Extinction in neutrally stable stochastic Lotka-Volterra models.

PubMed

Dobrinevski, Alexander; Frey, Erwin

2012-05-01

Populations of competing biological species exhibit a fascinating interplay between the nonlinear dynamics of evolutionary selection forces and random fluctuations arising from the stochastic nature of the interactions. The processes leading to extinction of species, whose understanding is a key component in the study of evolution and biodiversity, are influenced by both of these factors. Here, we investigate a class of stochastic population dynamics models based on generalized Lotka-Volterra systems. In the case of neutral stability of the underlying deterministic model, the impact of intrinsic noise on the survival of species is dramatic: It destroys coexistence of interacting species on a time scale proportional to the population size. We introduce a new method based on stochastic averaging which allows one to understand this extinction process quantitatively by reduction to a lower-dimensional effective dynamics. This is performed analytically for two highly symmetrical models and can be generalized numerically to more complex situations. The extinction probability distributions and other quantities of interest we obtain show excellent agreement with simulations.

Development of the ARISTOTLE webware for cloud-based rarefied gas flow modeling

NASA Astrophysics Data System (ADS)

Deschenes, Timothy R.; Grot, Jonathan; Cline, Jason A.

2016-11-01

Rarefied gas dynamics are important for a wide variety of applications. An improvement in the ability of general users to predict these gas flows will enable optimization of current, and discovery of future processes. Despite this potential, most rarefied simulation software is designed by and for experts in the community. This has resulted in low adoption of the methods outside of the immediate RGD community. This paper outlines an ongoing effort to create a rarefied gas dynamics simulation tool that can be used by a general audience. The tool leverages a direct simulation Monte Carlo (DSMC) library that is available to the entire community and a web-based simulation process that will enable all users to take advantage of high performance computing capabilities. First, the DSMC library and simulation architecture are described. Then the DSMC library is used to predict a number of representative transient gas flows that are applicable to the rarefied gas dynamics community. The paper closes with a summary and future direction.

Ergodicity convergence test suggests telomere motion obeys fractional dynamics

NASA Astrophysics Data System (ADS)

Kepten, E.; Bronshtein, I.; Garini, Y.

2011-04-01

Anomalous diffusion, observed in many biological processes, is a generalized description of a wide variety of processes, all obeying the same law of mean-square displacement. Identifying the basic mechanisms of these observations is important for deducing the nature of the biophysical systems measured. We implement a previously suggested method for distinguishing between fractional Langevin dynamics, fractional Brownian motion, and continuous time random walk based on the ergodic nature of the data. We apply the method together with the recently suggested P-variation test and the displacement correlation to the lately measured dynamics of telomeres in the nucleus of mammalian cells and find strong evidence that the telomeres motion obeys fractional dynamics. The ergodic dynamics are observed experimentally to fit fractional Brownian or Langevin dynamics.

Improving the quality of extracting dynamics from interspike intervals via a resampling approach

NASA Astrophysics Data System (ADS)

Pavlova, O. N.; Pavlov, A. N.

2018-04-01

We address the problem of improving the quality of characterizing chaotic dynamics based on point processes produced by different types of neuron models. Despite the presence of embedding theorems for non-uniformly sampled dynamical systems, the case of short data analysis requires additional attention because the selection of algorithmic parameters may have an essential influence on estimated measures. We consider how the preliminary processing of interspike intervals (ISIs) can increase the precision of computing the largest Lyapunov exponent (LE). We report general features of characterizing chaotic dynamics from point processes and show that independently of the selected mechanism for spike generation, the performed preprocessing reduces computation errors when dealing with a limited amount of data.

Analysis of Electrowetting Dynamics with Level Set Method

NASA Astrophysics Data System (ADS)

Park, Jun Kwon; Hong, Jiwoo; Kang, Kwan Hyoung

2009-11-01

Electrowetting is a versatile tool to handle tiny droplets and forms a backbone of digital microfluidics. Numerical analysis is necessary to fully understand the dynamics of electrowetting, especially in designing electrowetting-based liquid lenses and reflective displays. We developed a numerical method to analyze the general contact-line problems, incorporating dynamic contact angle models. The method was applied to the analysis of spreading process of a sessile droplet for step input voltages in electrowetting. The result was compared with experimental data and analytical result which is based on the spectral method. It is shown that contact line friction significantly affects the contact line motion and the oscillation amplitude. The pinning process of contact line was well represented by including the hysteresis effect in the contact angle models.

Coevolutionary dynamics in large, but finite populations

NASA Astrophysics Data System (ADS)

Traulsen, Arne; Claussen, Jens Christian; Hauert, Christoph

2006-07-01

Coevolving and competing species or game-theoretic strategies exhibit rich and complex dynamics for which a general theoretical framework based on finite populations is still lacking. Recently, an explicit mean-field description in the form of a Fokker-Planck equation was derived for frequency-dependent selection with two strategies in finite populations based on microscopic processes [A. Traulsen, J. C. Claussen, and C. Hauert, Phys. Rev. Lett. 95, 238701 (2005)]. Here we generalize this approach in a twofold way: First, we extend the framework to an arbitrary number of strategies and second, we allow for mutations in the evolutionary process. The deterministic limit of infinite population size of the frequency-dependent Moran process yields the adjusted replicator-mutator equation, which describes the combined effect of selection and mutation. For finite populations, we provide an extension taking random drift into account. In the limit of neutral selection, i.e., whenever the process is determined by random drift and mutations, the stationary strategy distribution is derived. This distribution forms the background for the coevolutionary process. In particular, a critical mutation rate uc is obtained separating two scenarios: above uc the population predominantly consists of a mixture of strategies whereas below uc the population tends to be in homogeneous states. For one of the fundamental problems in evolutionary biology, the evolution of cooperation under Darwinian selection, we demonstrate that the analytical framework provides excellent approximations to individual based simulations even for rather small population sizes. This approach complements simulation results and provides a deeper, systematic understanding of coevolutionary dynamics.

Representing Micro-Macro Linkages by Actor-Based Dynamic Network Models

PubMed Central

Snijders, Tom A.B.; Steglich, Christian E.G.

2014-01-01

Stochastic actor-based models for network dynamics have the primary aim of statistical inference about processes of network change, but may be regarded as a kind of agent-based models. Similar to many other agent-based models, they are based on local rules for actor behavior. Different from many other agent-based models, by including elements of generalized linear statistical models they aim to be realistic detailed representations of network dynamics in empirical data sets. Statistical parallels to micro-macro considerations can be found in the estimation of parameters determining local actor behavior from empirical data, and the assessment of goodness of fit from the correspondence with network-level descriptives. This article studies several network-level consequences of dynamic actor-based models applied to represent cross-sectional network data. Two examples illustrate how network-level characteristics can be obtained as emergent features implied by micro-specifications of actor-based models. PMID:25960578

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

PubMed

Ingram, James N; Howard, Ian S; Flanagan, J Randall; Wolpert, Daniel M

2011-09-01

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics, however, the representations can be engaged based on visual context, and are updated by a single-rate process.

Quantum theory of open systems based on stochastic differential equations of generalized Langevin (non-Wiener) type

NASA Astrophysics Data System (ADS)

Basharov, A. M.

2012-09-01

It is shown that the effective Hamiltonian representation, as it is formulated in author's papers, serves as a basis for distinguishing, in a broadband environment of an open quantum system, independent noise sources that determine, in terms of the stationary quantum Wiener and Poisson processes in the Markov approximation, the effective Hamiltonian and the equation for the evolution operator of the open system and its environment. General stochastic differential equations of generalized Langevin (non-Wiener) type for the evolution operator and the kinetic equation for the density matrix of an open system are obtained, which allow one to analyze the dynamics of a wide class of localized open systems in the Markov approximation. The main distinctive features of the dynamics of open quantum systems described in this way are the stabilization of excited states with respect to collective processes and an additional frequency shift of the spectrum of the open system. As an illustration of the general approach developed, the photon dynamics in a single-mode cavity without losses on the mirrors is considered, which contains identical intracavity atoms coupled to the external vacuum electromagnetic field. For some atomic densities, the photons of the cavity mode are "locked" inside the cavity, thus exhibiting a new phenomenon of radiation trapping and non-Wiener dynamics.

Dynamics of Quantum Causal Structures

NASA Astrophysics Data System (ADS)

Castro-Ruiz, Esteban; Giacomini, Flaminia; Brukner, Časlav

2018-01-01

It was recently suggested that causal structures are both dynamical, because of general relativity, and indefinite, because of quantum theory. The process matrix formalism furnishes a framework for quantum mechanics on indefinite causal structures, where the order between operations of local laboratories is not definite (e.g., one cannot say whether operation in laboratory A occurs before or after operation in laboratory B ). Here, we develop a framework for "dynamics of causal structures," i.e., for transformations of process matrices into process matrices. We show that, under continuous and reversible transformations, the causal order between operations is always preserved. However, the causal order between a subset of operations can be changed under continuous yet nonreversible transformations. An explicit example is that of the quantum switch, where a party in the past affects the causal order of operations of future parties, leading to a transition from a channel from A to B , via superposition of causal orders, to a channel from B to A . We generalize our framework to construct a hierarchy of quantum maps based on transformations of process matrices and transformations thereof.

A neural network model of metaphor understanding with dynamic interaction based on a statistical language analysis: targeting a human-like model.

PubMed

Terai, Asuka; Nakagawa, Masanori

2007-08-01

The purpose of this paper is to construct a model that represents the human process of understanding metaphors, focusing specifically on similes of the form an "A like B". Generally speaking, human beings are able to generate and understand many sorts of metaphors. This study constructs the model based on a probabilistic knowledge structure for concepts which is computed from a statistical analysis of a large-scale corpus. Consequently, this model is able to cover the many kinds of metaphors that human beings can generate. Moreover, the model implements the dynamic process of metaphor understanding by using a neural network with dynamic interactions. Finally, the validity of the model is confirmed by comparing model simulations with the results from a psychological experiment.

Intermittent dynamics in complex systems driven to depletion.

PubMed

Escobar, Juan V; Pérez Castillo, Isaac

2018-03-19

When complex systems are driven to depletion by some external factor, their non-stationary dynamics can present an intermittent behaviour between relative tranquility and burst of activity whose consequences are often catastrophic. To understand and ultimately be able to predict such dynamics, we propose an underlying mechanism based on sharp thresholds of a local generalized energy density that naturally leads to negative feedback. We find a transition from a continuous regime to an intermittent one, in which avalanches can be predicted despite the stochastic nature of the process. This model may have applications in many natural and social complex systems where a rapid depletion of resources or generalized energy drives the dynamics. In particular, we show how this model accurately describes the time evolution and avalanches present in a real social system.

A general science-based framework for dynamical spatio-temporal models

USGS Publications Warehouse

Wikle, C.K.; Hooten, M.B.

2010-01-01

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic nonlinearity and demonstrate that it accommodates many different classes of scientific-based parameterizations as special cases. The model is presented in a hierarchical Bayesian framework and is illustrated with examples from ecology and oceanography. ?? 2010 Sociedad de Estad??stica e Investigaci??n Operativa.

Integration agent-based models and GIS as a virtual urban dynamic laboratory

NASA Astrophysics Data System (ADS)

Chen, Peng; Liu, Miaolong

2007-06-01

Based on the Agent-based Model and spatial data model, a tight-coupling integrating method of GIS and Agent-based Model (ABM) is to be discussed in this paper. The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena such as urban dynamic. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, the agent-based model and spatial data model are discussed, and then the relationships affecting spatial data model and agent-based process models interaction. After that, a realistic crowd flow simulation experiment is presented. Using some tools provided by general GIS systems and a few specific programming languages, a new software system integrating GIS and MAS as a virtual laboratory applicable for simulating pedestrian flows in a crowd activity centre has been developed successfully. Under the environment supported by the software system, as an applicable case, a dynamic evolution process of the pedestrian's flows (dispersed process for the spectators) in a crowds' activity center - The Shanghai Stadium has been simulated successfully. At the end of the paper, some new research problems have been pointed out for the future.

Emotion unfolded by motion: a role for parietal lobe in decoding dynamic facial expressions.

PubMed

Sarkheil, Pegah; Goebel, Rainer; Schneider, Frank; Mathiak, Klaus

2013-12-01

Facial expressions convey important emotional and social information and are frequently applied in investigations of human affective processing. Dynamic faces may provide higher ecological validity to examine perceptual and cognitive processing of facial expressions. Higher order processing of emotional faces was addressed by varying the task and virtual face models systematically. Blood oxygenation level-dependent activation was assessed using functional magnetic resonance imaging in 20 healthy volunteers while viewing and evaluating either emotion or gender intensity of dynamic face stimuli. A general linear model analysis revealed that high valence activated a network of motion-responsive areas, indicating that visual motion areas support perceptual coding for the motion-based intensity of facial expressions. The comparison of emotion with gender discrimination task revealed increased activation of inferior parietal lobule, which highlights the involvement of parietal areas in processing of high level features of faces. Dynamic emotional stimuli may help to emphasize functions of the hypothesized 'extended' over the 'core' system for face processing.

Computational fluid dynamics: Transition to design applications

NASA Technical Reports Server (NTRS)

Bradley, R. G.; Bhateley, I. C.; Howell, G. A.

1987-01-01

The development of aerospace vehicles, over the years, was an evolutionary process in which engineering progress in the aerospace community was based, generally, on prior experience and data bases obtained through wind tunnel and flight testing. Advances in the fundamental understanding of flow physics, wind tunnel and flight test capability, and mathematical insights into the governing flow equations were translated into improved air vehicle design. The modern day field of Computational Fluid Dynamics (CFD) is a continuation of the growth in analytical capability and the digital mathematics needed to solve the more rigorous form of the flow equations. Some of the technical and managerial challenges that result from rapidly developing CFD capabilites, some of the steps being taken by the Fort Worth Division of General Dynamics to meet these challenges, and some of the specific areas of application for high performance air vehicles are presented.

Theoretical model of dynamic spin polarization of nuclei coupled to paramagnetic point defects in diamond and silicon carbide

NASA Astrophysics Data System (ADS)

Ivády, Viktor; Szász, Krisztián; Falk, Abram L.; Klimov, Paul V.; Christle, David J.; Janzén, Erik; Abrikosov, Igor A.; Awschalom, David D.; Gali, Adam

2015-09-01

Dynamic nuclear spin polarization (DNP) mediated by paramagnetic point defects in semiconductors is a key resource for both initializing nuclear quantum memories and producing nuclear hyperpolarization. DNP is therefore an important process in the field of quantum-information processing, sensitivity-enhanced nuclear magnetic resonance, and nuclear-spin-based spintronics. DNP based on optical pumping of point defects has been demonstrated by using the electron spin of nitrogen-vacancy (NV) center in diamond, and more recently, by using divacancy and related defect spins in hexagonal silicon carbide (SiC). Here, we describe a general model for these optical DNP processes that allows the effects of many microscopic processes to be integrated. Applying this theory, we gain a deeper insight into dynamic nuclear spin polarization and the physics of diamond and SiC defects. Our results are in good agreement with experimental observations and provide a detailed and unified understanding. In particular, our findings show that the defect electron spin coherence times and excited state lifetimes are crucial factors in the entire DNP process.

Identification of FOPDT and SOPDT process dynamics using closed loop test.

PubMed

Bajarangbali, Raghunath; Majhi, Somanath; Pandey, Saurabh

2014-07-01

In this paper, identification of stable and unstable first order, second order overdamped and underdamped process dynamics with time delay is presented. Relay with hysteresis is used to induce a limit cycle output and using this information, unknown process model parameters are estimated. State space based generalized analytical expressions are derived to achieve accurate results. To show the performance of the proposed method expressions are also derived for systems with a zero. In real time systems, measurement noise is an important issue during identification of process dynamics. A relay with hysteresis reduces the effect of measurement noise, in addition a new multiloop control strategy is proposed to recover the original limit cycle. Simulation results are included to validate the effectiveness of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Estimating rates of local extinction and colonization in colonial species and an extension to the metapopulation and community levels

USGS Publications Warehouse

Barbraud, C.; Nichols, J.D.; Hines, J.E.; Hafner, H.

2003-01-01

Coloniality has mainly been studied from an evolutionary perspective, but relatively few studies have developed methods for modelling colony dynamics. Changes in number of colonies over time provide a useful tool for predicting and evaluating the responses of colonial species to management and to environmental disturbance. Probabilistic Markov process models have been recently used to estimate colony site dynamics using presence-absence data when all colonies are detected in sampling efforts. Here, we define and develop two general approaches for the modelling and analysis of colony dynamics for sampling situations in which all colonies are, and are not, detected. For both approaches, we develop a general probabilistic model for the data and then constrain model parameters based on various hypotheses about colony dynamics. We use Akaike's Information Criterion (AIC) to assess the adequacy of the constrained models. The models are parameterised with conditional probabilities of local colony site extinction and colonization. Presence-absence data arising from Pollock's robust capture-recapture design provide the basis for obtaining unbiased estimates of extinction, colonization, and detection probabilities when not all colonies are detected. This second approach should be particularly useful in situations where detection probabilities are heterogeneous among colony sites. The general methodology is illustrated using presence-absence data on two species of herons (Purple Heron, Ardea purpurea and Grey Heron, Ardea cinerea). Estimates of the extinction and colonization rates showed interspecific differences and strong temporal and spatial variations. We were also able to test specific predictions about colony dynamics based on ideas about habitat change and metapopulation dynamics. We recommend estimators based on probabilistic modelling for future work on colony dynamics. We also believe that this methodological framework has wide application to problems in animal ecology concerning metapopulation and community dynamics.

Part 3 Specialized aspects of GIS and spatial analysis . Garage band science and dynamic spatial models

NASA Astrophysics Data System (ADS)

Box, Paul W.

GIS and spatial analysis is suited mainly for static pictures of the landscape, but many of the processes that need exploring are dynamic in nature. Dynamic processes can be complex when put in a spatial context; our ability to study such processes will probably come with advances in understanding complex systems in general. Cellular automata and agent-based models are two prime candidates for exploring complex spatial systems, but are difficult to implement. Innovative tools that help build complex simulations will create larger user communities, who will probably find novel solutions for understanding complexity. A significant source for such innovations is likely to be from the collective efforts of hobbyists and part-time programmers, who have been dubbed ``garage-band scientists'' in the popular press.

Three-wave resonant interactions: Multi-dark-dark-dark solitons, breathers, rogue waves, and their interactions and dynamics

NASA Astrophysics Data System (ADS)

Zhang, Guoqiang; Yan, Zhenya; Wen, Xiao-Yong

2018-03-01

We investigate three-wave resonant interactions through both the generalized Darboux transformation method and numerical simulations. Firstly, we derive a simple multi-dark-dark-dark-soliton formula through the generalized Darboux transformation. Secondly, we use the matrix analysis method to avoid the singularity of transformed potential functions and to find the general nonsingular breather solutions. Moreover, through a limit process, we deduce the general rogue wave solutions and give a classification by their dynamics including bright, dark, four-petals, and two-peaks rogue waves. Ever since the coexistence of dark soliton and rogue wave in non-zero background, their interactions naturally become a quite appealing topic. Based on the N-fold Darboux transformation, we can derive the explicit solutions to depict their interactions. Finally, by performing extensive numerical simulations we can predict whether these dark solitons and rogue waves are stable enough to propagate. These results can be available for several physical subjects such as fluid dynamics, nonlinear optics, solid state physics, and plasma physics.

Exact solutions for kinetic models of macromolecular dynamics.

PubMed

Chemla, Yann R; Moffitt, Jeffrey R; Bustamante, Carlos

2008-05-15

Dynamic biological processes such as enzyme catalysis, molecular motor translocation, and protein and nucleic acid conformational dynamics are inherently stochastic processes. However, when such processes are studied on a nonsynchronized ensemble, the inherent fluctuations are lost, and only the average rate of the process can be measured. With the recent development of methods of single-molecule manipulation and detection, it is now possible to follow the progress of an individual molecule, measuring not just the average rate but the fluctuations in this rate as well. These fluctuations can provide a great deal of detail about the underlying kinetic cycle that governs the dynamical behavior of the system. However, extracting this information from experiments requires the ability to calculate the general properties of arbitrarily complex theoretical kinetic schemes. We present here a general technique that determines the exact analytical solution for the mean velocity and for measures of the fluctuations. We adopt a formalism based on the master equation and show how the probability density for the position of a molecular motor at a given time can be solved exactly in Fourier-Laplace space. With this analytic solution, we can then calculate the mean velocity and fluctuation-related parameters, such as the randomness parameter (a dimensionless ratio of the diffusion constant and the velocity) and the dwell time distributions, which fully characterize the fluctuations of the system, both commonly used kinetic parameters in single-molecule measurements. Furthermore, we show that this formalism allows calculation of these parameters for a much wider class of general kinetic models than demonstrated with previous methods.

A PetriNet-Based Approach for Supporting Traceability in Cyber-Physical Manufacturing Systems

PubMed Central

Huang, Jiwei; Zhu, Yeping; Cheng, Bo; Lin, Chuang; Chen, Junliang

2016-01-01

With the growing popularity of complex dynamic activities in manufacturing processes, traceability of the entire life of every product has drawn significant attention especially for food, clinical materials, and similar items. This paper studies the traceability issue in cyber-physical manufacturing systems from a theoretical viewpoint. Petri net models are generalized for formulating dynamic manufacturing processes, based on which a detailed approach for enabling traceability analysis is presented. Models as well as algorithms are carefully designed, which can trace back the lifecycle of a possibly contaminated item. A practical prototype system for supporting traceability is designed, and a real-life case study of a quality control system for bee products is presented to validate the effectiveness of the approach. PMID:26999141

A PetriNet-Based Approach for Supporting Traceability in Cyber-Physical Manufacturing Systems.

PubMed

Huang, Jiwei; Zhu, Yeping; Cheng, Bo; Lin, Chuang; Chen, Junliang

2016-03-17

With the growing popularity of complex dynamic activities in manufacturing processes, traceability of the entire life of every product has drawn significant attention especially for food, clinical materials, and similar items. This paper studies the traceability issue in cyber-physical manufacturing systems from a theoretical viewpoint. Petri net models are generalized for formulating dynamic manufacturing processes, based on which a detailed approach for enabling traceability analysis is presented. Models as well as algorithms are carefully designed, which can trace back the lifecycle of a possibly contaminated item. A practical prototype system for supporting traceability is designed, and a real-life case study of a quality control system for bee products is presented to validate the effectiveness of the approach.

Structure-based control of complex networks with nonlinear dynamics.

PubMed

Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

2017-07-11

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

A biologically inspired neural network for dynamic programming.

PubMed

Francelin Romero, R A; Kacpryzk, J; Gomide, F

2001-12-01

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.

Machine Learning-based discovery of closures for reduced models of dynamical systems

NASA Astrophysics Data System (ADS)

Pan, Shaowu; Duraisamy, Karthik

2017-11-01

Despite the successful application of machine learning (ML) in fields such as image processing and speech recognition, only a few attempts has been made toward employing ML to represent the dynamics of complex physical systems. Previous attempts mostly focus on parameter calibration or data-driven augmentation of existing models. In this work we present a ML framework to discover closure terms in reduced models of dynamical systems and provide insights into potential problems associated with data-driven modeling. Based on exact closure models for linear system, we propose a general linear closure framework from viewpoint of optimization. The framework is based on trapezoidal approximation of convolution term. Hyperparameters that need to be determined include temporal length of memory effect, number of sampling points, and dimensions of hidden states. To circumvent the explicit specification of memory effect, a general framework inspired from neural networks is also proposed. We conduct both a priori and posteriori evaluations of the resulting model on a number of non-linear dynamical systems. This work was supported in part by AFOSR under the project ``LES Modeling of Non-local effects using Statistical Coarse-graining'' with Dr. Jean-Luc Cambier as the technical monitor.

CLIMATIC EFFECTS ON TUNDRA CARBON STORAGE INFERRED FROM EXPERIMENTAL DATA AND A MODEL

EPA Science Inventory

We used a process-based model of ecosystem carbon (C) and nitrogen (N)dynamics, MBL-GEM (Marine Biological Laboratory General Ecosystem Model), to integrated and analyze the results of several experiments that examined the response of arctic tussock tundra to manipulations of CO2...

Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Zheng, Jinde; Pan, Haiyang; Yang, Shubao; Cheng, Junsheng

2018-01-01

Multiscale permutation entropy (MPE) is a recently proposed nonlinear dynamic method for measuring the randomness and detecting the nonlinear dynamic change of time series and can be used effectively to extract the nonlinear dynamic fault feature from vibration signals of rolling bearing. To solve the drawback of coarse graining process in MPE, an improved MPE method called generalized composite multiscale permutation entropy (GCMPE) was proposed in this paper. Also the influence of parameters on GCMPE and its comparison with the MPE are studied by analyzing simulation data. GCMPE was applied to the fault feature extraction from vibration signal of rolling bearing and then based on the GCMPE, Laplacian score for feature selection and the Particle swarm optimization based support vector machine, a new fault diagnosis method for rolling bearing was put forward in this paper. Finally, the proposed method was applied to analyze the experimental data of rolling bearing. The analysis results show that the proposed method can effectively realize the fault diagnosis of rolling bearing and has a higher fault recognition rate than the existing methods.

Functional connectivity dynamics during film viewing reveal common networks for different emotional experiences.

PubMed

Raz, Gal; Touroutoglou, Alexandra; Wilson-Mendenhall, Christine; Gilam, Gadi; Lin, Tamar; Gonen, Tal; Jacob, Yael; Atzil, Shir; Admon, Roee; Bleich-Cohen, Maya; Maron-Katz, Adi; Hendler, Talma; Barrett, Lisa Feldman

2016-08-01

Recent theoretical and empirical work has highlighted the role of domain-general, large-scale brain networks in generating emotional experiences. These networks are hypothesized to process aspects of emotional experiences that are not unique to a specific emotional category (e.g., "sadness," "happiness"), but rather that generalize across categories. In this article, we examined the dynamic interactions (i.e., changing cohesiveness) between specific domain-general networks across time while participants experienced various instances of sadness, fear, and anger. We used a novel method for probing the network connectivity dynamics between two salience networks and three amygdala-based networks. We hypothesized, and found, that the functional connectivity between these networks covaried with the intensity of different emotional experiences. Stronger connectivity between the dorsal salience network and the medial amygdala network was associated with more intense ratings of emotional experience across six different instances of the three emotion categories examined. Also, stronger connectivity between the dorsal salience network and the ventrolateral amygdala network was associated with more intense ratings of emotional experience across five out of the six different instances. Our findings demonstrate that a variety of emotional experiences are associated with dynamic interactions of domain-general neural systems.

Optimal generalized multistep integration formulae for real-time digital simulation

NASA Technical Reports Server (NTRS)

Moerder, D. D.; Halyo, N.

1985-01-01

The problem of discretizing a dynamical system for real-time digital simulation is considered. Treating the system and its simulation as stochastic processes leads to a statistical characterization of simulator fidelity. A plant discretization procedure based on an efficient matrix generalization of explicit linear multistep discrete integration formulae is introduced, which minimizes a weighted sum of the mean squared steady-state and transient error between the system and simulator outputs.

Detection of a dynamic topography signal in last interglacial sea-level records

PubMed Central

Austermann, Jacqueline; Mitrovica, Jerry X.; Huybers, Peter; Rovere, Alessio

2017-01-01

Estimating minimum ice volume during the last interglacial based on local sea-level indicators requires that these indicators are corrected for processes that alter local sea level relative to the global average. Although glacial isostatic adjustment is generally accounted for, global scale dynamic changes in topography driven by convective mantle flow are generally not considered. We use numerical models of mantle flow to quantify vertical deflections caused by dynamic topography and compare predictions at passive margins to a globally distributed set of last interglacial sea-level markers. The deflections predicted as a result of dynamic topography are significantly correlated with marker elevations (>95% probability) and are consistent with construction and preservation attributes across marker types. We conclude that a dynamic topography signal is present in the elevation of last interglacial sea-level records and that the signal must be accounted for in any effort to determine peak global mean sea level during the last interglacial to within an accuracy of several meters. PMID:28695210

Ecosystem Restoration: Fact or Fancy?

Treesearch

John A. Stanturf; Callie J. Schweitzer; Stephen H. Schoenholtz; James P. Barnett; Charles K. McMahon; Donald J. Tomszak

1998-01-01

Ecological restoration is generally accepted as the reestablishment of natural ecological processes that produce certain dynamic ecosystem properties of structure, function, and processes. But restore to what? The most frequently used conceptual model for the restoration process is the shift of conditions from some current (degraded) dynamic state to some past dynamic...

Nanobiomimetic Active Shape Control - Fluidic and Swarm-Intelligence Embodiments for Planetary Exploration

NASA Astrophysics Data System (ADS)

Santoli, S.

The concepts of Active Shape Control ( ASC ) and of Generalized Quantum Holography ( GQH ), respectively embodying a closer approach to biomimicry than the current macrophysics-based attempts at bioinspired robotic systems, and realizing a non-connectionistic, life-like kind of information processing that allows increasingly depths of mimicking of the biological structure-function solidarity, which have been formulated in physical terms in previous papers, are here further investigated for application to bioinspired flying or swimming robots for planetary exploration. It is shown that nano-to-micro integration would give the deepest level of biomimicry, and that both low and very low Reynolds number ( Re ) fluidics would involve GQH and Fiber Bundle Topology ( FBT ) for processing information at the various levels of ASC bioinspired robotics. While very low Re flows lend themselves to geometrization of microrobot dynamics and to FBT design, the general design problem is geometrized through GQH , i.e. made independent of dynamic considerations, thus allowing possible problems of semantic dyscrasias in highly complex hierarchical dynamical chains of sensing information processing actuating to be overcome. A roadmap to near- and medium-term nanostructured and nano-to-micro integration realizations is suggested.

Model-based analysis of coupled equilibrium-kinetic processes: indirect kinetic studies of thermodynamic parameters using the dynamic data.

PubMed

Emami, Fereshteh; Maeder, Marcel; Abdollahi, Hamid

2015-05-07

Thermodynamic studies of equilibrium chemical reactions linked with kinetic procedures are mostly impossible by traditional approaches. In this work, the new concept of generalized kinetic study of thermodynamic parameters is introduced for dynamic data. The examples of equilibria intertwined with kinetic chemical mechanisms include molecular charge transfer complex formation reactions, pH-dependent degradation of chemical compounds and tautomerization kinetics in micellar solutions. Model-based global analysis with the possibility of calculating and embedding the equilibrium and kinetic parameters into the fitting algorithm has allowed the complete analysis of the complex reaction mechanisms. After the fitting process, the optimal equilibrium and kinetic parameters together with an estimate of their standard deviations have been obtained. This work opens up a promising new avenue for obtaining equilibrium constants through the kinetic data analysis for the kinetic reactions that involve equilibrium processes.

Tests of general relativity using Starprobe radio metric tracking data

NASA Technical Reports Server (NTRS)

Mease, K. D.; Anderson, J. D.; Wood, L. J.; White, L. K.

1982-01-01

The potential of a proposed spacecraft mission, called Starprobe, for testing general relativity and providing information on the interior structure and dynamics of the sun is investigated. Parametric, gravitational perturbation terms are derived which represent relativistic effects and effects due to spatial and temporal variations in the solar potential at a given radial distance. A covariance analysis based on Kalman filtering theory predicts the accuracies with which the free parameters in the perturbation terms can be estimated with radio metric tracking data through the process of trajectory reconstruction. It is concluded that Starprobe can contribute significant information on both the nature of gravitation and the structure and dynamics of the solar interior.

Simulation of quantum dynamics based on the quantum stochastic differential equation.

PubMed

Li, Ming

2013-01-01

The quantum stochastic differential equation derived from the Lindblad form quantum master equation is investigated. The general formulation in terms of environment operators representing the quantum state diffusion is given. The numerical simulation algorithm of stochastic process of direct photodetection of a driven two-level system for the predictions of the dynamical behavior is proposed. The effectiveness and superiority of the algorithm are verified by the performance analysis of the accuracy and the computational cost in comparison with the classical Runge-Kutta algorithm.

Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes

PubMed Central

2018-01-01

Single-cell experiments show that gene expression is stochastic and bursty, a feature that can emerge from slow switching between promoter states with different activities. In addition to slow chromatin and/or DNA looping dynamics, one source of long-lived promoter states is the slow binding and unbinding kinetics of transcription factors to promoters, i.e. the non-adiabatic binding regime. Here, we introduce a simple analytical framework, known as a piecewise deterministic Markov process (PDMP), that accurately describes the stochastic dynamics of gene expression in the non-adiabatic regime. We illustrate the utility of the PDMP on a non-trivial dynamical system by analysing the properties of a titration-based oscillator in the non-adiabatic limit. We first show how to transform the underlying chemical master equation into a PDMP where the slow transitions between promoter states are stochastic, but whose rates depend upon the faster deterministic dynamics of the transcription factors regulated by these promoters. We show that the PDMP accurately describes the observed periods of stochastic cycles in activator and repressor-based titration oscillators. We then generalize our PDMP analysis to more complicated versions of titration-based oscillators to explain how multiple binding sites lengthen the period and improve coherence. Last, we show how noise-induced oscillation previously observed in a titration-based oscillator arises from non-adiabatic and discrete binding events at the promoter site. PMID:29386401

A survey of automated methods for sensemaking support

NASA Astrophysics Data System (ADS)

Llinas, James

2014-05-01

Complex, dynamic problems in general present a challenge for the design of analysis support systems and tools largely because there is limited reliable a priori procedural knowledge descriptive of the dynamic processes in the environment. Problem domains that are non-cooperative or adversarial impute added difficulties involving suboptimal observational data and/or data containing the effects of deception or covertness. The fundamental nature of analysis in these environments is based on composite approaches involving mining or foraging over the evidence, discovery and learning processes, and the synthesis of fragmented hypotheses; together, these can be labeled as sensemaking procedures. This paper reviews and analyzes the features, benefits, and limitations of a variety of automated techniques that offer possible support to sensemaking processes in these problem domains.

Dynamical processes and epidemic threshold on nonlinear coupled multiplex networks

NASA Astrophysics Data System (ADS)

Gao, Chao; Tang, Shaoting; Li, Weihua; Yang, Yaqian; Zheng, Zhiming

2018-04-01

Recently, the interplay between epidemic spreading and awareness diffusion has aroused the interest of many researchers, who have studied models mainly based on linear coupling relations between information and epidemic layers. However, in real-world networks the relation between two layers may be closely correlated with the property of individual nodes and exhibits nonlinear dynamical features. Here we propose a nonlinear coupled information-epidemic model (I-E model) and present a comprehensive analysis in a more generalized scenario where the upload rate differs from node to node, deletion rate varies between susceptible and infected states, and infection rate changes between unaware and aware states. In particular, we develop a theoretical framework of the intra- and inter-layer dynamical processes with a microscopic Markov chain approach (MMCA), and derive an analytic epidemic threshold. Our results suggest that the change of upload and deletion rate has little effect on the diffusion dynamics in the epidemic layer.

A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive control.

PubMed

Han, Min; Fan, Jianchao; Wang, Jun

2011-09-01

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.

PubMed

Prezenski, Sabine; Brechmann, André; Wolff, Susann; Russwinkel, Nele

2017-01-01

Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

PubMed Central

Prezenski, Sabine; Brechmann, André; Wolff, Susann; Russwinkel, Nele

2017-01-01

Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks. PMID:28824512

Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

NASA Astrophysics Data System (ADS)

Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-04-01

One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community detection in complex networks.

When high working memory capacity is and is not beneficial for predicting nonlinear processes.

PubMed

Fischer, Helen; Holt, Daniel V

2017-04-01

Predicting the development of dynamic processes is vital in many areas of life. Previous findings are inconclusive as to whether higher working memory capacity (WMC) is always associated with using more accurate prediction strategies, or whether higher WMC can also be associated with using overly complex strategies that do not improve accuracy. In this study, participants predicted a range of systematically varied nonlinear processes based on exponential functions where prediction accuracy could or could not be enhanced using well-calibrated rules. Results indicate that higher WMC participants seem to rely more on well-calibrated strategies, leading to more accurate predictions for processes with highly nonlinear trajectories in the prediction region. Predictions of lower WMC participants, in contrast, point toward an increased use of simple exemplar-based prediction strategies, which perform just as well as more complex strategies when the prediction region is approximately linear. These results imply that with respect to predicting dynamic processes, working memory capacity limits are not generally a strength or a weakness, but that this depends on the process to be predicted.

Structure-based control of complex networks with nonlinear dynamics

NASA Astrophysics Data System (ADS)

Zanudo, Jorge G. T.; Yang, Gang; Albert, Reka

What can we learn about controlling a system solely from its underlying network structure? Here we use a framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors, regardless of the dynamic details and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of classical structural control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case, but not in specific model instances. This work was supported by NSF Grants PHY 1205840 and IIS 1160995. JGTZ is a recipient of a Stand Up To Cancer - The V Foundation Convergence Scholar Award.

Poissonian steady states: from stationary densities to stationary intensities.

PubMed

Eliazar, Iddo

2012-10-01

Markov dynamics are the most elemental and omnipresent form of stochastic dynamics in the sciences, with applications ranging from physics to chemistry, from biology to evolution, and from economics to finance. Markov dynamics can be either stationary or nonstationary. Stationary Markov dynamics represent statistical steady states and are quantified by stationary densities. In this paper, we generalize the notion of steady state to the case of general Markov dynamics. Considering an ensemble of independent motions governed by common Markov dynamics, we establish that the entire ensemble attains Poissonian steady states which are quantified by stationary Poissonian intensities and which hold valid also in the case of nonstationary Markov dynamics. The methodology is applied to a host of Markov dynamics, including Brownian motion, birth-death processes, random walks, geometric random walks, renewal processes, growth-collapse dynamics, decay-surge dynamics, Ito diffusions, and Langevin dynamics.

Poissonian steady states: From stationary densities to stationary intensities

NASA Astrophysics Data System (ADS)

Eliazar, Iddo

2012-10-01

Markov dynamics are the most elemental and omnipresent form of stochastic dynamics in the sciences, with applications ranging from physics to chemistry, from biology to evolution, and from economics to finance. Markov dynamics can be either stationary or nonstationary. Stationary Markov dynamics represent statistical steady states and are quantified by stationary densities. In this paper, we generalize the notion of steady state to the case of general Markov dynamics. Considering an ensemble of independent motions governed by common Markov dynamics, we establish that the entire ensemble attains Poissonian steady states which are quantified by stationary Poissonian intensities and which hold valid also in the case of nonstationary Markov dynamics. The methodology is applied to a host of Markov dynamics, including Brownian motion, birth-death processes, random walks, geometric random walks, renewal processes, growth-collapse dynamics, decay-surge dynamics, Ito diffusions, and Langevin dynamics.

A general modeling framework for describing spatially structured population dynamics

USGS Publications Warehouse

Sample, Christine; Fryxell, John; Bieri, Joanna; Federico, Paula; Earl, Julia; Wiederholt, Ruscena; Mattsson, Brady; Flockhart, Tyler; Nicol, Sam; Diffendorfer, James E.; Thogmartin, Wayne E.; Erickson, Richard A.; Norris, D. Ryan

2017-01-01

Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance that comparative analyses are colored by model details rather than general principles

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

PubMed

Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

2016-10-01

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

Assessment of Real-Time Time-Dependent Density Functional Theory (RT-TDDFT) in Radiation Chemistry: Ionized Water Dimer.

PubMed

Chalabala, Jan; Uhlig, Frank; Slavíček, Petr

2018-03-29

Ionization in the condensed phase and molecular clusters leads to a complicated chain of processes with coupled electron-nuclear dynamics. It is difficult to describe such dynamics with conventional nonadiabatic molecular dynamics schemes since the number of states swiftly increases as the molecular system grows. It is therefore attractive to use a direct electron and nuclear propagation such as the real-time time-dependent density functional theory (RT-TDDFT). Here we report a RT-TDDFT benchmark study on simulations of singly and doubly ionized states of a water monomer and dimer as a prototype for more complex processes in a condensed phase. We employed the RT-TDDFT based Ehrenfest molecular dynamics with a generalized gradient approximate (GGA) functional and compared it with wave-function-based surface hopping (SH) simulations. We found that the initial dynamics of a singly HOMO ionized water dimer is similar for both the RT-TDDFT/GGA and the SH simulations but leads to completely different reaction channels on a longer time scale. This failure is attributed to the self-interaction error in the GGA functionals and it can be avoided by using hybrid functionals with large fraction of exact exchange (represented here by the BHandHLYP functional). The simulations of doubly ionized states are reasonably described already at the GGA level. This suggests that the RT-TDDFT/GGA method could describe processes following the autoionization processes such as Auger emission, while its applicability to more complex processes such as intermolecular Coulombic decay remains limited.

Field dynamics inference via spectral density estimation

NASA Astrophysics Data System (ADS)

Frank, Philipp; Steininger, Theo; Enßlin, Torsten A.

2017-11-01

Stochastic differential equations are of utmost importance in various scientific and industrial areas. They are the natural description of dynamical processes whose precise equations of motion are either not known or too expensive to solve, e.g., when modeling Brownian motion. In some cases, the equations governing the dynamics of a physical system on macroscopic scales occur to be unknown since they typically cannot be deduced from general principles. In this work, we describe how the underlying laws of a stochastic process can be approximated by the spectral density of the corresponding process. Furthermore, we show how the density can be inferred from possibly very noisy and incomplete measurements of the dynamical field. Generally, inverse problems like these can be tackled with the help of Information Field Theory. For now, we restrict to linear and autonomous processes. To demonstrate its applicability, we employ our reconstruction algorithm on a time-series and spatiotemporal processes.

Field dynamics inference via spectral density estimation.

PubMed

Frank, Philipp; Steininger, Theo; Enßlin, Torsten A

2017-11-01

Stochastic differential equations are of utmost importance in various scientific and industrial areas. They are the natural description of dynamical processes whose precise equations of motion are either not known or too expensive to solve, e.g., when modeling Brownian motion. In some cases, the equations governing the dynamics of a physical system on macroscopic scales occur to be unknown since they typically cannot be deduced from general principles. In this work, we describe how the underlying laws of a stochastic process can be approximated by the spectral density of the corresponding process. Furthermore, we show how the density can be inferred from possibly very noisy and incomplete measurements of the dynamical field. Generally, inverse problems like these can be tackled with the help of Information Field Theory. For now, we restrict to linear and autonomous processes. To demonstrate its applicability, we employ our reconstruction algorithm on a time-series and spatiotemporal processes.

Invariance in the recurrence of large returns and the validation of models of price dynamics

NASA Astrophysics Data System (ADS)

Chang, Lo-Bin; Geman, Stuart; Hsieh, Fushing; Hwang, Chii-Ruey

2013-08-01

Starting from a robust, nonparametric definition of large returns (“excursions”), we study the statistics of their occurrences, focusing on the recurrence process. The empirical waiting-time distribution between excursions is remarkably invariant to year, stock, and scale (return interval). This invariance is related to self-similarity of the marginal distributions of returns, but the excursion waiting-time distribution is a function of the entire return process and not just its univariate probabilities. Generalized autoregressive conditional heteroskedasticity (GARCH) models, market-time transformations based on volume or trades, and generalized (Lévy) random-walk models all fail to fit the statistical structure of excursions.

Fractional quantum mechanics on networks: Long-range dynamics and quantum transport

NASA Astrophysics Data System (ADS)

Riascos, A. P.; Mateos, José L.

2015-11-01

In this paper we study the quantum transport on networks with a temporal evolution governed by the fractional Schrödinger equation. We generalize the dynamics based on continuous-time quantum walks, with transitions to nearest neighbors on the network, to the fractional case that allows long-range displacements. By using the fractional Laplacian matrix of a network, we establish a formalism that combines a long-range dynamics with the quantum superposition of states; this general approach applies to any type of connected undirected networks, including regular, random, and complex networks, and can be implemented from the spectral properties of the Laplacian matrix. We study the fractional dynamics and its capacity to explore the network by means of the transition probability, the average probability of return, and global quantities that characterize the efficiency of this quantum process. As a particular case, we explore analytically these quantities for circulant networks such as rings, interacting cycles, and complete graphs.

Fractional quantum mechanics on networks: Long-range dynamics and quantum transport.

PubMed

Riascos, A P; Mateos, José L

2015-11-01

In this paper we study the quantum transport on networks with a temporal evolution governed by the fractional Schrödinger equation. We generalize the dynamics based on continuous-time quantum walks, with transitions to nearest neighbors on the network, to the fractional case that allows long-range displacements. By using the fractional Laplacian matrix of a network, we establish a formalism that combines a long-range dynamics with the quantum superposition of states; this general approach applies to any type of connected undirected networks, including regular, random, and complex networks, and can be implemented from the spectral properties of the Laplacian matrix. We study the fractional dynamics and its capacity to explore the network by means of the transition probability, the average probability of return, and global quantities that characterize the efficiency of this quantum process. As a particular case, we explore analytically these quantities for circulant networks such as rings, interacting cycles, and complete graphs.

[Process orientation as a tool of strategic approaches to corporate governance and integrated management systems].

PubMed

Sens, Brigitte

2010-01-01

The concept of general process orientation as an instrument of organisation development is the core principle of quality management philosophy, i.e. the learning organisation. Accordingly, prestigious quality awards and certification systems focus on process configuration and continual improvement. In German health care organisations, particularly in hospitals, this general process orientation has not been widely implemented yet - despite enormous change dynamics and the requirements of both quality and economic efficiency of health care processes. But based on a consistent process architecture that considers key processes as well as management and support processes, the strategy of excellent health service provision including quality, safety and transparency can be realised in daily operative work. The core elements of quality (e.g., evidence-based medicine), patient safety and risk management, environmental management, health and safety at work can be embedded in daily health care processes as an integrated management system (the "all in one system" principle). Sustainable advantages and benefits for patients, staff, and the organisation will result: stable, high-quality, efficient, and indicator-based health care processes. Hospitals with their broad variety of complex health care procedures should now exploit the full potential of total process orientation. Copyright © 2010. Published by Elsevier GmbH.

A model of gene expression based on random dynamical systems reveals modularity properties of gene regulatory networks.

PubMed

Antoneli, Fernando; Ferreira, Renata C; Briones, Marcelo R S

2016-06-01

Here we propose a new approach to modeling gene expression based on the theory of random dynamical systems (RDS) that provides a general coupling prescription between the nodes of any given regulatory network given the dynamics of each node is modeled by a RDS. The main virtues of this approach are the following: (i) it provides a natural way to obtain arbitrarily large networks by coupling together simple basic pieces, thus revealing the modularity of regulatory networks; (ii) the assumptions about the stochastic processes used in the modeling are fairly general, in the sense that the only requirement is stationarity; (iii) there is a well developed mathematical theory, which is a blend of smooth dynamical systems theory, ergodic theory and stochastic analysis that allows one to extract relevant dynamical and statistical information without solving the system; (iv) one may obtain the classical rate equations form the corresponding stochastic version by averaging the dynamic random variables (small noise limit). It is important to emphasize that unlike the deterministic case, where coupling two equations is a trivial matter, coupling two RDS is non-trivial, specially in our case, where the coupling is performed between a state variable of one gene and the switching stochastic process of another gene and, hence, it is not a priori true that the resulting coupled system will satisfy the definition of a random dynamical system. We shall provide the necessary arguments that ensure that our coupling prescription does indeed furnish a coupled regulatory network of random dynamical systems. Finally, the fact that classical rate equations are the small noise limit of our stochastic model ensures that any validation or prediction made on the basis of the classical theory is also a validation or prediction of our model. We illustrate our framework with some simple examples of single-gene system and network motifs. Copyright © 2016 Elsevier Inc. All rights reserved.

Multi-Topic Tracking Model for dynamic social network

NASA Astrophysics Data System (ADS)

Li, Yuhua; Liu, Changzheng; Zhao, Ming; Li, Ruixuan; Xiao, Hailing; Wang, Kai; Zhang, Jun

2016-07-01

The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users' interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users' interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.

Investigating Long-Range Dependence in American Treasury Bills Variations and Volatilities during Stable and Unstable Periods

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-05-01

Detrended fluctuation analysis (DFA) is used to examine long-range dependence in variations and volatilities of American treasury bills (TB) during periods of low and high movements in TB rates. Volatility series are estimated by generalized autoregressive conditional heteroskedasticity (GARCH) model under Gaussian, Student, and the generalized error distribution (GED) assumptions. The DFA-based Hurst exponents from 3-month, 6-month, and 1-year TB data indicates that in general the dynamics of the TB variations process is characterized by persistence during stable time period (before 2008 international financial crisis) and anti-persistence during unstable time period (post-2008 international financial crisis). For volatility series, it is found that; for stable period; 3-month volatility process is more likely random, 6-month volatility process is anti-persistent, and 1-year volatility process is persistent. For unstable period, estimation results show that the generating process is persistent for all maturities and for all distributional assumptions.

Testing for significance of phase synchronisation dynamics in the EEG.

PubMed

Daly, Ian; Sweeney-Reed, Catherine M; Nasuto, Slawomir J

2013-06-01

A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.

Slow dynamics in translation-invariant quantum lattice models

NASA Astrophysics Data System (ADS)

Michailidis, Alexios A.; Žnidarič, Marko; Medvedyeva, Mariya; Abanin, Dmitry A.; Prosen, Tomaž; Papić, Z.

2018-03-01

Many-body quantum systems typically display fast dynamics and ballistic spreading of information. Here we address the open problem of how slow the dynamics can be after a generic breaking of integrability by local interactions. We develop a method based on degenerate perturbation theory that reveals slow dynamical regimes and delocalization processes in general translation invariant models, along with accurate estimates of their delocalization time scales. Our results shed light on the fundamental questions of the robustness of quantum integrable systems and the possibility of many-body localization without disorder. As an example, we construct a large class of one-dimensional lattice models where, despite the absence of asymptotic localization, the transient dynamics is exceptionally slow, i.e., the dynamics is indistinguishable from that of many-body localized systems for the system sizes and time scales accessible in experiments and numerical simulations.

General Relativistic Effects on Neutrino-driven Winds from Young, Hot Neutron Stars and r-Process Nucleosynthesis

NASA Astrophysics Data System (ADS)

Otsuki, Kaori; Tagoshi, Hideyuki; Kajino, Toshitaka; Wanajo, Shin-ya

2000-04-01

Neutrino-driven winds from young hot neutron stars, which are formed by supernova explosions, are the most promising candidate site for r-process nucleosynthesis. We study general relativistic effects on this wind in Schwarzschild geometry in order to look for suitable conditions for successful r-process nucleosynthesis. It is quantitatively demonstrated that general relativistic effects play a significant role in increasing the entropy and decreasing the dynamic timescale of the neutrino-driven wind. Exploring the wide parameter region that determines the expansion dynamics of the wind, we find interesting physical conditions that lead to successful r-process nucleosynthesis. The conditions that we found are realized in a neutrino-driven wind with a very short dynamic timescale, τdyn~6 ms, and a relatively low entropy, S~140. We carry out α-process and r-process nucleosynthesis calculations on these conditions with our single network code, which includes over 3000 isotopes, and confirm quantitatively that the second and third r-process abundance peaks are produced in neutrino-driven winds.

Full-degrees-of-freedom frequency based substructuring

NASA Astrophysics Data System (ADS)

Drozg, Armin; Čepon, Gregor; Boltežar, Miha

2018-01-01

Dividing the whole system into multiple subsystems and a separate dynamic analysis is common practice in the field of structural dynamics. The substructuring process improves the computational efficiency and enables an effective realization of the local optimization, modal updating and sensitivity analyses. This paper focuses on frequency-based substructuring methods using experimentally obtained data. An efficient substructuring process has already been demonstrated using numerically obtained frequency-response functions (FRFs). However, the experimental process suffers from several difficulties, among which, many of them are related to the rotational degrees of freedom. Thus, several attempts have been made to measure, expand or combine numerical correction methods in order to obtain a complete response model. The proposed methods have numerous limitations and are not yet generally applicable. Therefore, in this paper an alternative approach based on experimentally obtained data only, is proposed. The force-excited part of the FRF matrix is measured with piezoelectric translational and rotational direct accelerometers. The incomplete moment-excited part of the FRF matrix is expanded, based on the modal model. The proposed procedure is integrated in a Lagrange Multiplier Frequency Based Substructuring method and demonstrated on a simple beam structure, where the connection coordinates are mainly associated with the rotational degrees of freedom.

Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes

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

Lin, Yen Ting; Buchler, Nicolas E.

Single-cell experiments show that gene expression is stochastic and bursty, a feature that can emerge from slow switching between promoter states with different activities. In addition to slow chromatin and/or DNA looping dynamics, one source of long-lived promoter states is the slow binding and unbinding kinetics of transcription factors to promoters, i.e. the non-adiabatic binding regime. Here, we introduce a simple analytical framework, known as a piecewise deterministic Markov process (PDMP), that accurately describes the stochastic dynamics of gene expression in the non-adiabatic regime. We illustrate the utility of the PDMP on a non-trivial dynamical system by analysing the propertiesmore » of a titration-based oscillator in the non-adiabatic limit. We first show how to transform the underlying chemical master equation into a PDMP where the slow transitions between promoter states are stochastic, but whose rates depend upon the faster deterministic dynamics of the transcription factors regulated by these promoters. We show that the PDMP accurately describes the observed periods of stochastic cycles in activator and repressor-based titration oscillators. We then generalize our PDMP analysis to more complicated versions of titration-based oscillators to explain how multiple binding sites lengthen the period and improve coherence. Finally, we show how noise-induced oscillation previously observed in a titration-based oscillator arises from non-adiabatic and discrete binding events at the promoter site.« less

Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes

DOE PAGES

Lin, Yen Ting; Buchler, Nicolas E.

2018-01-31

Single-cell experiments show that gene expression is stochastic and bursty, a feature that can emerge from slow switching between promoter states with different activities. In addition to slow chromatin and/or DNA looping dynamics, one source of long-lived promoter states is the slow binding and unbinding kinetics of transcription factors to promoters, i.e. the non-adiabatic binding regime. Here, we introduce a simple analytical framework, known as a piecewise deterministic Markov process (PDMP), that accurately describes the stochastic dynamics of gene expression in the non-adiabatic regime. We illustrate the utility of the PDMP on a non-trivial dynamical system by analysing the propertiesmore » of a titration-based oscillator in the non-adiabatic limit. We first show how to transform the underlying chemical master equation into a PDMP where the slow transitions between promoter states are stochastic, but whose rates depend upon the faster deterministic dynamics of the transcription factors regulated by these promoters. We show that the PDMP accurately describes the observed periods of stochastic cycles in activator and repressor-based titration oscillators. We then generalize our PDMP analysis to more complicated versions of titration-based oscillators to explain how multiple binding sites lengthen the period and improve coherence. Finally, we show how noise-induced oscillation previously observed in a titration-based oscillator arises from non-adiabatic and discrete binding events at the promoter site.« less

Reservoir Computing Beyond Memory-Nonlinearity Trade-off.

PubMed

Inubushi, Masanobu; Yoshimura, Kazuyuki

2017-08-31

Reservoir computing is a brain-inspired machine learning framework that employs a signal-driven dynamical system, in particular harnessing common-signal-induced synchronization which is a widely observed nonlinear phenomenon. Basic understanding of a working principle in reservoir computing can be expected to shed light on how information is stored and processed in nonlinear dynamical systems, potentially leading to progress in a broad range of nonlinear sciences. As a first step toward this goal, from the viewpoint of nonlinear physics and information theory, we study the memory-nonlinearity trade-off uncovered by Dambre et al. (2012). Focusing on a variational equation, we clarify a dynamical mechanism behind the trade-off, which illustrates why nonlinear dynamics degrades memory stored in dynamical system in general. Moreover, based on the trade-off, we propose a mixture reservoir endowed with both linear and nonlinear dynamics and show that it improves the performance of information processing. Interestingly, for some tasks, significant improvements are observed by adding a few linear dynamics to the nonlinear dynamical system. By employing the echo state network model, the effect of the mixture reservoir is numerically verified for a simple function approximation task and for more complex tasks.

A Harris-Todaro Agent-Based Model to Rural-Urban Migration

NASA Astrophysics Data System (ADS)

Espíndola, Aquino L.; Silveira, Jaylson J.; Penna, T. J. P.

2006-09-01

The Harris-Todaro model of the rural-urban migration process is revisited under an agent-based approach. The migration of the workers is interpreted as a process of social learning by imitation, formalized by a computational model. By simulating this model, we observe a transitional dynamics with continuous growth of the urban fraction of overall population toward an equilibrium. Such an equilibrium is characterized by stabilization of rural-urban expected wages differential (generalized Harris-Todaro equilibrium condition), urban concentration and urban unemployment. These classic results obtained originally by Harris and Todaro are emergent properties of our model.

Molecular Dynamics based on a Generalized Born solvation model: application to protein folding

NASA Astrophysics Data System (ADS)

Onufriev, Alexey

2004-03-01

An accurate description of the aqueous environment is essential for realistic biomolecular simulations, but may become very expensive computationally. We have developed a version of the Generalized Born model suitable for describing large conformational changes in macromolecules. The model represents the solvent implicitly as continuum with the dielectric properties of water, and include charge screening effects of salt. The computational cost associated with the use of this model in Molecular Dynamics simulations is generally considerably smaller than the cost of representing water explicitly. Also, compared to traditional Molecular Dynamics simulations based on explicit water representation, conformational changes occur much faster in implicit solvation environment due to the absence of viscosity. The combined speed-up allow one to probe conformational changes that occur on much longer effective time-scales. We apply the model to folding of a 46-residue three helix bundle protein (residues 10-55 of protein A, PDB ID 1BDD). Starting from an unfolded structure at 450 K, the protein folds to the lowest energy state in 6 ns of simulation time, which takes about a day on a 16 processor SGI machine. The predicted structure differs from the native one by 2.4 A (backbone RMSD). Analysis of the structures seen on the folding pathway reveals details of the folding process unavailable form experiment.

