On the Limiting Markov Process of Energy Exchanges in a Rarely Interacting Ball-Piston Gas

NASA Astrophysics Data System (ADS)

Bálint, Péter; Gilbert, Thomas; Nándori, Péter; Szász, Domokos; Tóth, Imre Péter

2017-02-01

We analyse the process of energy exchanges generated by the elastic collisions between a point-particle, confined to a two-dimensional cell with convex boundaries, and a `piston', i.e. a line-segment, which moves back and forth along a one-dimensional interval partially intersecting the cell. This model can be considered as the elementary building block of a spatially extended high-dimensional billiard modeling heat transport in a class of hybrid materials exhibiting the kinetics of gases and spatial structure of solids. Using heuristic arguments and numerical analysis, we argue that, in a regime of rare interactions, the billiard process converges to a Markov jump process for the energy exchanges and obtain the expression of its generator.

Passive synchronization for Markov jump genetic oscillator networks with time-varying delays.

PubMed

Lu, Li; He, Bing; Man, Chuntao; Wang, Shun

2015-04-01

In this paper, the synchronization problem of coupled Markov jump genetic oscillator networks with time-varying delays and external disturbances is investigated. By introducing the drive-response concept, a novel mode-dependent control scheme is proposed, which guarantees that the synchronization can be achieved. By applying the Lyapunov-Krasovskii functional method and stochastic analysis, sufficient conditions are established based on passivity theory in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of our theoretical results. Copyright © 2015 Elsevier Inc. All rights reserved.

Markov Jump-Linear Performance Models for Recoverable Flight Control Computers

NASA Technical Reports Server (NTRS)

Zhang, Hong; Gray, W. Steven; Gonzalez, Oscar R.

2004-01-01

Single event upsets in digital flight control hardware induced by atmospheric neutrons can reduce system performance and possibly introduce a safety hazard. One method currently under investigation to help mitigate the effects of these upsets is NASA Langley s Recoverable Computer System. In this paper, a Markov jump-linear model is developed for a recoverable flight control system, which will be validated using data from future experiments with simulated and real neutron environments. The method of tracking error analysis and the plan for the experiments are also described.

A variational method for analyzing limit cycle oscillations in stochastic hybrid systems

NASA Astrophysics Data System (ADS)

Bressloff, Paul C.; MacLaurin, James

2018-06-01

Many systems in biology can be modeled through ordinary differential equations, which are piece-wise continuous, and switch between different states according to a Markov jump process known as a stochastic hybrid system or piecewise deterministic Markov process (PDMP). In the fast switching limit, the dynamics converges to a deterministic ODE. In this paper, we develop a phase reduction method for stochastic hybrid systems that support a stable limit cycle in the deterministic limit. A classic example is the Morris-Lecar model of a neuron, where the switching Markov process is the number of open ion channels and the continuous process is the membrane voltage. We outline a variational principle for the phase reduction, yielding an exact analytic expression for the resulting phase dynamics. We demonstrate that this decomposition is accurate over timescales that are exponential in the switching rate ɛ-1 . That is, we show that for a constant C, the probability that the expected time to leave an O(a) neighborhood of the limit cycle is less than T scales as T exp (-C a /ɛ ) .

Stochastic thermodynamics across scales: Emergent inter-attractoral discrete Markov jump process and its underlying continuous diffusion

NASA Astrophysics Data System (ADS)

Santillán, Moisés; Qian, Hong

2013-01-01

We investigate the internal consistency of a recently developed mathematical thermodynamic structure across scales, between a continuous stochastic nonlinear dynamical system, i.e., a diffusion process with Langevin and Fokker-Planck equations, and its emergent discrete, inter-attractoral Markov jump process. We analyze how the system’s thermodynamic state functions, e.g. free energy F, entropy S, entropy production ep, free energy dissipation Ḟ, etc., are related when the continuous system is described with coarse-grained discrete variables. It is shown that the thermodynamics derived from the underlying, detailed continuous dynamics gives rise to exactly the free-energy representation of Gibbs and Helmholtz. That is, the system’s thermodynamic structure is the same as if one only takes a middle road and starts with the natural discrete description, with the corresponding transition rates empirically determined. By natural we mean in the thermodynamic limit of a large system, with an inherent separation of time scales between inter- and intra-attractoral dynamics. This result generalizes a fundamental idea from chemistry, and the theory of Kramers, by incorporating thermodynamics: while a mechanical description of a molecule is in terms of continuous bond lengths and angles, chemical reactions are phenomenologically described by a discrete representation, in terms of exponential rate laws and a stochastic thermodynamics.

