott, Adam; Maric, Nevena; Bahar, Sonya

2014-03-01

The complexity of interactions among individuals and between individuals and the environment make agent based modeling ideal for studying emergent speciation. This is a dynamically complex problem that can be characterized via the critical behavior of a continuous phase transition. Concomitant with the main tenets of natural selection, we allow organisms to reproduce, mutate, and die within a neutral phenotype space. Previous work has shown phase transition behavior in an assortative mating model with variable fitness landscapes as the maximum mutation size (μ) was varied (Dees and Bahar, 2010). Similarly, this behavior was recently presented in the work of Scott et al. (2013), even on a completely neutral landscape, for bacterial-like fission as well as for assortative mating. Here we present another neutral model to investigate the `critical' phase transition behavior of three mating types - assortative, bacterial, and random - in a phenotype space as a function of the percentage of random death. Results show two types of phase transitions occurring for the parameters of the population size and the number of clusters (an analogue of species), indicating different evolutionary dynamics for system survival and clustering. This research was supported by funding from: University of Missouri Research Board and James S. McDonnell Foundation.

Characterizing time series: when Granger causality triggers complex networks

NASA Astrophysics Data System (ADS)

Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

2012-08-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

Controlling extreme events on complex networks

NASA Astrophysics Data System (ADS)

Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

2014-08-01

Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network ``mobile'' can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.

Effects of behavioral response and vaccination policy on epidemic spreading--an approach based on evolutionary-game dynamics.

PubMed

Zhang, Hai-Feng; Wu, Zhi-Xi; Tang, Ming; Lai, Ying-Cheng

2014-07-11

How effective are governmental incentives to achieve widespread vaccination coverage so as to prevent epidemic outbreak? The answer largely depends on the complex interplay among the type of incentive, individual behavioral responses, and the intrinsic epidemic dynamics. By incorporating evolutionary games into epidemic dynamics, we investigate the effects of two types of incentives strategies: partial-subsidy policy in which certain fraction of the cost of vaccination is offset, and free-subsidy policy in which donees are randomly selected and vaccinated at no cost. Through mean-field analysis and computations, we find that, under the partial-subsidy policy, the vaccination coverage depends monotonically on the sensitivity of individuals to payoff difference, but the dependence is non-monotonous for the free-subsidy policy. Due to the role models of the donees for relatively irrational individuals and the unchanged strategies of the donees for rational individuals, the free-subsidy policy can in general lead to higher vaccination coverage. Our findings indicate that any disease-control policy should be exercised with extreme care: its success depends on the complex interplay among the intrinsic mathematical rules of epidemic spreading, governmental policies, and behavioral responses of individuals.

Threshold behaviors of social dynamics and financial outcomes of Ponzi scheme diffusion in complex networks

NASA Astrophysics Data System (ADS)

Fu, Peihua; Zhu, Anding; Ni, He; Zhao, Xin; Li, Xiulin

2018-01-01

Ponzi schemes always lead to mass disasters after collapse. It is important to study the critical behaviors of both social dynamics and financial outcomes for Ponzi scheme diffusion in complex networks. We develop the potential-investor-divestor-investor (PIDI) model by considering the individual behavior of direct reinvestment. We find that only the spreading rate relates to the epidemic outbreak while the reinvestment rate relates to the zero and non-zero final states for social dynamics of both homo- and inhomogeneous networks. Financially, we find that there is a critical spreading threshold, above which the scheme needs not to use its own initial capital for taking off, i.e. the starting cost is covered by the rapidly inflowing funds. However, the higher the cost per recruit, the larger the critical spreading threshold and the worse the financial outcomes. Theoretical and simulation results also reveal that schemes are easier to take off in inhomogeneous networks. The reinvestment rate does not affect the starting. However, it improves the financial outcome in the early stages and postpones the outbreak of financial collapse. Some policy suggestions for the regulator from the perspective of social physics are proposed in the end of the paper.

Effects of behavioral response and vaccination policy on epidemic spreading - an approach based on evolutionary-game dynamics

NASA Astrophysics Data System (ADS)

Zhang, Hai-Feng; Wu, Zhi-Xi; Tang, Ming; Lai, Ying-Cheng

2014-07-01

How effective are governmental incentives to achieve widespread vaccination coverage so as to prevent epidemic outbreak? The answer largely depends on the complex interplay among the type of incentive, individual behavioral responses, and the intrinsic epidemic dynamics. By incorporating evolutionary games into epidemic dynamics, we investigate the effects of two types of incentives strategies: partial-subsidy policy in which certain fraction of the cost of vaccination is offset, and free-subsidy policy in which donees are randomly selected and vaccinated at no cost. Through mean-field analysis and computations, we find that, under the partial-subsidy policy, the vaccination coverage depends monotonically on the sensitivity of individuals to payoff difference, but the dependence is non-monotonous for the free-subsidy policy. Due to the role models of the donees for relatively irrational individuals and the unchanged strategies of the donees for rational individuals, the free-subsidy policy can in general lead to higher vaccination coverage. Our findings indicate that any disease-control policy should be exercised with extreme care: its success depends on the complex interplay among the intrinsic mathematical rules of epidemic spreading, governmental policies, and behavioral responses of individuals.

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

Noe, F; Diadone, Isabella; Lollmann, Marc

There is a gap between kinetic experiment and simulation in their views of the dynamics of complex biomolecular systems. Whereas experiments typically reveal only a few readily discernible exponential relaxations, simulations often indicate complex multistate behavior. Here, a theoretical framework is presented that reconciles these two approaches. The central concept is dynamical fingerprints which contain peaks at the time scales of the dynamical processes involved with amplitudes determined by the experimental observable. Fingerprints can be generated from both experimental and simulation data, and their comparison by matching peaks permits assignment of structural changes present in the simulation to experimentally observedmore » relaxation processes. The approach is applied here to a test case interpreting single molecule fluorescence correlation spectroscopy experiments on a set of fluorescent peptides with molecular dynamics simulations. The peptides exhibit complex kinetics shown to be consistent with the apparent simplicity of the experimental data. Moreover, the fingerprint approach can be used to design new experiments with site-specific labels that optimally probe specific dynamical processes in the molecule under investigation.« less

Dynamics of a network of phase oscillators with plastic couplings

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

Nekorkin, V. I.; Kasatkin, D. V.; Moscow Institute of Physics and Technology

The processes of synchronization and phase cluster formation are investigated in a complex network of dynamically coupled phase oscillators. Coupling weights evolve dynamically depending on the phase relations between the oscillators. It is shown that the network exhibits several types of behavior: the globally synchronized state, two-cluster and multi-cluster states, different synchronous states with a fixed phase relationship between the oscillators and chaotic desynchronized state.

An Information Theoretic Investigation Of Complex Adaptive Supply Networks With Organizational Topologies

DTIC Science & Technology

2016-12-22

assumptions of behavior. This research proposes an information theoretic methodology to discover such complex network structures and dynamics while overcoming...the difficulties historically associated with their study. Indeed, this was the first application of an information theoretic methodology as a tool...1 Research Objectives and Questions..............................................................................2 Methodology

A Complex Systems Framework for Research on Leadership and Organizational Dynamics in Academic Libraries

ERIC Educational Resources Information Center

Gilstrap, Donald L.

2009-01-01

This article provides a historiographical analysis of major leadership and organizational development theories that have shaped our thinking about how we lead and administrate academic libraries. Drawing from behavioral, cognitive, systems, and complexity theories, this article discusses major theorists and research studies appearing over the past…

Toward a Common Structure in Demographic Educational Modeling and Simulation: A Complex Systems Approach

ERIC Educational Resources Information Center

Guevara, Porfirio

2014-01-01

This article identifies elements and connections that seem to be relevant to explain persistent aggregate behavioral patterns in educational systems when using complex dynamical systems modeling and simulation approaches. Several studies have shown what factors are at play in educational fields, but confusion still remains about the underlying…

Dynamical Behaviors in Complex-Valued Love Model With or Without Time Delays

NASA Astrophysics Data System (ADS)

Deng, Wei; Liao, Xiaofeng; Dong, Tao

2017-12-01

In this paper, a novel version of nonlinear model, i.e. a complex-valued love model with two time delays between two individuals in a love affair, has been proposed. A notable feature in this model is that we separate the emotion of one individual into real and imaginary parts to represent the variation and complexity of psychophysiological emotion in romantic relationship instead of just real domain, and make our model much closer to reality. This is because love is a complicated cognitive and social phenomenon, full of complexity, diversity and unpredictability, which refers to the coexistence of different aspects of feelings, states and attitudes ranging from joy and trust to sadness and disgust. By analyzing associated characteristic equation of linearized equations for our model, it is found that the Hopf bifurcation occurs when the sum of time delays passes through a sequence of critical value. Stability of bifurcating cyclic love dynamics is also derived by applying the normal form theory and the center manifold theorem. In addition, it is also shown that, for some appropriate chosen parameters, chaotic behaviors can appear even without time delay.

Complexity and Competition in Appetitive and Aversive Neural Circuits

PubMed Central

Barberini, Crista L.; Morrison, Sara E.; Saez, Alex; Lau, Brian; Salzman, C. Daniel

2012-01-01

Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors – approaching or avoiding reinforcement depending upon its valence – these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors. PMID:23189037

Quantifying the buildup in extent and complexity of free exploration in mice

PubMed Central

Benjamini, Yoav; Fonio, Ehud; Galili, Tal; Havkin, Gregor Z.; Golani, Ilan

2011-01-01

To obtain a perspective on an animal's own functional world, we study its behavior in situations that allow the animal to regulate the growth rate of its behavior and provide us with the opportunity to quantify its moment-by-moment developmental dynamics. Thus, we are able to show that mouse exploratory behavior consists of sequences of repeated motion: iterative processes that increase in extent and complexity, whose presumed function is a systematic active management of input acquired during the exploration of a novel environment. We use this study to demonstrate our approach to quantifying behavior: targeting aspects of behavior that are shown to be actively managed by the animal, and using measures that are discriminative across strains and treatments and replicable across laboratories. PMID:21383149

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

Pigeons and humans use action and pose information to categorize complex human behaviors.

PubMed

Qadri, Muhammad A J; Cook, Robert G

2017-02-01

The biological mechanisms used to categorize and recognize behaviors are poorly understood in both human and non-human animals. Using animated digital models, we have recently shown that pigeons can categorize different locomotive animal gaits and types of complex human behaviors. In the current experiments, pigeons (go/no-go task) and humans (choice task) both learned to conditionally categorize two categories of human behaviors that did not repeat and were comprised of the coordinated motions of multiple limbs. These "martial arts" and "Indian dance" action sequences were depicted by a digital human model. Depending upon whether the model was in motion or not, each species was required to engage in different and opposing responses to the two behavioral categories. Both species learned to conditionally and correctly act on this dynamic and static behavioral information, indicating that both species use a combination of static pose cues that are available from stimulus onset in addition to less rapidly available action information in order to successfully discriminate between the behaviors. Human participants additionally demonstrated a bias towards the dynamic information in the display when re-learning the task. Theories that rely on generalized, non-specific visual mechanisms involving channels for motion and static cues offer a parsimonious account of how humans and pigeons recognize and categorize behaviors within and across species. Copyright © 2016 Elsevier Ltd. All rights reserved.

Characterization of Dynamical Phase Transitions in Quantum Jump Trajectories Beyond the Properties of the Stationary State

NASA Astrophysics Data System (ADS)

Lesanovsky, Igor; van Horssen, Merlijn; Guţă, Mădălin; Garrahan, Juan P.

2013-04-01

We describe how to characterize dynamical phase transitions in open quantum systems from a purely dynamical perspective, namely, through the statistical behavior of quantum jump trajectories. This approach goes beyond considering only properties of the steady state. While in small quantum systems dynamical transitions can only occur trivially at limiting values of the controlling parameters, in many-body systems they arise as collective phenomena and within this perspective they are reminiscent of thermodynamic phase transitions. We illustrate this in open models of increasing complexity: a three-level system, the micromaser, and a dissipative version of the quantum Ising model. In these examples dynamical transitions are accompanied by clear changes in static behavior. This is however not always the case, and, in general, dynamical phases need to be uncovered by observables which are strictly dynamical, e.g., dynamical counting fields. We demonstrate this via the example of a class of models of dissipative quantum glasses, whose dynamics can vary widely despite having identical (and trivial) stationary states.

Characterization of dynamical phase transitions in quantum jump trajectories beyond the properties of the stationary state.

PubMed

Lesanovsky, Igor; van Horssen, Merlijn; Guţă, Mădălin; Garrahan, Juan P

2013-04-12

We describe how to characterize dynamical phase transitions in open quantum systems from a purely dynamical perspective, namely, through the statistical behavior of quantum jump trajectories. This approach goes beyond considering only properties of the steady state. While in small quantum systems dynamical transitions can only occur trivially at limiting values of the controlling parameters, in many-body systems they arise as collective phenomena and within this perspective they are reminiscent of thermodynamic phase transitions. We illustrate this in open models of increasing complexity: a three-level system, the micromaser, and a dissipative version of the quantum Ising model. In these examples dynamical transitions are accompanied by clear changes in static behavior. This is however not always the case, and, in general, dynamical phases need to be uncovered by observables which are strictly dynamical, e.g., dynamical counting fields. We demonstrate this via the example of a class of models of dissipative quantum glasses, whose dynamics can vary widely despite having identical (and trivial) stationary states.

Complexity matching in dyadic conversation.

PubMed

Abney, Drew H; Paxton, Alexandra; Dale, Rick; Kello, Christopher T

2014-12-01

Recent studies of dyadic interaction have examined phenomena of synchronization, entrainment, alignment, and convergence. All these forms of behavioral matching have been hypothesized to play a supportive role in establishing coordination and common ground between interlocutors. In the present study, evidence is found for a new kind of coordination termed complexity matching. Temporal dynamics in conversational speech signals were analyzed through time series of acoustic onset events. Timing in periods of acoustic energy was found to exhibit behavioral matching that reflects complementary timing in turn-taking. In addition, acoustic onset times were found to exhibit power law clustering across a range of timescales, and these power law functions were found to exhibit complexity matching that is distinct from behavioral matching. Complexity matching is discussed in terms of interactive alignment and other theoretical principles that lead to new hypotheses about information exchange in dyadic conversation and interaction in general. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Message survival and decision dynamics in a class of reactive complex systems subject to external fields

NASA Astrophysics Data System (ADS)

Rodriguez Lucatero, C.; Schaum, A.; Alarcon Ramos, L.; Bernal-Jaquez, R.

2014-07-01

In this study, the dynamics of decisions in complex networks subject to external fields are studied within a Markov process framework using nonlinear dynamical systems theory. A mathematical discrete-time model is derived using a set of basic assumptions regarding the convincement mechanisms associated with two competing opinions. The model is analyzed with respect to the multiplicity of critical points and the stability of extinction states. Sufficient conditions for extinction are derived in terms of the convincement probabilities and the maximum eigenvalues of the associated connectivity matrices. The influences of exogenous (e.g., mass media-based) effects on decision behavior are analyzed qualitatively. The current analysis predicts: (i) the presence of fixed-point multiplicity (with a maximum number of four different fixed points), multi-stability, and sensitivity with respect to the process parameters; and (ii) the bounded but significant impact of exogenous perturbations on the decision behavior. These predictions were verified using a set of numerical simulations based on a scale-free network topology.

Agent-Based Modeling of Cancer Stem Cell Driven Solid Tumor Growth.

PubMed

Poleszczuk, Jan; Macklin, Paul; Enderling, Heiko

2016-01-01

Computational modeling of tumor growth has become an invaluable tool to simulate complex cell-cell interactions and emerging population-level dynamics. Agent-based models are commonly used to describe the behavior and interaction of individual cells in different environments. Behavioral rules can be informed and calibrated by in vitro assays, and emerging population-level dynamics may be validated with both in vitro and in vivo experiments. Here, we describe the design and implementation of a lattice-based agent-based model of cancer stem cell driven tumor growth.

The Dynamics of the Human Leukocyte Antigen Head Domain Modulates Its Recognition by the T-Cell Receptor.

PubMed

García-Guerrero, Estefanía; Pérez-Simón, José Antonio; Sánchez-Abarca, Luis Ignacio; Díaz-Moreno, Irene; De la Rosa, Miguel A; Díaz-Quintana, Antonio

2016-01-01

Generating the immune response requires the discrimination of peptides presented by the human leukocyte antigen complex (HLA) through the T-cell receptor (TCR). However, how a single amino acid substitution in the antigen bonded to HLA affects the response of T cells remains uncertain. Hence, we used molecular dynamics computations to analyze the molecular interactions between peptides, HLA and TCR. We compared immunologically reactive complexes with non-reactive and weakly reactive complexes. MD trajectories were produced to simulate the behavior of isolated components of the various p-HLA-TCR complexes. Analysis of the fluctuations showed that p-HLA binding barely restrains TCR motions, and mainly affects the CDR3 loops. Conversely, inactive p-HLA complexes displayed significant drop in their dynamics when compared with its free versus ternary forms (p-HLA-TCR). In agreement, the free non-reactive p-HLA complexes showed a lower amount of salt bridges than the responsive ones. This resulted in differences between the electrostatic potentials of reactive and inactive p-HLA species and larger vibrational entropies in non-elicitor complexes. Analysis of the ternary p-HLA-TCR complexes also revealed a larger number of salt bridges in the responsive complexes. To summarize, our computations indicate that the affinity of each p-HLA complex towards TCR is intimately linked to both, the dynamics of its free species and its ability to form specific intermolecular salt-bridges in the ternary complexes. Of outstanding interest is the emerging concept of antigen reactivity involving its interplay with the HLA head sidechain dynamics by rearranging its salt-bridges.

Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools.

PubMed

Siettos, Constantinos; Starke, Jens

2016-09-01

The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems.

PubMed

Ouyang, Fang-Yan; Zheng, Bo; Jiang, Xiong-Fei

2015-01-01

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode.

Emergent 1d Ising Behavior in AN Elementary Cellular Automaton Model

NASA Astrophysics Data System (ADS)

Kassebaum, Paul G.; Iannacchione, Germano S.

The fundamental nature of an evolving one-dimensional (1D) Ising model is investigated with an elementary cellular automaton (CA) simulation. The emergent CA simulation employs an ensemble of cells in one spatial dimension, each cell capable of two microstates interacting with simple nearest-neighbor rules and incorporating an external field. The behavior of the CA model provides insight into the dynamics of coupled two-state systems not expressible by exact analytical solutions. For instance, state progression graphs show the causal dynamics of a system through time in relation to the system's entropy. Unique graphical analysis techniques are introduced through difference patterns, diffusion patterns, and state progression graphs of the 1D ensemble visualizing the evolution. All analyses are consistent with the known behavior of the 1D Ising system. The CA simulation and new pattern recognition techniques are scalable (in both dimension, complexity, and size) and have many potential applications such as complex design of materials, control of agent systems, and evolutionary mechanism design.

Challenges in the analysis of complex systems: introduction and overview

NASA Astrophysics Data System (ADS)

Hastings, Harold M.; Davidsen, Jörn; Leung, Henry

2017-12-01

One of the main challenges of modern physics is to provide a systematic understanding of systems far from equilibrium exhibiting emergent behavior. Prominent examples of such complex systems include, but are not limited to the cardiac electrical system, the brain, the power grid, social systems, material failure and earthquakes, and the climate system. Due to the technological advances over the last decade, the amount of observations and data available to characterize complex systems and their dynamics, as well as the capability to process that data, has increased substantially. The present issue discusses a cross section of the current research on complex systems, with a focus on novel experimental and data-driven approaches to complex systems that provide the necessary platform to model the behavior of such systems.

Modeling the Complexity of Post-Treatment Drinking: It’s a Rocky Road to Relapse

PubMed Central

Witkiewitz, Katie; Marlatt, G. Alan

2007-01-01

The most widely cited road block to successful treatment outcomes for psychological and substance use disorders has been described as the return to the previous behavior, or “relapse.” The operational definition of “relapse” varies from study to study and between clinicians, but in general the term is used to indicate the return to previous levels of symptomatic behavior. One explanation for the variation in the operationalization of relapse is the wide variety of behaviors for which the term is applied, including (but not limited to): depression, substance abuse, schizophrenia, mania, sexual offending, risky sexual behavior, dieting, and the anxiety disorders. A second explanation for the multitude of definitions for relapse is the inherent complexity in the process of behavior change. In this paper we present the most recent treatment outcome research evaluating relapse rates, with a special focus on the substance use disorders. Following this review of the literature we present an argument for the operationalization of relapse as a dynamic process, which can be empirically characterized using dynamical systems theory. We support this argument by presenting results from the analysis of alcohol treatment outcomes using catastrophe modeling techniques. These results demonstrate the utility of catastrophe theory in modeling the alcohol relapse process. The implications of these analyses for the treatment of alcohol use disorders, as well as a discussion of future research incorporating nonlinear dynamical systems theory is provided. PMID:17355897

Detrended fluctuation analysis based on higher-order moments of financial time series

NASA Astrophysics Data System (ADS)

Teng, Yue; Shang, Pengjian

2018-01-01

In this paper, a generalized method of detrended fluctuation analysis (DFA) is proposed as a new measure to assess the complexity of a complex dynamical system such as stock market. We extend DFA and local scaling DFA to higher moments such as skewness and kurtosis (labeled SMDFA and KMDFA), so as to investigate the volatility scaling property of financial time series. Simulations are conducted over synthetic and financial data for providing the comparative study. We further report the results of volatility behaviors in three American countries, three Chinese and three European stock markets by using DFA and LSDFA method based on higher moments. They demonstrate the dynamics behaviors of time series in different aspects, which can quantify the changes of complexity for stock market data and provide us with more meaningful information than single exponent. And the results reveal some higher moments volatility and higher moments multiscale volatility details that cannot be obtained using the traditional DFA method.

Nonlinear complex dynamics and Keynesian rigidity: A short introduction

NASA Astrophysics Data System (ADS)

Jovero, Edgardo

2005-09-01

The topic of this paper is to show that the greater acceptance and intense use of complex nonlinear dynamics in macroeconomics makes sense only within the neoKeynesian tradition. An example is presented regarding the behavior of an open-economy two-sector growth model endowed with Keynesian rigidity. The Keynesian view that structural instability globally exists in the aggregate economy is put forward, and therefore the need arises for policy to alleviate this instability in the form of dampened fluctuations is presented as an alternative view for macroeconomic theorizing.

1991 Annual report on scientific programs: A broad research program on the sciences of complexity

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

Not Available

1991-01-01

1991 was continued rapid growth for the Santa Fe Institute (SFI) as it broadened its interdisciplinary research into the organization, evolution and operation of complex systems and sought deeply the principles underlying their dynamic behavior. Research on complex systems--the focus of work at SFI--involves an extraordinary range of topics normally studied in seemingly disparate fields. Natural systems displaying complex behavior range upwards from proteins and DNA through cells and evolutionary systems to human societies. Research models exhibiting complexity include nonlinear equations, spin glasses, cellular automata, genetic algorithms, classifier systems, and an array of other computational models. Some of the majormore » questions facing complex systems researchers are: (1) explaining how complexity arises from the nonlinear interaction of simples components, (2) describing the mechanisms underlying high-level aggregate behavior of complex systems (such as the overt behavior of an organism, the flow of energy in an ecology, the GNP of an economy), and (3) creating a theoretical framework to enable predictions about the likely behavior of such systems in various conditions. The importance of understanding such systems in enormous: many of the most serious challenges facing humanity--e.g., environmental sustainability, economic stability, the control of disease--as well as many of the hardest scientific questions--e.g., protein folding, the distinction between self and non-self in the immune system, the nature of intelligence, the origin of life--require deep understanding of complex systems.« less

1991 Annual report on scientific programs: A broad research program on the sciences of complexity

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

Not Available

1991-12-31

1991 was continued rapid growth for the Santa Fe Institute (SFI) as it broadened its interdisciplinary research into the organization, evolution and operation of complex systems and sought deeply the principles underlying their dynamic behavior. Research on complex systems--the focus of work at SFI--involves an extraordinary range of topics normally studied in seemingly disparate fields. Natural systems displaying complex behavior range upwards from proteins and DNA through cells and evolutionary systems to human societies. Research models exhibiting complexity include nonlinear equations, spin glasses, cellular automata, genetic algorithms, classifier systems, and an array of other computational models. Some of the majormore » questions facing complex systems researchers are: (1) explaining how complexity arises from the nonlinear interaction of simples components, (2) describing the mechanisms underlying high-level aggregate behavior of complex systems (such as the overt behavior of an organism, the flow of energy in an ecology, the GNP of an economy), and (3) creating a theoretical framework to enable predictions about the likely behavior of such systems in various conditions. The importance of understanding such systems in enormous: many of the most serious challenges facing humanity--e.g., environmental sustainability, economic stability, the control of disease--as well as many of the hardest scientific questions--e.g., protein folding, the distinction between self and non-self in the immune system, the nature of intelligence, the origin of life--require deep understanding of complex systems.« less

COREBA (cognition-oriented emergent behavior architecture)

NASA Astrophysics Data System (ADS)

Kwak, S. David

2000-06-01

Currently, many behavior implementation technologies are available for modeling human behaviors in Department of Defense (DOD) computerized systems. However, it is commonly known that any single currently adopted behavior implementation technology is not so capable of fully representing complex and dynamic human decision-making and cognition behaviors. The author views that the current situation can be greatly improved if multiple technologies are integrated within a well designed overarching architecture that amplifies the merits of each of the participating technologies while suppressing the limitations that are inherent with each of the technologies. COREBA uses an overarching behavior integration architecture that makes the multiple implementation technologies cooperate in a homogeneous environment while collectively transcending the limitations associated with the individual implementation technologies. Specifically, COREBA synergistically integrates Artificial Intelligence and Complex Adaptive System under Rational Behavior Model multi-level multi- paradigm behavior architecture. This paper will describe applicability of COREBA in DOD domain, behavioral capabilities and characteristics of COREBA and how the COREBA architectural integrates various behavior implementation technologies.

Saving Human Lives: What Complexity Science and Information Systems can Contribute

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Brockmann, Dirk; Chadefaux, Thomas; Donnay, Karsten; Blanke, Ulf; Woolley-Meza, Olivia; Moussaid, Mehdi; Johansson, Anders; Krause, Jens; Schutte, Sebastian; Perc, Matjaž

2015-02-01

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.

Saving Human Lives: What Complexity Science and Information Systems can Contribute.

PubMed

Helbing, Dirk; Brockmann, Dirk; Chadefaux, Thomas; Donnay, Karsten; Blanke, Ulf; Woolley-Meza, Olivia; Moussaid, Mehdi; Johansson, Anders; Krause, Jens; Schutte, Sebastian; Perc, Matjaž

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.

A Long-Lived Oscillatory Space-Time Correlation Function of Two Dimensional Colloids

NASA Astrophysics Data System (ADS)

Kim, Jeongmin; Sung, Bong June

2014-03-01

Diffusion of a colloid in solution has drawn significant attention for a century. A well-known behavior of the colloid is called Brownian motion : the particle displacement probability distribution (PDPD) is Gaussian and the mean-square displacement (MSD) is linear with time. However, recent simulation and experimental studies revealed the heterogeneous dynamics of colloids near glass transitions or in complex environments such as entangled actin, PDPD exhibited the exponential tail at a large length instead of being Gaussian at all length scales. More interestingly, PDPD is still exponential even when MSD was still linear with time. It requires a refreshing insight on the colloidal diffusion in the complex environments. In this work, we study heterogeneous dynamics of two dimensional (2D) colloids using molecular dynamics simulations. Unlike in three dimensions, 2D solids do not follow the Lindemann melting criterion. The Kosterlitz-Thouless-Halperin-Nelson-Young theory predicts two-step phase transitions with an intermediate phase, the hexatic phase between isotropic liquids and solids. Near solid-hexatic transition, PDPD shows interesting oscillatory behavior between a central Gaussian part and an exponential tail. Until 12 times longer than translational relaxation time, the oscillatory behavior still persists even after entering the Fickian regime. We also show that multi-layered kinetic clusters account for heterogeneous dynamics of 2D colloids with the long-lived anomalous oscillatory PDPD.

Conceptual shifts needed to understand the dynamic interactions of genes, environment, epigenetics, social processes, and behavioral choices.

PubMed

Jackson, Fatimah L C; Niculescu, Mihai D; Jackson, Robert T

2013-10-01

Social and behavioral research in public health is often intimately tied to profound, but frequently neglected, biological influences from underlying genetic, environmental, and epigenetic events. The dynamic interplay between the life, social, and behavioral sciences often remains underappreciated and underutilized in addressing complex diseases and disorders and in developing effective remediation strategies. Using a case-study format, we present examples as to how the inclusion of genetic, environmental, and epigenetic data can augment social and behavioral health research by expanding the parameters of such studies, adding specificity to phenotypic assessments, and providing additional internal control in comparative studies. We highlight the important roles of gene-environment interactions and epigenetics as sources of phenotypic change and as a bridge between the life and social and behavioral sciences in the development of robust interdisciplinary analyses.

MDcons: Intermolecular contact maps as a tool to analyze the interface of protein complexes from molecular dynamics trajectories

PubMed Central

2014-01-01

Background Molecular Dynamics (MD) simulations of protein complexes suffer from the lack of specific tools in the analysis step. Analyses of MD trajectories of protein complexes indeed generally rely on classical measures, such as the RMSD, RMSF and gyration radius, conceived and developed for single macromolecules. As a matter of fact, instead, researchers engaged in simulating the dynamics of a protein complex are mainly interested in characterizing the conservation/variation of its biological interface. Results On these bases, herein we propose a novel approach to the analysis of MD trajectories or other conformational ensembles of protein complexes, MDcons, which uses the conservation of inter-residue contacts at the interface as a measure of the similarity between different snapshots. A "consensus contact map" is also provided, where the conservation of the different contacts is drawn in a grey scale. Finally, the interface area of the complex is monitored during the simulations. To show its utility, we used this novel approach to study two protein-protein complexes with interfaces of comparable size and both dominated by hydrophilic interactions, but having binding affinities at the extremes of the experimental range. MDcons is demonstrated to be extremely useful to analyse the MD trajectories of the investigated complexes, adding important insight into the dynamic behavior of their biological interface. Conclusions MDcons specifically allows the user to highlight and characterize the dynamics of the interface in protein complexes and can thus be used as a complementary tool for the analysis of MD simulations of both experimental and predicted structures of protein complexes. PMID:25077693

MDcons: Intermolecular contact maps as a tool to analyze the interface of protein complexes from molecular dynamics trajectories.

PubMed

Abdel-Azeim, Safwat; Chermak, Edrisse; Vangone, Anna; Oliva, Romina; Cavallo, Luigi

2014-01-01

Molecular Dynamics (MD) simulations of protein complexes suffer from the lack of specific tools in the analysis step. Analyses of MD trajectories of protein complexes indeed generally rely on classical measures, such as the RMSD, RMSF and gyration radius, conceived and developed for single macromolecules. As a matter of fact, instead, researchers engaged in simulating the dynamics of a protein complex are mainly interested in characterizing the conservation/variation of its biological interface. On these bases, herein we propose a novel approach to the analysis of MD trajectories or other conformational ensembles of protein complexes, MDcons, which uses the conservation of inter-residue contacts at the interface as a measure of the similarity between different snapshots. A "consensus contact map" is also provided, where the conservation of the different contacts is drawn in a grey scale. Finally, the interface area of the complex is monitored during the simulations. To show its utility, we used this novel approach to study two protein-protein complexes with interfaces of comparable size and both dominated by hydrophilic interactions, but having binding affinities at the extremes of the experimental range. MDcons is demonstrated to be extremely useful to analyse the MD trajectories of the investigated complexes, adding important insight into the dynamic behavior of their biological interface. MDcons specifically allows the user to highlight and characterize the dynamics of the interface in protein complexes and can thus be used as a complementary tool for the analysis of MD simulations of both experimental and predicted structures of protein complexes.

Phase transitions in the first-passage time of scale-invariant correlated processes

PubMed Central

Carretero-Campos, Concepción; Bernaola-Galván, Pedro; Ch. Ivanov, Plamen

2012-01-01

A key quantity describing the dynamics of complex systems is the first-passage time (FPT). The statistical properties of FPT depend on the specifics of the underlying system dynamics. We present a unified approach to account for the diversity of statistical behaviors of FPT observed in real-world systems. We find three distinct regimes, separated by two transition points, with fundamentally different behavior for FPT as a function of increasing strength of the correlations in the system dynamics: stretched exponential, power-law, and saturation regimes. In the saturation regime, the average length of FPT diverges proportionally to the system size, with important implications for understanding electronic delocalization in one-dimensional correlated-disordered systems. PMID:22400544

Living in a network of scaling cities and finite resources.

PubMed

Qubbaj, Murad R; Shutters, Shade T; Muneepeerakul, Rachata

2015-02-01

Many urban phenomena exhibit remarkable regularity in the form of nonlinear scaling behaviors, but their implications on a system of networked cities has never been investigated. Such knowledge is crucial for our ability to harness the complexity of urban processes to further sustainability science. In this paper, we develop a dynamical modeling framework that embeds population-resource dynamics-a generalized Lotka-Volterra system with modifications to incorporate the urban scaling behaviors-in complex networks in which cities may be linked to the resources of other cities and people may migrate in pursuit of higher welfare. We find that isolated cities (i.e., no migration) are susceptible to collapse if they do not have access to adequate resources. Links to other cities may help cities that would otherwise collapse due to insufficient resources. The effects of inter-city links, however, can vary due to the interplay between the nonlinear scaling behaviors and network structure. The long-term population level of a city is, in many settings, largely a function of the city's access to resources over which the city has little or no competition. Nonetheless, careful investigation of dynamics is required to gain mechanistic understanding of a particular city-resource network because cities and resources may collapse and the scaling behaviors may influence the effects of inter-city links, thereby distorting what topological metrics really measure.

Cellulose synthase complexes display distinct dynamic behaviors during xylem transdifferentiation.

PubMed

Watanabe, Yoichiro; Schneider, Rene; Barkwill, Sarah; Gonzales-Vigil, Eliana; Hill, Joseph L; Samuels, A Lacey; Persson, Staffan; Mansfield, Shawn D

2018-06-05

In plants, plasma membrane-embedded CELLULOSE SYNTHASE (CESA) enzyme complexes deposit cellulose polymers into the developing cell wall. Cellulose synthesis requires two different sets of CESA complexes that are active during cell expansion and secondary cell wall thickening, respectively. Hence, developing xylem cells, which first undergo cell expansion and subsequently deposit thick secondary walls, need to completely reorganize their CESA complexes from primary wall- to secondary wall-specific CESAs. Using live-cell imaging, we analyzed the principles underlying this remodeling. At the onset of secondary wall synthesis, the primary wall CESAs ceased to be delivered to the plasma membrane and were gradually removed from both the plasma membrane and the Golgi. For a brief transition period, both primary wall- and secondary wall-specific CESAs coexisted in banded domains of the plasma membrane where secondary wall synthesis is concentrated. During this transition, primary and secondary wall CESAs displayed discrete dynamic behaviors and sensitivities to the inhibitor isoxaben. As secondary wall-specific CESAs were delivered and inserted into the plasma membrane, the primary wall CESAs became concentrated in prevacuolar compartments and lytic vacuoles. This adjustment in localization between the two CESAs was accompanied by concurrent decreased primary wall CESA and increased secondary wall CESA protein abundance. Our data reveal distinct and dynamic subcellular trafficking patterns that underpin the remodeling of the cellulose biosynthetic machinery, resulting in the removal and degradation of the primary wall CESA complex with concurrent production and recycling of the secondary wall CESAs. Copyright © 2018 the Author(s). Published by PNAS.

Chemotaxis in densely populated tissue determines germinal center anatomy and cell motility: a new paradigm for the development of complex tissues.

PubMed

Hawkins, Jared B; Jones, Mark T; Plassmann, Paul E; Thorley-Lawson, David A

2011-01-01

Germinal centers (GCs) are complex dynamic structures that form within lymph nodes as an essential process in the humoral immune response. They represent a paradigm for studying the regulation of cell movement in the development of complex anatomical structures. We have developed a simulation of a modified cyclic re-entry model of GC dynamics which successfully employs chemotaxis to recapitulate the anatomy of the primary follicle and the development of a mature GC, including correctly structured mantle, dark and light zones. We then show that correct single cell movement dynamics (including persistent random walk and inter-zonal crossing) arise from this simulation as purely emergent properties. The major insight of our study is that chemotaxis can only achieve this when constrained by the known biological properties that cells are incompressible, exist in a densely packed environment, and must therefore compete for space. It is this interplay of chemotaxis and competition for limited space that generates all the complex and biologically accurate behaviors described here. Thus, from a single simple mechanism that is well documented in the biological literature, we can explain both higher level structure and single cell movement behaviors. To our knowledge this is the first GC model that is able to recapitulate both correctly detailed anatomy and single cell movement. This mechanism may have wide application for modeling other biological systems where cells undergo complex patterns of movement to produce defined anatomical structures with sharp tissue boundaries.

Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification.

PubMed

Turner, Alexander P; Caves, Leo S D; Stepney, Susan; Tyrrell, Andy M; Lones, Michael A

2017-01-01

This paper describes the artificial epigenetic network, a recurrent connectionist architecture that is able to dynamically modify its topology in order to automatically decompose and solve dynamical problems. The approach is motivated by the behavior of gene regulatory networks, particularly the epigenetic process of chromatin remodeling that leads to topological change and which underlies the differentiation of cells within complex biological organisms. We expected this approach to be useful in situations where there is a need to switch between different dynamical behaviors, and do so in a sensitive and robust manner in the absence of a priori information about problem structure. This hypothesis was tested using a series of dynamical control tasks, each requiring solutions that could express different dynamical behaviors at different stages within the task. In each case, the addition of topological self-modification was shown to improve the performance and robustness of controllers. We believe this is due to the ability of topological changes to stabilize attractors, promoting stability within a dynamical regime while allowing rapid switching between different regimes. Post hoc analysis of the controllers also demonstrated how the partitioning of the networks could provide new insights into problem structure.

Dynamic stall characterization using modal analysis of phase-averaged pressure distributions

NASA Astrophysics Data System (ADS)

Harms, Tanner; Nikoueeyan, Pourya; Naughton, Jonathan

2017-11-01

Dynamic stall characterization by means of surface pressure measurements can simplify the time and cost associated with experimental investigation of unsteady airfoil aerodynamics. A unique test capability has been developed at University of Wyoming over the past few years that allows for time and cost efficient measurement of dynamic stall. A variety of rotorcraft and wind turbine airfoils have been tested under a variety of pitch oscillation conditions resulting in a range of dynamic stall behavior. Formation, development and separation of different flow structures are responsible for the complex aerodynamic loading behavior experienced during dynamic stall. These structures have unique signatures on the pressure distribution over the airfoil. This work investigates the statistical behavior of phase-averaged pressure distribution for different types of dynamic stall by means of modal analysis. The use of different modes to identify specific flow structures is being investigated. The use of these modes for different types of dynamic stall can provide a new approach for understanding and categorizing these flows. This work uses airfoil data acquired under Army contract W911W60160C-0021, DOE Grant DE-SC0001261, and a gift from BP Alternative Energy North America, Inc.