Modeling Dynamic Food Choice Processes to Understand Dietary Intervention Effects.

PubMed

Marcum, Christopher Steven; Goldring, Megan R; McBride, Colleen M; Persky, Susan

2018-02-17

Meal construction is largely governed by nonconscious and habit-based processes that can be represented as a collection of in dividual, micro-level food choices that eventually give rise to a final plate. Despite this, dietary behavior intervention research rarely captures these micro-level food choice processes, instead measuring outcomes at aggregated levels. This is due in part to a dearth of analytic techniques to model these dynamic time-series events. The current article addresses this limitation by applying a generalization of the relational event framework to model micro-level food choice behavior following an educational intervention. Relational event modeling was used to model the food choices that 221 mothers made for their child following receipt of an information-based intervention. Participants were randomized to receive either (a) control information; (b) childhood obesity risk information; (c) childhood obesity risk information plus a personalized family history-based risk estimate for their child. Participants then made food choices for their child in a virtual reality-based food buffet simulation. Micro-level aspects of the built environment, such as the ordering of each food in the buffet, were influential. Other dynamic processes such as choice inertia also influenced food selection. Among participants receiving the strongest intervention condition, choice inertia decreased and the overall rate of food selection increased. Modeling food selection processes can elucidate the points at which interventions exert their influence. Researchers can leverage these findings to gain insight into nonconscious and uncontrollable aspects of food selection that influence dietary outcomes, which can ultimately improve the design of dietary interventions.

Fractional Dynamics of Network Growth Constrained by Aging Node Interactions

PubMed Central

Safdari, Hadiseh; Zare Kamali, Milad; Shirazi, Amirhossein; Khalighi, Moein; Jafari, Gholamreza; Ausloos, Marcel

2016-01-01

In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of “commonly accepted beliefs” seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node’s age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model. PMID:27171424

Multibody dynamical modeling for spacecraft docking process with spring-damper buffering device: A new validation approach

NASA Astrophysics Data System (ADS)

Daneshjou, Kamran; Alibakhshi, Reza

2018-01-01

In the current manuscript, the process of spacecraft docking, as one of the main risky operations in an on-orbit servicing mission, is modeled based on unconstrained multibody dynamics. The spring-damper buffering device is utilized here in the docking probe-cone system for micro-satellites. Owing to the impact occurs inevitably during docking process and the motion characteristics of multibody systems are remarkably affected by this phenomenon, a continuous contact force model needs to be considered. Spring-damper buffering device, keeping the spacecraft stable in an orbit when impact occurs, connects a base (cylinder) inserted in the chaser satellite and the end of docking probe. Furthermore, by considering a revolute joint equipped with torsional shock absorber, between base and chaser satellite, the docking probe can experience both translational and rotational motions simultaneously. Although spacecraft docking process accompanied by the buffering mechanisms may be modeled by constrained multibody dynamics, this paper deals with a simple and efficient formulation to eliminate the surplus generalized coordinates and solve the impact docking problem based on unconstrained Lagrangian mechanics. By an example problem, first, model verification is accomplished by comparing the computed results with those recently reported in the literature. Second, according to a new alternative validation approach, which is based on constrained multibody problem, the accuracy of presented model can be also evaluated. This proposed verification approach can be applied to indirectly solve the constrained multibody problems by minimum required effort. The time history of impact force, the influence of system flexibility and physical interaction between shock absorber and penetration depth caused by impact are the issues followed in this paper. Third, the MATLAB/SIMULINK multibody dynamic analysis software will be applied to build impact docking model to validate computed results and then, investigate the trajectories of both satellites to take place the successful capture process.

Psychotherapy research needs theory. Outline for an epistemology of the clinical exchange.

PubMed

Salvatore, Sergio

2011-09-01

This paper provides an analysis of a basic assumption grounding the clinical research: the ontological autonomy of psychotherapy-based on the idea that the clinical exchange is sufficiently distinguished from other social objects (i.e. exchange between teacher and pupils, or between buyer and seller, or interaction during dinner, and so forth). A criticism of such an assumption is discussed together with the proposal of a different epistemological interpretation, based on the distinction between communicative dynamics and the process of psychotherapy-psychotherapy is a goal-oriented process based on the general dynamics of human communication. Theoretical and methodological implications are drawn from such a view: It allows further sources of knowledge to be integrated within clinical research (i.e. those coming from other domains of analysis of human communication); it also enables a more abstract definition of the psychotherapy process to be developed, leading to innovative views of classical critical issues, like the specific-nonspecific debate. The final part of the paper is devoted to presenting a model of human communication--the Semiotic Dialogical Dialectic Theory--which is meant as the framework for the analysis of psychotherapy.

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

NASA Astrophysics Data System (ADS)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Integrable Floquet dynamics, generalized exclusion processes and "fused" matrix ansatz

NASA Astrophysics Data System (ADS)

Vanicat, Matthieu

2018-04-01

We present a general method for constructing integrable stochastic processes, with two-step discrete time Floquet dynamics, from the transfer matrix formalism. The models can be interpreted as a discrete time parallel update. The method can be applied for both periodic and open boundary conditions. We also show how the stationary distribution can be built as a matrix product state. As an illustration we construct parallel discrete time dynamics associated with the R-matrix of the SSEP and of the ASEP, and provide the associated stationary distributions in a matrix product form. We use this general framework to introduce new integrable generalized exclusion processes, where a fixed number of particles is allowed on each lattice site in opposition to the (single particle) exclusion process models. They are constructed using the fusion procedure of R-matrices (and K-matrices for open boundary conditions) for the SSEP and ASEP. We develop a new method, that we named "fused" matrix ansatz, to build explicitly the stationary distribution in a matrix product form. We use this algebraic structure to compute physical observables such as the correlation functions and the mean particle current.

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.

Quantum Information Biology: From Theory of Open Quantum Systems to Adaptive Dynamics

NASA Astrophysics Data System (ADS)

Asano, Masanari; Basieva, Irina; Khrennikov, Andrei; Ohya, Masanori; Tanaka, Yoshiharu; Yamato, Ichiro

This chapter reviews quantum(-like) information biology (QIB). Here biology is treated widely as even covering cognition and its derivatives: psychology and decision making, sociology, and behavioral economics and finances. QIB provides an integrative description of information processing by bio-systems at all scales of life: from proteins and cells to cognition, ecological and social systems. Mathematically QIB is based on the theory of adaptive quantum systems (which covers also open quantum systems). Ideologically QIB is based on the quantum-like (QL) paradigm: complex bio-systems process information in accordance with the laws of quantum information and probability. This paradigm is supported by plenty of statistical bio-data collected at all bio-scales. QIB re ects the two fundamental principles: a) adaptivity; and, b) openness (bio-systems are fundamentally open). In addition, quantum adaptive dynamics provides the most generally possible mathematical representation of these principles.

[Current aspects of the physiopathology of the infectious process. II. Cybernetic elements in the pathogenetic structure of infectious diseases].

PubMed

Dragomirescu, M; Buzinschi, S

1980-01-01

The authors discuss the applicability of general cybernetic principles (the theory of systems and self-regulated mechanisms based on inversed connections) to the pathophysiologic structure of infections. With reference to concrete examples they outline the following elements: the appartenance of the infectious process to the notion of system (as conceived in the theory of systems), the previsible character of the functional potential of the structured system in the components of infection, and the sequental correspondence between system dynamics and the dynamics of the infectious process. Starting from the mechanism of action of the main microbial toxins, the aptitude of the latter to act upon the functional code of the macroorganism, altering the cellular and supracellular self-regulated biosystems, is demonstrated. Finally, the practical implications of assimilating cybernetic processes in the pathophysiology of infectious diseases are analyzed.

Fundamental limits on quantum dynamics based on entropy change

NASA Astrophysics Data System (ADS)

Das, Siddhartha; Khatri, Sumeet; Siopsis, George; Wilde, Mark M.

2018-01-01

It is well known in the realm of quantum mechanics and information theory that the entropy is non-decreasing for the class of unital physical processes. However, in general, the entropy does not exhibit monotonic behavior. This has restricted the use of entropy change in characterizing evolution processes. Recently, a lower bound on the entropy change was provided in the work of Buscemi, Das, and Wilde [Phys. Rev. A 93(6), 062314 (2016)]. We explore the limit that this bound places on the physical evolution of a quantum system and discuss how these limits can be used as witnesses to characterize quantum dynamics. In particular, we derive a lower limit on the rate of entropy change for memoryless quantum dynamics, and we argue that it provides a witness of non-unitality. This limit on the rate of entropy change leads to definitions of several witnesses for testing memory effects in quantum dynamics. Furthermore, from the aforementioned lower bound on entropy change, we obtain a measure of non-unitarity for unital evolutions.

Utilizing multiple state variables to improve the dynamic range of analog switching in a memristor

NASA Astrophysics Data System (ADS)

Jeong, YeonJoo; Kim, Sungho; Lu, Wei D.

2015-10-01

Memristors and memristive systems have been extensively studied for data storage and computing applications such as neuromorphic systems. To act as synapses in neuromorphic systems, the memristor needs to exhibit analog resistive switching (RS) behavior with incremental conductance change. In this study, we show that the dynamic range of the analog RS behavior can be significantly enhanced in a tantalum-oxide-based memristor. By controlling different state variables enabled by different physical effects during the RS process, the gradual filament expansion stage can be selectively enhanced without strongly affecting the abrupt filament length growth stage. Detailed physics-based modeling further verified the observed experimental effects and revealed the roles of oxygen vacancy drift and diffusion processes, and how the diffusion process can be selectively enhanced during the filament expansion stage. These findings lead to more desirable and reliable memristor behaviors for analog computing applications. Additionally, the ability to selectively control different internal physical processes demonstrated in the current study provides guidance for continued device optimization of memristor devices in general.

A dynamic model of reasoning and memory.

PubMed

Hawkins, Guy E; Hayes, Brett K; Heit, Evan

2016-02-01

Previous models of category-based induction have neglected how the process of induction unfolds over time. We conceive of induction as a dynamic process and provide the first fine-grained examination of the distribution of response times observed in inductive reasoning. We used these data to develop and empirically test the first major quantitative modeling scheme that simultaneously accounts for inductive decisions and their time course. The model assumes that knowledge of similarity relations among novel test probes and items stored in memory drive an accumulation-to-bound sequential sampling process: Test probes with high similarity to studied exemplars are more likely to trigger a generalization response, and more rapidly, than items with low exemplar similarity. We contrast data and model predictions for inductive decisions with a recognition memory task using a common stimulus set. Hierarchical Bayesian analyses across 2 experiments demonstrated that inductive reasoning and recognition memory primarily differ in the threshold to trigger a decision: Observers required less evidence to make a property generalization judgment (induction) than an identity statement about a previously studied item (recognition). Experiment 1 and a condition emphasizing decision speed in Experiment 2 also found evidence that inductive decisions use lower quality similarity-based information than recognition. The findings suggest that induction might represent a less cautious form of recognition. We conclude that sequential sampling models grounded in exemplar-based similarity, combined with hierarchical Bayesian analysis, provide a more fine-grained and informative analysis of the processes involved in inductive reasoning than is possible solely through examination of choice data. PsycINFO Database Record (c) 2016 APA, all rights reserved.

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.

PubMed

Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

2016-02-01

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter--describing somatic integration--and the spike-history filter--accounting for spike-frequency adaptation--dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations.

Equivalent formulations of “the equation of life”

NASA Astrophysics Data System (ADS)

Ao, Ping

2014-07-01

Motivated by progress in theoretical biology a recent proposal on a general and quantitative dynamical framework for nonequilibrium processes and dynamics of complex systems is briefly reviewed. It is nothing but the evolutionary process discovered by Charles Darwin and Alfred Wallace. Such general and structured dynamics may be tentatively named “the equation of life”. Three equivalent formulations are discussed, and it is also pointed out that such a quantitative dynamical framework leads naturally to the powerful Boltzmann-Gibbs distribution and the second law in physics. In this way, the equation of life provides a logically consistent foundation for thermodynamics. This view clarifies a particular outstanding problem and further suggests a unifying principle for physics and biology.

Hydrology team

NASA Technical Reports Server (NTRS)

Ragan, R.

1982-01-01

General problems faced by hydrologists when using historical records, real time data, statistical analysis, and system simulation in providing quantitative information on the temporal and spatial distribution of water are related to the limitations of these data. Major problem areas requiring multispectral imaging-based research to improve hydrology models involve: evapotranspiration rates and soil moisture dynamics for large areas; the three dimensional characteristics of bodies of water; flooding in wetlands; snow water equivalents; runoff and sediment yield from ungaged watersheds; storm rainfall; fluorescence and polarization of water and its contained substances; discriminating between sediment and chlorophyll in water; role of barrier island dynamics in coastal zone processes; the relationship between remotely measured surface roughness and hydraulic roughness of land surfaces and stream networks; and modeling the runoff process.

Coupled wave-packets for non-adiabatic molecular dynamics: a generalization of Gaussian wave-packet dynamics to multiple potential energy surfaces

DOE PAGES

White, Alexander James; Tretiak, Sergei; Mozyrsky, Dima V.

2016-04-25

Accurate simulation of the non-adiabatic dynamics of molecules in excited electronic states is key to understanding molecular photo-physical processes. Here we present a novel method, based on a semiclassical approximation, that is as efficient as the commonly used mean field Ehrenfest or ad hoc surface hopping methods and properly accounts for interference and decoherence effects. This novel method is an extension of Heller's thawed Gaussian wave-packet dynamics that includes coupling between potential energy surfaces. By studying several standard test problems we demonstrate that the accuracy of the method can be systematically improved while maintaining high efficiency. The method is suitablemore » for investigating the role of quantum coherence in the non-adiabatic dynamics of many-atom molecules.« less

Dynamic feature analysis for Voyager at the Image Processing Laboratory

NASA Technical Reports Server (NTRS)

Yagi, G. M.; Lorre, J. J.; Jepsen, P. L.

1978-01-01

Voyager 1 and 2 were launched from Cape Kennedy to Jupiter, Saturn, and beyond on September 5, 1977 and August 20, 1977. The role of the Image Processing Laboratory is to provide the Voyager Imaging Team with the necessary support to identify atmospheric features (tiepoints) for Jupiter and Saturn data, and to analyze and display them in a suitable form. This support includes the software needed to acquire and store tiepoints, the hardware needed to interactively display images and tiepoints, and the general image processing environment necessary for decalibration and enhancement of the input images. The objective is an understanding of global circulation in the atmospheres of Jupiter and Saturn. Attention is given to the Voyager imaging subsystem, the Voyager imaging science objectives, hardware, software, display monitors, a dynamic feature study, decalibration, navigation, and data base.

Theory of Cooperative Activated Structural Relaxation in Polymer Nanocomposites Composed of Small and Sticky Particles

NASA Astrophysics Data System (ADS)

Xie, Shijie; Schweizer, Kenneth

Recently, Cheng, Sokolov and coworkers have discovered qualitatively new dynamic behavior (exceptionally large Tg and fragility increases, unusual thermal and viscoelastic responses) in polymer nanocomposites composed of nanoparticles comparable in size to a polymer segment which form physical bonds with both themselves and segments. We generalize the Elastically Collective Nonlinear Langevin Equation theory of deeply supercooled molecular and polymer liquids to study the cooperative activated hopping dynamics of this system based on the dynamic free energy surface concept. The theoretical calculations are consistent with segmental relaxation time measurements as a function of temperature and nanoparticle volume fraction, and also the nearly linear growth of Tg with NP loading; predictions are made for the influence of nonuniversal chemical effects. The theory suggests the alpha process involves strongly coupled activated motion of segments and nanoparticles, consistent with the observed negligible change of the heat capacity jump with filler loading. Based on cohesive energy calculations and transient network ideas, full structural relaxation is suggested to involve a second, slower bond dissociation process with distinctive features and implications.

Perspective: Maximum caliber is a general variational principle for dynamical systems

NASA Astrophysics Data System (ADS)

Dixit, Purushottam D.; Wagoner, Jason; Weistuch, Corey; Pressé, Steve; Ghosh, Kingshuk; Dill, Ken A.

2018-01-01

We review here Maximum Caliber (Max Cal), a general variational principle for inferring distributions of paths in dynamical processes and networks. Max Cal is to dynamical trajectories what the principle of maximum entropy is to equilibrium states or stationary populations. In Max Cal, you maximize a path entropy over all possible pathways, subject to dynamical constraints, in order to predict relative path weights. Many well-known relationships of non-equilibrium statistical physics—such as the Green-Kubo fluctuation-dissipation relations, Onsager's reciprocal relations, and Prigogine's minimum entropy production—are limited to near-equilibrium processes. Max Cal is more general. While it can readily derive these results under those limits, Max Cal is also applicable far from equilibrium. We give examples of Max Cal as a method of inference about trajectory distributions from limited data, finding reaction coordinates in bio-molecular simulations, and modeling the complex dynamics of non-thermal systems such as gene regulatory networks or the collective firing of neurons. We also survey its basis in principle and some limitations.

Perspective: Maximum caliber is a general variational principle for dynamical systems.

PubMed

Dixit, Purushottam D; Wagoner, Jason; Weistuch, Corey; Pressé, Steve; Ghosh, Kingshuk; Dill, Ken A

2018-01-07

We review here Maximum Caliber (Max Cal), a general variational principle for inferring distributions of paths in dynamical processes and networks. Max Cal is to dynamical trajectories what the principle of maximum entropy is to equilibrium states or stationary populations. In Max Cal, you maximize a path entropy over all possible pathways, subject to dynamical constraints, in order to predict relative path weights. Many well-known relationships of non-equilibrium statistical physics-such as the Green-Kubo fluctuation-dissipation relations, Onsager's reciprocal relations, and Prigogine's minimum entropy production-are limited to near-equilibrium processes. Max Cal is more general. While it can readily derive these results under those limits, Max Cal is also applicable far from equilibrium. We give examples of Max Cal as a method of inference about trajectory distributions from limited data, finding reaction coordinates in bio-molecular simulations, and modeling the complex dynamics of non-thermal systems such as gene regulatory networks or the collective firing of neurons. We also survey its basis in principle and some limitations.

Exacerbated degradation and desertification of grassland in Central Asia

NASA Astrophysics Data System (ADS)

Zhang, G.; Xiao, X.; Biradar, C. M.; Dong, J.; Zhou, Y.; Qin, Y.; Zhang, Y.; Liu, F.; Ding, M.; Thomas, R. J.

2016-12-01

Grassland desertification is a complex process, including both state conversion (e.g., grasslands to deserts) and gradual within-state change (e.g., greenness dynamics). Existing studies generally did not separate the two components and analyzed them based on time series vegetation indices, which however cannot provide a clear and comprehensive picture for desertification. Here we proposed a desertification zone classification-based grassland degradation strategy to detect the grassland desertification process in Central Asia. First, annual spatially explicit maps of grasslands and deserts were generated to track the conversion between grasslands and deserts. The results showed that 13 % of grasslands were converted to deserts from 2000 to 2014, with an increasing desertification trend northward in the latitude range of 43-48°N. Second, a fragile and unstable Transitional zone was identified in southern Kazakhstan based on desert frequency maps. Third, gradual vegetation dynamics during the thermal growing season (EVITGS) were investigated using linear regression and Mann-Kendall approaches. The results indicated that grasslands generally experienced widespread degradation in Central Asia, with an additional hotspot identified in the northern Kazakhstan. Finally, attribution analyses of desertification were conducted by correlating vegetation dynamics with three different drought indices (Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Drought Severity Index (DSI)), precipitation, and temperature, and showed that grassland desertification was exacerbated by droughts, and persistent drought was the main factor for grassland desertification in Central Asia. This study provided essential information for taking practical actions to prevent the further desertification and targeting right spots for better intervention to combat the land degradation in the region.

Detecting dynamic causal inference in nonlinear two-phase fracture flow

NASA Astrophysics Data System (ADS)

Faybishenko, Boris

2017-08-01

Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.

Dynamic isoperimetry and the geometry of Lagrangian coherent structures

NASA Astrophysics Data System (ADS)

Froyland, Gary

2015-10-01

The study of transport and mixing processes in dynamical systems is particularly important for the analysis of mathematical models of physical systems. We propose a novel, direct geometric method to identify subsets of phase space that remain strongly coherent over a finite time duration. This new method is based on a dynamic extension of classical (static) isoperimetric problems; the latter are concerned with identifying submanifolds with the smallest boundary size relative to their volume. The present work introduces dynamic isoperimetric problems; the study of sets with small boundary size relative to volume as they are evolved by a general dynamical system. We formulate and prove dynamic versions of the fundamental (static) isoperimetric (in)equalities; a dynamic Federer-Fleming theorem and a dynamic Cheeger inequality. We introduce a new dynamic Laplace operator and describe a computational method to identify coherent sets based on eigenfunctions of the dynamic Laplacian. Our results include formal mathematical statements concerning geometric properties of finite-time coherent sets, whose boundaries can be regarded as Lagrangian coherent structures. The computational advantages of our new approach are a well-separated spectrum for the dynamic Laplacian, and flexibility in appropriate numerical approximation methods. Finally, we demonstrate that the dynamic Laplace operator can be realised as a zero-diffusion limit of a newly advanced probabilistic transfer operator method [9] for finding coherent sets, which is based on small diffusion. Thus, the present approach sits naturally alongside the probabilistic approach [9], and adds a formal geometric interpretation.

Low-cost digital dynamic visualization system

NASA Astrophysics Data System (ADS)

Asundi, Anand K.; Sajan, M. R.

1995-05-01

High speed photographic systems like the image rotation camera, the Cranz Schardin camera and the drum camera are typically used for recording and visualization of dynamic events in stress analysis, fluid mechanics, etc. All these systems are fairly expensive and generally not simple to use. Furthermore they are all based on photographic film recording systems requiring time consuming and tedious wet processing of the films. Currently digital cameras are replacing to certain extent the conventional cameras for static experiments. Recently, there is lot of interest in developing and modifying CCD architectures and recording arrangements for dynamic scene analysis. Herein we report the use of a CCD camera operating in the Time Delay and Integration (TDI) mode for digitally recording dynamic scenes. Applications in solid as well as fluid impact problems are presented.

A stochastic agent-based model of pathogen propagation in dynamic multi-relational social networks

PubMed Central

Khan, Bilal; Dombrowski, Kirk; Saad, Mohamed

2015-01-01

We describe a general framework for modeling and stochastic simulation of epidemics in realistic dynamic social networks, which incorporates heterogeneity in the types of individuals, types of interconnecting risk-bearing relationships, and types of pathogens transmitted across them. Dynamism is supported through arrival and departure processes, continuous restructuring of risk relationships, and changes to pathogen infectiousness, as mandated by natural history; dynamism is regulated through constraints on the local agency of individual nodes and their risk behaviors, while simulation trajectories are validated using system-wide metrics. To illustrate its utility, we present a case study that applies the proposed framework towards a simulation of HIV in artificial networks of intravenous drug users (IDUs) modeled using data collected in the Social Factors for HIV Risk survey. PMID:25859056

Information-based management mode based on value network analysis for livestock enterprises

NASA Astrophysics Data System (ADS)

Liu, Haoqi; Lee, Changhoon; Han, Mingming; Su, Zhongbin; Padigala, Varshinee Anu; Shen, Weizheng

2018-01-01

With the development of computer and IT technologies, enterprise management has gradually become information-based management. Moreover, due to poor technical competence and non-uniform management, most breeding enterprises show a lack of organisation in data collection and management. In addition, low levels of efficiency result in increasing production costs. This paper adopts 'struts2' in order to construct an information-based management system for standardised and normalised management within the process of production in beef cattle breeding enterprises. We present a radio-frequency identification system by studying multiple-tag anti-collision via a dynamic grouping ALOHA algorithm. This algorithm is based on the existing ALOHA algorithm and uses an improved packet dynamic of this algorithm, which is characterised by a high-throughput rate. This new algorithm can reach a throughput 42% higher than that of the general ALOHA algorithm. With a change in the number of tags, the system throughput is relatively stable.

Resonance energy transfer process in nanogap-based dual-color random lasing

NASA Astrophysics Data System (ADS)

Shi, Xiaoyu; Tong, Junhua; Liu, Dahe; Wang, Zhaona

2017-04-01

The resonance energy transfer (RET) process between Rhodamine 6G and oxazine in the nanogap-based random systems is systematically studied by revealing the variations and fluctuations of RET coefficients with pump power density. Three working regions stable fluorescence, dynamic laser, and stable laser are thus demonstrated in the dual-color random systems. The stable RET coefficients in fluorescence and lasing regions are generally different and greatly dependent on the donor concentration and the donor-acceptor ratio. These results may provide a way to reveal the energy distribution regulars in the random system and to design the tunable multi-color coherent random lasers for colorful imaging.

Kernel-based least squares policy iteration for reinforcement learning.

PubMed

Xu, Xin; Hu, Dewen; Lu, Xicheng

2007-07-01

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.

Driving the brain towards creativity and intelligence: A network control theory analysis.

PubMed

Kenett, Yoed N; Medaglia, John D; Beaty, Roger E; Chen, Qunlin; Betzel, Richard F; Thompson-Schill, Sharon L; Qiu, Jiang

2018-01-04

High-level cognitive constructs, such as creativity and intelligence, entail complex and multiple processes, including cognitive control processes. Recent neurocognitive research on these constructs highlight the importance of dynamic interaction across neural network systems and the role of cognitive control processes in guiding such a dynamic interaction. How can we quantitatively examine the extent and ways in which cognitive control contributes to creativity and intelligence? To address this question, we apply a computational network control theory (NCT) approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how NCT relates to individual differences in distinct measures of creative ability and intelligence. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that intelligence is related to the ability to "drive" the brain system into easy to reach neural states by the right inferior parietal lobe and lower integration abilities in the left retrosplenial cortex. We also find that creativity is related to the ability to "drive" the brain system into difficult to reach states by the right dorsolateral prefrontal cortex (inferior frontal junction) and higher integration abilities in sensorimotor areas. Furthermore, we found that different facets of creativity-fluency, flexibility, and originality-relate to generally similar but not identical network controllability processes. We relate our findings to general theories on intelligence and creativity. Copyright © 2018 Elsevier Ltd. All rights reserved.

Differentiation Driven Changes in the Dynamic Organization of Basal Transcription Initiation

PubMed Central

Giglia-Mari, Giuseppina; Mourgues, Sophie; Nonnekens, Julie; Andrieux, Lise O.; de Wit, Jan; Miquel, Catherine; Wijgers, Nils; Maas, Alex; Fousteri, Maria; Hoeijmakers, Jan H. J.; Vermeulen, Wim

2009-01-01

Studies based on cell-free systems and on in vitro–cultured living cells support the concept that many cellular processes, such as transcription initiation, are highly dynamic: individual proteins stochastically bind to their substrates and disassemble after reaction completion. This dynamic nature allows quick adaptation of transcription to changing conditions. However, it is unknown to what extent this dynamic transcription organization holds for postmitotic cells embedded in mammalian tissue. To allow analysis of transcription initiation dynamics directly into living mammalian tissues, we created a knock-in mouse model expressing fluorescently tagged TFIIH. Surprisingly and in contrast to what has been observed in cultured and proliferating cells, postmitotic murine cells embedded in their tissue exhibit a strong and long-lasting transcription-dependent immobilization of TFIIH. This immobilization is both differentiation driven and development dependent. Furthermore, although very statically bound, TFIIH can be remobilized to respond to new transcriptional needs. This divergent spatiotemporal transcriptional organization in different cells of the soma revisits the generally accepted highly dynamic concept of the kinetic framework of transcription and shows how basic processes, such as transcription, can be organized in a fundamentally different fashion in intact organisms as previously deduced from in vitro studies. PMID:19841728

The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis.

PubMed

Siemann, Julia; Herrmann, Manfred; Galashan, Daniela

2018-01-25

The present study examined whether feature-based cueing affects early or late stages of flanker conflict processing using EEG and fMRI. Feature cues either directed participants' attention to the upcoming colour of the target or were neutral. Validity-specific modulations during interference processing were investigated using the N200 event-related potential (ERP) component and BOLD signal differences. Additionally, both data sets were integrated using an fMRI-constrained source analysis. Finally, the results were compared with a previous study in which spatial instead of feature-based cueing was applied to an otherwise identical flanker task. Feature-based and spatial attention recruited a common fronto-parietal network during conflict processing. Irrespective of attention type (feature-based; spatial), this network responded to focussed attention (valid cueing) as well as context updating (invalid cueing), hinting at domain-general mechanisms. However, spatially and non-spatially directed attention also demonstrated domain-specific activation patterns for conflict processing that were observable in distinct EEG and fMRI data patterns as well as in the respective source analyses. Conflict-specific activity in visual brain regions was comparable between both attention types. We assume that the distinction between spatially and non-spatially directed attention types primarily applies to temporal differences (domain-specific dynamics) between signals originating in the same brain regions (domain-general localization).

Förster resonance energy transfer as a tool to study photoreceptor biology

PubMed Central

Hovan, Stephanie C.; Howell, Scott; Park, Paul S.-H.

2010-01-01

Vision is initiated in photoreceptor cells of the retina by a set of biochemical events called phototransduction. These events occur via coordinated dynamic processes that include changes in secondary messenger concentrations, conformational changes and post-translational modifications of signaling proteins, and protein-protein interactions between signaling partners. A complete description of the orchestration of these dynamic processes is still unavailable. Described in this work is the first step in the development of tools combining fluorescent protein technology, Förster resonance energy transfer (FRET), and transgenic animals that have the potential to reveal important molecular insights about the dynamic processes occurring in photoreceptor cells. We characterize the fluorescent proteins SCFP3A and SYFP2 for use as a donor-acceptor pair in FRET assays, which will facilitate the visualization of dynamic processes in living cells. We also demonstrate the targeted expression of these fluorescent proteins to the rod photoreceptor cells of Xenopus laevis, and describe a general method for detecting FRET in these cells. The general approaches described here can address numerous types of questions related to phototransduction and photoreceptor biology by providing a platform to visualize dynamic processes in molecular detail within a native context. PMID:21198205

System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling

DOE PAGES

Bacelli, Giorgio; Coe, Ryan; Patterson, David; ...

2017-04-01

Empirically based modeling is an essential aspect of design for a wave energy converter. These models are used in structural, mechanical and control design processes, as well as for performance prediction. The design of experiments and methods used to produce models from collected data have a strong impact on the quality of the model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followedmore » for wave tank testing. The general system identification processes are shown to have a number of advantages. The experimental data is then used to produce multiple models for the dynamics of the device. These models are validated and their performance is compared against one and other. Furthermore, while most models of wave energy converters use a formulation with wave elevation as an input, this study shows that a model using a hull pressure sensor to incorporate the wave excitation phenomenon has better accuracy.« less

System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling

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

Bacelli, Giorgio; Coe, Ryan; Patterson, David

Empirically based modeling is an essential aspect of design for a wave energy converter. These models are used in structural, mechanical and control design processes, as well as for performance prediction. The design of experiments and methods used to produce models from collected data have a strong impact on the quality of the model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followedmore » for wave tank testing. The general system identification processes are shown to have a number of advantages. The experimental data is then used to produce multiple models for the dynamics of the device. These models are validated and their performance is compared against one and other. Furthermore, while most models of wave energy converters use a formulation with wave elevation as an input, this study shows that a model using a hull pressure sensor to incorporate the wave excitation phenomenon has better accuracy.« less

Nonequilibrium thermodynamics and information theory: basic concepts and relaxing dynamics

NASA Astrophysics Data System (ADS)

Altaner, Bernhard

2017-11-01

Thermodynamics is based on the notions of energy and entropy. While energy is the elementary quantity governing physical dynamics, entropy is the fundamental concept in information theory. In this work, starting from first principles, we give a detailed didactic account on the relations between energy and entropy and thus physics and information theory. We show that thermodynamic process inequalities, like the second law, are equivalent to the requirement that an effective description for physical dynamics is strongly relaxing. From the perspective of information theory, strongly relaxing dynamics govern the irreversible convergence of a statistical ensemble towards the maximally non-commital probability distribution that is compatible with thermodynamic equilibrium parameters. In particular, Markov processes that converge to a thermodynamic equilibrium state are strongly relaxing. Our framework generalizes previous results to arbitrary open and driven systems, yielding novel thermodynamic bounds for idealized and real processes. , which features invited work from the best early-career researchers working within the scope of J. Phys. A. This project is part of the Journal of Physics series’ 50th anniversary celebrations in 2017. Bernhard Altaner was selected by the Editorial Board of J. Phys. A as an Emerging Talent.

Statistical inference for noisy nonlinear ecological dynamic systems.

PubMed

Wood, Simon N

2010-08-26

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

Locating the source of diffusion in complex networks by time-reversal backward spreading.

PubMed

Shen, Zhesi; Cao, Shinan; Wang, Wen-Xu; Di, Zengru; Stanley, H Eugene

2016-03-01

Locating the source that triggers a dynamical process is a fundamental but challenging problem in complex networks, ranging from epidemic spreading in society and on the Internet to cancer metastasis in the human body. An accurate localization of the source is inherently limited by our ability to simultaneously access the information of all nodes in a large-scale complex network. This thus raises two critical questions: how do we locate the source from incomplete information and can we achieve full localization of sources at any possible location from a given set of observable nodes. Here we develop a time-reversal backward spreading algorithm to locate the source of a diffusion-like process efficiently and propose a general locatability condition. We test the algorithm by employing epidemic spreading and consensus dynamics as typical dynamical processes and apply it to the H1N1 pandemic in China. We find that the sources can be precisely located in arbitrary networks insofar as the locatability condition is assured. Our tools greatly improve our ability to locate the source of diffusion in complex networks based on limited accessibility of nodal information. Moreover, they have implications for controlling a variety of dynamical processes taking place on complex networks, such as inhibiting epidemics, slowing the spread of rumors, pollution control, and environmental protection.

Evolution dynamics modeling and simulation of logistics enterprise's core competence based on service innovation

NASA Astrophysics Data System (ADS)

Yang, Bo; Tong, Yuting

2017-04-01

With the rapid development of economy, the development of logistics enterprises in China is also facing a huge challenge, especially the logistics enterprises generally lack of core competitiveness, and service innovation awareness is not strong. Scholars in the process of studying the core competitiveness of logistics enterprises are mainly from the perspective of static stability, not from the perspective of dynamic evolution to explore. So the author analyzes the influencing factors and the evolution process of the core competence of logistics enterprises, using the method of system dynamics to study the cause and effect of the evolution of the core competence of logistics enterprises, construct a system dynamics model of evolution of core competence logistics enterprises, which can be simulated by vensim PLE. The analysis for the effectiveness and sensitivity of simulation model indicates the model can be used as the fitting of the evolution process of the core competence of logistics enterprises and reveal the process and mechanism of the evolution of the core competence of logistics enterprises, and provide management strategies for improving the core competence of logistics enterprises. The construction and operation of computer simulation model offers a kind of effective method for studying the evolution of logistics enterprise core competence.

Locating the source of diffusion in complex networks by time-reversal backward spreading

NASA Astrophysics Data System (ADS)

Shen, Zhesi; Cao, Shinan; Wang, Wen-Xu; Di, Zengru; Stanley, H. Eugene

2016-03-01

Locating the source that triggers a dynamical process is a fundamental but challenging problem in complex networks, ranging from epidemic spreading in society and on the Internet to cancer metastasis in the human body. An accurate localization of the source is inherently limited by our ability to simultaneously access the information of all nodes in a large-scale complex network. This thus raises two critical questions: how do we locate the source from incomplete information and can we achieve full localization of sources at any possible location from a given set of observable nodes. Here we develop a time-reversal backward spreading algorithm to locate the source of a diffusion-like process efficiently and propose a general locatability condition. We test the algorithm by employing epidemic spreading and consensus dynamics as typical dynamical processes and apply it to the H1N1 pandemic in China. We find that the sources can be precisely located in arbitrary networks insofar as the locatability condition is assured. Our tools greatly improve our ability to locate the source of diffusion in complex networks based on limited accessibility of nodal information. Moreover, they have implications for controlling a variety of dynamical processes taking place on complex networks, such as inhibiting epidemics, slowing the spread of rumors, pollution control, and environmental protection.

Intrinsic and Extrinsic Neuromodulation of Olfactory Processing.

PubMed

Lizbinski, Kristyn M; Dacks, Andrew M

2017-01-01

Neuromodulation is a ubiquitous feature of neural systems, allowing flexible, context specific control over network dynamics. Neuromodulation was first described in invertebrate motor systems and early work established a basic dichotomy for neuromodulation as having either an intrinsic origin (i.e., neurons that participate in network coding) or an extrinsic origin (i.e., neurons from independent networks). In this conceptual dichotomy, intrinsic sources of neuromodulation provide a "memory" by adjusting network dynamics based upon previous and ongoing activation of the network itself, while extrinsic neuromodulators provide the context of ongoing activity of other neural networks. Although this dichotomy has been thoroughly considered in motor systems, it has received far less attention in sensory systems. In this review, we discuss intrinsic and extrinsic modulation in the context of olfactory processing in invertebrate and vertebrate model systems. We begin by discussing presynaptic modulation of olfactory sensory neurons by local interneurons (LNs) as a mechanism for gain control based on ongoing network activation. We then discuss the cell-class specific effects of serotonergic centrifugal neurons on olfactory processing. Finally, we briefly discuss the integration of intrinsic and extrinsic neuromodulation (metamodulation) as an effective mechanism for exerting global control over olfactory network dynamics. The heterogeneous nature of neuromodulation is a recurring theme throughout this review as the effects of both intrinsic and extrinsic modulation are generally non-uniform.

General purpose molecular dynamics simulations fully implemented on graphics processing units

NASA Astrophysics Data System (ADS)

Anderson, Joshua A.; Lorenz, Chris D.; Travesset, A.

2008-05-01

Graphics processing units (GPUs), originally developed for rendering real-time effects in computer games, now provide unprecedented computational power for scientific applications. In this paper, we develop a general purpose molecular dynamics code that runs entirely on a single GPU. It is shown that our GPU implementation provides a performance equivalent to that of fast 30 processor core distributed memory cluster. Our results show that GPUs already provide an inexpensive alternative to such clusters and discuss implications for the future.

Insights into numerical cognition: considering eye-fixations in number processing and arithmetic.

PubMed

Mock, J; Huber, S; Klein, E; Moeller, K

2016-05-01

Considering eye-fixation behavior is standard in reading research to investigate underlying cognitive processes. However, in numerical cognition research eye-tracking is used less often and less systematically. Nevertheless, we identified over 40 studies on this topic from the last 40 years with an increase of eye-tracking studies on numerical cognition during the last decade. Here, we review and discuss these empirical studies to evaluate the added value of eye-tracking for the investigation of number processing. Our literature review revealed that the way eye-fixation behavior is considered in numerical cognition research ranges from investigating basic perceptual aspects of processing non-symbolic and symbolic numbers, over assessing the common representational space of numbers and space, to evaluating the influence of characteristics of the base-10 place-value structure of Arabic numbers and executive control on number processing. Apart from basic results such as reading times of numbers increasing with their magnitude, studies revealed that number processing can influence domain-general processes such as attention shifting-but also the other way round. Domain-general processes such as cognitive control were found to affect number processing. In summary, eye-fixation behavior allows for new insights into both domain-specific and domain-general processes involved in number processing. Based thereon, a processing model of the temporal dynamics of numerical cognition is postulated, which distinguishes an early stage of stimulus-driven bottom-up processing from later more top-down controlled stages. Furthermore, perspectives for eye-tracking research in numerical cognition are discussed to emphasize the potential of this methodology for advancing our understanding of numerical cognition.

Application of numerical optimization techniques to control system design for nonlinear dynamic models of aircraft

NASA Technical Reports Server (NTRS)

Lan, C. Edward; Ge, Fuying

1989-01-01

Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.

Bursts and heavy tails in temporal and sequential dynamics of foraging decisions.

PubMed

Jung, Kanghoon; Jang, Hyeran; Kralik, Jerald D; Jeong, Jaeseung

2014-08-01

A fundamental understanding of behavior requires predicting when and what an individual will choose. However, the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear. In the current study, we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions. We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period. Regarding when choices were made, we found bursts of rapidly occurring actions, separated by time-varying inactive periods, partially based on a circadian rhythm. Regarding what was chosen, we found sequential dynamics in affective choices characterized by two key features: (a) a highly biased choice distribution; and (b) preferential attachment, in which the animals were more likely to choose what they had previously chosen. To capture the temporal dynamics, we propose a dual-state model consisting of active and inactive states. We also introduce a satiation-attainment process for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts. For the sequential dynamics, we propose a dual-control model consisting of goal-directed and habit systems, based on outcome valuation and choice history, respectively. This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems, leading to heavy tails in the distribution of behavior over time and choices.

Stochastic description of quantum Brownian dynamics

NASA Astrophysics Data System (ADS)

Yan, Yun-An; Shao, Jiushu

2016-08-01

Classical Brownian motion has well been investigated since the pioneering work of Einstein, which inspired mathematicians to lay the theoretical foundation of stochastic processes. A stochastic formulation for quantum dynamics of dissipative systems described by the system-plus-bath model has been developed and found many applications in chemical dynamics, spectroscopy, quantum transport, and other fields. This article provides a tutorial review of the stochastic formulation for quantum dissipative dynamics. The key idea is to decouple the interaction between the system and the bath by virtue of the Hubbard-Stratonovich transformation or Itô calculus so that the system and the bath are not directly entangled during evolution, rather they are correlated due to the complex white noises introduced. The influence of the bath on the system is thereby defined by an induced stochastic field, which leads to the stochastic Liouville equation for the system. The exact reduced density matrix can be calculated as the stochastic average in the presence of bath-induced fields. In general, the plain implementation of the stochastic formulation is only useful for short-time dynamics, but not efficient for long-time dynamics as the statistical errors go very fast. For linear and other specific systems, the stochastic Liouville equation is a good starting point to derive the master equation. For general systems with decomposable bath-induced processes, the hierarchical approach in the form of a set of deterministic equations of motion is derived based on the stochastic formulation and provides an effective means for simulating the dissipative dynamics. A combination of the stochastic simulation and the hierarchical approach is suggested to solve the zero-temperature dynamics of the spin-boson model. This scheme correctly describes the coherent-incoherent transition (Toulouse limit) at moderate dissipation and predicts a rate dynamics in the overdamped regime. Challenging problems such as the dynamical description of quantum phase transition (local- ization) and the numerical stability of the trace-conserving, nonlinear stochastic Liouville equation are outlined.

Disparate ultrafast dynamics of itinerant and localized magnetic moments in gadolinium metal

PubMed Central

Frietsch, B.; Bowlan, J.; Carley, R.; Teichmann, M.; Wienholdt, S.; Hinzke, D.; Nowak, U.; Carva, K.; Oppeneer, P. M.; Weinelt, M.

2015-01-01

The Heisenberg–Dirac intra-atomic exchange coupling is responsible for the formation of the atomic spin moment and thus the strongest interaction in magnetism. Therefore, it is generally assumed that intra-atomic exchange leads to a quasi-instantaneous aligning process in the magnetic moment dynamics of spins in separate, on-site atomic orbitals. Following ultrashort optical excitation of gadolinium metal, we concurrently record in photoemission the 4f magnetic linear dichroism and 5d exchange splitting. Their dynamics differ by one order of magnitude, with decay constants of 14 versus 0.8 ps, respectively. Spin dynamics simulations based on an orbital-resolved Heisenberg Hamiltonian combined with first-principles calculations explain the particular dynamics of 5d and 4f spin moments well, and corroborate that the 5d exchange splitting traces closely the 5d spin-moment dynamics. Thus gadolinium shows disparate dynamics of the localized 4f and the itinerant 5d spin moments, demonstrating a breakdown of their intra-atomic exchange alignment on a picosecond timescale. PMID:26355196

Dynamics and mechanics of motor-filament systems

NASA Astrophysics Data System (ADS)

Kruse, K.; Jülicher, F.

2006-08-01

Motivated by the cytoskeleton of eukaryotic cells, we develop a general framework for describing the large-scale dynamics of an active filament network. In the cytoskeleton, active cross-links are formed by motor proteins that are able to induce relative motion between filaments. Starting from pair-wise interactions of filaments via such active processes, our framework is based on momentum conservation and an analysis of the momentum flux. This allows us to calculate the stresses in the filament network generated by the action of motor proteins. We derive effective theories for the filament dynamics which can be related to continuum theories of active polar gels. As an example, we discuss the stability of homogenous isotropic filament distributions in two spatial dimensions.

Some Behavioral and Neurobiological Constraints on Theories of Audiovisual Speech Integration: A Review and Suggestions for New Directions

PubMed Central

Altieri, Nicholas; Pisoni, David B.; Townsend, James T.

2012-01-01

Summerfield (1987) proposed several accounts of audiovisual speech perception, a field of research that has burgeoned in recent years. The proposed accounts included the integration of discrete phonetic features, vectors describing the values of independent acoustical and optical parameters, the filter function of the vocal tract, and articulatory dynamics of the vocal tract. The latter two accounts assume that the representations of audiovisual speech perception are based on abstract gestures, while the former two assume that the representations consist of symbolic or featural information obtained from visual and auditory modalities. Recent converging evidence from several different disciplines reveals that the general framework of Summerfield’s feature-based theories should be expanded. An updated framework building upon the feature-based theories is presented. We propose a processing model arguing that auditory and visual brain circuits provide facilitatory information when the inputs are correctly timed, and that auditory and visual speech representations do not necessarily undergo translation into a common code during information processing. Future research on multisensory processing in speech perception should investigate the connections between auditory and visual brain regions, and utilize dynamic modeling tools to further understand the timing and information processing mechanisms involved in audiovisual speech integration. PMID:21968081

Some behavioral and neurobiological constraints on theories of audiovisual speech integration: a review and suggestions for new directions.

PubMed

Altieri, Nicholas; Pisoni, David B; Townsend, James T

2011-01-01

Summerfield (1987) proposed several accounts of audiovisual speech perception, a field of research that has burgeoned in recent years. The proposed accounts included the integration of discrete phonetic features, vectors describing the values of independent acoustical and optical parameters, the filter function of the vocal tract, and articulatory dynamics of the vocal tract. The latter two accounts assume that the representations of audiovisual speech perception are based on abstract gestures, while the former two assume that the representations consist of symbolic or featural information obtained from visual and auditory modalities. Recent converging evidence from several different disciplines reveals that the general framework of Summerfield's feature-based theories should be expanded. An updated framework building upon the feature-based theories is presented. We propose a processing model arguing that auditory and visual brain circuits provide facilitatory information when the inputs are correctly timed, and that auditory and visual speech representations do not necessarily undergo translation into a common code during information processing. Future research on multisensory processing in speech perception should investigate the connections between auditory and visual brain regions, and utilize dynamic modeling tools to further understand the timing and information processing mechanisms involved in audiovisual speech integration.

Eternal non-Markovianity: from random unitary to Markov chain realisations.

PubMed

Megier, Nina; Chruściński, Dariusz; Piilo, Jyrki; Strunz, Walter T

2017-07-25

The theoretical description of quantum dynamics in an intriguing way does not necessarily imply the underlying dynamics is indeed intriguing. Here we show how a known very interesting master equation with an always negative decay rate [eternal non-Markovianity (ENM)] arises from simple stochastic Schrödinger dynamics (random unitary dynamics). Equivalently, it may be seen as arising from a mixture of Markov (semi-group) open system dynamics. Both these approaches lead to a more general family of CPT maps, characterized by a point within a parameter triangle. Our results show how ENM quantum dynamics can be realised easily in the laboratory. Moreover, we find a quantum time-continuously measured (quantum trajectory) realisation of the dynamics of the ENM master equation based on unitary transformations and projective measurements in an extended Hilbert space, guided by a classical Markov process. Furthermore, a Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) representation of the dynamics in an extended Hilbert space can be found, with a remarkable property: there is no dynamics in the ancilla state. Finally, analogous constructions for two qubits extend these results from non-CP-divisible to non-P-divisible dynamics.

Technical Note: Approximate Bayesian parameterization of a complex tropical forest model

NASA Astrophysics Data System (ADS)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2013-08-01

Inverse parameter estimation of process-based models is a long-standing problem in ecology and evolution. A key problem of inverse parameter estimation is to define a metric that quantifies how well model predictions fit to the data. Such a metric can be expressed by general cost or objective functions, but statistical inversion approaches are based on a particular metric, the probability of observing the data given the model, known as the likelihood. Deriving likelihoods for dynamic models requires making assumptions about the probability for observations to deviate from mean model predictions. For technical reasons, these assumptions are usually derived without explicit consideration of the processes in the simulation. Only in recent years have new methods become available that allow generating likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional MCMC, performs well in retrieving known parameter values from virtual field data generated by the forest model. We analyze the results of the parameter estimation, examine the sensitivity towards the choice and aggregation of model outputs and observed data (summary statistics), and show results from using this method to fit the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss differences of this approach to Approximate Bayesian Computing (ABC), another commonly used method to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can successfully be applied to process-based models of high complexity. The methodology is particularly suited to heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models in ecology and evolution.

New levels of language processing complexity and organization revealed by granger causation.

PubMed

Gow, David W; Caplan, David N

2012-01-01

Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all non-redundant potentially interacting signals, and has shown that even "early" processes such as speech perception involve interactions of many areas in a strikingly large network that extends well beyond traditional left hemisphere perisylvian cortex that play out over hundreds of milliseconds. In this paper we describe this technique and review several general findings that reframe the way we think about language processing and brain function in general. These include the extent and complexity of language processing networks, the central role of interactive processing dynamics, the role of processing hubs where the input from many distinct brain regions are integrated, and the degree to which task requirements and stimulus properties influence processing dynamics and inform our understanding of "language-specific" localized processes.

Chrestenson transform FPGA embedded factorizations.

PubMed

Corinthios, Michael J

2016-01-01

Chrestenson generalized Walsh transform factorizations for parallel processing imbedded implementations on field programmable gate arrays are presented. This general base transform, sometimes referred to as the Discrete Chrestenson transform, has received special attention in recent years. In fact, the Discrete Fourier transform and Walsh-Hadamard transform are but special cases of the Chrestenson generalized Walsh transform. Rotations of a base-p hypercube, where p is an arbitrary integer, are shown to produce dynamic contention-free memory allocation, in processor architecture. The approach is illustrated by factorizations involving the processing of matrices of the transform which are function of four variables. Parallel operations are implemented matrix multiplications. Each matrix, of dimension N × N, where N = p (n) , n integer, has a structure that depends on a variable parameter k that denotes the iteration number in the factorization process. The level of parallelism, in the form of M = p (m) processors can be chosen arbitrarily by varying m between zero to its maximum value of n - 1. The result is an equation describing the generalised parallelism factorization as a function of the four variables n, p, k and m. Applications of the approach are shown in relation to configuring field programmable gate arrays for digital signal processing applications.

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons

PubMed Central

Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

2016-01-01

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter—describing somatic integration—and the spike-history filter—accounting for spike-frequency adaptation—dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations. PMID:26907675

Understanding rapid evolution in predator‐prey interactions using the theory of fast‐slow dynamical systems.

PubMed

Cortez, Michael H; Ellner, Stephen P

2010-11-01

The accumulation of evidence that ecologically important traits often evolve at the same time and rate as ecological dynamics (e.g., changes in species' abundances or spatial distributions) has outpaced theory describing the interplay between ecological and evolutionary processes with comparable timescales. The disparity between experiment and theory is partially due to the high dimensionality of models that include both evolutionary and ecological dynamics. Here we show how the theory of fast-slow dynamical systems can be used to reduce model dimension, and we use that body of theory to study a general predator-prey system exhibiting fast evolution in either the predator or the prey. Our approach yields graphical methods with predictive power about when new and unique dynamics (e.g., completely out-of-phase oscillations and cryptic dynamics) can arise in ecological systems exhibiting fast evolution. In addition, we derive analytical expressions for determining when such behavior arises and how evolution affects qualitative properties of the ecological dynamics. Finally, while the theory requires a separation of timescales between the ecological and evolutionary processes, our approach yields insight into systems where the rates of those processes are comparable and thus is a step toward creating a general ecoevolutionary theory.

Quantized Electromagnetic-Field Propagation in General Non-Local and Non-Stationary Dispersive and Absorbing Media

NASA Astrophysics Data System (ADS)

Jacobs, Verne

Dynamical descriptions for the propagation of quantized electromagnetic fields, in the presence of environmental interactions, are systematically and self-consistently developed in the complimentary Schrödinger and Heisenberg pictures. An open-systems (non-equilibrium) quantum-electrodynamics description is thereby provided for electromagnetic-field propagation in general non-local and non-stationary dispersive and absorbing optical media, including a fundamental microscopic treatment of decoherence and relaxation processes due to environmental collisional and electromagnetic interactions. Particular interest is centered on entangled states and other non-classical states of electromagnetic fields, which may be created by non-linear electromagnetic interactions and detected by the measurement of various electromagnetic-field correlation functions. Accordingly, we present dynamical descriptions based on general forms of electromagnetic-field correlation functions involving both the electric-field and the magnetic-field components of the electromagnetic field, which are treated on an equal footing. Work supported by the Office of Naval Research through the Basic Research Program at The Naval Research Laboratory.

Dynamic photoelasticity by TDI imaging

NASA Astrophysics Data System (ADS)

Asundi, Anand K.; Sajan, M. R.

2001-06-01

High speed photographic system like the image rotation camera, the Cranz Schardin camera and the drum camera are typically used for the recording and visualization of dynamic events in stress analysis, fluid mechanics, etc. All these systems are fairly expensive and generally not simple to use. Furthermore they are all based on photographic film recording system requiring time consuming and tedious wet processing of the films. Digital cameras are replacing the conventional cameras, to certain extent in static experiments. Recently, there is lots of interest in development and modifying CCD architectures and recording arrangements for dynamic scenes analysis. Herein we report the use of a CCD camera operating in the Time Delay and Integration mode for digitally recording dynamic photoelastic stress patterns. Applications in strobe and streak photoelastic pattern recording and system limitations will be explained in the paper.

Qualitative dynamics semantics for SBGN process description.

PubMed

Rougny, Adrien; Froidevaux, Christine; Calzone, Laurence; Paulevé, Loïc

2016-06-16

Qualitative dynamics semantics provide a coarse-grain modeling of networks dynamics by abstracting away kinetic parameters. They allow to capture general features of systems dynamics, such as attractors or reachability properties, for which scalable analyses exist. The Systems Biology Graphical Notation Process Description language (SBGN-PD) has become a standard to represent reaction networks. However, no qualitative dynamics semantics taking into account all the main features available in SBGN-PD had been proposed so far. We propose two qualitative dynamics semantics for SBGN-PD reaction networks, namely the general semantics and the stories semantics, that we formalize using asynchronous automata networks. While the general semantics extends standard Boolean semantics of reaction networks by taking into account all the main features of SBGN-PD, the stories semantics allows to model several molecules of a network by a unique variable. The obtained qualitative models can be checked against dynamical properties and therefore validated with respect to biological knowledge. We apply our framework to reason on the qualitative dynamics of a large network (more than 200 nodes) modeling the regulation of the cell cycle by RB/E2F. The proposed semantics provide a direct formalization of SBGN-PD networks in dynamical qualitative models that can be further analyzed using standard tools for discrete models. The dynamics in stories semantics have a lower dimension than the general one and prune multiple behaviors (which can be considered as spurious) by enforcing the mutual exclusiveness between the activity of different nodes of a same story. Overall, the qualitative semantics for SBGN-PD allow to capture efficiently important dynamical features of reaction network models and can be exploited to further refine them.

Atomic scale observation of oxygen delivery during silver–oxygen nanoparticle catalysed oxidation of carbon nanotubes

PubMed Central

Yue, Yonghai; Yuchi, Datong; Guan, Pengfei; Xu, Jia; Guo, Lin; Liu, Jingyue

2016-01-01

To probe the nature of metal-catalysed processes and to design better metal-based catalysts, atomic scale understanding of catalytic processes is highly desirable. Here we use aberration-corrected environmental transmission electron microscopy to investigate the atomic scale processes of silver-based nanoparticles, which catalyse the oxidation of multi-wall carbon nanotubes. A direct semi-quantitative estimate of the oxidized carbon atoms by silver-based nanoparticles is achieved. A mechanism similar to the Mars–van Krevelen process is invoked to explain the catalytic oxidation process. Theoretical calculations, together with the experimental data, suggest that the oxygen molecules dissociate on the surface of silver nanoparticles and diffuse through the silver nanoparticles to reach the silver/carbon interfaces and subsequently oxidize the carbon. The lattice distortion caused by oxygen concentration gradient within the silver nanoparticles provides the direct evidence for oxygen diffusion. Such direct observation of atomic scale dynamics provides an important general methodology for investigations of catalytic processes. PMID:27406595

Pilots Rate Augmented Generalized Predictive Control for Reconfiguration

NASA Technical Reports Server (NTRS)

Soloway, Don; Haley, Pam

2004-01-01

The objective of this paper is to report the results from the research being conducted in reconfigurable fight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft's control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive Control scheme and a Neural Network based Dynamic Inverse controller. This paper highlights the Predictive Control scheme where a simple augmentation to reduce zero steady-state error led to the neural network predictor model becoming redundant for the task. Instead of using a neural network predictor model, a nominal single point linear model was used and then augmented with an error corrector. This paper shows that the Generalized Predictive Controller and the Dynamic Inverse Neural Network controller perform equally well at reconfiguration, but with less rate requirements from the actuators. Also presented are the pilot ratings for each controller for various failure scenarios and two samples of the required control actuation during reconfiguration. Finally, the paper concludes by stepping through the Generalized Predictive Control's reconfiguration process for an elevator failure.