Strong diffusion formulation of Markov chain ensembles and its optimal weaker reductions

NASA Astrophysics Data System (ADS)

Güler, Marifi

2017-10-01

Two self-contained diffusion formulations, in the form of coupled stochastic differential equations, are developed for the temporal evolution of state densities over an ensemble of Markov chains evolving independently under a common transition rate matrix. Our first formulation derives from Kurtz's strong approximation theorem of density-dependent Markov jump processes [Stoch. Process. Their Appl. 6, 223 (1978), 10.1016/0304-4149(78)90020-0] and, therefore, strongly converges with an error bound of the order of lnN /N for ensemble size N . The second formulation eliminates some fluctuation variables, and correspondingly some noise terms, within the governing equations of the strong formulation, with the objective of achieving a simpler analytic formulation and a faster computation algorithm when the transition rates are constant or slowly varying. There, the reduction of the structural complexity is optimal in the sense that the elimination of any given set of variables takes place with the lowest attainable increase in the error bound. The resultant formulations are supported by numerical simulations.

Markov switching of the electricity supply curve and power prices dynamics

NASA Astrophysics Data System (ADS)

Mari, Carlo; Cananà, Lucianna

2012-02-01

Regime-switching models seem to well capture the main features of power prices behavior in deregulated markets. In a recent paper, we have proposed an equilibrium methodology to derive electricity prices dynamics from the interplay between supply and demand in a stochastic environment. In particular, assuming that the supply function is described by a power law where the exponent is a two-state strictly positive Markov process, we derived a regime switching dynamics of power prices in which regime switches are induced by transitions between Markov states. In this paper, we provide a dynamical model to describe the random behavior of power prices where the only non-Brownian component of the motion is endogenously introduced by Markov transitions in the exponent of the electricity supply curve. In this context, the stochastic process driving the switching mechanism becomes observable, and we will show that the non-Brownian component of the dynamics induced by transitions from Markov states is responsible for jumps and spikes of very high magnitude. The empirical analysis performed on three Australian markets confirms that the proposed approach seems quite flexible and capable of incorporating the main features of power prices time-series, thus reproducing the first four moments of log-returns empirical distributions in a satisfactory way.

American option pricing in Gauss-Markov interest rate models

NASA Astrophysics Data System (ADS)

Galluccio, Stefano

1999-07-01

In the context of Gaussian non-homogeneous interest-rate models, we study the problem of American bond option pricing. In particular, we show how to efficiently compute the exercise boundary in these models in order to decompose the price as a sum of a European option and an American premium. Generalizations to coupon-bearing bonds and jump-diffusion processes for the interest rates are also discussed.

Hybrid stochastic simplifications for multiscale gene networks.

PubMed

Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu

2009-09-07

Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Hybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

Development of reversible jump Markov Chain Monte Carlo algorithm in the Bayesian mixture modeling for microarray data in Indonesia

NASA Astrophysics Data System (ADS)

Astuti, Ani Budi; Iriawan, Nur; Irhamah, Kuswanto, Heri

2017-12-01

In the Bayesian mixture modeling requires stages the identification number of the most appropriate mixture components thus obtained mixture models fit the data through data driven concept. Reversible Jump Markov Chain Monte Carlo (RJMCMC) is a combination of the reversible jump (RJ) concept and the Markov Chain Monte Carlo (MCMC) concept used by some researchers to solve the problem of identifying the number of mixture components which are not known with certainty number. In its application, RJMCMC using the concept of the birth/death and the split-merge with six types of movement, that are w updating, θ updating, z updating, hyperparameter β updating, split-merge for components and birth/death from blank components. The development of the RJMCMC algorithm needs to be done according to the observed case. The purpose of this study is to know the performance of RJMCMC algorithm development in identifying the number of mixture components which are not known with certainty number in the Bayesian mixture modeling for microarray data in Indonesia. The results of this study represent that the concept RJMCMC algorithm development able to properly identify the number of mixture components in the Bayesian normal mixture model wherein the component mixture in the case of microarray data in Indonesia is not known for certain number.