Computation of the spectrum of spatial Lyapunov exponents for the spatially extended beam-plasma systems and electron-wave devices

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

Hramov, Alexander E.; Saratov State Technical University, Politechnicheskaja str., 77, Saratov 410054; Koronovskii, Alexey A.

2012-08-15

The spectrum of Lyapunov exponents is powerful tool for the analysis of the complex system dynamics. In the general framework of nonlinear dynamics, a number of the numerical techniques have been developed to obtain the spectrum of Lyapunov exponents for the complex temporal behavior of the systems with a few degree of freedom. Unfortunately, these methods cannot be applied directly to analysis of complex spatio-temporal dynamics of plasma devices which are characterized by the infinite phase space, since they are the spatially extended active media. In the present paper, we propose the method for the calculation of the spectrum ofmore » the spatial Lyapunov exponents (SLEs) for the spatially extended beam-plasma systems. The calculation technique is applied to the analysis of chaotic spatio-temporal oscillations in three different beam-plasma model: (1) simple plasma Pierce diode, (2) coupled Pierce diodes, and (3) electron-wave system with backward electromagnetic wave. We find an excellent agreement between the system dynamics and the behavior of the spectrum of the spatial Lyapunov exponents. Along with the proposed method, the possible problems of SLEs calculation are also discussed. It is shown that for the wide class of the spatially extended systems, the set of quantities included in the system state for SLEs calculation can be reduced using the appropriate feature of the plasma systems.« less

On the dynamic behavior of three readily available soft tissue simulants

NASA Astrophysics Data System (ADS)

Appleby-Thomas, G. J.; Hazell, P. J.; Wilgeroth, J. M.; Shepherd, C. J.; Wood, D. C.; Roberts, A.

2011-04-01

Plate-impact experiments have been employed to investigate the dynamic response of three readily available tissue simulants for ballistic purposes: gelatin, ballistic soap (both subdermal tissue simulants), and lard (adipose layers). All three materials exhibited linear Hugoniot equations-of-state in the US-uP plane. While gelatin behaved hydrodynamically under shock, soap and lard appeared to strengthen under increased loading. Interestingly, the simulants under test appeared to strengthen in a material-independent manner on shock arrival (tentatively attributed to a rearrangement of the amorphous molecular chains under loading). However, material-specific behavior was apparent behind the shock. This behavior appeared to correlate with microstructural complexity, suggesting a steric hindrance effect.

Scale Invariance in Lateral Head Scans During Spatial Exploration.

PubMed

Yadav, Chetan K; Doreswamy, Yoganarasimha

2017-04-14

Universality connects various natural phenomena through physical principles governing their dynamics, and has provided broadly accepted answers to many complex questions, including information processing in neuronal systems. However, its significance in behavioral systems is still elusive. Lateral head scanning (LHS) behavior in rodents might contribute to spatial navigation by actively managing (optimizing) the available sensory information. Our findings of scale invariant distributions in LHS lifetimes, interevent intervals and event magnitudes, provide evidence for the first time that the optimization takes place at a critical point in LHS dynamics. We propose that the LHS behavior is responsible for preprocessing of the spatial information content, critical for subsequent foolproof encoding by the respective downstream neural networks.

Scale Invariance in Lateral Head Scans During Spatial Exploration

NASA Astrophysics Data System (ADS)

Yadav, Chetan K.; Doreswamy, Yoganarasimha

2017-04-01

Universality connects various natural phenomena through physical principles governing their dynamics, and has provided broadly accepted answers to many complex questions, including information processing in neuronal systems. However, its significance in behavioral systems is still elusive. Lateral head scanning (LHS) behavior in rodents might contribute to spatial navigation by actively managing (optimizing) the available sensory information. Our findings of scale invariant distributions in LHS lifetimes, interevent intervals and event magnitudes, provide evidence for the first time that the optimization takes place at a critical point in LHS dynamics. We propose that the LHS behavior is responsible for preprocessing of the spatial information content, critical for subsequent foolproof encoding by the respective downstream neural networks.

Population-reaction model and microbial experimental ecosystems for understanding hierarchical dynamics of ecosystems.

PubMed

Hosoda, Kazufumi; Tsuda, Soichiro; Kadowaki, Kohmei; Nakamura, Yutaka; Nakano, Tadashi; Ishii, Kojiro

2016-02-01

Understanding ecosystem dynamics is crucial as contemporary human societies face ecosystem degradation. One of the challenges that needs to be recognized is the complex hierarchical dynamics. Conventional dynamic models in ecology often represent only the population level and have yet to include the dynamics of the sub-organism level, which makes an ecosystem a complex adaptive system that shows characteristic behaviors such as resilience and regime shifts. The neglect of the sub-organism level in the conventional dynamic models would be because integrating multiple hierarchical levels makes the models unnecessarily complex unless supporting experimental data are present. Now that large amounts of molecular and ecological data are increasingly accessible in microbial experimental ecosystems, it is worthwhile to tackle the questions of their complex hierarchical dynamics. Here, we propose an approach that combines microbial experimental ecosystems and a hierarchical dynamic model named population-reaction model. We present a simple microbial experimental ecosystem as an example and show how the system can be analyzed by a population-reaction model. We also show that population-reaction models can be applied to various ecological concepts, such as predator-prey interactions, climate change, evolution, and stability of diversity. Our approach will reveal a path to the general understanding of various ecosystems and organisms. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Common EEG features for behavioral estimation in disparate, real-world tasks.

PubMed

Touryan, Jon; Lance, Brent J; Kerick, Scott E; Ries, Anthony J; McDowell, Kaleb

2016-02-01

In this study we explored the potential for capturing the behavioral dynamics observed in real-world tasks from concurrent measures of EEG. In doing so, we sought to develop models of behavior that would enable the identification of common cross-participant and cross-task EEG features. To accomplish this we had participants perform both simulated driving and guard duty tasks while we recorded their EEG. For each participant we developed models to estimate their behavioral performance during both tasks. Sequential forward floating selection was used to identify the montage of independent components for each model. Linear regression was then used on the combined power spectra from these independent components to generate a continuous estimate of behavior. Our results show that oscillatory processes, evidenced in EEG, can be used to successfully capture slow fluctuations in behavior in complex, multi-faceted tasks. The average correlation coefficients between the actual and estimated behavior was 0.548 ± 0.117 and 0.701 ± 0.154 for the driving and guard duty tasks respectively. Interestingly, through a simple clustering approach we were able to identify a number of common components, both neural and eye-movement related, across participants and tasks. We used these component clusters to quantify the relative influence of common versus participant-specific features in the models of behavior. These findings illustrate the potential for estimating complex behavioral dynamics from concurrent measures from EEG using a finite library of universal features. Published by Elsevier B.V.

Aging, memory, and nonhierarchical energy landscape of spin jam

PubMed Central

Samarakoon, Anjana; Sato, Taku J.; Chen, Tianran; Chern, Gai-Wei; Yang, Junjie; Klich, Israel; Sinclair, Ryan; Zhou, Haidong; Lee, Seung-Hun

2016-01-01

The notion of complex energy landscape underpins the intriguing dynamical behaviors in many complex systems ranging from polymers, to brain activity, to social networks and glass transitions. The spin glass state found in dilute magnetic alloys has been an exceptionally convenient laboratory frame for studying complex dynamics resulting from a hierarchical energy landscape with rugged funnels. Here, we show, by a bulk susceptibility and Monte Carlo simulation study, that densely populated frustrated magnets in a spin jam state exhibit much weaker memory effects than spin glasses, and the characteristic properties can be reproduced by a nonhierarchical landscape with a wide and nearly flat but rough bottom. Our results illustrate that the memory effects can be used to probe different slow dynamics of glassy materials, hence opening a window to explore their distinct energy landscapes. PMID:27698141

Structurally Dynamic Spin Market Networks

NASA Astrophysics Data System (ADS)

Horváth, Denis; Kuscsik, Zoltán

The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.

Persistent topological features of dynamical systems

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

Maletić, Slobodan, E-mail: slobodan@hitsz.edu.cn; Institute of Nuclear Sciences Vinča, University of Belgrade, Belgrade; Zhao, Yi, E-mail: zhao.yi@hitsz.edu.cn

Inspired by an early work of Muldoon et al., Physica D 65, 1–16 (1993), we present a general method for constructing simplicial complex from observed time series of dynamical systems based on the delay coordinate reconstruction procedure. The obtained simplicial complex preserves all pertinent topological features of the reconstructed phase space, and it may be analyzed from topological, combinatorial, and algebraic aspects. In focus of this study is the computation of homology of the invariant set of some well known dynamical systems that display chaotic behavior. Persistent homology of simplicial complex and its relationship with the embedding dimensions are examinedmore » by studying the lifetime of topological features and topological noise. The consistency of topological properties for different dynamic regimes and embedding dimensions is examined. The obtained results shed new light on the topological properties of the reconstructed phase space and open up new possibilities for application of advanced topological methods. The method presented here may be used as a generic method for constructing simplicial complex from a scalar time series that has a number of advantages compared to the mapping of the same time series to a complex network.« less

Complex motion of a vehicle through a series of signals controlled by power-law phase

NASA Astrophysics Data System (ADS)

Nagatani, Takashi

2017-07-01

We study the dynamic motion of a vehicle moving through the series of traffic signals controlled by the position-dependent phase of power law. All signals are controlled by both cycle time and position-dependent phase. The dynamic model of the vehicular motion is described in terms of the nonlinear map. The vehicular motion varies in a complex manner by varying cycle time for various values of the power of the position-dependent phase. The vehicle displays the periodic motion with a long cycle for the integer power of the phase, while the vehicular motion exhibits the very complex behavior for the non-integer power of the phase.

New approaches in agent-based modeling of complex financial systems

NASA Astrophysics Data System (ADS)

Chen, Ting-Ting; Zheng, Bo; Li, Yan; Jiang, Xiong-Fei

2017-12-01

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents' behaviors with heterogeneous personal preferences and interactions, these models are successful in explaining the microscopic origination of the temporal and spatial correlations of financial markets. We then present a novel paradigm combining big-data analysis with agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces and develop an agent-based model to simulate the dynamic behaviors of complex financial systems.

Shape memory polymer network with thermally distinct elasticity and plasticity.

PubMed

Zhao, Qian; Zou, Weike; Luo, Yingwu; Xie, Tao

2016-01-01

Stimuli-responsive materials with sophisticated yet controllable shape-changing behaviors are highly desirable for real-world device applications. Among various shape-changing materials, the elastic nature of shape memory polymers allows fixation of temporary shapes that can recover on demand, whereas polymers with exchangeable bonds can undergo permanent shape change via plasticity. We integrate the elasticity and plasticity into a single polymer network. Rational molecular design allows these two opposite behaviors to be realized at different temperature ranges without any overlap. By exploring the cumulative nature of the plasticity, we demonstrate easy manipulation of highly complex shapes that is otherwise extremely challenging. The dynamic shape-changing behavior paves a new way for fabricating geometrically complex multifunctional devices.

Contemporary practice patterns of dynamic psychiatrists--survey results.

PubMed

Alfonso, César A; Olarte, Silvia W

2011-01-01

The authors examine the practice characteristics of dynamic psychiatrists, including the combined use of medication and psychotherapy, and adherence by self-report to psychodynamic, supportive, and cognitive behavioral therapy theoretical principles and techniques. Survey of 555 members of the American Academy of Psychoanalysis and Dynamic Psychiatry conducted in 2009. 24.1% response rate; 75% of respondents were between 61 and 80 years old, 61% had over 30 years of experience; 89% have a private practice but work on an average of 1.6 settings; 39% teach. Most respondents treat patients with complex comorbidities; 92.6% prescribe psychotropic medication. The preferred mode of practice is individual psychotherapy and the preferred frequency once a week; 94.5% of sessions are 45-60 minutes. Using Plakun's Y-model framework, psychoanalysts and dynamic psychiatrist subgroups equally support all core psychotherapy and psychodynamic features, with lesser emphasis but substantial endorsement of cognitive-behavioral psychotherapy features. Dynamic psychiatrists are committed to see most patients once a week for 45-60 minute sessions and use a variety of conceptual frameworks to guide their treatment plans. They endorse supportive and psychodynamic practice elements more than cognitive-behavioral principles.

Using nonlinear methods to quantify changes in infant limb movements and vocalizations.

PubMed

Abney, Drew H; Warlaumont, Anne S; Haussman, Anna; Ross, Jessica M; Wallot, Sebastian

2014-01-01

The pairing of dynamical systems theory and complexity science brings novel concepts and methods to the study of infant motor development. Accordingly, this longitudinal case study presents a new approach to characterizing the dynamics of infant limb and vocalization behaviors. A single infant's vocalizations and limb movements were recorded from 51-days to 305-days of age. On each recording day, accelerometers were placed on all four of the infant's limbs and an audio recorder was worn on the child's chest. Using nonlinear time series analysis methods, such as recurrence quantification analysis and Allan factor, we quantified changes in the stability and multiscale properties of the infant's behaviors across age as well as how these dynamics relate across modalities and effectors. We observed that particular changes in these dynamics preceded or coincided with the onset of various developmental milestones. For example, the largest changes in vocalization dynamics preceded the onset of canonical babbling. The results show that nonlinear analyses can help to understand the functional co-development of different aspects of infant behavior.

Using nonlinear methods to quantify changes in infant limb movements and vocalizations

PubMed Central

Abney, Drew H.; Warlaumont, Anne S.; Haussman, Anna; Ross, Jessica M.; Wallot, Sebastian

2014-01-01

The pairing of dynamical systems theory and complexity science brings novel concepts and methods to the study of infant motor development. Accordingly, this longitudinal case study presents a new approach to characterizing the dynamics of infant limb and vocalization behaviors. A single infant's vocalizations and limb movements were recorded from 51-days to 305-days of age. On each recording day, accelerometers were placed on all four of the infant's limbs and an audio recorder was worn on the child's chest. Using nonlinear time series analysis methods, such as recurrence quantification analysis and Allan factor, we quantified changes in the stability and multiscale properties of the infant's behaviors across age as well as how these dynamics relate across modalities and effectors. We observed that particular changes in these dynamics preceded or coincided with the onset of various developmental milestones. For example, the largest changes in vocalization dynamics preceded the onset of canonical babbling. The results show that nonlinear analyses can help to understand the functional co-development of different aspects of infant behavior. PMID:25161629

Joint action syntax in Japanese martial arts.

PubMed

Yamamoto, Yuji; Yokoyama, Keiko; Okumura, Motoki; Kijima, Akifumi; Kadota, Koji; Gohara, Kazutoshi

2013-01-01

Participation in interpersonal competitions, such as fencing or Japanese martial arts, requires players to make instantaneous decisions and execute appropriate motor behaviors in response to various situations. Such actions can be understood as complex phenomena emerging from simple principles. We examined the intentional switching dynamics associated with continuous movement during interpersonal competition in terms of their emergence from a simple syntax. Linear functions on return maps identified two attractors as well as the transitions between them. The effects of skill differences were evident in the second- and third-order state-transition diagrams for these two attractors. Our results suggest that abrupt switching between attractors is related to the diverse continuous movements resulting from quick responses to sudden changes in the environment. This abrupt-switching-quick-response behavior is characterized by a joint action syntax. The resulting hybrid dynamical system is composed of a higher module with discrete dynamics and a lower module with continuous dynamics. Our results suggest that intelligent human behavior and robust autonomy in real-life scenarios are based on this hybrid dynamical system, which connects interpersonal coordination and competition.

Differential Dynamic Engagement within 24 SH3 Domain: Peptide Complexes Revealed by Co-Linear Chemical Shift Perturbation Analysis

PubMed Central

Stollar, Elliott J.; Lin, Hong; Davidson, Alan R.; Forman-Kay, Julie D.

2012-01-01

There is increasing evidence for the functional importance of multiple dynamically populated states within single proteins. However, peptide binding by protein-protein interaction domains, such as the SH3 domain, has generally been considered to involve the full engagement of peptide to the binding surface with minimal dynamics and simple methods to determine dynamics at the binding surface for multiple related complexes have not been described. We have used NMR spectroscopy combined with isothermal titration calorimetry to comprehensively examine the extent of engagement to the yeast Abp1p SH3 domain for 24 different peptides. Over one quarter of the domain residues display co-linear chemical shift perturbation (CCSP) behavior, in which the position of a given chemical shift in a complex is co-linear with the same chemical shift in the other complexes, providing evidence that each complex exists as a unique dynamic rapidly inter-converting ensemble. The extent the specificity determining sub-surface of AbpSH3 is engaged as judged by CCSP analysis correlates with structural and thermodynamic measurements as well as with functional data, revealing the basis for significant structural and functional diversity amongst the related complexes. Thus, CCSP analysis can distinguish peptide complexes that may appear identical in terms of general structure and percent peptide occupancy but have significant local binding differences across the interface, affecting their ability to transmit conformational change across the domain and resulting in functional differences. PMID:23251481

Moving Word Learning to a Novel Space: A Dynamic Systems View of Referent Selection and Retention

ERIC Educational Resources Information Center

Samuelson, Larissa K.; Kucker, Sarah C.; Spencer, John P.

2017-01-01

Theories of cognitive development must address both the issue of how children bring their knowledge to bear on behavior in-the-moment, and how knowledge changes over time. We argue that seeking answers to these questions requires an appreciation of the dynamic nature of the developing system in its full, reciprocal complexity. We illustrate this…

Speculative behavior and asset price dynamics.

PubMed

Westerhoff, Frank

2003-07-01

This paper deals with speculative trading. Guided by empirical observations, a nonlinear deterministic asset pricing model is developed in which traders repeatedly choose between technical and fundamental analysis to determine their orders. The interaction between the trading rules produces complex dynamics. The model endogenously replicates the stylized facts of excess volatility, high trading volumes, shifts in the level of asset prices, and volatility clustering.

Chaos as a psychological construct: historical roots, principal findings, and current growth directions.

PubMed

Guastello, Stephen J

2009-07-01

The landmarks in the use of chaos and related constructs in psychology were entwined with the growing use of other nonlinear dynamical constructs, especially catastrophes and self-organization. The growth in substantive applications of chaos in psychology is partially related to the development of methodologies that work within the constraints of psychological data. The psychological literature includes rigorous theory with testable propositions, lighter-weight metaphorical uses of the construct, and colloquial uses of "chaos" with no particular theoretical intent. The current state of the chaos construct and supporting empirical research in psychological theory is summarized in neuroscience, psychophysics, psychomotor skill and other learning phenomena, clinical and abnormal psychology, and group dynamics and organizational behavior. Trends indicate that human systems do not remain chaotic indefinitely; they eventually self-organize, and the concept of the complex adaptive system has become prominent. Chaotic turbulence is generally higher in healthy systems compared to unhealthy systems, although opposite appears true in mood disorders. Group dynamics research shows trends consistent with the complex adaptive system, whereas organizational behavior lags behind in empirical studies relative to the quantity of its theory. Future directions for research involving the chaos construct and other nonlinear dynamics are outlined.

Combining a Toggle Switch and a Repressilator within the AC-DC Circuit Generates Distinct Dynamical Behaviors.

PubMed

Perez-Carrasco, Ruben; Barnes, Chris P; Schaerli, Yolanda; Isalan, Mark; Briscoe, James; Page, Karen M

2018-04-25

Although the structure of a genetically encoded regulatory circuit is an important determinant of its function, the relationship between circuit topology and the dynamical behaviors it can exhibit is not well understood. Here, we explore the range of behaviors available to the AC-DC circuit. This circuit consists of three genes connected as a combination of a toggle switch and a repressilator. Using dynamical systems theory, we show that the AC-DC circuit exhibits both oscillations and bistability within the same region of parameter space; this generates emergent behaviors not available to either the toggle switch or the repressilator alone. The AC-DC circuit can switch on oscillations via two distinct mechanisms, one of which induces coherence into ensembles of oscillators. In addition, we show that in the presence of noise, the AC-DC circuit can behave as an excitable system capable of spatial signal propagation or coherence resonance. Together, these results demonstrate how combinations of simple motifs can exhibit multiple complex behaviors. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Sharper Graph-Theoretical Conditions for the Stabilization of Complex Reaction Networks

PubMed Central

Knight, Daniel; Shinar, Guy; Feinberg, Martin

2015-01-01

Across the landscape of all possible chemical reaction networks there is a surprising degree of stable behavior, despite what might be substantial complexity and nonlinearity in the governing differential equations. At the same time there are reaction networks, in particular those that arise in biology, for which richer behavior is exhibited. Thus, it is of interest to understand network-structural features whose presence enforces dull, stable behavior and whose absence permits the dynamical richness that might be necessary for life. We present conditions on a network’s Species-Reaction Graph that ensure a high degree of stable behavior, so long as the kinetic rate functions satisfy certain weak and natural constraints. These graph-theoretical conditions are considerably more incisive than those reported earlier. PMID:25600138

Dynamic-compliance and viscosity of PET and PEN

NASA Astrophysics Data System (ADS)

Weick, Brian L.

2016-05-01

Complex dynamic-compliance and in-phase dynamic-viscosity data are presented and analyzed for PET and PEN advanced polyester substrates used for magnetic tapes. Frequency-temperature superposition is used to predict long-term behavior. Temperature and frequency ranges for the primary glass transition and secondary transitions are discussed and compared for PET and PEN. Shift factors from frequency-temperature superposition are used to determine activation energies for the transitions, and WLF parameters are determined for the polyester substrates.

Dynamic-compliance and viscosity of PET and PEN

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

Weick, Brian L.

Complex dynamic-compliance and in-phase dynamic-viscosity data are presented and analyzed for PET and PEN advanced polyester substrates used for magnetic tapes. Frequency-temperature superposition is used to predict long-term behavior. Temperature and frequency ranges for the primary glass transition and secondary transitions are discussed and compared for PET and PEN. Shift factors from frequency-temperature superposition are used to determine activation energies for the transitions, and WLF parameters are determined for the polyester substrates.

Chapter 11 - Post-hurricane fuel dynamics and implications for fire behavior (Project SO-EM-F-12-01)

Treesearch

Shanyue Guan; G. Geoff. Wang

2018-01-01

Hurricanes have long been a powerful and recurring disturbance in many coastal forest ecosystems. Intense hurricanes often produce a large amount of dead fuels in their affected forests. How the post-hurricane fuel complex changes with time, due todecomposition and management such as salvage, and its implications for fire behavior remain largely unknown....

The mechanisms of plastic strain accommodation and post critical behavior of heterogeneous reactive composites subject to dynamic loading

NASA Astrophysics Data System (ADS)

Olney, Karl L.

The dynamic behavior of granular/porous and laminate reactive materials is of interest due to their practical applications; reactive structural components, reactive fragments, etc. The mesostructural properties control meso- and macro-scale dynamic behavior of these heterogeneous composites including the behavior during the post-critical stage of deformation. They heavily influence mechanisms of fragment generation and the in situ development of local hot spots, which act as sites of ignition in these materials. This dissertation concentrates on understanding the mechanisms of plastic strain accommodation in two representative reactive material systems with different heterogeneous mesostructrues: Aluminum-Tungsten granular/porous and Nickel-Aluminum laminate composites. The main focus is on the interpretation of results of the following dynamic experiments conducted at different strain and strain rates: drop weight tests, explosively expanded ring experiments, and explosively collapsed thick walled cylinder experiments. Due to the natural limitations in the evaluation of the mesoscale behavior of these materials experimentally and the large variation in the size scales between the mesostructural level and the sample, it is extremely difficult, if not impossible, to examine the mesoscale behavior in situ. Therefore, numerical simulations of the corresponding experiments are used as the main tool to explore material behavior at the mesoscale. Numerical models were developed to elucidate the mechanisms of plastic strain accommodation and post critical behavior in these heterogeneous composites subjected to dynamic loading. These simulations were able to reproduce the qualitative and quantitative features that were observable in the experiments and provided insight into the evolution of the mechanisms of plastic strain accommodation and post critical behavior in these materials with complex mesotructure. Additionally, these simulations provided a framework to examine the influence of various mesoscale properties such as the bonding of interfaces, the role of material properties, and the influence of mesoscale geometry. The results of this research are helpful in the design of material mesotructures conducive to the desirable behavior under dynamic loading.

Review-Physicochemical hydrodynamics of gas bubbles in two phase electrochemical systems.

PubMed

Taqieddin, Amir; Nazari, Roya; Rajic, Ljiljana; Alshawabkeh, Akram

2017-01-01

Electrochemical systems suffer from poor management of evolving gas bubbles. Improved understanding of bubbles behavior helps to reduce overpotential, save energy and enhance the mass transfer during chemical reactions. This work investigates and reviews the gas bubbles hydrodynamics, behavior, and management in electrochemical cells. Although the rate of bubble growth over the electrode surface is well understood, there is no reliable prediction of bubbles break-off diameter from the electrode surface because of the complexity of bubbles motion near the electrode surface. Particle Image Velocimetry (PIV) and Laser Doppler Anemometry (LDA) are the most common experimental techniques to measure bubble dynamics. Although the PIV is faster than LDA, both techniques are considered expensive and time-consuming. This encourages adapting Computational Fluid Dynamics (CFD) methods as an alternative to study bubbles behavior. However, further development of CFD methods is required to include coalescence and break-up of bubbles for better understanding and accuracy. The disadvantages of CFD methods can be overcome by using hybrid methods. The behavior of bubbles in electrochemical systems is still a complex challenging topic which requires a better understanding of the gas bubbles hydrodynamics and their interactions with the electrode surface and bulk liquid, as well as between the bubbles itself.

Mapping Sub-Second Structure in Mouse Behavior

PubMed Central

Wiltschko, Alexander B.; Johnson, Matthew J.; Iurilli, Giuliano; Peterson, Ralph E.; Katon, Jesse M.; Pashkovski, Stan L.; Abraira, Victoria E.; Adams, Ryan P.; Datta, Sandeep Robert

2015-01-01

Summary Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that three-dimensional (3D) mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously-hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior. PMID:26687221

Fractal analysis on human dynamics of library loans

NASA Astrophysics Data System (ADS)

Fan, Chao; Guo, Jin-Li; Zha, Yi-Long

2012-12-01

In this paper, the fractal characteristic of human behaviors is investigated from the perspective of time series constructed with the amount of library loans. The values of the Hurst exponent and length of non-periodic cycle calculated through rescaled range analysis indicate that the time series of human behaviors and their sub-series are fractal with self-similarity and long-range dependence. Then the time series are converted into complex networks by the visibility algorithm. The topological properties of the networks such as scale-free property and small-world effect imply that there is a close relationship among the numbers of repetitious behaviors performed by people during certain periods of time. Our work implies that there is intrinsic regularity in the human collective repetitious behaviors. The conclusions may be helpful to develop some new approaches to investigate the fractal feature and mechanism of human dynamics, and provide some references for the management and forecast of human collective behaviors.

Social Insects: A Model System for Network Dynamics

NASA Astrophysics Data System (ADS)

Charbonneau, Daniel; Blonder, Benjamin; Dornhaus, Anna

Social insect colonies (ants, bees, wasps, and termites) show sophisticated collective problem-solving in the face of variable constraints. Individuals exchange information and materials such as food. The resulting network structure and dynamics can inform us about the mechanisms by which the insects achieve particular collective behaviors and these can be transposed to man-made and social networks. We discuss how network analysis can answer important questions about social insects, such as how effective task allocation or information flow is realized. We put forward the idea that network analysis methods are under-utilized in social insect research, and that they can provide novel ways to view the complexity of collective behavior, particularly if network dynamics are taken into account. To illustrate this, we present an example of network tasks performed by ant workers, linked by instances of workers switching from one task to another. We show how temporal network analysis can propose and test new hypotheses on mechanisms of task allocation, and how adding temporal elements to static networks can drastically change results. We discuss the benefits of using social insects as models for complex systems in general. There are multiple opportunities emergent technologies and analysis methods in facilitating research on social insect network. The potential for interdisciplinary work could significantly advance diverse fields such as behavioral ecology, computer sciences, and engineering.

Fundamental structures of dynamic social networks.

PubMed

Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune

2016-09-06

Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision.

Fundamental structures of dynamic social networks

PubMed Central

Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune

2016-01-01

Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision. PMID:27555584

Health behavior change in advance care planning: an agent-based model.

PubMed

Ernecoff, Natalie C; Keane, Christopher R; Albert, Steven M

2016-02-29

A practical and ethical challenge in advance care planning research is controlling and intervening on human behavior. Additionally, observing dynamic changes in advance care planning (ACP) behavior proves difficult, though tracking changes over time is important for intervention development. Agent-based modeling (ABM) allows researchers to integrate complex behavioral data about advance care planning behaviors and thought processes into a controlled environment that is more easily alterable and observable. Literature to date has not addressed how best to motivate individuals, increase facilitators and reduce barriers associated with ACP. We aimed to build an ABM that applies the Transtheoretical Model of behavior change to ACP as a health behavior and accurately reflects: 1) the rates at which individuals complete the process, 2) how individuals respond to barriers, facilitators, and behavioral variables, and 3) the interactions between these variables. We developed a dynamic ABM of the ACP decision making process based on the stages of change posited by the Transtheoretical Model. We integrated barriers, facilitators, and other behavioral variables that agents encounter as they move through the process. We successfully incorporated ACP barriers, facilitators, and other behavioral variables into our ABM, forming a plausible representation of ACP behavior and decision-making. The resulting distributions across the stages of change replicated those found in the literature, with approximately half of participants in the action-maintenance stage in both the model and the literature. Our ABM is a useful method for representing dynamic social and experiential influences on the ACP decision making process. This model suggests structural interventions, e.g. increasing access to ACP materials in primary care clinics, in addition to improved methods of data collection for behavioral studies, e.g. incorporating longitudinal data to capture behavioral dynamics.

Dynamic system simulation of small satellite projects

NASA Astrophysics Data System (ADS)

Raif, Matthias; Walter, Ulrich; Bouwmeester, Jasper

2010-11-01

A prerequisite to accomplish a system simulation is to have a system model holding all necessary project information in a centralized repository that can be accessed and edited by all parties involved. At the Institute of Astronautics of the Technische Universitaet Muenchen a modular approach for modeling and dynamic simulation of satellite systems has been developed called dynamic system simulation (DySyS). DySyS is based on the platform independent description language SysML to model a small satellite project with respect to the system composition and dynamic behavior. A library of specific building blocks and possible relations between these blocks have been developed. From this library a system model of the satellite of interest can be created. A mapping into a C++ simulation allows the creation of an executable system model with which simulations are performed to observe the dynamic behavior of the satellite. In this paper DySyS is used to model and simulate the dynamic behavior of small satellites, because small satellite projects can act as a precursor to demonstrate the feasibility of a system model since they are less complex compared to a large scale satellite project.

Revisiting the Quantum Brain Hypothesis: Toward Quantum (Neuro)biology?

PubMed Central

Jedlicka, Peter

2017-01-01

The nervous system is a non-linear dynamical complex system with many feedback loops. A conventional wisdom is that in the brain the quantum fluctuations are self-averaging and thus functionally negligible. However, this intuition might be misleading in the case of non-linear complex systems. Because of an extreme sensitivity to initial conditions, in complex systems the microscopic fluctuations may be amplified and thereby affect the system’s behavior. In this way quantum dynamics might influence neuronal computations. Accumulating evidence in non-neuronal systems indicates that biological evolution is able to exploit quantum stochasticity. The recent rise of quantum biology as an emerging field at the border between quantum physics and the life sciences suggests that quantum events could play a non-trivial role also in neuronal cells. Direct experimental evidence for this is still missing but future research should address the possibility that quantum events contribute to an extremely high complexity, variability and computational power of neuronal dynamics. PMID:29163041

Revisiting the Quantum Brain Hypothesis: Toward Quantum (Neuro)biology?

PubMed

Jedlicka, Peter

2017-01-01

The nervous system is a non-linear dynamical complex system with many feedback loops. A conventional wisdom is that in the brain the quantum fluctuations are self-averaging and thus functionally negligible. However, this intuition might be misleading in the case of non-linear complex systems. Because of an extreme sensitivity to initial conditions, in complex systems the microscopic fluctuations may be amplified and thereby affect the system's behavior. In this way quantum dynamics might influence neuronal computations. Accumulating evidence in non-neuronal systems indicates that biological evolution is able to exploit quantum stochasticity. The recent rise of quantum biology as an emerging field at the border between quantum physics and the life sciences suggests that quantum events could play a non-trivial role also in neuronal cells. Direct experimental evidence for this is still missing but future research should address the possibility that quantum events contribute to an extremely high complexity, variability and computational power of neuronal dynamics.

Size does Matter

NASA Astrophysics Data System (ADS)

Vespignani, Alessandro

From schools of fish and flocks of birds, to digital networks and self-organizing biopolymers, our understanding of spontaneously emergent phenomena, self-organization, and critical behavior is in large part due to complex systems science. The complex systems approach is indeed a very powerful conceptual framework to shed light on the link between the microscopic dynamical evolution of the basic elements of the system and the emergence of oscopic phenomena; often providing evidence for mathematical principles that go beyond the particulars of the individual system, thus hinting to general modeling principles. By killing the myth of the ant queen and shifting the focus on the dynamical interaction across the elements of the systems, complex systems science has ushered our way into the conceptual understanding of many phenomena at the core of major scientific and social challenges such as the emergence of consensus, social opinion dynamics, conflicts and cooperation, contagion phenomena. For many years though, these complex systems approaches to real-world problems were often suffering from being oversimplified and not grounded on actual data...

[Dynamic paradigm in psychopathology: "chaos theory", from physics to psychiatry].

PubMed

Pezard, L; Nandrino, J L

2001-01-01

For the last thirty years, progress in the field of physics, known as "Chaos theory"--or more precisely: non-linear dynamical systems theory--has increased our understanding of complex systems dynamics. This framework's formalism is general enough to be applied in other domains, such as biology or psychology, where complex systems are the rule rather than the exception. Our goal is to show here that this framework can become a valuable tool in scientific fields such as neuroscience and psychiatry where objects possess natural time dependency (i.e. dynamical properties) and non-linear characteristics. The application of non-linear dynamics concepts on these topics is more precise than a loose metaphor and can throw a new light on mental functioning and dysfunctioning. A class of neural networks (recurrent neural networks) constitutes an example of the implementation of the dynamical system concept and provides models of cognitive processes (15). The state of activity of the network is represented in its state space and the time evolution of this state is a trajectory in this space. After a period of time those networks settle on an equilibrium (a kind of attractor). The strength of connections between neurons define the number and relations between those attractors. The attractors of the network are usually interpreted as "mental representations". When an initial condition is imposed to the network, the evolution towards an attractor is considered as a model of information processing (27). This information processing is not defined in a symbolic manner but is a result of the interaction between distributed elements. Several properties of dynamical models can be used to define a way where the symbolic properties emerge from physical and dynamical properties (28) and thus they can be candidates for the definition of the emergence of mental properties on the basis of neuronal dynamics (42). Nevertheless, mental properties can also be considered as the result of an underlying dynamics without explicit mention of the neuronal one (47). In that case, dynamical tools can be used to elucidate the Freudian psychodynamics (34, 35). Recurrent neuronal networks have been used to propose interpretation of several mental dysfunctions (12). For example in the case of schizophrenia, it has been proposed that troubles in the cortical pruning during development (13) may cause a decrease in neural network storage ability and lead to the creation of spurious attractors. Those attractors do not correspond to stored memories and attract a large amount of initial conditions: they were thus associated to reality distorsion observed in schizophrenia (14). Nevertheless, the behavior of these models are too simple to be directly compared with real physiological data. In fact, equilibrium attractors are hardly met in biological dynamics. More complex behaviors (such as oscillations or chaos) should thus to be taken into account. The study of chaotic behavior have lead to the development of numerical methods devoted to the analysis of complex time series (17). These methods may be used to characterise the dynamical processes at the time-scales of both the cerebral dynamics and the clinical symptoms variations. The application of these methods to physiological signals have shown that complex behaviors are related to healthy states whereas simple dynamics are related to pathology (8). These studies have thus confirmed the notion of "dynamical disease" (20, 21) which denotes pathological conditions characterised by changes in physiological rhythms. Depression has been studied within this framework (25, 32) in order to define possible changes in brain electrical rhythms related to this trouble and its evolution. It has been shown that controls' brain dynamics is more complex than depressive one and that the recovery of a complex brain activity depends on the number of previous episodes. In the case of the symptoms time evolution, several studies have demonstrated that non-linear dynamical process may be involved in the recurrence of symptoms in troubles such as manic-depressive illness (9) or schizophrenia (51). These observations can contribute to more parcimonious interpretation of the time course of these illnesses than usual theories. In the search of a relationship between brain dynamics and mental troubles, it has been shown in three depressed patients an important correlation between the characteristics of brain dynamics and the intensity of depressive mood (49). This preliminary observation is in accordance with the emergence hypothesis according which changes in neuronal dynamics should be related to changes in mental processes. We reviewed here some theoretical and experimental results related to the use of "physical" dynamical theory in the field of psychopathology. It has been argued that these applications go beyond metaphor and that they are empirically founded. Nevertheless, these studies only constitute first steps on the way of a cautious development and definition of a "dynamical paradigm" in psychopathology. The introduction of concepts from dynamics such as complexity and dynamical changes (i.e. bifurcations) permits a new perspective on function and dysfunction of the mind/brain and the time evolution of symptoms. Moreover, it offers a ground for the hypothesis of the emergence of mental properties on the basis of neuronal dynamics (42). Since this theory can help to throw light on classical problems in psychopathology, we consider that a precise examination of both its theoretical and empirical consequences is requested to define its validity on this topic.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Synchronization in Complex Networks with Multiple Connections

NASA Astrophysics Data System (ADS)

Wu, Qing-Chu; Fu, Xin-Chu; Sun, Wei-Gang

2010-01-01

In this paper a class of networks with multiple connections are discussed. The multiple connections include two different types of links between nodes in complex networks. For this new model, we give a simple generating procedure. Furthermore, we investigate dynamical synchronization behavior in a delayed two-layer network, giving corresponding theoretical analysis and numerical examples.

Discrete Dynamics Lab

NASA Astrophysics Data System (ADS)

Wuensche, Andrew

DDLab is interactive graphics software for creating, visualizing, and analyzing many aspects of Cellular Automata, Random Boolean Networks, and Discrete Dynamical Networks in general and studying their behavior, both from the time-series perspective — space-time patterns, and from the state-space perspective — attractor basins. DDLab is relevant to research, applications, and education in the fields of complexity, self-organization, emergent phenomena, chaos, collision-based computing, neural networks, content addressable memory, genetic regulatory networks, dynamical encryption, generative art and music, and the study of the abstract mathematical/physical/dynamical phenomena in their own right.