Community and individual effects on SOD intensification in California redwood forests: implications for tanoak persistence

Treesearch

Richard C. Cobb; Joao A. N. Filipe; Ross K. Meentemeyer; Chris A. Gilligan; Shannon C. Lynch; David M. Rizzo

2010-01-01

Processes operating across different spatial scales (for example, individual, community, landscape) influence disease dynamics. Understanding these processes and their interactions can yield general insights into disease control, disease dynamics within communities, and community response to disease. For Phytophthora ramorum, pathogen establishment...

A Correlation-Based Transition Model using Local Variables. Part 1; Model Formation

NASA Technical Reports Server (NTRS)

Menter, F. R.; Langtry, R. B.; Likki, S. R.; Suzen, Y. B.; Huang, P. G.; Volker, S.

2006-01-01

A new correlation-based transition model has been developed, which is based strictly on local variables. As a result, the transition model is compatible with modern computational fluid dynamics (CFD) approaches, such as unstructured grids and massive parallel execution. The model is based on two transport equations, one for intermittency and one for the transition onset criteria in terms of momentum thickness Reynolds number. The proposed transport equations do not attempt to model the physics of the transition process (unlike, e.g., turbulence models) but from a framework for the implementation of correlation-based models into general-purpose CFD methods.

Detecting critical state before phase transition of complex systems by hidden Markov model

NASA Astrophysics Data System (ADS)

Liu, Rui; Chen, Pei; Li, Yongjun; Chen, Luonan

Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages, i.e., before-transition state, pre-transition state, and after-transition state, which can be considered as three different Markov processes. Thus, based on this dynamical feature, we present a novel computational method, i.e., hidden Markov model (HMM), to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process), thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets, and further identify the pre-transition states as well as their critical modules for three real datasets, i.e., the acute lung injury triggered by phosgene inhalation, MCF-7 human breast cancer caused by heregulin, and HCV-induced dysplasia and hepatocellular carcinoma.

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.

Some intriguing aspects of multiparticle production processes

NASA Astrophysics Data System (ADS)

Wilk, Grzegorz; Włodarczyk, Zbigniew

2018-04-01

Multiparticle production processes provide valuable information about the mechanism of the conversion of the initial energy of projectiles into a number of secondaries by measuring their multiplicity distributions and their distributions in phase space. They therefore serve as a reference point for more involved measurements. Distributions in phase space are usually investigated using the statistical approach, very successful in general but failing in cases of small colliding systems, small multiplicities, and at the edges of the allowed phase space, in which cases the underlying dynamical effects competing with the statistical distributions take over. We discuss an alternative approach, which applies to the whole phase space without detailed knowledge of dynamics. It is based on a modification of the usual statistics by generalizing it to a superstatistical form. We stress particularly the scaling and self-similar properties of such an approach manifesting themselves as the phenomena of the log-periodic oscillations and oscillations of temperature caused by sound waves in hadronic matter. Concerning the multiplicity distributions we discuss in detail the phenomenon of the oscillatory behavior of the modified combinants apparently observed in experimental data.

Identifying multiple influential spreaders based on generalized closeness centrality

NASA Astrophysics Data System (ADS)

Liu, Huan-Li; Ma, Chuang; Xiang, Bing-Bing; Tang, Ming; Zhang, Hai-Feng

2018-02-01

To maximize the spreading influence of multiple spreaders in complex networks, one important fact cannot be ignored: the multiple spreaders should be dispersively distributed in networks, which can effectively reduce the redundance of information spreading. For this purpose, we define a generalized closeness centrality (GCC) index by generalizing the closeness centrality index to a set of nodes. The problem converts to how to identify multiple spreaders such that an objective function has the minimal value. By comparing with the K-means clustering algorithm, we find that the optimization problem is very similar to the problem of minimizing the objective function in the K-means method. Therefore, how to find multiple nodes with the highest GCC value can be approximately solved by the K-means method. Two typical transmission dynamics-epidemic spreading process and rumor spreading process are implemented in real networks to verify the good performance of our proposed method.

Reorganization of the Connectivity between Elementary Functions – A Model Relating Conscious States to Neural Connections

PubMed Central

Mogensen, Jesper; Overgaard, Morten

2017-01-01

In the present paper it is argued that the “neural correlate of consciousness” (NCC) does not appear to be a separate “module” – but an aspect of information processing within the neural substrate of various cognitive processes. Consequently, NCC can only be addressed adequately within frameworks that model the general relationship between neural processes and mental states – and take into account the dynamic connectivity of the brain. We presently offer the REFGEN (general reorganization of elementary functions) model as such a framework. This model builds upon and expands the REF (reorganization of elementary functions) and REFCON (of elementary functions and consciousness) models. All three models integrate the relationship between the neural and mental layers of description via the construction of an intermediate level dealing with computational states. The importance of experience based organization of neural and cognitive processes is stressed. The models assume that the mechanisms of consciousness are in principle the same as the basic mechanisms of all aspects of cognition – when information is processed to a sufficiently “high level” it becomes available to conscious experience. The NCC is within the REFGEN model seen as aspects of the dynamic and experience driven reorganizations of the synaptic connectivity between the neurocognitive “building blocks” of the model – the elementary functions. PMID:28473797

Nature as a network of morphological infocomputational processes for cognitive agents

NASA Astrophysics Data System (ADS)

Dodig-Crnkovic, Gordana

2017-01-01

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted.

Satellite-enhanced dynamical downscaling for the analysis of extreme events

NASA Astrophysics Data System (ADS)

Nunes, Ana M. B.

2016-09-01

The use of regional models in the downscaling of general circulation models provides a strategy to generate more detailed climate information. In that case, boundary-forcing techniques can be useful to maintain the large-scale features from the coarse-resolution global models in agreement with the inner modes of the higher-resolution regional models. Although those procedures might improve dynamics, downscaling via regional modeling still aims for better representation of physical processes. With the purpose of improving dynamics and physical processes in regional downscaling of global reanalysis, the Regional Spectral Model—originally developed at the National Centers for Environmental Prediction—employs a newly reformulated scale-selective bias correction, together with the 3-hourly assimilation of the satellite-based precipitation estimates constructed from the Climate Prediction Center morphing technique. The two-scheme technique for the dynamical downscaling of global reanalysis can be applied in analyses of environmental disasters and risk assessment, with hourly outputs, and resolution of about 25 km. Here the satellite-enhanced dynamical downscaling added value is demonstrated in simulations of the first reported hurricane in the western South Atlantic Ocean basin through comparisons with global reanalyses and satellite products available in ocean areas.

Investigation of Dynamic and Physical Processes in the Upper Troposphere and Lower Stratosphere

NASA Technical Reports Server (NTRS)

Selkirk, Henry B.; Pfister, Leonhard (Technical Monitor)

2002-01-01

Research under this Cooperative Agreement has been funded by several NASA Earth Science programs: the Atmospheric Effects of Radiation Program (AEAP), the Upper Atmospheric Research Program (UARP), and most recently the Atmospheric Chemistry and Modeling Assessment Program (ACMAP). The purpose of the AEAP was to understand the impact of the present and future fleets of conventional jet traffic on the upper troposphere and lower stratosphere, while complementary airborne observations under UARP seek to understand the complex interactions of dynamical and chemical processes that affect the ozone layer. The ACMAP is a more general program of modeling and data analysis in the general area of atmospheric chemistry and dynamics, and the Radiation Sciences program.

Environment spectrum and coherence behaviours in a rare-earth doped crystal for quantum memory.

PubMed

Gong, Bo; Tu, Tao; Zhou, Zhong-Quan; Zhu, Xing-Yu; Li, Chuan-Feng; Guo, Guang-Can

2017-12-21

We theoretically investigate the dynamics of environment and coherence behaviours of the central ion in a quantum memory based on a rare-earth doped crystal. The interactions between the central ion and the bath spins suppress the flip-flop rate of the neighbour bath spins and yield a specific environment spectral density S(ω). Under dynamical decoupling pulses, this spectrum provides a general scaling for the coherence envelope and coherence time, which significantly extend over a range on an hour-long time scale. The characterized environment spectrum with ultra-long coherence time can be used to implement various quantum communication and information processing protocols.

Addressing the translational dilemma: dynamic knowledge representation of inflammation using agent-based modeling.

PubMed

An, Gary; Christley, Scott

2012-01-01

Given the panoply of system-level diseases that result from disordered inflammation, such as sepsis, atherosclerosis, cancer, and autoimmune disorders, understanding and characterizing the inflammatory response is a key target of biomedical research. Untangling the complex behavioral configurations associated with a process as ubiquitous as inflammation represents a prototype of the translational dilemma: the ability to translate mechanistic knowledge into effective therapeutics. A critical failure point in the current research environment is a throughput bottleneck at the level of evaluating hypotheses of mechanistic causality; these hypotheses represent the key step toward the application of knowledge for therapy development and design. Addressing the translational dilemma will require utilizing the ever-increasing power of computers and computational modeling to increase the efficiency of the scientific method in the identification and evaluation of hypotheses of mechanistic causality. More specifically, development needs to focus on facilitating the ability of non-computer trained biomedical researchers to utilize and instantiate their knowledge in dynamic computational models. This is termed "dynamic knowledge representation." Agent-based modeling is an object-oriented, discrete-event, rule-based simulation method that is well suited for biomedical dynamic knowledge representation. Agent-based modeling has been used in the study of inflammation at multiple scales. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggest that this modeling framework is well suited for addressing the translational dilemma. This review describes agent-based modeling, gives examples of its applications in the study of inflammation, and introduces a proposed general expansion of the use of modeling and simulation to augment the generation and evaluation of knowledge by the biomedical research community at large.

Dynamic effects of root system architecture improve root water uptake in 1-D process-based soil-root hydrodynamics

NASA Astrophysics Data System (ADS)

Bouda, Martin; Saiers, James E.

2017-12-01

Root system architecture (RSA) can significantly affect plant access to water, total transpiration, as well as its partitioning by soil depth, with implications for surface heat, water, and carbon budgets. Despite recent advances in land surface model (LSM) descriptions of plant hydraulics, descriptions of RSA have not been included because of their three-dimensional complexity, which makes them generally too computationally costly. Here we demonstrate a new, process-based 1D layered model that captures the dynamic shifts in water potential gradients of 3D RSA under different soil moisture conditions: the RSA stencil. Using root systems calibrated to the rooting profiles of four plant functional types (PFT) of the Community Land Model, we show that the RSA stencil predicts plant water potentials within 2% to the outputs of a full 3D model, under the same assumptions on soil moisture heterogeneity, despite its trivial computational cost, resulting in improved predictions of water uptake and soil moisture compared to a model without RSA in a transient simulation. Our results suggest that LSM predictions of soil moisture dynamics and dependent variables can be improved by the implementation of this model, calibrated for individual PFTs using field observations.

Evaluating the Generalization Value of Process-based Models in a Deep-in-time Machine Learning framework

NASA Astrophysics Data System (ADS)

Shen, C.; Fang, K.

2017-12-01

Deep Learning (DL) methods have made revolutionary strides in recent years. A core value proposition of DL is that abstract notions and patterns can be extracted purely from data, without the need for domain expertise. Process-based models (PBM), on the other hand, can be regarded as repositories of human knowledge or hypotheses about how systems function. Here, through computational examples, we argue that there is merit in integrating PBMs with DL due to the imbalance and lack of data in many situations, especially in hydrology. We trained a deep-in-time neural network, the Long Short-Term Memory (LSTM), to learn soil moisture dynamics from Soil Moisture Active Passive (SMAP) Level 3 product. We show that when PBM solutions are integrated into LSTM, the network is able to better generalize across regions. LSTM is able to better utilize PBM solutions than simpler statistical methods. Our results suggest PBMs have generalization value which should be carefully assessed and utilized. We also emphasize that when properly regularized, the deep network is robust and is of superior testing performance compared to simpler methods.

Potential-based dynamical reweighting for Markov state models of protein dynamics.

PubMed

Weber, Jeffrey K; Pande, Vijay S

2015-06-09

As simulators attempt to replicate the dynamics of large cellular components in silico, problems related to sampling slow, glassy degrees of freedom in molecular systems will be amplified manyfold. It is tempting to augment simulation techniques with external biases to overcome such barriers with ease; biased simulations, however, offer little utility unless equilibrium properties of interest (both kinetic and thermodynamic) can be recovered from the data generated. In this Article, we present a general scheme that harnesses the power of Markov state models (MSMs) to extract equilibrium kinetic properties from molecular dynamics trajectories collected on biased potential energy surfaces. We first validate our reweighting protocol on a simple two-well potential, and we proceed to test our method on potential-biased simulations of the Trp-cage miniprotein. In both cases, we find that equilibrium populations, time scales, and dynamical processes are reliably reproduced as compared to gold standard, unbiased data sets. We go on to discuss the limitations of our dynamical reweighting approach, and we suggest auspicious target systems for further application.

Assessing Similarity Among Individual Tumor Size Lesion Dynamics: The CICIL Methodology

PubMed Central

Girard, Pascal; Ioannou, Konstantinos; Klinkhardt, Ute; Munafo, Alain

2018-01-01

Mathematical models of tumor dynamics generally omit information on individual target lesions (iTLs), and consider the most important variable to be the sum of tumor sizes (TS). However, differences in lesion dynamics might be predictive of tumor progression. To exploit this information, we have developed a novel and flexible approach for the non‐parametric analysis of iTLs, which integrates knowledge from signal processing and machine learning. We called this new methodology ClassIfication Clustering of Individual Lesions (CICIL). We used CICIL to assess similarities among the TS dynamics of 3,223 iTLs measured in 1,056 patients with metastatic colorectal cancer treated with cetuximab combined with irinotecan, in two phase II studies. We mainly observed similar dynamics among lesions within the same tumor site classification. In contrast, lesions in anatomic locations with different features showed different dynamics in about 35% of patients. The CICIL methodology has also been implemented in a user‐friendly and efficient Java‐based framework. PMID:29388396

A dynamic subgrid scale model for Large Eddy Simulations based on the Mori-Zwanzig formalism

NASA Astrophysics Data System (ADS)

Parish, Eric J.; Duraisamy, Karthik

2017-11-01

The development of reduced models for complex multiscale problems remains one of the principal challenges in computational physics. The optimal prediction framework of Chorin et al. [1], which is a reformulation of the Mori-Zwanzig (M-Z) formalism of non-equilibrium statistical mechanics, provides a framework for the development of mathematically-derived reduced models of dynamical systems. Several promising models have emerged from the optimal prediction community and have found application in molecular dynamics and turbulent flows. In this work, a new M-Z-based closure model that addresses some of the deficiencies of existing methods is developed. The model is constructed by exploiting similarities between two levels of coarse-graining via the Germano identity of fluid mechanics and by assuming that memory effects have a finite temporal support. The appeal of the proposed model, which will be referred to as the 'dynamic-MZ-τ' model, is that it is parameter-free and has a structural form imposed by the mathematics of the coarse-graining process (rather than the phenomenological assumptions made by the modeler, such as in classical subgrid scale models). To promote the applicability of M-Z models in general, two procedures are presented to compute the resulting model form, helping to bypass the tedious error-prone algebra that has proven to be a hindrance to the construction of M-Z-based models for complex dynamical systems. While the new formulation is applicable to the solution of general partial differential equations, demonstrations are presented in the context of Large Eddy Simulation closures for the Burgers equation, decaying homogeneous turbulence, and turbulent channel flow. The performance of the model and validity of the underlying assumptions are investigated in detail.

143. GENERAL DYNAMICS SPACE SYSTEMS DIVISION SCHEDULE BOARD IN LUNCH ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

143. GENERAL DYNAMICS SPACE SYSTEMS DIVISION SCHEDULE BOARD IN LUNCH ROOM (120), LSB (BLDG. 770) - Vandenberg Air Force Base, Space Launch Complex 3, Launch Pad 3 West, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

Cloud fluid models of gas dynamics and star formation in galaxies

NASA Technical Reports Server (NTRS)

Struck-Marcell, Curtis; Scalo, John M.; Appleton, P. N.

1987-01-01

The large dynamic range of star formation in galaxies, and the apparently complex environmental influences involved in triggering or suppressing star formation, challenges the understanding. The key to this understanding may be the detailed study of simple physical models for the dominant nonlinear interactions in interstellar cloud systems. One such model is described, a generalized Oort model cloud fluid, and two simple applications of it are explored. The first of these is the relaxation of an isolated volume of cloud fluid following a disturbance. Though very idealized, this closed box study suggests a physical mechanism for starbursts, which is based on the approximate commensurability of massive cloud lifetimes and cloud collisional growth times. The second application is to the modeling of colliding ring galaxies. In this case, the driving processes operating on a dynamical timescale interact with the local cloud processes operating on the above timescale. The results is a variety of interesting nonequilibrium behaviors, including spatial variations of star formation that do not depend monotonically on gas density.

Dynamics of functional failures and recovery in complex road networks

NASA Astrophysics Data System (ADS)

Zhan, Xianyuan; Ukkusuri, Satish V.; Rao, P. Suresh C.

2017-11-01

We propose a new framework for modeling the evolution of functional failures and recoveries in complex networks, with traffic congestion on road networks as the case study. Differently from conventional approaches, we transform the evolution of functional states into an equivalent dynamic structural process: dual-vertex splitting and coalescing embedded within the original network structure. The proposed model successfully explains traffic congestion and recovery patterns at the city scale based on high-resolution data from two megacities. Numerical analysis shows that certain network structural attributes can amplify or suppress cascading functional failures. Our approach represents a new general framework to model functional failures and recoveries in flow-based networks and allows understanding of the interplay between structure and function for flow-induced failure propagation and recovery.

A penalty-based nodal discontinuous Galerkin method for spontaneous rupture dynamics

NASA Astrophysics Data System (ADS)

Ye, R.; De Hoop, M. V.; Kumar, K.

2017-12-01

Numerical simulation of the dynamic rupture processes with slip is critical to understand the earthquake source process and the generation of ground motions. However, it can be challenging due to the nonlinear friction laws interacting with seismicity, coupled with the discontinuous boundary conditions across the rupture plane. In practice, the inhomogeneities in topography, fault geometry, elastic parameters and permiability add extra complexity. We develop a nodal discontinuous Galerkin method to simulate seismic wave phenomenon with slipping boundary conditions, including the fluid-solid boundaries and ruptures. By introducing a novel penalty flux, we avoid solving Riemann problems on interfaces, which makes our method capable for general anisotropic and poro-elastic materials. Based on unstructured tetrahedral meshes in 3D, the code can capture various geometries in geological model, and use polynomial expansion to achieve high-order accuracy. We consider the rate and state friction law, in the spontaneous rupture dynamics, as part of a nonlinear transmitting boundary condition, which is weakly enforced across the fault surface as numerical flux. An iterative coupling scheme is developed based on implicit time stepping, containing a constrained optimization process that accounts for the nonlinear part. To validate the method, we proof the convergence of the coupled system with error estimates. We test our algorithm on a well-established numerical example (TPV102) of the SCEC/USGS Spontaneous Rupture Code Verification Project, and benchmark with the simulation of PyLith and SPECFEM3D with agreeable results.

Oceanic island biogeography through the lens of the general dynamic model: assessment and prospect.

PubMed

Borregaard, Michael K; Amorim, Isabel R; Borges, Paulo A V; Cabral, Juliano S; Fernández-Palacios, José M; Field, Richard; Heaney, Lawrence R; Kreft, Holger; Matthews, Thomas J; Olesen, Jens M; Price, Jonathan; Rigal, Francois; Steinbauer, Manuel J; Triantis, Konstantinos A; Valente, Luis; Weigelt, Patrick; Whittaker, Robert J

2017-05-01

The general dynamic model of oceanic island biogeography (GDM) has added a new dimension to theoretical island biogeography in recognizing that geological processes are key drivers of the evolutionary processes of diversification and extinction within remote islands. It provides a dynamic and essentially non-equilibrium framework generating novel predictions for emergent diversity properties of oceanic islands and archipelagos. Its publication in 2008 coincided with, and spurred on, renewed attention to the dynamics of remote islands. We review progress, both in testing the GDM's predictions and in developing and enhancing ecological-evolutionary understanding of oceanic island systems through the lens of the GDM. In particular, we focus on four main themes: (i) macroecological tests using a space-for-time rationale; (ii) extensions of theory to islands following different patterns of ontogeny; (iii) the implications of GDM dynamics for lineage diversification and trait evolution; and (iv) the potential for downscaling GDM dynamics to local-scale ecological patterns and processes within islands. We also consider the implications of the GDM for understanding patterns of non-native species diversity. We demonstrate the vitality of the field of island biogeography by identifying a range of potentially productive lines for future research. © 2016 Cambridge Philosophical Society.

Disclination mediated dynamic recrystallization in metals at low temperature.

PubMed

Aramfard, Mohammad; Deng, Chuang

2015-09-16

Recrystallization is one of the most important physical phenomena in condensed matter that has been utilized for materials processing for thousands of years in human history. It is generally believed that recrystallization is thermally activated and a minimum temperature must be achieved for the necessary atomic mechanisms to occur. Here, using atomistic simulations, we report a new mechanism of dynamic recrystallization that can operate at temperature as low as T = 10 K in metals during deformation. In contrast to previously proposed dislocation-based models, this mechanism relies on the generation of disclination quadrupoles, which are special defects that form during deformation when the grain boundary migration is restricted by structural defects such as triple junctions, cracks or obstacles. This mechanism offers an alternative explanation for the grain refinement in metals during severe plastic deformation at cryogenic temperature and may suggest a new method to tailor the microstructure in general crystalline materials.

Disclination mediated dynamic recrystallization in metals at low temperature

PubMed Central

Aramfard, Mohammad; Deng, Chuang

2015-01-01

Recrystallization is one of the most important physical phenomena in condensed matter that has been utilized for materials processing for thousands of years in human history. It is generally believed that recrystallization is thermally activated and a minimum temperature must be achieved for the necessary atomic mechanisms to occur. Here, using atomistic simulations, we report a new mechanism of dynamic recrystallization that can operate at temperature as low as T = 10 K in metals during deformation. In contrast to previously proposed dislocation-based models, this mechanism relies on the generation of disclination quadrupoles, which are special defects that form during deformation when the grain boundary migration is restricted by structural defects such as triple junctions, cracks or obstacles. This mechanism offers an alternative explanation for the grain refinement in metals during severe plastic deformation at cryogenic temperature and may suggest a new method to tailor the microstructure in general crystalline materials. PMID:26374603

Generalized master equations for non-Poisson dynamics on networks.

PubMed

Hoffmann, Till; Porter, Mason A; Lambiotte, Renaud

2012-10-01

The traditional way of studying temporal networks is to aggregate the dynamics of the edges to create a static weighted network. This implicitly assumes that the edges are governed by Poisson processes, which is not typically the case in empirical temporal networks. Accordingly, we examine the effects of non-Poisson inter-event statistics on the dynamics of edges, and we apply the concept of a generalized master equation to the study of continuous-time random walks on networks. We show that this equation reduces to the standard rate equations when the underlying process is Poissonian and that its stationary solution is determined by an effective transition matrix whose leading eigenvector is easy to calculate. We conduct numerical simulations and also derive analytical results for the stationary solution under the assumption that all edges have the same waiting-time distribution. We discuss the implications of our work for dynamical processes on temporal networks and for the construction of network diagnostics that take into account their nontrivial stochastic nature.

Generalized master equations for non-Poisson dynamics on networks

NASA Astrophysics Data System (ADS)

Hoffmann, Till; Porter, Mason A.; Lambiotte, Renaud

2012-10-01

The traditional way of studying temporal networks is to aggregate the dynamics of the edges to create a static weighted network. This implicitly assumes that the edges are governed by Poisson processes, which is not typically the case in empirical temporal networks. Accordingly, we examine the effects of non-Poisson inter-event statistics on the dynamics of edges, and we apply the concept of a generalized master equation to the study of continuous-time random walks on networks. We show that this equation reduces to the standard rate equations when the underlying process is Poissonian and that its stationary solution is determined by an effective transition matrix whose leading eigenvector is easy to calculate. We conduct numerical simulations and also derive analytical results for the stationary solution under the assumption that all edges have the same waiting-time distribution. We discuss the implications of our work for dynamical processes on temporal networks and for the construction of network diagnostics that take into account their nontrivial stochastic nature.

Active Polar Gels: a Paradigm for Cytoskeletal Dynamics

NASA Astrophysics Data System (ADS)

Julicher, Frank

2006-03-01

The cytoskeleton of eucaryotic cells is an intrinsically dynamic network of rod-like filaments. Active processes on the molecular scale such as the action of motor proteins and the polymerization and depolymerization of filaments drive active dynamic behaviors while consuming chemical energy in the form of a fuel. Such emergent dynamics is regulated by the cell and is important for many cellular processes such as cell locomotion and cell division. From a general point of view the cytoskeleton represents an active gel-like material with interesting material properties. We present a general theory of active viscoelastic materials made of polar filaments which is motivated by the the cytoskeleton. The continuous consumption of a fuel generates a non- equilibrium state characterized by the generation of flows and stresses. Our theory can be applied to experiments in which cytoskeletal patterns are set in motion by active processes such as those which are at work in cells. It can also capture generic aspects of the flows and stress profiles which occur during cell locomotion.

Water age and stream solute dynamics at the Hubbard Brook Experimental Forest (US)

NASA Astrophysics Data System (ADS)

Botter, Gianluca; Benettin, Paolo; McGuire, Kevin; Rinaldo, Andrea

2016-04-01

The contribution discusses experimental and modeling results from a headwater catchment at the Hubbard Brook Experimental Forest (New Hampshire, USA) to explore the link between stream solute dynamics and water age. A theoretical framework based on water age dynamics, which represents a general basis for characterizing solute transport at the catchment scale, is used to model both conservative and weathering-derived solutes. Based on the available information about the hydrology of the site, an integrated transport model was developed and used to estimate the relevant hydrochemical fluxes. The model was designed to reproduce the deuterium content of streamflow and allowed for the estimate of catchment water storage and dynamic travel time distributions (TTDs). Within this framework, dissolved silicon and sodium concentration in streamflow were simulated by implementing first-order chemical kinetics based explicitly on dynamic TTD, thus upscaling local geochemical processes to catchment scale. Our results highlight the key role of water stored within the subsoil glacial material in both the short-term and long-term solute circulation at Hubbard Brook. The analysis of the results provided by the calibrated model allowed a robust estimate of the emerging concentration-discharge relationship, streamflow age distributions (including the fraction of event water) and storage size, and their evolution in time due to hydrologic variability.

A Correlation-Based Transition Model using Local Variables. Part 2; Test Cases and Industrial Applications

NASA Technical Reports Server (NTRS)

Langtry, R. B.; Menter, F. R.; Likki, S. R.; Suzen, Y. B.; Huang, P. G.; Volker, S.

2006-01-01

A new correlation-based transition model has been developed, which is built strictly on local variables. As a result, the transition model is compatible with modern computational fluid dynamics (CFD) methods using unstructured grids and massive parallel execution. The model is based on two transport equations, one for the intermittency and one for the transition onset criteria in terms of momentum thickness Reynolds number. The proposed transport equations do not attempt to model the physics of the transition process (unlike, e.g., turbulence models), but form a framework for the implementation of correlation-based models into general-purpose CFD methods.

Dynamic Approaches to Language Processing

ERIC Educational Resources Information Center

Srinivasan, Narayanan

2007-01-01

Symbolic rule-based approaches have been a preferred way to study language and cognition. Dissatisfaction with rule-based approaches in the 1980s lead to alternative approaches to study language, the most notable being the dynamic approaches to language processing. Dynamic approaches provide a significant alternative by not being rule-based and…

Elastic facial movement influences part-based but not holistic processing

PubMed Central

Xiao, Naiqi G.; Quinn, Paul C.; Ge, Liezhong; Lee, Kang

2013-01-01

Face processing has been studied for decades. However, most of the empirical investigations have been conducted using static face images as stimuli. Little is known about whether static face processing findings can be generalized to real world contexts, in which faces are constantly moving. The present study investigates the nature of face processing (holistic vs. part-based) in elastic moving faces. Specifically, we focus on whether elastic moving faces, as compared to static ones, can facilitate holistic or part-based face processing. Using the composite paradigm, participants were asked to remember either an elastic moving face (i.e., a face that blinks and chews) or a static face, and then tested with a static composite face. The composite effect was (1) significantly smaller in the dynamic condition than in the static condition, (2) consistently found with different face encoding times (Experiments 1–3), and (3) present for the recognition of both upper and lower face parts (Experiment 4). These results suggest that elastic facial motion facilitates part-based processing, rather than holistic processing. Thus, while previous work with static faces has emphasized an important role for holistic processing, the current work highlights an important role for featural processing with moving faces. PMID:23398253

Nonlinear evolution of coarse-grained quantum systems with generalized purity constraints

NASA Astrophysics Data System (ADS)

Burić, Nikola

2010-12-01

Constrained quantum dynamics is used to propose a nonlinear dynamical equation for pure states of a generalized coarse-grained system. The relevant constraint is given either by the generalized purity or by the generalized invariant fluctuation, and the coarse-grained pure states correspond to the generalized coherent, i.e. generalized nonentangled states. Open system model of the coarse-graining is discussed. It is shown that in this model and in the weak coupling limit the constrained dynamical equations coincide with an equation for pointer states, based on Hilbert-Schmidt distance, that was previously suggested in the context of the decoherence theory.

Computational aerodynamics and artificial intelligence

NASA Technical Reports Server (NTRS)

Mehta, U. B.; Kutler, P.

1984-01-01

The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

Dynamics analysis of microsphere in a dual-beam fiber-optic trap with transverse offset.

PubMed

Chen, Xinlin; Xiao, Guangzong; Luo, Hui; Xiong, Wei; Yang, Kaiyong

2016-04-04

A comprehensive dynamics analysis of microsphere has been presented in a dual-beam fiber-optic trap with transverse offset. As the offset distance between two counterpropagating beams increases, the motion type of the microsphere starts with capture, then spiral motion, then orbital rotation, and ends with escape. We analyze the transformation process and mechanism of the four motion types based on ray optics approximation. Dynamic simulations show that the existence of critical offset distances at which different motion types transform. The result is an important step toward explaining physical phenomena in a dual-beam fiber-optic trap with transverse offset, and is generally applicable to achieving controllable motions of microspheres in integrated systems, such as microfluidic systems and lab-on-a-chip systems.

Classification framework for partially observed dynamical systems

NASA Astrophysics Data System (ADS)

Shen, Yuan; Tino, Peter; Tsaneva-Atanasova, Krasimira

2017-04-01

We present a general framework for classifying partially observed dynamical systems based on the idea of learning in the model space. In contrast to the existing approaches using point estimates of model parameters to represent individual data items, we employ posterior distributions over model parameters, thus taking into account in a principled manner the uncertainty due to both the generative (observational and/or dynamic noise) and observation (sampling in time) processes. We evaluate the framework on two test beds: a biological pathway model and a stochastic double-well system. Crucially, we show that the classification performance is not impaired when the model structure used for inferring posterior distributions is much more simple than the observation-generating model structure, provided the reduced-complexity inferential model structure captures the essential characteristics needed for the given classification task.

Identification of open quantum systems from observable time traces

DOE PAGES

Zhang, Jun; Sarovar, Mohan

2015-05-27

Estimating the parameters that dictate the dynamics of a quantum system is an important task for quantum information processing and quantum metrology, as well as fundamental physics. In our paper we develop a method for parameter estimation for Markovian open quantum systems using a temporal record of measurements on the system. Furthermore, the method is based on system realization theory and is a generalization of our previous work on identification of Hamiltonian parameters.

I. Cognitive and Instructional Factors Relating to Students' Development of Personal Models of Chemical Systems in the General Chemistry Laboratory II. Solvation in Supercritical Carbon Dioxide/Ethanol Mixtures Studied by Molecular Dynamics Simulation

ERIC Educational Resources Information Center

Anthony, Seth

2014-01-01

Part I: Students' participation in inquiry-based chemistry laboratory curricula, and, in particular, engagement with key thinking processes in conjunction with these experiences, is linked with success at the difficult task of "transfer"--applying their knowledge in new contexts to solve unfamiliar types of problems. We investigate…

Stability in chemical and biological systems: Multistage polyenzymatic reactions

NASA Astrophysics Data System (ADS)

Varfolomeev, S. D.; Lukovenkov, A. V.

2010-08-01

General principles of the theory of stability of solutions to differential equations are considered. The stability of equations describing the dynamics of changes in reagent concentrations in polyenzymatic biochemical chains is analyzed. Various mechanisms of formation of stable and unstable stationary states are considered, and unbalanced regimes and collapse are analyzed. The influence of systems of toxins and drugs on stability is studied. An interpretation of pathological processes based on stability theory is given.

"America Responds to AIDS": its content, development process, and outcome.

PubMed

Woods, D R; Davis, D; Westover, B J

1991-01-01

During the 1987-90 period, five phases of new AIDS information materials were released to the general public in the ARTA campaign, including a national mailer. The five were "General Awareness: Humanizing AIDS" in October 1987, "Understanding AIDS," the national mailout, April 1988, "Women at Risk/Multiple Partner, Sexually Active Adults," October 1988, "Parents and Youth," May 1989, and "Preventing HIV Infection and AIDS: Taking The Next Steps," July 1990. From planning to implementation to evaluation, ARTA is based on well-established theory and practice. Initially, the campaign was a response to an immediate crisis. It has evolved into the deliberate and systematic development of objectives to combat a chronic problem. ARTA represents one of the most comprehensive formative research processes in the history of public service campaigns. The dynamic process of carefully developing each new phase to include such important entities as State and local health agencies and community-based organizations is at least as important as the quality of the end materials. The objectives of each new phase are based on the needs of the public and of specific audiences. Maximum input from all relevant constituencies is obtained to ensure that they support the campaign's objectives and implementation strategy.

Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone

PubMed Central

Gerhard, Felipe; Kispersky, Tilman; Gutierrez, Gabrielle J.; Marder, Eve; Kramer, Mark; Eden, Uri

2013-01-01

Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities. PMID:23874181

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

IPA (v1): a framework for agent-based modelling of soil water movement

NASA Astrophysics Data System (ADS)

Mewes, Benjamin; Schumann, Andreas H.

2018-06-01

In the last decade, agent-based modelling (ABM) became a popular modelling technique in social sciences, medicine, biology, and ecology. ABM was designed to simulate systems that are highly dynamic and sensitive to small variations in their composition and their state. As hydrological systems, and natural systems in general, often show dynamic and non-linear behaviour, ABM can be an appropriate way to model these systems. Nevertheless, only a few studies have utilized the ABM method for process-based modelling in hydrology. The percolation of water through the unsaturated soil is highly responsive to the current state of the soil system; small variations in composition lead to major changes in the transport system. Hence, we present a new approach for modelling the movement of water through a soil column: autonomous water agents that transport water through the soil while interacting with their environment as well as with other agents under physical laws.

Efficient robust reconstruction of dynamic PET activity maps with radioisotope decay constraints.

PubMed

Gao, Fei; Liu, Huafeng; Shi, Pengcheng

2010-01-01

Dynamic PET imaging performs sequence of data acquisition in order to provide visualization and quantification of physiological changes in specific tissues and organs. The reconstruction of activity maps is generally the first step in dynamic PET. State space Hinfinity approaches have been proved to be a robust method for PET image reconstruction where, however, temporal constraints are not considered during the reconstruction process. In addition, the state space strategies for PET image reconstruction have been computationally prohibitive for practical usage because of the need for matrix inversion. In this paper, we present a minimax formulation of the dynamic PET imaging problem where a radioisotope decay model is employed as physics-based temporal constraints on the photon counts. Furthermore, a robust steady state Hinfinity filter is developed to significantly improve the computational efficiency with minimal loss of accuracy. Experiments are conducted on Monte Carlo simulated image sequences for quantitative analysis and validation.

High dynamic range hyperspectral imaging for camouflage performance test and evaluation

NASA Astrophysics Data System (ADS)

Pearce, D.; Feenan, J.

2016-10-01

This paper demonstrates the use of high dynamic range processing applied to the specific technique of hyper-spectral imaging with linescan spectrometers. The technique provides an improvement in signal to noise for reflectance estimation. This is demonstrated for field measurements of rural imagery collected from a ground-based linescan spectrometer of rural scenes. Once fully developed, the specific application is expected to improve the colour estimation approaches and consequently the test and evaluation accuracy of camouflage performance tests. Data are presented on both field and laboratory experiments that have been used to evaluate the improvements granted by the adoption of high dynamic range data acquisition in the field of hyperspectral imaging. High dynamic ranging imaging is well suited to the hyperspectral domain due to the large variation in solar irradiance across the visible and short wave infra-red (SWIR) spectrum coupled with the wavelength dependence of the nominal silicon detector response. Under field measurement conditions it is generally impractical to provide artificial illumination; consequently, an adaptation of the hyperspectral imaging and re ectance estimation process has been developed to accommodate the solar spectrum. This is shown to improve the signal to noise ratio for the re ectance estimation process of scene materials in the 400-500 nm and 700-900 nm regions.

Instantaneous Transfer Entropy for the Study of Cardiovascular and Cardiorespiratory Nonstationary Dynamics.

PubMed

Valenza, Gaetano; Faes, Luca; Citi, Luca; Orini, Michele; Barbieri, Riccardo

2018-05-01

Measures of transfer entropy (TE) quantify the direction and strength of coupling between two complex systems. Standard approaches assume stationarity of the observations, and therefore are unable to track time-varying changes in nonlinear information transfer with high temporal resolution. In this study, we aim to define and validate novel instantaneous measures of TE to provide an improved assessment of complex nonstationary cardiorespiratory interactions. We here propose a novel instantaneous point-process TE (ipTE) and validate its assessment as applied to cardiovascular and cardiorespiratory dynamics. In particular, heartbeat and respiratory dynamics are characterized through discrete time series, and modeled with probability density functions predicting the time of the next physiological event as a function of the past history. Likewise, nonstationary interactions between heartbeat and blood pressure dynamics are characterized as well. Furthermore, we propose a new measure of information transfer, the instantaneous point-process information transfer (ipInfTr), which is directly derived from point-process-based definitions of the Kolmogorov-Smirnov distance. Analysis on synthetic data, as well as on experimental data gathered from healthy subjects undergoing postural changes confirms that ipTE, as well as ipInfTr measures are able to dynamically track changes in physiological systems coupling. This novel approach opens new avenues in the study of hidden, transient, nonstationary physiological states involving multivariate autonomic dynamics in cardiovascular health and disease. The proposed method can also be tailored for the study of complex multisystem physiology (e.g., brain-heart or, more in general, brain-body interactions).

A new model for fluid velocity slip on a solid surface.

PubMed

Shu, Jian-Jun; Teo, Ji Bin Melvin; Chan, Weng Kong

2016-10-12

A general adsorption model is developed to describe the interactions between near-wall fluid molecules and solid surfaces. This model serves as a framework for the theoretical modelling of boundary slip phenomena. Based on this adsorption model, a new general model for the slip velocity of fluids on solid surfaces is introduced. The slip boundary condition at a fluid-solid interface has hitherto been considered separately for gases and liquids. In this paper, we show that the slip velocity in both gases and liquids may originate from dynamical adsorption processes at the interface. A unified analytical model that is valid for both gas-solid and liquid-solid slip boundary conditions is proposed based on surface science theory. The corroboration with the experimental data extracted from the literature shows that the proposed model provides an improved prediction compared to existing analytical models for gases at higher shear rates and close agreement for liquid-solid interfaces in general.

Detecting and modelling delayed density-dependence in abundance time series of a small mammal (Didelphis aurita)

NASA Astrophysics Data System (ADS)

Brigatti, E.; Vieira, M. V.; Kajin, M.; Almeida, P. J. A. L.; de Menezes, M. A.; Cerqueira, R.

2016-02-01

We study the population size time series of a Neotropical small mammal with the intent of detecting and modelling population regulation processes generated by density-dependent factors and their possible delayed effects. The application of analysis tools based on principles of statistical generality are nowadays a common practice for describing these phenomena, but, in general, they are more capable of generating clear diagnosis rather than granting valuable modelling. For this reason, in our approach, we detect the principal temporal structures on the bases of different correlation measures, and from these results we build an ad-hoc minimalist autoregressive model that incorporates the main drivers of the dynamics. Surprisingly our model is capable of reproducing very well the time patterns of the empirical series and, for the first time, clearly outlines the importance of the time of attaining sexual maturity as a central temporal scale for the dynamics of this species. In fact, an important advantage of this analysis scheme is that all the model parameters are directly biologically interpretable and potentially measurable, allowing a consistency check between model outputs and independent measurements.

Bursts and Heavy Tails in Temporal and Sequential Dynamics of Foraging Decisions

PubMed Central

Jung, Kanghoon; Jang, Hyeran; Kralik, Jerald D.; Jeong, Jaeseung

2014-01-01

A fundamental understanding of behavior requires predicting when and what an individual will choose. However, the actual temporal and sequential dynamics of successive choices made among multiple alternatives remain unclear. In the current study, we tested the hypothesis that there is a general bursting property in both the timing and sequential patterns of foraging decisions. We conducted a foraging experiment in which rats chose among four different foods over a continuous two-week time period. Regarding when choices were made, we found bursts of rapidly occurring actions, separated by time-varying inactive periods, partially based on a circadian rhythm. Regarding what was chosen, we found sequential dynamics in affective choices characterized by two key features: (a) a highly biased choice distribution; and (b) preferential attachment, in which the animals were more likely to choose what they had previously chosen. To capture the temporal dynamics, we propose a dual-state model consisting of active and inactive states. We also introduce a satiation-attainment process for bursty activity, and a non-homogeneous Poisson process for longer inactivity between bursts. For the sequential dynamics, we propose a dual-control model consisting of goal-directed and habit systems, based on outcome valuation and choice history, respectively. This study provides insights into how the bursty nature of behavior emerges from the interaction of different underlying systems, leading to heavy tails in the distribution of behavior over time and choices. PMID:25122498

Effect of Food Regulation on the Spanish Food Processing Industry: A Dynamic Productivity Analysis.

PubMed

Kapelko, Magdalena; Oude Lansink, Alfons; Stefanou, Spiro E

2015-01-01

This article develops the decomposition of the dynamic Luenberger productivity growth indicator into dynamic technical change, dynamic technical inefficiency change and dynamic scale inefficiency change in the dynamic directional distance function context using Data Envelopment Analysis. These results are used to investigate for the Spanish food processing industry the extent to which dynamic productivity growth and its components are affected by the introduction of the General Food Law in 2002 (Regulation (EC) No 178/2002). The empirical application uses panel data of Spanish meat, dairy, and oils and fats industries over the period 1996-2011. The results suggest that in the oils and fats industry the impact of food regulation on dynamic productivity growth is negative initially and then positive over the long run. In contrast, the opposite pattern is observed for the meat and dairy processing industries. The results further imply that firms in the meat processing and oils and fats industries face similar impacts of food safety regulation on dynamic technical change, dynamic inefficiency change and dynamic scale inefficiency change.

Effect of Food Regulation on the Spanish Food Processing Industry: A Dynamic Productivity Analysis

PubMed Central

Kapelko, Magdalena; Lansink, Alfons Oude; Stefanou, Spiro E.

2015-01-01

This article develops the decomposition of the dynamic Luenberger productivity growth indicator into dynamic technical change, dynamic technical inefficiency change and dynamic scale inefficiency change in the dynamic directional distance function context using Data Envelopment Analysis. These results are used to investigate for the Spanish food processing industry the extent to which dynamic productivity growth and its components are affected by the introduction of the General Food Law in 2002 (Regulation (EC) No 178/2002). The empirical application uses panel data of Spanish meat, dairy, and oils and fats industries over the period 1996-2011. The results suggest that in the oils and fats industry the impact of food regulation on dynamic productivity growth is negative initially and then positive over the long run. In contrast, the opposite pattern is observed for the meat and dairy processing industries. The results further imply that firms in the meat processing and oils and fats industries face similar impacts of food safety regulation on dynamic technical change, dynamic inefficiency change and dynamic scale inefficiency change. PMID:26057878

Early-Time Excited-State Relaxation Dynamics of Iridium Compounds: Distinct Roles of Electron and Hole Transfer.

PubMed

Liu, Xiang-Yang; Zhang, Ya-Hui; Fang, Wei-Hai; Cui, Ganglong

2018-06-28

Excited-state and photophysical properties of Ir-containing complexes have been extensively studied because of their potential applications as organic light-emitting diode emitting materials. However, their early time excited-state relaxation dynamics are less explored computationally. Herein we have employed our recently implemented TDDFT-based generalized surface-hopping dynamics method to simulate excited-state relaxation dynamics of three Ir(III) compounds having distinct ligands. According to our multistate dynamics simulations including five excited singlet states i.e., S n ( n = 1-5) and ten excited triplet states, i.e., T n ( n = 1-10), we have found that the intersystem crossing (ISC) processes from the S n to T n are very efficient and ultrafast in these three Ir(III) compounds. The corresponding ISC rates are estimated to be 65, 81, and 140 fs, which are reasonably close to the experimentally measured ca. 80, 80, and 110 fs. In addition, the internal conversion (IC) processes within respective singlet and triplet manifolds are also ultrafast. These ultrafast IC and ISC processes are caused by large nonadiabatic and spin-orbit couplings, respectively, as well as small energy gaps. Importantly, although these Ir(III) complexes share similar macroscopic phenomena, i.e., ultrafast IC and ISC, their microscopic excited-state relaxation mechanism and dynamics are qualitatively distinct. Specifically, the dynamical behaviors of electron and hole and their roles are variational in modulating the excited-state relaxation dynamics of these Ir(III) compounds. In other words, the electronic properties of the ligands that are coordinated with the central Ir(III) atom play important roles in regulating the microscopic excited-state relaxation dynamics. These gained insights could be useful for rationally designing Ir(III) compounds with excellent photoluminescence.

Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs

NASA Astrophysics Data System (ADS)

Schalkoff, Robert J.; Shaaban, Khaled M.

1999-07-01

Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.

Intrinsic and Extrinsic Neuromodulation of Olfactory Processing

PubMed Central

Lizbinski, Kristyn M.; Dacks, Andrew M.

2018-01-01

Neuromodulation is a ubiquitous feature of neural systems, allowing flexible, context specific control over network dynamics. Neuromodulation was first described in invertebrate motor systems and early work established a basic dichotomy for neuromodulation as having either an intrinsic origin (i.e., neurons that participate in network coding) or an extrinsic origin (i.e., neurons from independent networks). In this conceptual dichotomy, intrinsic sources of neuromodulation provide a “memory” by adjusting network dynamics based upon previous and ongoing activation of the network itself, while extrinsic neuromodulators provide the context of ongoing activity of other neural networks. Although this dichotomy has been thoroughly considered in motor systems, it has received far less attention in sensory systems. In this review, we discuss intrinsic and extrinsic modulation in the context of olfactory processing in invertebrate and vertebrate model systems. We begin by discussing presynaptic modulation of olfactory sensory neurons by local interneurons (LNs) as a mechanism for gain control based on ongoing network activation. We then discuss the cell-class specific effects of serotonergic centrifugal neurons on olfactory processing. Finally, we briefly discuss the integration of intrinsic and extrinsic neuromodulation (metamodulation) as an effective mechanism for exerting global control over olfactory network dynamics. The heterogeneous nature of neuromodulation is a recurring theme throughout this review as the effects of both intrinsic and extrinsic modulation are generally non-uniform. PMID:29375314

Directing Stem Cell Differentiation via Electrochemical Reversible Switching between Nanotubes and Nanotips of Polypyrrole Array.

PubMed

Wei, Yan; Mo, Xiaoju; Zhang, Pengchao; Li, Yingying; Liao, Jingwen; Li, Yongjun; Zhang, Jinxing; Ning, Chengyun; Wang, Shutao; Deng, Xuliang; Jiang, Lei

2017-06-27

Control of stem cell behaviors at solid biointerfaces is critical for stem-cell-based regeneration and generally achieved by engineering chemical composition, topography, and stiffness. However, the influence of dynamic stimuli at the nanoscale from solid biointerfaces on stem cell fate remains unclear. Herein, we show that electrochemical switching of a polypyrrole (Ppy) array between nanotubes and nanotips can alter surface adhesion, which can strongly influence mechanotransduction activation and guide differentiation of mesenchymal stem cells (MSCs). The Ppy array, prepared via template-free electrochemical polymerization, can be reversibly switched between highly adhesive hydrophobic nanotubes and poorly adhesive hydrophilic nanotips through an electrochemical oxidation/reduction process, resulting in dynamic attachment and detachment to MSCs at the nanoscale. Multicyclic attachment/detachment of the Ppy array to MSCs can activate intracellular mechanotransduction and osteogenic differentiation independent of surface stiffness and chemical induction. This smart surface, permitting transduction of nanoscaled dynamic physical inputs into biological outputs, provides an alternative to classical cell culture substrates for regulating stem cell fate commitment. This study represents a general strategy to explore nanoscaled interactions between stem cells and stimuli-responsive surfaces.

Requirements for efficient cell-type proportioning: regulatory timescales, stochasticity and lateral inhibition

NASA Astrophysics Data System (ADS)

Pfeuty, B.; Kaneko, K.

2016-04-01

The proper functioning of multicellular organisms requires the robust establishment of precise proportions between distinct cell types. This developmental differentiation process typically involves intracellular regulatory and stochastic mechanisms to generate cell-fate diversity as well as intercellular signaling mechanisms to coordinate cell-fate decisions at tissue level. We thus surmise that key insights about the developmental regulation of cell-type proportion can be captured by the modeling study of clustering dynamics in population of inhibitory-coupled noisy bistable systems. This general class of dynamical system is shown to exhibit a very stable two-cluster state, but also metastability, collective oscillations or noise-induced state hopping, which can prevent from timely and reliably reaching a robust and well-proportioned clustered state. To circumvent these obstacles or to avoid fine-tuning, we highlight a general strategy based on dual-time positive feedback loops, such as mediated through transcriptional versus epigenetic mechanisms, which improves proportion regulation by coordinating early and flexible lineage priming with late and firm commitment. This result sheds new light on the respective and cooperative roles of multiple regulatory feedback, stochasticity and lateral inhibition in developmental dynamics.

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

NASA Astrophysics Data System (ADS)

Altaf, Muhammad U.; Jana, Raghavendra B.; Hoteit, Ibrahim; McCabe, Matthew F.

2016-10-01

Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model's large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes

PubMed Central

Kuang, Zheng; Ji, Zhicheng

2018-01-01

Abstract Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes. PMID:29325176

Artificial neural networks for efficient clustering of conformational ensembles and their potential for medicinal chemistry.

PubMed

Pandini, Alessandro; Fraccalvieri, Domenico; Bonati, Laura

2013-01-01

The biological function of proteins is strictly related to their molecular flexibility and dynamics: enzymatic activity, protein-protein interactions, ligand binding and allosteric regulation are important mechanisms involving protein motions. Computational approaches, such as Molecular Dynamics (MD) simulations, are now routinely used to study the intrinsic dynamics of target proteins as well as to complement molecular docking approaches. These methods have also successfully supported the process of rational design and discovery of new drugs. Identification of functionally relevant conformations is a key step in these studies. This is generally done by cluster analysis of the ensemble of structures in the MD trajectory. Recently Artificial Neural Network (ANN) approaches, in particular methods based on Self-Organising Maps (SOMs), have been reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data-mining problems. In the specific case of conformational analysis, SOMs have been successfully used to compare multiple ensembles of protein conformations demonstrating a potential in efficiently detecting the dynamic signatures central to biological function. Moreover, examples of the use of SOMs to address problems relevant to other stages of the drug-design process, including clustering of docking poses, have been reported. In this contribution we review recent applications of ANN algorithms in analysing conformational and structural ensembles and we discuss their potential in computer-based approaches for medicinal chemistry.

Perspective: A Dynamics-Based Classification of Ventricular Arrhythmias

PubMed Central

Weiss, James N.; Garfinkel, Alan; Karagueuzian, Hrayr S.; Nguyen, Thao P.; Olcese, Riccardo; Chen, Peng-Sheng; Qu, Zhilin

2015-01-01

Despite key advances in the clinical management of life-threatening ventricular arrhythmias, culminating with the development of implantable cardioverter-defibrillators and catheter ablation techniques, pharmacologic/biologic therapeutics have lagged behind. The fundamental issue is that biological targets are molecular factors. Diseases, however, represent emergent properties at the scale of the organism that result from dynamic interactions between multiple constantly changing molecular factors. For a pharmacologic/biologic therapy to be effective, it must target the dynamic processes that underlie the disease. Here we propose a classification of ventricular arrhythmias that is based on our current understanding of the dynamics occurring at the subcellular, cellular, tissue and organism scales, which cause arrhythmias by simultaneously generating arrhythmia triggers and exacerbating tissue vulnerability. The goal is to create a framework that systematically links these key dynamic factors together with fixed factors (structural and electrophysiological heterogeneity) synergistically promoting electrical dispersion and increased arrhythmia risk to molecular factors that can serve as biological targets. We classify ventricular arrhythmias into three primary dynamic categories related generally to unstable Ca cycling, reduced repolarization, and excess repolarization, respectively. The clinical syndromes, arrhythmia mechanisms, dynamic factors and what is known about their molecular counterparts are discussed. Based on this framework, we propose a computational-experimental strategy for exploring the links between molecular factors, fixed factors and dynamic factors that underlie life-threatening ventricular arrhythmias. The ultimate objective is to facilitate drug development by creating an in silico platform to evaluate and predict comprehensively how molecular interventions affect not only a single targeted arrhythmia, but all primary arrhythmia dynamics categories as well as normal cardiac excitation-contraction coupling. PMID:25769672

Nonlinear Decoupling Control With ANFIS-Based Unmodeled Dynamics Compensation for a Class of Complex Industrial Processes.

PubMed

Zhang, Yajun; Chai, Tianyou; Wang, Hong; Wang, Dianhui; Chen, Xinkai

2018-06-01

Complex industrial processes are multivariable and generally exhibit strong coupling among their control loops with heavy nonlinear nature. These make it very difficult to obtain an accurate model. As a result, the conventional and data-driven control methods are difficult to apply. Using a twin-tank level control system as an example, a novel multivariable decoupling control algorithm with adaptive neural-fuzzy inference system (ANFIS)-based unmodeled dynamics (UD) compensation is proposed in this paper for a class of complex industrial processes. At first, a nonlinear multivariable decoupling controller with UD compensation is introduced. Different from the existing methods, the decomposition estimation algorithm using ANFIS is employed to estimate the UD, and the desired estimating and decoupling control effects are achieved. Second, the proposed method does not require the complicated switching mechanism which has been commonly used in the literature. This significantly simplifies the obtained decoupling algorithm and its realization. Third, based on some new lemmas and theorems, the conditions on the stability and convergence of the closed-loop system are analyzed to show the uniform boundedness of all the variables. This is then followed by the summary on experimental tests on a heavily coupled nonlinear twin-tank system that demonstrates the effectiveness and the practicability of the proposed method.

Autonomous Agents for Dynamic Process Planning in the Flexible Manufacturing System

NASA Astrophysics Data System (ADS)

Nik Nejad, Hossein Tehrani; Sugimura, Nobuhiro; Iwamura, Koji; Tanimizu, Yoshitaka

Rapid changes of market demands and pressures of competition require manufacturers to maintain highly flexible manufacturing systems to cope with a complex manufacturing environment. This paper deals with development of an agent-based architecture of dynamic systems for incremental process planning in the manufacturing systems. In consideration of alternative manufacturing processes and machine tools, the process plans and the schedules of the manufacturing resources are generated incrementally and dynamically. A negotiation protocol is discussed, in this paper, to generate suitable process plans for the target products real-timely and dynamically, based on the alternative manufacturing processes. The alternative manufacturing processes are presented by the process plan networks discussed in the previous paper, and the suitable process plans are searched and generated to cope with both the dynamic changes of the product specifications and the disturbances of the manufacturing resources. We initiatively combine the heuristic search algorithms of the process plan networks with the negotiation protocols, in order to generate suitable process plans in the dynamic manufacturing environment.

Predicted effect of dynamic load on pitting fatigue life for low-contact-ratio spur gears

NASA Technical Reports Server (NTRS)

Lewicki, David G.

1986-01-01

How dynamic load affects the surface pitting fatigue life of external spur gears was predicted by using the NASA computer program TELSGE. Parametric studies were performed over a range of various gear parameters modeling low-contact-ratio involute spur gears. In general, gear life predictions based on dynamic loads differed significantly from those based on static loads, with the predictions being strongly influenced by the maximum dynamic load during contact. Gear mesh operating speed strongly affected predicted dynamic load and life. Meshes operating at a resonant speed or one-half the resonant speed had significantly shorter lives. Dynamic life factors for gear surface pitting fatigue were developed on the basis of the parametric studies. In general, meshes with higher contact ratios had higher dynamic life factors than meshes with lower contact ratios. A design chart was developed for hand calculations of dynamic life factors.

154. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

154. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) STRUCTURAL PLANS FOR MST STATION 30, SHEET S84 - Vandenberg Air Force Base, Space Launch Complex 3, Launch Pad 3 East, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

225. Photocopy of drawing (1967 structural drawing by General Dynamics/Astronautics) ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

225. Photocopy of drawing (1967 structural drawing by General Dynamics/Astronautics) WIND DEFLECTOR FOR THE UMBILICAL MAST, SHEET S122 - Vandenberg Air Force Base, Space Launch Complex 3, Launch Pad 3 East, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

158. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

158. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) FRAMING PLANS FOR MST STATION 124, SHEET S94 - Vandenberg Air Force Base, Space Launch Complex 3, Launch Pad 3 East, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

Temporal coding in a silicon network of integrate-and-fire neurons.

PubMed

Liu, Shih-Chii; Douglas, Rodney

2004-09-01

Spatio-temporal processing of spike trains by neuronal networks depends on a variety of mechanisms distributed across synapses, dendrites, and somata. In natural systems, the spike trains and the processing mechanisms cohere though their common physical instantiation. This coherence is lost when the natural system is encoded for simulation on a general purpose computer. By contrast, analog VLSI circuits are, like neurons, inherently related by their real-time physics, and so, could provide a useful substrate for exploring neuronlike event-based processing. Here, we describe a hybrid analog-digital VLSI chip comprising a set of integrate-and-fire neurons and short-term dynamical synapses that can be configured into simple network architectures with some properties of neocortical neuronal circuits. We show that, despite considerable fabrication variance in the properties of individual neurons, the chip offers a viable substrate for exploring real-time spike-based processing in networks of neurons.

General Formalism of Decision Making Based on Theory of Open Quantum Systems

NASA Astrophysics Data System (ADS)

Asano, M.; Ohya, M.; Basieva, I.; Khrennikov, A.

2013-01-01

We present the general formalism of decision making which is based on the theory of open quantum systems. A person (decision maker), say Alice, is considered as a quantum-like system, i.e., a system which information processing follows the laws of quantum information theory. To make decision, Alice interacts with a huge mental bath. Depending on context of decision making this bath can include her social environment, mass media (TV, newspapers, INTERNET), and memory. Dynamics of an ensemble of such Alices is described by Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) equation. We speculate that in the processes of evolution biosystems (especially human beings) designed such "mental Hamiltonians" and GKSL-operators that any solution of the corresponding GKSL-equation stabilizes to a diagonal density operator (In the basis of decision making.) This limiting density operator describes population in which all superpositions of possible decisions has already been resolved. In principle, this approach can be used for the prediction of the distribution of possible decisions in human populations.

Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model

NASA Astrophysics Data System (ADS)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2014-02-01

Inverse parameter estimation of process-based models is a long-standing problem in many scientific disciplines. A key question for inverse parameter estimation is how to define the metric that quantifies how well model predictions fit to the data. This metric can be expressed by general cost or objective functions, but statistical inversion methods require a particular metric, the probability of observing the data given the model parameters, known as the likelihood. For technical and computational reasons, likelihoods for process-based stochastic models are usually based on general assumptions about variability in the observed data, and not on the stochasticity generated by the model. Only in recent years have new methods become available that allow the generation of likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional Markov chain Monte Carlo (MCMC) sampler, performs well in retrieving known parameter values from virtual inventory data generated by the forest model. We analyze the results of the parameter estimation, examine its sensitivity to the choice and aggregation of model outputs and observed data (summary statistics), and demonstrate the application of this method by fitting the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss how this approach differs from approximate Bayesian computation (ABC), another method commonly used to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can be successfully applied to process-based models of high complexity. The methodology is particularly suitable for heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models.

Stochastic Dynamics through Hierarchically Embedded Markov Chains

NASA Astrophysics Data System (ADS)

Vasconcelos, Vítor V.; Santos, Fernando P.; Santos, Francisco C.; Pacheco, Jorge M.

2017-02-01

Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects—such as mutations in evolutionary dynamics and a random exploration of choices in social systems—including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.

Stochastic Dynamics through Hierarchically Embedded Markov Chains.

PubMed

Vasconcelos, Vítor V; Santos, Fernando P; Santos, Francisco C; Pacheco, Jorge M

2017-02-03

Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects-such as mutations in evolutionary dynamics and a random exploration of choices in social systems-including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.

From Brain-Environment Connections to Temporal Dynamics and Social Interaction: Principles of Human Brain Function.

PubMed

Hari, Riitta

2017-06-07

Experimental data about brain function accumulate faster than does our understanding of how the brain works. To tackle some general principles at the grain level of behavior, I start from the omnipresent brain-environment connection that forces regularities of the physical world to shape the brain. Based on top-down processing, added by sparse sensory information, people are able to form individual "caricature worlds," which are similar enough to be shared among other people and which allow quick and purposeful reactions to abrupt changes. Temporal dynamics and social interaction in natural environments serve as further essential organizing principles of human brain function. Copyright © 2017 Elsevier Inc. All rights reserved.

153. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

153. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) PLANS, ELEVATIONS, AND DETAILS FOR MST STATION 3, SHEET A20 - Vandenberg Air Force Base, Space Launch Complex 3, Launch Pad 3 East, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

156. Photocopy of drawing (1963 architectural drawing by General Dynamics/Astronautics) ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

156. Photocopy of drawing (1963 architectural drawing by General Dynamics/Astronautics) PLAN AND DETAILS FOR MST STATION 85.5, SHEET A29 - Vandenberg Air Force Base, Space Launch Complex 3, Launch Pad 3 East, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

Stochastic, compartmental, and dynamic modeling of cross-contamination during mechanical smearing of cheeses.

PubMed

Aziza, Fanny; Mettler, Eric; Daudin, Jean-Jacques; Sanaa, Moez

2006-06-01

Cheese smearing is a complex process and the potential for cross-contamination with pathogenic or undesirable microorganisms is critical. During ripening, cheeses are salted and washed with brine to develop flavor and remove molds that could develop on the surfaces. Considering the potential for cross-contamination of this process in quantitative risk assessments could contribute to a better understanding of this phenomenon and, eventually, improve its control. The purpose of this article is to model the cross-contamination of smear-ripened cheeses due to the smearing operation under industrial conditions. A compartmental, dynamic, and stochastic model is proposed for mechanical brush smearing. This model has been developed to describe the exchange of microorganisms between compartments. Based on the analytical solution of the model equations and on experimental data collected with an industrial smearing machine, we assessed the values of the transfer parameters of the model. Monte Carlo simulations, using the distributions of transfer parameters, provide the final number of contaminated products in a batch and their final level of contamination for a given scenario taking into account the initial number of contaminated cheeses of the batch and their contaminant load. Based on analytical results, the model provides indicators for smearing efficiency and propensity of the process for cross-contamination. Unlike traditional approaches in mechanistic models, our approach captures the variability and uncertainty inherent in the process and the experimental data. More generally, this model could represent a generic base to use in modeling similar processes prone to cross-contamination.

Ecological Factors in Human Development.

PubMed

Cross, William E

2017-05-01

Urie Bronfenbrenner (1992) helped developmental psychologists comprehend and define "context" as a rich, thick multidimensional construct. His ecological systems theory consists of five layers, and within each layer are developmental processes unique to each layer. The four articles in this section limit the exploration of context to the three innermost systems: the individual plus micro- and macrolayers. Rather than examine both the physical features and processes, the articles tend to focus solely on processes associated with a niche. Processes explored include social identity development, social network dynamics, peer influences, and school-based friendship patterns. The works tend to extend the generalization of extant theory to the developmental experience of various minority group experiences. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

Differential Geometry Based Multiscale Models

PubMed Central

Wei, Guo-Wei

2010-01-01

Large chemical and biological systems such as fuel cells, ion channels, molecular motors, and viruses are of great importance to the scientific community and public health. Typically, these complex systems in conjunction with their aquatic environment pose a fabulous challenge to theoretical description, simulation, and prediction. In this work, we propose a differential geometry based multiscale paradigm to model complex macromolecular systems, and to put macroscopic and microscopic descriptions on an equal footing. In our approach, the differential geometry theory of surfaces and geometric measure theory are employed as a natural means to couple the macroscopic continuum mechanical description of the aquatic environment with the microscopic discrete atom-istic description of the macromolecule. Multiscale free energy functionals, or multiscale action functionals are constructed as a unified framework to derive the governing equations for the dynamics of different scales and different descriptions. Two types of aqueous macromolecular complexes, ones that are near equilibrium and others that are far from equilibrium, are considered in our formulations. We show that generalized Navier–Stokes equations for the fluid dynamics, generalized Poisson equations or generalized Poisson–Boltzmann equations for electrostatic interactions, and Newton's equation for the molecular dynamics can be derived by the least action principle. These equations are coupled through the continuum-discrete interface whose dynamics is governed by potential driven geometric flows. Comparison is given to classical descriptions of the fluid and electrostatic interactions without geometric flow based micro-macro interfaces. The detailed balance of forces is emphasized in the present work. We further extend the proposed multiscale paradigm to micro-macro analysis of electrohydrodynamics, electrophoresis, fuel cells, and ion channels. We derive generalized Poisson–Nernst–Planck equations that are coupled to generalized Navier–Stokes equations for fluid dynamics, Newton's equation for molecular dynamics, and potential and surface driving geometric flows for the micro-macro interface. For excessively large aqueous macromolecular complexes in chemistry and biology, we further develop differential geometry based multiscale fluid-electro-elastic models to replace the expensive molecular dynamics description with an alternative elasticity formulation. PMID:20169418

Dynamic taxonomies applied to a web-based relational database for geo-hydrological risk mitigation

NASA Astrophysics Data System (ADS)

Sacco, G. M.; Nigrelli, G.; Bosio, A.; Chiarle, M.; Luino, F.

2012-02-01

In its 40 years of activity, the Research Institute for Geo-hydrological Protection of the Italian National Research Council has amassed a vast and varied collection of historical documentation on landslides, muddy-debris flows, and floods in northern Italy from 1600 to the present. Since 2008, the archive resources have been maintained through a relational database management system. The database is used for routine study and research purposes as well as for providing support during geo-hydrological emergencies, when data need to be quickly and accurately retrieved. Retrieval speed and accuracy are the main objectives of an implementation based on a dynamic taxonomies model. Dynamic taxonomies are a general knowledge management model for configuring complex, heterogeneous information bases that support exploratory searching. At each stage of the process, the user can explore or browse the database in a guided yet unconstrained way by selecting the alternatives suggested for further refining the search. Dynamic taxonomies have been successfully applied to such diverse and apparently unrelated domains as e-commerce and medical diagnosis. Here, we describe the application of dynamic taxonomies to our database and compare it to traditional relational database query methods. The dynamic taxonomy interface, essentially a point-and-click interface, is considerably faster and less error-prone than traditional form-based query interfaces that require the user to remember and type in the "right" search keywords. Finally, dynamic taxonomy users have confirmed that one of the principal benefits of this approach is the confidence of having considered all the relevant information. Dynamic taxonomies and relational databases work in synergy to provide fast and precise searching: one of the most important factors in timely response to emergencies.

On the degree distribution of horizontal visibility graphs associated with Markov processes and dynamical systems: diagrammatic and variational approaches

NASA Astrophysics Data System (ADS)

Lacasa, Lucas

2014-09-01

Dynamical processes can be transformed into graphs through a family of mappings called visibility algorithms, enabling the possibility of (i) making empirical time series analysis and signal processing and (ii) characterizing classes of dynamical systems and stochastic processes using the tools of graph theory. Recent works show that the degree distribution of these graphs encapsulates much information on the signals' variability, and therefore constitutes a fundamental feature for statistical learning purposes. However, exact solutions for the degree distributions are only known in a few cases, such as for uncorrelated random processes. Here we analytically explore these distributions in a list of situations. We present a diagrammatic formalism which computes for all degrees their corresponding probability as a series expansion in a coupling constant which is the number of hidden variables. We offer a constructive solution for general Markovian stochastic processes and deterministic maps. As case tests we focus on Ornstein-Uhlenbeck processes, fully chaotic and quasiperiodic maps. Whereas only for certain degree probabilities can all diagrams be summed exactly, in the general case we show that the perturbation theory converges. In a second part, we make use of a variational technique to predict the complete degree distribution for special classes of Markovian dynamics with fast-decaying correlations. In every case we compare the theory with numerical experiments.

Connection forces in deformable multibody dynamics

NASA Technical Reports Server (NTRS)

Shabana, A. A.; Chang, C. W.

1989-01-01

In the dynamic formulation of holonomic and nonholonomic systems based on D'Alembert-Lagrange equation, the forces of constraints are maintained in the dynamic equations by introducing auxiliary variables, called Lagrange multipliers. This approach introduces a set of generalized reaction forces associated with the system generalized coordinates. Different sets of variables can be used as generalized coordinates and accordingly, the generalized reactions associated with these generalized coordinates may not be the actual reaction forces at the joints. In rigid body dynamics, the generalized reaction forces and the actual reaction forces at the joints represent equipollent systems of forces since they produce the same total forces and moments at and about any point on the rigid body. This is not, however, the case in deformable body analyses wherein the generalized reaction forces depend on the system generalized reference and elastic coordinates. In this paper, a method for determining the actual reaction forces at the joints from the generalized reaction forces in deformable multibody systems is presented.

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors

PubMed Central

Mishra, Abhishek; Ghosh, Rohan; Principe, Jose C.; Thakor, Nitish V.; Kukreja, Sunil L.

2017-01-01

Motion segmentation is a critical pre-processing step for autonomous robotic systems to facilitate tracking of moving objects in cluttered environments. Event based sensors are low power analog devices that represent a scene by means of asynchronous information updates of only the dynamic details at high temporal resolution and, hence, require significantly less calculations. However, motion segmentation using spatiotemporal data is a challenging task due to data asynchrony. Prior approaches for object tracking using neuromorphic sensors perform well while the sensor is static or a known model of the object to be followed is available. To address these limitations, in this paper we develop a technique for generalized motion segmentation based on spatial statistics across time frames. First, we create micromotion on the platform to facilitate the separation of static and dynamic elements of a scene, inspired by human saccadic eye movements. Second, we introduce the concept of spike-groups as a methodology to partition spatio-temporal event groups, which facilitates computation of scene statistics and characterize objects in it. Experimental results show that our algorithm is able to classify dynamic objects with a moving camera with maximum accuracy of 92%. PMID:28316563

Hierarchical Process Composition: Dynamic Maintenance of Structure in a Distributed Environment

DTIC Science & Technology

1988-01-01

One prominent hne of research stresses the independence of address space and thread of control, and the resulting efficiencies due to shared memory...cooperating processes. StarOS focuses on case of use and a general capability mechanism, while Medusa stresses the effect of distributed hardware on system...process structure and the asynchrony among agents and between agents and sources of failure. By stressing dynamic structure, we are led to adopt an

Quantitative imaging with fluorescent biosensors.

PubMed

Okumoto, Sakiko; Jones, Alexander; Frommer, Wolf B

2012-01-01

Molecular activities are highly dynamic and can occur locally in subcellular domains or compartments. Neighboring cells in the same tissue can exist in different states. Therefore, quantitative information on the cellular and subcellular dynamics of ions, signaling molecules, and metabolites is critical for functional understanding of organisms. Mass spectrometry is generally used for monitoring ions and metabolites; however, its temporal and spatial resolution are limited. Fluorescent proteins have revolutionized many areas of biology-e.g., fluorescent proteins can report on gene expression or protein localization in real time-yet promoter-based reporters are often slow to report physiologically relevant changes such as calcium oscillations. Therefore, novel tools are required that can be deployed in specific cells and targeted to subcellular compartments in order to quantify target molecule dynamics directly. We require tools that can measure enzyme activities, protein dynamics, and biophysical processes (e.g., membrane potential or molecular tension) with subcellular resolution. Today, we have an extensive suite of tools at our disposal to address these challenges, including translocation sensors, fluorescence-intensity sensors, and Förster resonance energy transfer sensors. This review summarizes sensor design principles, provides a database of sensors for more than 70 different analytes/processes, and gives examples of applications in quantitative live cell imaging.

Accommodating Dynamic Oceanographic Processes and Pelagic Biodiversity in Marine Conservation Planning

PubMed Central

Grantham, Hedley S.; Game, Edward T.; Lombard, Amanda T.; Hobday, Alistair J.; Richardson, Anthony J.; Beckley, Lynnath E.; Pressey, Robert L.; Huggett, Jenny A.; Coetzee, Janet C.; van der Lingen, Carl D.; Petersen, Samantha L.; Merkle, Dagmar; Possingham, Hugh P.

2011-01-01

Pelagic ecosystems support a significant and vital component of the ocean's productivity and biodiversity. They are also heavily exploited and, as a result, are the focus of numerous spatial planning initiatives. Over the past decade, there has been increasing enthusiasm for protected areas as a tool for pelagic conservation, however, few have been implemented. Here we demonstrate an approach to plan protected areas that address the physical and biological dynamics typical of the pelagic realm. Specifically, we provide an example of an approach to planning protected areas that integrates pelagic and benthic conservation in the southern Benguela and Agulhas Bank ecosystems off South Africa. Our aim was to represent species of importance to fisheries and species of conservation concern within protected areas. In addition to representation, we ensured that protected areas were designed to consider pelagic dynamics, characterized from time-series data on key oceanographic processes, together with data on the abundance of small pelagic fishes. We found that, to have the highest likelihood of reaching conservation targets, protected area selection should be based on time-specific data rather than data averaged across time. More generally, we argue that innovative methods are needed to conserve ephemeral and dynamic pelagic biodiversity. PMID:21311757

Method of conditional moments (MCM) for the Chemical Master Equation: a unified framework for the method of moments and hybrid stochastic-deterministic models.

PubMed

Hasenauer, J; Wolf, V; Kazeroonian, A; Theis, F J

2014-09-01

The time-evolution of continuous-time discrete-state biochemical processes is governed by the Chemical Master Equation (CME), which describes the probability of the molecular counts of each chemical species. As the corresponding number of discrete states is, for most processes, large, a direct numerical simulation of the CME is in general infeasible. In this paper we introduce the method of conditional moments (MCM), a novel approximation method for the solution of the CME. The MCM employs a discrete stochastic description for low-copy number species and a moment-based description for medium/high-copy number species. The moments of the medium/high-copy number species are conditioned on the state of the low abundance species, which allows us to capture complex correlation structures arising, e.g., for multi-attractor and oscillatory systems. We prove that the MCM provides a generalization of previous approximations of the CME based on hybrid modeling and moment-based methods. Furthermore, it improves upon these existing methods, as we illustrate using a model for the dynamics of stochastic single-gene expression. This application example shows that due to the more general structure, the MCM allows for the approximation of multi-modal distributions.

Simulating coupled dynamics of a rigid-flexible multibody system and compressible fluid

NASA Astrophysics Data System (ADS)

Hu, Wei; Tian, Qiang; Hu, HaiYan

2018-04-01

As a subsequent work of previous studies of authors, a new parallel computation approach is proposed to simulate the coupled dynamics of a rigid-flexible multibody system and compressible fluid. In this approach, the smoothed particle hydrodynamics (SPH) method is used to model the compressible fluid, the natural coordinate formulation (NCF) and absolute nodal coordinate formulation (ANCF) are used to model the rigid and flexible bodies, respectively. In order to model the compressible fluid properly and efficiently via SPH method, three measures are taken as follows. The first is to use the Riemann solver to cope with the fluid compressibility, the second is to define virtual particles of SPH to model the dynamic interaction between the fluid and the multibody system, and the third is to impose the boundary conditions of periodical inflow and outflow to reduce the number of SPH particles involved in the computation process. Afterwards, a parallel computation strategy is proposed based on the graphics processing unit (GPU) to detect the neighboring SPH particles and to solve the dynamic equations of SPH particles in order to improve the computation efficiency. Meanwhile, the generalized-alpha algorithm is used to solve the dynamic equations of the multibody system. Finally, four case studies are given to validate the proposed parallel computation approach.

Limitless capacity: a dynamic object-oriented approach to short-term memory.

PubMed

Macken, Bill; Taylor, John; Jones, Dylan

2015-01-01

The notion of capacity-limited processing systems is a core element of cognitive accounts of limited and variable performance, enshrined within the short-term memory construct. We begin with a detailed critical analysis of the conceptual bases of this view and argue that there are fundamental problems - ones that go to the heart of cognitivism more generally - that render it untenable. In place of limited capacity systems, we propose a framework for explaining performance that focuses on the dynamic interplay of three aspects of any given setting: the particular task that must be accomplished, the nature and form of the material upon which the task must be performed, and the repertoire of skills and perceptual-motor functions possessed by the participant. We provide empirical examples of the applications of this framework in areas of performance typically accounted for by reference to capacity-limited short-term memory processes.

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time.

PubMed

Galán, S F; Aguado, F; Díez, F J; Mira, J

2002-07-01

The spread of cancer is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is general enough to be applied to any other type of cancer.

Classical simulation of quantum error correction in a Fibonacci anyon code

NASA Astrophysics Data System (ADS)

Burton, Simon; Brell, Courtney G.; Flammia, Steven T.

2017-02-01

Classically simulating the dynamics of anyonic excitations in two-dimensional quantum systems is likely intractable in general because such dynamics are sufficient to implement universal quantum computation. However, processes of interest for the study of quantum error correction in anyon systems are typically drawn from a restricted class that displays significant structure over a wide range of system parameters. We exploit this structure to classically simulate, and thereby demonstrate the success of, an error-correction protocol for a quantum memory based on the universal Fibonacci anyon model. We numerically simulate a phenomenological model of the system and noise processes on lattice sizes of up to 128 ×128 sites, and find a lower bound on the error-correction threshold of approximately 0.125 errors per edge, which is comparable to those previously known for Abelian and (nonuniversal) non-Abelian anyon models.

On the line: worker democracy and the struggle over occupational health and safety.

PubMed

Granzow, Kara; Theberge, Nancy

2009-01-01

In this article we present a qualitative analysis of worker involvement in a participatory project to improve occupational health and safety at a Canadian manufacturing site. Based on interviews with workers in the plant, we consider the manner and degree to which workers experienced meaningful participation in the intervention process and some of the main barriers to worker participation. Findings emphasize the importance of the social and political context in conditioning the dynamics of joint management labor ventures specifically in relation to health initiatives. Interviews revealed few instances in which workers felt included in the participatory initiative; most often they felt marginalized. In the absence of structural change in the plant, workers described the health initiative as seriously limited in its ability to render meaningful worker participation. These results extend beyond this analysis of a participatory workplace health initiative, offering insights into the dynamics of institutional participatory process, and into participatory research practice generally.

Loss of 'complexity' and aging. Potential applications of fractals and chaos theory to senescence

NASA Technical Reports Server (NTRS)

Lipsitz, L. A.; Goldberger, A. L.

1992-01-01

The concept of "complexity," derived from the field of nonlinear dynamics, can be adapted to measure the output of physiologic processes that generate highly variable fluctuations resembling "chaos." We review data suggesting that physiologic aging is associated with a generalized loss of such complexity in the dynamics of healthy organ system function and hypothesize that such loss of complexity leads to an impaired ability to adapt to physiologic stress. This hypothesis is supported by observations showing an age-related loss of complex variability in multiple physiologic processes including cardiovascular control, pulsatile hormone release, and electroencephalographic potentials. If further research supports this hypothesis, measures of complexity based on chaos theory and the related geometric concept of fractals may provide new ways to monitor senescence and test the efficacy of specific interventions to modify the age-related decline in adaptive capacity.

Parallel Processing of Adaptive Meshes with Load Balancing

NASA Technical Reports Server (NTRS)

Das, Sajal K.; Harvey, Daniel J.; Biswas, Rupak; Biegel, Bryan (Technical Monitor)

2001-01-01

Many scientific applications involve grids that lack a uniform underlying structure. These applications are often also dynamic in nature in that the grid structure significantly changes between successive phases of execution. In parallel computing environments, mesh adaptation of unstructured grids through selective refinement/coarsening has proven to be an effective approach. However, achieving load balance while minimizing interprocessor communication and redistribution costs is a difficult problem. Traditional dynamic load balancers are mostly inadequate because they lack a global view of system loads across processors. In this paper, we propose a novel and general-purpose load balancer that utilizes symmetric broadcast networks (SBN) as the underlying communication topology, and compare its performance with a successful global load balancing environment, called PLUM, specifically created to handle adaptive unstructured applications. Our experimental results on an IBM SP2 demonstrate that the SBN-based load balancer achieves lower redistribution costs than that under PLUM by overlapping processing and data migration.

Controlled Folding, Motional, and Constitutional Dynamic Processes of Polyheterocyclic Molecular Strands.

PubMed

Barboiu, Mihail; Stadler, Adrian-Mihail; Lehn, Jean-Marie

2016-03-18

General design principles have been developed for the control of the structural features of polyheterocyclic strands and their effector-modulated shape changes. Induced defined molecular motions permit designed enforcement of helical as well as linear molecular shapes. The ability of such molecular strands to bind metal cations allows the generation of coiling/uncoiling processes between helically folded and extended linear states. Large molecular motions are produced on coordination of metal ions, which may be made reversible by competition with an ancillary complexing agent and fueled by sequential acid/base neutralization energy. The introduction of hydrazone units into the strands confers upon them constitutional dynamics, whereby interconversion between different strand compositions is achieved through component exchange. These features have relevance for nanomechanical devices. We present a morphological and functional analysis of such systems developed in our laboratories. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Use of a Computer Language in Teaching Dynamic Programming. Final Report.

ERIC Educational Resources Information Center

Trimble, C. J.; And Others

Most optimization problems of any degree of complexity must be solved using a computer. In the teaching of dynamic programing courses, it is often desirable to use a computer in problem solution. The solution process involves conceptual formulation and computational Solution. Generalized computer codes for dynamic programing problem solution…

Diagnosis of Processes Controlling Dissolved Organic Carbon (DOC) Export in a Subarctic Region by a Dynamic Ecosystem Model

NASA Astrophysics Data System (ADS)

Tang, J.

2015-12-01

Permafrost thawing in high latitudes allows more soil organic carbon (SOC) to become hydrologically accessible. This can increase dissolved organic carbon (DOC) exports and carbon release to the atmosphere as CO2 and CH4, with a positive feedback to regional and global climate warming. However, this portion of carbon loss through DOC export is often neglected in ecosystem models. In this paper, we incorporate a set of DOC-related processes (DOC production, mineralization, diffusion, sorption-desorption and leaching) into an Arctic-enabled version of the dynamic ecosystem model LPJ-GUESS (LPJ-GUESS WHyMe) to mechanistically model the DOC export, and to link this flux to other ecosystem processes. The extended LPJ-GUESS WHyMe with these DOC processes is applied to the Stordalen catchment in northern Sweden. The relative importance of different DOC-related processes for mineral and peatland soils for this region have been explored at both monthly and annual scales based on a detailed variance-based Sobol sensitivity analysis. For mineral soils, the annual DOC export is dominated by DOC fluxes in snowmelt seasons and the peak in spring is related to the runoff passing through top organic rich layers. Two processes, DOC sorption-desorption and production, are found to contribute most to the annual variance in DOC export. For peatland soils, the DOC export during snowmelt seasons is constrained by frozen soils and the processes of DOC production and mineralization, determining the magnitudes of DOC desorption in snowmelt seasons as well as DOC sorption in the rest of months, play the most important role in annual variances of DOC export. Generally, the seasonality of DOC fluxes is closely correlated with runoff seasonality in this region. The current implementation has demonstrated that DOC-related processes in the framework of LPJ-GUESS WHyMe are at an appropriate level of complexity to represent the main mechanism of DOC dynamics in soils. The quantified contributions from different processes on DOC export dynamics could be further linked to the climate change, vegetation composition change and permafrost thawing in this region.

157. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

157. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) PLANS, SECTIONS, AND DETAILS FOR MST STATION 85.5, SHEET S90 - Vandenberg Air Force Base, Space Launch Complex 3, Launch Pad 3 East, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

173. Photocopy of drawing (1963 piping drawing by General Dynamics/Astronautics) ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

173. Photocopy of drawing (1963 piping drawing by General Dynamics/Astronautics) COMPRESSED AIR AND WATER SYSTEM SCHEMATIC FOR THE MST, SHEET P38 - Vandenberg Air Force Base, Space Launch Complex 3, Launch Pad 3 East, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

152. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

152. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) STRUCTURAL DETAILS FOR MST STATIONS 21, 30, 39, AND 48, SHEET S98 - Vandenberg Air Force Base, Space Launch Complex 3, Launch Pad 3 East, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

224. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

224. Photocopy of drawing (1963 structural drawing by General Dynamics/Astronautics) UMBILICAL MAST WIND DEFLECTOR REQUIRED FOR 206 PROGRAM, PAD, SHEET S-101 - Vandenberg Air Force Base, Space Launch Complex 3, Launch Pad 3 East, Napa & Alden Roads, Lompoc, Santa Barbara County, CA

Epidemic spreading in weighted networks: an edge-based mean-field solution.

PubMed

Yang, Zimo; Zhou, Tao

2012-05-01

Weight distribution greatly impacts the epidemic spreading taking place on top of networks. This paper presents a study of a susceptible-infected-susceptible model on regular random networks with different kinds of weight distributions. Simulation results show that the more homogeneous weight distribution leads to higher epidemic prevalence, which, unfortunately, could not be captured by the traditional mean-field approximation. This paper gives an edge-based mean-field solution for general weight distribution, which can quantitatively reproduce the simulation results. This method could be applied to characterize the nonequilibrium steady states of dynamical processes on weighted networks.

Diagnosis of dynamic process over rainband of landfall typhoon

NASA Astrophysics Data System (ADS)

Ran, Ling-Kun; Yang, Wen-Xia; Chu, Yan-Li

2010-07-01

This paper introduces a new physical parameter — thermodynamic shear advection parameter combining the perturbation vertical component of convective vorticity vector with the coupling of horizontal divergence perturbation and vertical gradient of general potential temperature perturbation. For a heavy-rainfall event resulting from the landfall typhoon 'Wipha', the parameter is calculated by using National Centres for Enviromental Prediction/National Centre for Atmospheric Research global final analysis data. The results showed that the parameter corresponds to the observed 6 h accumulative rainband since it is capable of catching hold of the dynamic and thermodynamic disturbance in the lower troposphere over the observed rainband. Before the typhoon landed, the advection of the parameter by basic-state flow and the coupling of general potential temperature perturbation with curl of Coriolis force perturbation are the primary dynamic processes which are responsible for the local change of the parameter. After the typhoon landed, the disturbance is mainly driven by the combination of five primary dynamic processes. The advection of the parameter by basic-state flow was weakened after the typhoon landed.

SIERRA Multimechanics Module: Aria User Manual Version 4.44

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

Sierra Thermal /Fluid Team

2017-04-01

Aria is a Galerkin fnite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process fows via the incompressible Navier-Stokes equations specialized to a low Reynolds number ( %3C 1 ) regime. Enhanced modeling support of manufacturing processing is made possible through use of eithermore » arbitrary Lagrangian- Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newton's method with analytic or numerical sensitivities, fully-coupled Newton- Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic h -adaptivity and dynamic load balancing are some of Aria's more advanced capabilities. Aria is based upon the Sierra Framework.« less

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

Sierra Thermal/Fluid Team

Aria is a Galerkin fnite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process fows via the incompressible Navier-Stokes equations specialized to a low Reynolds number ( %3C 1 ) regime. Enhanced modeling support of manufacturing processing is made possible through use of eithermore » arbitrary Lagrangian- Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newton's method with analytic or numerical sensitivities, fully-coupled Newton- Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic h -adaptivity and dynamic load balancing are some of Aria's more advanced capabilities. Aria is based upon the Sierra Framework.« less

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

Sierra Thermal /Fluid Team

Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process flows via the incompressible Navier-Stokes equations specialized to a low Reynolds number (Re %3C 1) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrarymore » Lagrangian- Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newton's method with analytic or numerical sensitivities, fully-coupled Newton- Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic h-adaptivity and dynamic load balancing are some of Aria's more advanced capabilities. Aria is based upon the Sierra Framework.« less

Wavelet and adaptive methods for time dependent problems and applications in aerosol dynamics

NASA Astrophysics Data System (ADS)

Guo, Qiang

Time dependent partial differential equations (PDEs) are widely used as mathematical models of environmental problems. Aerosols are now clearly identified as an important factor in many environmental aspects of climate and radiative forcing processes, as well as in the health effects of air quality. The mathematical models for the aerosol dynamics with respect to size distribution are nonlinear partial differential and integral equations, which describe processes of condensation, coagulation and deposition. Simulating the general aerosol dynamic equations on time, particle size and space exhibits serious difficulties because the size dimension ranges from a few nanometer to several micrometer while the spatial dimension is usually described with kilometers. Therefore, it is an important and challenging task to develop efficient techniques for solving time dependent dynamic equations. In this thesis, we develop and analyze efficient wavelet and adaptive methods for the time dependent dynamic equations on particle size and further apply them to the spatial aerosol dynamic systems. Wavelet Galerkin method is proposed to solve the aerosol dynamic equations on time and particle size due to the fact that aerosol distribution changes strongly along size direction and the wavelet technique can solve it very efficiently. Daubechies' wavelets are considered in the study due to the fact that they possess useful properties like orthogonality, compact support, exact representation of polynomials to a certain degree. Another problem encountered in the solution of the aerosol dynamic equations results from the hyperbolic form due to the condensation growth term. We propose a new characteristic-based fully adaptive multiresolution numerical scheme for solving the aerosol dynamic equation, which combines the attractive advantages of adaptive multiresolution technique and the characteristics method. On the aspect of theoretical analysis, the global existence and uniqueness of solutions of continuous time wavelet numerical methods for the nonlinear aerosol dynamics are proved by using Schauder's fixed point theorem and the variational technique. Optimal error estimates are derived for both continuous and discrete time wavelet Galerkin schemes. We further derive reliable and efficient a posteriori error estimate which is based on stable multiresolution wavelet bases and an adaptive space-time algorithm for efficient solution of linear parabolic differential equations. The adaptive space refinement strategies based on the locality of corresponding multiresolution processes are proved to converge. At last, we develop efficient numerical methods by combining the wavelet methods proposed in previous parts and the splitting technique to solve the spatial aerosol dynamic equations. Wavelet methods along the particle size direction and the upstream finite difference method along the spatial direction are alternately used in each time interval. Numerical experiments are taken to show the effectiveness of our developed methods.

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes.

PubMed

Kuang, Zheng; Ji, Zhicheng; Boeke, Jef D; Ji, Hongkai

2018-01-09

Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Gender-Linked Language Effect: An Empirical Test of a General Process Model

ERIC Educational Resources Information Center

Mulac, Anthony; Giles, Howard; Bradac, James J.; Palomares, Nicholas A.

2013-01-01

The gender-linked language effect (GLLE) is a phenomenon in which transcripts of female communicators are rated higher on Socio-Intellectual Status and Aesthetic Quality and male communicators are rated higher on Dynamism. This study proposed and tested a new general process model explanation for the GLLE, a central mediating element of which…

Urban chaos and replacement dynamics in nature and society

NASA Astrophysics Data System (ADS)

Chen, Yanguang

2014-11-01

Replacements resulting from competition are ubiquitous phenomena in both nature and society. The evolution of a self-organized system is always a physical process substituting one type of components for another type of components. A logistic model of replacement dynamics has been proposed in terms of technical innovation and urbanization, but it fails to arouse widespread attention in the academia. This paper is devoted to laying the foundations of general replacement principle by using analogy and induction. The empirical base of this study is urban replacement, including urbanization and urban growth. The sigmoid functions can be employed to model various processes of replacement. Many mathematical methods such as allometric scaling and head/tail breaks can be applied to analyzing the processes and patterns of replacement. Among varied sigmoid functions, the logistic function is the basic and the simplest model of replacement dynamics. A new finding is that replacement can be associated with chaos in a nonlinear system, e.g., urban chaos is just a part of replacement dynamics. The aim of developing replacement theory is at understanding complex interaction and conversion. This theory provides a new way of looking at urbanization, technological innovation and diffusion, Volterra-Lotka’s predator-prey interaction, man-land relation, and dynastic changes resulting from peasant uprising, and all that. Especially, the periodic oscillations and chaos of replacement dynamics can be used to explain and predict the catastrophic occurrences in the physical and human systems.

Estimations of Vertical Velocities Using the Omega Equation in Different Flow Regimes in Preparation for the High Resolution Observations of the SWOT Altimetry Mission

NASA Astrophysics Data System (ADS)

Pietri, A.; Capet, X.; d'Ovidio, F.; Le Sommer, J.; Molines, J. M.; Doglioli, A. M.

2016-02-01

Vertical velocities (w) associated with meso and submesoscale processes play an essential role in ocean dynamics and physical-biological coupling due to their impact on the upper ocean vertical exchanges. However, their small intensity (O 1 cm/s) compared to horizontal motions and their important variability in space and time makes them very difficult to measure. Estimations of these velocities are thus usually inferred using a generalized approach based on frontogenesis theories. These estimations are often obtained by solving the diagnostic omega equation. This equation can be expressed in different forms from a simple quasi geostrophic formulation to more complex ones that take into account the ageostrophic advection and the turbulent fluxes. The choice of the method used generally depends on the data available and on the dominant processes in the region of study. Here we aim to provide a statistically robust evaluation of the scales at which the vertical velocity can be resolved with confidence depending on the formulation of the equation and the dynamics of the flow. A high resolution simulation (dx=1-1.5 km) of the North Atlantic was used to compare the calculations of w based on the omega equation to the modelled vertical velocity. The simulation encompasses regions with different atmospheric forcings, mesoscale activity, seasonality and energetic flows, allowing us to explore several different dynamical contexts. In a few years the SWOT mission will provide bi-dimensional images of sea level elevation at a significantly higher resolution than available today. This work helps assess the possible contribution of the SWOT data to the understanding of the submesoscale circulation and the associated vertical fluxes in the upper ocean.

The applications of Complexity Theory and Tsallis Non-extensive Statistics at Solar Plasma Dynamics

NASA Astrophysics Data System (ADS)

Pavlos, George

2015-04-01

As the solar plasma lives far from equilibrium it is an excellent laboratory for testing complexity theory and non-equilibrium statistical mechanics. In this study, we present the highlights of complexity theory and Tsallis non extensive statistical mechanics as concerns their applications at solar plasma dynamics, especially at sunspot, solar flare and solar wind phenomena. Generally, when a physical system is driven far from equilibrium states some novel characteristics can be observed related to the nonlinear character of dynamics. Generally, the nonlinearity in space plasma dynamics can generate intermittent turbulence with the typical characteristics of the anomalous diffusion process and strange topologies of stochastic space plasma fields (velocity and magnetic fields) caused by the strange dynamics and strange kinetics (Zaslavsky, 2002). In addition, according to Zelenyi and Milovanov (2004) the complex character of the space plasma system includes the existence of non-equilibrium (quasi)-stationary states (NESS) having the topology of a percolating fractal set. The stabilization of a system near the NESS is perceived as a transition into a turbulent state determined by self-organization processes. The long-range correlation effects manifest themselves as a strange non-Gaussian behavior of kinetic processes near the NESS plasma state. The complex character of space plasma can also be described by the non-extensive statistical thermodynamics pioneered by Tsallis, which offers a consistent and effective theoretical framework, based on a generalization of Boltzmann - Gibbs (BG) entropy, to describe far from equilibrium nonlinear complex dynamics (Tsallis, 2009). In a series of recent papers, the hypothesis of Tsallis non-extensive statistics in magnetosphere, sunspot dynamics, solar flares, solar wind and space plasma in general, was tested and verified (Karakatsanis et al., 2013; Pavlos et al., 2014; 2015). Our study includes the analysis of solar plasma time series at three cases: sunspot index, solar flare and solar wind data. The non-linear analysis of the sunspot index is embedded in the non-extensive statistical theory of Tsallis (1988; 2004; 2009). The q-triplet of Tsallis, as well as the correlation dimension and the Lyapunov exponent spectrum were estimated for the SVD components of the sunspot index timeseries. Also the multifractal scaling exponent spectrum f(a), the generalized Renyi dimension spectrum D(q) and the spectrum J(p) of the structure function exponents were estimated experimentally and theoretically by using the q-entropy principle included in Tsallis non-extensive statistical theory, following Arimitsu and Arimitsu (2000, 2001). Our analysis showed clearly the following: (a) a phase transition process in the solar dynamics from high dimensional non-Gaussian SOC state to a low dimensional non-Gaussian chaotic state, (b) strong intermittent solar turbulence and anomalous (multifractal) diffusion solar process, which is strengthened as the solar dynamics makes a phase transition to low dimensional chaos in accordance to Ruzmaikin, Zelenyi and Milovanov's studies (Zelenyi and Milovanov, 1991; Milovanov and Zelenyi, 1993; Ruzmakin et al., 1996), (c) faithful agreement of Tsallis non-equilibrium statistical theory with the experimental estimations of: (i) non-Gaussian probability distribution function P(x), (ii) multifractal scaling exponent spectrum f(a) and generalized Renyi dimension spectrum Dq, (iii) exponent spectrum J(p) of the structure functions estimated for the sunspot index and its underlying non equilibrium solar dynamics. Also, the q-triplet of Tsallis as well as the correlation dimension and the Lyapunov exponent spectrum were estimated for the singular value decomposition (SVD) components of the solar flares timeseries. Also the multifractal scaling exponent spectrum f(a), the generalized Renyi dimension spectrum D(q) and the spectrum J(p) of the structure function exponents were estimated experimentally and theoretically by using the q-entropy principle included in Tsallis non-extensive statistical theory, following Arimitsu and Arimitsu (2000). Our analysis showed clearly the following: (a) a phase transition process in the solar flare dynamics from a high dimensional non-Gaussian self-organized critical (SOC) state to a low dimensional also non-Gaussian chaotic state, (b) strong intermittent solar corona turbulence and an anomalous (multifractal) diffusion solar corona process, which is strengthened as the solar corona dynamics makes a phase transition to low dimensional chaos, (c) faithful agreement of Tsallis non-equilibrium statistical theory with the experimental estimations of the functions: (i) non-Gaussian probability distribution function P(x), (ii) f(a) and D(q), and (iii) J(p) for the solar flares timeseries and its underlying non-equilibrium solar dynamics, and (d) the solar flare dynamical profile is revealed similar to the dynamical profile of the solar corona zone as far as the phase transition process from self-organized criticality (SOC) to chaos state. However the solar low corona (solar flare) dynamical characteristics can be clearly discriminated from the dynamical characteristics of the solar convection zone. At last we present novel results revealing non-equilibrium phase transition processes in the solar wind plasma during a strong shock event, which can take place in Solar wind plasma system. The solar wind plasma as well as the entire solar plasma system is a typical case of stochastic spatiotemporal distribution of physical state variables such as force fields ( ) and matter fields (particle and current densities or bulk plasma distributions). This study shows clearly the non-extensive and non-Gaussian character of the solar wind plasma and the existence of multi-scale strong correlations from the microscopic to the macroscopic level. It also underlines the inefficiency of classical magneto-hydro-dynamic (MHD) or plasma statistical theories, based on the classical central limit theorem (CLT), to explain the complexity of the solar wind dynamics, since these theories include smooth and differentiable spatial-temporal functions (MHD theory) or Gaussian statistics (Boltzmann-Maxwell statistical mechanics). On the contrary, the results of this study indicate the presence of non-Gaussian non-extensive statistics with heavy tails probability distribution functions, which are related to the q-extension of CLT. Finally, the results of this study can be understood in the framework of modern theoretical concepts such as non-extensive statistical mechanics (Tsallis, 2009), fractal topology (Zelenyi and Milovanov, 2004), turbulence theory (Frisch, 1996), strange dynamics (Zaslavsky, 2002), percolation theory (Milovanov, 1997), anomalous diffusion theory and anomalous transport theory (Milovanov, 2001), fractional dynamics (Tarasov, 2013) and non-equilibrium phase transition theory (Chang, 1992). References 1. T. Arimitsu, N. Arimitsu, Tsallis statistics and fully developed turbulence, J. Phys. A: Math. Gen. 33 (2000) L235. 2. T. Arimitsu, N. Arimitsu, Analysis of turbulence by statistics based on generalized entropies, Physica A 295 (2001) 177-194. 3. T. Chang, Low-dimensional behavior and symmetry braking of stochastic systems near criticality can these effects be observed in space and in the laboratory, IEEE 20 (6) (1992) 691-694. 4. U. Frisch, Turbulence, Cambridge University Press, Cambridge, UK, 1996, p. 310. 5. L.P. Karakatsanis, G.P. Pavlos, M.N. Xenakis, Tsallis non-extensive statistics, intermittent turbulence, SOC and chaos in the solar plasma. Part two: Solar flares dynamics, Physica A 392 (2013) 3920-3944. 6. A.V. Milovanov, Topological proof for the Alexander-Orbach conjecture, Phys. Rev. E 56 (3) (1997) 2437-2446. 7. A.V. Milovanov, L.M. Zelenyi, Fracton excitations as a driving mechanism for the self-organized dynamical structuring in the solar wind, Astrophys. Space Sci. 264 (1-4) (1999) 317-345. 8. A.V. Milovanov, Stochastic dynamics from the fractional Fokker-Planck-Kolmogorov equation: large-scale behavior of the turbulent transport coefficient, Phys. Rev. E 63 (2001) 047301. 9. G.P. Pavlos, et al., Universality of non-extensive Tsallis statistics and time series analysis: Theory and applications, Physica A 395 (2014) 58-95. 10. G.P. Pavlos, et al., Tsallis non-extensive statistics and solar wind plasma complexity, Physica A 422 (2015) 113-135. 11. A.A. Ruzmaikin, et al., Spectral properties of solar convection and diffusion, ApJ 471 (1996) 1022. 12. V.E. Tarasov, Review of some promising fractional physical models, Internat. J. Modern Phys. B 27 (9) (2013) 1330005. 13. C. Tsallis, Possible generalization of BG statistics, J. Stat. Phys. J 52 (1-2) (1988) 479-487. 14. C. Tsallis, Nonextensive statistical mechanics: construction and physical interpretation, in: G.M. Murray, C. Tsallis (Eds.), Nonextensive Entropy-Interdisciplinary Applications, Oxford Univ. Press, 2004, pp. 1-53. 15. C. Tsallis, Introduction to Non-Extensive Statistical Mechanics, Springer, 2009. 16. G.M. Zaslavsky, Chaos, fractional kinetics, and anomalous transport, Physics Reports 371 (2002) 461-580. 17. L.M. Zelenyi, A.V. Milovanov, Fractal properties of sunspots, Sov. Astron. Lett. 17 (6) (1991) 425. 18. L.M. Zelenyi, A.V. Milovanov, Fractal topology and strange kinetics: from percolation theory to problems in cosmic electrodynamics, Phys.-Usp. 47 (8), (2004) 749-788.

Improved laser-based triangulation sensor with enhanced range and resolution through adaptive optics-based active beam control.

PubMed

Reza, Syed Azer; Khwaja, Tariq Shamim; Mazhar, Mohsin Ali; Niazi, Haris Khan; Nawab, Rahma

2017-07-20

Various existing target ranging techniques are limited in terms of the dynamic range of operation and measurement resolution. These limitations arise as a result of a particular measurement methodology, the finite processing capability of the hardware components deployed within the sensor module, and the medium through which the target is viewed. Generally, improving the sensor range adversely affects its resolution and vice versa. Often, a distance sensor is designed for an optimal range/resolution setting depending on its intended application. Optical triangulation is broadly classified as a spatial-signal-processing-based ranging technique and measures target distance from the location of the reflected spot on a position sensitive detector (PSD). In most triangulation sensors that use lasers as a light source, beam divergence-which severely affects sensor measurement range-is often ignored in calculations. In this paper, we first discuss in detail the limitations to ranging imposed by beam divergence, which, in effect, sets the sensor dynamic range. Next, we show how the resolution of laser-based triangulation sensors is limited by the interpixel pitch of a finite-sized PSD. In this paper, through the use of tunable focus lenses (TFLs), we propose a novel design of a triangulation-based optical rangefinder that improves both the sensor resolution and its dynamic range through adaptive electronic control of beam propagation parameters. We present the theory and operation of the proposed sensor and clearly demonstrate a range and resolution improvement with the use of TFLs. Experimental results in support of our claims are shown to be in strong agreement with theory.

Protein dynamics in a broad frequency range: Dielectric spectroscopy studies

DOE PAGES

Nakanishi, Masahiro; Sokolov, Alexei P.

2014-09-17

We present detailed dielectric spectroscopy studies of dynamics in two hydrated proteins, lysozyme and myoglobin. We emphasize the importance of explicit account for possible Maxwell-Wagner (MW) polarization effects in protein powder samples. Combining our data with earlier literature results, we demonstrate the existence of three major relaxation processes in globular proteins. To understand the mechanisms of these relaxations we involve literature data on neutron scattering, simulations and NMR studies. The faster process is ascribed to coupled protein-hydration water motions and has relaxation time similar to 10-50 Ps at room temperature. The intermediate process is similar to 10(2)-10(3) times slower thanmore » the faster process and might be strongly affected by MW polarizations. Based on the analysis of data obtained by different experimental techniques and simulations, we ascribe this process to large scale domain-like motions of proteins. The slowest observed process is similar to 10(6)-10(7) times slower than the faster process and has anomalously large dielectric amplitude Delta epsilon similar to 10(2)-10(4). The microscopic nature of this process is not clear, but it seems to be related to the glass transition of hydrated proteins. The presentedresults suggest a general classification of the relaxation processes in hydrated proteins. (c) 2014 Elsevier B.V. All rights reserved.« less

Tests of general relativity from gravitational wave observations of binary black holes

NASA Astrophysics Data System (ADS)

Del Pozzo, Walter

2017-01-01

Gravitational waves emitted during the coalescence of compact binary systems carry a wealth of information about the merging objects, the remnant object as well as their interaction with space-time. The description of the dynamics of such systems is based on solutions of the theory of general relativity. For any given physical configuration of masses, spins and orbital motion, general relativity predicts the dynamical evolution of the binary system as well as the corresponding gravitational wave signal. During the coalescence of extremely compact objects such as binary black holes, the typical curvature and velocity at play are such that, from the observation of the gravitational wave signal, we can access the most extreme dynamical regimes of gravity. In such conditions, we can test our understanding of gravity by looking for potential departures between the solutions of general relativity and the actual dynamics of space-time. The LIGO observations GW150914 and GW151226 provided wonderful testing grounds for general relativity in the, up to now unaccessible, strong-field dynamical regime of gravity. During my talk, I will review and discuss several of the tests that have been devised to detect violations of the predictions of general relativity from the observation of gravitational waves from coalescing binary systems. The discussion will be based on the results of the analysis of GW150914 and GW151226. Finally, I will conclude by discussing some of the future prospects of extending the current state-of-the-art methodologies to further aspects of general relativity.

Construction of non-Markovian coarse-grained models employing the Mori-Zwanzig formalism and iterative Boltzmann inversion

NASA Astrophysics Data System (ADS)

Yoshimoto, Yuta; Li, Zhen; Kinefuchi, Ikuya; Karniadakis, George Em

2017-12-01

We propose a new coarse-grained (CG) molecular simulation technique based on the Mori-Zwanzig (MZ) formalism along with the iterative Boltzmann inversion (IBI). Non-Markovian dissipative particle dynamics (NMDPD) taking into account memory effects is derived in a pairwise interaction form from the MZ-guided generalized Langevin equation. It is based on the introduction of auxiliary variables that allow for the replacement of a non-Markovian equation with a Markovian one in a higher dimensional space. We demonstrate that the NMDPD model exploiting MZ-guided memory kernels can successfully reproduce the dynamic properties such as the mean square displacement and velocity autocorrelation function of a Lennard-Jones system, as long as the memory kernels are appropriately evaluated based on the Volterra integral equation using the force-velocity and velocity-velocity correlations. Furthermore, we find that the IBI correction of a pair CG potential significantly improves the representation of static properties characterized by a radial distribution function and pressure, while it has little influence on the dynamic processes. Our findings suggest that combining the advantages of both the MZ formalism and IBI leads to an accurate representation of both the static and dynamic properties of microscopic systems that exhibit non-Markovian behavior.

Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

NASA Astrophysics Data System (ADS)

Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

2015-03-01

The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

Dynamic modeling and optimal joint torque coordination of advanced robotic systems

NASA Astrophysics Data System (ADS)

Kang, Hee-Jun

The development is documented of an efficient dynamic modeling algorithm and the subsequent optimal joint input load coordination of advanced robotic systems for industrial application. A closed-form dynamic modeling algorithm for the general closed-chain robotic linkage systems is presented. The algorithm is based on the transfer of system dependence from a set of open chain Lagrangian coordinates to any desired system generalized coordinate set of the closed-chain. Three different techniques for evaluation of the kinematic closed chain constraints allow the representation of the dynamic modeling parameters in terms of system generalized coordinates and have no restriction with regard to kinematic redundancy. The total computational requirement of the closed-chain system model is largely dependent on the computation required for the dynamic model of an open kinematic chain. In order to improve computational efficiency, modification of an existing open-chain KIC based dynamic formulation is made by the introduction of the generalized augmented body concept. This algorithm allows a 44 pct. computational saving over the current optimized one (O(N4), 5995 when N = 6). As means of resolving redundancies in advanced robotic systems, local joint torque optimization is applied for effectively using actuator power while avoiding joint torque limits. The stability problem in local joint torque optimization schemes is eliminated by using fictitious dissipating forces which act in the necessary null space. The performance index representing the global torque norm is shown to be satisfactory. In addition, the resulting joint motion trajectory becomes conservative, after a transient stage, for repetitive cyclic end-effector trajectories. The effectiveness of the null space damping method is shown. The modular robot, which is built of well defined structural modules from a finite-size inventory and is controlled by one general computer system, is another class of evolving, highly versatile, advanced robotic systems. Therefore, finally, a module based dynamic modeling algorithm is presented for the dynamic coordination of such reconfigurable modular robotic systems. A user interactive module based manipulator analysis program (MBMAP) has been coded in C language running on 4D/70 Silicon Graphics.

Dynamic self-assembly of charged colloidal strings and walls in simple fluid flows.

PubMed

Abe, Yu; Zhang, Bo; Gordillo, Leonardo; Karim, Alireza Mohammad; Francis, Lorraine F; Cheng, Xiang

2017-02-22

Colloidal particles can self-assemble into various ordered structures in fluid flows that have potential applications in biomedicine, materials synthesis and encryption. These dynamic processes are also of fundamental interest for probing the general principles of self-assembly under non-equilibrium conditions. Here, we report a simple microfluidic experiment, where charged colloidal particles self-assemble into flow-aligned 1D strings with regular particle spacing near a solid boundary. Using high-speed confocal microscopy, we systematically investigate the influence of flow rates, electrostatics and particle polydispersity on the observed string structures. By studying the detailed dynamics of stable flow-driven particle pairs, we quantitatively characterize interparticle interactions. Based on the results, we construct a simple model that explains the intriguing non-equilibrium self-assembly process. Our study shows that the colloidal strings arise from a delicate balance between attractive hydrodynamic coupling and repulsive electrostatic interaction between particles. Finally, we demonstrate that, with the assistance of transverse electric fields, a similar mechanism also leads to the formation of 2D colloidal walls.

Dynamics of Pure Shape, Relativity, and the Problem of Time

NASA Astrophysics Data System (ADS)

Barbour, Julian

A new approach to the dynamics of the universe based on work by Ó Murchadha, Foster, Anderson and the author is presented. The only kinematics presupposed is the spatial geometry needed to define configuration spaces in purely relational terms. A new formulation of the relativity principle based on Poincarés analysis of the problem of absolute and relative motion (Machs principle) is given. The entire dynamics is based on shape and nothing else. It leads to much stronger predictions than standard Newtonian theory. For the dynamics of Riemannian 3-geometries on which matter fields also evolve, implementation of the new relativity principle establishes unexpected links between special relativity, general relativity and the gauge principle. They all emerge together as a self-consistent complex from a unified and completely relational approach to dynamics. A connection between time and scale invariance is established. In particular, the representation of general relativity as evolution of the shape of space leads to a unique dynamical definition of simultaneity. This opens up the prospect of a solution of the problem of time in quantum gravity on the basis of a fundamental dynamical principle.

Dynamic functional connectivity of the default mode network tracks daydreaming.

PubMed

Kucyi, Aaron; Davis, Karen D

2014-10-15

Humans spend much of their time engaged in stimulus-independent thoughts, colloquially known as "daydreaming" or "mind-wandering." A fundamental question concerns how awake, spontaneous brain activity represents the ongoing cognition of daydreaming versus unconscious processes characterized as "intrinsic." Since daydreaming involves brief cognitive events that spontaneously fluctuate, we tested the hypothesis that the dynamics of brain network functional connectivity (FC) are linked with daydreaming. We determined the general tendency to daydream in healthy adults based on a daydreaming frequency scale (DDF). Subjects then underwent both resting state functional magnetic resonance imaging (rs-fMRI) and fMRI during sensory stimulation with intermittent thought probes to determine the occurrences of mind-wandering events. Brain regions within the default mode network (DMN), purported to be involved in daydreaming, were assessed for 1) static FC across the entire fMRI scans, and 2) dynamic FC based on FC variability (FCV) across 30s progressively sliding windows of 2s increments within each scan. We found that during both resting and sensory stimulation states, individual differences in DDF were negatively correlated with static FC between the posterior cingulate cortex and a ventral DMN subsystem involved in future-oriented thought. Dynamic FC analysis revealed that DDF was positively correlated with FCV within the same DMN subsystem in the resting state but not during stimulation. However, dynamic but not static FC, in this subsystem, was positively correlated with an individual's degree of self-reported mind-wandering during sensory stimulation. These findings identify temporal aspects of spontaneous DMN activity that reflect conscious and unconscious processes. Copyright © 2014 Elsevier Inc. All rights reserved.