Fluctuation theorem: A critical review

NASA Astrophysics Data System (ADS)

Malek Mansour, M.; Baras, F.

2017-10-01

Fluctuation theorem for entropy production is revisited in the framework of stochastic processes. The applicability of the fluctuation theorem to physico-chemical systems and the resulting stochastic thermodynamics were analyzed. Some unexpected limitations are highlighted in the context of jump Markov processes. We have shown that these limitations handicap the ability of the resulting stochastic thermodynamics to correctly describe the state of non-equilibrium systems in terms of the thermodynamic properties of individual processes therein. Finally, we considered the case of diffusion processes and proved that the fluctuation theorem for entropy production becomes irrelevant at the stationary state in the case of one variable systems.

Un formalisme de systemes a sauts pour la recirculation optimale des casses dans une machine a papier

NASA Astrophysics Data System (ADS)

Khanbaghi, Maryam

Increasing closure of white water circuits is making mill productivity and quality of paper produced increasingly affected by the occurrence of paper breaks. In this thesis the main objective is the development of white water and broke recirculation policies. The thesis consists of three main parts, respectively corresponding to the synthesis of a statistical model of paper breaks in a paper mill, the basic mathematical setup for the formulation of white water and broke recirculation policies in the mill as a jump linear quadratic regulation problem, and finally the tuning of the control law based on first passage-time theory, and its extension to the case of control sensitive paper break rates. More specifically, in the first part a statistical model of paper machine breaks is developed. We start from the hypothesis that the breaks process is a Markov chain with three states: the first state is the operational one, while the two others are associated with the general types of paper-breaks that can take place in the mill (wet breaks and dry breaks). The Markovian hypothesis is empirically validated. We also establish how paper-break rates are correlated with machine speed and broke recirculation ratio. Subsequently, we show how the obtained Markov chain model of paper-breaks can be used to formulate a machine operating speed parameter optimization problem. In the second part, upon recognizing that paper breaks can be modelled as a Markov chain type of process which, when interacting with the continuous mill dynamics, yields a jump Markov model, jump linear theory is proposed as a means of constructing white water and broke recirculation strategies which minimize process variability. Reduced process variability comes at the expense of relatively large swings in white water and broke tanks level. Since the linear design does not specifically account for constraints on the state-space, under the resulting law, damaging events of tank overflow or emptiness can occur. A heuristic simulation-based approach is proposed to choose the performance measure design parameters to keep the mean time between incidents of fluid in broke and white water tanks either overflowing, or reaching dangerously low levels, sufficiently long. In the third part, a methodology, mainly founded on the first passage-time theory of stochastic processes, is proposed to choose the performance measure design parameters to limit process variability while accounting for the possibility of undesirable tank overflows or tank emptiness. The heart of the approach is an approximation technique for evaluating mean first passage-times of the controlled tanks levels. This technique appears to have an applicability which largely exceeds the problem area it was designed for. Furthermore, the introduction of control sensitive break rates and the analysis of the ensuing control problem are presented. This is to account for the experimentally observed increase in breaks concomitant with flow rate variability.

Hybrid stochastic simplifications for multiscale gene networks

PubMed Central

Crudu, Alina; Debussche, Arnaud; Radulescu, Ovidiu

2009-01-01

Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion [1-3] which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered) approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach. PMID:19735554

Observer-based sliding mode control of Markov jump systems with random sensor delays and partly unknown transition rates

NASA Astrophysics Data System (ADS)

Yao, Deyin; Lu, Renquan; Xu, Yong; Ren, Hongru

2017-10-01

In this paper, the sliding mode control problem of Markov jump systems (MJSs) with unmeasured state, partly unknown transition rates and random sensor delays is probed. In the practical engineering control, the exact information of transition rates is hard to obtain and the measurement channel is supposed to subject to random sensor delay. Design a Luenberger observer to estimate the unmeasured system state, and an integral sliding mode surface is constructed to ensure the exponential stability of MJSs. A sliding mode controller based on estimator is proposed to drive the system state onto the sliding mode surface and render the sliding mode dynamics exponentially mean-square stable with H∞ performance index. Finally, simulation results are provided to illustrate the effectiveness of the proposed results.