Controlling uncertainty: a review of human behavior in complex dynamic environments.

PubMed

Osman, Magda

2010-01-01

Complex dynamic control (CDC) tasks are a type of problem-solving environment used for examining many cognitive activities (e.g., attention, control, decision making, hypothesis testing, implicit learning, memory, monitoring, planning, and problem solving). Because of their popularity, there have been many findings from diverse domains of research (economics, engineering, ergonomics, human-computer interaction, management, psychology), but they remain largely disconnected from each other. The objective of this article is to review theoretical developments and empirical work on CDC tasks, and to introduce a novel framework (monitoring and control framework) as a tool for integrating theory and findings. The main thesis of the monitoring and control framework is that CDC tasks are characteristically uncertain environments, and subjective judgments of uncertainty guide the way in which monitoring and control behaviors attempt to reduce it. The article concludes by discussing new insights into continuing debates and future directions for research on CDC tasks.

Neonatal Feeding Behavior as a Complex Dynamical System.

PubMed

Goldfield, Eugene C; Perez, Jennifer; Engstler, Katherine

2017-04-01

The requirements of evidence-based practice in 2017 are motivating new theoretical foundations and methodological tools for characterizing neonatal feeding behavior. Toward that end, this article offers a complex dynamical systems perspective. A set of critical concepts from this perspective frames challenges faced by speech-language pathologists and allied professionals: when to initiate oral feeds, how to determine the robustness of neonatal breathing during feeding and appropriate levels of respiratory support, what instrumental assessments of swallow function to use with preterm neonates, and whether or not to introduce thickened liquids. In the near future, we can expect vast amounts of new data to guide evidence-based practice. But unless practitioners are able to frame these issues in a systems context larger than the individual child, the availability of "big data" will not be effectively translated to clinical practice. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

A chaotic view of behavior change: a quantum leap for health promotion.

PubMed

Resnicow, Ken; Vaughan, Roger

2006-09-12

The study of health behavior change, including nutrition and physical activity behaviors, has been rooted in a cognitive-rational paradigm. Change is conceptualized as a linear, deterministic process where individuals weigh pros and cons, and at the point at which the benefits outweigh the cost change occurs. Consistent with this paradigm, the associated statistical models have almost exclusively assumed a linear relationship between psychosocial predictors and behavior. Such a perspective however, fails to account for non-linear, quantum influences on human thought and action. Consider why after years of false starts and failed attempts, a person succeeds at increasing their physical activity, eating healthier or losing weight. Or, why after years of success a person relapses. This paper discusses a competing view of health behavior change that was presented at the 2006 annual ISBNPA meeting in Boston. Rather than viewing behavior change from a linear perspective it can be viewed as a quantum event that can be understood through the lens of Chaos Theory and Complex Dynamic Systems. Key principles of Chaos Theory and Complex Dynamic Systems relevant to understanding health behavior change include: 1) Chaotic systems can be mathematically modeled but are nearly impossible to predict; 2) Chaotic systems are sensitive to initial conditions; 3) Complex Systems involve multiple component parts that interact in a nonlinear fashion; and 4) The results of Complex Systems are often greater than the sum of their parts. Accordingly, small changes in knowledge, attitude, efficacy, etc may dramatically alter motivation and behavioral outcomes. And the interaction of such variables can yield almost infinite potential patterns of motivation and behavior change. In the linear paradigm unaccounted for variance is generally relegated to the catch all "error" term, when in fact such "error" may represent the chaotic component of the process. The linear and chaotic paradigms are however, not mutually exclusive, as behavior change may include both chaotic and cognitive processes. Studies of addiction suggest that many decisions to change are quantum rather than planned events; motivation arrives as opposed to being planned. Moreover, changes made through quantum processes appear more enduring than those that involve more rational, planned processes. How such processes may apply to nutrition and physical activity behavior and related interventions merits examination.

A numerical and experimental study of temperature effects on deformation behavior of carbon steels at high strain rates

NASA Astrophysics Data System (ADS)

Pouya, M.; Winter, S.; Fritsch, S.; F-X Wagner, M.

2017-03-01

Both in research and in the light of industrial applications, there is a growing interest in methods to characterize the mechanical behavior of materials at high strain rates. This is particularly true for steels (the most important structural materials), where often the strain rate-dependent material behavior also needs to be characterized in a wide temperature range. In this study, we use the Finite Element Method (FEM), first, to model the compressive deformation behavior of carbon steels under quasi-static loading conditions. The results are then compared to experimental data (for a simple C75 steel) at room temperature, and up to testing temperatures of 1000 °C. Second, an explicit FEM model that captures wave propagation phenomena during dynamic loading is developed to closely reflect the complex loading conditions in a Split-Hopkinson Pressure Bar (SHPB) - an experimental setup that allows loading of compression samples with strain rates up to 104 s-1 The dynamic simulations provide a useful basis for an accurate analysis of dynamically measured experimental data, which considers reflected elastic waves. By combining numerical and experimental investigations, we derive material parameters that capture the strain rate- and temperature-dependent behavior of the C75 steel from room temperature to 1000 °C, and from quasi-static to dynamic loading.

The fractal geometry of Hartree-Fock

NASA Astrophysics Data System (ADS)

Theel, Friethjof; Karamatskou, Antonia; Santra, Robin

2017-12-01

The Hartree-Fock method is an important approximation for the ground-state electronic wave function of atoms and molecules so that its usage is widespread in computational chemistry and physics. The Hartree-Fock method is an iterative procedure in which the electronic wave functions of the occupied orbitals are determined. The set of functions found in one step builds the basis for the next iteration step. In this work, we interpret the Hartree-Fock method as a dynamical system since dynamical systems are iterations where iteration steps represent the time development of the system, as encountered in the theory of fractals. The focus is put on the convergence behavior of the dynamical system as a function of a suitable control parameter. In our case, a complex parameter λ controls the strength of the electron-electron interaction. An investigation of the convergence behavior depending on the parameter λ is performed for helium, neon, and argon. We observe fractal structures in the complex λ-plane, which resemble the well-known Mandelbrot set, determine their fractal dimension, and find that with increasing nuclear charge, the fragmentation increases as well.

The dynamical analysis of modified two-compartment neuron model and FPGA implementation

NASA Astrophysics Data System (ADS)

Lin, Qianjin; Wang, Jiang; Yang, Shuangming; Yi, Guosheng; Deng, Bin; Wei, Xile; Yu, Haitao

2017-10-01

The complexity of neural models is increasing with the investigation of larger biological neural network, more various ionic channels and more detailed morphologies, and the implementation of biological neural network is a task with huge computational complexity and power consumption. This paper presents an efficient digital design using piecewise linearization on field programmable gate array (FPGA), to succinctly implement the reduced two-compartment model which retains essential features of more complicated models. The design proposes an approximate neuron model which is composed of a set of piecewise linear equations, and it can reproduce different dynamical behaviors to depict the mechanisms of a single neuron model. The consistency of hardware implementation is verified in terms of dynamical behaviors and bifurcation analysis, and the simulation results including varied ion channel characteristics coincide with the biological neuron model with a high accuracy. Hardware synthesis on FPGA demonstrates that the proposed model has reliable performance and lower hardware resource compared with the original two-compartment model. These investigations are conducive to scalability of biological neural network in reconfigurable large-scale neuromorphic system.

Complexity in models of cultural niche construction with selection and homophily.

PubMed

Creanza, Nicole; Feldman, Marcus W

2014-07-22

Niche construction is the process by which organisms can alter the ecological environment for themselves, their descendants, and other species. As a result of niche construction, differences in selection pressures may be inherited across generations. Homophily, the tendency of like phenotypes to mate or preferentially associate, influences the evolutionary dynamics of these systems. Here we develop a model that includes selection and homophily as independent culturally transmitted traits that influence the fitness and mate choice determined by another focal cultural trait. We study the joint dynamics of a focal set of beliefs, a behavior that can differentially influence the fitness of those with certain beliefs, and a preference for partnering based on similar beliefs. Cultural transmission, selection, and homophily interact to produce complex evolutionary dynamics, including oscillations, stable polymorphisms of all cultural phenotypes, and simultaneous stability of oscillation and fixation, which have not previously been observed in models of cultural evolution or gene-culture interactions. We discuss applications of this model to the interaction of beliefs and behaviors regarding education, contraception, and animal domestication.

Pattern dynamics of the reaction-diffusion immune system.

PubMed

Zheng, Qianqian; Shen, Jianwei; Wang, Zhijie

2018-01-01

In this paper, we will investigate the effect of diffusion, which is ubiquitous in nature, on the immune system using a reaction-diffusion model in order to understand the dynamical behavior of complex patterns and control the dynamics of different patterns. Through control theory and linear stability analysis of local equilibrium, we obtain the optimal condition under which the system loses stability and a Turing pattern occurs. By combining mathematical analysis and numerical simulation, we show the possible patterns and how these patterns evolve. In addition, we establish a bridge between the complex patterns and the biological mechanism using the results from a previous study in Nature Cell Biology. The results in this paper can help us better understand the biological significance of the immune system.

Correlating contact line capillarity and dynamic contact angle hysteresis in surfactant-nanoparticle based complex fluids

NASA Astrophysics Data System (ADS)

Harikrishnan, A. R.; Dhar, Purbarun; Agnihotri, Prabhat K.; Gedupudi, Sateesh; Das, Sarit K.

2018-04-01

Dynamic wettability and contact angle hysteresis can be correlated to shed insight onto any solid-liquid interaction. Complex fluids are capable of altering the expected hysteresis and dynamic wetting behavior due to interfacial interactions. We report the effect of capillary number on the dynamic advancing and receding contact angles of surfactant-based nanocolloidal solutions on hydrophilic, near hydrophobic, and superhydrophobic surfaces by performing forced wetting and de-wetting experiments by employing the embedded needle method. A segregated study is performed to infer the contributing effects of the constituents and effects of particle morphology. The static contact angle hysteresis is found to be a function of particle and surfactant concentrations and greatly depends on the nature of the morphology of the particles. An order of estimate of line energy and a dynamic flow parameter called spreading factor and the transient variations of these parameters are explored which sheds light on the dynamics of contact line movement and response to perturbation of three-phase contact. The Cox-Voinov-Tanner law was found to hold for hydrophilic and a weak dependency on superhydrophobic surfaces with capillary number, and even for the complex fluids, with a varying degree of dependency for different fluids.

Glasslike dynamical behavior of the plastocyanin hydration water

NASA Astrophysics Data System (ADS)

Bizzarri, Anna Rita; Paciaroni, Alessandro; Cannistraro, Salvatore

2000-09-01

The dynamical behavior of water around plastocyanin has been investigated in a wide temperature range by molecular dynamics simulation. The mean square displacements of water oxygen atoms show, at long times, a tα trend for all temperatures. Below 150 K, α is constant and equal to 1; at higher temperatures it drops to a value significantly smaller than 1, and thereafter decreases with increasing temperature. The occurrence of such an anomalous diffusion matches the onset of the dynamical transition observed in the protein. The intermediate scattering function of water is characterized, at high temperature, by a stretched exponential decay evolving, at low temperature, toward a two step relaxation behavior, which becomes more evident on increasing the exchanged wave vector q. Both the mean square displacements and the intermediate scattering functions show, beyond the ballistic regime, a plateau, which progressively extends for longer times as long as the temperature is lowered, such behavior reflecting trapping of water molecules within a cage formed by the nearest neighbors. At low temperature, a low frequency broad inelastic peak is observed in the dynamical structure factor of hydration water; such an excess of vibrational modes being reminiscent of the boson peak, characteristic of disordered, amorphous systems. All these features, which are typical of complex systems, can be traced back to the glassy character of the hydration water and suggest a dynamical coupling occurring at the macromolecule-solvent interface.

Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex

PubMed Central

Procyk, Emmanuel; Dominey, Peter Ford

2016-01-01

Primates display a remarkable ability to adapt to novel situations. Determining what is most pertinent in these situations is not always possible based only on the current sensory inputs, and often also depends on recent inputs and behavioral outputs that contribute to internal states. Thus, one can ask how cortical dynamics generate representations of these complex situations. It has been observed that mixed selectivity in cortical neurons contributes to represent diverse situations defined by a combination of the current stimuli, and that mixed selectivity is readily obtained in randomly connected recurrent networks. In this context, these reservoir networks reproduce the highly recurrent nature of local cortical connectivity. Recombining present and past inputs, random recurrent networks from the reservoir computing framework generate mixed selectivity which provides pre-coded representations of an essentially universal set of contexts. These representations can then be selectively amplified through learning to solve the task at hand. We thus explored their representational power and dynamical properties after training a reservoir to perform a complex cognitive task initially developed for monkeys. The reservoir model inherently displayed a dynamic form of mixed selectivity, key to the representation of the behavioral context over time. The pre-coded representation of context was amplified by training a feedback neuron to explicitly represent this context, thereby reproducing the effect of learning and allowing the model to perform more robustly. This second version of the model demonstrates how a hybrid dynamical regime combining spatio-temporal processing of reservoirs, and input driven attracting dynamics generated by the feedback neuron, can be used to solve a complex cognitive task. We compared reservoir activity to neural activity of dorsal anterior cingulate cortex of monkeys which revealed similar network dynamics. We argue that reservoir computing is a pertinent framework to model local cortical dynamics and their contribution to higher cognitive function. PMID:27286251

Development and application of coupled system dynamics and game theory: A dynamic water conflict resolution method.

PubMed

Zomorodian, Mehdi; Lai, Sai Hin; Homayounfar, Mehran; Ibrahim, Shaliza; Pender, Gareth

2017-01-01

Conflicts over water resources can be highly dynamic and complex due to the various factors which can affect such systems, including economic, engineering, social, hydrologic, environmental and even political, as well as the inherent uncertainty involved in many of these factors. Furthermore, the conflicting behavior, preferences and goals of stakeholders can often make such conflicts even more challenging. While many game models, both cooperative and non-cooperative, have been suggested to deal with problems over utilizing and sharing water resources, most of these are based on a static viewpoint of demand points during optimization procedures. Moreover, such models are usually developed for a single reservoir system, and so are not really suitable for application to an integrated decision support system involving more than one reservoir. This paper outlines a coupled simulation-optimization modeling method based on a combination of system dynamics (SD) and game theory (GT). The method harnesses SD to capture the dynamic behavior of the water system, utilizing feedback loops between the system components in the course of the simulation. In addition, it uses GT concepts, including pure-strategy and mixed-strategy games as well as the Nash Bargaining Solution (NBS) method, to find the optimum allocation decisions over available water in the system. To test the capability of the proposed method to resolve multi-reservoir and multi-objective conflicts, two different deterministic simulation-optimization models with increasing levels of complexity were developed for the Langat River basin in Malaysia. The later is a strategic water catchment that has a range of different stakeholders and managerial bodies, which are however willing to cooperate in order to avoid unmet demand. In our first model, all water users play a dynamic pure-strategy game. The second model then adds in dynamic behaviors to reservoirs to factor in inflow uncertainty and adjust the strategies for the reservoirs using the mixed-strategy game and Markov chain methods. The two models were then evaluated against three performance indices: Reliability, Resilience and Vulnerability (R-R-V). The results showed that, while both models were well capable of dealing with conflict resolution over water resources in the Langat River basin, the second model achieved a substantially improved performance through its ability to deal with dynamicity, complexity and uncertainty in the river system.

Development and application of coupled system dynamics and game theory: A dynamic water conflict resolution method

PubMed Central

Lai, Sai Hin; Homayounfar, Mehran; Ibrahim, Shaliza; Pender, Gareth

2017-01-01

Conflicts over water resources can be highly dynamic and complex due to the various factors which can affect such systems, including economic, engineering, social, hydrologic, environmental and even political, as well as the inherent uncertainty involved in many of these factors. Furthermore, the conflicting behavior, preferences and goals of stakeholders can often make such conflicts even more challenging. While many game models, both cooperative and non-cooperative, have been suggested to deal with problems over utilizing and sharing water resources, most of these are based on a static viewpoint of demand points during optimization procedures. Moreover, such models are usually developed for a single reservoir system, and so are not really suitable for application to an integrated decision support system involving more than one reservoir. This paper outlines a coupled simulation-optimization modeling method based on a combination of system dynamics (SD) and game theory (GT). The method harnesses SD to capture the dynamic behavior of the water system, utilizing feedback loops between the system components in the course of the simulation. In addition, it uses GT concepts, including pure-strategy and mixed-strategy games as well as the Nash Bargaining Solution (NBS) method, to find the optimum allocation decisions over available water in the system. To test the capability of the proposed method to resolve multi-reservoir and multi-objective conflicts, two different deterministic simulation-optimization models with increasing levels of complexity were developed for the Langat River basin in Malaysia. The later is a strategic water catchment that has a range of different stakeholders and managerial bodies, which are however willing to cooperate in order to avoid unmet demand. In our first model, all water users play a dynamic pure-strategy game. The second model then adds in dynamic behaviors to reservoirs to factor in inflow uncertainty and adjust the strategies for the reservoirs using the mixed-strategy game and Markov chain methods. The two models were then evaluated against three performance indices: Reliability, Resilience and Vulnerability (R-R-V). The results showed that, while both models were well capable of dealing with conflict resolution over water resources in the Langat River basin, the second model achieved a substantially improved performance through its ability to deal with dynamicity, complexity and uncertainty in the river system. PMID:29216200

The magnetosphere as system

NASA Astrophysics Data System (ADS)

Siscoe, G. L.

2012-12-01

What is a system? A group of elements interacting with each other so as to create feedback loops. A system gets complex as the number of feedback loops increases and as the feedback loops exhibit time delays. Positive and negative feedback loops with time delays can give a system intrinsic time dependence and emergent properties. A system generally has input and output flows of something (matter, energy, money), which, if time variable, add an extrinsic component to its behavior. The magnetosphere is a group of elements interacting through feedback loops, some with time delays, driven by energy and mass inflow from a variable solar wind and outflow into the atmosphere and solar wind. The magnetosphere is a complex system. With no solar wind, there is no behavior. With solar wind, there is behavior from intrinsic and extrinsic causes. As a contribution to taking a macroscopic view of magnetospheric dynamics, to treating the magnetosphere as a globally integrated, complex entity, I will discus the magnetosphere as a system, its feedback loops, time delays, emergent behavior, and intrinsic and extrinsic behavior modes.

Robustness of Oscillatory Behavior in Correlated Networks

PubMed Central

Sasai, Takeyuki; Morino, Kai; Tanaka, Gouhei; Almendral, Juan A.; Aihara, Kazuyuki

2015-01-01

Understanding network robustness against failures of network units is useful for preventing large-scale breakdowns and damages in real-world networked systems. The tolerance of networked systems whose functions are maintained by collective dynamical behavior of the network units has recently been analyzed in the framework called dynamical robustness of complex networks. The effect of network structure on the dynamical robustness has been examined with various types of network topology, but the role of network assortativity, or degree–degree correlations, is still unclear. Here we study the dynamical robustness of correlated (assortative and disassortative) networks consisting of diffusively coupled oscillators. Numerical analyses for the correlated networks with Poisson and power-law degree distributions show that network assortativity enhances the dynamical robustness of the oscillator networks but the impact of network disassortativity depends on the detailed network connectivity. Furthermore, we theoretically analyze the dynamical robustness of correlated bimodal networks with two-peak degree distributions and show the positive impact of the network assortativity. PMID:25894574

Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems

PubMed Central

Ouyang, Fang-Yan; Zheng, Bo; Jiang, Xiong-Fei

2015-01-01

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode. PMID:26427063

Application of Dynamic Mode Decomposition: Temporal Evolution of Flow Structures in an Aneurysm

NASA Astrophysics Data System (ADS)

Conlin, William; Yu, Paulo; Durgesh, Vibhav

2017-11-01

An aneurysm is an enlargement of a weakened arterial wall that can be fatal or debilitating on rupture. Aneurysm hemodynamics is integral to developing an understanding of aneurysm formation, growth, and rupture. The flow in an aneurysm exhibits complex fluid dynamics behavior due to an inherent unsteady inflow condition and its interactions with large-scale flow structures present in the aneurysm. The objective of this study is to identify the large-scale structures in the aneurysm, study temporal behavior, and quantify their interaction with the inflow condition. For this purpose, detailed Particle Image Velocimetry (PIV) measurements were performed at the center plane of an idealized aneurysm model for a range of inflow conditions. Inflow conditions were precisely controlled using a ViVitro SuperPump system. Dynamic Modal Decomposition (DMD) of the velocity field was used to identify coherent structures and their temporal behavior. DMD was successful in capturing the large-scale flow structures and their temporal behavior. A low dimensional approximation to the flow field was obtained with the most relevant dynamic modes and was used to obtain temporal information about the coherent structures and their interaction with the inflow, formation, evolution, and growth.

Biomechanical behavior of muscle-tendon complex during dynamic human movements.

PubMed

Fukashiro, Senshi; Hay, Dean C; Nagano, Akinori

2006-05-01

This paper reviews the research findings regarding the force and length changes of the muscle-tendon complex during dynamic human movements, especially those using ultrasonography and computer simulation. The use of ultrasonography demonstrated that the tendinous structures of the muscle-tendon complex are compliant enough to influence the biomechanical behavior (length change, shortening velocity, and so on) of fascicles substantially. It was discussed that the fascicles are a force generator rather than a work generator; the tendinous structures function not only as an energy re-distributor but also as a power amplifier, and the interaction between fascicles and tendinous structures is essential for generating higher joint power outputs during the late pushoff phase in human vertical jumping. This phenomenon could be explained based on the force-length/velocity relationships of each element (contractile and series elastic elements) in the muscle-tendon complex during movements. Through computer simulation using a Hill-type muscle-tendon complex model, the benefit of making a countermovement was examined in relation to the compliance of the muscle-tendon complex and the length ratio between the contractile and series elastic elements. Also, the integral roles of the series elastic element were simulated in a cyclic human heel-raise exercise. It was suggested that the storage and reutilization of elastic energy by the tendinous structures play an important role in enhancing work output and movement efficiency in many sorts of human movements.

Modeling of dielectric elastomer as electromechanical resonator

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

Li, Bo, E-mail: liboxjtu@mail.xjtu.edu.cn; Liu, Lei; Chen, Hualing

Dielectric elastomers (DEs) feature nonlinear dynamics resulting from an electromechanical coupling. Under alternating voltage, the DE resonates with tunable performances. We present an analysis of the nonlinear dynamics of a DE as electromechanical resonator (DEER) configured as a pure shear actuator. A theoretical model is developed to characterize the complex performance under different boundary conditions. Physical mechanisms are presented and discussed. Chaotic behavior is also predicted, illustrating instabilities in the dynamics. The results provide a guide to the design and application of DEER in haptic devices.

Towards a unified study of extreme events using universality concepts and transdisciplinary analysis methods

NASA Astrophysics Data System (ADS)

Balasis, George; Donner, Reik V.; Donges, Jonathan F.; Radebach, Alexander; Eftaxias, Konstantinos; Kurths, Jürgen

2013-04-01

The dynamics of many complex systems is characterized by the same universal principles. In particular, systems which are otherwise quite different in nature show striking similarities in their behavior near tipping points (bifurcations, phase transitions, sudden regime shifts) and associated extreme events. Such critical phenomena are frequently found in diverse fields such as climate, seismology, or financial markets. Notably, the observed similarities include a high degree of organization, persistent behavior, and accelerated energy release, which are common to (among others) phenomena related to geomagnetic variability of the terrestrial magnetosphere (intense magnetic storms), seismic activity (electromagnetic emissions prior to earthquakes), solar-terrestrial physics (solar flares), neurophysiology (epileptic seizures), and socioeconomic systems (stock market crashes). It is an open question whether the spatial and temporal complexity associated with extreme events arises from the system's structural organization (geometry) or from the chaotic behavior inherent to the nonlinear equations governing the dynamics of these phenomena. On the one hand, the presence of scaling laws associated with earthquakes and geomagnetic disturbances suggests understanding these events as generalized phase transitions similar to nucleation and critical phenomena in thermal and magnetic systems. On the other hand, because of the structural organization of the systems (e.g., as complex networks) the associated spatial geometry and/or topology of interactions plays a fundamental role in the emergence of extreme events. Here, a few aspects of the interplay between geometry and dynamics (critical phase transitions) that could result in the emergence of extreme events, which is an open problem, will be discussed.

Mouse Social Network Dynamics and Community Structure are Associated with Plasticity-Related Brain Gene Expression

PubMed Central

Williamson, Cait M.; Franks, Becca; Curley, James P.

2016-01-01

Laboratory studies of social behavior have typically focused on dyadic interactions occurring within a limited spatiotemporal context. However, this strategy prevents analyses of the dynamics of group social behavior and constrains identification of the biological pathways mediating individual differences in behavior. In the current study, we aimed to identify the spatiotemporal dynamics and hierarchical organization of a large social network of male mice. We also sought to determine if standard assays of social and exploratory behavior are predictive of social behavior in this social network and whether individual network position was associated with the mRNA expression of two plasticity-related genes, DNA methyltransferase 1 and 3a. Mice were observed to form a hierarchically organized social network and self-organized into two separate social network communities. Members of both communities exhibited distinct patterns of socio-spatial organization within the vivaria that was not limited to only agonistic interactions. We further established that exploratory and social behaviors in standard behavioral assays conducted prior to placing the mice into the large group was predictive of initial network position and behavior but were not associated with final social network position. Finally, we determined that social network position is associated with variation in mRNA levels of two neural plasticity genes, DNMT1 and DNMT3a, in the hippocampus but not the mPOA. This work demonstrates the importance of understanding the role of social context and complex social dynamics in determining the relationship between individual differences in social behavior and brain gene expression. PMID:27540359

Complex dynamics and enhanced photosensitivity in a modified Belousov-Zhabotinsky reaction

NASA Astrophysics Data System (ADS)

Li, Nan; Zhao, Jinpei; Wang, Jichang

2008-06-01

This study presents an experimental investigation of nonlinear dynamics in a modified Belousov-Zhabotinsky (BZ) reaction, in which the addition of 1,4-benzoquinone induced various complex behaviors such as mixed-mode oscillations and consecutive period-adding bifurcations. In addition, the presence of 1,4-benzoquinone significantly enhanced the photosensitivity of the ferroin-catalyzed BZ system, in which light-induced transitions between simple and complex oscillations have been achieved. Mechanistic study suggests that the influence of benzoquinone may arise from its interactions with the metal catalyst ferroin/ferriin, where cyclic voltammograms illustrate that the presence of benzoquinone causes an increase in the redox potential of ferroin/ferriin couple, which may consequently alternate the oxidation and reduction paths of the catalyst.

UAV Swarm Mission Planning Development Using Evolutionary Algorithms - Part I

DTIC Science & Technology

2008-05-01

desired behaviors in autonomous vehicles is a difficult problem at best and in general prob- ably impossible to completely resolve in complex dynamic...associated behaviors. Various techniques inspired by biological self-organized systems as found in forging insects and flocking birds, revolve around...swarms of heterogeneous vehicles in a distributed simulation system with animated graphics. Statistical measurements and observations indicate that bio

Profilin2 contributes to synaptic vesicle exocytosis, neuronal excitability, and novelty-seeking behavior

PubMed Central

Pilo Boyl, Pietro; Di Nardo, Alessia; Mulle, Christophe; Sassoè-Pognetto, Marco; Panzanelli, Patrizia; Mele, Andrea; Kneussel, Matthias; Costantini, Vivian; Perlas, Emerald; Massimi, Marzia; Vara, Hugo; Giustetto, Maurizio; Witke, Walter

2007-01-01

Profilins are actin binding proteins essential for regulating cytoskeletal dynamics, however, their function in the mammalian nervous system is unknown. Here, we provide evidence that in mouse brain profilin1 and profilin2 have distinct roles in regulating synaptic actin polymerization with profilin2 preferring a WAVE-complex-mediated pathway. Mice lacking profilin2 show a block in synaptic actin polymerization in response to depolarization, which is accompanied by increased synaptic excitability of glutamatergic neurons due to higher vesicle exocytosis. These alterations in neurotransmitter release correlate with a hyperactivation of the striatum and enhanced novelty-seeking behavior in profilin2 mutant mice. Our results highlight a novel, profilin2-dependent pathway, regulating synaptic physiology, neuronal excitability, and complex behavior. PMID:17541406

Shape memory polymer network with thermally distinct elasticity and plasticity

PubMed Central

Zhao, Qian; Zou, Weike; Luo, Yingwu; Xie, Tao

2016-01-01

Stimuli-responsive materials with sophisticated yet controllable shape-changing behaviors are highly desirable for real-world device applications. Among various shape-changing materials, the elastic nature of shape memory polymers allows fixation of temporary shapes that can recover on demand, whereas polymers with exchangeable bonds can undergo permanent shape change via plasticity. We integrate the elasticity and plasticity into a single polymer network. Rational molecular design allows these two opposite behaviors to be realized at different temperature ranges without any overlap. By exploring the cumulative nature of the plasticity, we demonstrate easy manipulation of highly complex shapes that is otherwise extremely challenging. The dynamic shape-changing behavior paves a new way for fabricating geometrically complex multifunctional devices. PMID:26824077

Vegetation-hydrology dynamics in complex terrain of semiarid areas: 1. A mechanistic approach to modeling dynamic feedbacks

NASA Astrophysics Data System (ADS)

Ivanov, Valeriy Y.; Bras, Rafael L.; Vivoni, Enrique R.

2008-03-01

Vegetation, particularly its dynamics, is the often-ignored linchpin of the land-surface hydrology. This work emphasizes the coupled nature of vegetation-water-energy dynamics by considering linkages at timescales that vary from hourly to interannual. A series of two papers is presented. A dynamic ecohydrological model [tRIBS + VEGGIE] is described in this paper. It reproduces essential water and energy processes over the complex topography of a river basin and links them to the basic plant life regulatory processes. The framework focuses on ecohydrology of semiarid environments exhibiting abundant input of solar energy but limiting soil water that correspondingly affects vegetation structure and organization. The mechanisms through which water limitation influences plant dynamics are related to carbon assimilation via the control of photosynthesis and stomatal behavior, carbon allocation, stress-induced foliage loss, as well as recruitment and phenology patterns. This first introductory paper demonstrates model performance using observations for a site located in a semiarid environment of central New Mexico.

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks

PubMed Central

Miconi, Thomas

2017-01-01

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior. DOI: http://dx.doi.org/10.7554/eLife.20899.001 PMID:28230528

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

PubMed

Miconi, Thomas

2017-02-23

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

Power-law scaling for macroscopic entropy and microscopic complexity: Evidence from human movement and posture

NASA Astrophysics Data System (ADS)

Hong, S. Lee; Bodfish, James W.; Newell, Karl M.

2006-03-01

We investigated the relationship between macroscopic entropy and microscopic complexity of the dynamics of body rocking and sitting still across adults with stereotyped movement disorder and mental retardation (profound and severe) against controls matched for age, height, and weight. This analysis was performed through the examination of center of pressure (COP) motion on the mediolateral (side-to-side) and anteroposterior (fore-aft) dimensions and the entropy of the relative phase between the two dimensions of motion. Intentional body rocking and stereotypical body rocking possessed similar slopes for their respective frequency spectra, but differences were revealed during maintenance of sitting postures. The dynamics of sitting in the control group produced lower spectral slopes and higher complexity (approximate entropy). In the controls, the higher complexity found on each dimension of motion was related to a weaker coupling between dimensions. Information entropy of the relative phase between the two dimensions of COP motion and irregularity (complexity) of their respective motions fitted a power-law function, revealing a relationship between macroscopic entropy and microscopic complexity across both groups and behaviors. This power-law relation affords the postulation that the organization of movement and posture dynamics occurs as a fractal process.

Markovian robots: Minimal navigation strategies for active particles

NASA Astrophysics Data System (ADS)

Nava, Luis Gómez; Großmann, Robert; Peruani, Fernando

2018-04-01

We explore minimal navigation strategies for active particles in complex, dynamical, external fields, introducing a class of autonomous, self-propelled particles which we call Markovian robots (MR). These machines are equipped with a navigation control system (NCS) that triggers random changes in the direction of self-propulsion of the robots. The internal state of the NCS is described by a Boolean variable that adopts two values. The temporal dynamics of this Boolean variable is dictated by a closed Markov chain—ensuring the absence of fixed points in the dynamics—with transition rates that may depend exclusively on the instantaneous, local value of the external field. Importantly, the NCS does not store past measurements of this value in continuous, internal variables. We show that despite the strong constraints, it is possible to conceive closed Markov chain motifs that lead to nontrivial motility behaviors of the MR in one, two, and three dimensions. By analytically reducing the complexity of the NCS dynamics, we obtain an effective description of the long-time motility behavior of the MR that allows us to identify the minimum requirements in the design of NCS motifs and transition rates to perform complex navigation tasks such as adaptive gradient following, detection of minima or maxima, or selection of a desired value in a dynamical, external field. We put these ideas in practice by assembling a robot that operates by the proposed minimalistic NCS to evaluate the robustness of MR, providing a proof of concept that is possible to navigate through complex information landscapes with such a simple NCS whose internal state can be stored in one bit. These ideas may prove useful for the engineering of miniaturized robots.

Single-molecule visualization of Saccharomyces cerevisiae leading-strand synthesis reveals dynamic interaction between MTC and the replisome

PubMed Central

Lewis, Jacob S.; Spenkelink, Lisanne M.; Schauer, Grant D.; Hill, Flynn R.; Georgescu, Roxanna E.; O’Donnell, Michael E.; van Oijen, Antoine M.

2017-01-01

The replisome, the multiprotein system responsible for genome duplication, is a highly dynamic complex displaying a large number of different enzyme activities. Recently, the Saccharomyces cerevisiae minimal replication reaction has been successfully reconstituted in vitro. This provided an opportunity to uncover the enzymatic activities of many of the components in a eukaryotic system. Their dynamic behavior and interactions in the context of the replisome, however, remain unclear. We use a tethered-bead assay to provide real-time visualization of leading-strand synthesis by the S. cerevisiae replisome at the single-molecule level. The minimal reconstituted leading-strand replisome requires 24 proteins, forming the CMG helicase, the Pol ε DNA polymerase, the RFC clamp loader, the PCNA sliding clamp, and the RPA single-stranded DNA binding protein. We observe rates and product lengths similar to those obtained from ensemble biochemical experiments. At the single-molecule level, we probe the behavior of two components of the replication progression complex and characterize their interaction with active leading-strand replisomes. The Minichromosome maintenance protein 10 (Mcm10), an important player in CMG activation, increases the number of productive replication events in our assay. Furthermore, we show that the fork protection complex Mrc1–Tof1–Csm3 (MTC) enhances the rate of the leading-strand replisome threefold. The introduction of periods of fast replication by MTC leads to an average rate enhancement of a factor of 2, similar to observations in cellular studies. We observe that the MTC complex acts in a dynamic fashion with the moving replisome, leading to alternating phases of slow and fast replication. PMID:28923950

(A)biotic processes control soil carbon dynamics: quantitative assessment of model complexity, stability and response to perturbations for improving ESMs

NASA Astrophysics Data System (ADS)

Georgiou, K.; Abramoff, R. Z.; Harte, J.; Riley, W. J.; Torn, M. S.

2016-12-01

As global temperatures and atmospheric CO2 concentrations continue to increase, soil microbial activity and decomposition of soil organic matter (SOM) are expected to follow suit, potentially limiting soil carbon storage. Traditional global- and ecosystem-scale models simulate SOM decomposition using linear kinetics, which are inherently unable to reproduce carbon-concentration feedbacks, such as priming of native SOM at elevated CO2 concentrations. Recent studies using nonlinear microbial models of SOM decomposition seek to capture these interactions, and several groups are currently integrating these microbial models into Earth System Models (ESMs). However, despite their widespread ability to exhibit nonlinear responses, these models vary tremendously in complexity and, consequently, dynamics. In this study, we explore, both analytically and numerically, the emergent oscillatory behavior and insensitivity of SOM stocks to carbon inputs that have been deemed `unrealistic' in recent microbial models. We discuss the sources of instability in four models of varying complexity, by sequentially reducing complexity of a detailed model that includes microbial physiology, a mineral sorption isotherm, and enzyme dynamics. We also present an alternative representation of microbial turnover that limits population sizes and, thus, reduces oscillations. We compare these models to several long-term carbon input manipulations, including the Detritus Input and Removal Treatment (DIRT) experiments, to show that there are clear metrics that can be used to distinguish and validate the inherent dynamics of each model structure. We find that traditional linear and nonlinear models cannot readily capture the range of long-term responses observed across the DIRT experiments as a direct consequence of their model structures, and that modifying microbial turnover results in more realistic predictions. Finally, we discuss our findings in the context of improving microbial model behavior for inclusion in ESMs.

Weighted fractional permutation entropy and fractional sample entropy for nonlinear Potts financial dynamics

NASA Astrophysics Data System (ADS)

Xu, Kaixuan; Wang, Jun

2017-02-01

In this paper, recently introduced permutation entropy and sample entropy are further developed to the fractional cases, weighted fractional permutation entropy (WFPE) and fractional sample entropy (FSE). The fractional order generalization of information entropy is utilized in the above two complexity approaches, to detect the statistical characteristics of fractional order information in complex systems. The effectiveness analysis of proposed methods on the synthetic data and the real-world data reveals that tuning the fractional order allows a high sensitivity and more accurate characterization to the signal evolution, which is useful in describing the dynamics of complex systems. Moreover, the numerical research on nonlinear complexity behaviors is compared between the returns series of Potts financial model and the actual stock markets. And the empirical results confirm the feasibility of the proposed model.

How to identify the most effective control measures based on disease-behavior coupled mechanisms?. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Sun, Gui-Quan; Jin, Zhen

2015-12-01

Modelling infectious diseases on complex networks is a significant tool to understand the transmission of epidemics in human society, and consequently it has commanded increasing attention in the community of mathematicians, physicists, epidemiologists, public health policy-makers and so on [1-4]. Human behavior responses are associated with the emergence of infectious disease, for instance, wearing masks [5], staying away from a thick crowd [6], cutting contacts with infected individuals [7] and receiving a vaccination [8]. However, infectious diseases and human behavior were often modeled as independent systems in the literature, despite the fact that in the real world they are often mutually influential on each other, and hence their coupling exerts significant impacts on disease spread [9,10].

Behavior of composite/metal aircraft structural elements and components under crash type loads: What are they telling us

NASA Technical Reports Server (NTRS)

Carden, Huey D.; Boitnott, Richard L.; Fasanella, Edwin L.