A Physicist's view on Chopin's Études

NASA Astrophysics Data System (ADS)

Blasone, Massimo

2017-07-01

We propose the use of specific dynamical processes and more in general of ideas from Physics to model the evolution in time of musical structures. We apply this approach to two Études by F. Chopin, namely Op.10 n.3 and Op.25 n.1, proposing some original description based on concepts of symmetry breaking/restoration and quantum coherence, which could be useful for interpretation. In this analysis, we take advantage of colored musical scores, obtained by implementing Scriabin's color code for sounds to musical notation.

Training trajectories by continuous recurrent multilayer networks.

PubMed

Leistritz, L; Galicki, M; Witte, H; Kochs, E

2002-01-01

This paper addresses the problem of training trajectories by means of continuous recurrent neural networks whose feedforward parts are multilayer perceptrons. Such networks can approximate a general nonlinear dynamic system with arbitrary accuracy. The learning process is transformed into an optimal control framework where the weights are the controls to be determined. A training algorithm based upon a variational formulation of Pontryagin's maximum principle is proposed for such networks. Computer examples demonstrating the efficiency of the given approach are also presented.

Can longitudinal generalized estimating equation models distinguish network influence and homophily? An agent-based modeling approach to measurement characteristics.

PubMed

Sauser Zachrison, Kori; Iwashyna, Theodore J; Gebremariam, Achamyeleh; Hutchins, Meghan; Lee, Joyce M

2016-12-28

Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence. In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present. The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily.

Capturing the Interplay of Dynamics and Networks through Parameterizations of Laplacian Operators

DTIC Science & Technology

2016-08-24

important vertices and communities in the network. Specifically, for each dynamical process in this framework, we define a centrality measure that...vertices as a potential cluster (or community ) with respect to this process. We show that the subset-quality function generalizes the traditional conductance...compare the different perspectives they create on network structure. Subjects Network Science and Online Social Networks Keywords Network, Community

Exploring the Role of Social Media and Individual Behaviors in Flood Evacuation Processes: An Agent-Based Modeling Approach

NASA Astrophysics Data System (ADS)

Du, Erhu; Cai, Ximing; Sun, Zhiyong; Minsker, Barbara

2017-11-01

Flood warnings from various information sources are important for individuals to make evacuation decisions during a flood event. In this study, we develop a general opinion dynamics model to simulate how individuals update their flood hazard awareness when exposed to multiple information sources, including global broadcast, social media, and observations of neighbors' actions. The opinion dynamics model is coupled with a traffic model to simulate the evacuation processes of a residential community with a given transportation network. Through various scenarios, we investigate how social media affect the opinion dynamics and evacuation processes. We find that stronger social media can make evacuation processes more sensitive to the change of global broadcast and neighbor observations, and thus, impose larger uncertainty on evacuation rates (i.e., a large range of evacuation rates corresponding to sources of information). For instance, evacuation rates are lower when social media become more influential and individuals have less trust in global broadcast. Stubborn individuals can significantly affect the opinion dynamics and reduce evacuation rates. In addition, evacuation rates respond to the percentage of stubborn agents in a nonlinear manner, i.e., above a threshold, the impact of stubborn agents will be intensified by stronger social media. These results highlight the role of social media in flood evacuation processes and the need to monitor social media so that misinformation can be corrected in a timely manner. The joint impacts of social media, quality of flood warnings, and transportation capacity on evacuation rates are also discussed.

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

PubMed Central

Almquist, Zack W.; Butts, Carter T.

2015-01-01

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

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

PubMed

Almquist, Zack W; Butts, Carter T

2014-08-01

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

Galactic cannibalism. IV. The evidence-correlations between dynamical time scales and Bautz-Morgan type

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

McGlynn, T.A.; Ostriker, J.P.

1980-11-01

If the luminosity of supergiant cD galaxies in particular, and the Bautz-Morgan sequence of galaxy types in general, is produced by dynamical evolutionary processes, then one expects to find a correlation between dynamical times and ..delta..M/sub 12/, the magnitude difference between first and second brightest cluster members.

Indoor emission, dispersion and exposure of total particle-bound polycyclic aromatic hydrocarbons during cooking

NASA Astrophysics Data System (ADS)

Gao, Jun; Jian, Yating; Cao, Changsheng; Chen, Lei; Zhang, Xu

2015-11-01

Cooking processes highly contribute to indoor polycyclic aromatic hydrocarbon (PAH) pollution. High molecular weight and potentially carcinogenic PAHs are generally found attached to small particles, i.e., particulate phase PAHs (PPAHs). Due to the fact that indoor particle dynamics have been clear, describing the indoor dynamics of cooking-generated PPAHs within a specific time span is possible. This paper attempted to quantify the dynamic emission rate, simultaneous spatial dispersion and individual exposure of PPAHs using a cooking source. Experiments were conducted in a real-scale kitchen chamber to elucidate the time-resolved emission and effect of edible oil temperature and mass. Numerical simulations based on indoor particle dynamics were performed to obtain the spatial dispersion and individual inhalation intake of PPAHs under different emission and ventilation conditions. The present work examined the preheating cooking stage, at which edible oil is heated up to beyond its smoke point. The dynamic emission rate peak point occurred much earlier than the oil heating temperature. The total PPAH emission ranged from 2258 to 6578 ng upon heating 40-85 g of edible oil. The overall intake fraction by an individual within a period of 10 min, including 3 min for heating and 7 min for natural cooling, was generally ∼1/10,000. An important outcome of this work was that the overall intake fraction could be represented by multiplying the range hood escape efficiency by the inhalation-to-ventilation rate ratio, which would be no greater than the same ratio. The methodology and results of this work were extendible for the number-based assessment of PPAHs. This work is expected to help us understand the health risks due to inhalation exposure to cooking-generated PPAHs in the kitchen.

Modeling and Fault Simulation of Propellant Filling System

NASA Astrophysics Data System (ADS)

Jiang, Yunchun; Liu, Weidong; Hou, Xiaobo

2012-05-01

Propellant filling system is one of the key ground plants in launching site of rocket that use liquid propellant. There is an urgent demand for ensuring and improving its reliability and safety, and there is no doubt that Failure Mode Effect Analysis (FMEA) is a good approach to meet it. Driven by the request to get more fault information for FMEA, and because of the high expense of propellant filling, in this paper, the working process of the propellant filling system in fault condition was studied by simulating based on AMESim. Firstly, based on analyzing its structure and function, the filling system was modular decomposed, and the mathematic models of every module were given, based on which the whole filling system was modeled in AMESim. Secondly, a general method of fault injecting into dynamic system was proposed, and as an example, two typical faults - leakage and blockage - were injected into the model of filling system, based on which one can get two fault models in AMESim. After that, fault simulation was processed and the dynamic characteristics of several key parameters were analyzed under fault conditions. The results show that the model can simulate effectively the two faults, and can be used to provide guidance for the filling system maintain and amelioration.

A Control Scheme That Uses Dynamic Postural Synergies to Coordinate a Hybrid Walking Neuroprosthesis: Theory and Experiments.

PubMed

Alibeji, Naji A; Molazadeh, Vahidreza; Dicianno, Brad E; Sharma, Nitin

2018-01-01

A hybrid walking neuroprosthesis that combines functional electrical stimulation (FES) with a powered lower limb exoskeleton can be used to restore walking in persons with paraplegia. It provides therapeutic benefits of FES and torque reliability of the powered exoskeleton. Moreover, by harnessing metabolic power of muscles via FES, the hybrid combination has a potential to lower power consumption and reduce actuator size in the powered exoskeleton. Its control design, however, must overcome the challenges of actuator redundancy due to the combined use of FES and electric motor. Further, dynamic disturbances such as electromechanical delay (EMD) and muscle fatigue must be considered during the control design process. This ensures stability and control performance despite disparate dynamics of FES and electric motor. In this paper, a general framework to coordinate FES of multiple gait-governing muscles with electric motors is presented. A muscle synergy-inspired control framework is used to derive the controller and is motivated mainly to address the actuator redundancy issue. Dynamic postural synergies between FES of the muscles and the electric motors were artificially generated through optimizations and result in key dynamic postures when activated. These synergies were used in the feedforward path of the control system. A dynamic surface control technique, modified with a delay compensation term, is used as the feedback controller to address model uncertainty, the cascaded muscle activation dynamics, and EMD. To address muscle fatigue, the stimulation levels in the feedforward path were gradually increased based on a model-based fatigue estimate. A Lyapunov-based stability approach was used to derive the controller and guarantee its stability. The synergy-based controller was demonstrated experimentally on an able-bodied subject and person with an incomplete spinal cord injury.

Perceptions of the Effectiveness of System Dynamics-Based Interactive Learning Environments: An Empirical Study

ERIC Educational Resources Information Center

Qudrat-Ullah, Hassan

2010-01-01

The use of simulations in general and of system dynamics simulation based interactive learning environments (SDILEs) in particular is well recognized as an effective way of improving users' decision making and learning in complex, dynamic tasks. However, the effectiveness of SDILEs in classrooms has rarely been evaluated. This article describes…

Modeling the Structural Dynamic of Industrial Networks

NASA Astrophysics Data System (ADS)

Wilkinson, Ian F.; Wiley, James B.; Lin, Aizhong

Market systems consist of locally interacting agents who continuously pursue advantageous opportunities. Since the time of Adam Smith, a fundamental task of economics has been to understand how market systems develop and to explain their operation. During the intervening years, theory largely has stressed comparative statics analysis. Based on the assumptions of rational, utility or profit-maximizing agents, and negative, diminishing returns) feedback process, traditional economic analysis seeks to describe the, generally) unique state of an economy corresponding to an initial set of assumptions. The analysis is tatic in the sense that it does not describe the process by which an economy might get from one state to another.

Self-organized shocks in the sedimentation of a granular gas

NASA Astrophysics Data System (ADS)

Almazán, Lidia; Serero, Dan; Salueña, Clara; Pöschel, Thorsten

2015-06-01

A granular gas in gravity heated from below develops a certain stationary density profile. When the heating is switched off, the granular gas collapses. We investigate the process of sedimentation using computational hydrodynamics, based on the Jenkins-Richman theory, and find that the process is significantly more complex than generally acknowledged. In particular, during its evolution, the system passes several stages which reveal distinct spatial regions of inertial (supersonic) and diffusive (subsonic) dynamics. During the supersonic stages, characterized by Mach>1 , the system develops supersonic shocks which are followed by a steep front of the hydrodynamic fields of temperature and density, traveling upward.

Implications of the Turing machine model of computation for processor and programming language design

NASA Astrophysics Data System (ADS)

Hunter, Geoffrey

2004-01-01

A computational process is classified according to the theoretical model that is capable of executing it; computational processes that require a non-predeterminable amount of intermediate storage for their execution are Turing-machine (TM) processes, while those whose storage are predeterminable are Finite Automation (FA) processes. Simple processes (such as traffic light controller) are executable by Finite Automation, whereas the most general kind of computation requires a Turing Machine for its execution. This implies that a TM process must have a non-predeterminable amount of memory allocated to it at intermediate instants of its execution; i.e. dynamic memory allocation. Many processes encountered in practice are TM processes. The implication for computational practice is that the hardware (CPU) architecture and its operating system must facilitate dynamic memory allocation, and that the programming language used to specify TM processes must have statements with the semantic attribute of dynamic memory allocation, for in Alan Turing"s thesis on computation (1936) the "standard description" of a process is invariant over the most general data that the process is designed to process; i.e. the program describing the process should never have to be modified to allow for differences in the data that is to be processed in different instantiations; i.e. data-invariant programming. Any non-trivial program is partitioned into sub-programs (procedures, subroutines, functions, modules, etc). Examination of the calls/returns between the subprograms reveals that they are nodes in a tree-structure; this tree-structure is independent of the programming language used to encode (define) the process. Each sub-program typically needs some memory for its own use (to store values intermediate between its received data and its computed results); this locally required memory is not needed before the subprogram commences execution, and it is not needed after its execution terminates; it may be allocated as its execution commences, and deallocated as its execution terminates, and if the amount of this local memory is not known until just before execution commencement, then it is essential that it be allocated dynamically as the first action of its execution. This dynamically allocated/deallocated storage of each subprogram"s intermediate values, conforms with the stack discipline; i.e. last allocated = first to be deallocated, an incidental benefit of which is automatic overlaying of variables. This stack-based dynamic memory allocation was a semantic implication of the nested block structure that originated in the ALGOL-60 programming language. AGLOL-60 was a TM language, because the amount of memory allocated on subprogram (block/procedure) entry (for arrays, etc) was computable at execution time. A more general requirement of a Turing machine process is for code generation at run-time; this mandates access to the source language processor (compiler/interpretor) during execution of the process. This fundamental aspect of computer science is important to the future of system design, because it has been overlooked throughout the 55 years since modern computing began in 1048. The popular computer systems of this first half-century of computing were constrained by compile-time (or even operating system boot-time) memory allocation, and were thus limited to executing FA processes. The practical effect was that the distinction between the data-invariant program and its variable data was blurred; programmers had to make trial and error executions, modifying the program"s compile-time constants (array dimensions) to iterate towards the values required at run-time by the data being processed. This era of trial and error computing still persists; it pervades the culture of current (2003) computing practice.

Characterizing time series via complexity-entropy curves

NASA Astrophysics Data System (ADS)

Ribeiro, Haroldo V.; Jauregui, Max; Zunino, Luciano; Lenzi, Ervin K.

2017-06-01

The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q -complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.

Self-organizing neural integration of pose-motion features for human action recognition

PubMed Central

Parisi, German I.; Weber, Cornelius; Wermter, Stefan

2015-01-01

The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented toward human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR) networks that obtain progressively generalized representations of sensory inputs and learn inherent spatio-temporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions. PMID:26106323

High Temporal Resolution Characterization of the Carbonate Chemistry and the Relative Influence of Community Metabolic Processes on Controlling Coral Reef Dynamics at La Parguera, Puerto Rico

NASA Astrophysics Data System (ADS)

Melendez, M.; Salisbury, J.; Gledhill, D. K.; Musielewicz, S.; Morell, J. M.; Manzello, D.

2016-02-01

Diverse metabolic processes in conjunction with thermodynamic, physical and benthic related processes modulate seawater carbonate chemistry in near-shore environments. Such processes operate at different time scales. In the open ocean, dynamics and trends in carbonate chemistry are reasonably well constrained and often characterized based on TA-salinity and pCO2-temperature relationships. However, in near-shore environments benthic and coastal processes can convolute these relationships and careful direct measurement of the carbonate system (e.g. through alkalinity and dissolved inorganic carbon) is needed. To this end, we characterized seasonal and inter-annual carbonate dynamics from 2009 to 2014 at the Class III fixed climate station of La Parguera Marine Reserve, Puerto Rico. This high-temporal resolution chemical monitoring at Enrique reef facilitated an examination of what local processes might prove dominant, and how changes in community-scale metabolic performance might alter the dynamics of the carbonate system within the near-shore reef waters. Changes in pCO2,sw at Enrique reef are strongly associated with both community inorganic and organic carbon production processes. Enrique reef is a persistent source of CO2 to the atmosphere (1.8 mmol CO2 m-2 d-1, SE = 0.04) with at maximum peak during the summer and fall seasons. During the same time, carbonate mineral saturation state are generally lower along the fore-reef relative to offshore waters and dominantly controlled by short-term pCO2,sw dynamics primarily driven by benthic community organic matter productivity, temperature and salinity seasonal changes. At this time, high temperatures coincide with intense local rainfall and the influx of the low-salinity Amazon and Orinoco River plumes into the eastern Caribbean. One benefit of such measurements is that they provide data for a more accurate determination of TA-salinity relationships for our region and site-specific algorithms for first order derivations of other carbonate system parameters.

Point process modeling and estimation: Advances in the analysis of dynamic neural spiking data

NASA Astrophysics Data System (ADS)

Deng, Xinyi

2016-08-01

A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in population spiking data. Lastly, we proposed a general three-step paradigm that allows us to relate behavioral outcomes of various tasks to simultaneously recorded neural activity across multiple brain areas, which is a step towards closed-loop therapies for psychological diseases using real-time neural stimulation. These methods are suitable for real-time implementation for content-based feedback experiments.

Communication: GAIMS—Generalized Ab Initio Multiple Spawning for both internal conversion and intersystem crossing processes

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

Curchod, Basile F. E.; Martínez, Todd J., E-mail: toddjmartinez@gmail.com; SLAC National Accelerator Laboratory, Menlo Park, California 94025

2016-03-14

Full multiple spawning is a formally exact method to describe the excited-state dynamics of molecular systems beyond the Born-Oppenheimer approximation. However, it has been limited until now to the description of radiationless transitions taking place between electronic states with the same spin multiplicity. This Communication presents a generalization of the full and ab initio multiple spawning methods to both internal conversion (mediated by nonadiabatic coupling terms) and intersystem crossing events (triggered by spin-orbit coupling matrix elements) based on a spin-diabatic representation. The results of two numerical applications, a model system and the deactivation of thioformaldehyde, validate the presented formalism andmore » its implementation.« less

Adressing optimality principles in DGVMs: Dynamics of Carbon allocation changes

NASA Astrophysics Data System (ADS)

Pietsch, Stephan

2017-04-01

DGVMs are designed to reproduce and quantify ecosystem processes. Based on plant functions or species specific parameter sets, the energy, carbon, nitrogen and water cycles of different ecosystems are assessed. These models have been proven to be important tools to investigate ecosystem fluxes as they are derived by plant, site and environmental factors. The general model approach assumes steady state conditions and constant model parameters. Both assumptions, however, are wrong, since: (i) No given ecosystem ever is at steady state! (ii) Ecosystems have the capability to adapt to changes in growth conditions, e.g. via changes in allocation patterns! This presentation will give examples how these general failures within current DGVMs may be addressed.

Adressing optimality principles in DGVMs: Dynamics of Carbon allocation changes.

NASA Astrophysics Data System (ADS)

Pietsch, S.

2016-12-01

DGVMs are designed to reproduce and quantify ecosystem processes. Based on plant functions or species specific parameter sets, the energy, carbon, nitrogen and water cycles of different ecosystems are assessed. These models have been proven to be important tools to investigate ecosystem fluxes as they are derived by plant, site and environmental factors. The general model approach assumes steady state conditions and constant model parameters. Both assumptions, however, are wrong. Any given ecosystem never is at steady state! Ecosystems have the capability to adapt to changes in growth conditions, e.g. via changes in allocation patterns! This presentation will give examples how these general failures within current DGVMs may be addressed.

Communication: GAIMS—generalized ab initio multiple spawning for both internal conversion and intersystem crossing processes

DOE PAGES

Curchod, Basile F. E.; Rauer, Clemens; Marquetand, Philipp; ...

2016-03-11

Full Multiple Spawning is a formally exact method to describe the excited-state dynamics of molecular systems beyond the Born-Oppenheimer approximation. However, it has been limited until now to the description of radiationless transitions taking place between electronic states with the same spin multiplicity. This Communication presents a generalization of the full and ab initio Multiple Spawning methods to both internal conversion (mediated by nonadiabatic coupling terms) and intersystem crossing events (triggered by spin-orbit coupling matrix elements) based on a spin-diabatic representation. Lastly, the results of two numerical applications, a model system and the deactivation of thioformaldehyde, validate the presented formalismmore » and its implementation.« less

Evolution equation for quantum entanglement

NASA Astrophysics Data System (ADS)

Konrad, Thomas; de Melo, Fernando; Tiersch, Markus; Kasztelan, Christian; Aragão, Adriano; Buchleitner, Andreas

2008-02-01

Quantum information technology largely relies on a precious and fragile resource, quantum entanglement, a highly non-trivial manifestation of the coherent superposition of states of composite quantum systems. However, our knowledge of the time evolution of this resource under realistic conditions-that is, when corrupted by environment-induced decoherence-is so far limited, and general statements on entanglement dynamics in open systems are scarce. Here we prove a simple and general factorization law for quantum systems shared by two parties, which describes the time evolution of entanglement on passage of either component through an arbitrary noisy channel. The robustness of entanglement-based quantum information processing protocols is thus easily and fully characterized by a single quantity.

Adiabatic markovian dynamics.

PubMed

Oreshkov, Ognyan; Calsamiglia, John

2010-07-30

We propose a theory of adiabaticity in quantum markovian dynamics based on a decomposition of the Hilbert space induced by the asymptotic behavior of the Lindblad semigroup. A central idea of our approach is that the natural generalization of the concept of eigenspace of the Hamiltonian in the case of markovian dynamics is a noiseless subsystem with a minimal noisy cofactor. Unlike previous attempts to define adiabaticity for open systems, our approach deals exclusively with physical entities and provides a simple, intuitive picture at the Hilbert-space level, linking the notion of adiabaticity to the theory of noiseless subsystems. As two applications of our theory, we propose a general framework for decoherence-assisted computation in noiseless codes and a dissipation-driven approach to holonomic computation based on adiabatic dragging of subsystems that is generally not achievable by nondissipative means.

Prediction of dynamical systems by symbolic regression

NASA Astrophysics Data System (ADS)

Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.

2016-07-01

We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.

Quantum decision-maker theory and simulation

NASA Astrophysics Data System (ADS)

Zak, Michail; Meyers, Ronald E.; Deacon, Keith S.

2000-07-01

A quantum device simulating the human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and of classical neural nets describing the evolution of probabilities of these processes which represent the mental dynamics. The autonomy of the decision making process is achieved by a feedback from the mental to motor dynamics which changes the stochastic matrix based upon the probability distribution. This feedback replaces unavailable external information by an internal knowledge- base stored in the mental model in the form of probability distributions. As a result, the coupled motor-mental dynamics is described by a nonlinear version of Markov chains which can decrease entropy without an external source of information. Applications to common sense based decisions as well as to evolutionary games are discussed. An example exhibiting self-organization is computed using quantum computer simulation. Force on force and mutual aircraft engagements using the quantum decision maker dynamics are considered.

Research on Finite Element Model Generating Method of General Gear Based on Parametric Modelling

NASA Astrophysics Data System (ADS)

Lei, Yulong; Yan, Bo; Fu, Yao; Chen, Wei; Hou, Liguo

2017-06-01

Aiming at the problems of low efficiency and poor quality of gear meshing in the current mainstream finite element software, through the establishment of universal gear three-dimensional model, and explore the rules of unit and node arrangement. In this paper, a finite element model generation method of universal gear based on parameterization is proposed. Visual Basic program is used to realize the finite element meshing, give the material properties, and set the boundary / load conditions and other pre-processing work. The dynamic meshing analysis of the gears is carried out with the method proposed in this pape, and compared with the calculated values to verify the correctness of the method. The method greatly shortens the workload of gear finite element pre-processing, improves the quality of gear mesh, and provides a new idea for the FEM pre-processing.

The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping.

PubMed

Bahlmann, Claus; Burkhardt, Hans

2004-03-01

In this paper, we give a comprehensive description of our writer-independent online handwriting recognition system frog on hand. The focus of this work concerns the presentation of the classification/training approach, which we call cluster generative statistical dynamic time warping (CSDTW). CSDTW is a general, scalable, HMM-based method for variable-sized, sequential data that holistically combines cluster analysis and statistical sequence modeling. It can handle general classification problems that rely on this sequential type of data, e.g., speech recognition, genome processing, robotics, etc. Contrary to previous attempts, clustering and statistical sequence modeling are embedded in a single feature space and use a closely related distance measure. We show character recognition experiments of frog on hand using CSDTW on the UNIPEN online handwriting database. The recognition accuracy is significantly higher than reported results of other handwriting recognition systems. Finally, we describe the real-time implementation of frog on hand on a Linux Compaq iPAQ embedded device.

Controlled ultrafast transfer and stability degree of generalized coherent states of a kicked two-level ion

NASA Astrophysics Data System (ADS)

Chen, Hao; Kong, Chao; Hai, Wenhua

2018-06-01

We investigate quantum dynamics of a two-level ion trapped in the Lamb-Dicke regime of a δ -kicked optical lattice, based on the exact generalized coherent states rotated by a π / 2 pulse of Ramsey type experiment. The spatiotemporal evolutions of the spin-motion entangled states in different parameter regions are illustrated, and the parameter regions of different degrees of quantum stability described by the quantum fidelity are found. Time evolutions of the probability for the ion being in different pseudospin states reveal that the ultrafast entanglement generation and population transfers of the system can be analytically controlled by managing the laser pulses. The probability in an initially disentangled state shows periodic collapses (entanglement) and revivals (de-entanglement). Reduction of the stability degree results in enlarging the period of de-entanglement, while the instability and potential chaos will cause the sustained entanglement. The results could be justified experimentally in the existing setups and may be useful in engineering quantum dynamics for quantum information processing.

Computer simulation of surface and film processes

NASA Technical Reports Server (NTRS)

Tiller, W. A.; Halicioglu, M. T.

1984-01-01

All the investigations which were performed employed in one way or another a computer simulation technique based on atomistic level considerations. In general, three types of simulation methods were used for modeling systems with discrete particles that interact via well defined potential functions: molecular dynamics (a general method for solving the classical equations of motion of a model system); Monte Carlo (the use of Markov chain ensemble averaging technique to model equilibrium properties of a system); and molecular statics (provides properties of a system at T = 0 K). The effects of three-body forces on the vibrational frequencies of triatomic cluster were investigated. The multilayer relaxation phenomena for low index planes of an fcc crystal was analyzed also as a function of the three-body interactions. Various surface properties for Si and SiC system were calculated. Results obtained from static simulation calculations for slip formation were presented. The more elaborate molecular dynamics calculations on the propagation of cracks in two-dimensional systems were outlined.

Social Cognition in Schizophrenia: From Social Stimuli Processing to Social Engagement

PubMed Central

Billeke, Pablo; Aboitiz, Francisco

2013-01-01

Social cognition consists of several skills which allow us to interact with other humans. These skills include social stimuli processing, drawing inferences about others’ mental states, and engaging in social interactions. In recent years, there has been growing evidence of social cognitive impairments in patients with schizophrenia. Apparently, these impairments are separable from general neurocognitive impairments, such as attention, memory, and executive functioning. Moreover, social cognition seems to be a main determinant of functional outcome and could be used as a guide to elaborate new pharmacological and psychological treatments. However, most of these studies focus on individual mechanisms and observational perspectives; only few of them study schizophrenic patients during interactive situations. We first review evidences of social cognitive impairments both in social stimuli processing and in mental state attribution. We focus on the relationship between these functions and both general cognitive impairments and functional outcome. We next review recent game theory approaches to the study of how social engagement occurs in schizophrenic patients. The advantage of using game theory is that game-oriented tasks can assess social decision making in an interactive everyday situation model. Finally, we review proposed theoretical models used to explain social alterations and their underlying biological mechanisms. Based on interactive studies, we propose a framework which takes into account the dynamic nature of social processes. Thus, understanding social skills as a result of dynamical systems could facilitate the development of both basic research and clinical applications oriented to psychiatric populations. PMID:23444313

A Comparative Analysis of Options for Preserving the Tank Industrial Base

DTIC Science & Technology

1993-03-01

4 G. LITERATURE REVIEW ........................... 5 II. BACKGROUND ..................................... 6 A. THE...INDUSTRIAL BASE .......................... 6 B. THE TANK INDUSTRIAL BASE, 1917-1945 ................ 7 C. THE TANK INDUSTRIAL BASE, 1945-1980...published studies on the subject. General Dynamics-Land Systems Division ( GDLS ), Congressional Budget Office (CBO) and the General Accounting Office

A Sensitivity Analysis Method to Study the Behavior of Complex Process-based Models

NASA Astrophysics Data System (ADS)

Brugnach, M.; Neilson, R.; Bolte, J.

2001-12-01

The use of process-based models as a tool for scientific inquiry is becoming increasingly relevant in ecosystem studies. Process-based models are artificial constructs that simulate the system by mechanistically mimicking the functioning of its component processes. Structurally, a process-based model can be characterized, in terms of its processes and the relationships established among them. Each process comprises a set of functional relationships among several model components (e.g., state variables, parameters and input data). While not encoded explicitly, the dynamics of the model emerge from this set of components and interactions organized in terms of processes. It is the task of the modeler to guarantee that the dynamics generated are appropriate and semantically equivalent to the phenomena being modeled. Despite the availability of techniques to characterize and understand model behavior, they do not suffice to completely and easily understand how a complex process-based model operates. For example, sensitivity analysis studies model behavior by determining the rate of change in model output as parameters or input data are varied. One of the problems with this approach is that it considers the model as a "black box", and it focuses on explaining model behavior by analyzing the relationship input-output. Since, these models have a high degree of non-linearity, understanding how the input affects an output can be an extremely difficult task. Operationally, the application of this technique may constitute a challenging task because complex process-based models are generally characterized by a large parameter space. In order to overcome some of these difficulties, we propose a method of sensitivity analysis to be applicable to complex process-based models. This method focuses sensitivity analysis at the process level, and it aims to determine how sensitive the model output is to variations in the processes. Once the processes that exert the major influence in the output are identified, the causes of its variability can be found. Some of the advantages of this approach are that it reduces the dimensionality of the search space, it facilitates the interpretation of the results and it provides information that allows exploration of uncertainty at the process level, and how it might affect model output. We present an example using the vegetation model BIOME-BGC.

Gradient Dynamics and Entropy Production Maximization

NASA Astrophysics Data System (ADS)

Janečka, Adam; Pavelka, Michal

2018-01-01

We compare two methods for modeling dissipative processes, namely gradient dynamics and entropy production maximization. Both methods require similar physical inputs-how energy (or entropy) is stored and how it is dissipated. Gradient dynamics describes irreversible evolution by means of dissipation potential and entropy, it automatically satisfies Onsager reciprocal relations as well as their nonlinear generalization (Maxwell-Onsager relations), and it has statistical interpretation. Entropy production maximization is based on knowledge of free energy (or another thermodynamic potential) and entropy production. It also leads to the linear Onsager reciprocal relations and it has proven successful in thermodynamics of complex materials. Both methods are thermodynamically sound as they ensure approach to equilibrium, and we compare them and discuss their advantages and shortcomings. In particular, conditions under which the two approaches coincide and are capable of providing the same constitutive relations are identified. Besides, a commonly used but not often mentioned step in the entropy production maximization is pinpointed and the condition of incompressibility is incorporated into gradient dynamics.

Effects of fundamentals acquisition and strategy switch on stock price dynamics

NASA Astrophysics Data System (ADS)

Wu, Songtao; He, Jianmin; Li, Shouwei

2018-02-01

An agent-based artificial stock market is developed to simulate trading behavior of investors. In the market, acquisition and employment of information about fundamentals and strategy switch are investigated to explain stock price dynamics. Investors could obtain the information from both market and neighbors resided on their social networks. Depending on information status and performances of different strategies, an informed investor may switch to the strategy of fundamentalist. This in turn affects the information acquisition process, since fundamentalists are more inclined to search and spread the information than chartists. Further investigation into price dynamics generated from three typical networks, i.e. regular lattice, small-world network and random graph, are conducted after general relation between network structures and price dynamics is revealed. In each network, integrated effects of different combinations of information efficiency and switch intensity are investigated. Results have shown that, along with increasing switch intensity, market and social information efficiency play different roles in the formation of price distortion, standard deviation and kurtosis of returns.

Self-similarity in nature

NASA Astrophysics Data System (ADS)

Timashev, S. F.

2000-02-01

A general phenomenological approach to the analysis of experimental temporal, spatial and energetic series for extracting truly physical non-model parameters ("passport data") is presented, which may be used to characterize and distinguish the evolution as well as the spatial and energetic structure of any open nonlinear dissipative system. This methodology is based on a postulate concerning the crucial information contained in the sequences of non-regularities of the measured dynamic variable (temporal, spatial, energetic). In accordance with this approach, multi-parametric formulas for dynamic variable power spectra as well as for structural functions of different orders are identical for every spatial-temporal-energetic level of the system under consideration. In effect, this entails the introduction of a new kind of self-similarity in Nature. An algorithm has been developed for obtaining as many "passport data" as are necessary for the characterization of a dynamic system. Applications of this approach in the analysis of various experimental series (temporal, spatial, energetic) demonstrate its potential for defining adequate phenomenological parameters of different dynamic processes and structures.

Informations in Models of Evolutionary Dynamics

NASA Astrophysics Data System (ADS)

Rivoire, Olivier

2016-03-01

Biological organisms adapt to changes by processing informations from different sources, most notably from their ancestors and from their environment. We review an approach to quantify these informations by analyzing mathematical models of evolutionary dynamics and show how explicit results are obtained for a solvable subclass of these models. In several limits, the results coincide with those obtained in studies of information processing for communication, gambling or thermodynamics. In the most general case, however, information processing by biological populations shows unique features that motivate the analysis of specific models.

A multilevel-skin neighbor list algorithm for molecular dynamics simulation

NASA Astrophysics Data System (ADS)

Zhang, Chenglong; Zhao, Mingcan; Hou, Chaofeng; Ge, Wei

2018-01-01

Searching of the interaction pairs and organization of the interaction processes are important steps in molecular dynamics (MD) algorithms and are critical to the overall efficiency of the simulation. Neighbor lists are widely used for these steps, where thicker skin can reduce the frequency of list updating but is discounted by more computation in distance check for the particle pairs. In this paper, we propose a new neighbor-list-based algorithm with a precisely designed multilevel skin which can reduce unnecessary computation on inter-particle distances. The performance advantages over traditional methods are then analyzed against the main simulation parameters on Intel CPUs and MICs (many integrated cores), and are clearly demonstrated. The algorithm can be generalized for various discrete simulations using neighbor lists.

Deformation and breakup of liquid-liquid threads, jets, and drops

NASA Astrophysics Data System (ADS)

Doshi, Pankaj

The formation and breakup of two-fluid jets and drops find application in various industrially important processes like microencapsulation, inkjet printing, dispersion and emulsion formation, micro fluidics. Two important aspects of these problems are studied in this thesis. The first regards the study of the dynamics of a two-fluid jet issuing out of a concentric nozzle and breaking into multiple liquid drops. The second aspect concerns the study of the dynamics of liquid-liquid interface rupture. Highly robust and accurate numerical algorithms based on the Galerkin finite element method (G/FEM) and elliptic mesh generation technique are developed. The most important results of this research are the prediction of compound drop formation and volume partitioning between primary drop and satellite drops, which are of critical importance for microencapsulation technology. Another equally important result is computational and experimental demonstration of a self-similar behavior for the rupture of liquid-liquid interface. The final focus is the study of the pinch-off dynamics of generalized-Newtonian fluids with deformation-rate-dependent rheology using asymptotic analysis and numerical computation. A significant result is the first ever prediction of self-similar pinch-off of liquid threads of generalized Newtonian fluids.

Chern-Simons Modified Gravity

NASA Astrophysics Data System (ADS)

Efstratiou, P.

2013-09-01

This presentation will be based on my, undergraduate, thesis at Aristotle University of Thessoliniki with the same subject, supervised by Professor Demetrios Papadopoulos. I will first present the general mathematical formulation of the Chern-Simons (CS) modified gravity, which is split in a dynamical and a non-dynamical context, and the different physical theories which suggest this modification. Then proceed by examing the possibility that the CS theory shares solutions with General Relativity in both contexts. In the non-dynamical context I will present a new, undocumented solution as well as all the other possible solutions found to date. I will conclude by arguing that General Relativity and CS Theory share any solutions in the dynamical context.

Nano-Wilhelmy investigation of dynamic wetting properties of AFM tips through tip-nanobubble interaction

PubMed Central

Wang, Yuliang; Wang, Huimin; Bi, Shusheng; Guo, Bin

2016-01-01

The dynamic wetting properties of atomic force microscopy (AFM) tips are of much concern in many AFM-related measurement, fabrication, and manipulation applications. In this study, the wetting properties of silicon and silicon nitride AFM tips are investigated through dynamic contact angle measurement using a nano-Wilhelmy balance based method. This is done by capillary force measurement during extension and retraction motion of AFM tips relative to interfacial nanobubbles. The working principle of the proposed method and mathematic models for dynamic contact angle measurement are presented. Geometric models of AFM tips were constructed using scanning electronic microscopy (SEM) images taken from different view directions. The detailed process of tip-nanobubble interaction was investigated using force-distance curves of AFM on nanobubbles. Several parameters including nanobubble height, adhesion and capillary force between tip and nanobubbles are extracted. The variation of these parameters was studied over nanobubble surfaces. The dynamic contact angles of the AFM tips were calculated from the capillary force measurements. The proposed method provides direct measurement of dynamic contact angles for AFM tips and can also be taken as a general approach for nanoscale dynamic wetting property investigation. PMID:27452115

Nano-Wilhelmy investigation of dynamic wetting properties of AFM tips through tip-nanobubble interaction

NASA Astrophysics Data System (ADS)

Wang, Yuliang; Wang, Huimin; Bi, Shusheng; Guo, Bin

2016-07-01

The dynamic wetting properties of atomic force microscopy (AFM) tips are of much concern in many AFM-related measurement, fabrication, and manipulation applications. In this study, the wetting properties of silicon and silicon nitride AFM tips are investigated through dynamic contact angle measurement using a nano-Wilhelmy balance based method. This is done by capillary force measurement during extension and retraction motion of AFM tips relative to interfacial nanobubbles. The working principle of the proposed method and mathematic models for dynamic contact angle measurement are presented. Geometric models of AFM tips were constructed using scanning electronic microscopy (SEM) images taken from different view directions. The detailed process of tip-nanobubble interaction was investigated using force-distance curves of AFM on nanobubbles. Several parameters including nanobubble height, adhesion and capillary force between tip and nanobubbles are extracted. The variation of these parameters was studied over nanobubble surfaces. The dynamic contact angles of the AFM tips were calculated from the capillary force measurements. The proposed method provides direct measurement of dynamic contact angles for AFM tips and can also be taken as a general approach for nanoscale dynamic wetting property investigation.

Holistic processing of static and moving faces.

PubMed

Zhao, Mintao; Bülthoff, Isabelle

2017-07-01

Humans' face ability develops and matures with extensive experience in perceiving, recognizing, and interacting with faces that move most of the time. However, how facial movements affect 1 core aspect of face ability-holistic face processing-remains unclear. Here we investigated the influence of rigid facial motion on holistic and part-based face processing by manipulating the presence of facial motion during study and at test in a composite face task. The results showed that rigidly moving faces were processed as holistically as static faces (Experiment 1). Holistic processing of moving faces persisted whether facial motion was presented during study, at test, or both (Experiment 2). Moreover, when faces were inverted to eliminate the contributions of both an upright face template and observers' expertise with upright faces, rigid facial motion facilitated holistic face processing (Experiment 3). Thus, holistic processing represents a general principle of face perception that applies to both static and dynamic faces, rather than being limited to static faces. These results support an emerging view that both perceiver-based and face-based factors contribute to holistic face processing, and they offer new insights on what underlies holistic face processing, how information supporting holistic face processing interacts with each other, and why facial motion may affect face recognition and holistic face processing differently. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A compositional framework for reaction networks

NASA Astrophysics Data System (ADS)

Baez, John C.; Pollard, Blake S.

Reaction networks, or equivalently Petri nets, are a general framework for describing processes in which entities of various kinds interact and turn into other entities. In chemistry, where the reactions are assigned ‘rate constants’, any reaction network gives rise to a nonlinear dynamical system called its ‘rate equation’. Here we generalize these ideas to ‘open’ reaction networks, which allow entities to flow in and out at certain designated inputs and outputs. We treat open reaction networks as morphisms in a category. Composing two such morphisms connects the outputs of the first to the inputs of the second. We construct a functor sending any open reaction network to its corresponding ‘open dynamical system’. This provides a compositional framework for studying the dynamics of reaction networks. We then turn to statics: that is, steady state solutions of open dynamical systems. We construct a ‘black-boxing’ functor that sends any open dynamical system to the relation that it imposes between input and output variables in steady states. This extends our earlier work on black-boxing for Markov processes.

Semiclassical theory of electronically nonadiabatic transitions in molecular collision processes

NASA Technical Reports Server (NTRS)

Lam, K. S.; George, T. F.

1979-01-01

An introductory account of the semiclassical theory of the S-matrix for molecular collision processes is presented, with special emphasis on electronically nonadiabatic transitions. This theory is based on the incorporation of classical mechanics with quantum superposition, and in practice makes use of the analytic continuation of classical mechanics into the complex space of time domain. The relevant concepts of molecular scattering theory and related dynamical models are described and the formalism is developed and illustrated with simple examples - collinear collision of the A+BC type. The theory is then extended to include the effects of laser-induced nonadiabatic transitions. Two bound continuum processes collisional ionization and collision-induced emission also amenable to the same general semiclassical treatment are discussed.

CHIMERA II - A real-time multiprocessing environment for sensor-based robot control

NASA Technical Reports Server (NTRS)

Stewart, David B.; Schmitz, Donald E.; Khosla, Pradeep K.

1989-01-01

A multiprocessing environment for a wide variety of sensor-based robot system, providing the flexibility, performance, and UNIX-compatible interface needed for fast development of real-time code is addressed. The requirements imposed on the design of a programming environment for sensor-based robotic control is outlined. The details of the current hardware configuration are presented, along with the details of the CHIMERA II software. Emphasis is placed on the kernel, low-level interboard communication, user interface, extended file system, user-definable and dynamically selectable real-time schedulers, remote process synchronization, and generalized interprocess communication. A possible implementation of a hierarchical control model, the NASA/NBS standard reference model for telerobot control system is demonstrated.

The Co-simulation of Humanoid Robot Based on Solidworks, ADAMS and Simulink

NASA Astrophysics Data System (ADS)

Song, Dalei; Zheng, Lidan; Wang, Li; Qi, Weiwei; Li, Yanli

A simulation method of adaptive controller is proposed for the humanoid robot system based on co-simulation of Solidworks, ADAMS and Simulink. A complex mathematical modeling process is avoided by this method, and the real time dynamic simulating function of Simulink would be exerted adequately. This method could be generalized to other complicated control system. This method is adopted to build and analyse the model of humanoid robot. The trajectory tracking and adaptive controller design also proceed based on it. The effect of trajectory tracking is evaluated by fitting-curve theory of least squares method. The anti-interference capability of the robot is improved a lot through comparative analysis.

Enhanced embodied response following ambiguous emotional processing.

PubMed

Beffara, Brice; Ouellet, Marc; Vermeulen, Nicolas; Basu, Anamitra; Morisseau, Tiffany; Mermillod, Martial

2012-08-01

It has generally been assumed that high-level cognitive and emotional processes are based on amodal conceptual information. In contrast, however, "embodied simulation" theory states that the perception of an emotional signal can trigger a simulation of the related state in the motor, somatosensory, and affective systems. To study the effect of social context on the mimicry effect predicted by the "embodied simulation" theory, we recorded the electromyographic (EMG) activity of participants when looking at emotional facial expressions. We observed an increase in embodied responses when the participants were exposed to a context involving social valence before seeing the emotional facial expressions. An examination of the dynamic EMG activity induced by two socially relevant emotional expressions (namely joy and anger) revealed enhanced EMG responses of the facial muscles associated with the related social prime (either positive or negative). These results are discussed within the general framework of embodiment theory.

Structural Stability of Mathematical Models of National Economy

NASA Astrophysics Data System (ADS)

Ashimov, Abdykappar A.; Sultanov, Bahyt T.; Borovskiy, Yuriy V.; Adilov, Zheksenbek M.; Ashimov, Askar A.

2011-12-01

In the paper we test robustness of particular dynamic systems in a compact regions of a plane and a weak structural stability of one dynamic system of high order in a compact region of its phase space. The test was carried out based on the fundamental theory of dynamical systems on a plane and based on the conditions for weak structural stability of high order dynamic systems. A numerical algorithm for testing the weak structural stability of high order dynamic systems has been proposed. Based on this algorithm we assess the weak structural stability of one computable general equilibrium model.

Some dynamic resource allocation problems in wireless networks

NASA Astrophysics Data System (ADS)

Berry, Randall

2001-07-01

We consider dynamic resource allocation problems that arise in wireless networking. Specifically transmission scheduling problems are studied in cases where a user can dynamically allocate communication resources such as transmission rate and power based on current channel knowledge as well as traffic variations. We assume that arriving data is stored in a transmission buffer, and investigate the trade-off between average transmission power and average buffer delay. A general characterization of this trade-off is given and the behavior of this trade-off in the regime of asymptotically large buffer delays is explored. An extension to a more general utility based quality of service definition is also discussed.

Multi-issue Agent Negotiation Based on Fairness

NASA Astrophysics Data System (ADS)

Zuo, Baohe; Zheng, Sue; Wu, Hong

Agent-based e-commerce service has become a hotspot now. How to make the agent negotiation process quickly and high-efficiently is the main research direction of this area. In the multi-issue model, MAUT(Multi-attribute Utility Theory) or its derived theory usually consider little about the fairness of both negotiators. This work presents a general model of agent negotiation which considered the satisfaction of both negotiators via autonomous learning. The model can evaluate offers from the opponent agent based on the satisfaction degree, learn online to get the opponent's knowledge from interactive instances of history and negotiation of this time, make concessions dynamically based on fair object. Through building the optimal negotiation model, the bilateral negotiation achieved a higher efficiency and fairer deal.

Mussel dynamics model: A hydroinformatics tool for analyzing the effects of different stressors on the dynamics of freshwater mussel communities

USGS Publications Warehouse

Morales, Y.; Weber, L.J.; Mynett, A.E.; Newton, T.J.

2006-01-01

A model for simulating freshwater mussel population dynamics is presented. The model is a hydroinformatics tool that integrates principles from ecology, river hydraulics, fluid mechanics and sediment transport, and applies the individual-based modelling approach for simulating population dynamics. The general model layout, data requirements, and steps of the simulation process are discussed. As an illustration, simulation results from an application in a 10 km reach of the Upper Mississippi River are presented. The model was used to investigate the spatial distribution of mussels and the effects of food competition in native unionid mussel communities, and communities infested by Dreissena polymorpha, the zebra mussel. Simulation results were found to be realistic and coincided with data obtained from the literature. These results indicate that the model can be a useful tool for assessing the potential effects of different stressors on long-term population dynamics, and consequently, may improve the current understanding of cause and effect relationships in freshwater mussel communities. ?? 2006 Elsevier B.V. All rights reserved.

Molecular dynamics for dense matter

NASA Astrophysics Data System (ADS)

Maruyama, Toshiki; Watanabe, Gentaro; Chiba, Satoshi

2012-08-01

We review a molecular dynamics method for nucleon many-body systems called quantum molecular dynamics (QMD), and our studies using this method. These studies address the structure and the dynamics of nuclear matter relevant to neutron star crusts, supernova cores, and heavy-ion collisions. A key advantage of QMD is that we can study dynamical processes of nucleon many-body systems without any assumptions about the nuclear structure. First, we focus on the inhomogeneous structures of low-density nuclear matter consisting not only of spherical nuclei but also of nuclear "pasta", i.e., rod-like and slab-like nuclei. We show that pasta phases can appear in the ground and equilibrium states of nuclear matter without assuming nuclear shape. Next, we show our simulation of compression of nuclear matter which corresponds to the collapsing stage of supernovae. With the increase in density, a crystalline solid of spherical nuclei changes to a triangular lattice of rods by connecting neighboring nuclei. Finally, we discuss fragment formation in expanding nuclear matter. Our results suggest that a generally accepted scenario based on the liquid-gas phase transition is not plausible at lower temperatures.

Langevin Dynamics Simulations of Genome Packing in Bacteriophage

PubMed Central

Forrey, Christopher; Muthukumar, M.

2006-01-01

We use Langevin dynamics simulations to study the process by which a coarse-grained DNA chain is packaged within an icosahedral container. We focus our inquiry on three areas of interest in viral packing: the evolving structure of the packaged DNA condensate; the packing velocity; and the internal buildup of energy and resultant forces. Each of these areas has been studied experimentally, and we find that we can qualitatively reproduce experimental results. However, our findings also suggest that the phage genome packing process is fundamentally different than that suggested by the inverse spool model. We suggest that packing in general does not proceed in the deterministic fashion of the inverse-spool model, but rather is stochastic in character. As the chain configuration becomes compressed within the capsid, the structure, energy, and packing velocity all become dependent upon polymer dynamics. That many observed features of the packing process are rooted in condensed-phase polymer dynamics suggests that statistical mechanics, rather than mechanics, should serve as the proper theoretical basis for genome packing. Finally we suggest that, as a result of an internal protein unique to bacteriophage T7, the T7 genome may be significantly more ordered than is true for bacteriophage in general. PMID:16617089

Langevin dynamics simulations of genome packing in bacteriophage.

PubMed

Forrey, Christopher; Muthukumar, M

2006-07-01

We use Langevin dynamics simulations to study the process by which a coarse-grained DNA chain is packaged within an icosahedral container. We focus our inquiry on three areas of interest in viral packing: the evolving structure of the packaged DNA condensate; the packing velocity; and the internal buildup of energy and resultant forces. Each of these areas has been studied experimentally, and we find that we can qualitatively reproduce experimental results. However, our findings also suggest that the phage genome packing process is fundamentally different than that suggested by the inverse spool model. We suggest that packing in general does not proceed in the deterministic fashion of the inverse-spool model, but rather is stochastic in character. As the chain configuration becomes compressed within the capsid, the structure, energy, and packing velocity all become dependent upon polymer dynamics. That many observed features of the packing process are rooted in condensed-phase polymer dynamics suggests that statistical mechanics, rather than mechanics, should serve as the proper theoretical basis for genome packing. Finally we suggest that, as a result of an internal protein unique to bacteriophage T7, the T7 genome may be significantly more ordered than is true for bacteriophage in general.

Risk assessment and adaptive runoff utilization in water resource system considering the complex relationship among water supply, electricity generation and environment

NASA Astrophysics Data System (ADS)

Zhou, J.; Zeng, X.; Mo, L.; Chen, L.; Jiang, Z.; Feng, Z.; Yuan, L.; He, Z.

2017-12-01

Generally, the adaptive utilization and regulation of runoff in the source region of China's southwest rivers is classified as a typical multi-objective collaborative optimization problem. There are grim competitions and incidence relation in the subsystems of water supply, electricity generation and environment, which leads to a series of complex problems represented by hydrological process variation, blocked electricity output and water environment risk. Mathematically, the difficulties of multi-objective collaborative optimization focus on the description of reciprocal relationships and the establishment of evolving model of adaptive systems. Thus, based on the theory of complex systems science, this project tries to carry out the research from the following aspects: the changing trend of coupled water resource, the covariant factor and driving mechanism, the dynamic evolution law of mutual feedback dynamic process in the supply-generation-environment coupled system, the environmental response and influence mechanism of coupled mutual feedback water resource system, the relationship between leading risk factor and multiple risk based on evolutionary stability and dynamic balance, the transfer mechanism of multiple risk response with the variation of the leading risk factor, the multidimensional coupled feedback system of multiple risk assessment index system and optimized decision theory. Based on the above-mentioned research results, the dynamic method balancing the efficiency of multiple objectives in the coupled feedback system and optimized regulation model of water resources is proposed, and the adaptive scheduling mode considering the internal characteristics and external response of coupled mutual feedback system of water resource is established. In this way, the project can make a contribution to the optimal scheduling theory and methodology of water resource management under uncertainty in the source region of Southwest River.

A Survey of Solver-Related Geometry and Meshing Issues

NASA Technical Reports Server (NTRS)

Masters, James; Daniel, Derick; Gudenkauf, Jared; Hine, David; Sideroff, Chris

2016-01-01

There is a concern in the computational fluid dynamics community that mesh generation is a significant bottleneck in the CFD workflow. This is one of several papers that will help set the stage for a moderated panel discussion addressing this issue. Although certain general "rules of thumb" and a priori mesh metrics can be used to ensure that some base level of mesh quality is achieved, inadequate consideration is often given to the type of solver or particular flow regime on which the mesh will be utilized. This paper explores how an analyst may want to think differently about a mesh based on considerations such as if a flow is compressible vs. incompressible or hypersonic vs. subsonic or if the solver is node-centered vs. cell-centered. This paper is a high-level investigation intended to provide general insight into how considering the nature of the solver or flow when performing mesh generation has the potential to increase the accuracy and/or robustness of the solution and drive the mesh generation process to a state where it is no longer a hindrance to the analysis process.

LANDPLANER (LANDscape, Plants, LANdslide and ERosion): a model to describe the dynamic response of slopes (or basins) under different changing scenarios

NASA Astrophysics Data System (ADS)

Rossi, Mauro; Torri, Dino; Santi, Elisa; Bacaro, Giovanni; Marchesini, Ivan

2014-05-01

Landslide phenomena and erosion processes are widespread and cause every year extensive damages to the environment and sensible reduction of ecosystem services. These processes are in competition among them, and their complex interaction control the landscapes evolution. Landslide phenomena and erosion processes can be strongly influenced by land use, vegetation, soil characteristics and anthropic actions. Such type of phenomena are mainly model separately using empirical and physically based approaches. The former rely upon the identification of simple empirical laws correlating/relating the occurrence of instability processes to some of their potential causes. The latter are based on physical descriptions of the processes, and depending on the degree of complexity they can integrate different variables characterizing the process and their trigger. Those model often couple an hydrological model with an erosion or a landslide model. The spatial modeling schemas are heterogeneous, but mostly the raster (i.e. matrices of data) or the conceptual (i.e. cascading planes and channels) description of the terrain are used. The two model types are generally designed and applied at different scales. Empirical models, less demanding in terms of input data cannot consider explicitly the real process triggering mechanisms and commonly they are exploited to assess the potential occurrence of instability phenomena over large areas (small scale assessment). Physically-based models are high-demanding in term of input data, difficult to obtain over large areas if not with large uncertainty, and their applicability is often limited to small catchments or single slopes (large scale assessment). More those models, even if physically-based, are simplified description of the instability processes and can neglect significant issues of the real triggering mechanisms. For instance the influence of vegetation has been considered just partially. Although in the literature a variety of model approaches have been proposed to model separately landslide and erosion processes, only few attempts were made to model both jointly, mostly integrating pre-existing models. To overcome this limitation we develop a new model called LANDPLANER (LANDscape, Plants, LANdslide and ERosion), specifically design to describe the dynamic response of slopes (or basins) under different changing scenarios including: (i) changes of meteorological factors, (ii) changes of vegetation or land-use, (iii) and changes of slope morphology. The was applied in different study area in order to check its basic assumptions, and to test its general operability and applicability. Results show a reasonable model behaviors and confirm its easy applicability in real cases.

Universality of human microbial dynamics

NASA Astrophysics Data System (ADS)

Bashan, Amir; Gibson, Travis E.; Friedman, Jonathan; Carey, Vincent J.; Weiss, Scott T.; Hohmann, Elizabeth L.; Liu, Yang-Yu

2016-06-01

Human-associated microbial communities have a crucial role in determining our health and well-being, and this has led to the continuing development of microbiome-based therapies such as faecal microbiota transplantation. These microbial communities are very complex, dynamic and highly personalized ecosystems, exhibiting a high degree of inter-individual variability in both species assemblages and abundance profiles. It is not known whether the underlying ecological dynamics of these communities, which can be parameterized by growth rates, and intra- and inter-species interactions in population dynamics models, are largely host-independent (that is, universal) or host-specific. If the inter-individual variability reflects host-specific dynamics due to differences in host lifestyle, physiology or genetics, then generic microbiome manipulations may have unintended consequences, rendering them ineffective or even detrimental. Alternatively, microbial ecosystems of different subjects may exhibit universal dynamics, with the inter-individual variability mainly originating from differences in the sets of colonizing species. Here we develop a new computational method to characterize human microbial dynamics. By applying this method to cross-sectional data from two large-scale metagenomic studies—the Human Microbiome Project and the Student Microbiome Project—we show that gut and mouth microbiomes display pronounced universal dynamics, whereas communities associated with certain skin sites are probably shaped by differences in the host environment. Notably, the universality of gut microbial dynamics is not observed in subjects with recurrent Clostridium difficile infection but is observed in the same set of subjects after faecal microbiota transplantation. These results fundamentally improve our understanding of the processes that shape human microbial ecosystems, and pave the way to designing general microbiome-based therapies.

Generalized Bell states map physical systems’ quantum evolution into a grammar for quantum information processing

NASA Astrophysics Data System (ADS)

Delgado, Francisco

2017-12-01

Quantum information processing should be generated through control of quantum evolution for physical systems being used as resources, such as superconducting circuits, spinspin couplings in ions and artificial anyons in electronic gases. They have a quantum dynamics which should be translated into more natural languages for quantum information processing. On this terrain, this language should let to establish manipulation operations on the associated quantum information states as classical information processing does. This work shows how a kind of processing operations can be settled and implemented for quantum states design and quantum processing for systems fulfilling a SU(2) reduction in their dynamics.

Power Laws are Disguised Boltzmann Laws

NASA Astrophysics Data System (ADS)

Richmond, Peter; Solomon, Sorin

Using a previously introduced model on generalized Lotka-Volterra dynamics together with some recent results for the solution of generalized Langevin equations, we derive analytically the equilibrium mean field solution for the probability distribution of wealth and show that it has two characteristic regimes. For large values of wealth, it takes the form of a Pareto style power law. For small values of wealth, w<=wm, the distribution function tends sharply to zero. The origin of this law lies in the random multiplicative process built into the model. Whilst such results have been known since the time of Gibrat, the present framework allows for a stable power law in an arbitrary and irregular global dynamics, so long as the market is ``fair'', i.e., there is no net advantage to any particular group or individual. We further show that the dynamics of relative wealth is independent of the specific nature of the agent interactions and exhibits a universal character even though the total wealth may follow an arbitrary and complicated dynamics. In developing the theory, we draw parallels with conventional thermodynamics and derive for the system some new relations for the ``thermodynamics'' associated with the Generalized Lotka-Volterra type of stochastic dynamics. The power law that arises in the distribution function is identified with new additional logarithmic terms in the familiar Boltzmann distribution function for the system. These are a direct consequence of the multiplicative stochastic dynamics and are absent for the usual additive stochastic processes.