Schrödinger problem, Lévy processes, and noise in relativistic quantum mechanics

NASA Astrophysics Data System (ADS)

Garbaczewski, Piotr; Klauder, John R.; Olkiewicz, Robert

1995-05-01

The main purpose of the paper is an essentially probabilistic analysis of relativistic quantum mechanics. It is based on the assumption that whenever probability distributions arise, there exists a stochastic process that is either responsible for the temporal evolution of a given measure or preserves the measure in the stationary case. Our departure point is the so-called Schrödinger problem of probabilistic evolution, which provides for a unique Markov stochastic interpolation between any given pair of boundary probability densities for a process covering a fixed, finite duration of time, provided we have decided a priori what kind of primordial dynamical semigroup transition mechanism is involved. In the nonrelativistic theory, including quantum mechanics, Feynman-Kac-like kernels are the building blocks for suitable transition probability densities of the process. In the standard ``free'' case (Feynman-Kac potential equal to zero) the familiar Wiener noise is recovered. In the framework of the Schrödinger problem, the ``free noise'' can also be extended to any infinitely divisible probability law, as covered by the Lévy-Khintchine formula. Since the relativistic Hamiltonians ||∇|| and √-Δ+m2 -m are known to generate such laws, we focus on them for the analysis of probabilistic phenomena, which are shown to be associated with the relativistic wave (D'Alembert) and matter-wave (Klein-Gordon) equations, respectively. We show that such stochastic processes exist and are spatial jump processes. In general, in the presence of external potentials, they do not share the Markov property, except for stationary situations. A concrete example of the pseudodifferential Cauchy-Schrödinger evolution is analyzed in detail. The relativistic covariance of related wave equations is exploited to demonstrate how the associated stochastic jump processes comply with the principles of special relativity.

Probability-based constrained MPC for structured uncertain systems with state and random input delays

NASA Astrophysics Data System (ADS)

Lu, Jianbo; Li, Dewei; Xi, Yugeng

2013-07-01

This article is concerned with probability-based constrained model predictive control (MPC) for systems with both structured uncertainties and time delays, where a random input delay and multiple fixed state delays are included. The process of input delay is governed by a discrete-time finite-state Markov chain. By invoking an appropriate augmented state, the system is transformed into a standard structured uncertain time-delay Markov jump linear system (MJLS). For the resulting system, a multi-step feedback control law is utilised to minimise an upper bound on the expected value of performance objective. The proposed design has been proved to stabilise the closed-loop system in the mean square sense and to guarantee constraints on control inputs and system states. Finally, a numerical example is given to illustrate the proposed results.

Detection of multiple airborne targets from multisensor data

NASA Astrophysics Data System (ADS)

Foltz, Mark A.; Srivastava, Anuj; Miller, Michael I.; Grenander, Ulf

1995-08-01

Previously we presented a jump-diffusion based random sampling algorithm for generating conditional mean estimates of scene representations for the tracking and recongition of maneuvering airborne targets. These representations include target positions and orientations along their trajectories and the target type associated with each trajectory. Taking a Bayesian approach, a posterior measure is defined on the parameter space by combining sensor models with a sophisticated prior based on nonlinear airplane dynamics. The jump-diffusion algorithm constructs a Markov process which visits the elements of the parameter space with frequencies proportional to the posterior probability. It consititutes both the infinitesimal, local search via a sample path continuous diffusion transform and the larger, global steps through discrete jump moves. The jump moves involve the addition and deletion of elements from the scene configuration or changes in the target type assoviated with each target trajectory. One such move results in target detection by the addition of a track seed to the inference set. This provides initial track data for the tracking/recognition algorithm to estimate linear graph structures representing tracks using the other jump moves and the diffusion process, as described in our earlier work. Target detection ideally involves a continuous research over a continuum of the observation space. In this work we conclude that for practical implemenations the search space must be discretized with lattice granularity comparable to sensor resolution, and discuss how fast Fourier transforms are utilized for efficient calcuation of sufficient statistics given our array models. Some results are also presented from our implementation on a networked system including a massively parallel machine architecture and a silicon graphics onyx workstation.