1990-01-01

Failure behavior results are presented from crash dynamics research using concepts of aircraft elements and substructure not necessarily designed or optimized for energy absorption or crash loading considerations. To achieve desired new designs which incorporate improved energy absorption capabilities often requires an understanding of how more conventional designs behave under crash loadings. Experimental and analytical data are presented which indicate some general trends in the failure behavior of a class of composite structures which include individual fuselage frames, skeleton subfloors with stringers and floor beams but without skin covering, and subfloors with skin added to the frame-stringer arrangement. Although the behavior is complex, a strong similarity in the static and dynamic failure behavior among these structures is illustrated through photographs of the experimental results and through analytical data of generic composite structural models. It is believed that the similarity in behavior is giving the designer and dynamists much information about what to expect in the crash behavior of these structures and can guide designs for improving the energy absorption and crash behavior of such structures.

Behavior of composite/metal aircraft structural elements and components under crash type loads - What are they telling us?

NASA Technical Reports Server (NTRS)

Carden, Huey D.; Boitnott, Richard L.; Fasanella, Edwin L.

1990-01-01

Failure behavior results are presented from crash dynamics research using concepts of aircraft elements and substructure not necessarily designed or optimized for energy absorption or crash loading considerations. To achieve desired new designs which incorporate improved energy absorption capabilities often requires an understanding of how more conventional designs behave under crash loadings. Experimental and analytical data are presented which indicate some general trends in the failure behavior of a class of composite structures which include individual fuselage frames, skeleton subfloors with stringers and floor beams but without skin covering, and subfloors with skin added to the frame-stringer arrangement. Although the behavior is complex, a strong similarity in the static and dynamic failure behavior among these structures is illustrated through photographs of the experimental results and through analytical data of generic composite structural models. It is believed that the similarity in behavior is giving the designer and dynamists much information about what to expect in the crash behavior of these structures and can guide designs for improving the energy absorption and crash behavior of such structures.

Unique failure behavior of metal/composite aircraft structural components under crash type loads

NASA Technical Reports Server (NTRS)

Carden, Huey D.

1990-01-01

Failure behavior results are presented on some of the crash dynamics research conducted with concepts of aircraft elements and substructure which have not necessarily been designed or optimized for energy absorption or crash loading considerations. To achieve desired new designs which incorporate improved energy absorption capabilities often requires an understanding of how more conventional designs behave under crash type loadings. Experimental and analytical data are presented which indicate some general trends in the failure behavior of a class of composite structures which include individual fuselage frames, skeleton subfloors with stringers and floor beams but without skin covering, and subfloors with skin added to the frame-stringer arrangement. Although the behavior is complex, a strong similarity in the static/dynamic failure behavior among these structures is illustrated through photographs of the experimental results and through analytical data of generic composite structural models. It is believed that the thread of similarity in behavior is telling the designer and dynamists a great deal about what to expect in the crash behavior of these structures and can guide designs for improving the energy absorption and crash behavior of such structures.

Complexity for Survival of Living Systems

NASA Technical Reports Server (NTRS)

Zak, Michail

2009-01-01

A logical connection between the survivability of living systems and the complexity of their behavior (equivalently, mental complexity) has been established. This connection is an important intermediate result of continuing research on mathematical models that could constitute a unified representation of the evolution of both living and non-living systems. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the two most relevant being Characteristics of Dynamics of Intelligent Systems (NPO- 21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; and Self-Supervised Dynamical Systems (NPO- 30634) NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72. As used here, living systems is synonymous with active systems and intelligent systems. The quoted terms can signify artificial agents (e.g., suitably programmed computers) or natural biological systems ranging from single-cell organisms at one extreme to the whole of human society at the other extreme. One of the requirements that must be satisfied in mathematical modeling of living systems is reconciliation of evolution of life with the second law of thermodynamics. In the approach followed in this research, this reconciliation is effected by means of a model, inspired partly by quantum mechanics, in which the quantum potential is replaced with an information potential. The model captures the most fundamental property of life - the ability to evolve from disorder to order without any external interference. The model incorporates the equations of classical dynamics, including Newton s equations of motion and equations for random components caused by uncertainties in initial conditions and by Langevin forces. The equations of classical dynamics are coupled with corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces that are gradients of the information potential, which, in turn, is a function of the probability densities. The probability densities are associated with mental images both self-image and nonself images (images of external objects that can include other agents). The evolution of the probability densities represents mental dynamics. Then the interaction between the physical and metal aspects of behavior is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. The interaction of a system with its self and nonself images affords unlimited capacity for increase of complexity. There is a biological basis for this model of mental dynamics in the discovery of mirror neurons that learn by imitation. The levels of complexity attained by use of this model match those observed in living systems. To establish a mechanism for increasing the complexity of dynamics of an active system, the model enables exploitation of a chain of reflections exemplified by questions of the form, "What do you think that I think that you think...?" Mathematically, each level of reflection is represented in the form of an attractor performing the corresponding level of abstraction with more details removed from higher levels. The model can be used to describe the behaviors, not only of biological systems, but also of ecological, social, and economics ones.

Spectral simplicity of apparent complexity. I. The nondiagonalizable metadynamics of prediction

NASA Astrophysics Data System (ADS)

Riechers, Paul M.; Crutchfield, James P.

2018-03-01

Virtually all questions that one can ask about the behavioral and structural complexity of a stochastic process reduce to a linear algebraic framing of a time evolution governed by an appropriate hidden-Markov process generator. Each type of question—correlation, predictability, predictive cost, observer synchronization, and the like—induces a distinct generator class. Answers are then functions of the class-appropriate transition dynamic. Unfortunately, these dynamics are generically nonnormal, nondiagonalizable, singular, and so on. Tractably analyzing these dynamics relies on adapting the recently introduced meromorphic functional calculus, which specifies the spectral decomposition of functions of nondiagonalizable linear operators, even when the function poles and zeros coincide with the operator's spectrum. Along the way, we establish special properties of the spectral projection operators that demonstrate how they capture the organization of subprocesses within a complex system. Circumventing the spurious infinities of alternative calculi, this leads in the sequel, Part II [P. M. Riechers and J. P. Crutchfield, Chaos 28, 033116 (2018)], to the first closed-form expressions for complexity measures, couched either in terms of the Drazin inverse (negative-one power of a singular operator) or the eigenvalues and projection operators of the appropriate transition dynamic.

Recurrence quantity analysis based on matrix eigenvalues

NASA Astrophysics Data System (ADS)

Yang, Pengbo; Shang, Pengjian

2018-06-01

Recurrence plots is a powerful tool for visualization and analysis of dynamical systems. Recurrence quantification analysis (RQA), based on point density and diagonal and vertical line structures in the recurrence plots, is considered to be alternative measures to quantify the complexity of dynamical systems. In this paper, we present a new measure based on recurrence matrix to quantify the dynamical properties of a given system. Matrix eigenvalues can reflect the basic characteristics of the complex systems, so we show the properties of the system by exploring the eigenvalues of the recurrence matrix. Considering that Shannon entropy has been defined as a complexity measure, we propose the definition of entropy of matrix eigenvalues (EOME) as a new RQA measure. We confirm that EOME can be used as a metric to quantify the behavior changes of the system. As a given dynamical system changes from a non-chaotic to a chaotic regime, the EOME will increase as well. The bigger EOME values imply higher complexity and lower predictability. We also study the effect of some factors on EOME,including data length, recurrence threshold, the embedding dimension, and additional noise. Finally, we demonstrate an application in physiology. The advantage of this measure lies in a high sensitivity and simple computation.

Topics in Complexity: Dynamical Patterns in the Cyberworld

NASA Astrophysics Data System (ADS)

Qi, Hong

Quantitative understanding of mechanism in complex systems is a common "difficult" problem across many fields such as physical, biological, social and economic sciences. Investigation on underlying dynamics of complex systems and building individual-based models have recently been fueled by big data resulted from advancing information technology. This thesis investigates complex systems in social science, focusing on civil unrests on streets and relevant activities online. Investigation consists of collecting data of unrests from open digital source, featuring dynamical patterns underlying, making predictions and constructing models. A simple law governing the progress of two-sided confrontations is proposed with data of activities at micro-level. Unraveling the connections between activity of organizing online and outburst of unrests on streets gives rise to a further meso-level pattern of human behavior, through which adversarial groups evolve online and hyper-escalate ahead of real-world uprisings. Based on the patterns found, noticeable improvement of prediction of civil unrests is achieved. Meanwhile, novel model created from combination of mobility dynamics in the cyberworld and a traditional contagion model can better capture the characteristics of modern civil unrests and other contagion-like phenomena than the original one.

Multi-stage complex contagions.

PubMed

Melnik, Sergey; Ward, Jonathan A; Gleeson, James P; Porter, Mason A

2013-03-01

The spread of ideas across a social network can be studied using complex contagion models, in which agents are activated by contact with multiple activated neighbors. The investigation of complex contagions can provide crucial insights into social influence and behavior-adoption cascades on networks. In this paper, we introduce a model of a multi-stage complex contagion on networks. Agents at different stages-which could, for example, represent differing levels of support for a social movement or differing levels of commitment to a certain product or idea-exert different amounts of influence on their neighbors. We demonstrate that the presence of even one additional stage introduces novel dynamical behavior, including interplay between multiple cascades, which cannot occur in single-stage contagion models. We find that cascades-and hence collective action-can be driven not only by high-stage influencers but also by low-stage influencers.

Multi-stage complex contagions

NASA Astrophysics Data System (ADS)

Melnik, Sergey; Ward, Jonathan A.; Gleeson, James P.; Porter, Mason A.

2013-03-01

The spread of ideas across a social network can be studied using complex contagion models, in which agents are activated by contact with multiple activated neighbors. The investigation of complex contagions can provide crucial insights into social influence and behavior-adoption cascades on networks. In this paper, we introduce a model of a multi-stage complex contagion on networks. Agents at different stages—which could, for example, represent differing levels of support for a social movement or differing levels of commitment to a certain product or idea—exert different amounts of influence on their neighbors. We demonstrate that the presence of even one additional stage introduces novel dynamical behavior, including interplay between multiple cascades, which cannot occur in single-stage contagion models. We find that cascades—and hence collective action—can be driven not only by high-stage influencers but also by low-stage influencers.

A computational framework for testing arrhythmia marker sensitivities to model parameters in functionally calibrated populations of atrial cells

NASA Astrophysics Data System (ADS)

Vagos, Márcia R.; Arevalo, Hermenegild; de Oliveira, Bernardo Lino; Sundnes, Joakim; Maleckar, Mary M.

2017-09-01

Models of cardiac cell electrophysiology are complex non-linear systems which can be used to gain insight into mechanisms of cardiac dynamics in both healthy and pathological conditions. However, the complexity of cardiac models can make mechanistic insight difficult. Moreover, these are typically fitted to averaged experimental data which do not incorporate the variability in observations. Recently, building populations of models to incorporate inter- and intra-subject variability in simulations has been combined with sensitivity analysis (SA) to uncover novel ionic mechanisms and potentially clarify arrhythmogenic behaviors. We used the Koivumäki human atrial cell model to create two populations, representing normal Sinus Rhythm (nSR) and chronic Atrial Fibrillation (cAF), by varying 22 key model parameters. In each population, 14 biomarkers related to the action potential and dynamic restitution were extracted. Populations were calibrated based on distributions of biomarkers to obtain reasonable physiological behavior, and subjected to SA to quantify correlations between model parameters and pro-arrhythmia markers. The two populations showed distinct behaviors under steady state and dynamic pacing. The nSR population revealed greater variability, and more unstable dynamic restitution, as compared to the cAF population, suggesting that simulated cAF remodeling rendered cells more stable to parameter variation and rate adaptation. SA revealed that the biomarkers depended mainly on five ionic currents, with noted differences in sensitivities to these between nSR and cAF. Also, parameters could be selected to produce a model variant with no alternans and unaltered action potential morphology, highlighting that unstable dynamical behavior may be driven by specific cell parameter settings. These results ultimately suggest that arrhythmia maintenance in cAF may not be due to instability in cell membrane excitability, but rather due to tissue-level effects which promote initiation and maintenance of reentrant arrhythmia.

Complexities’ day-to-day dynamic evolution analysis and prediction for a Didi taxi trip network based on complex network theory

NASA Astrophysics Data System (ADS)

Zhang, Lin; Lu, Jian; Zhou, Jialin; Zhu, Jinqing; Li, Yunxuan; Wan, Qian

2018-03-01

Didi Dache is the most popular taxi order mobile app in China, which provides online taxi-hailing service. The obtained big database from this app could be used to analyze the complexities’ day-to-day dynamic evolution of Didi taxi trip network (DTTN) from the level of complex network dynamics. First, this paper proposes the data cleaning and modeling methods for expressing Nanjing’s DTTN as a complex network. Second, the three consecutive weeks’ data are cleaned to establish 21 DTTNs based on the proposed big data processing technology. Then, multiple topology measures that characterize the complexities’ day-to-day dynamic evolution of these networks are provided. Third, these measures of 21 DTTNs are calculated and subsequently explained with actual implications. They are used as a training set for modeling the BP neural network which is designed for predicting DTTN complexities evolution. Finally, the reliability of the designed BP neural network is verified by comparing with the actual data and the results obtained from ARIMA method simultaneously. Because network complexities are the basis for modeling cascading failures and conducting link prediction in complex system, this proposed research framework not only provides a novel perspective for analyzing DTTN from the level of system aggregated behavior, but can also be used to improve the DTTN management level.

Tour time in a two-route traffic system controlled by signals

NASA Astrophysics Data System (ADS)

Nagatani, Takashi; Naito, Yuichi

2011-11-01

We study the dynamic behavior of vehicular traffic in a two-route system with a series of signals (traffic lights) at low density where the number of signals on route A is different from that on route B. We investigate the dependence of the tour time on the route for some strategies of signal control. The nonlinear dynamic model of a two-route traffic system controlled by signals is presented by nonlinear maps. The vehicular traffic exhibits a very complex behavior, depending on the cycle time, the phase difference, and the irregularity. The dependence of the tour time on the route choice is clarified for the signal strategies.

Recurrence analysis of ant activity patterns

PubMed Central

2017-01-01

In this study, we used recurrence quantification analysis (RQA) and recurrence plots (RPs) to compare the movement activity of individual workers of three ant species, as well as a gregarious beetle species. RQA and RPs quantify the number and duration of recurrences of a dynamical system, including a detailed quantification of signals that could be stochastic, deterministic, or both. First, we found substantial differences between the activity dynamics of beetles and ants, with the results suggesting that the beetles have quasi-periodic dynamics and the ants do not. Second, workers from different ant species varied with respect to their dynamics, presenting degrees of predictability as well as stochastic signals. Finally, differences were found among minor and major caste of the same (dimorphic) ant species. Our results underscore the potential of RQA and RPs in the analysis of complex behavioral patterns, as well as in general inferences on animal behavior and other biological phenomena. PMID:29016648

Quincke rotation of an ellipsoid

NASA Astrophysics Data System (ADS)

Vlahovska, Petia; Brosseau, Quentin

2016-11-01

The Quincke effect - spontaneous spinning of a sphere in a uniform DC electric field - has attracted considerable interest in recent year because of the intriguing dynamics exhibited by a Quincke-rotating drop and the emergent collective behavior of confined suspensions of Quincke-rotating spheres. Shape anisotropy, e.g., due to drop deformation or particle asphericity, is predicted to give rise to complex particle dynamics. Analysis of the dynamics of rigid prolate ellipsoid in a uniform DC electric field shows two possible stable states characterized by the orientation of the ellipsoid long axis relative to the applied electric field : spinless (parallel) and spinning (perpendicular). Here we report an experimental study testing the theoretical predictions. The phase diagram of ellipsoid behavior as a function of field strength and aspect ratio is in close agreement with theory. We also investigated the dynamics of the ellipsoidal Quincke "roller": an ellipsoid near a planar surface with normal perpendicular to the field direction. We find novel behaviors such as swinging (long axis oscillating around the applied field direction) and tumbling due to the confinement. Supported by NSF CBET awards 1437545 and 1544196.

Nonlinear analysis of EEGs of patients with major depression during different emotional states.

PubMed

Akdemir Akar, Saime; Kara, Sadık; Agambayev, Sümeyra; Bilgiç, Vedat

2015-12-01

Although patients with major depressive disorder (MDD) have dysfunctions in cognitive behaviors and the regulation of emotions, the underlying brain dynamics of the pathophysiology are unclear. Therefore, nonlinear techniques can be used to understand the dynamic behavior of the EEG signals of MDD patients. To investigate and clarify the dynamics of MDD patients׳ brains during different emotional states, EEG recordings were analyzed using nonlinear techniques. The purpose of the present study was to assess whether there are different EEG complexities that discriminate between MDD patients and healthy controls during emotional processing. Therefore, nonlinear parameters, such as Katz fractal dimension (KFD), Higuchi fractal dimension (HFD), Shannon entropy (ShEn), Lempel-Ziv complexity (LZC) and Kolmogorov complexity (KC), were computed from the EEG signals of two groups under different experimental states: noise (negative emotional content) and music (positive emotional content) periods. First, higher complexity values were generated by MDD patients relative to controls. Significant differences were obtained in the frontal and parietal scalp locations using KFD (p<0.001), HFD (p<0.05), and LZC (p=0.05). Second, lower complexities were observed only in the controls when they were subjected to music compared to the resting baseline state in the frontal (p<0.05) and parietal (p=0.005) regions. In contrast, the LZC and KFD values of patients increased in the music period compared to the resting state in the frontal region (p<0.05). Third, the patients׳ brains had higher complexities when they were exposed to noise stimulus than did the controls׳ brains. Moreover, MDD patients׳ negative emotional bias was demonstrated by their higher brain complexities during the noise period than the music stimulus. Additionally, we found that the KFD, HFD and LZC values were more sensitive in discriminating between patients and controls than the ShEn and KC measures, according to the results of ANOVA and ROC calculations. It can be concluded that the nonlinear analysis may be a useful and discriminative tool in investigating the neuro-dynamic properties of the brain in patients with MDD during emotional stimulation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparing the Ability of Enhanced Sampling Molecular Dynamics Methods To Reproduce the Behavior of Fluorescent Labels on Proteins.

PubMed

Walczewska-Szewc, Katarzyna; Deplazes, Evelyne; Corry, Ben

2015-07-14

Adequately sampling the large number of conformations accessible to proteins and other macromolecules is one of the central challenges in molecular dynamics (MD) simulations; this activity can be difficult, even for relatively simple systems. An example where this problem arises is in the simulation of dye-labeled proteins, which are now being widely used in the design and interpretation of Förster resonance energy transfer (FRET) experiments. In this study, MD simulations are used to characterize the motion of two commonly used FRET dyes attached to an immobilized chain of polyproline. Even in this simple system, the dyes exhibit complex behavior that is a mixture of fast and slow motions. Consequently, very long MD simulations are required to sufficiently sample the entire range of dye motion. Here, we compare the ability of enhanced sampling methods to reproduce the behavior of fluorescent labels on proteins. In particular, we compared Accelerated Molecular Dynamics (AMD), metadynamics, Replica Exchange Molecular Dynamics (REMD), and High Temperature Molecular Dynamics (HTMD) to equilibrium MD simulations. We find that, in our system, all of these methods improve the sampling of the dye motion, but the most significant improvement is achieved using REMD.

Mechanically dynamic PDMS substrates to investigate changing cell environments

PubMed Central

Yeh, Yi-Cheun; Corbin, Elise A.; Caliari, Steven R.; Ouyang, Liu; Vega, Sebastián L.; Truitt, Rachel; Han, Lin; Margulies, Kenneth B.; Burdick, Jason A.

2018-01-01

Mechanics of the extracellular matrix (ECM) play a pivotal role in governing cell behavior, such as cell spreading and differentiation. ECM mechanics have been recapitulated primarily in elastic hydrogels, including with dynamic properties to mimic complex behaviors (e.g., fibrosis); however, these dynamic hydrogels fail to introduce the viscoelastic nature of many tissues. Here, we developed a two-step crosslinking strategy to first form (via platinum-catalyzed crosslinking) networks of polydimethylsiloxane (PDMS) and then to increase PDMS crosslinking (via thiol-ene click reaction) in a temporally-controlled manner. This photoinitiated reaction increased the compressive modulus of PDMS up to 10-fold within minutes and was conducted under cytocompatible conditions. With stiffening, cells displayed increased spreading, changing from ~1300 to 1900 μm2 and from ~2700 to 4600 μm2 for fibroblasts and mesenchymal stem cells, respectively. In addition, higher myofibroblast activation (from ~2 to 20%) for cardiac fibroblasts was observed with increasing PDMS substrate stiffness. These results indicate a cellular response to changes in PDMS substrate mechanics, along with a demonstration of a mechanically dynamic and photoresponsive PDMS substrate platform to model the dynamic behavior of ECM. PMID:28843064

Mechanically dynamic PDMS substrates to investigate changing cell environments.

PubMed

Yeh, Yi-Cheun; Corbin, Elise A; Caliari, Steven R; Ouyang, Liu; Vega, Sebastián L; Truitt, Rachel; Han, Lin; Margulies, Kenneth B; Burdick, Jason A

2017-11-01

Mechanics of the extracellular matrix (ECM) play a pivotal role in governing cell behavior, such as cell spreading and differentiation. ECM mechanics have been recapitulated primarily in elastic hydrogels, including with dynamic properties to mimic complex behaviors (e.g., fibrosis); however, these dynamic hydrogels fail to introduce the viscoelastic nature of many tissues. Here, we developed a two-step crosslinking strategy to first form (via platinum-catalyzed crosslinking) networks of polydimethylsiloxane (PDMS) and then to increase PDMS crosslinking (via thiol-ene click reaction) in a temporally-controlled manner. This photoinitiated reaction increased the compressive modulus of PDMS up to 10-fold within minutes and was conducted under cytocompatible conditions. With stiffening, cells displayed increased spreading, changing from ∼1300 to 1900 μm 2 and from ∼2700 to 4600 μm 2 for fibroblasts and mesenchymal stem cells, respectively. In addition, higher myofibroblast activation (from ∼2 to 20%) for cardiac fibroblasts was observed with increasing PDMS substrate stiffness. These results indicate a cellular response to changes in PDMS substrate mechanics, along with a demonstration of a mechanically dynamic and photoresponsive PDMS substrate platform to model the dynamic behavior of ECM. Copyright © 2017 Elsevier Ltd. All rights reserved.

Speech as a breakthrough signaling resource in the cognitive evolution of biological complex adaptive systems.

PubMed

Mattei, Tobias A

2014-12-01

In self-adapting dynamical systems, a significant improvement in the signaling flow among agents constitutes one of the most powerful triggering events for the emergence of new complex behaviors. Ackermann and colleagues' comprehensive phylogenetic analysis of the brain structures involved in acoustic communication provides further evidence of the essential role which speech, as a breakthrough signaling resource, has played in the evolutionary development of human cognition viewed from the standpoint of complex adaptive system analysis.

Sustainable Water Systems for the City of Tomorrow—A Conceptual Framework

EPA Science Inventory

Urban water systems are an example of complex, dynamic human-environment coupled systems, which exhibit emergent behaviors that transcends individual scientific disciplines. While previous siloed approaches to water services (i.e., water resources, drinking water, wastewater, and...

Dynamic modelling and response characteristics of a magnetic bearing rotor system with auxiliary bearings

NASA Technical Reports Server (NTRS)

Free, April M.; Flowers, George T.; Trent, Victor S.

1995-01-01

Auxiliary bearings are a critical feature of any magnetic bearing system. They protect the soft iron core of the magnetic bearing during an overload or failure. An auxiliary bearing typically consists of a rolling element bearing or bushing with a clearance gap between the rotor and the inner race of the support. The dynamics of such systems can be quite complex. It is desired to develop a rotordynamic model which describes the dynamic behavior of a flexible rotor system with magnetic bearings including auxiliary bearings. The model is based upon an experimental test facility. Some simulation studies are presented to illustrate the behavior of the model. In particular, the effects of introducing sideloading from the magnetic bearing when one coil fails is studied.

Scaling behavior in the dynamics of citations to scientific journals

NASA Astrophysics Data System (ADS)

Picoli, S., Jr.; Mendes, R. S.; Malacarne, L. C.; Lenzi, E. K.

2006-08-01

We analyze a database comprising the impact factor (citations per recent items published) of scientific journals for a 13-year period (1992 2004). We find that i) the distribution of impact factors follows asymptotic power law behavior, ii) the distribution of annual logarithmic growth rates has an exponential form, and iii) the width of this distribution decays with the impact factor as a power law with exponent β simeq 0.22. The results ii) and iii) are surprising similar to those observed in the growth dynamics of organizations with complex internal structure suggesting the existence of common mechanisms underlying the dynamics of these systems. We propose a general model for such systems, an extension of the simplest model for firm growth, and compare their predictions with our empirical results.

The Complexity of Dynamics in Small Neural Circuits

PubMed Central

Panzeri, Stefano

2016-01-01

Mean-field approximations are a powerful tool for studying large neural networks. However, they do not describe well the behavior of networks composed of a small number of neurons. In this case, major differences between the mean-field approximation and the real behavior of the network can arise. Yet, many interesting problems in neuroscience involve the study of mesoscopic networks composed of a few tens of neurons. Nonetheless, mathematical methods that correctly describe networks of small size are still rare, and this prevents us to make progress in understanding neural dynamics at these intermediate scales. Here we develop a novel systematic analysis of the dynamics of arbitrarily small networks composed of homogeneous populations of excitatory and inhibitory firing-rate neurons. We study the local bifurcations of their neural activity with an approach that is largely analytically tractable, and we numerically determine the global bifurcations. We find that for strong inhibition these networks give rise to very complex dynamics, caused by the formation of multiple branching solutions of the neural dynamics equations that emerge through spontaneous symmetry-breaking. This qualitative change of the neural dynamics is a finite-size effect of the network, that reveals qualitative and previously unexplored differences between mesoscopic cortical circuits and their mean-field approximation. The most important consequence of spontaneous symmetry-breaking is the ability of mesoscopic networks to regulate their degree of functional heterogeneity, which is thought to help reducing the detrimental effect of noise correlations on cortical information processing. PMID:27494737

Constructing food choice decisions.

PubMed

Sobal, Jeffery; Bisogni, Carole A

2009-12-01

Food choice decisions are frequent, multifaceted, situational, dynamic, and complex and lead to food behaviors where people acquire, prepare, serve, give away, store, eat, and clean up. Many disciplines and fields examine decision making. Several classes of theories are applicable to food decision making, including social behavior, social facts, and social definition perspectives. Each offers some insights but also makes limiting assumptions that prevent fully explaining food choice decisions. We used constructionist social definition perspectives to inductively develop a food choice process model that organizes a broad scope of factors and dynamics involved in food behaviors. This food choice process model includes (1) life course events and experiences that establish a food choice trajectory through transitions, turning points, timing, and contexts; (2) influences on food choices that include cultural ideals, personal factors, resources, social factors, and present contexts; and (3) a personal system that develops food choice values, negotiates and balances values, classifies foods and situations, and forms/revises food choice strategies, scripts, and routines. The parts of the model dynamically interact to make food choice decisions leading to food behaviors. No single theory can fully explain decision making in food behavior. Multiple perspectives are needed, including constructionist thinking.

Static and dynamic properties of smoothed dissipative particle dynamics

NASA Astrophysics Data System (ADS)

Alizadehrad, Davod; Fedosov, Dmitry A.

2018-03-01

In this paper, static and dynamic properties of the smoothed dissipative particle dynamics (SDPD) method are investigated. We study the effect of method parameters on SDPD fluid properties, such as structure, speed of sound, and transport coefficients, and show that a proper choice of parameters leads to a well-behaved and accurate fluid model. In particular, the speed of sound, the radial distribution function (RDF), shear-thinning of viscosity, the mean-squared displacement (〈R2 〉 ∝ t), and the Schmidt number (Sc ∼ O (103) - O (104)) can be controlled, such that the model exhibits a fluid-like behavior for a wide range of temperatures in simulations. Furthermore, in addition to the consideration of fluid density variations for fluid compressibility, a more challenging test of incompressibility is performed by considering the Poisson ratio and divergence of velocity field in an elongational flow. Finally, as an example of complex-fluid flow, we present the applicability and validity of the SDPD method with an appropriate choice of parameters for the simulation of cellular blood flow in irregular geometries. In conclusion, the results demonstrate that the SDPD method is able to approximate well a nearly incompressible fluid behavior, which includes hydrodynamic interactions and consistent thermal fluctuations, thereby providing, a powerful approach for simulations of complex mesoscopic systems.

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.

A cluster phase analysis for collective behavior in team sports.

PubMed

López-Felip, Maurici A; Davis, Tehran J; Frank, Till D; Dixon, James A

2018-06-01

Collective behavior can be defined as the ability of humans to coordinate with others through a complex environment. Sports offer exquisite examples of this dynamic interplay, requiring decision making and other perceptual-cognitive skills to adjust individual decisions to the team self-organization and vice versa. Considering players of a team as periodic phase oscillators, synchrony analyses can be used to model the coordination of a team. Nonetheless, a main limitation of current models is that collective behavior is context independent. In other words, players on a team can be highly synchronized without this corresponding to a meaningful coordination dynamics relevant to the context of the game. Considering these issues, the aim of this study was to develop a method of analysis sensitive to the context for evidence-based measures of collective behavior. Copyright © 2018 Elsevier B.V. All rights reserved.

Unnatural selection: talent identification and development in sport.

PubMed

Abbott, Angela; Button, Chris; Pepping, Gert-Jan; Collins, Dave

2005-01-01

The early identification of talented individuals has become increasingly important across many performance domains. Current talent identification (TI) schemes in sport typically select on the basis of discrete, unidimensional measures at unstable periods in the athlete's development. In this article, the concept of talent is revised as a complex, dynamical system in which future behaviors emerge from an interaction of key performance determinants such as psychological behaviors, motor abilities, and physical characteristics. Key nonlinear dynamics concepts are related to TI approaches such as sensitivity to initial conditions, transitions, and exponential behavioral distributions. It is concluded that many TI models place an overemphasis on early identification rather than the development of potentially talented performers. A generic model of talent identification and development is proposed that addresses these issues and provides direction for future research.

Granular chaos and mixing: Whirled in a grain of sand.

PubMed

Shinbrot, Troy

2015-09-01

In this paper, we overview examples of chaos in granular flows. We begin by reviewing several remarkable behaviors that have intrigued researchers over the past few decades, and we then focus on three areas in which chaos plays an intrinsic role in granular behavior. First, we discuss pattern formation in vibrated beds, which we show is a direct result of chaotic scattering combined with dynamical dissipation. Next, we consider stick-slip motion, which involves chaotic scattering on the micro-scale, and which results in complex and as yet unexplained peculiarities on the macro-scale. Finally, we examine granular mixing, which we show combines micro-scale chaotic scattering and macro-scale stick-slip motion into behaviors that are well described by dynamical systems tools, such as iterative mappings.

Granular chaos and mixing: Whirled in a grain of sand

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

Shinbrot, Troy, E-mail: shinbrot@rutgers.edu

2015-09-15

In this paper, we overview examples of chaos in granular flows. We begin by reviewing several remarkable behaviors that have intrigued researchers over the past few decades, and we then focus on three areas in which chaos plays an intrinsic role in granular behavior. First, we discuss pattern formation in vibrated beds, which we show is a direct result of chaotic scattering combined with dynamical dissipation. Next, we consider stick-slip motion, which involves chaotic scattering on the micro-scale, and which results in complex and as yet unexplained peculiarities on the macro-scale. Finally, we examine granular mixing, which we show combinesmore » micro-scale chaotic scattering and macro-scale stick-slip motion into behaviors that are well described by dynamical systems tools, such as iterative mappings.« less

Wigner flow reveals topological order in quantum phase space dynamics.

PubMed

Steuernagel, Ole; Kakofengitis, Dimitris; Ritter, Georg

2013-01-18

The behavior of classical mechanical systems is characterized by their phase portraits, the collections of their trajectories. Heisenberg's uncertainty principle precludes the existence of sharply defined trajectories, which is why traditionally only the time evolution of wave functions is studied in quantum dynamics. These studies are quite insensitive to the underlying structure of quantum phase space dynamics. We identify the flow that is the quantum analog of classical particle flow along phase portrait lines. It reveals hidden features of quantum dynamics and extra complexity. Being constrained by conserved flow winding numbers, it also reveals fundamental topological order in quantum dynamics that has so far gone unnoticed.

A complex relationship between calving glaciers and climate

USGS Publications Warehouse

Post, A.; O'Neel, S.; Motyka, R.J.; Streveler, G.

2011-01-01

Many terrestrial glaciers are sensitive indicators of past and present climate change as atmospheric temperature and snowfall modulate glacier volume. However, climate interpretations based on glacier behavior require careful selection of representative glaciers, as was recently pointed out for surging and debris-covered glaciers, whose behavior often defies regional glacier response to climate [Yde and Paasche, 2010]. Tidewater calving glaciers (TWGs)mountain glaciers whose termini reach the sea and are generally grounded on the seaflooralso fall into the category of non-representative glaciers because the regional-scale asynchronous behavior of these glaciers clouds their complex relationship with climate. TWGs span the globe; they can be found both fringing ice sheets and in high-latitude regions of each hemisphere. TWGs are known to exhibit cyclic behavior, characterized by slow advance and rapid, unstable retreat, largely independent of short-term climate forcing. This so-called TWG cycle, first described by Post [1975], provides a solid foundation upon which modern investigations of TWG stability are built. Scientific understanding has developed rapidly as a result of the initial recognition of their asynchronous cyclicity, rendering greater insight into the hierarchy of processes controlling regional behavior. This has improved the descriptions of the strong dynamic feedbacks present during retreat, the role of the ocean in TWG dynamics, and the similarities and differences between TWG and ice sheet outlet glaciers that can often support floating tongues.

Wavelet transform analysis of dynamic speckle patterns texture

NASA Astrophysics Data System (ADS)

Limia, Margarita Fernandez; Nunez, Adriana Mavilio; Rabal, Hector; Trivi, Marcelo

2002-11-01

We propose the use of the wavelet transform to characterize the time evolution of dynamic speckle patterns. We describe it by using as an example a method used for the assessment of the drying of paint. Optimal texture features are determined and the time evolution is described in terms of the Mahalanobis distance to the final (dry) state. From the behavior of this distance function, two parameters are defined that characterize the evolution. Because detailed knowledge of the involved dynamics is not required, the methodology could be implemented for other complex or poorly understood dynamic phenomena.

Do endothelial cells dream of eclectic shape?

PubMed

Bentley, Katie; Philippides, Andrew; Ravasz Regan, Erzsébet

2014-04-28

Endothelial cells (ECs) exhibit dramatic plasticity of form at the single- and collective-cell level during new vessel growth, adult vascular homeostasis, and pathology. Understanding how, when, and why individual ECs coordinate decisions to change shape, in relation to the myriad of dynamic environmental signals, is key to understanding normal and pathological blood vessel behavior. However, this is a complex spatial and temporal problem. In this review we show that the multidisciplinary field of Adaptive Systems offers a refreshing perspective, common biological language, and straightforward toolkit that cell biologists can use to untangle the complexity of dynamic, morphogenetic systems. Copyright © 2014 Elsevier Inc. All rights reserved.

Catalytic dimer nanomotors: continuum theory and microscopic dynamics.

PubMed

Reigh, Shang Yik; Kapral, Raymond

2015-04-28

Synthetic chemically-powered motors with various geometries have potentially new applications involving dynamics on very small scales. Self-generated concentration and fluid flow fields, which depend on geometry, play essential roles in motor dynamics. Sphere-dimer motors, comprising linked catalytic and noncatalytic spheres, display more complex versions of such fields, compared to the often-studied spherical Janus motors. By making use of analytical continuum theory and particle-based simulations we determine the concentration fields, and both the complex structure of the near-field and point-force dipole nature of the far-field behavior of the solvent velocity field that are important for studies of collective motor motion. We derive the dependence of motor velocity on geometric factors such as sphere size and dimer bond length and, thus, show how to construct motors with specific characteristics.

Dynamical analysis of uterine cell electrical activity model.

PubMed

Rihana, S; Santos, J; Mondie, S; Marque, C

2006-01-01

The uterus is a physiological system consisting of a large number of interacting smooth muscle cells. The uterine excitability changes remarkably with time, generally quiescent during pregnancy, the uterus exhibits forceful synchronized contractions at term leading to fetus expulsion. These changes characterize thus a dynamical system susceptible of being studied through formal mathematical tools. Multiple physiological factors are involved in the regulation process of this complex system. Our aim is to relate the physiological factors to the uterine cell dynamic behaviors. Taking into account a previous work presented, in which the electrical activity of a uterine cell is described by a set of ordinary differential equations, we analyze the impact of physiological parameters on the response of the model, and identify the main subsystems generating the complex uterine electrical activity, with respect to physiological data.

Review—Physicochemical hydrodynamics of gas bubbles in two phase electrochemical systems

PubMed Central

Taqieddin, Amir; Nazari, Roya; Rajic, Ljiljana; Alshawabkeh, Akram

2018-01-01

Electrochemical systems suffer from poor management of evolving gas bubbles. Improved understanding of bubbles behavior helps to reduce overpotential, save energy and enhance the mass transfer during chemical reactions. This work investigates and reviews the gas bubbles hydrodynamics, behavior, and management in electrochemical cells. Although the rate of bubble growth over the electrode surface is well understood, there is no reliable prediction of bubbles break-off diameter from the electrode surface because of the complexity of bubbles motion near the electrode surface. Particle Image Velocimetry (PIV) and Laser Doppler Anemometry (LDA) are the most common experimental techniques to measure bubble dynamics. Although the PIV is faster than LDA, both techniques are considered expensive and time-consuming. This encourages adapting Computational Fluid Dynamics (CFD) methods as an alternative to study bubbles behavior. However, further development of CFD methods is required to include coalescence and break-up of bubbles for better understanding and accuracy. The disadvantages of CFD methods can be overcome by using hybrid methods. The behavior of bubbles in electrochemical systems is still a complex challenging topic which requires a better understanding of the gas bubbles hydrodynamics and their interactions with the electrode surface and bulk liquid, as well as between the bubbles itself. PMID:29731515

Exploration of bulk and interface behavior of gas molecules and 1-butyl-3-methylimidazolium tetrafluoroborate ionic liquid using equilibrium and nonequilibrium molecular dynamics simulation and quantum chemical calculation.

PubMed

Yang, Quan; Achenie, Luke E K

2018-04-18

Ionic liquids (ILs) show brilliant performance in separating gas impurities, but few researchers have performed an in-depth exploration of the bulk and interface behavior of penetrants and ILs thoroughly. In this research, we have performed a study on both molecular dynamics (MD) simulation and quantum chemical (QC) calculation to explore the transport of acetylene and ethylene in the bulk and interface regions of 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM]-[BF4]). The diffusivity, solubility and permeability of gas molecules in the bulk were researched with MD simulation first. The subdiffusion behavior of gas molecules is induced by coupling between the motion of gas molecules and the ions, and the relaxation processes of the ions after the disturbance caused by gas molecules. Then, QC calculation was performed to explore the optical geometry of ions, ion pairs and complexes of ions and penetrants, and interaction potential for pairs and complexes. Finally, nonequilibrium MD simulation was performed to explore the interface structure and properties of the IL-gas system and gas molecule behavior in the interface region. The research results may be used in the design of IL separation media.