Model for macroevolutionary dynamics.

PubMed

Maruvka, Yosef E; Shnerb, Nadav M; Kessler, David A; Ricklefs, Robert E

2013-07-02

The highly skewed distribution of species among genera, although challenging to macroevolutionists, provides an opportunity to understand the dynamics of diversification, including species formation, extinction, and morphological evolution. Early models were based on either the work by Yule [Yule GU (1925) Philos Trans R Soc Lond B Biol Sci 213:21-87], which neglects extinction, or a simple birth-death (speciation-extinction) process. Here, we extend the more recent development of a generic, neutral speciation-extinction (of species)-origination (of genera; SEO) model for macroevolutionary dynamics of taxon diversification. Simulations show that deviations from the homogeneity assumptions in the model can be detected in species-per-genus distributions. The SEO model fits observed species-per-genus distributions well for class-to-kingdom-sized taxonomic groups. The model's predictions for the appearance times (the time of the first existing species) of the taxonomic groups also approximately match estimates based on molecular inference and fossil records. Unlike estimates based on analyses of phylogenetic reconstruction, fitted extinction rates for large clades are close to speciation rates, consistent with high rates of species turnover and the relatively slow change in diversity observed in the fossil record. Finally, the SEO model generally supports the consistency of generic boundaries based on morphological differences between species and provides a comparator for rates of lineage splitting and morphological evolution.

Domain Specific vs Domain General: Implications for Dynamic Assessment

ERIC Educational Resources Information Center

Kaniel, Shlomo

2010-01-01

The article responds to the need for evidence-based dynamic assessment. The article is divided into two sections: In Part 1 we examine the scientific answer to the question of how far human mental activities and capabilities are domain general (DG) / domain specific (DS). A highly complex answer emerges from the literature review of domains such…

Peer feedback for examiner quality assurance on MRCGP International South Asia: a mixed methods study.

PubMed

Perera, D P; Andrades, Marie; Wass, Val

2017-12-08

The International Membership Examination (MRCGP[INT]) of the Royal College of General Practitioners UK is a unique collaboration between four South Asian countries with diverse cultures, epidemiology, clinical facilities and resources. In this setting good quality assurance is imperative to achieve acceptable standards of inter rater reliability. This study aims to explore the process of peer feedback for examiner quality assurance with regard to factors affecting the implementation and acceptance of the method. A sequential mixed methods approach was used based on focus group discussions with examiners (n = 12) and clinical examination convenors who acted as peer reviewers (n = 4). A questionnaire based on emerging themes and literature review was then completed by 20 examiners at the subsequent OSCE exam. Qualitative data were analysed using an iterative reflexive process. Quantitative data were integrated by interpretive analysis looking for convergence, complementarity or dissonance. The qualitative data helped understand the issues and informed the process of developing the questionnaire. The quantitative data allowed for further refining of issues, wider sampling of examiners and giving voice to different perspectives. Examiners stated specifically that peer feedback gave an opportunity for discussion, standardisation of judgments and improved discriminatory abilities. Interpersonal dynamics, hierarchy and perception of validity of feedback were major factors influencing acceptance of feedback. Examiners desired increased transparency, accountability and the opportunity for equal partnership within the process. The process was stressful for examiners and reviewers; however acceptance increased with increasing exposure to receiving feedback. The process could be refined to improve acceptability through scrupulous attention to training and selection of those giving feedback to improve the perceived validity of feedback and improved reviewer feedback skills to enable better interpersonal dynamics and a more equitable feedback process. It is important to highlight the role of quality assurance and peer feedback as a tool for continuous improvement and maintenance of standards to examiners during training. Examiner quality assurance using peer feedback was generally a successful and accepted process. The findings highlight areas for improvement and guide the path towards a model of feedback that is responsive to examiner views and cultural sensibilities.

Effect of solvation-related interaction on the low-temperature dynamics of proteins

NASA Astrophysics Data System (ADS)

Zuo, Guanghong; Wang, Jun; Qin, Meng; Xue, Bin; Wang, Wei

2010-03-01

The effect of solvation-related interaction on the low-temperature dynamics of proteins is studied by taking into account the desolvation barriers in the interactions of native contacts. It is found out that about the folding transition temperature, the protein folds in a cooperative manner, and the water molecules are expelled from the hydrophobic core at the final stage in the folding process. At low temperature, however, the protein would generally be trapped in many metastable conformations with some water molecules frozen inside the protein. The desolvation takes an important role in these processes. The number of frozen water molecules and that of frozen states of proteins are further analyzed with the methods based on principal component analysis (PCA) and the clustering of conformations. It is found out that both the numbers of frozen water molecules and the frozen states of the protein increase quickly below a certain temperature. Especially, the number of frozen states of the protein increases exponentially following the decrease in the temperature, which resembles the basic features of glassy dynamics. Interestingly, it is observed that the freezing of water molecules and that of protein conformations happen at almost the same temperature. This suggests that the solvation-related interaction performs an important role for the low-temperature dynamics of the model protein.

A dynamic spatio-temporal model for spatial data

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin; Walsh, Daniel P.

2017-01-01

Analyzing spatial data often requires modeling dependencies created by a dynamic spatio-temporal data generating process. In many applications, a generalized linear mixed model (GLMM) is used with a random effect to account for spatial dependence and to provide optimal spatial predictions. Location-specific covariates are often included as fixed effects in a GLMM and may be collinear with the spatial random effect, which can negatively affect inference. We propose a dynamic approach to account for spatial dependence that incorporates scientific knowledge of the spatio-temporal data generating process. Our approach relies on a dynamic spatio-temporal model that explicitly incorporates location-specific covariates. We illustrate our approach with a spatially varying ecological diffusion model implemented using a computationally efficient homogenization technique. We apply our model to understand individual-level and location-specific risk factors associated with chronic wasting disease in white-tailed deer from Wisconsin, USA and estimate the location the disease was first introduced. We compare our approach to several existing methods that are commonly used in spatial statistics. Our spatio-temporal approach resulted in a higher predictive accuracy when compared to methods based on optimal spatial prediction, obviated confounding among the spatially indexed covariates and the spatial random effect, and provided additional information that will be important for containing disease outbreaks.

Collaboration processes and perceived effectiveness of integrated care projects in primary care: a longitudinal mixed-methods study.

PubMed

Valentijn, Pim P; Ruwaard, Dirk; Vrijhoef, Hubertus J M; de Bont, Antoinette; Arends, Rosa Y; Bruijnzeels, Marc A

2015-10-09

Collaborative partnerships are considered an essential strategy for integrating local disjointed health and social services. Currently, little evidence is available on how integrated care arrangements between professionals and organisations are achieved through the evolution of collaboration processes over time. The first aim was to develop a typology of integrated care projects (ICPs) based on the final degree of integration as perceived by multiple stakeholders. The second aim was to study how types of integration differ in changes of collaboration processes over time and final perceived effectiveness. A longitudinal mixed-methods study design based on two data sources (surveys and interviews) was used to identify the perceived degree of integration and patterns in collaboration among 42 ICPs in primary care in The Netherlands. We used cluster analysis to identify distinct subgroups of ICPs based on the final perceived degree of integration from a professional, organisational and system perspective. With the use of ANOVAs, the subgroups were contrasted based on: 1) changes in collaboration processes over time (shared ambition, interests and mutual gains, relationship dynamics, organisational dynamics and process management) and 2) final perceived effectiveness (i.e. rated success) at the professional, organisational and system levels. The ICPs were classified into three subgroups with: 'United Integration Perspectives (UIP)', 'Disunited Integration Perspectives (DIP)' and 'Professional-oriented Integration Perspectives (PIP)'. ICPs within the UIP subgroup made the strongest increase in trust-based (mutual gains and relationship dynamics) as well as control-based (organisational dynamics and process management) collaboration processes and had the highest overall effectiveness rates. On the other hand, ICPs with the DIP subgroup decreased on collaboration processes and had the lowest overall effectiveness rates. ICPs within the PIP subgroup increased in control-based collaboration processes (organisational dynamics and process management) and had the highest effectiveness rates at the professional level. The differences across the three subgroups in terms of the development of collaboration processes and the final perceived effectiveness provide evidence that united stakeholders' perspectives are achieved through a constructive collaboration process over time. Disunited perspectives at the professional, organisation and system levels can be aligned by both trust-based and control-based collaboration processes.

Professional judgement and decision-making in adventure sports coaching: the role of interaction.

PubMed

Collins, Loel; Collins, Dave

2016-01-01

This qualitative study presents the view that coaching practice places demands on the coach's adaptability and flexibility. These requirements for being adaptive and flexible are met through a careful process of professional judgement and decision-making based on context-appropriate bodies of knowledge. Adventure sports coaches were selected for study on the basis that adventure sports create a hyper-dynamic environment in which these features can be examined. Thematic analysis revealed that coaches were generally well informed and practised with respect to the technical aspects of their sporting disciplines. Less positively, however, they often relied on ad hoc contextualisation of generalised theories of coaching practice to respond to the hyper-dynamic environments encountered in adventure sports. We propose that coaching practice reflects the demands of the environment, individual learning needs of the students and the task at hand. Together, these factors outwardly resemble a constraints-led approach but, we suggest, actually reflect manipulation of these parameters from a cognitive rather than an ecological perspective. This process is facilitated by a refined judgement and decision-making process, sophisticated epistemology and an explicit interaction of coaching components.

[Dynamics of adaptation processes and morbidity risk for the popupation of the territory of industrial cities].

PubMed

Prusakov, V M; Prusakova, A V

2014-01-01

There was investigated the character of the adaptation processes in the population residing in conditions ofprolonged exposure to environmental pollution in the territory of the industrial cities of the Irkutsk region in order to identify the possible periodicity of their manifestations in the formation of the morbidity risk for the population of different age groups. Under conditions of prolonged exposure to air pollution and other adverse unfavorable factors of industrial cities in the population of all age groups long cyclic changes of adaptation processes in the body in the form of repeated 11-15-years cycles in which a period of relative destabilization of physiological functions with lowered resistance is replaced by the period with the state of elevated nonspecific resistance were established to be observed. Undulating changes of the dynamics of the relative risks of general morbidity should be considered in the assessment of the medical and environmental situation in the territory and making the managing decisions at the base on the data of public health monitoring.

Dynamical mechanism of atrial fibrillation: A topological approach

NASA Astrophysics Data System (ADS)

Marcotte, Christopher D.; Grigoriev, Roman O.

2017-09-01

While spiral wave breakup has been implicated in the emergence of atrial fibrillation, its role in maintaining this complex type of cardiac arrhythmia is less clear. We used the Karma model of cardiac excitation to investigate the dynamical mechanisms that sustain atrial fibrillation once it has been established. The results of our numerical study show that spatiotemporally chaotic dynamics in this regime can be described as a dynamical equilibrium between topologically distinct types of transitions that increase or decrease the number of wavelets, in general agreement with the multiple wavelets' hypothesis. Surprisingly, we found that the process of continuous excitation waves breaking up into discontinuous pieces plays no role whatsoever in maintaining spatiotemporal complexity. Instead, this complexity is maintained as a dynamical balance between wave coalescence—a unique, previously unidentified, topological process that increases the number of wavelets—and wave collapse—a different topological process that decreases their number.

Dynamic Buffer Capacity in Acid-Base Systems.

PubMed

Michałowska-Kaczmarczyk, Anna M; Michałowski, Tadeusz

The generalized concept of 'dynamic' buffer capacity β V is related to electrolytic systems of different complexity where acid-base equilibria are involved. The resulting formulas are presented in a uniform and consistent form. The detailed calculations are related to two Britton-Robinson buffers, taken as examples.

General framework for dynamic large deformation contact problems based on phantom-node X-FEM

NASA Astrophysics Data System (ADS)

Broumand, P.; Khoei, A. R.

2018-04-01

This paper presents a general framework for modeling dynamic large deformation contact-impact problems based on the phantom-node extended finite element method. The large sliding penalty contact formulation is presented based on a master-slave approach which is implemented within the phantom-node X-FEM and an explicit central difference scheme is used to model the inertial effects. The method is compared with conventional contact X-FEM; advantages, limitations and implementational aspects are also addressed. Several numerical examples are presented to show the robustness and accuracy of the proposed method.

An agent-based modelling framework to explore the role of social media and stubborn people on evacuation rates during flooding events

NASA Astrophysics Data System (ADS)

Du, E.; Cai, X.; Minsker, B. S.; Sun, Z.

2017-12-01

Flood warnings from various information sources are important for individuals to make evacuation decisions during a flood event. In this study, we develop a general opinion dynamics model to simulate how individuals update their flood hazard awareness when exposed to multiple information sources, including global broadcast, social media, and observations of neighbors' actions. The opinion dynamics model is coupled with a traffic model to simulate the evacuation processes of a residential community with a given transportation network. Through various scenarios, we investigate how social media affect the opinion dynamics and evacuation processes. We find that stronger social media can make evacuation processes more sensitive to the change of global broadcast and neighbor observations, and thus, impose larger uncertainty on evacuation rates (i.e., a large range of evacuation rates corresponding to sources of information). For instance, evacuation rates are lower when social media become more influential and individuals have less trust in global broadcast. Stubborn individuals can significantly affect the opinion dynamics and reduce evacuation rates. In addition, evacuation rates respond to the percentage of stubborn agents in a non-linear manner, i.e., above a threshold, the impact of stubborn agents will be intensified by stronger social media. These results highlight the role of social media in flood evacuation processes and the need to monitor social media so that misinformation can be corrected in a timely manner. The joint impacts of social media, quality of flood warnings and transportation capacity on evacuation rates are also discussed.

Process simulation and dynamic control for marine oily wastewater treatment using UV irradiation.

PubMed

Jing, Liang; Chen, Bing; Zhang, Baiyu; Li, Pu

2015-09-15

UV irradiation and advanced oxidation processes have been recently regarded as promising solutions in removing polycyclic aromatic hydrocarbons (PAHs) from marine oily wastewater. However, such treatment methods are generally not sufficiently understood in terms of reaction mechanisms, process simulation and process control. These deficiencies can drastically hinder their application in shipping and offshore petroleum industries which produce bilge/ballast water and produced water as the main streams of marine oily wastewater. In this study, the factorial design of experiment was carried out to investigate the degradation mechanism of a typical PAH, namely naphthalene, under UV irradiation in seawater. Based on the experimental results, a three-layer feed-forward artificial neural network simulation model was developed to simulate the treatment process and to forecast the removal performance. A simulation-based dynamic mixed integer nonlinear programming (SDMINP) approach was then proposed to intelligently control the treatment process by integrating the developed simulation model, genetic algorithm and multi-stage programming. The applicability and effectiveness of the developed approach were further tested though a case study. The experimental results showed that the influences of fluence rate and temperature on the removal of naphthalene were greater than those of salinity and initial concentration. The developed simulation model could well predict the UV-induced removal process under varying conditions. The case study suggested that the SDMINP approach, with the aid of the multi-stage control strategy, was able to significantly reduce treatment cost when comparing to the traditional single-stage process optimization. The developed approach and its concept/framework have high potential of applicability in other environmental fields where a treatment process is involved and experimentation and modeling are used for process simulation and control. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dynamic Range Across Music Genres and the Perception of Dynamic Compression in Hearing-Impaired Listeners

PubMed Central

Kirchberger, Martin

2016-01-01

Dynamic range compression serves different purposes in the music and hearing-aid industries. In the music industry, it is used to make music louder and more attractive to normal-hearing listeners. In the hearing-aid industry, it is used to map the variable dynamic range of acoustic signals to the reduced dynamic range of hearing-impaired listeners. Hence, hearing-aided listeners will typically receive a dual dose of compression when listening to recorded music. The present study involved an acoustic analysis of dynamic range across a cross section of recorded music as well as a perceptual study comparing the efficacy of different compression schemes. The acoustic analysis revealed that the dynamic range of samples from popular genres, such as rock or rap, was generally smaller than the dynamic range of samples from classical genres, such as opera and orchestra. By comparison, the dynamic range of speech, based on recordings of monologues in quiet, was larger than the dynamic range of all music genres tested. The perceptual study compared the effect of the prescription rule NAL-NL2 with a semicompressive and a linear scheme. Music subjected to linear processing had the highest ratings for dynamics and quality, followed by the semicompressive and the NAL-NL2 setting. These findings advise against NAL-NL2 as a prescription rule for recorded music and recommend linear settings. PMID:26868955

Dynamic Range Across Music Genres and the Perception of Dynamic Compression in Hearing-Impaired Listeners.

PubMed

Kirchberger, Martin; Russo, Frank A

2016-02-10

Dynamic range compression serves different purposes in the music and hearing-aid industries. In the music industry, it is used to make music louder and more attractive to normal-hearing listeners. In the hearing-aid industry, it is used to map the variable dynamic range of acoustic signals to the reduced dynamic range of hearing-impaired listeners. Hence, hearing-aided listeners will typically receive a dual dose of compression when listening to recorded music. The present study involved an acoustic analysis of dynamic range across a cross section of recorded music as well as a perceptual study comparing the efficacy of different compression schemes. The acoustic analysis revealed that the dynamic range of samples from popular genres, such as rock or rap, was generally smaller than the dynamic range of samples from classical genres, such as opera and orchestra. By comparison, the dynamic range of speech, based on recordings of monologues in quiet, was larger than the dynamic range of all music genres tested. The perceptual study compared the effect of the prescription rule NAL-NL2 with a semicompressive and a linear scheme. Music subjected to linear processing had the highest ratings for dynamics and quality, followed by the semicompressive and the NAL-NL2 setting. These findings advise against NAL-NL2 as a prescription rule for recorded music and recommend linear settings. © The Author(s) 2016.

Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks

PubMed Central

Vestergaard, Christian L.; Génois, Mathieu

2015-01-01

Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling. PMID:26517860

Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks.

PubMed

Vestergaard, Christian L; Génois, Mathieu

2015-10-01

Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling.

Modeling Firn Compaction in Dynamic Regions

NASA Astrophysics Data System (ADS)

Horlings, Annika N.; Christianson, Knut; Waddington, Edwin D.; Stevens, C. Max; Holschuh, Nicholas

2017-04-01

Firn compaction remains the largest source of uncertainty in assessments of ice-sheet mass balance from repeat altimetry measurements due to our limited understanding of the physical processes responsible for the transformation of snow into ice. In addition to the lack of a comprehensive, physically-based constitutive relationship that describes firn compaction, dynamic thinning is an important process in some regions, but is generally neglected in firn-compaction models due to their one-dimensional nature. Here, we report on preliminary results incorporating dynamic strain thinning into firn compaction models. Using a Lagrangian (material-following) reference frame, we first compact each firn element using a standard 1-D firn-compaction model without longitudinal strain. Then, we stretch each firn parcel at each time step by applying a prescribed longitudinal strain rate in the absence of further density changes; this produces additional vertical thinning. To assess variations among firn models, we compare results from eight firn densification models currently included in the UW Community Firn Model. We focus on the Northeast Greenland Ice Stream due to the high extensile strain rates (10-3 yr-1 or higher) in the ice stream's shear margins and the extensive firn-density data in this area from seismic measurements and shallow firn/ice cores. For temperatures and accumulation rates typical for northeast Greenland, our preliminary results indicate up to an 18-meter decrease in bubble close-off depth in the shear margins compared to nearby areas either inside or outside the ice stream, which compares favorably to field data. Further work includes incorporating physically-based constitutive relations and applying these improved models to other dynamic regions, such as the Amundsen Sea Embayment, where dynamic strain thinning has accelerated in recent decades.

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

PubMed Central

Wijeakumar, Sobanawartiny; Ambrose, Joseph P.; Spencer, John P.; Curtu, Rodica

2017-01-01

A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the ‘standard’ for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus-response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations’ dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior. PMID:29118459

Conduction at the onset of chaos

NASA Astrophysics Data System (ADS)

Baldovin, Fulvio

2017-02-01

After a general discussion of the thermodynamics of conductive processes, we introduce specific observables enabling the connection of the diffusive transport properties with the microscopic dynamics. We solve the case of Brownian particles, both analytically and numerically, and address then whether aspects of the classic Onsager's picture generalize to the non-local non-reversible dynamics described by logistic map iterates. While in the chaotic case numerical evidence of a monotonic relaxation is found, at the onset of chaos complex relaxation patterns emerge.

Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

NASA Astrophysics Data System (ADS)

Sidelnikov, O. S.; Redyuk, A. A.; Sygletos, S.

2017-12-01

We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

Dynamic reasoning in a knowledge-based system

NASA Technical Reports Server (NTRS)

Rao, Anand S.; Foo, Norman Y.

1988-01-01

Any space based system, whether it is a robot arm assembling parts in space or an onboard system monitoring the space station, has to react to changes which cannot be foreseen. As a result, apart from having domain-specific knowledge as in current expert systems, a space based AI system should also have general principles of change. This paper presents a modal logic which can not only represent change but also reason with it. Three primitive operations, expansion, contraction and revision are introduced and axioms which specify how the knowledge base should change when the external world changes are also specified. Accordingly the notion of dynamic reasoning is introduced, which unlike the existing forms of reasoning, provide general principles of change. Dynamic reasoning is based on two main principles, namely minimize change and maximize coherence. A possible-world semantics which incorporates the above two principles is also discussed. The paper concludes by discussing how the dynamic reasoning system can be used to specify actions and hence form an integral part of an autonomous reasoning and planning system.

SMOG 2: A Versatile Software Package for Generating Structure-Based Models.

PubMed

Noel, Jeffrey K; Levi, Mariana; Raghunathan, Mohit; Lammert, Heiko; Hayes, Ryan L; Onuchic, José N; Whitford, Paul C

2016-03-01

Molecular dynamics simulations with coarse-grained or simplified Hamiltonians have proven to be an effective means of capturing the functionally important long-time and large-length scale motions of proteins and RNAs. Originally developed in the context of protein folding, structure-based models (SBMs) have since been extended to probe a diverse range of biomolecular processes, spanning from protein and RNA folding to functional transitions in molecular machines. The hallmark feature of a structure-based model is that part, or all, of the potential energy function is defined by a known structure. Within this general class of models, there exist many possible variations in resolution and energetic composition. SMOG 2 is a downloadable software package that reads user-designated structural information and user-defined energy definitions, in order to produce the files necessary to use SBMs with high performance molecular dynamics packages: GROMACS and NAMD. SMOG 2 is bundled with XML-formatted template files that define commonly used SBMs, and it can process template files that are altered according to the needs of each user. This computational infrastructure also allows for experimental or bioinformatics-derived restraints or novel structural features to be included, e.g. novel ligands, prosthetic groups and post-translational/transcriptional modifications. The code and user guide can be downloaded at http://smog-server.org/smog2.

Alternating event processes during lifetimes: population dynamics and statistical inference.

PubMed

Shinohara, Russell T; Sun, Yifei; Wang, Mei-Cheng

2018-01-01

In the literature studying recurrent event data, a large amount of work has been focused on univariate recurrent event processes where the occurrence of each event is treated as a single point in time. There are many applications, however, in which univariate recurrent events are insufficient to characterize the feature of the process because patients experience nontrivial durations associated with each event. This results in an alternating event process where the disease status of a patient alternates between exacerbations and remissions. In this paper, we consider the dynamics of a chronic disease and its associated exacerbation-remission process over two time scales: calendar time and time-since-onset. In particular, over calendar time, we explore population dynamics and the relationship between incidence, prevalence and duration for such alternating event processes. We provide nonparametric estimation techniques for characteristic quantities of the process. In some settings, exacerbation processes are observed from an onset time until death; to account for the relationship between the survival and alternating event processes, nonparametric approaches are developed for estimating exacerbation process over lifetime. By understanding the population dynamics and within-process structure, the paper provide a new and general way to study alternating event processes.

A KPI-based process monitoring and fault detection framework for large-scale processes.

PubMed

Zhang, Kai; Shardt, Yuri A W; Chen, Zhiwen; Yang, Xu; Ding, Steven X; Peng, Kaixiang

2017-05-01

Large-scale processes, consisting of multiple interconnected subprocesses, are commonly encountered in industrial systems, whose performance needs to be determined. A common approach to this problem is to use a key performance indicator (KPI)-based approach. However, the different KPI-based approaches are not developed with a coherent and consistent framework. Thus, this paper proposes a framework for KPI-based process monitoring and fault detection (PM-FD) for large-scale industrial processes, which considers the static and dynamic relationships between process and KPI variables. For the static case, a least squares-based approach is developed that provides an explicit link with least-squares regression, which gives better performance than partial least squares. For the dynamic case, using the kernel representation of each subprocess, an instrument variable is used to reduce the dynamic case to the static case. This framework is applied to the TE benchmark process and the hot strip mill rolling process. The results show that the proposed method can detect faults better than previous methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency

NASA Astrophysics Data System (ADS)

Liu, Changjiang; Cheng, Irene; Zhang, Yi; Basu, Anup

2017-06-01

This paper presents an improved multi-scale Retinex (MSR) based enhancement for ariel images under low visibility. For traditional multi-scale Retinex, three scales are commonly employed, which limits its application scenarios. We extend our research to a general purpose enhanced method, and design an MSR with more than three scales. Based on the mathematical analysis and deductions, an explicit multi-scale representation is proposed that balances image contrast and color consistency. In addition, a histogram truncation technique is introduced as a post-processing strategy to remap the multi-scale Retinex output to the dynamic range of the display. Analysis of experimental results and comparisons with existing algorithms demonstrate the effectiveness and generality of the proposed method. Results on image quality assessment proves the accuracy of the proposed method with respect to both objective and subjective criteria.

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

PubMed Central

Carmena, Jose M.

2016-01-01

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics. PMID:27035820

Reduction of Large Dynamical Systems by Minimization of Evolution Rate

NASA Technical Reports Server (NTRS)

Girimaji, Sharath S.

1999-01-01

Reduction of a large system of equations to a lower-dimensional system of similar dynamics is investigated. For dynamical systems with disparate timescales, a criterion for determining redundant dimensions and a general reduction method based on the minimization of evolution rate are proposed.

A general CPL-AdS methodology for fixing dynamic parameters in dual environments.

PubMed

Huang, De-Shuang; Jiang, Wen

2012-10-01

The algorithm of Continuous Point Location with Adaptive d-ary Search (CPL-AdS) strategy exhibits its efficiency in solving stochastic point location (SPL) problems. However, there is one bottleneck for this CPL-AdS strategy which is that, when the dimension of the feature, or the number of divided subintervals for each iteration, d is large, the decision table for elimination process is almost unavailable. On the other hand, the larger dimension of the features d can generally make this CPL-AdS strategy avoid oscillation and converge faster. This paper presents a generalized universal decision formula to solve this bottleneck problem. As a matter of fact, this decision formula has a wider usage beyond handling out this SPL problems, such as dealing with deterministic point location problems and searching data in Single Instruction Stream-Multiple Data Stream based on Concurrent Read and Exclusive Write parallel computer model. Meanwhile, we generalized the CPL-AdS strategy with an extending formula, which is capable of tracking an unknown dynamic parameter λ in both informative and deceptive environments. Furthermore, we employed different learning automata in the generalized CPL-AdS method to find out if faster learning algorithm will lead to better realization of the generalized CPL-AdS method. All of these aforementioned contributions are vitally important whether in theory or in practical applications. Finally, extensive experiments show that our proposed approaches are efficient and feasible.

Fidelity of Majorana-based quantum operations

NASA Astrophysics Data System (ADS)

Tanhayi Ahari, Mostafa; Ortiz, Gerardo; Seradjeh, Babak

2015-03-01

It is well known that one-dimensional p-wave superconductor, the so-called Kitaev model, has topologically distinct phases that are distinguished by the presence of Majorana fermions. Owing to their topological protection, these Majorana fermions have emerged as candidates for fault-tolerant quantum computation. They furnish the operation of such a computation via processes that produce, braid, and annihilate them in pairs. In this work we study some of these processes from the dynamical perspective. In particular, we determine the fidelity of the Majorana fermions when they are produced or annihilated by tuning the system through the corresponding topological phase transition. For a simple linear protocol, we derive analytical expressions for fidelity and test various perturbative schemes. For more general protocols, we present exact numerics. Our results are relevant for the operation of Majorana-based quantum gates and quantum memories.

Distributed Processing Tools Definition. Volume 1. Hardware and Software Technologies for Tightly-Coupled Distributed Systems.

DTIC Science & Technology

1983-06-01

LOSARDO Project Engineer APPROVED: .MARMCINIhI, Colonel. USAF Chief, Coaud and Control Division FOR THE CCOaIDKR: Acting Chief, Plea Off ice * **711...WORK UNIT NUMBERS General Dynamics Corporation 62702F Data Systems Division P 0 Box 748, Fort Worth TX 76101 55811829 I1. CONTROLLING OFFICE NAME AND...Processing System for 29 the Operation/Direction Center(s) 4-3 Distribution of Processing Control 30 for the Operation/Direction Center(s) 4-4 Generalized

Ecological and Dynamical Study of the Creative Process and Affects of Scientific Students Working in Groups

ERIC Educational Resources Information Center

Peilloux, Aurélien; Botella, Marion

2016-01-01

Although creativity has drawn the attention of researchers during the past century, collaborative processes have barely been investigated. In this article, the collective dimension of a creative process is investigated, based on a dynamic and ecological approach that includes an affective component. "Dynamic" means that the creative…

Event coincidence analysis for quantifying statistical interrelationships between event time series. On the role of flood events as triggers of epidemic outbreaks

NASA Astrophysics Data System (ADS)

Donges, J. F.; Schleussner, C.-F.; Siegmund, J. F.; Donner, R. V.

2016-05-01

Studying event time series is a powerful approach for analyzing the dynamics of complex dynamical systems in many fields of science. In this paper, we describe the method of event coincidence analysis to provide a framework for quantifying the strength, directionality and time lag of statistical interrelationships between event series. Event coincidence analysis allows to formulate and test null hypotheses on the origin of the observed interrelationships including tests based on Poisson processes or, more generally, stochastic point processes with a prescribed inter-event time distribution and other higher-order properties. Applying the framework to country-level observational data yields evidence that flood events have acted as triggers of epidemic outbreaks globally since the 1950s. Facing projected future changes in the statistics of climatic extreme events, statistical techniques such as event coincidence analysis will be relevant for investigating the impacts of anthropogenic climate change on human societies and ecosystems worldwide.

CubeSats in Hydrology: Ultrahigh-Resolution Insights Into Vegetation Dynamics and Terrestrial Evaporation

NASA Astrophysics Data System (ADS)

McCabe, M. F.; Aragon, B.; Houborg, R.; Mascaro, J.

2017-12-01

Satellite-based remote sensing has generally necessitated a trade-off between spatial resolution and temporal frequency, affecting the capacity to observe fast hydrological processes and rapidly changing land surface conditions. An avenue for overcoming these spatiotemporal restrictions is the concept of using constellations of satellites, as opposed to the mission focus exemplified by the more conventional space-agency approach to earth observation. Referred to as CubeSats, these platforms offer the potential to provide new insights into a range of earth system variables and processes. Their emergence heralds a paradigm shift from single-sensor launches to an operational approach that envisions tens to hundreds of small, lightweight, and comparatively inexpensive satellites placed into a range of low earth orbits. Although current systems are largely limited to sensing in the optical portion of the electromagnetic spectrum, we demonstrate the opportunity and potential that CubeSats present the hydrological community via the retrieval of vegetation dynamics and terrestrial evaporation and foreshadow future sensing capabilities.

Upper Atmosphere Research Satellite (UARS) onboard attitude determination using a Kalman filter

NASA Technical Reports Server (NTRS)

Garrick, Joseph

1993-01-01

The Upper Atmospheric Research Satellite (UARS) requires a highly accurate knowledge of its attitude to accomplish its mission. Propagation of the attitude state using gyro measurements is not sufficient to meet the accuracy requirements, and must be supplemented by a observer/compensation process to correct for dynamics and observation anomalies. The process of amending the attitude state utilizes a well known method, the discrete Kalman Filter. This study is a sensitivity analysis of the discrete Kalman Filter as implemented in the UARS Onboard Computer (OBC). The stability of the Kalman Filter used in the normal on-orbit control mode within the OBC, is investigated for the effects of corrupted observations and nonlinear errors. Also, a statistical analysis on the residuals of the Kalman Filter is performed. These analysis is based on simulations using the UARS Dynamics Simulator (UARSDSIM) and compared against attitude requirements as defined by General Electric (GE). An independent verification of expected accuracies is performed using the Attitude Determination Error Analysis System (ADEAS).

Effects of stiffness and volume on the transit time of an erythrocyte through a slit.

PubMed

Salehyar, Sara; Zhu, Qiang

2017-06-01

By using a fully coupled fluid-cell interaction model, we numerically simulate the dynamic process of a red blood cell passing through a slit driven by an incoming flow. The model is achieved by combining a multiscale model of the composite cell membrane with a boundary element fluid dynamics model based on the Stokes flow assumption. Our concentration is on the correlation between the transit time (the time it takes to finish the whole translocation process) and different conditions (flow speed, cell orientation, cell stiffness, cell volume, etc.) that are involved. According to the numerical prediction (with some exceptions), the transit time rises as the cell is stiffened. It is also highly sensitive to volume increase inside the cell. In general, even slightly swollen cells (i.e., the internal volume is increased while the surface area of the cell kept unchanged) travel dramatically slower through the slit. For these cells, there is also an increased chance of blockage.

Hierarchy of Efficiently Computable and Faithful Lower Bounds to Quantum Discord

NASA Astrophysics Data System (ADS)

Piani, Marco

2016-08-01

Quantum discord expresses a fundamental nonclassicality of correlations that is more general than entanglement, but that, in its standard definition, is not easily evaluated. We derive a hierarchy of computationally efficient lower bounds to the standard quantum discord. Every nontrivial element of the hierarchy constitutes by itself a valid discordlike measure, based on a fundamental feature of quantum correlations: their lack of shareability. Our approach emphasizes how the difference between entanglement and discord depends on whether shareability is intended as a static property or as a dynamical process.

Path-space variational inference for non-equilibrium coarse-grained systems

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

Harmandaris, Vagelis, E-mail: harman@uoc.gr; Institute of Applied and Computational Mathematics; Kalligiannaki, Evangelia, E-mail: ekalligian@tem.uoc.gr

In this paper we discuss information-theoretic tools for obtaining optimized coarse-grained molecular models for both equilibrium and non-equilibrium molecular simulations. The latter are ubiquitous in physicochemical and biological applications, where they are typically associated with coupling mechanisms, multi-physics and/or boundary conditions. In general the non-equilibrium steady states are not known explicitly as they do not necessarily have a Gibbs structure. The presented approach can compare microscopic behavior of molecular systems to parametric and non-parametric coarse-grained models using the relative entropy between distributions on the path space and setting up a corresponding path-space variational inference problem. The methods can become entirelymore » data-driven when the microscopic dynamics are replaced with corresponding correlated data in the form of time series. Furthermore, we present connections and generalizations of force matching methods in coarse-graining with path-space information methods. We demonstrate the enhanced transferability of information-based parameterizations to different observables, at a specific thermodynamic point, due to information inequalities. We discuss methodological connections between information-based coarse-graining of molecular systems and variational inference methods primarily developed in the machine learning community. However, we note that the work presented here addresses variational inference for correlated time series due to the focus on dynamics. The applicability of the proposed methods is demonstrated on high-dimensional stochastic processes given by overdamped and driven Langevin dynamics of interacting particles.« less

Precipitation, pH and metal load in AMD river basins: an application of fuzzy clustering algorithms to the process characterization.

PubMed

Grande, J A; Andújar, J M; Aroba, J; de la Torre, M L; Beltrán, R

2005-04-01

In the present work, Acid Mine Drainage (AMD) processes in the Chorrito Stream, which flows into the Cobica River (Iberian Pyrite Belt, Southwest Spain) are characterized by means of clustering techniques based on fuzzy logic. Also, pH behavior in contrast to precipitation is clearly explained, proving that the influence of rainfall inputs on the acidity and, as a result, on the metal load of a riverbed undergoing AMD processes highly depends on the moment when it occurs. In general, the riverbed dynamic behavior is the response to the sum of instant stimuli produced by isolated rainfall, the seasonal memory depending on the moment of the target hydrological year and, finally, the own inertia of the river basin, as a result of an accumulation process caused by age-long mining activity.

Beating Landauer's Bound: Tradeoff between Accuracy and Heat Dissipation

NASA Astrophysics Data System (ADS)

Talukdar, Saurav; Bhaban, Shreyas; Salapaka, Murti

The Landauer's Principle states that erasing of one bit of stored information is necessarily accompanied by heat dissipation of at least kb Tln 2 per bit. However, this is true only if the erasure process is always successful. We demonstrate that if the erasure process has a success probability p, the minimum heat dissipation per bit is given by kb T(plnp + (1 - p) ln (1 - p) + ln 2), referred to as the Generalized Landauer Bound, which is kb Tln 2 if the erasure process is always successful and decreases to zero as p reduces to 0.5. We present a model for a one-bit memory based on a Brownian particle in a double well potential motivated from optical tweezers and achieve erasure by manipulation of the optical fields. The method uniquely provides with a handle on the success proportion of the erasure. The thermodynamics framework for Langevin dynamics developed by Sekimoto is used for computation of heat dissipation in each realization of the erasure process. Using extensive Monte Carlo simulations, we demonstrate that the Landauer Bound of kb Tln 2 is violated by compromising on the success of the erasure process, while validating the existence of the Generalized Landauer Bound.

Are genes faster than crabs? Mitochondrial introgression exceeds larval dispersal during population expansion of the invasive crab Carcinus maenas.

PubMed

Darling, John A; Tsai, Yi-Hsin Erica; Blakeslee, April M H; Roman, Joe

2014-10-01

Biological invasions offer unique opportunities to investigate evolutionary dynamics at the peripheries of expanding populations. Here, we examine genetic patterns associated with admixture between two distinct invasive lineages of the European green crab, Carcinus maenas L., independently introduced to the northwest Atlantic. Previous investigations based on mitochondrial DNA sequences demonstrated that larval dispersal driven by advective currents could explain observed southward displacement of an admixture zone between the two invasions. Comparison of published mitochondrial results with new nuclear data from nine microsatellite loci, however, reveals striking discordance in their introgression patterns. Specifically, introgression of mitochondrial genomes relative to nuclear background suggests that demographic processes such as sex-biased reproductive dynamics and population size imbalances-and not solely larval dispersal-play an important role in driving the evolution of the genetic cline. In particular, the unpredicted introgression of mitochondrial alleles against the direction of mean larval dispersal in the region is consistent with recent models invoking similar demographic processes to explain movements of genes into invading populations. These observations have important implications for understanding historical shifts in C. maenas range limits, and more generally for inferences of larval dispersal based on genetic data.

Monitoring dynamic electrochemical processes with in situ ptychography

NASA Astrophysics Data System (ADS)

Kourousias, George; Bozzini, Benedetto; Jones, Michael W. M.; Van Riessen, Grant A.; Dal Zilio, Simone; Billè, Fulvio; Kiskinova, Maya; Gianoncelli, Alessandra

2018-03-01

The present work reports novel soft X-ray Fresnel CDI ptychography results, demonstrating the potential of this method for dynamic in situ studies. Specifically, in situ ptychography experiments explored the electrochemical fabrication of Co-doped Mn-oxide/polypyrrole nanocomposites for sustainable and cost-effective fuel-cell air-electrodes. Oxygen-reduction catalysts based on Mn-oxides exhibit relatively high activity, but poor durability: doping with Co has been shown to improve both reduction rate and stability. In this study, we examine the chemical state distribution of the catalytically crucial Co dopant to elucidate details of the Co dopant incorporation into the Mn/polymer matrix. The measurements were performed using a custom-made three-electrode thin-layer microcell, developed at the TwinMic beamline of Elettra Synchrotron during a series of experiments that were continued at the SXRI beamline of the Australian Synchrotron. Our time-resolved ptychography-based investigation was carried out in situ after two representative growth steps, controlled by electrochemical bias. In addition to the observation of morphological changes, we retrieved the spectroscopic information, provided by multiple ptychographic energy scans across Co L3-edge, shedding light on the doping mechanism and demonstrating a general approach for the molecular-level investigation complex multimaterial electrodeposition processes.

Are genes faster than crabs? Mitochondrial introgression exceeds larval dispersal during population expansion of the invasive crab Carcinus maenas

PubMed Central

Darling, John A.; Tsai, Yi-Hsin Erica; Blakeslee, April M. H.; Roman, Joe

2014-01-01

Biological invasions offer unique opportunities to investigate evolutionary dynamics at the peripheries of expanding populations. Here, we examine genetic patterns associated with admixture between two distinct invasive lineages of the European green crab, Carcinus maenas L., independently introduced to the northwest Atlantic. Previous investigations based on mitochondrial DNA sequences demonstrated that larval dispersal driven by advective currents could explain observed southward displacement of an admixture zone between the two invasions. Comparison of published mitochondrial results with new nuclear data from nine microsatellite loci, however, reveals striking discordance in their introgression patterns. Specifically, introgression of mitochondrial genomes relative to nuclear background suggests that demographic processes such as sex-biased reproductive dynamics and population size imbalances—and not solely larval dispersal—play an important role in driving the evolution of the genetic cline. In particular, the unpredicted introgression of mitochondrial alleles against the direction of mean larval dispersal in the region is consistent with recent models invoking similar demographic processes to explain movements of genes into invading populations. These observations have important implications for understanding historical shifts in C. maenas range limits, and more generally for inferences of larval dispersal based on genetic data. PMID:26064543

Research 0n Incentive Mechanism of General Contractor and Subcontractors Dynamic Alliance in Construction Project Based on Team Cooperation

NASA Astrophysics Data System (ADS)

Yin, Honglian; Sun, Aihua; Liu, Quanru; Chen, Zhiyi

2018-03-01

It is the key of motivating sub-contractors working hard and mutual cooperation, ensuring implementation overall goal of the project that to design rational incentive mechanism for general contractor. Based on the principal-agency theory, the subcontractor efforts is divided into two parts, one for individual efforts, another helping other subcontractors, team Cooperation incentive models of multiple subcontractors are set up, incentive schemes and intensities are also given. The results show that the general contractor may provide individual and team motivation incentives when subcontractors working independently, not affecting each other in time and space; otherwise, the general contractor may only provide individual incentive to entice teams collaboration between subcontractors and helping each other. The conclusions can provide a reference for the subcontract design of general and sub-contractor dynamic alliances.

Grounding explanations in evolving, diagnostic situations

NASA Technical Reports Server (NTRS)

Johannesen, Leila J.; Cook, Richard I.; Woods, David D.

1994-01-01

Certain fields of practice involve the management and control of complex dynamic systems. These include flight deck operations in commercial aviation, control of space systems, anesthetic management during surgery or chemical or nuclear process control. Fault diagnosis of these dynamic systems generally must occur with the monitored process on-line and in conjunction with maintaining system integrity.This research seeks to understand in more detail what it means for an intelligent system to function cooperatively, or as a 'team player' in complex, dynamic environments. The approach taken was to study human practitioners engaged in the management of a complex, dynamic process: anesthesiologists during neurosurgical operations. The investigation focused on understanding how team members cooperate in management and fault diagnosis and comparing this interaction to the situation with an Artificial Intelligence(AI) system that provides diagnoses and explanations. Of particular concern was to study the ways in which practitioners support one another in keeping aware of relevant information concerning the state of the monitored process and of the problem solving process.

Imitation versus payoff: Duality of the decision-making process demonstrates criticality and consensus formation

NASA Astrophysics Data System (ADS)

Turalska, M.; West, B. J.

2014-11-01

We consider a dual model of decision making, in which an individual forms its opinion based on contrasting mechanisms of imitation and rational calculation. The decision-making model (DMM) implements imitating behavior by means of a network of coupled two-state master equations that undergoes a phase transition at a critical value of a control parameter. The evolutionary spatial game, being a generalization of the prisoner's dilemma game, is used to determine in objective fashion the cooperative or anticooperative strategy adopted by individuals. Interactions between two sources of dynamics increases the domain of initial states attracted to phase transition dynamics beyond that of the DMM network in isolation. Additionally, on average the influence of the DMM on the game increases the final observed fraction of cooperators in the system.

Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: computational study

PubMed Central

Marmarelis, Vasilis Z.; Berger, Theodore W.

2009-01-01

Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input–output data. Results show that the non-parametric models accurately and efficiently replicate the input–output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP. PMID:18506609

Super-stable Poissonian structures

NASA Astrophysics Data System (ADS)

Eliazar, Iddo

2012-10-01

In this paper we characterize classes of Poisson processes whose statistical structures are super-stable. We consider a flow generated by a one-dimensional ordinary differential equation, and an ensemble of particles ‘surfing’ the flow. The particles start from random initial positions, and are propagated along the flow by stochastic ‘wave processes’ with general statistics and general cross correlations. Setting the initial positions to be Poisson processes, we characterize the classes of Poisson processes that render the particles’ positions—at all times, and invariantly with respect to the wave processes—statistically identical to their initial positions. These Poisson processes are termed ‘super-stable’ and facilitate the generalization of the notion of stationary distributions far beyond the realm of Markov dynamics.

Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process.

PubMed

Ding, Mingtao; He, Lihan; Dunson, David; Carin, Lawrence

2012-12-01

A nonparametric Bayesian model is proposed for segmenting time-evolving multivariate spatial point process data. An inhomogeneous Poisson process is assumed, with a logistic stick-breaking process (LSBP) used to encourage piecewise-constant spatial Poisson intensities. The LSBP explicitly favors spatially contiguous segments, and infers the number of segments based on the observed data. The temporal dynamics of the segmentation and of the Poisson intensities are modeled with exponential correlation in time, implemented in the form of a first-order autoregressive model for uniformly sampled discrete data, and via a Gaussian process with an exponential kernel for general temporal sampling. We consider and compare two different inference techniques: a Markov chain Monte Carlo sampler, which has relatively high computational complexity; and an approximate and efficient variational Bayesian analysis. The model is demonstrated with a simulated example and a real example of space-time crime events in Cincinnati, Ohio, USA.

Complex Networks in Psychological Models

NASA Astrophysics Data System (ADS)

Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

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.

A New Look at Stratospheric Sudden Warmings. Part II: Evaluation of Numerical Model Simulations

NASA Technical Reports Server (NTRS)

Charlton, Andrew J.; Polvani, Lorenza M.; Perlwitz, Judith; Sassi, Fabrizio; Manzini, Elisa; Shibata, Kiyotaka; Pawson, Steven; Nielsen, J. Eric; Rind, David

2007-01-01

The simulation of major midwinter stratospheric sudden warmings (SSWs) in six stratosphere-resolving general circulation models (GCMs) is examined. The GCMs are compared to a new climatology of SSWs, based on the dynamical characteristics of the events. First, the number, type, and temporal distribution of SSW events are evaluated. Most of the models show a lower frequency of SSW events than the climatology, which has a mean frequency of 6.0 SSWs per decade. Statistical tests show that three of the six models produce significantly fewer SSWs than the climatology, between 1.0 and 2.6 SSWs per decade. Second, four process-based diagnostics are calculated for all of the SSW events in each model. It is found that SSWs in the GCMs compare favorably with dynamical benchmarks for SSW established in the first part of the study. These results indicate that GCMs are capable of quite accurately simulating the dynamics required to produce SSWs, but with lower frequency than the climatology. Further dynamical diagnostics hint that, in at least one case, this is due to a lack of meridional heat flux in the lower stratosphere. Even though the SSWs simulated by most GCMs are dynamically realistic when compared to the NCEP-NCAR reanalysis, the reasons for the relative paucity of SSWs in GCMs remains an important and open question.

Measuring the impact of computer resource quality on the software development process and product

NASA Technical Reports Server (NTRS)

Mcgarry, Frank; Valett, Jon; Hall, Dana

1985-01-01

The availability and quality of computer resources during the software development process was speculated to have measurable, significant impact on the efficiency of the development process and the quality of the resulting product. Environment components such as the types of tools, machine responsiveness, and quantity of direct access storage may play a major role in the effort to produce the product and in its subsequent quality as measured by factors such as reliability and ease of maintenance. During the past six years, the NASA Goddard Space Flight Center has conducted experiments with software projects in an attempt to better understand the impact of software development methodologies, environments, and general technologies on the software process and product. Data was extracted and examined from nearly 50 software development projects. All were related to support of satellite flight dynamics ground-based computations. The relationship between computer resources and the software development process and product as exemplified by the subject NASA data was examined. Based upon the results, a number of computer resource-related implications are provided.

Nonlinear flight control design using backstepping methodology

NASA Astrophysics Data System (ADS)

Tran, Thanh Trung

The subject of nonlinear flight control design using backstepping control methodology is investigated in the dissertation research presented here. Control design methods based on nonlinear models of the dynamic system provide higher utility and versatility because the design model more closely matches the physical system behavior. Obtaining requisite model fidelity is only half of the overall design process, however. Design of the nonlinear control loops can lessen the effects of nonlinearity, or even exploit nonlinearity, to achieve higher levels of closed-loop stability, performance, and robustness. The goal of the research is to improve control quality for a general class of strict-feedback dynamic systems and provide flight control architectures to augment the aircraft motion. The research is divided into two parts: theoretical control development for the strict-feedback form of nonlinear dynamic systems and application of the proposed theory for nonlinear flight dynamics. In the first part, the research is built on two components: transforming the nonlinear dynamic model to a canonical strict-feedback form and then applying backstepping control theory to the canonical model. The research considers a process to determine when this transformation is possible, and when it is possible, a systematic process to transfer the model is also considered when practical. When this is not the case, certain modeling assumptions are explored to facilitate the transformation. After achieving the canonical form, a systematic design procedure for formulating a backstepping control law is explored in the research. Starting with the simplest subsystem and ending with the full system, pseudo control concepts based on Lyapunov control functions are used to control each successive subsystem. Typically each pseudo control must be solved from a nonlinear algebraic equation. At the end of this process, the physical control input must be re-expressed in terms of the physical states by eliminating the pseudo control transformations. In the second part, the research focuses on nonlinear control design for flight dynamics of aircraft motion. Some assumptions on aerodynamics of the aircraft are addressed to transform full nonlinear flight dynamics into the canonical strict-feedback form. The assumptions are also analyzed, validated, and compared to show the advantages and disadvantages of the design models. With the achieved models, investigation focuses on formulating the backstepping control laws and provides an advanced control algorithm for nonlinear flight dynamics of the aircraft. Experimental and simulation studies are successfully implemented to validate the proposed control method. Advancement of nonlinear backstepping control theory and its application to nonlinear flight control are achieved in the dissertation research.

A hybrid approach for nondestructive assessment and design optimisation and testing of in-service machinery

NASA Astrophysics Data System (ADS)

Rahman, Abdul Ghaffar Abdul; Noroozi, Siamak; Dupac, Mihai; Mahathir Syed Mohd Al-Attas, Syed; Vinney, John E.

2013-03-01

Complex rotating machinery requires regular condition monitoring inspections to assess their running conditions and their structural integrity to prevent catastrophic failures. Machine failures can be divided into two categories. First is the wear and tear during operation, they range from bearing defects, gear damage, misalignment, imbalance or mechanical looseness, for which simple condition-based maintenance techniques can easily detect the root cause and trigger remedial action process. The second factor in machine failure is caused by the inherent design faults that usually happened due to many reasons such as improper installation, poor servicing, bad workmanship and structural dynamics design deficiency. In fact, individual machines components are generally dynamically well designed and rigorously tested. However, when these machines are assembled on sight and linked together, their dynamic characteristics will change causing unexpected behaviour of the system. Since nondestructive evaluation provides an excellent alternative to the classical monitoring and proved attractive due to the possibility of performing reliable assessments of all types of machinery, the novel dynamic design verification procedure - based on the combination of in-service operation deflection shape measurement, experimental modal analysis and iterative inverse finite element analysis - proposed here allows quick identification of structural weakness, and helps to provide and verify the solutions.

Individual heterogeneity in life histories and eco-evolutionary dynamics

PubMed Central

Vindenes, Yngvild; Langangen, Øystein

2015-01-01

Individual heterogeneity in life history shapes eco-evolutionary processes, and unobserved heterogeneity can affect demographic outputs characterising life history and population dynamical properties. Demographic frameworks like matrix models or integral projection models represent powerful approaches to disentangle mechanisms linking individual life histories and population-level processes. Recent developments have provided important steps towards their application to study eco-evolutionary dynamics, but so far individual heterogeneity has largely been ignored. Here, we present a general demographic framework that incorporates individual heterogeneity in a flexible way, by separating static and dynamic traits (discrete or continuous). First, we apply the framework to derive the consequences of ignoring heterogeneity for a range of widely used demographic outputs. A general conclusion is that besides the long-term growth rate lambda, all parameters can be affected. Second, we discuss how the framework can help advance current demographic models of eco-evolutionary dynamics, by incorporating individual heterogeneity. For both applications numerical examples are provided, including an empirical example for pike. For instance, we demonstrate that predicted demographic responses to climate warming can be reversed by increased heritability. We discuss how applications of this demographic framework incorporating individual heterogeneity can help answer key biological questions that require a detailed understanding of eco-evolutionary dynamics. PMID:25807980

Determination of the smoke-plume heights and their dynamics with ground-based scanning LIDAR

Treesearch

V. Kovalev; A. Petkov; C. Wold; S. Urbanski; W. M. Hao

2015-01-01

Lidar-data processing techniques are analyzed, which allow determining smoke-plume heights and their dynamics and can be helpful for the improvement of smoke dispersion and air quality models. The data processing algorithms considered in the paper are based on the analysis of two alternative characteristics related to the smoke dispersion process: the regularized...

The Fragility of Individual-Based Explanations of Social Hierarchies: A Test Using Animal Pecking Orders

PubMed Central

2016-01-01

The standard approach in accounting for hierarchical differentiation in biology and the social sciences considers a hierarchy as a static distribution of individuals possessing differing amounts of some valued commodity, assumes that the hierarchy is generated by micro-level processes involving individuals, and attempts to reverse engineer the processes that produced the hierarchy. However, sufficient experimental and analytical results are available to evaluate this standard approach in the case of animal dominance hierarchies (pecking orders). Our evaluation using evidence from hierarchy formation in small groups of both hens and cichlid fish reveals significant deficiencies in the three tenets of the standard approach in accounting for the organization of dominance hierarchies. In consequence, we suggest that a new approach is needed to explain the organization of pecking orders and, very possibly, by implication, for other kinds of social hierarchies. We develop an example of such an approach that considers dominance hierarchies to be dynamic networks, uses dynamic sequences of interaction (dynamic network motifs) to explain the organization of dominance hierarchies, and derives these dynamic sequences directly from observation of hierarchy formation. We test this dynamical explanation using computer simulation and find a good fit with actual dynamics of hierarchy formation in small groups of hens. We hypothesize that the same dynamic sequences are used in small groups of many other animal species forming pecking orders, and we discuss the data required to evaluate our hypothesis. Finally, we briefly consider how our dynamic approach may be generalized to other kinds of social hierarchies using the example of the distribution of empty gastropod (snail) shells occupied in populations of hermit crabs. PMID:27410230

Dynamic Modeling of Process Technologies for Closed-Loop Water Recovery Systems

NASA Technical Reports Server (NTRS)

Allada, Rama Kumar; Lange, Kevin E.; Anderson, Molly S.

2012-01-01

Detailed chemical process simulations are a useful tool in designing and optimizing complex systems and architectures for human life support. Dynamic and steady-state models of these systems help contrast the interactions of various operating parameters and hardware designs, which become extremely useful in trade-study analyses. NASA s Exploration Life Support technology development project recently made use of such models to compliment a series of tests on different waste water distillation systems. This paper presents dynamic simulations of chemical process for primary processor technologies including: the Cascade Distillation System (CDS), the Vapor Compression Distillation (VCD) system, the Wiped-Film Rotating Disk (WFRD), and post-distillation water polishing processes such as the Volatiles Removal Assembly (VRA). These dynamic models were developed using the Aspen Custom Modeler (Registered TradeMark) and Aspen Plus(Registered TradeMark) process simulation tools. The results expand upon previous work for water recovery technology models and emphasize dynamic process modeling and results. The paper discusses system design, modeling details, and model results for each technology and presents some comparisons between the model results and available test data. Following these initial comparisons, some general conclusions and forward work are discussed.