Bootstrapping Least Squares Estimates in Biochemical Reaction Networks

PubMed Central

Linder, Daniel F.

2015-01-01

The paper proposes new computational methods of computing confidence bounds for the least squares estimates (LSEs) of rate constants in mass-action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large volume limit of a reaction network, to network’s partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods. PMID:25898769

Stochastic volatility of the futures prices of emission allowances: A Bayesian approach

NASA Astrophysics Data System (ADS)

Kim, Jungmu; Park, Yuen Jung; Ryu, Doojin

2017-01-01

Understanding the stochastic nature of the spot volatility of emission allowances is crucial for risk management in emissions markets. In this study, by adopting a stochastic volatility model with or without jumps to represent the dynamics of European Union Allowances (EUA) futures prices, we estimate the daily volatilities and model parameters by using the Markov Chain Monte Carlo method for stochastic volatility (SV), stochastic volatility with return jumps (SVJ) and stochastic volatility with correlated jumps (SVCJ) models. Our empirical results reveal three important features of emissions markets. First, the data presented herein suggest that EUA futures prices exhibit significant stochastic volatility. Second, the leverage effect is noticeable regardless of whether or not jumps are included. Third, the inclusion of jumps has a significant impact on the estimation of the volatility dynamics. Finally, the market becomes very volatile and large jumps occur at the beginning of a new phase. These findings are important for policy makers and regulators.

Global exponential stability of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays.

PubMed

Huang, Haiying; Du, Qiaosheng; Kang, Xibing

2013-11-01

In this paper, a class of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays is investigated. The jumping parameters are modeled as a continuous-time finite-state Markov chain. At first, the existence of equilibrium point for the addressed neural networks is studied. By utilizing the Lyapunov stability theory, stochastic analysis theory and linear matrix inequality (LMI) technique, new delay-dependent stability criteria are presented in terms of linear matrix inequalities to guarantee the neural networks to be globally exponentially stable in the mean square. Numerical simulations are carried out to illustrate the main results. © 2013 ISA. Published by ISA. All rights reserved.

Mixed H2/H∞ pitch control of wind turbine with a Markovian jump model

NASA Astrophysics Data System (ADS)

Lin, Zhongwei; Liu, Jizhen; Wu, Qiuwei; Niu, Yuguang

2018-01-01

This paper proposes a Markovian jump model and the corresponding H2/H∞ control strategy for the wind turbine driven by the stochastic switching wind speed, which can be used to regulate the generator speed in order to harvest the rated power while reducing the fatigue loads on the mechanical side of wind turbine. Through sampling the low-frequency wind speed data into separate intervals, the stochastic characteristic of the steady wind speed can be represented as a Markov process, while the high-frequency wind speed in the each interval is regarded as the disturbance input. Then, the traditional operating points of wind turbine can be divided into separate subregions correspondingly, where the model parameters and the control mode can be fixed in each mode. Then, the mixed H2/H∞ control problem is discussed for such a class of Markovian jump wind turbine working above the rated wind speed to guarantee both the disturbance rejection and the mechanical loads objectives, which can reduce the power volatility and the generator torque fluctuation of the whole transmission mechanism efficiently. Simulation results for a 2 MW wind turbine show the effectiveness of the proposed method.

Design of robust reliable control for T-S fuzzy Markovian jumping delayed neutral type neural networks with probabilistic actuator faults and leakage delays: An event-triggered communication scheme.

PubMed

Syed Ali, M; Vadivel, R; Saravanakumar, R

2018-06-01

This study examines the problem of robust reliable control for Takagi-Sugeno (T-S) fuzzy Markovian jumping delayed neural networks with probabilistic actuator faults and leakage terms. An event-triggered communication scheme. First, the randomly occurring actuator faults and their failures rates are governed by two sets of unrelated random variables satisfying certain probabilistic failures of every actuator, new type of distribution based event triggered fault model is proposed, which utilize the effect of transmission delay. Second, Takagi-Sugeno (T-S) fuzzy model is adopted for the neural networks and the randomness of actuators failures is modeled in a Markov jump model framework. Third, to guarantee the considered closed-loop system is exponential mean square stable with a prescribed reliable control performance, a Markov jump event-triggered scheme is designed in this paper, which is the main purpose of our study. Fourth, by constructing appropriate Lyapunov-Krasovskii functional, employing Newton-Leibniz formulation and integral inequalities, several delay-dependent criteria for the solvability of the addressed problem are derived. The obtained stability criteria are stated in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Finally, numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results over the existing ones, among them one example was supported by real-life application of the benchmark problem. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Population density equations for stochastic processes with memory kernels