Protein stability and dynamics influenced by ligands in extremophilic complexes - a molecular dynamics investigation.

PubMed

Khan, Sara; Farooq, Umar; Kurnikova, Maria

2017-08-22

In this study, we explore the structural and dynamic adaptations of the Tryptophan synthase α-subunit in a ligand bound state in psychrophilic, mesophilic and hyperthermophilic organisms at different temperatures by MD simulations. We quantify the global and local fluctuations in the 40 ns time scale by analyzing the root mean square deviation/fluctuations. The distinct behavior of the active site and loop 6 is observed with the elevation of temperature. Protein stability relies more on electrostatic interactions, and these interactions might be responsible for the stability of varying temperature evolved proteins. The paper also focuses on the effect of temperature on protein dynamics and stability governed by the distinct behavior of the ligand associated with its retention, binding and dissociation over the course of time. The integration of principle component analysis and a free energy landscape was useful in identifying the conformational space accessible to ligand bound homologues and how the presence of the ligand alters the conformational and dynamic properties of the protein.

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.

ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.

PubMed

Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

2017-07-20

Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

Further steps in the modeling of behavioural crowd dynamics, good news for safe handling. Comment on "Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management" by Nicola Bellomo et al.

NASA Astrophysics Data System (ADS)

Knopoff, Damián A.

2016-09-01

The recent review paper [4] constitutes a valuable contribution on the understanding, modeling and simulation of crowd dynamics in extreme situations. It provides a very comprehensive revision about the complexity features of the system under consideration, scaling and the consequent justification of the used methods. In particular, macro and microscopic models have so far been used to model crowd dynamics [9] and authors appropriately explain that working at the mesoscale is a good choice to deal with the heterogeneous behaviour of walkers as well as with the difficulty of their deterministic identification. In this way, methods based on the kinetic theory and statistical dynamics are employed, more precisely the so-called kinetic theory for active particles [7]. This approach has successfully been applied in the modeling of several complex dynamics, with recent applications to learning [2,8] that constitutes the key to understand communication and is of great importance in social dynamics and behavioral sciences.

A novel approach to the dynamical complexity of the Earth's magnetosphere at geomagnetic storm time-scales based on recurrences

NASA Astrophysics Data System (ADS)

Donner, Reik; Balasis, Georgios; Stolbova, Veronika; Wiedermann, Marc; Georgiou, Marina; Kurths, Jürgen

2016-04-01

Magnetic storms are the most prominent global manifestations of out-of-equilibrium magnetospheric dynamics. Investigating the dynamical complexity exhibited by geomagnetic observables can provide valuable insights into relevant physical processes as well as temporal scales associated with this phenomenon. In this work, we introduce several innovative data analysis techniques enabling a quantitative analysis of the Dst index non-stationary behavior. Using recurrence quantification analysis (RQA) and recurrence network analysis (RNA), we obtain a variety of complexity measures serving as markers of quiet- and storm-time magnetospheric dynamics. We additionally apply these techniques to the main driver of Dst index variations, the V BSouth coupling function and interplanetary medium parameters Bz and Pdyn in order to discriminate internal processes from the magnetosphere's response directly induced by the external forcing by the solar wind. The derived recurrence-based measures allow us to improve the accuracy with which magnetospheric storms can be classified based on ground-based observations. The new methodology presented here could be of significant interest for the space weather research community working on time series analysis for magnetic storm forecasts.

Fixation, transient landscape, and diffusion dilemma in stochastic evolutionary game dynamics

NASA Astrophysics Data System (ADS)

Zhou, Da; Qian, Hong

2011-09-01

Agent-based stochastic models for finite populations have recently received much attention in the game theory of evolutionary dynamics. Both the ultimate fixation and the pre-fixation transient behavior are important to a full understanding of the dynamics. In this paper, we study the transient dynamics of the well-mixed Moran process through constructing a landscape function. It is shown that the landscape playing a central theoretical “device” that integrates several lines of inquiries: the stable behavior of the replicator dynamics, the long-time fixation, and continuous diffusion approximation associated with asymptotically large population. Several issues relating to the transient dynamics are discussed: (i) multiple time scales phenomenon associated with intra- and inter-attractoral dynamics; (ii) discontinuous transition in stochastically stationary process akin to Maxwell construction in equilibrium statistical physics; and (iii) the dilemma diffusion approximation facing as a continuous approximation of the discrete evolutionary dynamics. It is found that rare events with exponentially small probabilities, corresponding to the uphill movements and barrier crossing in the landscape with multiple wells that are made possible by strong nonlinear dynamics, plays an important role in understanding the origin of the complexity in evolutionary, nonlinear biological systems.

Deciphering complex dynamics of water counteraction around secondary structural elements of allosteric protein complex: Case study of SAP-SLAM system in signal transduction cascade

NASA Astrophysics Data System (ADS)

Samanta, Sudipta; Mukherjee, Sanchita

2018-01-01

The first hydration shell of a protein exhibits heterogeneous behavior owing to several attributes, majorly local polarity and structural flexibility as revealed by solvation dynamics of secondary structural elements. We attempt to recognize the change in complex water counteraction generated due to substantial alteration in flexibility during protein complex formation. The investigation is carried out with the signaling lymphocytic activation molecule (SLAM) family of receptors, expressed by an array of immune cells, and interacting with SLAM-associated protein (SAP), composed of one SH2 domain. All atom molecular dynamics simulations are employed to the aqueous solutions of free SAP and SLAM-peptide bound SAP. We observed that water dynamics around different secondary structural elements became highly affected as well as nicely correlated with the SLAM-peptide induced change in structural rigidity obtained by thermodynamic quantification. A few instances of contradictory dynamic features of water to the change in structural flexibility are explained by means of occluded polar residues by the peptide. For βD, EFloop, and BGloop, both structural flexibility and solvent accessibility of the residues confirm the obvious contribution. Most importantly, we have quantified enhanced restriction in water dynamics around the second Fyn-binding site of the SAP due to SAP-SLAM complexation, even prior to the presence of Fyn. This observation leads to a novel argument that SLAM induced more restricted water molecules could offer more water entropic contribution during the subsequent Fyn binding and provide enhanced stability to the SAP-Fyn complex in the signaling cascade. Finally, SLAM induced water counteraction around the second binding site of the SAP sheds light on the allosteric property of the SAP, which becomes an integral part of the underlying signal transduction mechanism.

The Emergence of Temporal Structures in Dynamical Systems

NASA Astrophysics Data System (ADS)

Mainzer, Klaus

2010-10-01

Dynamical systems in classical, relativistic and quantum physics are ruled by laws with time reversibility. Complex dynamical systems with time-irreversibility are known from thermodynamics, biological evolution, growth of organisms, brain research, aging of people, and historical processes in social sciences. Complex systems are systems that compromise many interacting parts with the ability to generate a new quality of macroscopic collective behavior the manifestations of which are the spontaneous emergence of distinctive temporal, spatial or functional structures. But, emergence is no mystery. In a general meaning, the emergence of macroscopic features results from the nonlinear interactions of the elements in a complex system. Mathematically, the emergence of irreversible structures is modelled by phase transitions in non-equilibrium dynamics of complex systems. These methods have been modified even for chemical, biological, economic and societal applications (e.g., econophysics). Emergence of irreversible structures can also be simulated by computational systems. The question arises how the emergence of irreversible structures is compatible with the reversibility of fundamental physical laws. It is argued that, according to quantum cosmology, cosmic evolution leads from symmetry to complexity of irreversible structures by symmetry breaking and phase transitions. Thus, arrows of time and aging processes are not only subjective experiences or even contradictions to natural laws, but they can be explained by quantum cosmology and the nonlinear dynamics of complex systems. Human experiences and religious concepts of arrows of time are considered in a modern scientific framework. Platonic ideas of eternity are at least understandable with respect to mathematical invariance and symmetry of physical laws. Heraclit’s world of change and dynamics can be mapped onto our daily real-life experiences of arrows of time.

Deciphering complex dynamics of water counteraction around secondary structural elements of allosteric protein complex: Case study of SAP-SLAM system in signal transduction cascade.

PubMed

Samanta, Sudipta; Mukherjee, Sanchita

2018-01-28

The first hydration shell of a protein exhibits heterogeneous behavior owing to several attributes, majorly local polarity and structural flexibility as revealed by solvation dynamics of secondary structural elements. We attempt to recognize the change in complex water counteraction generated due to substantial alteration in flexibility during protein complex formation. The investigation is carried out with the signaling lymphocytic activation molecule (SLAM) family of receptors, expressed by an array of immune cells, and interacting with SLAM-associated protein (SAP), composed of one SH2 domain. All atom molecular dynamics simulations are employed to the aqueous solutions of free SAP and SLAM-peptide bound SAP. We observed that water dynamics around different secondary structural elements became highly affected as well as nicely correlated with the SLAM-peptide induced change in structural rigidity obtained by thermodynamic quantification. A few instances of contradictory dynamic features of water to the change in structural flexibility are explained by means of occluded polar residues by the peptide. For βD, EFloop, and BGloop, both structural flexibility and solvent accessibility of the residues confirm the obvious contribution. Most importantly, we have quantified enhanced restriction in water dynamics around the second Fyn-binding site of the SAP due to SAP-SLAM complexation, even prior to the presence of Fyn. This observation leads to a novel argument that SLAM induced more restricted water molecules could offer more water entropic contribution during the subsequent Fyn binding and provide enhanced stability to the SAP-Fyn complex in the signaling cascade. Finally, SLAM induced water counteraction around the second binding site of the SAP sheds light on the allosteric property of the SAP, which becomes an integral part of the underlying signal transduction mechanism.

Inferring the Limit Behavior of Some Elementary Cellular Automata

NASA Astrophysics Data System (ADS)

Ruivo, Eurico L. P.; de Oliveira, Pedro P. B.

Cellular automata locally define dynamical systems, discrete in space, time and in the state variables, capable of displaying arbitrarily complex global emergent behavior. One core question in the study of cellular automata refers to their limit behavior, that is, to the global dynamical features in an infinite time evolution. Previous works have shown that for finite time evolutions, the dynamics of one-dimensional cellular automata can be described by regular languages and, therefore, by finite automata. Such studies have shown the existence of growth patterns in the evolution of such finite automata for some elementary cellular automata rules and also inferred the limit behavior of such rules based upon the growth patterns; however, the results on the limit behavior were obtained manually, by direct inspection of the structures that arise during the time evolution. Here we present the formalization of an automatic method to compute such structures. Based on this, the rules of the elementary cellular automata space were classified according to the existence of a growth pattern in their finite automata. Also, we present a method to infer the limit graph of some elementary cellular automata rules, derived from the analysis of the regular expressions that describe their behavior in finite time. Finally, we analyze some attractors of two rules for which we could not compute the whole limit set.

The application of complex network time series analysis in turbulent heated jets

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

Charakopoulos, A. K.; Karakasidis, T. E., E-mail: thkarak@uth.gr; Liakopoulos, A.

In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topologicalmore » properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.« less

The application of complex network time series analysis in turbulent heated jets

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

Charakopoulos, A. K.; Karakasidis, T. E., E-mail: thkarak@uth.gr; Liakopoulos, A.

2014-06-15

In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topologicalmore » properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.« less

Dynamic Cross-Entropy.

PubMed

Aur, Dorian; Vila-Rodriguez, Fidel

2017-01-01

Complexity measures for time series have been used in many applications to quantify the regularity of one dimensional time series, however many dynamical systems are spatially distributed multidimensional systems. We introduced Dynamic Cross-Entropy (DCE) a novel multidimensional complexity measure that quantifies the degree of regularity of EEG signals in selected frequency bands. Time series generated by discrete logistic equations with varying control parameter r are used to test DCE measures. Sliding window DCE analyses are able to reveal specific period doubling bifurcations that lead to chaos. A similar behavior can be observed in seizures triggered by electroconvulsive therapy (ECT). Sample entropy data show the level of signal complexity in different phases of the ictal ECT. The transition to irregular activity is preceded by the occurrence of cyclic regular behavior. A significant increase of DCE values in successive order from high frequencies in gamma to low frequencies in delta band reveals several phase transitions into less ordered states, possible chaos in the human brain. To our knowledge there are no reliable techniques able to reveal the transition to chaos in case of multidimensional times series. In addition, DCE based on sample entropy appears to be robust to EEG artifacts compared to DCE based on Shannon entropy. The applied technique may offer new approaches to better understand nonlinear brain activity. Copyright © 2016 Elsevier B.V. All rights reserved.

Alternation of regular and chaotic dynamics in a simple two-degree-of-freedom system with nonlinear inertial coupling.

PubMed

Sigalov, G; Gendelman, O V; AL-Shudeifat, M A; Manevitch, L I; Vakakis, A F; Bergman, L A

2012-03-01

We show that nonlinear inertial coupling between a linear oscillator and an eccentric rotator can lead to very interesting interchanges between regular and chaotic dynamical behavior. Indeed, we show that this model demonstrates rather unusual behavior from the viewpoint of nonlinear dynamics. Specifically, at a discrete set of values of the total energy, the Hamiltonian system exhibits non-conventional nonlinear normal modes, whose shape is determined by phase locking of rotatory and oscillatory motions of the rotator at integer ratios of characteristic frequencies. Considering the weakly damped system, resonance capture of the dynamics into the vicinity of these modes brings about regular motion of the system. For energy levels far from these discrete values, the motion of the system is chaotic. Thus, the succession of resonance captures and escapes by a discrete set of the normal modes causes a sequence of transitions between regular and chaotic behavior, provided that the damping is sufficiently small. We begin from the Hamiltonian system and present a series of Poincaré sections manifesting the complex structure of the phase space of the considered system with inertial nonlinear coupling. Then an approximate analytical description is presented for the non-conventional nonlinear normal modes. We confirm the analytical results by numerical simulation and demonstrate the alternate transitions between regular and chaotic dynamics mentioned above. The origin of the chaotic behavior is also discussed.

Architecture Analysis of Evolving Complex Systems of Systems: Technical Presentation [and Executive Status Report

NASA Technical Reports Server (NTRS)

Lindvall, Mikael; Godfrey, Sally; Ackermann, Chris; Ray, Arnab; Yonkwa, Lyly; Ganesan, Dharma; Stratton, William C.; Sibol, Deane E.

2008-01-01

Analyze, Visualize, and Evaluate structure and behavior using static and dynamic information, individual systems as well as systems of systems. Next steps: Refine software tool support; Apply to other systems; and Apply earlier in system life cycle.

Dynamic stiffness characteristics of high eccentricity ratio bearings and seals by perturbation testing

NASA Technical Reports Server (NTRS)

Bently, D. E.; Muszynska, A.

1984-01-01

The complex behavior of cylindrical bearings and seals that are statically loaded to eccentricities in excess of 0.7 are examined. The stiffness algorithms as a function of static load are developed from perturbation methodology by empirical modeling.

Molecular dynamic simulation of weakly magnetized complex plasmas

NASA Astrophysics Data System (ADS)

Funk, Dylan; Konopka, Uwe; Thomas, Edward

2017-10-01

A complex plasma consists of the usual plasma components (electrons, ions and neutrals), as well as a heavier component made of solid, micrometer-sized particles. The particles are in general highly charged as a result of the interaction with the other plasma components. The static and dynamic properties of a complex plasma such as its crystal structure or wave properties are influenced by many forces acting on the individual particles such as the dust particle interaction (a screened Coulomb interaction), neutral (Epstein) drag, the particle inertia and various plasma drag or thermophoretic forces. To study the behavior of complex plasmas we setup an experiment accompanying molecular dynamic simulation. We will present the approach taken in our simulation and give an overview of experimental situations that we want to cover with our simulation such as the particle charge under microgravity condition as performed on the PK-4 space experiment, or to study the detailed influences of high magnetic fields. This work was supported by the US Dept. of Energy (DE-SC0016330), NSF (PHY-1613087) and JPL/NASA (JPL-RSA 1571699).

Cooperative particle motion in complex (dusty) plasmas

NASA Astrophysics Data System (ADS)

Zhdanov, Sergey; Morfill, Gregor

2014-05-01

Strongly coupled complex (dusty) plasmas give us a unique opportunity to go beyond the limits of continuous media and study various generic processes occurring in liquids or solids at the kinetic level. A particularly interesting and challenging topic is to study dynamic cooperativity at local and intermediate scales. As an important element of self-organization, cooperative particle motion is present in many physical, astrophysical and biological systems. As a rule, cooperative dynamics, bringing to life 'abnormal' effects like enhanced diffusion, self-dragging, or self-propelling of particles, hold aspects of 'strange' kinetics. The synergy effects are also important. Such kind of cooperative behavior was evidenced for string-like formations of colloidal rods, dynamics of mono- and di-vacancies in 2d colloidal crystals. Externally manipulated 'dust molecules' and self-assembled strings in driven 3d particle clusters were other noticeable examples. There is a certain advantage to experiment with complex plasmas merely because these systems are easy to manipulate in a controllable way. We report on the first direct observation of microparticle cooperative movements occurring under natural conditions in a 2d complex plasma.

Topics in Complexity: From Physical to Life Science Systems

NASA Astrophysics Data System (ADS)

Charry, Pedro David Manrique

Complexity seeks to unwrap the mechanisms responsible for collective phenomena across the physical, biological, chemical, economic and social sciences. This thesis investigates real-world complex dynamical systems ranging from the quantum/natural domain to the social domain. The following novel understandings are developed concerning these systems' out-of-equilibrium and nonlinear behavior. Standard quantum techniques show divergent outcomes when a quantum system comprising more than one subunit is far from thermodynamic equilibrium. Abnormal photon inter-arrival times help fulfill the metabolic needs of a terrestrial photosynthetic bacterium. Spatial correlations within incident light can act as a driving mechanism for an organism's adaptation toward more ordered structures. The group dynamics of non-identical objects, whose assembly rules depend on mutual heterogeneity, yield rich transition dynamics between isolation and cohesion, with the cohesion regime reproducing a particular universal pattern commonly found in many real-world systems. Analyses of covert networks reveal collective gender superiority in the connectivity that provides benefits for system robustness and survival. Nodal migration in a network generates complex contagion profiles that lie beyond traditional approaches and yet resemble many modern-day outbreaks.

Modeling spatial and temporal dynamics of wind flow and potential fire behavior following a mountain pine beetle outbreak in a lodgepole pine forest

Treesearch

Chad M. Hoffman; Rodman Linn; Russell Parsons; Carolyn Sieg; Judith Winterkamp

2015-01-01

Patches of live, dead, and dying trees resulting from bark beetle-caused mortality alter spatial and temporal variability in the canopy and surface fuel complex through changes in the foliar moisture content of attacked trees and through the redistribution of canopy fuels. The resulting heterogeneous fuels complexes alter within-canopy wind flow, wind fluctuations, and...

Complexity in Dynamical Systems

NASA Astrophysics Data System (ADS)

Moore, Cristopher David

The study of chaos has shown us that deterministic systems can have a kind of unpredictability, based on a limited knowledge of their initial conditions; after a finite time, the motion appears essentially random. This observation has inspired a general interest in the subject of unpredictability, and more generally, complexity; how can we characterize how "complex" a dynamical system is?. In this thesis, we attempt to answer this question with a paradigm of complexity that comes from computer science, we extract sets of symbol sequences, or languages, from a dynamical system using standard methods of symbolic dynamics; we then ask what kinds of grammars or automata are needed a generate these languages. This places them in the Chomsky heirarchy, which in turn tells us something about how subtle and complex the dynamical system's behavior is. This gives us insight into the question of unpredictability, since these automata can also be thought of as computers attempting to predict the system. In the culmination of the thesis, we find a class of smooth, two-dimensional maps which are equivalent to the highest class in the Chomsky heirarchy, the turning machine; they are capable of universal computation. Therefore, these systems possess a kind of unpredictability qualitatively different from the usual "chaos": even if the initial conditions are known exactly, questions about the system's long-term dynamics are undecidable. No algorithm exists to answer them. Although this kind of unpredictability has been discussed in the context of distributed, many-degree-of -freedom systems (for instance, cellular automata) we believe this is the first example of such phenomena in a smooth, finite-degree-of-freedom system.

Temporal and spatial complexity of maternal thermoregulation in tropical pythons.

PubMed

Stahlschmidt, Zachary Ross; Shine, Richard; Denardo, Dale F

2012-01-01

Parental care is a widespread adaptation that evolved independently in a broad range of taxa. Although the dynamics by which two parents meet the developmental needs of offspring are well studied in birds, we lack understanding about the temporal and spatial complexity of parental care in taxa exhibiting female-only care, the predominant mode of parental care. Thus, we examined the behavioral and physiological mechanisms by which female water pythons Liasis fuscus meet a widespread developmental need (thermoregulation) in a natural setting. Although female L. fuscus were not facultatively thermogenic, they did use behaviors on multiple spatial scales (e.g., shifts in egg-brooding postures and surface activity patterns) to balance the thermal needs of their offspring throughout reproduction (gravidity and egg brooding). Maternal behaviors in L. fuscus varied by stage within reproduction and were mediated by interindividual variation in body size and fecundity. Female pythons with relatively larger clutch sizes were cooler during egg brooding, suggesting a trade-off between reproductive quantity (size of clutch) and quality (developmental temperature). In nature, caregiving parents of all taxa must navigate both extrinsic factors (temporal and spatial complexity) and intrinsic factors (body size and fecundity) to meet the needs of their offspring. Our study used a comprehensive approach that can be used as a general template for future research examining the dynamics by which parents meet other developmental needs (e.g., predation risk or energy balance).

Postnatal brain development: Structural imaging of dynamic neurodevelopmental processes

PubMed Central

Jernigan, Terry L.; Baaré, William F. C.; Stiles, Joan; Madsen, Kathrine Skak

2013-01-01

After birth, there is striking biological and functional development of the brain’s fiber tracts as well as remodeling of cortical and subcortical structures. Behavioral development in children involves a complex and dynamic set of genetically guided processes by which neural structures interact constantly with the environment. This is a protracted process, beginning in the third week of gestation and continuing into early adulthood. Reviewed here are studies using structural imaging techniques, with a special focus on diffusion weighted imaging, describing age-related brain maturational changes in children and adolescents, as well as studies that link these changes to behavioral differences. Finally, we discuss evidence for effects on the brain of several factors that may play a role in mediating these brain–behavior associations in children, including genetic variation, behavioral interventions, and hormonal variation associated with puberty. At present longitudinal studies are few, and we do not yet know how variability in individual trajectories of biological development in specific neural systems map onto similar variability in behavioral trajectories. PMID:21489384

Sub-diffusion and trapped dynamics of neutral and charged probes in DNA-protein coacervates

NASA Astrophysics Data System (ADS)

Arfin, Najmul; Yadav, Avinash Chand; Bohidar, H. B.

2013-11-01

The physical mechanism leading to the formation of large intermolecular DNA-protein complexes has been studied. Our study aims to explain the occurrence of fast coacervation dynamics at the charge neutralization point, followed by the appearance of smaller complexes and slower coacervation dynamics as the complex experiences overcharging. Furthermore, the electrostatic potential and probe mobility was investigated to mimic the transport of DNA / DNA-protein complex in a DNA-protein complex coacervate medium [N. Arfin and H. B. Bohidar, J. Phys. Chem. B 116, 13192 (2012)] by assigning neutral, negative, or positive charge to the probe particle. The mobility of the neutral probe was maximal at low matrix concentrations and showed random walk behavior, while its mobility ceased at the jamming concentration of c = 0.6, showing sub-diffusion and trapped dynamics. The positively charged probe showed sub-diffusive random walk followed by trapped dynamics, while the negatively charged probe showed trapping with occasional hopping dynamics at much lower concentrations. Sub-diffusion of the probe was observed in all cases under consideration, where the electrostatic interaction was used exclusively as the dominant force involved in the dynamics. For neutral and positive probes, the mean square displacement ⟨R2⟩ exhibits a scaling with time as ⟨R2⟩ ˜ tα, distinguishing random walk and trapped dynamics at α = 0.64 ± 0.04 at c = 0.12 and c = 0.6, respectively. In addition, the same scaling factors with the exponent β = 0.64 ± 0.04 can be used to distinguish random walk and trapped dynamics for the neutral and positive probes using the relation between the number of distinct sites visited by the probe, S(t), which follows the scaling, S(t) ˜ tβ/ln (t). Our results established the occurrence of a hierarchy of diffusion dynamics experienced by a probe in a dense medium that is either charged or neutral.

Complexity and network dynamics in physiological adaptation: an integrated view.

PubMed

Baffy, György; Loscalzo, Joseph

2014-05-28

Living organisms constantly interact with their surroundings and sustain internal stability against perturbations. This dynamic process follows three fundamental strategies (restore, explore, and abandon) articulated in historical concepts of physiological adaptation such as homeostasis, allostasis, and the general adaptation syndrome. These strategies correspond to elementary forms of behavior (ordered, chaotic, and static) in complex adaptive systems and invite a network-based analysis of the operational characteristics, allowing us to propose an integrated framework of physiological adaptation from a complex network perspective. Applicability of this concept is illustrated by analyzing molecular and cellular mechanisms of adaptation in response to the pervasive challenge of obesity, a chronic condition resulting from sustained nutrient excess that prompts chaotic exploration for system stability associated with tradeoffs and a risk of adverse outcomes such as diabetes, cardiovascular disease, and cancer. Deconstruction of this complexity holds the promise of gaining novel insights into physiological adaptation in health and disease. Published by Elsevier Inc.

A Symbolic and Graphical Computer Representation of Dynamical Systems

NASA Astrophysics Data System (ADS)

Gould, Laurence I.

2005-04-01

AUTONO is a Macsyma/Maxima program, designed at the University of Hartford, for solving autonomous systems of differential equations as well as for relating Lagrangians and Hamiltonians to their associated dynamical equations. AUTONO can be used in a number of fields to decipher a variety of complex dynamical systems with ease, producing their Lagrangian and Hamiltonian equations in seconds. These equations can then be incorporated into VisSim, a modeling and simulation program, which yields graphical representations of motion in a given system through easily chosen input parameters. The program, along with the VisSim differential-equations graphical package, allows for resolution and easy understanding of complex problems in a relatively short time; thus enabling quicker and more advanced computing of dynamical systems on any number of platforms---from a network of sensors on a space probe, to the behavior of neural networks, to the effects of an electromagnetic field on components in a dynamical system. A flowchart of AUTONO, along with some simple applications and VisSim output, will be shown.

Complex Dynamics in a Model of Common Fishery Resource Harvested by Multiagents with Heterogeneous Strategy

NASA Astrophysics Data System (ADS)

Gu, En-Guo

In this paper, we formulate a dynamical model of common fishery resource harvested by multiagents with heterogeneous strategy: profit maximizers and gradient learners. Special attention is paid to the problem of heterogeneity of strategic behaviors. We mainly study the existence and the local stability of non-negative equilibria for the model through mathematical analysis. We analyze local bifurcations and complex dynamics such as coexisting attractors by numerical simulations. We also study the local and global dynamics of the exclusive gradient learners as a special case of the model. We discover that when adjusting the speed to be slightly high, the increasing ratio of gradient learners may lead to instability of the fixed point and makes the system sink into complicated dynamics such as quasiperiodic or chaotic attractor. The results reveal that gradient learners with high adjusting speed may ultimately be more harmful to the sustainable use of fish stock than the profit maximizers.

The Internet As a Large-Scale Complex System

NASA Astrophysics Data System (ADS)

Park, Kihong; Willinger, Walter

2005-06-01

The Internet may be viewed as a "complex system" with diverse features and many components that can give rise to unexpected emergent phenomena, revealing much about its own engineering. This book brings together chapter contributions from a workshop held at the Santa Fe Institute in March 2001. This volume captures a snapshot of some features of the Internet that may be fruitfully approached using a complex systems perspective, meaning using interdisciplinary tools and methods to tackle the subject area. The Internet penetrates the socioeconomic fabric of everyday life; a broader and deeper grasp of the Internet may be needed to meet the challenges facing the future. The resulting empirical data have already proven to be invaluable for gaining novel insights into the network's spatio-temporal dynamics, and can be expected to become even more important when tryin to explain the Internet's complex and emergent behavior in terms of elementary networking-based mechanisms. The discoveries of fractal or self-similar network traffic traces, power-law behavior in network topology and World Wide Web connectivity are instances of unsuspected, emergent system traits. Another important factor at the heart of fair, efficient, and stable sharing of network resources is user behavior. Network systems, when habited by selfish or greedy users, take on the traits of a noncooperative multi-party game, and their stability and efficiency are integral to understanding the overall system and its dynamics. Lastly, fault-tolerance and robustness of large-scale network systems can exhibit spatial and temporal correlations whose effective analysis and management may benefit from rescaling techniques applied in certain physical and biological systems. The present book will bring together several of the leading workers involved in the analysis of complex systems with the future development of the Internet.

Evidence of complex contagion of information in social media: An experiment using Twitter bots.

PubMed

Mønsted, Bjarke; Sapieżyński, Piotr; Ferrara, Emilio; Lehmann, Sune

2017-01-01

It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using 'social bots' deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.

Thermomechanical deformation testing and modeling in the presence of metallurgical instabilities. M.S. Thesis - Akron Univ. Final Report

NASA Technical Reports Server (NTRS)

Castelli, Michael G.

1990-01-01

A number of viscoplastic constitutive models were developed to describe deformation behavior under complex combinations of thermal and mechanical loading. Questions remain, however, regarding the validity of procedures used to characterize these models for specific structural alloys. One area of concern is that the majority of experimental data available for this purpose are determined under isothermal conditions. This experimental study is aimed at determining whether viscoplastic constitutive theories characterized using an isothermal data base can adequately model material response under the complex thermomechanical loading conditions typical of power generation service. The approach adopted was to conduct a series of carefully controlled thermomechanical experiments on a nickel-based superalloy, Hastelloy Alloy X. Previous investigations had shown that this material experiences metallurgical instabilities leading to complex hardening behavior, termed dynamic strain aging. Investigating this phenomenon under full thermomechanical conditions leads to a number of challenging experimental difficulties which up to the present work were unresolved. To correct this situation, a number of advances were made in thermomechanical testing techniques. Advanced methods for dynamic temperature gradient control, phasing control and thermal strain compensation were developed and incorporated into real time test control software. These advances allowed the thermomechanical data to be analyzed with minimal experimental uncertainty. The thermomechanical results were evaluated on both a phenomenological and microstructural basis. Phenomenological results revealed that the thermomechanical hardening trends were not bounded by those displayed under isothermal conditions. For the case of Hastelloy Alloy X (and similar dynamic strain aging materials), this strongly suggests that some form of thermomechanical testing is necessary when characterizing a thermoviscoplastic deformation model. Transmission electron microscopy was used to study the microstructural physics, and analyze the unique phenomenological behavior.

Controlling Complex Systems and Developing Dynamic Technology

NASA Astrophysics Data System (ADS)

Avizienis, Audrius Victor

In complex systems, control and understanding become intertwined. Following Ilya Prigogine, we define complex systems as having control parameters which mediate transitions between distinct modes of dynamical behavior. From this perspective, determining the nature of control parameters and demonstrating the associated dynamical phase transitions are practically equivalent and fundamental to engaging with complexity. In the first part of this work, a control parameter is determined for a non-equilibrium electrochemical system by studying a transition in the morphology of structures produced by an electroless deposition reaction. Specifically, changing the size of copper posts used as the substrate for growing metallic silver structures by the reduction of Ag+ from solution under diffusion-limited reaction conditions causes a dynamical phase transition in the crystal growth process. For Cu posts with edge lengths on the order of one micron, local forces promoting anisotropic growth predominate, and the reaction produces interconnected networks of Ag nanowires. As the post size is increased above 10 microns, the local interfacial growth reaction dynamics couple with the macroscopic diffusion field, leading to spatially propagating instabilities in the electrochemical potential which induce periodic branching during crystal growth, producing dendritic deposits. This result is interesting both as an example of control and understanding in a complex system, and as a useful combination of top-down lithography with bottom-up electrochemical self-assembly. The second part of this work focuses on the technological development of devices fabricated using this non-equilibrium electrochemical process, towards a goal of integrating a complex network as a dynamic functional component in a neuromorphic computing device. Self-assembled networks of silver nanowires were reacted with sulfur to produce interfacial "atomic switches": silver-silver sulfide junctions, which exhibit complex dynamics (e.g. both short- and long-term changes in conductivity) in response to applied voltage signals. Characterization of these atomic switch networks (ASNs) brought out interesting parallels to biological neural networks, including power-law scaling in the statistics of electrical signal propagation and dynamic self-organization of differentiated subnetworks. A reservoir computing (RC) strategy was employed to utilize measurements of electrical signals dynamically generated in ASNs to perform time-series memory and manipulation tasks including a parity test and arbitrary waveform generation. These results represent the useful integration of a complex network into a dynamic physical RC device.

Synchronization of networked chaotic oscillators under external periodic driving.

PubMed

Yang, Wenchao; Lin, Weijie; Wang, Xingang; Huang, Liang

2015-03-01

The dynamical responses of a complex system to external perturbations are of both fundamental interest and practical significance. Here, by the model of networked chaotic oscillators, we investigate how the synchronization behavior of a complex network is influenced by an externally added periodic driving. Interestingly, it is found that by a slight change of the properties of the external driving, e.g., the frequency or phase lag between its intrinsic oscillation and external driving, the network synchronizability could be significantly modified. We demonstrate this phenomenon by different network models and, based on the method of master stability function, give an analysis on the underlying mechanisms. Our studies highlight the importance of external perturbations on the collective behaviors of complex networks, and also provide an alternate approach for controlling network synchronization.

Active Curved Polymers Form Vortex Patterns on Membranes.

PubMed

Denk, Jonas; Huber, Lorenz; Reithmann, Emanuel; Frey, Erwin

2016-04-29

Recent in vitro experiments with FtsZ polymers show self-organization into different dynamic patterns, including structures reminiscent of the bacterial Z ring. We model FtsZ polymers as active particles moving along chiral, circular paths by Brownian dynamics simulations and a Boltzmann approach. Our two conceptually different methods point to a generic phase behavior. At intermediate particle densities, we find self-organization into vortex structures including closed rings. Moreover, we show that the dynamics at the onset of pattern formation is described by a generalized complex Ginzburg-Landau equation.

Dynamic analysis of space structures including elastic, multibody, and control behavior

NASA Technical Reports Server (NTRS)

Pinson, Larry; Soosaar, Keto

1989-01-01

The problem is to develop analysis methods, modeling stategies, and simulation tools to predict with assurance the on-orbit performance and integrity of large complex space structures that cannot be verified on the ground. The problem must incorporate large reliable structural models, multi-body flexible dynamics, multi-tier controller interaction, environmental models including 1g and atmosphere, various on-board disturbances, and linkage to mission-level performance codes. All areas are in serious need of work, but the weakest link is multi-body flexible dynamics.

Moving alcohol prevention research forward-Part II: new directions grounded in community-based system dynamics modeling.

PubMed

Apostolopoulos, Yorghos; Lemke, Michael K; Barry, Adam E; Lich, Kristen Hassmiller

2018-02-01

Given the complexity of factors contributing to alcohol misuse, appropriate epistemologies and methodologies are needed to understand and intervene meaningfully. We aimed to (1) provide an overview of computational modeling methodologies, with an emphasis on system dynamics modeling; (2) explain how community-based system dynamics modeling can forge new directions in alcohol prevention research; and (3) present a primer on how to build alcohol misuse simulation models using system dynamics modeling, with an emphasis on stakeholder involvement, data sources and model validation. Throughout, we use alcohol misuse among college students in the United States as a heuristic example for demonstrating these methodologies. System dynamics modeling employs a top-down aggregate approach to understanding dynamically complex problems. Its three foundational properties-stocks, flows and feedbacks-capture non-linearity, time-delayed effects and other system characteristics. As a methodological choice, system dynamics modeling is amenable to participatory approaches; in particular, community-based system dynamics modeling has been used to build impactful models for addressing dynamically complex problems. The process of community-based system dynamics modeling consists of numerous stages: (1) creating model boundary charts, behavior-over-time-graphs and preliminary system dynamics models using group model-building techniques; (2) model formulation; (3) model calibration; (4) model testing and validation; and (5) model simulation using learning-laboratory techniques. Community-based system dynamics modeling can provide powerful tools for policy and intervention decisions that can result ultimately in sustainable changes in research and action in alcohol misuse prevention. © 2017 Society for the Study of Addiction.

Fast social-like learning of complex behaviors based on motor motifs.

PubMed

Calvo Tapia, Carlos; Tyukin, Ivan Y; Makarov, Valeri A

2018-05-01

Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n-1)! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire "on the fly" its synaptic couplings in no more than (n-1) learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.

Fast social-like learning of complex behaviors based on motor motifs

NASA Astrophysics Data System (ADS)

Calvo Tapia, Carlos; Tyukin, Ivan Y.; Makarov, Valeri A.

2018-05-01

Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n -1 )! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire "on the fly" its synaptic couplings in no more than (n -1 ) learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.

Tendon exhibits complex poroelastic behavior at the nanoscale as revealed by high-frequency AFM-based rheology.

PubMed

Connizzo, Brianne K; Grodzinsky, Alan J

2017-03-21

Tendons transmit load from muscle to bone by utilizing their unique static and viscoelastic tensile properties. These properties are highly dependent on the composition and structure of the tissue matrix, including the collagen I hierarchy, proteoglycans, and water. While the role of matrix constituents in the tensile response has been studied, their role in compression, particularly in matrix pressurization via regulation of fluid flow, is not well understood. Injured or diseased tendons and tendon regions that naturally experience compression are known to have alterations in glycosaminoglycan content, which could modulate fluid flow and ultimately mechanical function. While recent theoretical studies have predicted tendon mechanics using poroelastic theory, no experimental data have directly demonstrated such behavior. In this study, we use high-bandwidth AFM-based rheology to determine the dynamic response of tendons to compressive loading at the nanoscale and to determine the presence of poroelastic behavior. Tendons are found to have significant characteristic dynamic relaxation behavior occurring at both low and high frequencies. Classic poroelastic behavior is observed, although we hypothesize that the full dynamic response is caused by a combination of flow-dependent poroelasticity as well as flow-independent viscoelasticity. Tendons also demonstrate regional dependence in their dynamic response, particularly near the junction of tendon and bone, suggesting that the structural and compositional heterogeneity in tendon may be responsible for regional poroelastic behavior. Overall, these experiments provide the foundation for understanding fluid-flow-dependent poroelastic mechanics of tendon, and the methodology is valuable for assessing changes in tendon matrix compressive behavior at the nanoscale. Copyright © 2017 Elsevier Ltd. All rights reserved.