Sub-500 fs electronically nonadiabatic chemical dynamics of energetic molecules from the S1 excited state: Ab initio multiple spawning study

NASA Astrophysics Data System (ADS)

Ghosh, Jayanta; Gajapathy, Harshad; Konar, Arindam; Narasimhaiah, Gowrav M.; Bhattacharya, Atanu

2017-11-01

Energetic materials store a large amount of chemical energy. Different ignition processes, including laser ignition and shock or compression wave, initiate the energy release process by first promoting energetic molecules to the electronically excited states. This is why a full understanding of initial steps of the chemical dynamics of energetic molecules from the excited electronic states is highly desirable. In general, conical intersection (CI), which is the crossing point of multidimensional electronic potential energy surfaces, is well established as a controlling factor in the initial steps of chemical dynamics of energetic molecules following their electronic excitations. In this article, we have presented different aspects of the ultrafast unimolecular relaxation dynamics of energetic molecules through CIs. For this task, we have employed ab initio multiple spawning (AIMS) simulation using the complete active space self-consistent field (CASSCF) electronic wavefunction and frozen Gaussian-based nuclear wavefunction. The AIMS simulation results collectively reveal that the ultrafast relaxation step of the best energetic molecules (which are known to exhibit very good detonation properties) is completed in less than 500 fs. Many, however, exhibit sub-50 fs dynamics. For example, nitro-containing molecules (including C-NO2, N-NO2, and O-NO2 active moieties) relax back to the ground state in approximately 40 fs through similar (S1/S0)CI conical intersections. The N3-based energetic molecule undergoes the N2 elimination process in 40 fs through the (S1/S0)CI conical intersection. Nitramine-Fe complexes exhibit sub-50 fs Fe-O and N-O bond dissociation through the respective (S1/S0)CI conical intersection. On the other hand, tetrazine-N-oxides, which are known to exhibit better detonation properties than tetrazines, undergo internal conversion in a 400-fs time scale, while the relaxation time of tetrazine is very long (about 100 ns). Many other characteristics of sub-500 fs nonadiabatic decay of energetic molecules are discussed. In the end, many unresolved issues associated with the ultrafast nonadiabatic chemical dynamics of energetic molecules are presented.

Spatio-Temporal Regression Based Clustering of Precipitation Extremes in a Presence of Systematically Missing Covariates

NASA Astrophysics Data System (ADS)

Kaiser, Olga; Martius, Olivia; Horenko, Illia

2017-04-01

Regression based Generalized Pareto Distribution (GPD) models are often used to describe the dynamics of hydrological threshold excesses relying on the explicit availability of all of the relevant covariates. But, in real application the complete set of relevant covariates might be not available. In this context, it was shown that under weak assumptions the influence coming from systematically missing covariates can be reflected by a nonstationary and nonhomogenous dynamics. We present a data-driven, semiparametric and an adaptive approach for spatio-temporal regression based clustering of threshold excesses in a presence of systematically missing covariates. The nonstationary and nonhomogenous behavior of threshold excesses is describes by a set of local stationary GPD models, where the parameters are expressed as regression models, and a non-parametric spatio-temporal hidden switching process. Exploiting nonparametric Finite Element time-series analysis Methodology (FEM) with Bounded Variation of the model parameters (BV) for resolving the spatio-temporal switching process, the approach goes beyond strong a priori assumptions made is standard latent class models like Mixture Models and Hidden Markov Models. Additionally, the presented FEM-BV-GPD provides a pragmatic description of the corresponding spatial dependence structure by grouping together all locations that exhibit similar behavior of the switching process. The performance of the framework is demonstrated on daily accumulated precipitation series over 17 different locations in Switzerland from 1981 till 2013 - showing that the introduced approach allows for a better description of the historical data.

BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling.

PubMed

Feng, Song; Ollivier, Julien F; Swain, Peter S; Soyer, Orkun S

2015-10-30

Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Modelling information dissemination under privacy concerns in social media

NASA Astrophysics Data System (ADS)

Zhu, Hui; Huang, Cheng; Lu, Rongxing; Li, Hui

2016-05-01

Social media has recently become an important platform for users to share news, express views, and post messages. However, due to user privacy preservation in social media, many privacy setting tools are employed, which inevitably change the patterns and dynamics of information dissemination. In this study, a general stochastic model using dynamic evolution equations was introduced to illustrate how privacy concerns impact the process of information dissemination. Extensive simulations and analyzes involving the privacy settings of general users, privileged users, and pure observers were conducted on real-world networks, and the results demonstrated that user privacy settings affect information differently. Finally, we also studied the process of information diffusion analytically and numerically with different privacy settings using two classic networks.

Understanding system dynamics of an adaptive enzyme network from globally profiled kinetic parameters.

PubMed

Chiang, Austin W T; Liu, Wei-Chung; Charusanti, Pep; Hwang, Ming-Jing

2014-01-15

A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system's dynamics. We demonstrate our methodology on a prototype biological system of perfect adaptation dynamics, namely the chemotaxis model for Escherichia coli. Our results agreed well with those derived from experimental data and theoretical studies in the literature. Using this model system, we showed that there are motifs in kinetic parameters and that these motifs are governed by constraints of the specified system dynamics. A systematic approach based on enrichment statistical tests has been developed to elucidate the relationships between model parameters and the roles they play in affecting system dynamics of a prototype biological network. The proposed approach is generally applicable and therefore can find wide use in systems biology modeling research.

Dynamic Speckle Imaging with Low-Cost Devices

ERIC Educational Resources Information Center

Vannoni, Maurizio; Trivi, Marcelo; Arizaga, Ricardo; Rabal, Hector; Molesini, Giuseppe

2008-01-01

Light from a rough sample surface illuminated with a laser consists of a speckle pattern. If the surface evolves with time, the pattern becomes dynamic, following the activity of the sample. This phenomenon is used both in research and in industry to monitor processes and systems that change with time. The measuring equipment generally includes…

Study Abroad: The Reality of Building Dynamic Group Learning.

ERIC Educational Resources Information Center

Ransbury, Molly K.; Harris, Sandra A.

1994-01-01

The collaborative effort of a professor of human development with expertise in group process and a general education professor with expertise in Greek mythology and culture uses a case study format to apply theoretical models of group dynamics to the travel and learning experience of study abroad. Implications for course design and group process…

Source-sink dynamics sustain central stonerollers (Campostoma anomalum) in a heavily urbanized catchment

Treesearch

Eric R. Waits; Mark J. Bagley; Michael J. Blum; Frank H. McCormick; James M. Lazorchak

2008-01-01

Relating local demographic processes to spatial structure (e.g. habitat heterogeneity) is essential for understanding population and species persistence (Hanski & Gilpin, 1997; Fagan, 2002). Yet few studies have tested general hypotheses about the importance of spatial patterns in determining population dynamics within river­stream networks (Lowe, Likens &...

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

van Rij, Jennifer A; Yu, Yi-Hsiang; Guo, Yi

This study explores and verifies the generalized body-modes method for evaluating the structural loads on a wave energy converter (WEC). Historically, WEC design methodologies have focused primarily on accurately evaluating hydrodynamic loads, while methodologies for evaluating structural loads have yet to be fully considered and incorporated into the WEC design process. As wave energy technologies continue to advance, however, it has become increasingly evident that an accurate evaluation of the structural loads will enable an optimized structural design, as well as the potential utilization of composites and flexible materials, and hence reduce WEC costs. Although there are many computational fluidmore » dynamics, structural analyses and fluid-structure-interaction (FSI) codes available, the application of these codes is typically too computationally intensive to be practical in the early stages of the WEC design process. The generalized body-modes method, however, is a reduced order, linearized, frequency-domain FSI approach, performed in conjunction with the linear hydrodynamic analysis, with computation times that could realistically be incorporated into the WEC design process. The objective of this study is to verify the generalized body-modes approach in comparison to high-fidelity FSI simulations to accurately predict structural deflections and stress loads in a WEC. Two verification cases are considered, a free-floating barge and a fixed-bottom column. Details for both the generalized body-modes models and FSI models are first provided. Results for each of the models are then compared and discussed. Finally, based on the verification results obtained, future plans for incorporating the generalized body-modes method into the WEC simulation tool, WEC-Sim, and the overall WEC design process are discussed.« less

Residence-time framework for modeling multicomponent reactive transport in stream hyporheic zones

NASA Astrophysics Data System (ADS)

Painter, S. L.; Coon, E. T.; Brooks, S. C.

2017-12-01

Process-based models for transport and transformation of nutrients and contaminants in streams require tractable representations of solute exchange between the stream channel and biogeochemically active hyporheic zones. Residence-time based formulations provide an alternative to detailed three-dimensional simulations and have had good success in representing hyporheic exchange of non-reacting solutes. We extend the residence-time formulation for hyporheic transport to accommodate general multicomponent reactive transport. To that end, the integro-differential form of previous residence time models is replaced by an equivalent formulation based on a one-dimensional advection dispersion equation along the channel coupled at each channel location to a one-dimensional transport model in Lagrangian travel-time form. With the channel discretized for numerical solution, the associated Lagrangian model becomes a subgrid model representing an ensemble of streamlines that are diverted into the hyporheic zone before returning to the channel. In contrast to the previous integro-differential forms of the residence-time based models, the hyporheic flowpaths have semi-explicit spatial representation (parameterized by travel time), thus allowing coupling to general biogeochemical models. The approach has been implemented as a stream-corridor subgrid model in the open-source integrated surface/subsurface modeling software ATS. We use bedform-driven flow coupled to a biogeochemical model with explicit microbial biomass dynamics as an example to show that the subgrid representation is able to represent redox zonation in sediments and resulting effects on metal biogeochemical dynamics in a tractable manner that can be scaled to reach scales.

Microscopic derivation of particle-based coarse-grained dynamics: Exact expression for memory function

NASA Astrophysics Data System (ADS)

Izvekov, Sergei

2017-03-01

We consider the generalized Langevin equations of motion describing exactly the particle-based coarse-grained dynamics in the classical microscopic ensemble that were derived recently within the Mori-Zwanzig formalism based on new projection operators [S. Izvekov, J. Chem. Phys. 138(13), 134106 (2013)]. The fundamental difference between the new family of projection operators and the standard Zwanzig projection operator used in the past to derive the coarse-grained equations of motion is that the new operators average out the explicit irrelevant trajectories leading to the possibility of solving the projected dynamics exactly. We clarify the definition of the projection operators and revisit the formalism to compute the projected dynamics exactly for the microscopic system in equilibrium. The resulting expression for the projected force is in the form of a "generalized additive fluctuating force" describing the departure of the generalized microscopic force associated with the coarse-grained coordinate from its projection. Starting with this key expression, we formulate a new exact formula for the memory function in terms of microscopic and coarse-grained conservative forces. We conclude by studying two independent limiting cases of practical importance: the Markov limit (vanishing correlations of projected force) and the limit of weak dependence of the memory function on the particle momenta. We present computationally affordable expressions which can be efficiently evaluated from standard molecular dynamics simulations.

Examining the equivalence of Bakamjian-Thomas mass operators in different forms of dynamics

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

Polyzou, W. N.

We discus the proof of the equivalence of relativistic quantum mechanical models based on the generalized Bakamjian-Thomas construction in all of Dirac's forms of dynamics. Explicit representations of the equivalent mass operators are given in all three of Dirac's forms of dynamics.

Targeted quantification of functional enzyme dynamics in environmental samples for microbially mediated biogeochemical processes: Targeted quantification of functional enzyme dynamics

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

Li, Minjing; Gao, Yuqian; Qian, Wei-Jun

Microbially mediated biogeochemical processes are catalyzed by enzymes that control the transformation of carbon, nitrogen, and other elements in environment. The dynamic linkage between enzymes and biogeochemical species transformation has, however, rarely been investigated because of the lack of analytical approaches to efficiently and reliably quantify enzymes and their dynamics in soils and sediments. Herein, we developed a signature peptide-based technique for sensitively quantifying dissimilatory and assimilatory enzymes using nitrate-reducing enzymes in a hyporheic zone sediment as an example. Moreover, the measured changes in enzyme concentration were found to correlate with the nitrate reduction rate in a way different frommore » that inferred from biogeochemical models based on biomass or functional genes as surrogates for functional enzymes. This phenomenon has important implications for understanding and modeling the dynamics of microbial community functions and biogeochemical processes in environments. Our results also demonstrate the importance of enzyme quantification for the identification and interrogation of those biogeochemical processes with low metabolite concentrations as a result of faster enzyme-catalyzed consumption of metabolites than their production. The dynamic enzyme behaviors provide a basis for the development of enzyme-based models to describe the relationship between the microbial community and biogeochemical processes.« less

The Relationship between Supervisors' Power Bases and Supervisory Styles

ERIC Educational Resources Information Center

Tanaka, Hideyuki

2009-01-01

Despite its critical role in counselor training, empirical research on clinical supervision is generally limited (Bernard & Goodyear, 2003; Ellis & Ladany, 2007). This is also applied to an area of power dynamics in supervision. This study tested the relationship between the two aspects of power dynamics; namely, supervisors' power bases (i.e.,…

Dynamic Attack Tree Tool for Risk Assessments

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

Black, Karl

2012-03-13

DATT enables interactive visualization, qualitative analysis and recording of cyber and other forms of risk. It facilitates dynamic risk-based approaches (as opposed to static compliance-based) to security and risk management in general. DATT allows decision makers to consistently prioritize risk mitigation strategies and quickly see where attention is most needed across the enterprise.

Quantitative analysis of rib movement based on dynamic chest bone images: preliminary results

NASA Astrophysics Data System (ADS)

Tanaka, R.; Sanada, S.; Oda, M.; Mitsutaka, M.; Suzuki, K.; Sakuta, K.; Kawashima, H.

2014-03-01

Rib movement during respiration is one of the diagnostic criteria in pulmonary impairments. In general, the rib movement is assessed in fluoroscopy. However, the shadows of lung vessels and bronchi overlapping ribs prevent accurate quantitative analysis of rib movement. Recently, an image-processing technique for separating bones from soft tissue in static chest radiographs, called "bone suppression technique", has been developed. Our purpose in this study was to evaluate the usefulness of dynamic bone images created by the bone suppression technique in quantitative analysis of rib movement. Dynamic chest radiographs of 10 patients were obtained using a dynamic flat-panel detector (FPD). Bone suppression technique based on a massive-training artificial neural network (MTANN) was applied to the dynamic chest images to create bone images. Velocity vectors were measured in local areas on the dynamic bone images, which formed a map. The velocity maps obtained with bone and original images for scoliosis and normal cases were compared to assess the advantages of bone images. With dynamic bone images, we were able to quantify and distinguish movements of ribs from those of other lung structures accurately. Limited rib movements of scoliosis patients appeared as reduced rib velocity vectors. Vector maps in all normal cases exhibited left-right symmetric distributions, whereas those in abnormal cases showed nonuniform distributions. In conclusion, dynamic bone images were useful for accurate quantitative analysis of rib movements: Limited rib movements were indicated as a reduction of rib movement and left-right asymmetric distribution on vector maps. Thus, dynamic bone images can be a new diagnostic tool for quantitative analysis of rib movements without additional radiation dose.

Supplier Perspective: Paints & Finishes for Corrosion Protection of Military Vehicles

DTIC Science & Technology

2010-06-15

Systems , Oshkosh Truck, General Dynamics, AM General. Automotive approvals include Honda , Toyota , General Motors, Ford, Chrysler, BMW, Subaru, Nissan...Military Compliance EXAMPLE OF AN INFERIOR PROCESS MIL-DTL-5541 Aluminum pretreatment conveyor system using a trivalent chrome pretreatment...Automotive has become a leader in corrosion prevention. Vehicles last much longer due to improved paint systems . Much of this is attributed to

A dynamic scheduling algorithm for singe-arm two-cluster tools with flexible processing times

NASA Astrophysics Data System (ADS)

Li, Xin; Fung, Richard Y. K.

2018-02-01

This article presents a dynamic algorithm for job scheduling in two-cluster tools producing multi-type wafers with flexible processing times. Flexible processing times mean that the actual times for processing wafers should be within given time intervals. The objective of the work is to minimize the completion time of the newly inserted wafer. To deal with this issue, a two-cluster tool is decomposed into three reduced single-cluster tools (RCTs) in a series based on a decomposition approach proposed in this article. For each single-cluster tool, a dynamic scheduling algorithm based on temporal constraints is developed to schedule the newly inserted wafer. Three experiments have been carried out to test the dynamic scheduling algorithm proposed, comparing with the results the 'earliest starting time' heuristic (EST) adopted in previous literature. The results show that the dynamic algorithm proposed in this article is effective and practical.

Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems

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

Ferrandi, Fabrizio; Lanzi, Pier Luca; Pilato, Christian

Modern heterogeneous embedded platforms, com- posed of several digital signal, application specific and general purpose processors, also include reconfigurable devices support- ing partial dynamic reconfiguration. These devices can change the behavior of some of their parts during execution, allowing hardware acceleration of more sections of the applications. Never- theless, partial dynamic reconfiguration imposes severe overheads in terms of latency. For such systems, a critical part of the design phase is deciding on which processing elements (mapping) and when (scheduling) executing a task, but also how to place them on the reconfigurable device to guarantee the most efficient reuse of themore » programmable logic. In this paper we propose an algorithm based on Ant Colony Optimization (ACO) that simultaneously executes the scheduling, the mapping and the linear placing of tasks, hiding reconfiguration overheads through prefetching. Our heuristic gradually constructs solutions and then searches around the best ones, cutting out non-promising areas of the design space. We show how to consider the partial dynamic reconfiguration constraints in the scheduling, placing and mapping problems and compare our formulation to other heuristics that address the same problems. We demonstrate that our proposal is more general and robust, and finds better solutions (16.5% in average) with respect to competing solutions.« less

How Accurate Are Transition States from Simulations of Enzymatic Reactions?

PubMed Central

2015-01-01

The rate expression of traditional transition state theory (TST) assumes no recrossing of the transition state (TS) and thermal quasi-equilibrium between the ground state and the TS. Currently, it is not well understood to what extent these assumptions influence the nature of the activated complex obtained in traditional TST-based simulations of processes in the condensed phase in general and in enzymes in particular. Here we scrutinize these assumptions by characterizing the TSs for hydride transfer catalyzed by the enzyme Escherichia coli dihydrofolate reductase obtained using various simulation approaches. Specifically, we compare the TSs obtained with common TST-based methods and a dynamics-based method. Using a recently developed accurate hybrid quantum mechanics/molecular mechanics potential, we find that the TST-based and dynamics-based methods give considerably different TS ensembles. This discrepancy, which could be due equilibrium solvation effects and the nature of the reaction coordinate employed and its motion, raises major questions about how to interpret the TSs determined by common simulation methods. We conclude that further investigation is needed to characterize the impact of various TST assumptions on the TS phase-space ensemble and on the reaction kinetics. PMID:24860275

Optimal exploitation strategies for an animal population in a Markovian environment: A theory and an example

USGS Publications Warehouse

Anderson, D.R.

1975-01-01

Optimal exploitation strategies were studied for an animal population in a Markovian (stochastic, serially correlated) environment. This is a general case and encompasses a number of important special cases as simplifications. Extensive empirical data on the Mallard (Anas platyrhynchos) were used as an example of general theory. The number of small ponds on the central breeding grounds was used as an index to the state of the environment. A general mathematical model was formulated to provide a synthesis of the existing literature, estimates of parameters developed from an analysis of data, and hypotheses regarding the specific effect of exploitation on total survival. The literature and analysis of data were inconclusive concerning the effect of exploitation on survival. Therefore, two hypotheses were explored: (1) exploitation mortality represents a largely additive form of mortality, and (2) exploitation mortality is compensatory with other forms of mortality, at least to some threshold level. Models incorporating these two hypotheses were formulated as stochastic dynamic programming models and optimal exploitation strategies were derived numerically on a digital computer. Optimal exploitation strategies were found to exist under the rather general conditions. Direct feedback control was an integral component in the optimal decision-making process. Optimal exploitation was found to be substantially different depending upon the hypothesis regarding the effect of exploitation on the population. If we assume that exploitation is largely an additive force of mortality in Mallards, then optimal exploitation decisions are a convex function of the size of the breeding population and a linear or slight concave function of the environmental conditions. Under the hypothesis of compensatory mortality forces, optimal exploitation decisions are approximately linearly related to the size of the Mallard breeding population. Dynamic programming is suggested as a very general formulation for realistic solutions to the general optimal exploitation problem. The concepts of state vectors and stage transformations are completely general. Populations can be modeled stochastically and the objective function can include extra-biological factors. The optimal level of exploitation in year t must be based on the observed size of the population and the state of the environment in year t unless the dynamics of the population, the state of the environment, and the result of the exploitation decisions are completely deterministic. Exploitation based on an average harvest, or harvest rate, or designed to maintain a constant breeding population size is inefficient.

Rupture Dynamics and Seismic Radiation on Rough Faults for Simulation-Based PSHA

NASA Astrophysics Data System (ADS)

Mai, P. M.; Galis, M.; Thingbaijam, K. K. S.; Vyas, J. C.; Dunham, E. M.

2017-12-01

Simulation-based ground-motion predictions may augment PSHA studies in data-poor regions or provide additional shaking estimations, incl. seismic waveforms, for critical facilities. Validation and calibration of such simulation approaches, based on observations and GMPE's, is important for engineering applications, while seismologists push to include the precise physics of the earthquake rupture process and seismic wave propagation in 3D heterogeneous Earth. Geological faults comprise both large-scale segmentation and small-scale roughness that determine the dynamics of the earthquake rupture process and its radiated seismic wavefield. We investigate how different parameterizations of fractal fault roughness affect the rupture evolution and resulting near-fault ground motions. Rupture incoherence induced by fault roughness generates realistic ω-2 decay for high-frequency displacement amplitude spectra. Waveform characteristics and GMPE-based comparisons corroborate that these rough-fault rupture simulations generate realistic synthetic seismogram for subsequent engineering application. Since dynamic rupture simulations are computationally expensive, we develop kinematic approximations that emulate the observed dynamics. Simplifying the rough-fault geometry, we find that perturbations in local moment tensor orientation are important, while perturbations in local source location are not. Thus, a planar fault can be assumed if the local strike, dip, and rake are maintained. The dynamic rake angle variations are anti-correlated with local dip angles. Based on a dynamically consistent Yoffe source-time function, we show that the seismic wavefield of the approximated kinematic rupture well reproduces the seismic radiation of the full dynamic source process. Our findings provide an innovative pseudo-dynamic source characterization that captures fault roughness effects on rupture dynamics. Including the correlations between kinematic source parameters, we present a new pseudo-dynamic rupture modeling approach for computing broadband ground-motion time-histories for simulation-based PSHA

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.

Dynamic Modeling of Process Technologies for Closed-Loop Water Recovery Systems

NASA Technical Reports Server (NTRS)

Allada, Rama Kumar; Lange, Kevin; Anderson, Molly

2011-01-01

Detailed chemical process simulations are a useful tool in designing and optimizing complex systems and architectures for human life support. Dynamic and steady-state models of these systems help contrast the interactions of various operating parameters and hardware designs, which become extremely useful in trade-study analyses. NASA s Exploration Life Support technology development project recently made use of such models to compliment a series of tests on different waste water distillation systems. This paper presents dynamic simulations of chemical process for primary processor technologies including: the Cascade Distillation System (CDS), the Vapor Compression Distillation (VCD) system, the Wiped-Film Rotating Disk (WFRD), and post-distillation water polishing processes such as the Volatiles Removal Assembly (VRA) that were developed using the Aspen Custom Modeler and Aspen Plus process simulation tools. The results expand upon previous work for water recovery technology models and emphasize dynamic process modeling and results. The paper discusses system design, modeling details, and model results for each technology and presents some comparisons between the model results and available test data. Following these initial comparisons, some general conclusions and forward work are discussed.

General method to find the attractors of discrete dynamic models of biological systems.

PubMed

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

General method to find the attractors of discrete dynamic models of biological systems

NASA Astrophysics Data System (ADS)

Gan, Xiao; Albert, Réka

2018-04-01

Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function. We then create an expanded representation of the multilevel network, find multilevel stable motifs and oscillating motifs, and identify attractors by successive network reduction. In this way, we find both fixed point attractors and complex attractors. We implemented an algorithm, which we test and validate on representative synthetic networks and on published multilevel models of biological networks. Despite its primary motivation to analyze biological networks, our motif-based method is general and can be applied to any finite discrete dynamical system.

Threshold exceedance risk assessment in complex space-time systems

NASA Astrophysics Data System (ADS)

Angulo, José M.; Madrid, Ana E.; Romero, José L.

2015-04-01

Environmental and health impact risk assessment studies most often involve analysis and characterization of complex spatio-temporal dynamics. Recent developments in this context are addressed, among other objectives, to proper representation of structural heterogeneities, heavy-tailed processes, long-range dependence, intermittency, scaling behavior, etc. Extremal behaviour related to spatial threshold exceedances can be described in terms of geometrical characteristics and distribution patterns of excursion sets, which are the basis for construction of risk-related quantities, such as in the case of evolutionary study of 'hotspots' and long-term indicators of occurrence of extremal episodes. Derivation of flexible techniques, suitable for both the application under general conditions and the interpretation on singularities, is important for practice. Modern risk theory, a developing discipline motivated by the need to establish solid general mathematical-probabilistic foundations for rigorous definition and characterization of risk measures, has led to the introduction of a variety of classes and families, ranging from some conceptually inspired by specific fields of applications, to some intended to provide generality and flexibility to risk analysts under parametric specifications, etc. Quantile-based risk measures, such as Value-at-Risk (VaR), Average Value-at-Risk (AVaR), and generalization to spectral measures, are of particular interest for assessment under very general conditions. In this work, we study the application of quantile-based risk measures in the spatio-temporal context in relation to certain geometrical characteristics of spatial threshold exceedance sets. In particular, we establish a closed-form relationship between VaR, AVaR, and the expected value of threshold exceedance areas and excess volumes. Conditional simulation allows us, by means of empirical global and local spatial cumulative distributions, the derivation of various statistics of practical interest, and subsequent construction of dynamic risk maps. Further, we study the implementation of static and dynamic spatial deformation under this setup, meaningful, among other aspects, for incorporation of heterogeneities and/or covariate effects, or consideration of external factors for risk measurement. We illustrate this approach though Environment and Health applications. This work is partially supported by grant MTM2012-32666 of the Spanish Ministry of Economy and Competitiveness (co-financed by FEDER).

Fixation probability on clique-based graphs

NASA Astrophysics Data System (ADS)

Choi, Jeong-Ok; Yu, Unjong

2018-02-01

The fixation probability of a mutant in the evolutionary dynamics of Moran process is calculated by the Monte-Carlo method on a few families of clique-based graphs. It is shown that the complete suppression of fixation can be realized with the generalized clique-wheel graph in the limit of small wheel-clique ratio and infinite size. The family of clique-star is an amplifier, and clique-arms graph changes from amplifier to suppressor as the fitness of the mutant increases. We demonstrate that the overall structure of a graph can be more important to determine the fixation probability than the degree or the heat heterogeneity. The dependence of the fixation probability on the position of the first mutant is discussed.

Quantum-like model of unconscious–conscious dynamics

PubMed Central

Khrennikov, Andrei

2015-01-01

We present a quantum-like model of sensation–perception dynamics (originated in Helmholtz theory of unconscious inference) based on the theory of quantum apparatuses and instruments. We illustrate our approach with the model of bistable perception of a particular ambiguous figure, the Schröder stair. This is a concrete model for unconscious and conscious processing of information and their interaction. The starting point of our quantum-like journey was the observation that perception dynamics is essentially contextual which implies impossibility of (straightforward) embedding of experimental statistical data in the classical (Kolmogorov, 1933) framework of probability theory. This motivates application of nonclassical probabilistic schemes. And the quantum formalism provides a variety of the well-approved and mathematically elegant probabilistic schemes to handle results of measurements. The theory of quantum apparatuses and instruments is the most general quantum scheme describing measurements and it is natural to explore it to model the sensation–perception dynamics. In particular, this theory provides the scheme of indirect quantum measurements which we apply to model unconscious inference leading to transition from sensations to perceptions. PMID:26283979

Three-Dimensional Geometric Modeling of Membrane-bound Organelles in Ventricular Myocytes: Bridging the Gap between Microscopic Imaging and Mathematical Simulation

PubMed Central

Yu, Zeyun; Holst, Michael J.; Hayashi, Takeharu; Bajaj, Chandrajit L.; Ellisman, Mark H.; McCammon, J. Andrew; Hoshijima, Masahiko

2009-01-01

A general framework of image-based geometric processing is presented to bridge the gap between three-dimensional (3D) imaging that provides structural details of a biological system and mathematical simulation where high-quality surface or volumetric meshes are required. A 3D density map is processed in the order of image pre-processing (contrast enhancement and anisotropic filtering), feature extraction (boundary segmentation and skeletonization), and high-quality and realistic surface (triangular) and volumetric (tetrahedral) mesh generation. While the tool-chain described is applicable to general types of 3D imaging data, the performance is demonstrated specifically on membrane-bound organelles in ventricular myocytes that are imaged and reconstructed with electron microscopic (EM) tomography and two-photon microscopy (T-PM). Of particular interest in this study are two types of membrane-bound Ca2+-handling organelles, namely, transverse tubules (T-tubules) and junctional sarcoplasmic reticulum (jSR), both of which play an important role in regulating the excitation-contraction (E-C) coupling through dynamic Ca2+ mobilization in cardiomyocytes. PMID:18835449

Three-dimensional geometric modeling of membrane-bound organelles in ventricular myocytes: bridging the gap between microscopic imaging and mathematical simulation.

PubMed

Yu, Zeyun; Holst, Michael J; Hayashi, Takeharu; Bajaj, Chandrajit L; Ellisman, Mark H; McCammon, J Andrew; Hoshijima, Masahiko

2008-12-01

A general framework of image-based geometric processing is presented to bridge the gap between three-dimensional (3D) imaging that provides structural details of a biological system and mathematical simulation where high-quality surface or volumetric meshes are required. A 3D density map is processed in the order of image pre-processing (contrast enhancement and anisotropic filtering), feature extraction (boundary segmentation and skeletonization), and high-quality and realistic surface (triangular) and volumetric (tetrahedral) mesh generation. While the tool-chain described is applicable to general types of 3D imaging data, the performance is demonstrated specifically on membrane-bound organelles in ventricular myocytes that are imaged and reconstructed with electron microscopic (EM) tomography and two-photon microscopy (T-PM). Of particular interest in this study are two types of membrane-bound Ca(2+)-handling organelles, namely, transverse tubules (T-tubules) and junctional sarcoplasmic reticulum (jSR), both of which play an important role in regulating the excitation-contraction (E-C) coupling through dynamic Ca(2+) mobilization in cardiomyocytes.

Detection of generalized synchronization using echo state networks

NASA Astrophysics Data System (ADS)

Ibáñez-Soria, D.; Garcia-Ojalvo, J.; Soria-Frisch, A.; Ruffini, G.

2018-03-01

Generalized synchronization between coupled dynamical systems is a phenomenon of relevance in applications that range from secure communications to physiological modelling. Here, we test the capabilities of reservoir computing and, in particular, echo state networks for the detection of generalized synchronization. A nonlinear dynamical system consisting of two coupled Rössler chaotic attractors is used to generate temporal series consisting of time-locked generalized synchronized sequences interleaved with unsynchronized ones. Correctly tuned, echo state networks are able to efficiently discriminate between unsynchronized and synchronized sequences even in the presence of relatively high levels of noise. Compared to other state-of-the-art techniques of synchronization detection, the online capabilities of the proposed Echo State Network based methodology make it a promising choice for real-time applications aiming to monitor dynamical synchronization changes in continuous signals.

Restoration of rhythmicity in diffusively coupled dynamical networks.

PubMed

Zou, Wei; Senthilkumar, D V; Nagao, Raphael; Kiss, István Z; Tang, Yang; Koseska, Aneta; Duan, Jinqiao; Kurths, Jürgen

2015-07-15

Oscillatory behaviour is essential for proper functioning of various physical and biological processes. However, diffusive coupling is capable of suppressing intrinsic oscillations due to the manifestation of the phenomena of amplitude and oscillation deaths. Here we present a scheme to revoke these quenching states in diffusively coupled dynamical networks, and demonstrate the approach in experiments with an oscillatory chemical reaction. By introducing a simple feedback factor in the diffusive coupling, we show that the stable (in)homogeneous steady states can be effectively destabilized to restore dynamic behaviours of coupled systems. Even a feeble deviation from the normal diffusive coupling drastically shrinks the death regions in the parameter space. The generality of our method is corroborated in diverse non-linear systems of diffusively coupled paradigmatic models with various death scenarios. Our study provides a general framework to strengthen the robustness of dynamic activity in diffusively coupled dynamical networks.

Application of Local Discretization Methods in the NASA Finite-Volume General Circulation Model

NASA Technical Reports Server (NTRS)

Yeh, Kao-San; Lin, Shian-Jiann; Rood, Richard B.

2002-01-01

We present the basic ideas of the dynamics system of the finite-volume General Circulation Model developed at NASA Goddard Space Flight Center for climate simulations and other applications in meteorology. The dynamics of this model is designed with emphases on conservative and monotonic transport, where the property of Lagrangian conservation is used to maintain the physical consistency of the computational fluid for long-term simulations. As the model benefits from the noise-free solutions of monotonic finite-volume transport schemes, the property of Lagrangian conservation also partly compensates the accuracy of transport for the diffusion effects due to the treatment of monotonicity. By faithfully maintaining the fundamental laws of physics during the computation, this model is able to achieve sufficient accuracy for the global consistency of climate processes. Because the computing algorithms are based on local memory, this model has the advantage of efficiency in parallel computation with distributed memory. Further research is yet desirable to reduce the diffusion effects of monotonic transport for better accuracy, and to mitigate the limitation due to fast-moving gravity waves for better efficiency.

Research a Novel Integrated and Dynamic Multi-object Trade-Off Mechanism in Software Project

NASA Astrophysics Data System (ADS)

Jiang, Weijin; Xu, Yuhui

Aiming at practical requirements of present software project management and control, the paper presented to construct integrated multi-object trade-off model based on software project process management, so as to actualize integrated and dynamic trade-oil of the multi-object system of project. Based on analyzing basic principle of dynamic controlling and integrated multi-object trade-off system process, the paper integrated method of cybernetics and network technology, through monitoring on some critical reference points according to the control objects, emphatically discussed the integrated and dynamic multi- object trade-off model and corresponding rules and mechanism in order to realize integration of process management and trade-off of multi-object system.

A 3-D model of tumor progression based on complex automata driven by particle dynamics.

PubMed

Wcisło, Rafał; Dzwinel, Witold; Yuen, David A; Dudek, Arkadiusz Z

2009-12-01

The dynamics of a growing tumor involving mechanical remodeling of healthy tissue and vasculature is neglected in most of the existing tumor models. This is due to the lack of efficient computational framework allowing for simulation of mechanical interactions. Meanwhile, just these interactions trigger critical changes in tumor growth dynamics and are responsible for its volumetric and directional progression. We describe here a novel 3-D model of tumor growth, which combines particle dynamics with cellular automata concept. The particles represent both tissue cells and fragments of the vascular network. They interact with their closest neighbors via semi-harmonic central forces simulating mechanical resistance of the cell walls. The particle dynamics is governed by both the Newtonian laws of motion and the cellular automata rules. These rules can represent cell life-cycle and other biological interactions involving smaller spatio-temporal scales. We show that our complex automata, particle based model can reproduce realistic 3-D dynamics of the entire system consisting of the tumor, normal tissue cells, blood vessels and blood flow. It can explain phenomena such as the inward cell motion in avascular tumor, stabilization of tumor growth by the external pressure, tumor vascularization due to the process of angiogenesis, trapping of healthy cells by invading tumor, and influence of external (boundary) conditions on the direction of tumor progression. We conclude that the particle model can serve as a general framework for designing advanced multiscale models of tumor dynamics and it is very competitive to the modeling approaches presented before.

Ecological communities with Lotka-Volterra dynamics

NASA Astrophysics Data System (ADS)

Bunin, Guy

2017-04-01

Ecological communities in heterogeneous environments assemble through the combined effect of species interaction and migration. Understanding the effect of these processes on the community properties is central to ecology. Here we study these processes for a single community subject to migration from a pool of species, with population dynamics described by the generalized Lotka-Volterra equations. We derive exact results for the phase diagram describing the dynamical behaviors, and for the diversity and species abundance distributions. A phase transition is found from a phase where a unique globally attractive fixed point exists to a phase where multiple dynamical attractors exist, leading to history-dependent community properties. The model is shown to possess a symmetry that also establishes a connection with other well-known models.

Ecological communities with Lotka-Volterra dynamics.

PubMed

Bunin, Guy

2017-04-01

Ecological communities in heterogeneous environments assemble through the combined effect of species interaction and migration. Understanding the effect of these processes on the community properties is central to ecology. Here we study these processes for a single community subject to migration from a pool of species, with population dynamics described by the generalized Lotka-Volterra equations. We derive exact results for the phase diagram describing the dynamical behaviors, and for the diversity and species abundance distributions. A phase transition is found from a phase where a unique globally attractive fixed point exists to a phase where multiple dynamical attractors exist, leading to history-dependent community properties. The model is shown to possess a symmetry that also establishes a connection with other well-known models.

Cultural ecologies of adaptive vs. maladaptive traits: A simple nonlinear model

NASA Astrophysics Data System (ADS)

Antoci, Angelo; Russu, Paolo; Sacco, Pier Luigi

2018-05-01

In this paper, we generalize a model by Enquist and Ghirlanda [12] to analyze the "macro" dynamics of cumulative culture in a context where there is a coexistence of adaptive and maladaptive cultural traits. In particular, we introduce a different, nonlinear specification of the main processes at work in the cumulative culture dynamics: imperfect transmission of traits, generation of new traits, and switches from adaptive to maladaptive and vice-versa. We find that the system exhibits a variety of dynamic behaviors where the crucial force is the switching between the adaptive and maladaptive nature of a certain trait, with the other processes playing a modulating role. We identify in particular a number of dynamic regimes with distinctive characteristics.

ON THE DYNAMICAL DERIVATION OF EQUILIBRIUM STATISTICAL MECHANICS

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

Prigogine, I.; Balescu, R.; Henin, F.

1960-12-01

Work on nonequilibrium statistical mechanics, which allows an extension of the kinetic proof to all results of equilibrium statistical mechanics involving a finite number of degrees of freedom, is summarized. As an introduction to the general N-body problem, the scattering theory in classical mechanics is considered. The general N-body problem is considered for the case of classical mechanics, quantum mechanics with Boltzmann statistics, and quantum mechanics including quantum statistics. Six basic diagrams, which describe the elementary processes of the dynamics of correlations, were obtained. (M.C.G.)

Spectrum of the Laplace-Beltrami operator and the phase structure of causal dynamical triangulations

NASA Astrophysics Data System (ADS)

Clemente, Giuseppe; D'Elia, Massimo

2018-06-01

We propose a new method to characterize the different phases observed in the nonperturbative numerical approach to quantum gravity known as causal dynamical triangulations. The method is based on the analysis of the eigenvalues and the eigenvectors of the Laplace-Beltrami operator computed on the triangulations: it generalizes previous works based on the analysis of diffusive processes and proves capable of providing more detailed information on the geometric properties of the triangulations. In particular, we apply the method to the analysis of spatial slices, showing that the different phases can be characterized by a new order parameter related to the presence or absence of a gap in the spectrum of the Laplace-Beltrami operator, and deriving an effective dimensionality of the slices at the different scales. We also propose quantities derived from the spectrum that could be used to monitor the running to the continuum limit around a suitable critical point in the phase diagram, if any is found.

Just-in-time adaptive classifiers-part II: designing the classifier.

PubMed

Alippi, Cesare; Roveri, Manuel

2008-12-01

Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.

Electronic Devices Based on Oxide Thin Films Fabricated by Fiber-to-Film Process.

PubMed

Meng, You; Liu, Ao; Guo, Zidong; Liu, Guoxia; Shin, Byoungchul; Noh, Yong-Young; Fortunato, Elvira; Martins, Rodrigo; Shan, Fukai

2018-05-30

Technical development for thin-film fabrication is essential for emerging metal-oxide (MO) electronics. Although impressive progress has been achieved in fabricating MO thin films, the challenges still remain. Here, we report a versatile and general thermal-induced nanomelting technique for fabricating MO thin films from the fiber networks, briefly called fiber-to-film (FTF) process. The high quality of the FTF-processed MO thin films was confirmed by various investigations. The FTF process is generally applicable to numerous technologically relevant MO thin films, including semiconducting thin films (e.g., In 2 O 3 , InZnO, and InZrZnO), conducting thin films (e.g., InSnO), and insulating thin films (e.g., AlO x ). By optimizing the fabrication process, In 2 O 3 /AlO x thin-film transistors (TFTs) were successfully integrated by fully FTF processes. High-performance TFT was achieved with an average mobility of ∼25 cm 2 /(Vs), an on/off current ratio of ∼10 7 , a threshold voltage of ∼1 V, and a device yield of 100%. As a proof of concept, one-transistor-driven pixel circuit was constructed, which exhibited high controllability over the light-emitting diodes. Logic gates based on fully FTF-processed In 2 O 3 /AlO x TFTs were further realized, which exhibited good dynamic logic responses and voltage amplification by a factor of ∼4. The FTF technique presented here offers great potential in large-area and low-cost manufacturing for flexible oxide electronics.

Development of dynamic Bayesian models for web application test management

NASA Astrophysics Data System (ADS)

Azarnova, T. V.; Polukhin, P. V.; Bondarenko, Yu V.; Kashirina, I. L.

2018-03-01

The mathematical apparatus of dynamic Bayesian networks is an effective and technically proven tool that can be used to model complex stochastic dynamic processes. According to the results of the research, mathematical models and methods of dynamic Bayesian networks provide a high coverage of stochastic tasks associated with error testing in multiuser software products operated in a dynamically changing environment. Formalized representation of the discrete test process as a dynamic Bayesian model allows us to organize the logical connection between individual test assets for multiple time slices. This approach gives an opportunity to present testing as a discrete process with set structural components responsible for the generation of test assets. Dynamic Bayesian network-based models allow us to combine in one management area individual units and testing components with different functionalities and a direct influence on each other in the process of comprehensive testing of various groups of computer bugs. The application of the proposed models provides an opportunity to use a consistent approach to formalize test principles and procedures, methods used to treat situational error signs, and methods used to produce analytical conclusions based on test results.

MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing

DTIC Science & Technology

2013-09-01

recognition, a Gaussian Process Dynamic Model with Social Network Analysis (GPDM-SNA) for a small human group action recognition, an extended GPDM-SNA...44  3.2. Small Human Group Activity Modeling Based on Gaussian Process Dynamic Model and Social Network Analysis (SN-GPDM...51  Approved for public release; distribution unlimited. 3 3.2.3. Gaussian Process Dynamical Model and

The Jarzynski identity derived from general Hamiltonian or non-Hamiltonian dynamics reproducing NVT or NPT ensembles

NASA Astrophysics Data System (ADS)

Cuendet, Michel A.

2006-10-01

The Jarzynski identity (JI) relates nonequilibrium work averages to thermodynamic free energy differences. It was shown in a recent contribution [M. A. Cuendet, Phys. Rev. Lett. 96, 120602 (2006)] that the JI can, in particular, be derived directly from the Nosé-Hoover thermostated dynamics. This statistical mechanical derivation is particularly relevant in the framework of molecular dynamics simulation, because it is based solely on the equations of motion considered and is free of any additional assumptions on system size or bath coupling. Here, this result is generalized to a variety of dynamics, along two directions. On the one hand, specific improved thermostating schemes used in practical applications are treated. These include Nosé-Hoover chains, higher moment thermostats, as well as an isothermal-isobaric scheme yielding the JI in the NPT ensemble. On the other hand, the theoretical generality of the new derivation is explored. Generic dynamics with arbitrary coupling terms and an arbitrary number of thermostating variables, both non-Hamiltonian and Hamiltonian, are shown to imply the JI. In particular, a nonautonomous formulation of the generalized Nosé-Poincaré thermostat is proposed. Finally, general conditions required for the JI derivation are briefly discussed.

Efficient implementation of constant pH molecular dynamics on modern graphics processors.

PubMed

Arthur, Evan J; Brooks, Charles L

2016-09-15

The treatment of pH sensitive ionization states for titratable residues in proteins is often omitted from molecular dynamics (MD) simulations. While static charge models can answer many questions regarding protein conformational equilibrium and protein-ligand interactions, pH-sensitive phenomena such as acid-activated chaperones and amyloidogenic protein aggregation are inaccessible to such models. Constant pH molecular dynamics (CPHMD) coupled with the Generalized Born with a Simple sWitching function (GBSW) implicit solvent model provide an accurate framework for simulating pH sensitive processes in biological systems. Although this combination has demonstrated success in predicting pKa values of protein structures, and in exploring dynamics of ionizable side-chains, its speed has been an impediment to routine application. The recent availability of low-cost graphics processing unit (GPU) chipsets with thousands of processing cores, together with the implementation of the accurate GBSW implicit solvent model on those chipsets (Arthur and Brooks, J. Comput. Chem. 2016, 37, 927), provide an opportunity to improve the speed of CPHMD and ionization modeling greatly. Here, we present a first implementation of GPU-enabled CPHMD within the CHARMM-OpenMM simulation package interface. Depending on the system size and nonbonded force cutoff parameters, we find speed increases of between one and three orders of magnitude. Additionally, the algorithm scales better with system size than the CPU-based algorithm, thus allowing for larger systems to be modeled in a cost effective manner. We anticipate that the improved performance of this methodology will open the door for broad-spread application of CPHMD in its modeling pH-mediated biological processes. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

F-BAR family proteins, emerging regulators for cell membrane dynamic changes-from structure to human diseases.

PubMed

Liu, Suxuan; Xiong, Xinyu; Zhao, Xianxian; Yang, Xiaofeng; Wang, Hong

2015-05-09

Eukaryotic cell membrane dynamics change in curvature during physiological and pathological processes. In the past ten years, a novel protein family, Fes/CIP4 homology-Bin/Amphiphysin/Rvs (F-BAR) domain proteins, has been identified to be the most important coordinators in membrane curvature regulation. The F-BAR domain family is a member of the Bin/Amphiphysin/Rvs (BAR) domain superfamily that is associated with dynamic changes in cell membrane. However, the molecular basis in membrane structure regulation and the biological functions of F-BAR protein are unclear. The pathophysiological role of F-BAR protein is unknown. This review summarizes the current understanding of structure and function in the BAR domain superfamily, classifies F-BAR family proteins into nine subfamilies based on domain structure, and characterizes F-BAR protein structure, domain interaction, and functional relevance. In general, F-BAR protein binds to cell membrane via F-BAR domain association with membrane phospholipids and initiates membrane curvature and scission via Src homology-3 (SH3) domain interaction with its partner proteins. This process causes membrane dynamic changes and leads to seven important cellular biological functions, which include endocytosis, phagocytosis, filopodium, lamellipodium, cytokinesis, adhesion, and podosome formation, via distinct signaling pathways determined by specific domain-binding partners. These cellular functions play important roles in many physiological and pathophysiological processes. We further summarize F-BAR protein expression and mutation changes observed in various diseases and developmental disorders. Considering the structure feature and functional implication of F-BAR proteins, we anticipate that F-BAR proteins modulate physiological and pathophysiological processes via transferring extracellular materials, regulating cell trafficking and mobility, presenting antigens, mediating extracellular matrix degradation, and transmitting signaling for cell proliferation.

F*** Yeah Fluid Dynamics: Inside the science communication process

NASA Astrophysics Data System (ADS)

Sharp, Nicole

2016-11-01

Communicating scientific research to general audiences may seem daunting, but it does not have to be. For six years, fluid dynamics outreach blog FYFD has been sharing the community's scientific output with an audience of nearly a quarter of a million readers and viewers of all ages and backgrounds. This talk will focus on the process behind science communication and some of the steps and exercises that can help scientists communicate to broad audiences more effectively. Using examples from the FYFD blog and YouTube channel, the talk will illustrate this communication process in action.

Evolutionary dynamics on graphs: Efficient method for weak selection

NASA Astrophysics Data System (ADS)

Fu, Feng; Wang, Long; Nowak, Martin A.; Hauert, Christoph

2009-04-01

Investigating the evolutionary dynamics of game theoretical interactions in populations where individuals are arranged on a graph can be challenging in terms of computation time. Here, we propose an efficient method to study any type of game on arbitrary graph structures for weak selection. In this limit, evolutionary game dynamics represents a first-order correction to neutral evolution. Spatial correlations can be empirically determined under neutral evolution and provide the basis for formulating the game dynamics as a discrete Markov process by incorporating a detailed description of the microscopic dynamics based on the neutral correlations. This framework is then applied to one of the most intriguing questions in evolutionary biology: the evolution of cooperation. We demonstrate that the degree heterogeneity of a graph impedes cooperation and that the success of tit for tat depends not only on the number of rounds but also on the degree of the graph. Moreover, considering the mutation-selection equilibrium shows that the symmetry of the stationary distribution of states under weak selection is skewed in favor of defectors for larger selection strengths. In particular, degree heterogeneity—a prominent feature of scale-free networks—generally results in a more pronounced increase in the critical benefit-to-cost ratio required for evolution to favor cooperation as compared to regular graphs. This conclusion is corroborated by an analysis of the effects of population structures on the fixation probabilities of strategies in general 2×2 games for different types of graphs. Computer simulations confirm the predictive power of our method and illustrate the improved accuracy as compared to previous studies.

Modeling Common-Sense Decisions

NASA Astrophysics Data System (ADS)

Zak, Michail

This paper presents a methodology for efficient synthesis of dynamical model simulating a common-sense decision making process. The approach is based upon the extension of the physics' First Principles that includes behavior of living systems. The new architecture consists of motor dynamics simulating actual behavior of the object, and mental dynamics representing evolution of the corresponding knowledge-base and incorporating it in the form of information flows into the motor dynamics. The autonomy of the decision making process is achieved by a feedback from mental to motor dynamics. This feedback replaces unavailable external information by an internal knowledgebase stored in the mental model in the form of probability distributions.

Accelerating Large Data Analysis By Exploiting Regularities

NASA Technical Reports Server (NTRS)

Moran, Patrick J.; Ellsworth, David

2003-01-01

We present techniques for discovering and exploiting regularity in large curvilinear data sets. The data can be based on a single mesh or a mesh composed of multiple submeshes (also known as zones). Multi-zone data are typical to Computational Fluid Dynamics (CFD) simulations. Regularities include axis-aligned rectilinear and cylindrical meshes as well as cases where one zone is equivalent to a rigid-body transformation of another. Our algorithms can also discover rigid-body motion of meshes in time-series data. Next, we describe a data model where we can utilize the results from the discovery process in order to accelerate large data visualizations. Where possible, we replace general curvilinear zones with rectilinear or cylindrical zones. In rigid-body motion cases we replace a time-series of meshes with a transformed mesh object where a reference mesh is dynamically transformed based on a given time value in order to satisfy geometry requests, on demand. The data model enables us to make these substitutions and dynamic transformations transparently with respect to the visualization algorithms. We present results with large data sets where we combine our mesh replacement and transformation techniques with out-of-core paging in order to achieve significant speed-ups in analysis.

Dimension reduction for stochastic dynamical systems forced onto a manifold by large drift: a constructive approach with examples from theoretical biology

NASA Astrophysics Data System (ADS)

Parsons, Todd L.; Rogers, Tim

2017-10-01

Systems composed of large numbers of interacting agents often admit an effective coarse-grained description in terms of a multidimensional stochastic dynamical system, driven by small-amplitude intrinsic noise. In applications to biological, ecological, chemical and social dynamics it is common for these models to posses quantities that are approximately conserved on short timescales, in which case system trajectories are observed to remain close to some lower-dimensional subspace. Here, we derive explicit and general formulae for a reduced-dimension description of such processes that is exact in the limit of small noise and well-separated slow and fast dynamics. The Michaelis-Menten law of enzyme-catalysed reactions, and the link between the Lotka-Volterra and Wright-Fisher processes are explored as a simple worked examples. Extensions of the method are presented for infinite dimensional systems and processes coupled to non-Gaussian noise sources.

Refinement of a Conceptual Model for Adolescent Readiness to Engage in End-of-Life Discussions.

PubMed

Bell, Cynthia J; Zimet, Gregory D; Hinds, Pamela S; Broome, Marion E; McDaniel, Anna M; Mays, Rose M; Champion, Victoria L

Adolescents living with incurable cancer require ongoing support to process grief, emotions, and information as disease progresses including treatment options (phase 1 clinical trials and/or hospice/palliative care). Little is known about how adolescents become ready for such discussions. The purpose of this study was to explore the process of adolescent readiness for end-of-life preparedness discussions, generating a theoretical understanding for guiding clinical conversations when curative options are limited. We explored 2 in-depth cases across time using case-study methodology. An à priori conceptual model based on current end-of-life research guided data collection and analysis. Multiple sources including in-depth adolescent interviews generated data collection on model constructs. Analysis followed a logical sequence establishing a chain of evidence linking raw data to study conclusions. Synthesis and data triangulation across cases and time led to theoretical generalizations. Initially, we proposed a linear process of readiness with 3 domains: a cognitive domain (awareness), an emotional domain (acceptance), and a behavioral domain (willingness), which preceded preparedness. Findings led to conceptual model refinement showing readiness is a dynamic internal process that interacts with preparedness. Current awareness context facilitates the type of preparedness discussions (cognitive or emotional). Furthermore, social constraint inhibits discussions. Data support theoretical understanding of the dynamism of readiness. Future research that validates adolescent conceptualization will ensure age-appropriate readiness representation. Understanding the dynamic process of readiness for engaging in end-of-life preparedness provides clinician insight for guiding discussions that facilitate shared decision making and promote quality of life for adolescents and their families.

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Dynamics of ejecta from a binary asteroid impact in the framework of the AIDA mission: a NEOShield-2 contribution

NASA Astrophysics Data System (ADS)

Yu, Y.; Schwartz, S. R.; Michel, P.; Benner, L. A. M.

2015-10-01

The dynamics of the ejecta cloud that results from a binary asteroid impact is one of the tasks of the NEOShield-2 project, funded by the European Commission in its program Horizon 2020. Results from such an investigation will have great relevance to the Phase-A study of the AIDA space mission, a collaborative effort between ESA and NASA, which aims to perform a kinetic impactor demonstration. Our study presents a multi-scale dynamical model of the ejecta cloud produced by a hypervelocity impact, which enables us to check the behaviors of the ejecta at different spatial and time scales. This model is applied to the impact into the small moon of the binary Near- Earth asteroid (65803) Didymos on October 2022 as considered by the AIDA mission. We attempt to model the process by including as much practical information as possible, e.g., the gravitational environment influenced by the non-spherical shapes of the bodies based on observed shape of the primary), the solar tides, and the solar radiation pressure. Our simulations show the general patterns of motion of the ejecta cloud, which we use to assess the potential hazard to an observing spacecraft. We also look into the grain-scale dynamics of the ejecta during this process, which has influence on the re-accumulation of particles orbiting in the vicinity.

Simulations of NLC formation using a microphysical model driven by three-dimensional dynamics

NASA Astrophysics Data System (ADS)

Kirsch, Annekatrin; Becker, Erich; Rapp, Markus; Megner, Linda; Wilms, Henrike

2014-05-01

Noctilucent clouds (NLCs) represent an optical phenomenon occurring in the polar summer mesopause region. These clouds have been known since the late 19th century. Current physical understanding of NLCs is based on numerous observational and theoretical studies, in recent years especially observations from satellites and by lidars from ground. Theoretical studies based on numerical models that simulate NLCs with the underlying microphysical processes are uncommon. Up to date no three-dimensional numerical simulations of NLCs exist that take all relevant dynamical scales into account, i.e., from the planetary scale down to gravity waves and turbulence. Rather, modeling is usually restricted to certain flow regimes. In this study we make a more rigorous attempt and simulate NLC formation in the environment of the general circulation of the mesopause region by explicitly including gravity waves motions. For this purpose we couple the Community Aerosol and Radiation Model for Atmosphere (CARMA) to gravity-wave resolving dynamical fields simulated beforehand with the Kuehlungsborn Mechanistic Circulation Model (KMCM). In our case, the KMCM is run with a horizontal resolution of T120 which corresponds to a minimum horizontal wavelength of 350 km. This restriction causes the resolved gravity waves to be somewhat biased to larger scales. The simulated general circulation is dynamically controlled by these waves in a self-consitent fashion and provides realistic temperatures and wind-fields for July conditions. Assuming a water vapor mixing ratio profile in agreement with current observations results in reasonable supersaturations of up to 100. In a first step, CARMA is applied to a horizontal section covering the Northern hemisphere. The vertical resolution is 120 levels ranging from 72 to 101 km. In this paper we will present initial results of this coupled dynamical microphysical model focussing on the interaction of waves and turbulent diffusion with NLC-microphysics.

Carbon membranes for efficient water-ethanol separation.

PubMed

Gravelle, Simon; Yoshida, Hiroaki; Joly, Laurent; Ybert, Christophe; Bocquet, Lydéric

2016-09-28

We demonstrate, on the basis of molecular dynamics simulations, the possibility of an efficient water-ethanol separation using nanoporous carbon membranes, namely, carbon nanotube membranes, nanoporous graphene sheets, and multilayer graphene membranes. While these carbon membranes are in general permeable to both pure liquids, they exhibit a counter-intuitive "self-semi-permeability" to water in the presence of water-ethanol mixtures. This originates in a preferred ethanol adsorption in nanoconfinement that prevents water molecules from entering the carbon nanopores. An osmotic pressure is accordingly expressed across the carbon membranes for the water-ethanol mixture, which agrees with the classic van't Hoff type expression. This suggests a robust and versatile membrane-based separation, built on a pressure-driven reverse-osmosis process across these carbon-based membranes. In particular, the recent development of large-scale "graphene-oxide" like membranes then opens an avenue for a versatile and efficient ethanol dehydration using this separation process, with possible application for bio-ethanol fabrication.