NASA Astrophysics Data System (ADS)

Lai, Yi Ming; de Kamps, Marc

2017-06-01

We present a method for solving population density equations (PDEs)-a mean-field technique describing homogeneous populations of uncoupled neurons—where the populations can be subject to non-Markov noise for arbitrary distributions of jump sizes. The method combines recent developments in two different disciplines that traditionally have had limited interaction: computational neuroscience and the theory of random networks. The method uses a geometric binning scheme, based on the method of characteristics, to capture the deterministic neurodynamics of the population, separating the deterministic and stochastic process cleanly. We can independently vary the choice of the deterministic model and the model for the stochastic process, leading to a highly modular numerical solution strategy. We demonstrate this by replacing the master equation implicit in many formulations of the PDE formalism by a generalization called the generalized Montroll-Weiss equation—a recent result from random network theory—describing a random walker subject to transitions realized by a non-Markovian process. We demonstrate the method for leaky- and quadratic-integrate and fire neurons subject to spike trains with Poisson and gamma-distributed interspike intervals. We are able to model jump responses for both models accurately to both excitatory and inhibitory input under the assumption that all inputs are generated by one renewal process.

Upscaling transport of a reacting solute through a peridocially converging-diverging channel at pre-asymptotic times

NASA Astrophysics Data System (ADS)

Sund, Nicole L.; Bolster, Diogo; Dawson, Clint

2015-11-01

In this study we extend the Spatial Markov model, which has been successfully used to upscale conservative transport across a diverse range of porous media flows, to test if it can accurately upscale reactive transport, defined by a spatially heterogeneous first order degradation rate. We test the model in a well known highly simplified geometry, commonly considered as an idealized pore or fracture structure, a periodic channel with wavy boundaries. The edges of the flow domain have a layer through which there is no flow, but in which diffusion of a solute still occurs. Reactions are confined to this region. We demonstrate that the Spatial Markov model, an upscaled random walk model that enforces correlation between successive jumps, can reproduce breakthrough curves measured from microscale simulations that explicitly resolve all pertinent processes. We also demonstrate that a similar random walk model that does not enforce successive correlations is unable to reproduce all features of the measured breakthrough curves.

Distributions-per-level: a means of testing level detectors and models of patch-clamp data.

PubMed

Schröder, I; Huth, T; Suitchmezian, V; Jarosik, J; Schnell, S; Hansen, U P

2004-01-01

Level or jump detectors generate the reconstructed time series from a noisy record of patch-clamp current. The reconstructed time series is used to create dwell-time histograms for the kinetic analysis of the Markov model of the investigated ion channel. It is shown here that some additional lines in the software of such a detector can provide a powerful new means of patch-clamp analysis. For each current level that can be recognized by the detector, an array is declared. The new software assigns every data point of the original time series to the array that belongs to the actual state of the detector. From the data sets in these arrays distributions-per-level are generated. Simulated and experimental time series analyzed by Hinkley detectors are used to demonstrate the benefits of these distributions-per-level. First, they can serve as a test of the reliability of jump and level detectors. Second, they can reveal beta distributions as resulting from fast gating that would usually be hidden in the overall amplitude histogram. Probably the most valuable feature is that the malfunctions of the Hinkley detectors turn out to depend on the Markov model of the ion channel. Thus, the errors revealed by the distributions-per-level can be used to distinguish between different putative Markov models of the measured time series.