Geometric Universality in Brain Allosteric Protein Dynamics: Complex Hydrophobic Transformation Predicts Mutual Recognition by Polypeptides and Proteins,

DTIC Science & Technology

1986-10-01

organic acids using the Hammett equation , has been called the hydrophobic effect.’ Water adjusts its geometry to maximize the number of intact hydrogen...understanding both structural stability with respect to the underlying equations (not initial values) and phase transitions in these dynamical hierarchies...for quantitative characterization. Although the complicated behavior is gen- erated by deterministic equations , its description in entropies leads to

Complex Dynamical Behavior in Hybrid Systems

DTIC Science & Technology

2012-09-29

stability for a class of hybrid dynamical systems via averaging”, Mathematics of Control , Signals, and Systems , vol. 23, no. 4, pp...no. 7, pp. 1636-1649, 2011. J9. A.R. Teel and L. Marconi, `` Stabilization for a class of minimum phase hybrid systems under an average dwell- time ...functions for L2 and input-to-state stability in a class of quantized control systems ”, 50th IEEE Conference on Decision and Control , Dec.

Probing dynamics and pinning of single vortices in superconductors at nanometer scales.

PubMed

Embon, L; Anahory, Y; Suhov, A; Halbertal, D; Cuppens, J; Yakovenko, A; Uri, A; Myasoedov, Y; Rappaport, M L; Huber, M E; Gurevich, A; Zeldov, E

2015-01-07

The dynamics of quantized magnetic vortices and their pinning by materials defects determine electromagnetic properties of superconductors, particularly their ability to carry non-dissipative currents. Despite recent advances in the understanding of the complex physics of vortex matter, the behavior of vortices driven by current through a multi-scale potential of the actual materials defects is still not well understood, mostly due to the scarcity of appropriate experimental tools capable of tracing vortex trajectories on nanometer scales. Using a novel scanning superconducting quantum interference microscope we report here an investigation of controlled dynamics of vortices in lead films with sub-Angstrom spatial resolution and unprecedented sensitivity. We measured, for the first time, the fundamental dependence of the elementary pinning force of multiple defects on the vortex displacement, revealing a far more complex behavior than has previously been recognized, including striking spring softening and broken-spring depinning, as well as spontaneous hysteretic switching between cellular vortex trajectories. Our results indicate the importance of thermal fluctuations even at 4.2 K and of the vital role of ripples in the pinning potential, giving new insights into the mechanisms of magnetic relaxation and electromagnetic response of superconductors.

Probing dynamics and pinning of single vortices in superconductors at nanometer scales

NASA Astrophysics Data System (ADS)

Embon, L.; Anahory, Y.; Suhov, A.; Halbertal, D.; Cuppens, J.; Yakovenko, A.; Uri, A.; Myasoedov, Y.; Rappaport, M. L.; Huber, M. E.; Gurevich, A.; Zeldov, E.

2015-01-01

The dynamics of quantized magnetic vortices and their pinning by materials defects determine electromagnetic properties of superconductors, particularly their ability to carry non-dissipative currents. Despite recent advances in the understanding of the complex physics of vortex matter, the behavior of vortices driven by current through a multi-scale potential of the actual materials defects is still not well understood, mostly due to the scarcity of appropriate experimental tools capable of tracing vortex trajectories on nanometer scales. Using a novel scanning superconducting quantum interference microscope we report here an investigation of controlled dynamics of vortices in lead films with sub-Angstrom spatial resolution and unprecedented sensitivity. We measured, for the first time, the fundamental dependence of the elementary pinning force of multiple defects on the vortex displacement, revealing a far more complex behavior than has previously been recognized, including striking spring softening and broken-spring depinning, as well as spontaneous hysteretic switching between cellular vortex trajectories. Our results indicate the importance of thermal fluctuations even at 4.2 K and of the vital role of ripples in the pinning potential, giving new insights into the mechanisms of magnetic relaxation and electromagnetic response of superconductors.

Prediction of Thermal Transport Properties of Materials with Microstructural Complexity

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

Chen, Youping

This project aims at overcoming the major obstacle standing in the way of progress in dynamic multiscale simulation, which is the lack of a concurrent atomistic-continuum method that allows phonons, heat and defects to pass through the atomistic-continuum interface. The research has led to the development of a concurrent atomistic-continuum (CAC) methodology for multiscale simulations of materials microstructural, mechanical and thermal transport behavior. Its efficacy has been tested and demonstrated through simulations of dislocation dynamics and phonon transport coupled with microstructural evolution in a variety of materials and through providing visual evidences of the nature of phonon transport, such asmore » showing the propagation of heat pulses in single and polycrystalline solids is partially ballistic and partially diffusive. In addition to providing understanding on phonon scattering with phase interface and with grain boundaries, the research has contributed a multiscale simulation tool for understanding of the behavior of complex materials and has demonstrated the capability of the tool in simulating the dynamic, in situ experimental studies of nonequilibrium transient transport processes in material samples that are at length scales typically inaccessible by atomistically resolved methods.« less

Solution-state structure and affinities of cyclodextrin: Fentanyl complexes by nuclear magnetic resonance spectroscopy and molecular dynamics simulation

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

Mayer, Brian P.; Kennedy, Daniel J.; Lau, Edmond Y.

Cyclodextrins (CDs) are investigated for their ability to form inclusion complexes with the analgesic fentanyl and three similar molecules: acetylfentanyl, thiofentanyl, and acetylthiofentanyl. Stoichiometry, binding strength, and complex structure are revealed through nuclear magnetic resonance (NMR) techniques and discussed in terms of molecular dynamics (MD) simulations. It was found that β-cyclodextrin is generally capable of forming the strongest complexes with the fentanyl panel. Two-dimensional NMR data and computational chemical calculations are used to derive solution-state structures of the complexes. Binding of the fentanyls to the CDs occurs at the amide phenyl ring, leaving the majority of the molecule solvated bymore » water, an observation common to all four fentanyls. This finding suggests a universal binding behavior, as the vast majority of previously synthesized fentanyl analogues contain this structural moiety. Furthermore, this baseline study serves as the most complete work on CD:fentanyl complexes to date and provides the insights into strategies for producing future generations of designer cyclodextrins capable of stronger and more selective complexation of fentanyl and its analogues.« less

Solution-state structure and affinities of cyclodextrin: Fentanyl complexes by nuclear magnetic resonance spectroscopy and molecular dynamics simulation

DOE PAGES

Mayer, Brian P.; Kennedy, Daniel J.; Lau, Edmond Y.; ...

2016-02-04

Cyclodextrins (CDs) are investigated for their ability to form inclusion complexes with the analgesic fentanyl and three similar molecules: acetylfentanyl, thiofentanyl, and acetylthiofentanyl. Stoichiometry, binding strength, and complex structure are revealed through nuclear magnetic resonance (NMR) techniques and discussed in terms of molecular dynamics (MD) simulations. It was found that β-cyclodextrin is generally capable of forming the strongest complexes with the fentanyl panel. Two-dimensional NMR data and computational chemical calculations are used to derive solution-state structures of the complexes. Binding of the fentanyls to the CDs occurs at the amide phenyl ring, leaving the majority of the molecule solvated bymore » water, an observation common to all four fentanyls. This finding suggests a universal binding behavior, as the vast majority of previously synthesized fentanyl analogues contain this structural moiety. Furthermore, this baseline study serves as the most complete work on CD:fentanyl complexes to date and provides the insights into strategies for producing future generations of designer cyclodextrins capable of stronger and more selective complexation of fentanyl and its analogues.« less

Interference in the classical probabilistic model and its representation in complex Hilbert space

NASA Astrophysics Data System (ADS)

Khrennikov, Andrei Yu.

2005-10-01

The notion of a context (complex of physical conditions, that is to say: specification of the measurement setup) is basic in this paper.We show that the main structures of quantum theory (interference of probabilities, Born's rule, complex probabilistic amplitudes, Hilbert state space, representation of observables by operators) are present already in a latent form in the classical Kolmogorov probability model. However, this model should be considered as a calculus of contextual probabilities. In our approach it is forbidden to consider abstract context independent probabilities: “first context and only then probability”. We construct the representation of the general contextual probabilistic dynamics in the complex Hilbert space. Thus dynamics of the wave function (in particular, Schrödinger's dynamics) can be considered as Hilbert space projections of a realistic dynamics in a “prespace”. The basic condition for representing of the prespace-dynamics is the law of statistical conservation of energy-conservation of probabilities. In general the Hilbert space projection of the “prespace” dynamics can be nonlinear and even irreversible (but it is always unitary). Methods developed in this paper can be applied not only to quantum mechanics, but also to classical statistical mechanics. The main quantum-like structures (e.g., interference of probabilities) might be found in some models of classical statistical mechanics. Quantum-like probabilistic behavior can be demonstrated by biological systems. In particular, it was recently found in some psychological experiments.

Interconversion of dynamic modulus to creep compliance and relaxation modulus : numerical modeling and laboratory validation - final report.

DOT National Transportation Integrated Search

2016-09-01

Viscoelastic material functions such as time domain functions, such as, relaxation modulus and creep compliance, : or frequency domain function, such as, complex modulus can be used to characterize the linear viscoelastic behavior : of asphalt concre...

Recent developments in computer modeling add ecological realism to landscape genetics

EPA Science Inventory

Background / Question / Methods A factor limiting the rate of progress in landscape genetics has been the shortage of spatial models capable of linking life history attributes such as dispersal behavior to complex dynamic landscape features. The recent development of new models...

Organization and evolution of parieto-frontal processing streams in macaque monkeys and humans.

PubMed

Caminiti, Roberto; Innocenti, Giorgio M; Battaglia-Mayer, Alexandra

2015-09-01

The functional organization of the parieto-frontal system is crucial for understanding cognitive-motor behavior and provides the basis for interpreting the consequences of parietal lesions in humans from a neurobiological perspective. The parieto-frontal connectivity defines some main information streams that, rather than being devoted to restricted functions, underlie a rich behavioral repertoire. Surprisingly, from macaque to humans, evolution has added only a few, new functional streams, increasing however their complexity and encoding power. In fact, the characterization of the conduction times of parietal and frontal areas to different target structures has recently opened a new window on cortical dynamics, suggesting that evolution has amplified the probability of dynamic interactions between the nodes of the network, thanks to communication patterns based on temporally-dispersed conduction delays. This might allow the representation of sensory-motor signals within multiple neural assemblies and reference frames, as to optimize sensory-motor remapping within an action space characterized by different and more complex demands across evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

What Studying Leadership Can Teach Us About the Science of Behavior.

PubMed

Malott, Maria E

2016-05-01

Throughout history, individuals have changed the world in significant ways, forging new paths; demonstrating remarkable capacity to inspire others to follow; and repeatedly showing independence, resilience, consistency, and commitment to principle. However, significant cultural change is rarely accomplished single-handedly; instead, it results from the complex and dynamic interaction of groups of individuals. To illustrate how leaders participate in cultural phenomena, I describe how a few individuals helped to establish the Cold War. In this analysis, I distinguish two types of cultural phenomena: metacontingencies, involving lineages of interlocking behavioral contingencies, and cultural cusps, involving complicated, unique, and nonreplicable interrelations between individuals and circumstances. I conclude that by analyzing leaders' actions and their results, we can appreciate that cultural and behavioral phenomena are different, and although cultural phenomena are inherently complex and in many cases do not lend themselves to replication, not only should the science of behavior account for them, cultural phenomena should also constitute a major area of behavior analysis study and application.

Characteristics of pattern formation and evolution in approximations of Physarum transport networks.

PubMed

Jones, Jeff

2010-01-01

Most studies of pattern formation place particular emphasis on its role in the development of complex multicellular body plans. In simpler organisms, however, pattern formation is intrinsic to growth and behavior. Inspired by one such organism, the true slime mold Physarum polycephalum, we present examples of complex emergent pattern formation and evolution formed by a population of simple particle-like agents. Using simple local behaviors based on chemotaxis, the mobile agent population spontaneously forms complex and dynamic transport networks. By adjusting simple model parameters, maps of characteristic patterning are obtained. Certain areas of the parameter mapping yield particularly complex long term behaviors, including the circular contraction of network lacunae and bifurcation of network paths to maintain network connectivity. We demonstrate the formation of irregular spots and labyrinthine and reticulated patterns by chemoattraction. Other Turing-like patterning schemes were obtained by using chemorepulsion behaviors, including the self-organization of regular periodic arrays of spots, and striped patterns. We show that complex pattern types can be produced without resorting to the hierarchical coupling of reaction-diffusion mechanisms. We also present network behaviors arising from simple pre-patterning cues, giving simple examples of how the emergent pattern formation processes evolve into networks with functional and quasi-physical properties including tensionlike effects, network minimization behavior, and repair to network damage. The results are interpreted in relation to classical theories of biological pattern formation in natural systems, and we suggest mechanisms by which emergent pattern formation processes may be used as a method for spatially represented unconventional computation.

Simulating living organisms with populations of point vortices

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

Schmieder, R.W.

1995-07-01

The author has found that time-averaged images of small populations of point vortices can exhibit motions suggestive of the behavior of individual organisms. As an example, the author shows that collections of point vortices confined in a box and subjected to heating can generate patterns that are broadly similar to interspecies defense in certain sea anemones. It is speculated that other simple dynamical systems can be found to produce similar complex organism-like behavior.

Adaptive functional systems: learning with chaos.

PubMed

Komarov, M A; Osipov, G V; Burtsev, M S

2010-12-01

We propose a new model of adaptive behavior that combines a winnerless competition principle and chaos to learn new functional systems. The model consists of a complex network of nonlinear dynamical elements producing sequences of goal-directed actions. Each element describes dynamics and activity of the functional system which is supposed to be a distributed set of interacting physiological elements such as nerve or muscle that cooperates to obtain certain goal at the level of the whole organism. During "normal" behavior, the dynamics of the system follows heteroclinic channels, but in the novel situation chaotic search is activated and a new channel leading to the target state is gradually created simulating the process of learning. The model was tested in single and multigoal environments and had demonstrated a good potential for generation of new adaptations. © 2010 American Institute of Physics.

Photophysical Behaviors of Single Fluorophores Localized on Zinc Oxide Nanostructures

PubMed Central

Fu, Yi; Zhang, Jian; Lakowicz, Joseph R.

2012-01-01

Single-molecule fluorescence spectroscopy has now been widely used to investigate complex dynamic processes which would normally be obscured in an ensemble-averaged measurement. In this report we studied photophysical behaviors of single fluorophores in proximity to zinc oxide nanostructures by single-molecule fluorescence spectroscopy and time-correlated single-photon counting (TCSPC). Single fluorophores on ZnO surfaces showed enhanced fluorescence brightness to various extents compared with those on glass; the single-molecule time trajectories also illustrated pronounced fluctuations of emission intensities, with time periods distributed from milliseconds to seconds. We attribute fluorescence fluctuations to the interfacial electron transfer (ET) events. The fluorescence fluctuation dynamics were found to be inhomogeneous from molecule to molecule and from time to time, showing significant static and dynamic disorders in the interfacial electron transfer reaction processes. PMID:23109903

Spectral simplicity of apparent complexity. II. Exact complexities and complexity spectra

NASA Astrophysics Data System (ADS)

Riechers, Paul M.; Crutchfield, James P.

2018-03-01

The meromorphic functional calculus developed in Part I overcomes the nondiagonalizability of linear operators that arises often in the temporal evolution of complex systems and is generic to the metadynamics of predicting their behavior. Using the resulting spectral decomposition, we derive closed-form expressions for correlation functions, finite-length Shannon entropy-rate approximates, asymptotic entropy rate, excess entropy, transient information, transient and asymptotic state uncertainties, and synchronization information of stochastic processes generated by finite-state hidden Markov models. This introduces analytical tractability to investigating information processing in discrete-event stochastic processes, symbolic dynamics, and chaotic dynamical systems. Comparisons reveal mathematical similarities between complexity measures originally thought to capture distinct informational and computational properties. We also introduce a new kind of spectral analysis via coronal spectrograms and the frequency-dependent spectra of past-future mutual information. We analyze a number of examples to illustrate the methods, emphasizing processes with multivariate dependencies beyond pairwise correlation. This includes spectral decomposition calculations for one representative example in full detail.

Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach.

PubMed

Perez-Acle, Tomas; Fuenzalida, Ignacio; Martin, Alberto J M; Santibañez, Rodrigo; Avaria, Rodrigo; Bernardin, Alejandro; Bustos, Alvaro M; Garrido, Daniel; Dushoff, Jonathan; Liu, James H

2018-03-29

Computational simulation is a widely employed methodology to study the dynamic behavior of complex systems. Although common approaches are based either on ordinary differential equations or stochastic differential equations, these techniques make several assumptions which, when it comes to biological processes, could often lead to unrealistic models. Among others, model approaches based on differential equations entangle kinetics and causality, failing when complexity increases, separating knowledge from models, and assuming that the average behavior of the population encompasses any individual deviation. To overcome these limitations, simulations based on the Stochastic Simulation Algorithm (SSA) appear as a suitable approach to model complex biological systems. In this work, we review three different models executed in PISKaS: a rule-based framework to produce multiscale stochastic simulations of complex systems. These models span multiple time and spatial scales ranging from gene regulation up to Game Theory. In the first example, we describe a model of the core regulatory network of gene expression in Escherichia coli highlighting the continuous model improvement capacities of PISKaS. The second example describes a hypothetical outbreak of the Ebola virus occurring in a compartmentalized environment resembling cities and highways. Finally, in the last example, we illustrate a stochastic model for the prisoner's dilemma; a common approach from social sciences describing complex interactions involving trust within human populations. As whole, these models demonstrate the capabilities of PISKaS providing fertile scenarios where to explore the dynamics of complex systems. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Exploring the movement dynamics of deception.

PubMed

Duran, Nicholas D; Dale, Rick; Kello, Christopher T; Street, Chris N H; Richardson, Daniel C

2013-01-01

BOTH THE SCIENCE AND THE EVERYDAY PRACTICE OF DETECTING A LIE REST ON THE SAME ASSUMPTION: hidden cognitive states that the liar would like to remain hidden nevertheless influence observable behavior. This assumption has good evidence. The insights of professional interrogators, anecdotal evidence, and body language textbooks have all built up a sizeable catalog of non-verbal cues that have been claimed to distinguish deceptive and truthful behavior. Typically, these cues are discrete, individual behaviors-a hand touching a mouth, the rise of a brow-that distinguish lies from truths solely in terms of their frequency or duration. Research to date has failed to establish any of these non-verbal cues as a reliable marker of deception. Here we argue that perhaps this is because simple tallies of behavior can miss out on the rich but subtle organization of behavior as it unfolds over time. Research in cognitive science from a dynamical systems perspective has shown that behavior is structured across multiple timescales, with more or less regularity and structure. Using tools that are sensitive to these dynamics, we analyzed body motion data from an experiment that put participants in a realistic situation of choosing, or not, to lie to an experimenter. Our analyses indicate that when being deceptive, continuous fluctuations of movement in the upper face, and somewhat in the arms, are characterized by dynamical properties of less stability, but greater complexity. For the upper face, these distinctions are present despite no apparent differences in the overall amount of movement between deception and truth. We suggest that these unique dynamical signatures of motion are indicative of both the cognitive demands inherent to deception and the need to respond adaptively in a social context.

Transoceanic migration, spatial dynamics, and population linkages of white sharks.

PubMed

Bonfil, Ramón; Meÿer, Michael; Scholl, Michael C; Johnson, Ryan; O'Brien, Shannon; Oosthuizen, Herman; Swanson, Stephan; Kotze, Deon; Paterson, Michael

2005-10-07

The large-scale spatial dynamics and population structure of marine top predators are poorly known. We present electronic tag and photographic identification data showing a complex suite of behavioral patterns in white sharks. These include coastal return migrations and the fastest known transoceanic return migration among swimming fauna, which provide direct evidence of a link between widely separated populations in South Africa and Australia. Transoceanic return migration involved a return to the original capture location, dives to depths of 980 meters, and the tolerance of water temperatures as low as 3.4 degrees C. These findings contradict previous ideas that female white sharks do not make transoceanic migrations, and they suggest natal homing behavior.

Estradiol Membrane-Initiated Signaling in the Brain Mediates Reproduction.

PubMed

Micevych, Paul E; Mermelstein, Paul G; Sinchak, Kevin

2017-11-01

Over the past few years our understanding of estrogen signaling in the brain has expanded rapidly. Estrogens are synthesized in the periphery and in the brain, acting on multiple receptors to regulate gene transcription, neural function, and behavior. Various estrogen-sensitive signaling pathways often operate in concert within the same cell, increasing the complexity of the system. In females, estrogen concentrations fluctuate over the estrous/menstrual cycle, dynamically modulating estrogen receptor (ER) expression, activity, and trafficking. These dynamic changes influence multiple behaviors but are particularly important for reproduction. Using the female rodent model, we review our current understanding of estradiol signaling in the regulation of sexual receptivity. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Strategies and Rubrics for Teaching Complex Systems Theory to Novices (Invited)

NASA Astrophysics Data System (ADS)

Fichter, L. S.

2010-12-01

Bifurcation. Self-similarity. Fractal. Sensitive dependent. Agents. Self-organized criticality. Avalanche behavior. Power laws. Strange attractors. Emergence. The language of complexity is fundamentally different from the language of equilibrium. If students do not know these phenomena, and what they tell us about the pulse of dynamic systems, complex systems will be opaque. A complex system is a group of agents. (individual interacting units, like birds in a flock, sand grains in a ripple, or individual friction units along a fault zone), existing far from equilibrium, interacting through positive and negative feedbacks, following simple rules, forming interdependent, dynamic, evolutionary networks. Complex systems produce behaviors that cannot be predicted deductively from knowledge of the behaviors of the individual components themselves; they must be experienced. What complexity theory demonstrates is that, by following simple rules, all the agents end up coordinating their behavior—self organizing—so that what emerges is not chaos, but meaningful patterns. How can we introduce Freshman, non-science, general education students to complex systems theories, in 3 to 5 classes; in a way they really get it, and can use the principles to understand real systems? Complex systems theories are not a series of unconnected or disconnected equations or models; they are developed as narratives that makes sense of how all the pieces and properties are interrelated. The principles of complex systems must be taught as deliberately and systematically as the equilibrium principles normally taught; as, say, the systematic training from pre-algebra and geometry to algebra. We have developed a sequence of logically connected narratives (strategies and rubrics) that introduce complex systems principles using models that can be simulated in a computer, in class, in real time. The learning progression has a series of 12 models (e.g. logistic system, bifurcation diagrams, genetic algorithms, etc.) leading to 19 learning outcomes that encompass most of the universality properties that characterize complex systems. They are developed in a specific order to achieve specific ends of understanding. We use these models in various depths and formats in courses ranging from gened courses, to evolutionary systems and environmental systems, to upper level geology courses. Depending on the goals of a course, the learning outcomes can be applied to understanding many other complex systems; e.g. oscillating chemical reactions (reaction-diffusion and activator-inhibitor systems), autocatalytic networks, hysteresis (bistable) systems, networks, and the rise/collapse of complex societies. We use these and other complex systems concepts in various classes to talk about the origin of life, ecosystem organization, game theory, extinction events, and environmental system behaviors. The applications are almost endless. The complete learning progression with models, computer programs, experiments, and learning outcomes is available at: www.jmu.edu/geology/ComplexEvolutionarySystems/

Practical Measurement of Complexity In Dynamic Systems

DTIC Science & Technology

2012-01-01

policies that produce highly complex behaviors , yet yield no benefit. 21Jason B. Clark and David R. Jacques / Procedia Computer Science 8 (2012) 14... Procedia Computer Science 8 (2012) 14 – 21 1877-0509 © 2012 Published by Elsevier B.V. doi:10.1016/j.procs.2012.01.008 Available online at...www.sciencedirect.com Procedia Computer Science Procedia Computer Science 00 (2012) 000–000 www.elsevier.com/locate/ procedia Available online at

Scaling up complexity in host-pathogens interaction models. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Aguiar, Maíra

2015-12-01

Caused by micro-organisms that are pathogenic to the host, infectious diseases have caused debilitation and premature death to large portions of the human population, leading to serious social-economic concerns. The persistence and increase in the occurrence of infectious diseases as well the emergence or resurgence of vector-borne diseases are closely related with demographic factors such as the uncontrolled urbanization and remarkable population growth, political, social and economical changes, deforestation, development of resistance to insecticides and drugs and increased human travel. In recent years, mathematical modeling became an important tool for the understanding of infectious disease epidemiology and dynamics, addressing ideas about the components of host-pathogen interactions. Acting as a possible tool to understand, predict the spread of infectious diseases these models are also used to evaluate the introduction of intervention strategies like vector control and vaccination. Many scientific papers have been published recently on these topics, and most of the models developed try to incorporate factors focusing on several different aspects of the disease (and eventually biological aspects of the vector), which can imply rich dynamic behavior even in the most basic dynamical models. As one example to be cited, there is a minimalistic dengue model that has shown rich dynamic structures, with bifurcations (Hopf, pitchfork, torus and tangent bifurcations) up to chaotic attractors in unexpected parameter regions [1,2], which was able to describe the large fluctuations observed in empirical outbreak data [3,4].

Protein Folding and Self-Organized Criticality

NASA Astrophysics Data System (ADS)

Bajracharya, Arun; Murray, Joelle

Proteins are known to fold into tertiary structures that determine their functionality in living organisms. However, the complex dynamics of protein folding and the way they consistently fold into the same structures is not fully understood. Self-organized criticality (SOC) has provided a framework for understanding complex systems in various systems (earthquakes, forest fires, financial markets, and epidemics) through scale invariance and the associated power law behavior. In this research, we use a simple hydrophobic-polar lattice-bound computational model to investigate self-organized criticality as a possible mechanism for generating complexity in protein folding.

Characterization of complexities in combustion instability in a lean premixed gas-turbine model combustor.

PubMed

Gotoda, Hiroshi; Amano, Masahito; Miyano, Takaya; Ikawa, Takuya; Maki, Koshiro; Tachibana, Shigeru

2012-12-01

We characterize complexities in combustion instability in a lean premixed gas-turbine model combustor by nonlinear time series analysis to evaluate permutation entropy, fractal dimensions, and short-term predictability. The dynamic behavior in combustion instability near lean blowout exhibits a self-affine structure and is ascribed to fractional Brownian motion. It undergoes chaos by the onset of combustion oscillations with slow amplitude modulation. Our results indicate that nonlinear time series analysis is capable of characterizing complexities in combustion instability close to lean blowout.

Complexity reduction of rate-equations models for two-choice decision-making.

PubMed

Carrillo, José Antonio; Cordier, Stéphane; Deco, Gustavo; Mancini, Simona

2013-01-01

We are concerned with the complexity reduction of a stochastic system of differential equations governing the dynamics of a neuronal circuit describing a decision-making task. This reduction is based on the slow-fast behavior of the problem and holds on the whole phase space and not only locally around the spontaneous state. Macroscopic quantities, such as performance and reaction times, computed applying this reduction are in agreement with previous works in which the complexity reduction is locally performed at the spontaneous point by means of a Taylor expansion.

Interplay between local dynamics and mechanical reinforcement in glassy polymer nanocomposites

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

Holt, Adam P.; Bocharova, Vera; Cheng, Shiwang

The modification of polymer dynamics in the presence of strongly interacting nanoparticles has been shown to significantly change themacroscopic properties above the glass transition temperature of polymer nanocomposites (PNCs). However, much less attention has been paid to changes in the dynamics of glassy PNCs. Analysis of neutron and light scattering data presented herein reveals a surprising enhancement of local dynamics, e.g., fast picosecond and secondary relaxations, in glassy PNCs accompanied with a strengthening of mechanical modulus. Here we ascribe this counter-intuitive behavior to the complex interplay between chain packing and stretching within the interfacial layer formed at the polymer-nanoparticle interface.

Interplay between local dynamics and mechanical reinforcement in glassy polymer nanocomposites

DOE PAGES

Holt, Adam P.; Bocharova, Vera; Cheng, Shiwang; ...

2017-11-17

The modification of polymer dynamics in the presence of strongly interacting nanoparticles has been shown to significantly change themacroscopic properties above the glass transition temperature of polymer nanocomposites (PNCs). However, much less attention has been paid to changes in the dynamics of glassy PNCs. Analysis of neutron and light scattering data presented herein reveals a surprising enhancement of local dynamics, e.g., fast picosecond and secondary relaxations, in glassy PNCs accompanied with a strengthening of mechanical modulus. Here we ascribe this counter-intuitive behavior to the complex interplay between chain packing and stretching within the interfacial layer formed at the polymer-nanoparticle interface.

System Dynamics Modeling for Public Health: Background and Opportunities

PubMed Central

Homer, Jack B.; Hirsch, Gary B.

2006-01-01

The systems modeling methodology of system dynamics is well suited to address the dynamic complexity that characterizes many public health issues. The system dynamics approach involves the development of computer simulation models that portray processes of accumulation and feedback and that may be tested systematically to find effective policies for overcoming policy resistance. System dynamics modeling of chronic disease prevention should seek to incorporate all the basic elements of a modern ecological approach, including disease outcomes, health and risk behaviors, environmental factors, and health-related resources and delivery systems. System dynamics shows promise as a means of modeling multiple interacting diseases and risks, the interaction of delivery systems and diseased populations, and matters of national and state policy. PMID:16449591

Multiscale multifractal DCCA and complexity behaviors of return intervals for Potts price model

NASA Astrophysics Data System (ADS)

Wang, Jie; Wang, Jun; Stanley, H. Eugene

2018-02-01

To investigate the characteristics of extreme events in financial markets and the corresponding return intervals among these events, we use a Potts dynamic system to construct a random financial time series model of the attitudes of market traders. We use multiscale multifractal detrended cross-correlation analysis (MM-DCCA) and Lempel-Ziv complexity (LZC) perform numerical research of the return intervals for two significant China's stock market indices and for the proposed model. The new MM-DCCA method is based on the Hurst surface and provides more interpretable cross-correlations of the dynamic mechanism between different return interval series. We scale the LZC method with different exponents to illustrate the complexity of return intervals in different scales. Empirical studies indicate that the proposed return intervals from the Potts system and the real stock market indices hold similar statistical properties.

ADAPT: The Agent Development and Prototyping Testbed.

PubMed

Shoulson, Alexander; Marshak, Nathan; Kapadia, Mubbasir; Badler, Norman I

2014-07-01

We present ADAPT, a flexible platform for designing and authoring functional, purposeful human characters in a rich virtual environment. Our framework incorporates character animation, navigation, and behavior with modular interchangeable components to produce narrative scenes. The animation system provides locomotion, reaching, gaze tracking, gesturing, sitting, and reactions to external physical forces, and can easily be extended with more functionality due to a decoupled, modular structure. The navigation component allows characters to maneuver through a complex environment with predictive steering for dynamic obstacle avoidance. Finally, our behavior framework allows a user to fully leverage a character's animation and navigation capabilities when authoring both individual decision-making and complex interactions between actors using a centralized, event-driven model.

Fluctuation Dynamics of Exchange Rates on Indian Financial Market

NASA Astrophysics Data System (ADS)

Sarkar, A.; Barat, P.

Here we investigate the scaling behavior and the complexity of the average daily exchange rate returns of the Indian Rupee against four foreign currencies namely US Dollar, Euro, Great Britain Pound and Japanese Yen. Our analysis revealed that the average daily exchange rate return of the Indian Rupee against the US Dollar exhibits a persistent scaling behavior and follow Levy stable distribution. On the contrary the average daily exchange rate returns of the other three foreign currencies show randomness and follow Gaussian distribution. Moreover, it is seen that the complexity of the average daily exchange rate return of the Indian Rupee against US Dollar is less than the other three exchange rate returns.

The 14th Annual Conference on Manual Control. [digital simulation of human operator dynamics

NASA Technical Reports Server (NTRS)

1978-01-01

Human operator dynamics during actual manual control or while monitoring the automatic control systems involved in air-to-air tracking, automobile driving, the operator of undersea vehicles, and remote handling are examined. Optimal control models and the use of mathematical theory in representing man behavior in complex man machine system tasks are discussed with emphasis on eye/head tracking and scanning; perception and attention allocation; decision making; and motion simulation and effects.

Mechanical Analyses for coupled Vegetation-Flow System

NASA Astrophysics Data System (ADS)

Chen, L.; Acharya, K.; Stone, M.

2010-12-01

Vegetation in riparian areas plays important roles in hydrology, geomorphology and ecology in local environment. Mechanical response of the aquatic vegetation to hydraulic forces and its impact on flow hydraulics have received considerable attention due to implications for flood control, habitat restoration, and water resources management. This study aims to advance understanding of the mechanical properties of in-stream vegetation including drag force, moment and stress. Dynamic changes of these properties under various flow conditions largely determine vegetation affected flow field and dynamic resistance with progressive bending, and hydraulic conditions for vegetation failure (rupture or wash-out) thus are critical for understanding the coupled vegetation-flow system. A new approach combining fluid and material mechanics is developed in this study to examine the behavior of both rigid and flexible vegetation. The major advantage of this approach is its capability to treat large deflection (bending) of plants and associated changes of mechanical properties in both vegetation and flow. Starting from simple emergent vegetation, both static and dynamic formulations of the problem are presented and the solutions are compared. Results show the dynamic behavior of a simplified system mimicking complex and real systems, implying the approach is able to disclose the physical essence of the coupled system. The approach is extended to complex vegetation under both submerged and emergent conditions using more realistic representation of biomechanical properties for vegetation.

Unraveling the structural complexity in a single-stranded RNA tail: implications for efficient ligand binding in the prequeuosine riboswitch

PubMed Central

Eichhorn, Catherine D.; Feng, Jun; Suddala, Krishna C.; Walter, Nils G.; Brooks, Charles L.; Al-Hashimi, Hashim M.

2012-01-01

Single-stranded RNAs (ssRNAs) are ubiquitous RNA elements that serve diverse functional roles. Much of our understanding of ssRNA conformational behavior is limited to structures in which ssRNA directly engages in tertiary interactions or is recognized by proteins. Little is known about the structural and dynamic behavior of free ssRNAs at atomic resolution. Here, we report the collaborative application of nuclear magnetic resonance (NMR) and replica exchange molecular dynamics (REMD) simulations to characterize the 12 nt ssRNA tail derived from the prequeuosine riboswitch. NMR carbon spin relaxation data and residual dipolar coupling measurements reveal a flexible yet stacked core adopting an A-form-like conformation, with the level of order decreasing toward the terminal ends. An A-to-C mutation within the polyadenine tract alters the observed dynamics consistent with the introduction of a dynamic kink. Pre-ordering of the tail may increase the efficacy of ligand binding above that achieved by a random-coil ssRNA. The REMD simulations recapitulate important trends in the NMR data, but suggest more internal motions than inferred from the NMR analysis. Our study unmasks a previously unappreciated level of complexity in ssRNA, which we believe will also serve as an excellent model system for testing and developing computational force fields. PMID:22009676

Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems.

PubMed

Kim, Jason Z; Soffer, Jonathan M; Kahn, Ari E; Vettel, Jean M; Pasqualetti, Fabio; Bassett, Danielle S

2018-01-01

Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behavior. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behavior in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

A system dynamics optimization framework to achieve population desired of average weight target

NASA Astrophysics Data System (ADS)

Abidin, Norhaslinda Zainal; Zulkepli, Jafri Haji; Zaibidi, Nerda Zura

2017-11-01

Obesity is becoming a serious problem in Malaysia as it has been rated as the highest among Asian countries. The aim of the paper is to propose a system dynamics (SD) optimization framework to achieve population desired weight target based on the changes in physical activity behavior and its association to weight and obesity. The system dynamics approach of stocks and flows diagram was used to quantitatively model the impact of both behavior on the population's weight and obesity trends. This work seems to bring this idea together and highlighting the interdependence of the various aspects of eating and physical activity behavior on the complex of human weight regulation system. The model was used as an experimentation vehicle to investigate the impacts of changes in physical activity on weight and prevalence of obesity implications. This framework paper provides evidence on the usefulness of SD optimization as a strategic decision making approach to assist in decision making related to obesity prevention. SD applied in this research is relatively new in Malaysia and has a high potential to apply to any feedback models that address the behavior cause to obesity.

Thermal dynamic behavior during selective laser melting of K418 superalloy: numerical simulation and experimental verification

NASA Astrophysics Data System (ADS)

Chen, Zhen; Xiang, Yu; Wei, Zhengying; Wei, Pei; Lu, Bingheng; Zhang, Lijuan; Du, Jun

2018-04-01

During selective laser melting (SLM) of K418 powder, the influence of the process parameters, such as laser power P and scanning speed v, on the dynamic thermal behavior and morphology of the melted tracks was investigated numerically. A 3D finite difference method was established to predict the dynamic thermal behavior and flow mechanism of K418 powder irradiated by a Gaussian laser beam. A three-dimensional randomly packed powder bed composed of spherical particles was established by discrete element method. The powder particle information including particle size distribution and packing density were taken into account. The volume shrinkage and temperature-dependent thermophysical parameters such as thermal conductivity, specific heat, and other physical properties were also considered. The volume of fluid method was applied to reconstruct the free surface of the molten pool during SLM. The geometrical features, continuity boundaries, and irregularities of the molten pool were proved to be largely determined by the laser energy density. The numerical results are in good agreement with the experiments, which prove to be reasonable and effective. The results provide us some in-depth insight into the complex physical behavior during SLM and guide the optimization of process parameters.

Real-Time Assessment of Wellness and Disease in Daily Life.

PubMed

Ausiello, Dennis; Lipnick, Scott

2015-09-01

The next frontier in medicine involves better quantifying human traits, known as "phenotypes." Biological markers have been directly associated with disease risks, but poor measurement of behaviors such as diet and exercise limits our understanding of preventive measures. By joining together an uncommonly wide range of disciplines and expertise, the Kavli HUMAN Project will advance measurement of behavioral phenotypes, as well as environmental factors that impact behavior. By following the same individuals over time, KHP will liberate new understanding of dynamic links between behavioral phenotypes, disease, and the broader environment. As KHP advances understanding of the bio-behavioral complex, it will seed new approaches to the diagnosis, prevention, and treatment of human disease.

On The Ubiquity of Nonstationary Fluvial Suspended Sediment Dynamics: A Call for Long Term Monitoring and Dynamical Sediment Management Strategies

NASA Astrophysics Data System (ADS)

Gray, A. B.