Carbon membranes for efficient water-ethanol separation

NASA Astrophysics Data System (ADS)

Gravelle, Simon; Yoshida, Hiroaki; Joly, Laurent; Ybert, Christophe; Bocquet, Lydéric

2016-09-01

We demonstrate, on the basis of molecular dynamics simulations, the possibility of an efficient water-ethanol separation using nanoporous carbon membranes, namely, carbon nanotube membranes, nanoporous graphene sheets, and multilayer graphene membranes. While these carbon membranes are in general permeable to both pure liquids, they exhibit a counter-intuitive "self-semi-permeability" to water in the presence of water-ethanol mixtures. This originates in a preferred ethanol adsorption in nanoconfinement that prevents water molecules from entering the carbon nanopores. An osmotic pressure is accordingly expressed across the carbon membranes for the water-ethanol mixture, which agrees with the classic van't Hoff type expression. This suggests a robust and versatile membrane-based separation, built on a pressure-driven reverse-osmosis process across these carbon-based membranes. In particular, the recent development of large-scale "graphene-oxide" like membranes then opens an avenue for a versatile and efficient ethanol dehydration using this separation process, with possible application for bio-ethanol fabrication.

An accurate European option pricing model under Fractional Stable Process based on Feynman Path Integral

NASA Astrophysics Data System (ADS)

Ma, Chao; Ma, Qinghua; Yao, Haixiang; Hou, Tiancheng

2018-03-01

In this paper, we propose to use the Fractional Stable Process (FSP) for option pricing. The FSP is one of the few candidates to directly model a number of desired empirical properties of asset price risk neutral dynamics. However, pricing the vanilla European option under FSP is difficult and problematic. In the paper, built upon the developed Feynman Path Integral inspired techniques, we present a novel computational model for option pricing, i.e. the Fractional Stable Process Path Integral (FSPPI) model under a general fractional stable distribution that tackles this problem. Numerical and empirical experiments show that the proposed pricing model provides a correction of the Black-Scholes pricing error - overpricing long term options, underpricing short term options; overpricing out-of-the-money options, underpricing in-the-money options without any additional structures such as stochastic volatility and a jump process.

The Generalized Support Software (GSS) Domain Engineering Process: An Object-Oriented Implementation and Reuse Success at Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Condon, Steven; Hendrick, Robert; Stark, Michael E.; Steger, Warren

1997-01-01

The Flight Dynamics Division (FDD) of NASA's Goddard Space Flight Center (GSFC) recently embarked on a far-reaching revision of its process for developing and maintaining satellite support software. The new process relies on an object-oriented software development method supported by a domain specific library of generalized components. This Generalized Support Software (GSS) Domain Engineering Process is currently in use at the NASA GSFC Software Engineering Laboratory (SEL). The key facets of the GSS process are (1) an architecture for rapid deployment of FDD applications, (2) a reuse asset library for FDD classes, and (3) a paradigm shift from developing software to configuring software for mission support. This paper describes the GSS architecture and process, results of fielding the first applications, lessons learned, and future directions

Dynamical Analysis in the Mathematical Modelling of Human Blood Glucose

ERIC Educational Resources Information Center

Bae, Saebyok; Kang, Byungmin

2012-01-01

We want to apply the geometrical method to a dynamical system of human blood glucose. Due to the educational importance of model building, we show a relatively general modelling process using observational facts. Next, two models of some concrete forms are analysed in the phase plane by means of linear stability, phase portrait and vector…

A Social Approach to High-Level Context Generation for Supporting Context-Aware M-Learning

ERIC Educational Resources Information Center

Pan, Xu-Wei; Ding, Ling; Zhu, Xi-Yong; Yang, Zhao-Xiang

2017-01-01

In m-learning environments, context-awareness is for wide use where learners' situations are varied, dynamic and unpredictable. We are facing the challenge of requirements of both generality and depth in generating and processing high-level context. In this paper, we present a social approach which exploits social dynamics and social computing for…

On a generalized Ablowitz-Kaup-Newell-Segur hierarchy in inhomogeneities of media: soliton solutions and wave propagation influenced from coefficient functions and scattering data

NASA Astrophysics Data System (ADS)

Zhang, Sheng; Hong, Siyu

2018-07-01

In this paper, a generalized Ablowitz-Kaup-Newell-Segur (AKNS) hierarchy in inhomogeneities of media described by variable coefficients is investigated, which includes some important nonlinear evolution equations as special cases, for example, the celebrated Korteweg-de Vries equation modeling waves on shallow water surfaces. To be specific, the known AKNS spectral problem and its time evolution equation are first generalized by embedding a finite number of differentiable and time-dependent functions. Starting from the generalized AKNS spectral problem and its generalized time evolution equation, a generalized AKNS hierarchy with variable coefficients is then derived. Furthermore, based on a systematic analysis on the time dependence of related scattering data of the generalized AKNS spectral problem, exact solutions of the generalized AKNS hierarchy are formulated through the inverse scattering transform method. In the case of reflectionless potentials, the obtained exact solutions are reduced to n-soliton solutions. It is graphically shown that the dynamical evolutions of such soliton solutions are influenced by not only the time-dependent coefficients but also the related scattering data in the process of propagations.

Generic equilibration dynamics of planar defects in trapped atomic superfluids

DOE PAGES

Scherpelz, Peter; Padavić, Karmela; Murray, Andy; ...

2015-03-18

Here, we investigate equilibration processes shortly after sudden perturbations are applied to ultracold trapped superfluids. We show the similarity of phase imprinting and localized density depletion perturbations, both of which initially are found to produce “phase walls”. These planar defects are associated with a sharp gradient in the phase. Importantly they relax following a quite general sequence. Our studies, based on simulations of the complex time-dependent Ginzburg-Landau equation, address the challenge posed by these experiments: how a superfluid eventually eliminatesa spatially extended planar defect. The processes involved are necessarily more complex than equilibration involving simpler line vortices. An essential mechanismmore » form relaxation involves repeated formation and loss of vortex rings near the trap edge.« less

Detection of abrupt changes in dynamic systems

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1984-01-01

Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

ICT-Based Dynamic Assessment to Reveal Special Education Students' Potential in Mathematics

ERIC Educational Resources Information Center

Peltenburg, Marjolijn; van den Heuvel-Panhuizen, Marja; Robitzsch, Alexander

2010-01-01

This paper reports on a research project on information and communication technology (ICT)-based dynamic assessment. The project aims to reveal the mathematical potential of students in special education. The focus is on a topic that is generally recognised as rather difficult for weak students: subtraction up to 100 with crossing the ten. The…

Various-scale controls of complex subduction dynamics on magmatic-hydrothermal processes in eastern Mediterranean

NASA Astrophysics Data System (ADS)

Menant, Armel; Jolivet, Laurent; Sternai, Pietro; Ducoux, Maxime; Augier, Romain; Rabillard, Aurélien; Gerya, Taras; Guillou-Frottier, Laurent

2014-05-01

In subduction environment, magmatic-hydrothermal processes, responsible for the emplacement of magmatic bodies and related mineralization, are strongly controlled by slab dynamics. This 3D dynamics is often complex, resulting notably in spatial evolution through time of mineralization and magmatism types and in fast kinematic changes at the surface. Study at different scales of the distribution of these magmatic and hydrothermal products is useful to better constrain subduction dynamics. This work is focused on the eastern Mediterranean, where the complex dynamics of the Tethyan active margin since the upper Cretaceous is still largely debated. We propose new kinematic reconstructions of the region also showing the distribution of magmatic products and mineralization in space and time. Three main periods have thus been identified with a general southward migration of magmatic and ore bodies. (1) From late Cretaceous to lower Paleocene, calc-alkaline magmatism and porphyry Cu deposits emplaced notably in the Balkans, along a long linear cordillera. (2) From late Paleocene to Eocene, a barren period occurred while the Pelagonian microcontinent was buried within the subduction zone. (3) Since the Oligocene, Au-rich deposits and related K-rich magmatism emplaced in the Rhodopes, the Aegean and western Anatolian extensional domains in response to fast slab retreat and related mantle flow inducing the partial melting of the lithospheric mantle or the base of the upper crust where Au was previously stored. The emplacement at shallow level of this mineralization was largely controlled by large-scale structures that drained the magmatic-hydrothermal fluids. In the Cyclades for instance, field studies show that Au-rich but also base metal-rich ore deposits are syn-extensional and spatially related to large-scale detachment systems (e.g. on Tinos, Mykonos, Serifos islands), which are recognized as subduction-related structures. These results highlight the importance at different scales of subduction dynamics and related mantle flow on the emplacement of mineralization and magmatic bodies. Indeed, besides a general southward migration of the magmatic-hydrothermal activity since the upper Cretaceous from the Balkans to the present-day Aegean volcanic arc, a secondary westward migration is observed during the Miocene from the Menderes massif to the Cyclades. This feature is a possible consequence of a slab tearing event and related mantle flow, as suggested notably by tomographic models below western Anatolia. To further test the effects of slab retreat and tearing on the flow and temperature field within the mantle, we performed 3D thermo-mechanical numerical modeling. Models suggest that the asthenospheric flow induced by the development of a slab tear controls the migration of magmatic products stored at the base of the crust, influencing the distribution of potentially fertile magmas within the upper crust.

Dynamic Allocation of SPM Based on Time-Slotted Cache Conflict Graph for System Optimization

NASA Astrophysics Data System (ADS)

Wu, Jianping; Ling, Ming; Zhang, Yang; Mei, Chen; Wang, Huan

This paper proposes a novel dynamic Scratch-pad Memory allocation strategy to optimize the energy consumption of the memory sub-system. Firstly, the whole program execution process is sliced into several time slots according to the temporal dimension; thereafter, a Time-Slotted Cache Conflict Graph (TSCCG) is introduced to model the behavior of Data Cache (D-Cache) conflicts within each time slot. Then, Integer Nonlinear Programming (INP) is implemented, which can avoid time-consuming linearization process, to select the most profitable data pages. Virtual Memory System (VMS) is adopted to remap those data pages, which will cause severe Cache conflicts within a time slot, to SPM. In order to minimize the swapping overhead of dynamic SPM allocation, a novel SPM controller with a tightly coupled DMA is introduced to issue the swapping operations without CPU's intervention. Last but not the least, this paper discusses the fluctuation of system energy profit based on different MMU page size as well as the Time Slot duration quantitatively. According to our design space exploration, the proposed method can optimize all of the data segments, including global data, heap and stack data in general, and reduce the total energy consumption by 27.28% on average, up to 55.22% with a marginal performance promotion. And comparing to the conventional static CCG (Cache Conflicts Graph), our approach can obtain 24.7% energy profit on average, up to 30.5% with a sight boost in performance.

An improved EMD method for modal identification and a combined static-dynamic method for damage detection

NASA Astrophysics Data System (ADS)

Yang, Jinping; Li, Peizhen; Yang, Youfa; Xu, Dian

2018-04-01

Empirical mode decomposition (EMD) is a highly adaptable signal processing method. However, the EMD approach has certain drawbacks, including distortions from end effects and mode mixing. In the present study, these two problems are addressed using an end extension method based on the support vector regression machine (SVRM) and a modal decomposition method based on the characteristics of the Hilbert transform. The algorithm includes two steps: using the SVRM, the time series data are extended at both endpoints to reduce the end effects, and then, a modified EMD method using the characteristics of the Hilbert transform is performed on the resulting signal to reduce mode mixing. A new combined static-dynamic method for identifying structural damage is presented. This method combines the static and dynamic information in an equilibrium equation that can be solved using the Moore-Penrose generalized matrix inverse. The combination method uses the differences in displacements of the structure with and without damage and variations in the modal force vector. Tests on a four-story, steel-frame structure were conducted to obtain static and dynamic responses of the structure. The modal parameters are identified using data from the dynamic tests and improved EMD method. The new method is shown to be more accurate and effective than the traditional EMD method. Through tests with a shear-type test frame, the higher performance of the proposed static-dynamic damage detection approach, which can detect both single and multiple damage locations and the degree of the damage, is demonstrated. For structures with multiple damage, the combined approach is more effective than either the static or dynamic method. The proposed EMD method and static-dynamic damage detection method offer improved modal identification and damage detection, respectively, in structures.

Differential morphology and image processing.

PubMed

Maragos, P

1996-01-01

Image processing via mathematical morphology has traditionally used geometry to intuitively understand morphological signal operators and set or lattice algebra to analyze them in the space domain. We provide a unified view and analytic tools for morphological image processing that is based on ideas from differential calculus and dynamical systems. This includes ideas on using partial differential or difference equations (PDEs) to model distance propagation or nonlinear multiscale processes in images. We briefly review some nonlinear difference equations that implement discrete distance transforms and relate them to numerical solutions of the eikonal equation of optics. We also review some nonlinear PDEs that model the evolution of multiscale morphological operators and use morphological derivatives. Among the new ideas presented, we develop some general 2-D max/min-sum difference equations that model the space dynamics of 2-D morphological systems (including the distance computations) and some nonlinear signal transforms, called slope transforms, that can analyze these systems in a transform domain in ways conceptually similar to the application of Fourier transforms to linear systems. Thus, distance transforms are shown to be bandpass slope filters. We view the analysis of the multiscale morphological PDEs and of the eikonal PDE solved via weighted distance transforms as a unified area in nonlinear image processing, which we call differential morphology, and briefly discuss its potential applications to image processing and computer vision.

BIOMAP A Daily Time Step, Mechanistic Model for the Study of Ecosystem Dynamics

NASA Astrophysics Data System (ADS)

Wells, J. R.; Neilson, R. P.; Drapek, R. J.; Pitts, B. S.

2010-12-01

BIOMAP simulates competition between two Plant Functional Types (PFT) at any given point in the conterminous U.S. using a time series of daily temperature (mean, minimum, maximum), precipitation, humidity, light and nutrients, with PFT-specific rooting within a multi-layer soil. The model employs a 2-layer canopy biophysics, Farquhar photosynthesis, the Beer-Lambert Law for light attenuation and a mechanistic soil hydrology. In essence, BIOMAP is a re-built version of the biogeochemistry model, BIOME-BGC, into the form of the MAPSS biogeography model. Specific enhancements are: 1) the 2-layer canopy biophysics of Dolman (1993); 2) the unique MAPSS-based hydrology, which incorporates canopy evaporation, snow dynamics, infiltration and saturated and unsaturated percolation with ‘fast’ flow and base flow and a ‘tunable aquifer’ capacity, a metaphor of D’Arcy’s Law; and, 3) a unique MAPSS-based stomatal conductance algorithm, which simultaneously incorporates vapor pressure and soil water potential constraints, based on physiological information and many other improvements. Over small domains the PFTs can be parameterized as individual species to investigate fundamental vs. potential niche theory; while, at more coarse scales the PFTs can be rendered as more general functional groups. Since all of the model processes are intrinsically leaf to plot scale (physiology to PFT competition), it essentially has no ‘intrinsic’ scale and can be implemented on a grid of any size, taking on the characteristics defined by the homogeneous climate of each grid cell. Currently, the model is implemented on the VEMAP 1/2 degree, daily grid over the conterminous U.S. Although both the thermal and water-limited ecotones are dynamic, following climate variability, the PFT distributions remain fixed. Thus, the model is currently being fitted with a ‘reproduction niche’ to allow full dynamic operation as a Dynamic General Vegetation Model (DGVM). While global simulations of both climate and ecosystems must be done at coarse grid resolutions; smaller domains require higher resolution for the simulation of natural resource processes at the landscape scale and that of on-the-ground management practices. Via a combined multi-agency and private conservation effort we have implemented a Nested Scale Experiment (NeScE) that ranges from 1/2 degree resolution (global, ca. 50 km) to ca. 8km (North America) and 800 m (conterminous U.S.). Our first DGVM, MC1, has been implemented at all 3 scales. We are just beginning to implement BIOMAP into NeScE, with its unique features, and daily time step, as a counterpoint to MC1. We believe it will be more accurate at all resolutions providing better simulations of vegetation distribution, carbon balance, runoff, fire regimes and drought impacts.

Event detection and localization for small mobile robots using reservoir computing.

PubMed

Antonelo, E A; Schrauwen, B; Stroobandt, D

2008-08-01

Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the input stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.

Scale Dependend Investigations of the Dynamic State Index Concerning the QG-Theory

NASA Astrophysics Data System (ADS)

Mueller, Annette; Névir, Peter

2017-04-01

The Dynamic State Index (DSI) indicates local deviations of the atmospheric flow field from a steady wind solution based on the primitive equations under adiabatic and inviscid conditions. We represent generalizations of the DSI for reduced models given by the quasi-geostrophic theory and the Rossby-model. By applying a Fourier transformation to the circumpolar geopotential height field we demonstrate the characteristic dipole structure of the DSI-field related to atmospheric waves. Furthermore, by applying data of the COSMO-DE model of the German Weather Service (DWD), we compare the vertical profile of all three DSI-parameters concerning classes with and without precipitation. We work out that the relation to precipitation decreases with increasing approximation, but in all scales, it can be shown that the DSI is highly correlated to diabatic processes.

The quantum dynamics of electronically nonadiabatic chemical reactions

NASA Technical Reports Server (NTRS)

Truhlar, Donald G.

1993-01-01

Considerable progress was achieved on the quantum mechanical treatment of electronically nonadiabatic collisions involving energy transfer and chemical reaction in the collision of an electronically excited atom with a molecule. In the first step, a new diabatic representation for the coupled potential energy surfaces was created. A two-state diabatic representation was developed which was designed to realistically reproduce the two lowest adiabatic states of the valence bond model and also to have the following three desirable features: (1) it is more economical to evaluate; (2) it is more portable; and (3) all spline fits are replaced by analytic functions. The new representation consists of a set of two coupled diabatic potential energy surfaces plus a coupling surface. It is suitable for dynamics calculations on both the electronic quenching and reaction processes in collisions of Na(3p2p) with H2. The new two-state representation was obtained by a three-step process from a modified eight-state diatomics-in-molecules (DIM) representation of Blais. The second step required the development of new dynamical methods. A formalism was developed for treating reactions with very general basis functions including electronically excited states. Our formalism is based on the generalized Newton, scattered wave, and outgoing wave variational principles that were used previously for reactive collisions on a single potential energy surface, and it incorporates three new features: (1) the basis functions include electronic degrees of freedom, as required to treat reactions involving electronic excitation and two or more coupled potential energy surfaces; (2) the primitive electronic basis is assumed to be diabatic, and it is not assumed that it diagonalizes the electronic Hamiltonian even asymptotically; and (3) contracted basis functions for vibrational-rotational-orbital degrees of freedom are included in a very general way, similar to previous prescriptions for locally adiabatic functions in various quantum scattering algorithms.

TOWARDS A MULTI-SCALE AGENT-BASED PROGRAMMING LANGUAGE METHODOLOGY

PubMed Central

Somogyi, Endre; Hagar, Amit; Glazier, James A.

2017-01-01

Living tissues are dynamic, heterogeneous compositions of objects, including molecules, cells and extra-cellular materials, which interact via chemical, mechanical and electrical process and reorganize via transformation, birth, death and migration processes. Current programming language have difficulty describing the dynamics of tissues because: 1: Dynamic sets of objects participate simultaneously in multiple processes, 2: Processes may be either continuous or discrete, and their activity may be conditional, 3: Objects and processes form complex, heterogeneous relationships and structures, 4: Objects and processes may be hierarchically composed, 5: Processes may create, destroy and transform objects and processes. Some modeling languages support these concepts, but most cannot translate models into executable simulations. We present a new hybrid executable modeling language paradigm, the Continuous Concurrent Object Process Methodology (CCOPM) which naturally expresses tissue models, enabling users to visually create agent-based models of tissues, and also allows computer simulation of these models. PMID:29282379

TOWARDS A MULTI-SCALE AGENT-BASED PROGRAMMING LANGUAGE METHODOLOGY.

PubMed

Somogyi, Endre; Hagar, Amit; Glazier, James A

2016-12-01

Living tissues are dynamic, heterogeneous compositions of objects , including molecules, cells and extra-cellular materials, which interact via chemical, mechanical and electrical process and reorganize via transformation, birth, death and migration processes . Current programming language have difficulty describing the dynamics of tissues because: 1: Dynamic sets of objects participate simultaneously in multiple processes, 2: Processes may be either continuous or discrete, and their activity may be conditional, 3: Objects and processes form complex, heterogeneous relationships and structures, 4: Objects and processes may be hierarchically composed, 5: Processes may create, destroy and transform objects and processes. Some modeling languages support these concepts, but most cannot translate models into executable simulations. We present a new hybrid executable modeling language paradigm, the Continuous Concurrent Object Process Methodology ( CCOPM ) which naturally expresses tissue models, enabling users to visually create agent-based models of tissues, and also allows computer simulation of these models.

A simple criterion for determining the dynamical stability of three-body systems

NASA Technical Reports Server (NTRS)

Black, D. C.

1982-01-01

Coplanar, prograde three-body systems (TBS) are discussed, emphasizing the specification of general criteria for determining whether such systems are dynamically stable. It is shown that the Graziani-Black (1981) criteria provide a quantitatively accurate characterization of the onset of dynamic instability for values of the dimensionless mass ranging from one millionth to one million. Harrington's (1977) general criterion and the Graziani-Black criterion are compared with results from analytic work that spans a 12-orders-of-magnitude variation in the mass ratios of the TBS components. Comparison of the Graziani-Black criteria with data for eight well-studied triple-star systems indicates that the observed lower limit for the ratio of periastron distance of the tertiary orbit to the semimajor axis of the binary orbit is due to dynamical instability rather than to cosmogonic processes.

A generalized framework for nucleosynthesis calculations

NASA Astrophysics Data System (ADS)

Sprouse, Trevor; Mumpower, Matthew; Aprahamian, Ani

2014-09-01

Simulating astrophysical events is a difficult process, requiring a detailed pairing of knowledge from both astrophysics and nuclear physics. Astrophysics guides the thermodynamic evolution of an astrophysical event. We present a nucleosynthesis framework written in Fortran that combines as inputs a thermodynamic evolution and nuclear data to time evolve the abundances of nuclear species. Through our coding practices, we have emphasized the applicability of our framework to any astrophysical event, including those involving nuclear fission. Because these calculations are often very complicated, our framework dynamically optimizes itself based on the conditions at each time step in order to greatly minimize total computation time. To highlight the power of this new approach, we demonstrate the use of our framework to simulate both Big Bang nucleosynthesis and r-process nucleosynthesis with speeds competitive with current solutions dedicated to either process alone.

From one plot to many and from hillslopes to streams: Improving our understanding of catchment hydrology with a multi-scale experimental approach

NASA Astrophysics Data System (ADS)

Blume, Theresa; Weiler, Markus; Angermann, Lisa; Beiter, Daniel; Hassler, Sibylle; Kaplan, Nils; Lieder, Ernestine; Sprenger, Matthias

2017-04-01

Sustainable water resources management needs to be based on sound process understanding. This is especially true in a changing world, where boundary conditions change and models calibrated to the status quo are no longer helpful. There is a general agreement in the hydrologic community that we are in need of a better process understanding and that one of the most promising ways to achieve this is by using nested experimental designs that cover a range of scales. In the here presented study we argue that while we might be able to investigate a certain process at a plot or hillslope in detail, the real power of advancing our understanding lies in site intercomparison and if possible knowledge transfer and generalization. The experimental design of the CAOS observatory is based on sensor clusters measuring ground-, soil and stream water, sap flow and climate variables in 45 hydrological functional units which were chosen from a matrix of site characteristics (geology, land use, hillslope aspect, and topographic positions). This design allows for site intercomparisons that are based on more than one member per class and thus does not only characterize between class differences but also attempts to identify within-class variability. These distributed plot scale investigations offer a large amount of information on plot scale processes and their variability in space and time (e.g. water storage dynamics and patterns, vertical flow processes and vadose zone transit times, transpiration dynamics and patterns). However, if we want to improve our understanding of runoff generation (and thus also of nutrient and contaminant transport and export to the stream) we need to also understand how these plots link up within hillslopes and how and when these hillslopes are connected to the stream. And certainly, this is again most helpful if we do not focus on single sites but attempt experimental designs that aim at intercomparison and generalization. At the same time, the investigation of hillslope-stream connectivity is extremely challenging due to the fact that there is a high 4-dimensional variability of the involved processes and most of them are hidden from view in the subsurface. To tackle this challenge we employed a number of different field methods ranging from hillslope scale irrigation and flow-through experiments, to in depth analyses of near stream piezometer responses and stream reach tracer experiments, and then moving on to the mesoscale catchment with network wide investigations of spatial patterns of stream temperature and electric conductivity as well as of the expansion and shrinkage of the network itself. In this presentation we will provide an overview of the rationale, approach, experimental design and ongoing work, the challenges we encountered and a synthesis of exemplary results.

Noise, chaos, and (ɛ, τ)-entropy per unit time

NASA Astrophysics Data System (ADS)

Gaspard, Pierre; Wang, Xiao-Jing

1993-12-01

The degree of dynamical randomness of different time processes is characterized in terms of the (ε, τ)-entropy per unit time. The (ε, τ)-entropy is the amount of information generated per unit time, at different scales τ of time and ε of the observables. This quantity generalizes the Kolmogorov-Sinai entropy per unit time from deterministic chaotic processes, to stochastic processes such as fluctuations in mesoscopic physico-chemical phenomena or strong turbulence in macroscopic spacetime dynamics. The random processes that are characterized include chaotic systems, Bernoulli and Markov chains, Poisson and birth-and-death processes, Ornstein-Uhlenbeck and Yaglom noises, fractional Brownian motions, different regimes of hydrodynamical turbulence, and the Lorentz-Boltzmann process of nonequilibrium statistical mechanics. We also extend the (ε, τ)-entropy to spacetime processes like cellular automata, Conway's game of life, lattice gas automata, coupled maps, spacetime chaos in partial differential equations, as well as the ideal, the Lorentz, and the hard sphere gases. Through these examples it is demonstrated that the (ε, τ)-entropy provides a unified quantitative measure of dynamical randomness to both chaos and noises, and a method to detect transitions between dynamical states of different degrees of randomness as a parameter of the system is varied.

Self-organization principles of intracellular pattern formation.

PubMed

Halatek, J; Brauns, F; Frey, E

2018-05-26

Dynamic patterning of specific proteins is essential for the spatio-temporal regulation of many important intracellular processes in prokaryotes, eukaryotes and multicellular organisms. The emergence of patterns generated by interactions of diffusing proteins is a paradigmatic example for self-organization. In this article, we review quantitative models for intracellular Min protein patterns in Escherichia coli , Cdc42 polarization in Saccharomyces cerevisiae and the bipolar PAR protein patterns found in Caenorhabditis elegans By analysing the molecular processes driving these systems we derive a theoretical perspective on general principles underlying self-organized pattern formation. We argue that intracellular pattern formation is not captured by concepts such as 'activators', 'inhibitors' or 'substrate depletion'. Instead, intracellular pattern formation is based on the redistribution of proteins by cytosolic diffusion, and the cycling of proteins between distinct conformational states. Therefore, mass-conserving reaction-diffusion equations provide the most appropriate framework to study intracellular pattern formation. We conclude that directed transport, e.g. cytosolic diffusion along an actively maintained cytosolic gradient, is the key process underlying pattern formation. Thus the basic principle of self-organization is the establishment and maintenance of directed transport by intracellular protein dynamics.This article is part of the theme issue 'Self-organization in cell biology'. © 2018 The Authors.

A Coupled Ocean General Circulation, Biogeochemical, and Radiative Model of the Global Oceans: Seasonal Distributions of Ocean Chlorophyll and Nutrients

NASA Technical Reports Server (NTRS)

Gregg, Watson W.; Busalacchi, Antonio (Technical Monitor)

2000-01-01

A coupled ocean general circulation, biogeochemical, and radiative model was constructed to evaluate and understand the nature of seasonal variability of chlorophyll and nutrients in the global oceans. Biogeochemical processes in the model are determined from the influences of circulation and turbulence dynamics, irradiance availability. and the interactions among three functional phytoplankton groups (diatoms. chlorophytes, and picoplankton) and three nutrients (nitrate, ammonium, and silicate). Basin scale (greater than 1000 km) model chlorophyll results are in overall agreement with CZCS pigments in many global regions. Seasonal variability observed in the CZCS is also represented in the model. Synoptic scale (100-1000 km) comparisons of imagery are generally in conformance although occasional departures are apparent. Model nitrate distributions agree with in situ data, including seasonal dynamics, except for the equatorial Atlantic. The overall agreement of the model with satellite and in situ data sources indicates that the model dynamics offer a reasonably realistic simulation of phytoplankton and nutrient dynamics on synoptic scales. This is especially true given that initial conditions are homogenous chlorophyll fields. The success of the model in producing a reasonable representation of chlorophyll and nutrient distributions and seasonal variability in the global oceans is attributed to the application of a generalized, processes-driven approach as opposed to regional parameterization and the existence of multiple phytoplankton groups with different physiological and physical properties. These factors enable the model to simultaneously represent many aspects of the great diversity of physical, biological, chemical, and radiative environments encountered in the global oceans.

Classical molecular dynamics simulation of electronically non-adiabatic processes.

PubMed

Miller, William H; Cotton, Stephen J

2016-12-22

Both classical and quantum mechanics (as well as hybrids thereof, i.e., semiclassical approaches) find widespread use in simulating dynamical processes in molecular systems. For large chemical systems, however, which involve potential energy surfaces (PES) of general/arbitrary form, it is usually the case that only classical molecular dynamics (MD) approaches are feasible, and their use is thus ubiquitous nowadays, at least for chemical processes involving dynamics on a single PES (i.e., within a single Born-Oppenheimer electronic state). This paper reviews recent developments in an approach which extends standard classical MD methods to the treatment of electronically non-adiabatic processes, i.e., those that involve transitions between different electronic states. The approach treats nuclear and electronic degrees of freedom (DOF) equivalently (i.e., by classical mechanics, thereby retaining the simplicity of standard MD), and provides "quantization" of the electronic states through a symmetrical quasi-classical (SQC) windowing model. The approach is seen to be capable of treating extreme regimes of strong and weak coupling between the electronic states, as well as accurately describing coherence effects in the electronic DOF (including the de-coherence of such effects caused by coupling to the nuclear DOF). A survey of recent applications is presented to illustrate the performance of the approach. Also described is a newly developed variation on the original SQC model (found universally superior to the original) and a general extension of the SQC model to obtain the full electronic density matrix (at no additional cost/complexity).

A model to create an efficient and equitable admission policy for patients arriving to the cardiothoracic ICU.

PubMed

Yang, Muer; Fry, Michael J; Raikhelkar, Jayashree; Chin, Cynthia; Anyanwu, Anelechi; Brand, Jordan; Scurlock, Corey

2013-02-01

To develop queuing and simulation-based models to understand the relationship between ICU bed availability and operating room schedule to maximize the use of critical care resources and minimize case cancellation while providing equity to patients and surgeons. Retrospective analysis of 6-month unit admission data from a cohort of cardiothoracic surgical patients, to create queuing and simulation-based models of ICU bed flow. Three different admission policies (current admission policy, shortest-processing-time policy, and a dynamic policy) were then analyzed using simulation models, representing 10 yr worth of potential admissions. Important output data consisted of the "average waiting time," a proxy for unit efficiency, and the "maximum waiting time," a surrogate for patient equity. A cardiothoracic surgical ICU in a tertiary center in New York, NY. Six hundred thirty consecutive cardiothoracic surgical patients admitted to the cardiothoracic surgical ICU. None. Although the shortest-processing-time admission policy performs best in terms of unit efficiency (0.4612 days), it did so at expense of patient equity prolonging surgical waiting time by as much as 21 days. The current policy gives the greatest equity but causes inefficiency in unit bed-flow (0.5033 days). The dynamic policy performs at a level (0.4997 days) 8.3% below that of the shortest-processing-time in average waiting time; however, it balances this with greater patient equity (maximum waiting time could be shortened by 4 days compared to the current policy). Queuing theory and computer simulation can be used to model case flow through a cardiothoracic operating room and ICU. A dynamic admission policy that looks at current waiting time and expected ICU length of stay allows for increased equity between patients with only minimum losses of efficiency. This dynamic admission policy would seem to be a superior in maximizing case-flow. These results may be generalized to other surgical ICUs.

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

Ilieva, N., E-mail: nevena.ilieva@parallel.bas.bg; Dai, J., E-mail: daijing491@gmail.com; Sieradzan, A., E-mail: adams86@wp.pl

Protein folding [1] is the process of formation of a functional 3D structure from a random coil — the shape in which amino-acid chains leave the ribosome. Anfinsen’s dogma states that the native 3D shape of a protein is completely determined by protein’s amino acid sequence. Despite the progress in understanding the process rate and the success in folding prediction for some small proteins, with presently available physics-based methods it is not yet possible to reliably deduce the shape of a biologically active protein from its amino acid sequence. The protein-folding problem endures as one of the most important unresolvedmore » problems in science; it addresses the origin of life itself. Furthermore, a wrong fold is a common cause for a protein to lose its function or even endanger the living organism. Soliton solutions of a generalized discrete non-linear Schrödinger equation (GDNLSE) obtained from the energy function in terms of bond and torsion angles κ and τ provide a constructive theoretical framework for describing protein folds and folding patterns [2]. Here we study the dynamics of this process by means of molecular-dynamics simulations. The soliton manifestation is the pattern helix–loop–helix in the secondary structure of the protein, which explains the importance of understanding loop formation in helical proteins. We performed in silico experiments for unfolding one subunit of the core structure of gp41 from the HIV envelope glycoprotein (PDB ID: 1AIK [3]) by molecular-dynamics simulations with the MD package GROMACS. We analyzed 80 ns trajectories, obtained with one united-atom and two different all-atom force fields, to justify the side-chain orientation quantification scheme adopted in the studies and to eliminate force-field based artifacts. Our results are compatible with the soliton model of protein folding and provide first insight into soliton-formation dynamics.« less

Solitons and protein folding: An In Silico experiment

NASA Astrophysics Data System (ADS)

Ilieva, N.; Dai, J.; Sieradzan, A.; Niemi, A.

2015-10-01

Protein folding [1] is the process of formation of a functional 3D structure from a random coil — the shape in which amino-acid chains leave the ribosome. Anfinsen's dogma states that the native 3D shape of a protein is completely determined by protein's amino acid sequence. Despite the progress in understanding the process rate and the success in folding prediction for some small proteins, with presently available physics-based methods it is not yet possible to reliably deduce the shape of a biologically active protein from its amino acid sequence. The protein-folding problem endures as one of the most important unresolved problems in science; it addresses the origin of life itself. Furthermore, a wrong fold is a common cause for a protein to lose its function or even endanger the living organism. Soliton solutions of a generalized discrete non-linear Schrödinger equation (GDNLSE) obtained from the energy function in terms of bond and torsion angles κ and τ provide a constructive theoretical framework for describing protein folds and folding patterns [2]. Here we study the dynamics of this process by means of molecular-dynamics simulations. The soliton manifestation is the pattern helix-loop-helix in the secondary structure of the protein, which explains the importance of understanding loop formation in helical proteins. We performed in silico experiments for unfolding one subunit of the core structure of gp41 from the HIV envelope glycoprotein (PDB ID: 1AIK [3]) by molecular-dynamics simulations with the MD package GROMACS. We analyzed 80 ns trajectories, obtained with one united-atom and two different all-atom force fields, to justify the side-chain orientation quantification scheme adopted in the studies and to eliminate force-field based artifacts. Our results are compatible with the soliton model of protein folding and provide first insight into soliton-formation dynamics.

Non-Markovian quantum feedback networks II: Controlled flows

NASA Astrophysics Data System (ADS)

Gough, John E.

2017-06-01

The concept of a controlled flow of a dynamical system, especially when the controlling process feeds information back about the system, is of central importance in control engineering. In this paper, we build on the ideas presented by Bouten and van Handel [Quantum Stochastics and Information: Statistics, Filtering and Control (World Scientific, 2008)] and develop a general theory of quantum feedback. We elucidate the relationship between the controlling processes, Z, and the measured processes, Y, and to this end we make a distinction between what we call the input picture and the output picture. We should note that the input-output relations for the noise fields have additional terms not present in the standard theory but that the relationship between the control processes and measured processes themselves is internally consistent—we do this for the two main cases of quadrature measurement and photon-counting measurement. The theory is general enough to include a modulating filter which post-processes the measurement readout Y before returning to the system. This opens up the prospect of applying very general engineering feedback control techniques to open quantum systems in a systematic manner, and we consider a number of specific modulating filter problems. Finally, we give a brief argument as to why most of the rules for making instantaneous feedback connections [J. Gough and M. R. James, Commun. Math. Phys. 287, 1109 (2009)] ought to apply for controlled dynamical networks as well.

GOBF-ARMA based model predictive control for an ideal reactive distillation column.

PubMed

Seban, Lalu; Kirubakaran, V; Roy, B K; Radhakrishnan, T K

2015-11-01

This paper discusses the control of an ideal reactive distillation column (RDC) using model predictive control (MPC) based on a combination of deterministic generalized orthonormal basis filter (GOBF) and stochastic autoregressive moving average (ARMA) models. Reactive distillation (RD) integrates reaction and distillation in a single process resulting in process and energy integration promoting green chemistry principles. Improved selectivity of products, increased conversion, better utilization and control of reaction heat, scope for difficult separations and the avoidance of azeotropes are some of the advantages that reactive distillation offers over conventional technique of distillation column after reactor. The introduction of an in situ separation in the reaction zone leads to complex interactions between vapor-liquid equilibrium, mass transfer rates, diffusion and chemical kinetics. RD with its high order and nonlinear dynamics, and multiple steady states is a good candidate for testing and verification of new control schemes. Here a combination of GOBF-ARMA models is used to catch and represent the dynamics of the RDC. This GOBF-ARMA model is then used to design an MPC scheme for the control of product purity of RDC under different operating constraints and conditions. The performance of proposed modeling and control using GOBF-ARMA based MPC is simulated and analyzed. The proposed controller is found to perform satisfactorily for reference tracking and disturbance rejection in RDC. Copyright © 2015 Elsevier Inc. All rights reserved.

Studying non-equilibrium many-body dynamics using one-dimensional Bose gases

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

Langen, Tim; Gring, Michael; Kuhnert, Maximilian

2014-12-04

Non-equilibrium dynamics of isolated quantum many-body systems play an important role in many areas of physics. However, a general answer to the question of how these systems relax is still lacking. We experimentally study the dynamics of ultracold one-dimensional (1D) Bose gases. This reveals the existence of a quasi-steady prethermalized state which differs significantly from the thermal equilibrium of the system. Our results demonstrate that the dynamics of non-equilibrium quantum many-body systems is a far richer process than has been assumed in the past.

From microscopic rules to macroscopic dynamics with active colloidal snakes

NASA Astrophysics Data System (ADS)

Zhang, Jie; Yan, Jing; Granick, Steve

Seeking to learn about self-assembly far from equilibrium, these imaging experiments inspect self-propelled colloidal particles whose heads and tails attract other particles reversibly as they swim. We observe processes akin to polymerization (short times) and chain scission and recombination (long times). The steady-state of dilute systems consists of discrete rings rotating in place with largely quenched dynamics, but when concentration is high, the system dynamics share features with turbulence. The dynamical rules of this model system appear to be scale-independent and hence potentially relevant more generally.

The Teaching-Learning Environment, an Information-Dynamic Approach

ERIC Educational Resources Information Center

De Blasio, Cataldo; Järvinen, Mika

2014-01-01

In the present study a generalized approach is given for the description of acquisition procedures with a particular focus on the knowledge acquisition process. The learning progression is given as an example here letting the theory to be applied to different situations. An analytical approach is performed starting from the generalized fundamental…

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run.

PubMed

Armeanu, Daniel; Andrei, Jean Vasile; Lache, Leonard; Panait, Mirela

2017-01-01

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets.

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run

PubMed Central

Armeanu, Daniel; Lache, Leonard; Panait, Mirela

2017-01-01

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets. PMID:28742100

Flux-split algorithms for flows with non-equilibrium chemistry and vibrational relaxation

NASA Technical Reports Server (NTRS)

Grossman, B.; Cinnella, P.

1990-01-01

The present consideration of numerical computation methods for gas flows with nonequilibrium chemistry thermodynamics gives attention to an equilibrium model, a general nonequilibrium model, and a simplified model based on vibrational relaxation. Flux-splitting procedures are developed for the fully-coupled inviscid equations encompassing fluid dynamics and both chemical and internal energy-relaxation processes. A fully coupled and implicit large-block structure is presented which embodies novel forms of flux-vector split and flux-difference split algorithms valid for nonequilibrium flow; illustrative high-temperature shock tube and nozzle flow examples are given.

Authoring Issues beyond Tools

NASA Astrophysics Data System (ADS)

Spierling, Ulrike; Szilas, Nicolas

Authoring is still considered a bottleneck in successful Interactive Storytelling and Drama. The claim for intuitive authoring tools is high, especially for tools that allow storytellers and artists to define dynamic content that can be run with an AI-based story engine. We explored two concrete authoring processes in depth, using various Interactive Storytelling prototypes, and have provided feedback from the practical steps. The result is a presentation of general issues in authoring Interactive Storytelling, rather than of particular problems with a specific system that could be overcome by 'simply' designing the right interface. Priorities for future developments are also outlined.

Nice Guys Finish Fast and Bad Guys Finish Last: Facilitatory vs. Inhibitory Interaction in Parallel Systems

PubMed Central

Eidels, Ami; Houpt, Joseph W.; Altieri, Nicholas; Pei, Lei; Townsend, James T.

2011-01-01

Systems Factorial Technology is a powerful framework for investigating the fundamental properties of human information processing such as architecture (i.e., serial or parallel processing) and capacity (how processing efficiency is affected by increased workload). The Survivor Interaction Contrast (SIC) and the Capacity Coefficient are effective measures in determining these underlying properties, based on response-time data. Each of the different architectures, under the assumption of independent processing, predicts a specific form of the SIC along with some range of capacity. In this study, we explored SIC predictions of discrete-state (Markov process) and continuous-state (Linear Dynamic) models that allow for certain types of cross-channel interaction. The interaction can be facilitatory or inhibitory: one channel can either facilitate, or slow down processing in its counterpart. Despite the relative generality of these models, the combination of the architecture-oriented plus the capacity oriented analyses provide for precise identification of the underlying system. PMID:21516183

Nice Guys Finish Fast and Bad Guys Finish Last: Facilitatory vs. Inhibitory Interaction in Parallel Systems.

PubMed

Eidels, Ami; Houpt, Joseph W; Altieri, Nicholas; Pei, Lei; Townsend, James T

2011-04-01

Systems Factorial Technology is a powerful framework for investigating the fundamental properties of human information processing such as architecture (i.e., serial or parallel processing) and capacity (how processing efficiency is affected by increased workload). The Survivor Interaction Contrast (SIC) and the Capacity Coefficient are effective measures in determining these underlying properties, based on response-time data. Each of the different architectures, under the assumption of independent processing, predicts a specific form of the SIC along with some range of capacity. In this study, we explored SIC predictions of discrete-state (Markov process) and continuous-state (Linear Dynamic) models that allow for certain types of cross-channel interaction. The interaction can be facilitatory or inhibitory: one channel can either facilitate, or slow down processing in its counterpart. Despite the relative generality of these models, the combination of the architecture-oriented plus the capacity oriented analyses provide for precise identification of the underlying system.

The influence of interspecific interactions on species range expansion rates

USGS Publications Warehouse

Svenning, Jens-Christian; Gravel, Dominique; Holt, Robert D.; Schurr, Frank M.; Thuiller, Wilfried; Münkemüller, Tamara; Schiffers, Katja H.; Dullinger, Stefan; Edwards, Thomas C.; Hickler, Thomas; Higgins, Steven I.; Nabel, Julia E.M.S.; Pagel, Jörn; Normand, Signe

2014-01-01

Ongoing and predicted global change makes understanding and predicting species’ range shifts an urgent scientific priority. Here, we provide a synthetic perspective on the so far poorly understood effects of interspecific interactions on range expansion rates. We present theoretical foundations for how interspecific interactions may modulate range expansion rates, consider examples from empirical studies of biological invasions and natural range expansions as well as process-based simulations, and discuss how interspecific interactions can be more broadly represented in process-based, spatiotemporally explicit range forecasts. Theory tells us that interspecific interactions affect expansion rates via alteration of local population growth rates and spatial displacement rates, but also via effects on other demographic parameters. The best empirical evidence for interspecific effects on expansion rates comes from studies of biological invasions. Notably, invasion studies indicate that competitive dominance and release from specialized enemies can enhance expansion rates. Studies of natural range expansions especially point to the potential for competition from resident species to reduce expansion rates. Overall, it is clear that interspecific interactions may have important consequences for range dynamics, but also that their effects have received too little attention to robustly generalize on their importance. We then discuss how interspecific interactions effects can be more widely incorporated in dynamic modeling of range expansions. Importantly, models must describe spatiotemporal variation in both local population dynamics and dispersal. Finally, we derive the following guidelines for when it is particularly important to explicitly represent interspecific interactions in dynamic range expansion forecasts: if most interacting species show correlated spatial or temporal trends in their effects on the target species, if the number of interacting species is low, and if the abundance of one or more strongly interacting species is not closely linked to the abundance of the target species.

The influence of interspecific interactions on species range expansion rates.

PubMed

Svenning, Jens-Christian; Gravel, Dominique; Holt, Robert D; Schurr, Frank M; Thuiller, Wilfried; Münkemüller, Tamara; Schiffers, Katja H; Dullinger, Stefan; Edwards, Thomas C; Hickler, Thomas; Higgins, Steven I; Nabel, Julia E M S; Pagel, Jörn; Normand, Signe

2014-12-01

Ongoing and predicted global change makes understanding and predicting species' range shifts an urgent scientific priority. Here, we provide a synthetic perspective on the so far poorly understood effects of interspecific interactions on range expansion rates. We present theoretical foundations for how interspecific interactions may modulate range expansion rates, consider examples from empirical studies of biological invasions and natural range expansions as well as process-based simulations, and discuss how interspecific interactions can be more broadly represented in process-based, spatiotemporally explicit range forecasts. Theory tells us that interspecific interactions affect expansion rates via alteration of local population growth rates and spatial displacement rates, but also via effects on other demographic parameters. The best empirical evidence for interspecific effects on expansion rates comes from studies of biological invasions. Notably, invasion studies indicate that competitive dominance and release from specialized enemies can enhance expansion rates. Studies of natural range expansions especially point to the potential for competition from resident species to reduce expansion rates. Overall, it is clear that interspecific interactions may have important consequences for range dynamics, but also that their effects have received too little attention to robustly generalize on their importance. We then discuss how interspecific interactions effects can be more widely incorporated in dynamic modeling of range expansions. Importantly, models must describe spatiotemporal variation in both local population dynamics and dispersal. Finally, we derive the following guidelines for when it is particularly important to explicitly represent interspecific interactions in dynamic range expansion forecasts: if most interacting species show correlated spatial or temporal trends in their effects on the target species, if the number of interacting species is low, and if the abundance of one or more strongly interacting species is not closely linked to the abundance of the target species.

The influence of interspecific interactions on species range expansion rates

PubMed Central

Svenning, Jens-Christian; Gravel, Dominique; Holt, Robert D.; Schurr, Frank M.; Thuiller, Wilfried; Münkemüller, Tamara; Schiffers, Katja H.; Dullinger, Stefan; Edwards, Thomas C.; Hickler, Thomas; Higgins, Steven I.; Nabel, Julia E. M. S.; Pagel, Jörn; Normand, Signe

2014-01-01

Ongoing and predicted global change makes understanding and predicting species’ range shifts an urgent scientific priority. Here, we provide a synthetic perspective on the so far poorly understood effects of interspecific interactions on range expansion rates. We present theoretical foundations for how interspecific interactions may modulate range expansion rates, consider examples from empirical studies of biological invasions and natural range expansions as well as process-based simulations, and discuss how interspecific interactions can be more broadly represented in process-based, spatiotemporally explicit range forecasts. Theory tells us that interspecific interactions affect expansion rates via alteration of local population growth rates and spatial displacement rates, but also via effects on other demographic parameters. The best empirical evidence for interspecific effects on expansion rates comes from studies of biological invasions. Notably, invasion studies indicate that competitive dominance and release from specialized enemies can enhance expansion rates. Studies of natural range expansions especially point to the potential for competition from resident species to reduce expansion rates. Overall, it is clear that interspecific interactions may have important consequences for range dynamics, but also that their effects have received too little attention to robustly generalize on their importance. We then discuss how interspecific interactions effects can be more widely incorporated in dynamic modeling of range expansions. Importantly, models must describe spatiotemporal variation in both local population dynamics and dispersal. Finally, we derive the following guidelines for when it is particularly important to explicitly represent interspecific interactions in dynamic range expansion forecasts: if most interacting species show correlated spatial or temporal trends in their effects on the target species, if the number of interacting species is low, and if the abundance of one or more strongly interacting species is not closely linked to the abundance of the target species. PMID:25722537

Real-time stereo matching using orthogonal reliability-based dynamic programming.

PubMed

Gong, Minglun; Yang, Yee-Hong

2007-03-01

A novel algorithm is presented in this paper for estimating reliable stereo matches in real time. Based on the dynamic programming-based technique we previously proposed, the new algorithm can generate semi-dense disparity maps using as few as two dynamic programming passes. The iterative best path tracing process used in traditional dynamic programming is replaced by a local minimum searching process, making the algorithm suitable for parallel execution. Most computations are implemented on programmable graphics hardware, which improves the processing speed and makes real-time estimation possible. The experiments on the four new Middlebury stereo datasets show that, on an ATI Radeon X800 card, the presented algorithm can produce reliable matches for 60% approximately 80% of pixels at the rate of 10 approximately 20 frames per second. If needed, the algorithm can be configured for generating full density disparity maps.

Adiabatic reduction of a model of stochastic gene expression with jump Markov process.

PubMed

Yvinec, Romain; Zhuge, Changjing; Lei, Jinzhi; Mackey, Michael C

2014-04-01

This paper considers adiabatic reduction in a model of stochastic gene expression with bursting transcription considered as a jump Markov process. In this model, the process of gene expression with auto-regulation is described by fast/slow dynamics. The production of mRNA is assumed to follow a compound Poisson process occurring at a rate depending on protein levels (the phenomena called bursting in molecular biology) and the production of protein is a linear function of mRNA numbers. When the dynamics of mRNA is assumed to be a fast process (due to faster mRNA degradation than that of protein) we prove that, with appropriate scalings in the burst rate, jump size or translational rate, the bursting phenomena can be transmitted to the slow variable. We show that, depending on the scaling, the reduced equation is either a stochastic differential equation with a jump Poisson process or a deterministic ordinary differential equation. These results are significant because adiabatic reduction techniques seem to have not been rigorously justified for a stochastic differential system containing a jump Markov process. We expect that the results can be generalized to adiabatic methods in more general stochastic hybrid systems.

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.

Optimization of the dressing parameters in cylindrical grinding based on a generalized utility function

NASA Astrophysics Data System (ADS)

Aleksandrova, Irina

2016-01-01

The existing studies, concerning the dressing process, focus on the major influence of the dressing conditions on the grinding response variables. However, the choice of the dressing conditions is often made, based on the experience of the qualified staff or using data from reference books. The optimal dressing parameters, which are only valid for the particular methods and dressing and grinding conditions, are also used. The paper presents a methodology for optimization of the dressing parameters in cylindrical grinding. The generalized utility function has been chosen as an optimization parameter. It is a complex indicator determining the economic, dynamic and manufacturing characteristics of the grinding process. The developed methodology is implemented for the dressing of aluminium oxide grinding wheels by using experimental diamond roller dressers with different grit sizes made of medium- and high-strength synthetic diamonds type ??32 and ??80. To solve the optimization problem, a model of the generalized utility function is created which reflects the complex impact of dressing parameters. The model is built based on the results from the conducted complex study and modeling of the grinding wheel lifetime, cutting ability, production rate and cutting forces during grinding. They are closely related to the dressing conditions (dressing speed ratio, radial in-feed of the diamond roller dresser and dress-out time), the diamond roller dresser grit size/grinding wheel grit size ratio, the type of synthetic diamonds and the direction of dressing. Some dressing parameters are determined for which the generalized utility function has a maximum and which guarantee an optimum combination of the following: the lifetime and cutting ability of the abrasive wheels, the tangential cutting force magnitude and the production rate of the grinding process. The results obtained prove the possibility of control and optimization of grinding by selecting particular dressing parameters.

Economic communication model set

NASA Astrophysics Data System (ADS)

Zvereva, Olga M.; Berg, Dmitry B.

2017-06-01

This paper details findings from the research work targeted at economic communications investigation with agent-based models usage. The agent-based model set was engineered to simulate economic communications. Money in the form of internal and external currencies was introduced into the models to support exchanges in communications. Every model, being based on the general concept, has its own peculiarities in algorithm and input data set since it was engineered to solve the specific problem. Several and different origin data sets were used in experiments: theoretic sets were estimated on the basis of static Leontief's equilibrium equation and the real set was constructed on the basis of statistical data. While simulation experiments, communication process was observed in dynamics, and system macroparameters were estimated. This research approved that combination of an agent-based and mathematical model can cause a synergetic effect.

The historical dynamics of social-ecological traps.

PubMed

Boonstra, Wiebren J; de Boer, Florianne W

2014-04-01

Environmental degradation is a typical unintended outcome of collective human behavior. Hardin's metaphor of the "tragedy of the commons" has become a conceived wisdom that captures the social dynamics leading to environmental degradation. Recently, "traps" has gained currency as an alternative concept to explain the rigidity of social and ecological processes that produce environmental degradation and livelihood impoverishment. The trap metaphor is, however, a great deal more complex compared to Hardin's insight. This paper takes stock of studies using the trap metaphor. It argues that the concept includes time and history in the analysis, but only as background conditions and not as a factor of causality. From a historical-sociological perspective this is remarkable since social-ecological traps are clearly path-dependent processes, which are causally produced through a conjunction of events. To prove this point the paper conceptualizes social-ecological traps as a process instead of a condition, and systematically compares history and timing in one classic and three recent studies of social-ecological traps. Based on this comparison it concludes that conjunction of social and environmental events contributes profoundly to the production of trap processes. The paper further discusses the implications of this conclusion for policy intervention and outlines how future research might generalize insights from historical-sociological studies of traps.

Intrusive and Non-Intrusive Instruction in Dynamic Skill Training.

DTIC Science & Technology

1981-10-01

less sensitive to the processing load imposed by the dynaic task together with instructional feedback processing than were the decison - making and...betwee computer based instruction of knowledge systems and computer based instruction of dynamic skills. There is reason to expect that the findings of...knowledge 3Ytm and computer based instruction of dynlamic skill.. There is reason to expect that the findings of research on knowledge system

Generalized trajectory surface-hopping method for internal conversion and intersystem crossing

NASA Astrophysics Data System (ADS)

Cui, Ganglong; Thiel, Walter

2014-09-01

Trajectory-based fewest-switches surface-hopping (FSSH) dynamics simulations have become a popular and reliable theoretical tool to simulate nonadiabatic photophysical and photochemical processes. Most available FSSH methods model internal conversion. We present a generalized trajectory surface-hopping (GTSH) method for simulating both internal conversion and intersystem crossing processes on an equal footing. We consider hops between adiabatic eigenstates of the non-relativistic electronic Hamiltonian (pure spin states), which is appropriate for sufficiently small spin-orbit coupling. This choice allows us to make maximum use of existing electronic structure programs and to minimize the changes to available implementations of the traditional FSSH method. The GTSH method is formulated within the quantum mechanics (QM)/molecular mechanics framework, but can of course also be applied at the pure QM level. The algorithm implemented in the GTSH code is specified step by step. As an initial GTSH application, we report simulations of the nonadiabatic processes in the lowest four electronic states (S0, S1, T1, and T2) of acrolein both in vacuo and in acetonitrile solution, in which the acrolein molecule is treated at the ab initio complete-active-space self-consistent-field level. These dynamics simulations provide detailed mechanistic insight by identifying and characterizing two nonadiabatic routes to the lowest triplet state, namely, direct S1 → T1 hopping as major pathway and sequential S1 → T2 → T1 hopping as minor pathway, with the T2 state acting as a relay state. They illustrate the potential of the GTSH approach to explore photoinduced processes in complex systems, in which intersystem crossing plays an important role.

Stochastic assembly in a subtropical forest chronosequence: evidence from contrasting changes of species, phylogenetic and functional dissimilarity over succession.

PubMed

Mi, Xiangcheng; Swenson, Nathan G; Jia, Qi; Rao, Mide; Feng, Gang; Ren, Haibao; Bebber, Daniel P; Ma, Keping

2016-09-07

Deterministic and stochastic processes jointly determine the community dynamics of forest succession. However, it has been widely held in previous studies that deterministic processes dominate forest succession. Furthermore, inference of mechanisms for community assembly may be misleading if based on a single axis of diversity alone. In this study, we evaluated the relative roles of deterministic and stochastic processes along a disturbance gradient by integrating species, functional, and phylogenetic beta diversity in a subtropical forest chronosequence in Southeastern China. We found a general pattern of increasing species turnover, but little-to-no change in phylogenetic and functional turnover over succession at two spatial scales. Meanwhile, the phylogenetic and functional beta diversity were not significantly different from random expectation. This result suggested a dominance of stochastic assembly, contrary to the general expectation that deterministic processes dominate forest succession. On the other hand, we found significant interactions of environment and disturbance and limited evidence for significant deviations of phylogenetic or functional turnover from random expectations for different size classes. This result provided weak evidence of deterministic processes over succession. Stochastic assembly of forest succession suggests that post-disturbance restoration may be largely unpredictable and difficult to control in subtropical forests.

Genetic algorithm dynamics on a rugged landscape

NASA Astrophysics Data System (ADS)

Bornholdt, Stefan

1998-04-01

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.