2017-12-01

Watersheds with sufficient monitoring data have been predominantly found to display nonstationary suspended sediment dynamics, whereby the relationship between suspended sediment concentration and discharge changes over time. Despite the importance of suspended sediment as a keystone of geophysical and biochemical processes, and as a primary mediator of water quality, stationary behavior remains largely assumed in the context of these applications. This study presents an investigation into the time dependent behavior of small mountainous rivers draining the coastal ranges of the western continental US over interannual to interdecadal time scales. Of the 250+ small coastal (drainage area < 2x104 km2) watersheds in this region, only 23 have discharge associated suspended sediment concentration time series with base periods of 10 years or more. Event to interdecadal scale nonstationary suspended sediment dynamics were identified throughout these systems. Temporal patterns of non-stationary behavior provided some evidence for spatial coherence, which may be related to synoptic hydro-metrological patterns and regional scale changes in land use patterns. However, the results also highlight the complex, integrative nature of watershed scale fluvial suspended sediment dynamics. This underscores the need for in-depth, forensic approaches for initial processes identification, which require long term, high resolution monitoring efforts in order to adequately inform management. The societal implications of nonstationary sediment dynamics and their controls were further explored through the case of California, USA, where over 150 impairment listings have resulted in more than 50 sediment TMDLs, only 3 of which are flux based - none of which account for non-stationary behavior.

Dynamic range in small-world networks of Hodgkin-Huxley neurons with chemical synapses

NASA Astrophysics Data System (ADS)

Batista, C. A. S.; Viana, R. L.; Lopes, S. R.; Batista, A. M.

2014-09-01

According to Stevens' law the relationship between stimulus and response is a power-law within an interval called the dynamic range. The dynamic range of sensory organs is found to be larger than that of a single neuron, suggesting that the network structure plays a key role in the behavior of both the scaling exponent and the dynamic range of neuron assemblies. In order to verify computationally the relationships between stimulus and response for spiking neurons, we investigate small-world networks of neurons described by the Hodgkin-Huxley equations connected by chemical synapses. We found that the dynamic range increases with the network size, suggesting that the enhancement of the dynamic range observed in sensory organs, with respect to single neurons, is an emergent property of complex network dynamics.

Actin Out: Regulation of the Synaptic Cytoskeleton

PubMed Central

Spence, Erin F.; Soderling, Scott H.

2015-01-01

The small size of dendritic spines belies the elaborate role they play in excitatory synaptic transmission and ultimately complex behaviors. The cytoskeletal architecture of the spine is predominately composed of actin filaments. These filaments, which at first glance might appear simple, are also surprisingly complex. They dynamically assemble into different structures and serve as a platform for orchestrating the elaborate responses of the spine during spinogenesis and experience-dependent plasticity. Multiple mutations associated with human neurodevelopmental and psychiatric disorders involve genes that encode regulators of the synaptic cytoskeleton. A major, unresolved question is how the disruption of specific actin filament structures leads to the onset and progression of complex synaptic and behavioral phenotypes. This review will cover established and emerging mechanisms of actin cytoskeletal remodeling and how this influences specific aspects of spine biology that are implicated in disease. PMID:26453304

Physics, stability, and dynamics of supply networks

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Lämmer, Stefan; Seidel, Thomas; Šeba, Pétr; Płatkowski, Tadeusz

2004-12-01

We show how to treat supply networks as physical transport problems governed by balance equations and equations for the adaptation of production speeds. Although the nonlinear behavior is different, the linearized set of coupled differential equations is formally related to those of mechanical or electrical oscillator networks. Supply networks possess interesting features due to their complex topology and directed links. We derive analytical conditions for absolute and convective instabilities. The empirically observed “bullwhip effect” in supply chains is explained as a form of convective instability based on resonance effects. Moreover, it is generalized to arbitrary supply networks. Their related eigenvalues are usually complex, depending on the network structure (even without loops). Therefore, their generic behavior is characterized by damped or growing oscillations. We also show that regular distribution networks possess two negative eigenvalues only, but perturbations generate a spectrum of complex eigenvalues.

Nouvelles techniques pratiques pour la modelisation du comportement dynamique des systèmes eau-structure

NASA Astrophysics Data System (ADS)

Miquel, Benjamin

The dynamic or seismic behavior of hydraulic structures is, as for conventional structures, essential to assure protection of human lives. These types of analyses also aim at limiting structural damage caused by an earthquake to prevent rupture or collapse of the structure. The particularity of these hydraulic structures is that not only the internal displacements are caused by the earthquake, but also by the hydrodynamic loads resulting from fluid-structure interaction. This thesis reviews the existing complex and simplified methods to perform such dynamic analysis for hydraulic structures. For the complex existing methods, attention is placed on the difficulties arising from their use. Particularly, interest is given in this work on the use of transmitting boundary conditions to simulate the semi infinity of reservoirs. A procedure has been developed to estimate the error that these boundary conditions can introduce in finite element dynamic analysis. Depending on their formulation and location, we showed that they can considerably affect the response of such fluid-structure systems. For practical engineering applications, simplified procedures are still needed to evaluate the dynamic behavior of structures in contact with water. A review of the existing simplified procedures showed that these methods are based on numerous simplifications that can affect the prediction of the dynamic behavior of such systems. One of the main objectives of this thesis has been to develop new simplified methods that are more accurate than those existing. First, a new spectral analysis method has been proposed. Expressions for the fundamental frequency of fluid-structure systems, key parameter of spectral analysis, have been developed. We show that this new technique can easily be implemented in a spreadsheet or program, and that its calculation time is near instantaneous. When compared to more complex analytical or numerical method, this new procedure yields excellent prediction of the dynamic behavior of fluid-structure systems. Spectral analyses ignore the transient and oscillatory nature of vibrations. When such dynamic analyses show that some areas of the studied structure undergo excessive stresses, time history analyses allow a better estimate of the extent of these zones as well as a time notion of these excessive stresses. Furthermore, the existing spectral analyses methods for fluid-structure systems account only for the static effect of higher modes. Thought this can generally be sufficient for dams, for flexible structures the dynamic effect of these modes should be accounted for. New methods have been developed for fluid-structure systems to account for these observations as well as the flexibility of foundations. A first method was developed to study structures in contact with one or two finite or infinite water domains. This new technique includes flexibility of structures and foundations as well as the dynamic effect of higher vibration modes and variations of the levels of the water domains. Extension of this method was performed to study beam structures in contact with fluids. These new developments have also allowed extending existing analytical formulations of the dynamic properties of a dry beam to a new formulation that includes effect of fluid-structure interaction. The method yields a very good estimate of the dynamic behavior of beam-fluid systems or beam like structures in contact with fluid. Finally, a Modified Accelerogram Method (MAM) has been developed to modify the design earthquake into a new accelerogram that directly accounts for the effect of fluid-structure interaction. This new accelerogram can therefore be applied directly to the dry structure (i.e. without water) in order to calculate the dynamic response of the fluid-structure system. This original technique can include numerous parameters that influence the dynamic response of such systems and allows to treat analytically the fluid-structure interaction while keeping the advantages of finite element modeling.

Dynamical Analysis and Visualization of Tornadoes Time Series

PubMed Central

2015-01-01

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns. PMID:25790281

Dynamical analysis and visualization of tornadoes time series.

PubMed

Lopes, António M; Tenreiro Machado, J A

2015-01-01

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

An agent-based model of cattle grazing toxic Geyer's larkspur.

PubMed

Jablonski, Kevin E; Boone, Randall B; Meiman, Paul J

2018-01-01

By killing cattle and otherwise complicating management, the many species of larkspur (Delphinium spp.) present a serious, intractable, and complex challenge to livestock grazing management in the western United States. Among the many obstacles to improving our understanding of cattle-larkspur dynamics has been the difficulty of testing different grazing management strategies in the field, as the risk of dead animals is too great. Agent-based models (ABMs) provide an effective method of testing alternate management strategies without risk to livestock. ABMs are especially useful for modeling complex systems such as livestock grazing management, and allow for realistic bottom-up encoding of cattle behavior. Here, we introduce a spatially-explicit, behavior-based ABM of cattle grazing in a pasture with a dangerous amount of Geyer's larkspur (D. geyeri). This model tests the role of herd cohesion and stocking density in larkspur intake, finds that both are key drivers of larkspur-induced toxicosis, and indicates that alteration of these factors within realistic bounds can mitigate risk. Crucially, the model points to herd cohesion, which has received little attention in the discipline, as playing an important role in lethal acute toxicosis. As the first ABM to model grazing behavior at realistic scales, this study also demonstrates the tremendous potential of ABMs to illuminate grazing management dynamics, including fundamental aspects of livestock behavior amidst ecological heterogeneity.

Heart rate dynamics in patients with stable angina pectoris and utility of fractal and complexity measures

NASA Technical Reports Server (NTRS)

Makikallio, T. H.; Ristimae, T.; Airaksinen, K. E.; Peng, C. K.; Goldberger, A. L.; Huikuri, H. V.

1998-01-01

Dynamic analysis techniques may uncover abnormalities in heart rate (HR) behavior that are not easily detectable with conventional statistical measures. However, the applicability of these new methods for detecting possible abnormalities in HR behavior in various cardiovascular disorders is not well established. Conventional measures of HR variability were compared with short-term (< or = 11 beats, alpha1) and long-term (> 11 beats, alpha2) fractal correlation properties and with approximate entropy of RR interval data in 38 patients with stable angina pectoris without previous myocardial infarction or cardiac medication at the time of the study and 38 age-matched healthy controls. The short- and long-term fractal scaling exponents (alpha1, alpha2) were significantly higher in the coronary patients than in the healthy controls (1.34 +/- 0.15 vs 1.11 +/- 0.12 [p <0.001] and 1.10 +/- 0.08 vs 1.04 +/- 0.06 [p <0.01], respectively), and they also had lower approximate entropy (p <0.05), standard deviation of all RR intervals (p <0.01), and high-frequency spectral component of HR variability (p <0.05). The short-term fractal scaling exponent performed better than other heart rate variability parameters in differentiating patients with coronary artery disease from healthy subjects, but it was not related to the clinical or angiographic severity of coronary artery disease or any single nonspectral or spectral measure of HR variability in this retrospective study. Patients with stable angina pectoris have altered fractal properties and reduced complexity in their RR interval dynamics relative to age-matched healthy subjects. Dynamic analysis may complement traditional analyses in detecting altered HR behavior in patients with stable angina pectoris.

Deterministic chaos and fractal complexity in the dynamics of cardiovascular behavior: perspectives on a new frontier.

PubMed

Sharma, Vijay

2009-09-10

Physiological systems such as the cardiovascular system are capable of five kinds of behavior: equilibrium, periodicity, quasi-periodicity, deterministic chaos and random behavior. Systems adopt one or more these behaviors depending on the function they have evolved to perform. The emerging mathematical concepts of fractal mathematics and chaos theory are extending our ability to study physiological behavior. Fractal geometry is observed in the physical structure of pathways, networks and macroscopic structures such the vasculature and the His-Purkinje network of the heart. Fractal structure is also observed in processes in time, such as heart rate variability. Chaos theory describes the underlying dynamics of the system, and chaotic behavior is also observed at many levels, from effector molecules in the cell to heart function and blood pressure. This review discusses the role of fractal structure and chaos in the cardiovascular system at the level of the heart and blood vessels, and at the cellular level. Key functional consequences of these phenomena are highlighted, and a perspective provided on the possible evolutionary origins of chaotic behavior and fractal structure. The discussion is non-mathematical with an emphasis on the key underlying concepts.

Deterministic Chaos and Fractal Complexity in the Dynamics of Cardiovascular Behavior: Perspectives on a New Frontier

PubMed Central

Sharma, Vijay

2009-01-01

Physiological systems such as the cardiovascular system are capable of five kinds of behavior: equilibrium, periodicity, quasi-periodicity, deterministic chaos and random behavior. Systems adopt one or more these behaviors depending on the function they have evolved to perform. The emerging mathematical concepts of fractal mathematics and chaos theory are extending our ability to study physiological behavior. Fractal geometry is observed in the physical structure of pathways, networks and macroscopic structures such the vasculature and the His-Purkinje network of the heart. Fractal structure is also observed in processes in time, such as heart rate variability. Chaos theory describes the underlying dynamics of the system, and chaotic behavior is also observed at many levels, from effector molecules in the cell to heart function and blood pressure. This review discusses the role of fractal structure and chaos in the cardiovascular system at the level of the heart and blood vessels, and at the cellular level. Key functional consequences of these phenomena are highlighted, and a perspective provided on the possible evolutionary origins of chaotic behavior and fractal structure. The discussion is non-mathematical with an emphasis on the key underlying concepts. PMID:19812706

The Use of Computer Simulation Techniques in Educational Planning.

ERIC Educational Resources Information Center

Wilson, Charles Z.

Computer simulations provide powerful models for establishing goals, guidelines, and constraints in educational planning. They are dynamic models that allow planners to examine logical descriptions of organizational behavior over time as well as permitting consideration of the large and complex systems required to provide realistic descriptions of…

Informing Biological Design by Integration of Systems and Synthetic Biology

PubMed Central

Smolke, Christina D.; Silver, Pamela A.

2011-01-01

Synthetic biology aims to make the engineering of biology faster and more predictable. In contrast, systems biology focuses on the interaction of myriad components and how these give rise to the dynamic and complex behavior of biological systems. Here, we examine the synergies between these two fields. PMID:21414477

A Platform for Development of Mathematical Games on Silverlight

ERIC Educational Resources Information Center

Radakovic, Davorka; Herceg, Dorde

2013-01-01

Dynamic geometry software (DGS) is often used for development of interactive teaching materials in many subjects, not only mathematics. These interactive materials can contain hundreds of elements in order to represent complex objects, and script programs to control their behavior. We propose an approach for creating, importing and using…

A Proposed Approach for Screening Level Assessment of Risk to Songbird Populations from Pesticide Application in Cotton Fields

EPA Science Inventory

Due to complex population dynamics and migration behaviors, the well-being of animal populations that host human diseases sometimes varies across landscapes in ways that cannot be deduced from geographic abundance patterns alone. In such cases, efficient management of ecologica...

Can a simple dynamical system describe the interplay between drag and buoyancy in terrain-induced canopy flows?

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

De Roo, Frederik; Banerjee, Tirtha

Under non-neutral stratification and in the presence of topography the dynamics of turbulent flow within a canopy is not yet completely understood. This has, among others, serious implications for the measurement of surface – atmosphere exchange by means of eddy-covariance: for example the measurement of carbon dioxide fluxes are strongly influenced if drainage flows occur during night, when the flow within the canopy decouples from the flow aloft. An improved physical understanding of the behavior of scalars under canopy turbulence in complex terrain is urgently needed. In the present work, we investigate the dynamics of turbulent flow within sloped canopies,more » focusing on the slope wind and potential temperature. We concentrate on the presence of oscillatory behavior in the flow variables in terms of switching of flow regimes by conducting linear stability analysis. We revisit and correct the simplified theory that exists in the literature, which is based on the interplay between the drag force and the buoyancy. We find that the simplified description of this dynamical system cannot exhibit the observed richness of the dynamics. To augment the simplified dynamical system’s analysis, we make use of large-eddy simulation of a three-dimensional hill covered by a homogeneous forest and analyze the phase synchronization behavior of the buoyancy and drag forces in the momentum budget to explore the turbulent dynamics in more detail.« less

Can a simple dynamical system describe the interplay between drag and buoyancy in terrain-induced canopy flows?

DOE PAGES

De Roo, Frederik; Banerjee, Tirtha

2018-02-23

Under non-neutral stratification and in the presence of topography the dynamics of turbulent flow within a canopy is not yet completely understood. This has, among others, serious implications for the measurement of surface – atmosphere exchange by means of eddy-covariance: for example the measurement of carbon dioxide fluxes are strongly influenced if drainage flows occur during night, when the flow within the canopy decouples from the flow aloft. An improved physical understanding of the behavior of scalars under canopy turbulence in complex terrain is urgently needed. In the present work, we investigate the dynamics of turbulent flow within sloped canopies,more » focusing on the slope wind and potential temperature. We concentrate on the presence of oscillatory behavior in the flow variables in terms of switching of flow regimes by conducting linear stability analysis. We revisit and correct the simplified theory that exists in the literature, which is based on the interplay between the drag force and the buoyancy. We find that the simplified description of this dynamical system cannot exhibit the observed richness of the dynamics. To augment the simplified dynamical system’s analysis, we make use of large-eddy simulation of a three-dimensional hill covered by a homogeneous forest and analyze the phase synchronization behavior of the buoyancy and drag forces in the momentum budget to explore the turbulent dynamics in more detail.« less

LoyalTracker: Visualizing Loyalty Dynamics in Search Engines.

PubMed

Shi, Conglei; Wu, Yingcai; Liu, Shixia; Zhou, Hong; Qu, Huamin

2014-12-01

The huge amount of user log data collected by search engine providers creates new opportunities to understand user loyalty and defection behavior at an unprecedented scale. However, this also poses a great challenge to analyze the behavior and glean insights into the complex, large data. In this paper, we introduce LoyalTracker, a visual analytics system to track user loyalty and switching behavior towards multiple search engines from the vast amount of user log data. We propose a new interactive visualization technique (flow view) based on a flow metaphor, which conveys a proper visual summary of the dynamics of user loyalty of thousands of users over time. Two other visualization techniques, a density map and a word cloud, are integrated to enable analysts to gain further insights into the patterns identified by the flow view. Case studies and the interview with domain experts are conducted to demonstrate the usefulness of our technique in understanding user loyalty and switching behavior in search engines.

Piping benchmark problems. Volume 1. Dynamic analysis uniform support motion response spectrum method

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

Bezler, P.; Hartzman, M.; Reich, M.

1980-08-01

A set of benchmark problems and solutions have been developed for verifying the adequacy of computer programs used for dynamic analysis and design of nuclear piping systems by the Response Spectrum Method. The problems range from simple to complex configurations which are assumed to experience linear elastic behavior. The dynamic loading is represented by uniform support motion, assumed to be induced by seismic excitation in three spatial directions. The solutions consist of frequencies, participation factors, nodal displacement components and internal force and moment components. Solutions to associated anchor point motion static problems are not included.

Persistence and stochastic periodicity in the intensity dynamics of a fiber laser during the transition to optical turbulence

NASA Astrophysics Data System (ADS)

Carpi, Laura; Masoller, Cristina

2018-02-01

Many natural systems display transitions among different dynamical regimes, which are difficult to identify when the data are noisy and high dimensional. A technologically relevant example is a fiber laser, which can display complex dynamical behaviors that involve nonlinear interactions of millions of cavity modes. Here we study the laminar-turbulence transition that occurs when the laser pump power is increased. By applying various data analysis tools to empirical intensity time series we characterize their persistence and demonstrate that at the transition temporal correlations can be precisely represented by a surprisingly simple model.

Simplifying the complexity of a coupled carbon turnover and pesticide degradation model

NASA Astrophysics Data System (ADS)

Marschmann, Gianna; Erhardt, André H.; Pagel, Holger; Kügler, Philipp; Streck, Thilo

2016-04-01

The mechanistic one-dimensional model PECCAD (PEsticide degradation Coupled to CArbon turnover in the Detritusphere; Pagel et al. 2014, Biogeochemistry 117, 185-204) has been developed as a tool to elucidate regulation mechanisms of pesticide degradation in soil. A feature of this model is that it integrates functional traits of microorganisms, identifiable by molecular tools, and physicochemical processes such as transport and sorption that control substrate availability. Predicting the behavior of microbially active interfaces demands a fundamental understanding of factors controlling their dynamics. Concepts from dynamical systems theory allow us to study general properties of the model such as its qualitative behavior, intrinsic timescales and dynamic stability: Using a Latin hypercube method we sampled the parameter space for physically realistic steady states of the PECCAD ODE system and set up a numerical continuation and bifurcation problem with the open-source toolbox MatCont in order to obtain a complete classification of the dynamical system's behaviour. Bifurcation analysis reveals an equilibrium state of the system entirely controlled by fungal kinetic parameters. The equilibrium is generally unstable in response to small perturbations except for a small band in parameter space where the pesticide pool is stable. Time scale separation is a phenomenon that occurs in almost every complex open physical system. Motivated by the notion of "initial-stage" and "late-stage" decomposers and the concept of r-, K- or L-selected microbial life strategies, we test the applicability of geometric singular perturbation theory to identify fast and slow time scales of PECCAD. Revealing a generic fast-slow structure would greatly simplify the analysis of complex models of organic matter turnover by reducing the number of unknowns and parameters and providing a systematic mathematical framework for studying their properties.

Novel readout method for molecular diagnostic assays based on optical measurements of magnetic nanobead dynamics.

PubMed

Donolato, Marco; Antunes, Paula; Bejhed, Rebecca S; Zardán Gómez de la Torre, Teresa; Østerberg, Frederik W; Strömberg, Mattias; Nilsson, Mats; Strømme, Maria; Svedlindh, Peter; Hansen, Mikkel F; Vavassori, Paolo

2015-02-03

We demonstrate detection of DNA coils formed from a Vibrio cholerae DNA target at picomolar concentrations using a novel optomagnetic approach exploiting the dynamic behavior and optical anisotropy of magnetic nanobead (MNB) assemblies. We establish that the complex second harmonic optical transmission spectra of MNB suspensions measured upon application of a weak uniaxial AC magnetic field correlate well with the rotation dynamics of the individual MNBs. Adding a target analyte to the solution leads to the formation of permanent MNB clusters, namely, to the suppression of the dynamic MNB behavior. We prove that the optical transmission spectra are highly sensitive to the formation of permanent MNB clusters and, thereby to the target analyte concentration. As a specific clinically relevant diagnostic case, we detect DNA coils formed via padlock probe recognition and isothermal rolling circle amplification and benchmark against a commercial equipment. The results demonstrate the fast optomagnetic readout of rolling circle products from bacterial DNA utilizing the dynamic properties of MNBs in a miniaturized and low-cost platform requiring only a transparent window in the chip.

The resonant system: Linking brain-body-environment in sport performance☆.

PubMed

Teques, Pedro; Araújo, Duarte; Seifert, Ludovic; Del Campo, Vicente L; Davids, Keith

2017-01-01

The ecological dynamics approach offers new insights to understand how athlete nervous systems are embedded within the body-environment system in sport. Cognitive neuroscience focuses on the neural bases of athlete behaviors in terms of perceptual, cognitive, and motor functions defined within specific brain structures. Here, we discuss some limitations of this traditional perspective, addressing how athletes functionally adapt perception and action to the dynamics of complex performance environments by continuously perceiving information to regulate goal-directed actions. We examine how recent neurophysiological evidence of functioning in diverse cortical and subcortical regions appears more compatible with an ecological dynamics perspective, than traditional views in cognitive neuroscience. We propose how athlete behaviors in sports may be related to the tuning of resonant mechanisms indicating that perception is a dynamic process involving the whole body of the athlete. We emphasize the important role of metastable dynamics in the brain-body-environment system facilitating continuous interactions with a landscape of affordances (opportunities for action) in a performance environment. We discuss implications of these ideas for performance preparation and practice design in sport. © 2017 Elsevier B.V. All rights reserved.

Embracing Complexity beyond Systems Medicine: A New Approach to Chronic Immune Disorders

PubMed Central

te Velde, Anje A.; Bezema, Tjitske; van Kampen, Antoine H. C.; Kraneveld, Aletta D.; 't Hart, Bert A.; van Middendorp, Henriët; Hack, Erik C.; van Montfrans, Joris M.; Belzer, Clara; Jans-Beken, Lilian; Pieters, Raymond H.; Knipping, Karen; Huber, Machteld; Boots, Annemieke M. H.; Garssen, Johan; Radstake, Tim R.; Evers, Andrea W. M.; Prakken, Berent J.; Joosten, Irma

2016-01-01

In order to combat chronic immune disorders (CIDs), it is an absolute necessity to understand the bigger picture, one that goes beyond insights at a one-disease, molecular, cellular, and static level. To unravel this bigger picture we advocate an integral, cross-disciplinary approach capable of embracing the complexity of the field. This paper discusses the current knowledge on common pathways in CIDs including general psychosocial and lifestyle factors associated with immune functioning. We demonstrate the lack of more in-depth psychosocial and lifestyle factors in current research cohorts and most importantly the need for an all-encompassing analysis of these factors. The second part of the paper discusses the challenges of understanding immune system dynamics and effectively integrating all key perspectives on immune functioning, including the patient’s perspective itself. This paper suggests the use of techniques from complex systems science in describing and simulating healthy or deviating behavior of the immune system in its biopsychosocial surroundings. The patient’s perspective data are suggested to be generated by using specific narrative techniques. We conclude that to gain more insight into the behavior of the whole system and to acquire new ways of combatting CIDs, we need to construct and apply new techniques in the field of computational and complexity science, to an even wider variety of dynamic data than used in today’s systems medicine. PMID:28018353

Dynamic complexities in a pest control model with birth pulse and harvesting

NASA Astrophysics Data System (ADS)

Goel, A.; Gakkhar, S.

2016-04-01

In this paper, an impulsive model is discussed for an integrated pest management approach comprising of chemical and mechanical controls. The pesticides and harvesting are used to control the stage-structured pest population. The mature pest give birth to immature pest in pulses at regular intervals. The pest is controlled by spraying chemical pesticides affecting immature as well as mature pest. The harvesting of both immature and mature pest further reduce the pest population. The discrete dynamical system obtained from stroboscopic map is analyzed. The threshold conditions for stability of pest-free state as well as non-trivial period-1 solution is obtained. The effect of pesticide spray timing and harvesting on immature as well as mature pest are shown. Finally, by numerical simulation with MATLAB, the dynamical behaviors of the model is found to be complex. Above the threshold level there is a characteristic sequence of bifurcations leading to chaotic dynamics. Route to chaos is found to be period-doubling. Period halving bifurcations are also observed.

Attachment and Jealousy: Understanding the Dynamic Experience of Jealousy Using the Response Escalation Paradigm.

PubMed

Huelsnitz, Chloe O; Farrell, Allison K; Simpson, Jeffry A; Griskevicius, Vladas; Szepsenwol, Ohad

2018-04-01

Jealousy is a complex, dynamic experience that unfolds over time in relationship-threatening situations. Prior research has used retrospective reports that cannot disentangle initial levels and change in jealousy in response to escalating threat. In three studies, we examined responses to the Response Escalation Paradigm (REP)-a 5-stage hypothetical scenario in which individuals are exposed to increasing levels of relationship threat-as a function of attachment orientations. Highly anxious individuals exhibited hypervigilant, slow escalation response patterns, interfered earlier in the REP, felt more jealousy, sadness, and worry when they interfered, and wanted to engage in more vigilant, destructive, and passive behaviors aimed at their partner. Highly avoidant individuals felt more anger when they interfered in the REP and wanted to engage in more partner-focused, destructive behaviors. The REP offers a dynamic method for inducing and examining jealousy and introduces a novel approach to studying other emotional experiences.

The internal architecture of dendritic spines revealed by super-resolution imaging: What did we learn so far?

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

MacGillavry, Harold D., E-mail: h.d.macgillavry@uu.nl; Hoogenraad, Casper C., E-mail: c.hoogenraad@uu.nl

2015-07-15

The molecular architecture of dendritic spines defines the efficiency of signal transmission across excitatory synapses. It is therefore critical to understand the mechanisms that control the dynamic localization of the molecular constituents within spines. However, because of the small scale at which most processes within spines take place, conventional light microscopy techniques are not adequate to provide the necessary level of resolution. Recently, super-resolution imaging techniques have overcome the classical barrier imposed by the diffraction of light, and can now resolve the localization and dynamic behavior of proteins within small compartments with nanometer precision, revolutionizing the study of dendritic spinemore » architecture. Here, we highlight exciting new findings from recent super-resolution studies on neuronal spines, and discuss how these studies revealed important new insights into how protein complexes are assembled and how their dynamic behavior shapes the efficiency of synaptic transmission.« less

Hybrid automata models of cardiac ventricular electrophysiology for real-time computational applications.

PubMed

Andalam, Sidharta; Ramanna, Harshavardhan; Malik, Avinash; Roop, Parthasarathi; Patel, Nitish; Trew, Mark L

2016-08-01

Virtual heart models have been proposed for closed loop validation of safety-critical embedded medical devices, such as pacemakers. These models must react in real-time to off-the-shelf medical devices. Real-time performance can be obtained by implementing models in computer hardware, and methods of compiling classes of Hybrid Automata (HA) onto FPGA have been developed. Models of ventricular cardiac cell electrophysiology have been described using HA which capture the complex nonlinear behavior of biological systems. However, many models that have been used for closed-loop validation of pacemakers are highly abstract and do not capture important characteristics of the dynamic rate response. We developed a new HA model of cardiac cells which captures dynamic behavior and we implemented the model in hardware. This potentially enables modeling the heart with over 1 million dynamic cells, making the approach ideal for closed loop testing of medical devices.

Adaptive and Resilient Soft Tensegrity Robots.

PubMed

Rieffel, John; Mouret, Jean-Baptiste

2018-04-17

Living organisms intertwine soft (e.g., muscle) and hard (e.g., bones) materials, giving them an intrinsic flexibility and resiliency often lacking in conventional rigid robots. The emerging field of soft robotics seeks to harness these same properties to create resilient machines. The nature of soft materials, however, presents considerable challenges to aspects of design, construction, and control-and up until now, the vast majority of gaits for soft robots have been hand-designed through empirical trial-and-error. This article describes an easy-to-assemble tensegrity-based soft robot capable of highly dynamic locomotive gaits and demonstrating structural and behavioral resilience in the face of physical damage. Enabling this is the use of a machine learning algorithm able to discover effective gaits with a minimal number of physical trials. These results lend further credence to soft-robotic approaches that seek to harness the interaction of complex material dynamics to generate a wealth of dynamical behaviors.

From electromyographic activity to frequency modulation in zebra finch song.

PubMed

Döppler, Juan F; Bush, Alan; Goller, Franz; Mindlin, Gabriel B

2018-02-01

Behavior emerges from the interaction between the nervous system and peripheral devices. In the case of birdsong production, a delicate and fast control of several muscles is required to control the configuration of the syrinx (the avian vocal organ) and the respiratory system. In particular, the syringealis ventralis muscle is involved in the control of the tension of the vibrating labia and thus affects the frequency modulation of the sound. Nevertheless, the translation of the instructions (which are electrical in nature) into acoustical features is complex and involves nonlinear, dynamical processes. In this work, we present a model of the dynamics of the syringealis ventralis muscle and the labia, which allows calculating the frequency of the generated sound, using as input the electrical activity recorded in the muscle. In addition, the model provides a framework to interpret inter-syllabic activity and hints at the importance of the biomechanical dynamics in determining behavior.

Self-organization in suspensions of end-functionalized semiflexible polymers under shear flow

NASA Astrophysics Data System (ADS)

Myung, Jin Suk; Winkler, Roland G.; Gompper, Gerhard

2015-12-01

The nonequilibrium dynamical behavior and structure formation of end-functionalized semiflexible polymer suspensions under flow are investigated by mesoscale hydrodynamic simulations. The hybrid simulation approach combines the multiparticle collision dynamics method for the fluid, which accounts for hydrodynamic interactions, with molecular dynamics simulations for the semiflexible polymers. In equilibrium, various kinds of scaffold-like network structures are observed, depending on polymer flexibility and end-attraction strength. We investigate the flow behavior of the polymer networks under shear and analyze their nonequilibrium structural and rheological properties. The scaffold structure breaks up and densified aggregates are formed at low shear rates, while the structural integrity is completely lost at high shear rates. We provide a detailed analysis of the shear- rate-dependent flow-induced structures. The studies provide a deeper understanding of the formation and deformation of network structures in complex materials.

Fault reactivation due to the M7.6 Nicoya earthquake at the Turrialba-Irazú volcanic complex, Costa Rica: Effects of dynamic stress triggering

NASA Astrophysics Data System (ADS)

Lupi, M.; Fuchs, Florian; Pacheco, Javier F.

2014-06-01

The M7.6 Nicoya earthquake struck at the interface between the Cocos plate and the Caribbean plate on 5 September 2012 inducing a ground acceleration of 0.5 m s-2 at the Irazú-Turrialba volcanic complex. We use data from six seismic stations deployed around and atop the Irazú-Turrialba volcanic complex to show the increase of local seismic activity after the M7.6 Nicoya earthquake. The response consists in more than 300 locatable earthquakes occurring in swarm sequences along a fault system that intersects the Irazú-Turrialba volcanic complex. In addition, we point out that major aftershocks are followed by increases of seismic activity in the same region. The weak static stress variation imposed by the main slip of the Nicoya earthquake at the Irazú-Turrialba volcanic complex suggests a dynamic triggering mechanism. We expand this concept suggesting that this behavior may be similar to the one observed in the Chilean and Japanese volcanic arcs during the M8.8 2010 Maule, Chile, and M9.0 2011 Tohoku, Japan, earthquakes. Finally, we highlight that the combined action of dynamic stress and short-lived coseismic relaxation may trigger seismic activity in geological systems in near-critical conditions.

Singular spectrum and singular entropy used in signal processing of NC table

NASA Astrophysics Data System (ADS)

Wang, Linhong; He, Yiwen

2011-12-01

NC (numerical control) table is a complex dynamic system. The dynamic characteristics caused by backlash, friction and elastic deformation among each component are so complex that they have become the bottleneck of enhancing the positioning accuracy, tracking accuracy and dynamic behavior of NC table. This paper collects vibration acceleration signals from NC table, analyzes the signals with SVD (singular value decomposition) method, acquires the singular spectrum and calculates the singular entropy of the signals. The signal characteristics and their regulations of NC table are revealed via the characteristic quantities such as singular spectrum, singular entropy etc. The steep degrees of singular spectrums can be used to discriminate complex degrees of signals. The results show that the signals in direction of driving axes are the simplest and the signals in perpendicular direction are the most complex. The singular entropy values can be used to study the indetermination of signals. The results show that the signals of NC table are not simple signal nor white noise, the entropy values in direction of driving axe are lower, the entropy values increase along with the increment of driving speed and the entropy values at the abnormal working conditions such as resonance or creeping etc decrease obviously.

On the origin of the decrease in stability of the DNA hairpin d(GCGAAGC) on complexation with aromatic drugs.

PubMed

Kostjukov, V V; Lantushenko, A O; Davies, D B; Evstigneev, M P

2007-08-01

Molecular dynamics simulations of drug-DNA complexes have been carried out in order to explain the experimentally observed decrease in thermal stability of the DNA hairpin d(GCGAAGC) on binding the aromatic drug molecules, daunomycin, ethidium bromide, novantrone and proflavine. This complexation behavior is in contrast to the stabilizing effect of the same aromatic drug molecules on DNA duplexes. Analysis of the energy parameters and the hydration properties of the complexes shows that the main factor correlating with the decrease in melting temperatures of the drug-hairpin complexes is the number of water bridges, with a reduction of at least 40% on ligand binding.

Agent-based modeling of autophagy reveals emergent regulatory behavior of spatio-temporal autophagy dynamics.

PubMed

Börlin, Christoph S; Lang, Verena; Hamacher-Brady, Anne; Brady, Nathan R

2014-09-10

Autophagy is a vesicle-mediated pathway for lysosomal degradation, essential under basal and stressed conditions. Various cellular components, including specific proteins, protein aggregates, organelles and intracellular pathogens, are targets for autophagic degradation. Thereby, autophagy controls numerous vital physiological and pathophysiological functions, including cell signaling, differentiation, turnover of cellular components and pathogen defense. Moreover, autophagy enables the cell to recycle cellular components to metabolic substrates, thereby permitting prolonged survival under low nutrient conditions. Due to the multi-faceted roles for autophagy in maintaining cellular and organismal homeostasis and responding to diverse stresses, malfunction of autophagy contributes to both chronic and acute pathologies. We applied a systems biology approach to improve the understanding of this complex cellular process of autophagy. All autophagy pathway vesicle activities, i.e. creation, movement, fusion and degradation, are highly dynamic, temporally and spatially, and under various forms of regulation. We therefore developed an agent-based model (ABM) to represent individual components of the autophagy pathway, subcellular vesicle dynamics and metabolic feedback with the cellular environment, thereby providing a framework to investigate spatio-temporal aspects of autophagy regulation and dynamic behavior. The rules defining our ABM were derived from literature and from high-resolution images of autophagy markers under basal and activated conditions. Key model parameters were fit with an iterative method using a genetic algorithm and a predefined fitness function. From this approach, we found that accurate prediction of spatio-temporal behavior required increasing model complexity by implementing functional integration of autophagy with the cellular nutrient state. The resulting model is able to reproduce short-term autophagic flux measurements (up to 3 hours) under basal and activated autophagy conditions, and to measure the degree of cell-to-cell variability. Moreover, we experimentally confirmed two model predictions, namely (i) peri-nuclear concentration of autophagosomes and (ii) inhibitory lysosomal feedback on mTOR signaling. Agent-based modeling represents a novel approach to investigate autophagy dynamics, function and dysfunction with high biological realism. Our model accurately recapitulates short-term behavior and cell-to-cell variability under basal and activated conditions of autophagy. Further, this approach also allows investigation of long-term behaviors emerging from biologically-relevant alterations to vesicle trafficking and metabolic state.

Social Determinants and Health Behaviors: Conceptual Frames and Empirical Advances

PubMed Central

Short, Susan E.; Mollborn, Stefanie

2015-01-01

Health behaviors shape health and well-being in individuals and populations. Drawing on recent research, we review applications of the widely applied “social determinants” approach to health behaviors. This approach shifts the lens from individual attribution and responsibility to societal organization and the myriad institutions, structures, inequalities, and ideologies undergirding health behaviors. Recent scholarship integrates a social determinants perspective with biosocial approaches to health behavior dynamics. Empirical advances model feedback among social, psychological and biological factors. Health behaviors are increasingly recognized as multidimensional and embedded in health lifestyles, varying over the life course and across place and reflecting dialectic between structure and agency that necessitates situating individuals in context. Advances in measuring and modeling health behaviors promise to enhance representations of this complexity. PMID:26213711

The Origin of Time in the Songbird Motor Pathway

NASA Astrophysics Data System (ADS)

Long, Michael

2010-03-01

Many complex behaviors, like speech or music, have a hierarchical organization with structure on many timescales. How does the brain control the timing and ordering of behavioral sequences? Do different circuits control different timescales of the behavior? To begin answering these questions, we use temperature to manipulate the biophysical dynamics in different regions of the songbird forebrain involved in song production. We found that cooling premotor nucleus HVC (high vocal center) uniformly slows song speed by up to 40% while only slightly altering the acoustic structure, whereas cooling downstream motor nucleus RA (robust nucleus of the arcopallium) has no observable effect on song timing, despite a marked affect of RA spiking activity. To better understand the circuit mechanisms of precise premotor timing, we perform intracellular recordings in RA-projecting HVC neurons during singing. Our observations suggest highly ordered dynamics within HVC which are consistent with a synfire-like neuronal architecture.

Social influence and bullying behavior: intervention-based network dynamics of the fairplayer.manual bullying prevention program.

PubMed

Wölfer, Ralf; Scheithauer, Herbert

2014-01-01

Bullying is a social phenomenon and although preventive interventions consequently address social mechanisms, evaluations hardly consider the complexity of peer processes. Therefore, the present study analyzes the efficacy of the fairplayer.manual bullying prevention program from a social network perspective. Within a pretest-posttest control group design, longitudinal data were available from 328 middle-school students (MAge  = 13.7 years; 51% girls), who provided information on bullying behavior and interaction patterns. The revealed network parameters were utilized to examine the network change (MANCOVA) and the network dynamics (SIENA). Across both forms of analyses, findings revealed the hypothesized intervention-based decrease of bullies' social influence. Hence the present bullying prevention program, as one example of programs that successfully addresses both individual skills and social mechanisms, demonstrates the desired effect of reducing contextual opportunities for the exhibition of bullying behavior. © 2014 Wiley Periodicals, Inc.

Coordinated Dynamic Behaviors for Multirobot Systems With Collision Avoidance.

PubMed

Sabattini, Lorenzo; Secchi, Cristian; Fantuzzi, Cesare

2017-12-01

In this paper, we propose a novel methodology for achieving complex dynamic behaviors in multirobot systems. In particular, we consider a multirobot system partitioned into two subgroups: 1) dependent and 2) independent robots. Independent robots are utilized as a control input, and their motion is controlled in such a way that the dependent robots solve a tracking problem, that is following arbitrarily defined setpoint trajectories, in a coordinated manner. The control strategy proposed in this paper explicitly addresses the collision avoidance problem, utilizing a null space-based behavioral approach: this leads to combining, in a non conflicting manner, the tracking control law with a collision avoidance strategy. The combination of these control actions allows the robots to execute their task in a safe way. Avoidance of collisions is formally proven in this paper, and the proposed methodology is validated by means of simulations and experiments on real robots.

Coevolutionary dynamics with clustering behaviors on cyclic competition

NASA Astrophysics Data System (ADS)

Dong, Linrong; Yang, Guangcan

2012-05-01

We propose a dynamic model for describing clustering behaviors on a cyclic game, in which the same species form a cluster to compete. The rates of consuming the prey depend not only on the individual competing ability v, but also on the two interacting cluster’s sizes. The fragmentation and coagulation rates of the clusters are related to the cohesive strength among the individuals. A new parameter u is introduced to indicate the uniting degree. We find that the probability distribution of the clustering sizes is almost a power law in a large regime specified by the two parameters, which reflects the scale-free behavior in complex systems. In addition, the exponential magnitudes are mostly in the range of real social systems. Our simulation shows that clustering promotes biodiversity. At steady state, the amounts about the three species evolve tempestuously with asymmetric period; the aggregations about big size’s clusters to compete are obvious and on-off intermittence.

Biting the hand that feeds: current opinion on the interpersonal causes, correlates, and consequences of borderline personality disorder.

PubMed

Crowell, Sheila E

2016-01-01

Borderline personality disorder (BPD) is a complex psychiatric diagnosis characterized by dysregulated behaviors, emotions, cognitions, and interpersonal relationships. In recent years, developmental psychopathologists have sought to identify early origins of BPD, with the ultimate goal of developing and providing effective preventative interventions for those at highest risk. In addition to heritable biological sensitivities, many scholars assert that environmental and interpersonal risk factors contribute to the emergence and maintenance of key borderline traits. Nonetheless, many BPD researchers examine only affected individuals, neglecting the family, peer, couple, and other dynamic contextual forces that impinge upon individual-level behavior. In the past decade, however, theoretical and empirical research has increasingly explored the interpersonal causes, correlates, and consequences of BPD. Such work has resulted in novel research and clinical theories intended to better understand and improve interpersonal dynamics among those with borderline traits. A major objective for the field is to better characterize how interpersonal dynamics affect (and are affected by) the behaviors, emotions, and thoughts of vulnerable individuals to either reduce or heighten risk for BPD.

Dynamic Electromechanical Characterization of the Ferroelectric Ceramic PZT 95/5

NASA Astrophysics Data System (ADS)

Setchell, R. E.; Chhabildas, L. C.; Furnish, M. D.; Montgomery, S. T.; Holman, G. T.

1997-07-01

Shock-induced depoling of the ferroelectric ceramic PZT 95/5 has been utilized in a number of pulsed power applications. The dynamic behavior of the poled ceramic is complex, with nonlinear coupling between mechanical and electrical variables. Recent efforts to improve numerical simulations of this process have been limited by the scarcity of relevant experimental studies within the last twenty years. Consequently, we have initiated an extensive experimental study of the dynamic electromechanical behavior of this material. Samples of the poled ceramic are shocked to axial stresses from 0.5 to 5 GPa in planar impact experiments and observed with laser interferometry (VISAR) to obtain transmitted wave profiles. Current generation due to shock-induced depoling is observed using different external loads to vary electric field strengths within the samples. Experimental configurations either have the remanent polarization parallel to the direction of shock motion (axially poled) or perpendicular (normally poled). Initial experiments on unpoled samples utilized PVDF stress gauges as well as VISAR, and extended prior data on shock loading and release behavior. (Supported by the U. S. Department of Energy under contract DE-AC04-94AL85000). abstract.

An integrated approach to evaluate policies for controlling traffic law violations.

PubMed

Mehmood, Arif

2010-03-01

Modeling dynamics of the driver behavior is a complex problem. In this paper a system approach is introduced to model and to analyze the driver behavior related to traffic law violations in the Emirate of Abu Dhabi. This paper demonstrates how the theoretical relationships between different factors can be expressed formally, and how the resulting model can assist in evaluating potential benefits of various policies to control the traffic law violations Using system approach, an integrated dynamic simulation model is developed, and model is tested to simulate the driver behavior for violating traffic laws during 2002-2007 in the Emirate of Abu Dhabi. The dynamic simulation model attempts to address the questions: (1) "what" interventions should be implemented to reduce and eventually control traffic violations which will lead to improving road safety and (2) "how" to justify those interventions will be effective or ineffective to control the violations in different transportation conditions. The simulation results reveal promising capability of applying system approach in the policy evaluation studies. Copyright 2009 Elsevier Ltd. All rights reserved.

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.

Importance of Age on the Dynamic Mechanical Behavior of Intertubular and Peritubular Dentin

PubMed Central

Ryou, Heonjune; Romberg, Elaine; Pashley, David H.; Tay, Franklin R.; Arola, Dwayne

2014-01-01

An experimental evaluation of human coronal dentin was performed using nanoscopic Dynamic Mechanical Analysis (nanoDMA). The primary objectives were to quantify any unique changes in mechanical behavior of intertubular and peritubular dentin with age, and to evaluate the microstructure and mechanical behavior of the mineral deposited within the lumens. Specimens of coronal dentin were evaluated by nanoDMA using single indents and in scanning mode via scanning probe microscopy. Results showed that there were no significant differences in the storage modulus or complex modulus between the two age groups (18–25 versus 54–83 yrs) for either the intertubular or peritubular tissue. However, there were significant differences in the dampening behavior between the young and old dentin, as represented in the loss modulus and tanδ responses. For both the intertubular and peritubular components, the capacity for dampening was significantly lower in the old group. Scanning based nanoDMA showed that the tubules of old dentin exhibit a gradient in elastic behavior, with decrease in elastic modulus from the cuff to the center of tubules filled with newly deposited mineral. PMID:25498296

Multiscale analysis of information dynamics for linear multivariate processes.

PubMed

Faes, Luca; Montalto, Alessandro; Stramaglia, Sebastiano; Nollo, Giandomenico; Marinazzo, Daniele

2016-08-01

In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving average (MA) component, we describe how to represent the resulting VARMA process using statespace (SS) models and how to exploit the SS model parameters to compute analytical measures of information storage and information transfer for the original and rescaled processes. The framework is then used to quantify multiscale information dynamics for simulated unidirectionally and bidirectionally coupled VAR processes, showing that rescaling may lead to insightful patterns of information storage and transfer but also to potentially misleading behaviors.

Purely-elastic flow instabilities and elastic turbulence in microfluidic cross-slot devices

PubMed Central

Sousa, P. C.; Pinho, F. T.

2018-01-01

We experimentally investigate the dynamics of viscoelastic fluid flows in cross-slot microgeometries under creeping flow conditions. We focus on the unsteady flow regime observed at high Weissenberg numbers (Wi) with the purpose of understanding the underlying flow signature of elastic turbulence. The effects of the device aspect ratio and fluid rheology on the unsteady flow state are investigated. Visualization of the flow patterns and time-resolved micro-particle image velocimetry were carried out to study the fluid flow behavior for a wide range of Weissenberg numbers. A periodic flow behavior is observed at low Weissenberg numbers followed by a more complex dynamics as Wi increases, eventually leading to the onset of elastic turbulence for very high Weissenberg numbers. PMID:29376533

Dynamical behavior of the random field on the pulsating and snaking solitons in cubic-quintic complex Ginzburg-Landau equation

NASA Astrophysics Data System (ADS)

Bakhtiar, Nurizatul Syarfinas Ahmad; Abdullah, Farah Aini; Hasan, Yahya Abu

2017-08-01

In this paper, we consider the dynamical behaviour of the random field on the pulsating and snaking solitons in a dissipative systems described by the one-dimensional cubic-quintic complex Ginzburg-Landau equation (cqCGLE). The dynamical behaviour of the random filed was simulated by adding a random field to the initial pulse. Then, we solve it numerically by fixing the initial amplitude profile for the pulsating and snaking solitons without losing any generality. In order to create the random field, we choose 0 ≤ ɛ ≤ 1.0. As a result, multiple soliton trains are formed when the random field is applied to a pulse like initial profile for the parameters of the pulsating and snaking solitons. The results also show the effects of varying the random field of the transient energy peaks in pulsating and snaking solitons.

Emergence of grouping in multi-resource minority game dynamics

NASA Astrophysics Data System (ADS)

Huang, Zi-Gang; Zhang, Ji-Qiang; Dong, Jia-Qi; Huang, Liang; Lai, Ying-Cheng

2012-10-01

Complex systems arising in a modern society typically have many resources and strategies available for their dynamical evolutions. To explore quantitatively the behaviors of such systems, we propose a class of models to investigate Minority Game (MG) dynamics with multiple strategies. In particular, agents tend to choose the least used strategies based on available local information. A striking finding is the emergence of grouping states defined in terms of distinct strategies. We develop an analytic theory based on the mean-field framework to understand the ``bifurcations'' of the grouping states. The grouping phenomenon has also been identified in the Shanghai Stock-Market system, and we discuss its prevalence in other real-world systems. Our work demonstrates that complex systems obeying the MG rules can spontaneously self-organize themselves into certain divided states, and our model represents a basic and general mathematical framework to address this kind of phenomena in social, economical and political systems.

Dynamics of venom composition across a complex life cycle

PubMed Central

Macrander, Jason; Fridrich, Arie; Modepalli, Vengamanaidu; Reitzel, Adam M; Sunagar, Kartik

2018-01-01

Little is known about venom in young developmental stages of animals. The appearance of toxins and stinging cells during early embryonic stages in the sea anemone Nematostella vectensis suggests that venom is already expressed in eggs and larvae of this species. Here, we harness transcriptomic, biochemical and transgenic tools to study venom production dynamics in Nematostella. We find that venom composition and arsenal of toxin-producing cells change dramatically between developmental stages of this species. These findings can be explained by the vastly different interspecific interactions of each life stage, as individuals develop from a miniature non-feeding mobile planula to a larger sessile polyp that predates on other animals and interact differently with predators. Indeed, behavioral assays involving prey, predators and Nematostella are consistent with this hypothesis. Further, the results of this work suggest a much wider and dynamic venom landscape than initially appreciated in animals with a complex life cycle. PMID:29424690

Modeling infectious disease dynamics in the complex landscape of global health

PubMed Central

Heesterbeek, Hans; Anderson, Roy; Andreasen, Viggo; Bansal, Shweta; De Angelis, Daniela; Dye, Chris; Eames, Ken; Edmunds, John; Frost, Simon; Funk, Sebastian; Hollingsworth, Deirdre; House, Thomas; Isham, Valerie; Klepac, Petra; Lessler, Justin; Lloyd-Smith, James; Metcalf, Jessica; Mollison, Denis; Pellis, Lorenzo; Pulliam, Juliet; Roberts, Mick; Viboud, Cecile

2015-01-01

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational and spatial scales, which even within a single pathogen often span hours to months, cellular to ecosystem levels, and local to pandemic spread. Some pathogens are directly transmitted between individuals of a single species, while others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity, and dynamic human behavior, raise prevention and control from formerly national to international issues. In the face of this complexity, mathematical models offer essential tools for synthesizing information to understand epidemiological patterns, and for developing the quantitative evidence base for decision-making in global health. PMID:25766240

Water resources planning based on complex system dynamics: A case study of Tianjin city

NASA Astrophysics Data System (ADS)

Zhang, X. H.; Zhang, H. W.; Chen, B.; Chen, G. Q.; Zhao, X. H.

2008-12-01

A complex system dynamic (SD) model focusing on water resources, termed as TianjinSD, is developed for the integrated and scientific management of the water resources of Tianjin, which contains information feedback that governs interactions in the system and is capable of synthesizing component-level knowledge into system behavior simulation at an integrated level, thus presenting reasonable predictive results for policy-making on water resources allocation and management. As for the Tianjin city, interactions among 96 components for 12 years are explored and four planning alternatives are chosen, one of which is based on the conventional mode assuming that the existing pattern of human activities will be prevailed, while the others are alternative planning designs based on the interaction of local authorities and planning researchers. Optimal mode is therefore obtained according to different scenarios when compared the simulation results for evaluation of different decisions and dynamic consequences.

Intrinsic information carriers in combinatorial dynamical systems

NASA Astrophysics Data System (ADS)

Harmer, Russ; Danos, Vincent; Feret, Jérôme; Krivine, Jean; Fontana, Walter

2010-09-01

Many proteins are composed of structural and chemical features—"sites" for short—characterized by definite interaction capabilities, such as noncovalent binding or covalent modification of other proteins. This modularity allows for varying degrees of independence, as the behavior of a site might be controlled by the state of some but not all sites of the ambient protein. Independence quickly generates a startling combinatorial complexity that shapes most biological networks, such as mammalian signaling systems, and effectively prevents their study in terms of kinetic equations—unless the complexity is radically trimmed. Yet, if combinatorial complexity is key to the system's behavior, eliminating it will prevent, not facilitate, understanding. A more adequate representation of a combinatorial system is provided by a graph-based framework of rewrite rules where each rule specifies only the information that an interaction mechanism depends on. Unlike reactions, which deal with molecular species, rules deal with patterns, i.e., multisets of molecular species. Although the stochastic dynamics induced by a collection of rules on a mixture of molecules can be simulated, it appears useful to capture the system's average or deterministic behavior by means of differential equations. However, expansion of the rules into kinetic equations at the level of molecular species is not only impractical, but conceptually indefensible. If rules describe bona fide patterns of interaction, molecular species are unlikely to constitute appropriate units of dynamics. Rather, we must seek aggregate variables reflective of the causal structure laid down by the rules. We call these variables "fragments" and the process of identifying them "fragmentation." Ideally, fragments are aspects of the system's microscopic population that the set of rules can actually distinguish on average; in practice, it may only be feasible to identify an approximation to this. Most importantly, fragments are self-consistent descriptors of system dynamics in that their time-evolution is governed by a closed system of kinetic equations. Taken together, fragments are endogenous distinctions that matter for the dynamics of a system, which warrants viewing them as the carriers of information. Although fragments can be thought of as multisets of molecular species (an extensional view), their self-consistency suggests treating them as autonomous aspects cut off from their microscopic realization (an intensional view). Fragmentation is a seeded process that depends on the choice of observables whose dynamics one insists to describe. Different observables can cause distinct fragmentations, in effect altering the set of information carriers that govern the behavior of a system, even though nothing has changed in its microscopic constitution. In this contribution, we present a mathematical specification of fragments, but not an algorithmic implementation. We have described the latter elsewhere in rather technical terms that, although effective, were lacking an embedding into a more general conceptual framework, which we here provide.

Intrinsic information carriers in combinatorial dynamical systems.

PubMed

Harmer, Russ; Danos, Vincent; Feret, Jérôme; Krivine, Jean; Fontana, Walter

2010-09-01

Many proteins are composed of structural and chemical features--"sites" for short--characterized by definite interaction capabilities, such as noncovalent binding or covalent modification of other proteins. This modularity allows for varying degrees of independence, as the behavior of a site might be controlled by the state of some but not all sites of the ambient protein. Independence quickly generates a startling combinatorial complexity that shapes most biological networks, such as mammalian signaling systems, and effectively prevents their study in terms of kinetic equations-unless the complexity is radically trimmed. Yet, if combinatorial complexity is key to the system's behavior, eliminating it will prevent, not facilitate, understanding. A more adequate representation of a combinatorial system is provided by a graph-based framework of rewrite rules where each rule specifies only the information that an interaction mechanism depends on. Unlike reactions, which deal with molecular species, rules deal with patterns, i.e., multisets of molecular species. Although the stochastic dynamics induced by a collection of rules on a mixture of molecules can be simulated, it appears useful to capture the system's average or deterministic behavior by means of differential equations. However, expansion of the rules into kinetic equations at the level of molecular species is not only impractical, but conceptually indefensible. If rules describe bona fide patterns of interaction, molecular species are unlikely to constitute appropriate units of dynamics. Rather, we must seek aggregate variables reflective of the causal structure laid down by the rules. We call these variables "fragments" and the process of identifying them "fragmentation." Ideally, fragments are aspects of the system's microscopic population that the set of rules can actually distinguish on average; in practice, it may only be feasible to identify an approximation to this. Most importantly, fragments are self-consistent descriptors of system dynamics in that their time-evolution is governed by a closed system of kinetic equations. Taken together, fragments are endogenous distinctions that matter for the dynamics of a system, which warrants viewing them as the carriers of information. Although fragments can be thought of as multisets of molecular species (an extensional view), their self-consistency suggests treating them as autonomous aspects cut off from their microscopic realization (an intensional view). Fragmentation is a seeded process that depends on the choice of observables whose dynamics one insists to describe. Different observables can cause distinct fragmentations, in effect altering the set of information carriers that govern the behavior of a system, even though nothing has changed in its microscopic constitution. In this contribution, we present a mathematical specification of fragments, but not an algorithmic implementation. We have described the latter elsewhere in rather technical terms that, although effective, were lacking an embedding into a more general conceptual framework, which we here provide.

Fundamental Properties of Soils for Complex Dynamic Loadings: Dynamic Constitutive Modeling of Sandy Soils.

DTIC Science & Technology

1983-04-01

1.0 INTRODUCTION AND SCOPE 1 2.0 PROGRESS SUMMARY 3 2.1 Soil Element Model Development 3 2.2 U.S. Any Engineer Waterways Experiment Station (WES...LABORATORY BEHAVIOR OF SAND 8 3.1 Introduction 8 3.2 Material Description 8 3.3 Laboratory Tests Performed 9 3.4 Laboratory Test Results 14 4.0 MODELING THE... INTRODUCTION AND SCOPE The subject of this annual report is constitutive modeling of cohesionless soil, for both laboratory standard static test conditions

N-soliton interactions: Effects of linear and nonlinear gain and loss

NASA Astrophysics Data System (ADS)

Carretero-González, R.; Gerdjikov, V. S.; Todorov, M. D.

2017-10-01

We analyze the dynamical behavior of the N-soliton train in the adiabatic approximation of the nonlinear Schrödinger equation perturbed simultaneously by linear and nonlinear gain/loss terms. We derive the corresponding perturbed complex Toda chain in the case of a combination of linear, cubic, and/or quintic terms. We show that the soliton interactions dynamics for this reduced PCTC model compares favorably to full numerical results of the original perturbed nonlinear Schrödinger equation.

Autonomous cycling between excitatory and inhibitory coupling in photosensitive chemical oscillators

NASA Astrophysics Data System (ADS)

Yengi, Desmond; Tinsley, Mark R.; Showalter, Kenneth

2018-04-01

Photochemically coupled Belousov-Zhabotinsky micro-oscillators are studied in experiments and simulations. The photosensitive oscillators exhibit excitatory or inhibitory coupling depending on the surrounding reaction mixture composition, which can be systematically varied. In-phase or out-of-phase synchronization is observed with predominantly excitatory or inhibitory coupling, respectively, and complex frequency cycling between excitatory and inhibitory coupling is found between these extremes. The dynamical behavior is characterized in terms of the corresponding phase response curves, and a map representation of the dynamics is presented.

Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains

PubMed Central

Liu, Chang-Chia; Pardalos, Panos M.; Chaovalitwongse, W. Art; Shiau, Deng-Shan; Ghacibeh, Georges; Suharitdamrong, Wichai; Sackellares, J. Chris

2008-01-01

Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than 10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical measurements appear to reflect the changes of EEG’s dynamical structure. We suggest that the nonlinear dynamical analysis can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional linear analysis. PMID:19079790

Exploring the movement dynamics of deception

PubMed Central

Duran, Nicholas D.; Dale, Rick; Kello, Christopher T.; Street, Chris N. H.; Richardson, Daniel C.

2013-01-01

Both the science and the everyday practice of detecting a lie rest on the same assumption: hidden cognitive states that the liar would like to remain hidden nevertheless influence observable behavior. This assumption has good evidence. The insights of professional interrogators, anecdotal evidence, and body language textbooks have all built up a sizeable catalog of non-verbal cues that have been claimed to distinguish deceptive and truthful behavior. Typically, these cues are discrete, individual behaviors—a hand touching a mouth, the rise of a brow—that distinguish lies from truths solely in terms of their frequency or duration. Research to date has failed to establish any of these non-verbal cues as a reliable marker of deception. Here we argue that perhaps this is because simple tallies of behavior can miss out on the rich but subtle organization of behavior as it unfolds over time. Research in cognitive science from a dynamical systems perspective has shown that behavior is structured across multiple timescales, with more or less regularity and structure. Using tools that are sensitive to these dynamics, we analyzed body motion data from an experiment that put participants in a realistic situation of choosing, or not, to lie to an experimenter. Our analyses indicate that when being deceptive, continuous fluctuations of movement in the upper face, and somewhat in the arms, are characterized by dynamical properties of less stability, but greater complexity. For the upper face, these distinctions are present despite no apparent differences in the overall amount of movement between deception and truth. We suggest that these unique dynamical signatures of motion are indicative of both the cognitive demands inherent to deception and the need to respond adaptively in a social context. PMID:23543852

Brownian dynamics of a protein-polymer chain complex in a solid-state nanopore

NASA Astrophysics Data System (ADS)

Wells, Craig C.; Melnikov, Dmitriy V.; Gracheva, Maria E.

2017-08-01

We study the movement of a polymer attached to a large protein inside a nanopore in a thin silicon dioxide membrane submerged in an electrolyte solution. We use Brownian dynamics to describe the motion of a negatively charged polymer chain of varying lengths attached to a neutral protein modeled as a spherical bead with a radius larger than that of the nanopore, allowing the chain to thread the nanopore but preventing it from translocating. The motion of the protein-polymer complex within the pore is also compared to that of a freely translocating polymer. Our results show that the free polymer's standard deviations in the direction normal to the pore axis is greater than that of the protein-polymer complex. We find that restrictions imposed by the protein, bias, and neighboring chain segments aid in controlling the position of the chain in the pore. Understanding the behavior of the protein-polymer chain complex may lead to methods that improve molecule identification by increasing the resolution of ionic current measurements.

Brownian dynamics of a protein-polymer chain complex in a solid-state nanopore.

PubMed

Wells, Craig C; Melnikov, Dmitriy V; Gracheva, Maria E

2017-08-07

We study the movement of a polymer attached to a large protein inside a nanopore in a thin silicon dioxide membrane submerged in an electrolyte solution. We use Brownian dynamics to describe the motion of a negatively charged polymer chain of varying lengths attached to a neutral protein modeled as a spherical bead with a radius larger than that of the nanopore, allowing the chain to thread the nanopore but preventing it from translocating. The motion of the protein-polymer complex within the pore is also compared to that of a freely translocating polymer. Our results show that the free polymer's standard deviations in the direction normal to the pore axis is greater than that of the protein-polymer complex. We find that restrictions imposed by the protein, bias, and neighboring chain segments aid in controlling the position of the chain in the pore. Understanding the behavior of the protein-polymer chain complex may lead to methods that improve molecule identification by increasing the resolution of ionic current measurements.

From supramolecular polymers to multi-component biomaterials.

PubMed

Goor, Olga J G M; Hendrikse, Simone I S; Dankers, Patricia Y W; Meijer, E W

2017-10-30

The most striking and general property of the biological fibrous architectures in the extracellular matrix (ECM) is the strong and directional interaction between biologically active protein subunits. These fibers display rich dynamic behavior without losing their architectural integrity. The complexity of the ECM taking care of many essential properties has inspired synthetic chemists to mimic these properties in artificial one-dimensional fibrous structures with the aim to arrive at multi-component biomaterials. Due to the dynamic character required for interaction with natural tissue, supramolecular biomaterials are promising candidates for regenerative medicine. Depending on the application area, and thereby the design criteria of these multi-component fibrous biomaterials, they are used as elastomeric materials or hydrogel systems. Elastomeric materials are designed to have load bearing properties whereas hydrogels are proposed to support in vitro cell culture. Although the chemical structures and systems designed and studied today are rather simple compared to the complexity of the ECM, the first examples of these functional supramolecular biomaterials reaching the clinic have been reported. The basic concept of many of these supramolecular biomaterials is based on their ability to adapt to cell behavior as a result of dynamic non-covalent interactions. In this review, we show the translation of one-dimensional supramolecular polymers into multi-component functional biomaterials for regenerative medicine applications.

Quantitative 4D analyses of epithelial folding during Drosophila gastrulation.

PubMed

Khan, Zia; Wang, Yu-Chiun; Wieschaus, Eric F; Kaschube, Matthias

2014-07-01

Understanding the cellular and mechanical processes that underlie the shape changes of individual cells and their collective behaviors in a tissue during dynamic and complex morphogenetic events is currently one of the major frontiers in developmental biology. The advent of high-speed time-lapse microscopy and its use in monitoring the cellular events in fluorescently labeled developing organisms demonstrate tremendous promise in establishing detailed descriptions of these events and could potentially provide a foundation for subsequent hypothesis-driven research strategies. However, obtaining quantitative measurements of dynamic shapes and behaviors of cells and tissues in a rapidly developing metazoan embryo using time-lapse 3D microscopy remains technically challenging, with the main hurdle being the shortage of robust imaging processing and analysis tools. We have developed EDGE4D, a software tool for segmenting and tracking membrane-labeled cells using multi-photon microscopy data. Our results demonstrate that EDGE4D enables quantification of the dynamics of cell shape changes, cell interfaces and neighbor relations at single-cell resolution during a complex epithelial folding event in the early Drosophila embryo. We expect this tool to be broadly useful for the analysis of epithelial cell geometries and movements in a wide variety of developmental contexts. © 2014. Published by The Company of Biologists Ltd.

Atomic-scale dynamics of a model glass-forming metallic liquid: Dynamical crossover, dynamical decoupling, and dynamical clustering

DOE PAGES

Jaiswal, Abhishek; Egami, Takeshi; Zhang, Yang

2015-04-01

The phase behavior of multi-component metallic liquids is exceedingly complex because of the convoluted many-body and many-elemental interactions. Herein, we present systematic studies of the dynamic aspects of such a model ternary metallic liquid Cu 40Zr 51Al 9 using molecular dynamics simulation with embedded atom method. We observed a dynamical crossover from Arrhenius to super-Arrhenius behavior in the transport properties (diffusion coefficient, relaxation times, and shear viscosity) bordered at T x ~1300K. Unlike in many molecular and macromolecular liquids, this crossover phenomenon occurs in the equilibrium liquid state well above the melting temperature of the system (T m ~ 900K),more » and the crossover temperature is roughly twice of the glass-transition temperature (T g). Below T x, we found the elemental dynamics decoupled and the Stokes-Einstein relation broke down, indicating the onset of heterogeneous spatially correlated dynamics in the system mediated by dynamic communications among local configurational excitations. To directly characterize and visualize the correlated dynamics, we employed a non-parametric, unsupervised machine learning technique and identified dynamical clusters of atoms with similar atomic mobility. The revealed average dynamical cluster size shows an accelerated increase below T x and mimics the trend observed in other ensemble averaged quantities that are commonly used to quantify the spatially heterogeneous dynamics such as the non-Gaussian parameter and the four-point correlation function.« less

Generalized Synchronization in AN Array of Nonlinear Dynamic Systems with Applications to Chaotic Cnn

NASA Astrophysics Data System (ADS)

Min, Lequan; Chen, Guanrong

This paper establishes some generalized synchronization (GS) theorems for a coupled discrete array of difference systems (CDADS) and a coupled continuous array of differential systems (CCADS). These constructive theorems provide general representations of GS in CDADS and CCADS. Based on these theorems, one can design GS-driven CDADS and CCADS via appropriate (invertible) transformations. As applications, the results are applied to autonomous and nonautonomous coupled Chen cellular neural network (CNN) CDADS and CCADS, discrete bidirectional Lorenz CNN CDADS, nonautonomous bidirectional Chua CNN CCADS, and nonautonomously bidirectional Chen CNN CDADS and CCADS, respectively. Extensive numerical simulations show their complex dynamic behaviors. These theorems provide new means for understanding the GS phenomena of complex discrete and continuously differentiable networks.

Excitonic magnet in external field: Complex order parameter and spin currents

NASA Astrophysics Data System (ADS)

Geffroy, D.; Hariki, A.; Kuneš, J.

2018-04-01

We investigate spin-triplet exciton condensation in the two-orbital Hubbard model close to half-filling by means of dynamical mean-field theory. Employing an impurity solver that handles complex off-diagonal hybridization functions, we study the behavior of excitonic condensate in stoichiometric and doped systems subject to external magnetic field. We find a general tendency of the triplet order parameter to lie perpendicular with the applied field and identify exceptions from this rule. For solutions exhibiting k -odd spin textures, we discuss the Bloch theorem, which, in the absence of spin-orbit coupling, forbids the appearance of spontaneous net spin current. We demonstrate that the Bloch theorem is not obeyed by the dynamical mean-field theory.

Delay-dependent dynamical analysis of complex-valued memristive neural networks: Continuous-time and discrete-time cases.

PubMed

Wang, Jinling; Jiang, Haijun; Ma, Tianlong; Hu, Cheng

2018-05-01

This paper considers the delay-dependent stability of memristive complex-valued neural networks (MCVNNs). A novel linear mapping function is presented to transform the complex-valued system into the real-valued system. Under such mapping function, both continuous-time and discrete-time MCVNNs are analyzed in this paper. Firstly, when activation functions are continuous but not Lipschitz continuous, an extended matrix inequality is proved to ensure the stability of continuous-time MCVNNs. Furthermore, if activation functions are discontinuous, a discontinuous adaptive controller is designed to acquire its stability by applying Lyapunov-Krasovskii functionals. Secondly, compared with techniques in continuous-time MCVNNs, the Halanay-type inequality and comparison principle are firstly used to exploit the dynamical behaviors of discrete-time MCVNNs. Finally, the effectiveness of theoretical results is illustrated through numerical examples. Copyright © 2018 Elsevier Ltd. All rights reserved.

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].

Visualizing Parallel Computer System Performance

NASA Technical Reports Server (NTRS)

Malony, Allen D.; Reed, Daniel A.

1988-01-01

Parallel computer systems are among the most complex of man's creations, making satisfactory performance characterization difficult. Despite this complexity, there are strong, indeed, almost irresistible, incentives to quantify parallel system performance using a single metric. The fallacy lies in succumbing to such temptations. A complete performance characterization requires not only an analysis of the system's constituent levels, it also requires both static and dynamic characterizations. Static or average behavior analysis may mask transients that dramatically alter system performance. Although the human visual system is remarkedly adept at interpreting and identifying anomalies in false color data, the importance of dynamic, visual scientific data presentation has only recently been recognized Large, complex parallel system pose equally vexing performance interpretation problems. Data from hardware and software performance monitors must be presented in ways that emphasize important events while eluding irrelevant details. Design approaches and tools for performance visualization are the subject of this paper.

Single-Molecule Spectroscopy and Imaging Studies of Protein Dynamics

NASA Astrophysics Data System (ADS)

Lu, H. Peter

2012-04-01

Enzymatic reactions and protein-protein interactions are traditionally studied at the ensemble level, despite significant static and dynamic inhomogeneities. Subtle conformational changes play a crucial role in protein functions, and these protein conformations are highly dynamic rather than being static. We applied AFM-enhanced single-molecule spectroscopy to study the mechanisms and dynamics of enzymatic reactions involved with kinase and lysozyme proteins. Enzymatic reaction turnovers and the associated structure changes of individual protein molecules were observed simultaneously in real-time by single-molecule FRET detections. Our single-molecule spectroscopy measurements of T4 lysozyme and HPPK enzymatic conformational dynamics have revealed time bunching effect and intermittent coherence in conformational state change dynamics involving in enzymatic reaction cycles. The coherent conformational state dynamics suggests that the enzymatic catalysis involves a multi-step conformational motion along the coordinates of substrate-enzyme complex formation and product releasing, presenting as an extreme dynamic behavior intrinsically related to the time bunching effect that we have reported previously. Our results of HPPK interaction with substrate support a multiple-conformational state model, being consistent with a complementary conformation selection and induced-fit enzymatic loop-gated conformational change mechanism in substrate-enzyme active complex formation. Our new approach is applicable to a wide range of single-molecule FRET measurements for protein conformational changes under enzymatic reactions.

Experimental and Numerical Investigation of Reduced Gravity Fluid Slosh Dynamics for the Characterization of Cryogenic Launch and Space Vehicle Propellants

NASA Technical Reports Server (NTRS)

Walls, Laurie K.; Kirk, Daniel; deLuis, Kavier; Haberbusch, Mark S.

2011-01-01

As space programs increasingly investigate various options for long duration space missions the accurate prediction of propellant behavior over long periods of time in microgravity environment has become increasingly imperative. This has driven the development of a detailed, physics-based understanding of slosh behavior of cryogenic propellants over a range of conditions and environments that are relevant for rocket and space storage applications. Recent advancements in computational fluid dynamics (CFD) models and hardware capabilities have enabled the modeling of complex fluid behavior in microgravity environment. Historically, launch vehicles with moderate duration upper stage coast periods have contained very limited instrumentation to quantify propellant stratification and boil-off in these environments, thus the ability to benchmark these complex computational models is of great consequence. To benchmark enhanced CFD models, recent work focuses on establishing an extensive experimental database of liquid slosh under a wide range of relevant conditions. In addition, a mass gauging system specifically designed to provide high fidelity measurements for both liquid stratification and liquid/ullage position in a micro-gravity environment has been developed. This pUblication will summarize the various experimental programs established to produce this comprehensive database and unique flight measurement techniques.

Dynamics of Complex Systems Built as Coupled Physical, Communication and Decision Layers

PubMed Central

Kühnlenz, Florian; Nardelli, Pedro H. J.

2016-01-01

This paper proposes a simple model to capture the complexity of multilayer systems where their constituent layers affect, and are affected by, each other. The physical layer is a circuit composed by a power source and resistors in parallel. Every individual agent aims at maximizing its own delivered power by adding, removing or keeping the resistors it has; the delivered power is in turn a non-linear function that depends on the other agents’ behavior, its own internal state, its global state perception, the information received from its neighbors via the communication network and a randomized selfishness. We develop an agent-based simulation to analyze the effects of number of agents (system size), communication network topology, communication errors and the minimum power gain that triggers a behavioral change on the system dynamic. Our results show that a wave-like behavior at macro-level (caused by individual changes in the decision layer) can only emerge for a specific system size. The ratio between cooperators and defectors depends on the minimum gain assumed—lower minimal gains lead to less cooperation, and vice-versa. Different communication network topologies imply different levels of power utilization and fairness at the physical layer, and a certain level of error in the communication layer induces more cooperation. PMID:26730590

Putting Humpty-Dumpty Together: Clustering the Functional Dynamics of Single Biomolecular Machines Such as the Spliceosome.

PubMed

Rohlman, C E; Blanco, M R; Walter, N G

2016-01-01

The spliceosome is a biomolecular machine that, in all eukaryotes, accomplishes site-specific splicing of introns from precursor messenger RNAs (pre-mRNAs) with high fidelity. Operating at the nanometer scale, where inertia and friction have lost the dominant role they play in the macroscopic realm, the spliceosome is highly dynamic and assembles its active site around each pre-mRNA anew. To understand the structural dynamics underlying the molecular motors, clocks, and ratchets that achieve functional accuracy in the yeast spliceosome (a long-standing model system), we have developed single-molecule fluorescence resonance energy transfer (smFRET) approaches that report changes in intra- and intermolecular interactions in real time. Building on our work using hidden Markov models (HMMs) to extract kinetic and conformational state information from smFRET time trajectories, we recognized that HMM analysis of individual state transitions as independent stochastic events is insufficient for a biomolecular machine as complex as the spliceosome. In this chapter, we elaborate on the recently developed smFRET-based Single-Molecule Cluster Analysis (SiMCAn) that dissects the intricate conformational dynamics of a pre-mRNA through the splicing cycle in a model-free fashion. By leveraging hierarchical clustering techniques developed for Bioinformatics, SiMCAn efficiently analyzes large datasets to first identify common molecular behaviors. Through a second level of clustering based on the abundance of dynamic behaviors exhibited by defined functional intermediates that have been stalled by biochemical or genetic tools, SiMCAn then efficiently assigns pre-mRNA FRET states and transitions to specific splicing complexes, with the potential to find heretofore undescribed conformations. SiMCAn thus arises as a general tool to analyze dynamic cellular machines more broadly. © 2016 Elsevier Inc. All rights reserved.

Complexities, Catastrophes and Cities: Emergency Dynamics in Varying Scenarios and Urban Topologies

NASA Astrophysics Data System (ADS)

Narzisi, Giuseppe; Mysore, Venkatesh; Byeon, Jeewoong; Mishra, Bud

Complex Systems are often characterized by agents capable of interacting with each other dynamically, often in non-linear and non-intuitive ways. Trying to characterize their dynamics often results in partial differential equations that are difficult, if not impossible, to solve. A large city or a city-state is an example of such an evolving and self-organizing complex environment that efficiently adapts to different and numerous incremental changes to its social, cultural and technological infrastructure [1]. One powerful technique for analyzing such complex systems is Agent-Based Modeling (ABM) [9], which has seen an increasing number of applications in social science, economics and also biology. The agent-based paradigm facilitates easier transfer of domain specific knowledge into a model. ABM provides a natural way to describe systems in which the overall dynamics can be described as the result of the behavior of populations of autonomous components: agents, with a fixed set of rules based on local information and possible central control. As part of the NYU Center for Catastrophe Preparedness and Response (CCPR1), we have been exploring how ABM can serve as a powerful simulation technique for analyzing large-scale urban disasters. The central problem in Disaster Management is that it is not immediately apparent whether the current emergency plans are robust against such sudden, rare and punctuated catastrophic events.