Lévy processes on a generalized fractal comb

NASA Astrophysics Data System (ADS)

Sandev, Trifce; Iomin, Alexander; Méndez, Vicenç

2016-09-01

Comb geometry, constituted of a backbone and fingers, is one of the most simple paradigm of a two-dimensional structure, where anomalous diffusion can be realized in the framework of Markov processes. However, the intrinsic properties of the structure can destroy this Markovian transport. These effects can be described by the memory and spatial kernels. In particular, the fractal structure of the fingers, which is controlled by the spatial kernel in both the real and the Fourier spaces, leads to the Lévy processes (Lévy flights) and superdiffusion. This generalization of the fractional diffusion is described by the Riesz space fractional derivative. In the framework of this generalized fractal comb model, Lévy processes are considered, and exact solutions for the probability distribution functions are obtained in terms of the Fox H-function for a variety of the memory kernels, and the rate of the superdiffusive spreading is studied by calculating the fractional moments. For a special form of the memory kernels, we also observed a competition between long rests and long jumps. Finally, we considered the fractal structure of the fingers controlled by a Weierstrass function, which leads to the power-law kernel in the Fourier space. This is a special case, when the second moment exists for superdiffusion in this competition between long rests and long jumps.

Automatic crown cover mapping to improve forest inventory

Treesearch

Claude Vidal; Jean-Guy Boureau; Nicolas Robert; Nicolas Py; Josiane Zerubia; Xavier Descombes; Guillaume Perrin

2009-01-01

To automatically analyze near infrared aerial photographs, the French National Institute for Research in Computer Science and Control developed together with the French National Forest Inventory (NFI) a method for automatic crown cover mapping. This method uses a Reverse Jump Monte Carlo Markov Chain algorithm to locate the crowns and describe those using ellipses or...

A new look at the robust control of discrete-time Markov jump linear systems

NASA Astrophysics Data System (ADS)

Todorov, M. G.; Fragoso, M. D.

2016-03-01

In this paper, we make a foray in the role played by a set of four operators on the study of robust H2 and mixed H2/H∞ control problems for discrete-time Markov jump linear systems. These operators appear in the study of mean square stability for this class of systems. By means of new linear matrix inequality (LMI) characterisations of controllers, which include slack variables that, to some extent, separate the robustness and performance objectives, we introduce four alternative approaches to the design of controllers which are robustly stabilising and at the same time provide a guaranteed level of H2 performance. Since each operator provides a different degree of conservatism, the results are unified in the form of an iterative LMI technique for designing robust H2 controllers, whose convergence is attained in a finite number of steps. The method yields a new way of computing mixed H2/H∞ controllers, whose conservatism decreases with iteration. Two numerical examples illustrate the applicability of the proposed results for the control of a small unmanned aerial vehicle, and for an underactuated robotic arm.

A comparison between MS-VECM and MS-VECMX on economic time series data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Wai; Ismail, Mohd Tahir; Sek, Siok-Kun

2014-07-01

Multivariate Markov switching models able to provide useful information on the study of structural change data since the regime switching model can analyze the time varying data and capture the mean and variance in the series of dependence structure. This paper will investigates the oil price and gold price effects on Malaysia, Singapore, Thailand and Indonesia stock market returns. Two forms of Multivariate Markov switching models are used namely the mean adjusted heteroskedasticity Markov Switching Vector Error Correction Model (MSMH-VECM) and the mean adjusted heteroskedasticity Markov Switching Vector Error Correction Model with exogenous variable (MSMH-VECMX). The reason for using these two models are to capture the transition probabilities of the data since real financial time series data always exhibit nonlinear properties such as regime switching, cointegrating relations, jumps or breaks passing the time. A comparison between these two models indicates that MSMH-VECM model able to fit the time series data better than the MSMH-VECMX model. In addition, it was found that oil price and gold price affected the stock market changes in the four selected countries.

Appraisal of jump distributions in ensemble-based sampling algorithms

NASA Astrophysics Data System (ADS)

Dejanic, Sanda; Scheidegger, Andreas; Rieckermann, Jörg; Albert, Carlo

2017-04-01

Sampling Bayesian posteriors of model parameters is often required for making model-based probabilistic predictions. For complex environmental models, standard Monte Carlo Markov Chain (MCMC) methods are often infeasible because they require too many sequential model runs. Therefore, we focused on ensemble methods that use many Markov chains in parallel, since they can be run on modern cluster architectures. Little is known about how to choose the best performing sampler, for a given application. A poor choice can lead to an inappropriate representation of posterior knowledge. We assessed two different jump moves, the stretch and the differential evolution move, underlying, respectively, the software packages EMCEE and DREAM, which are popular in different scientific communities. For the assessment, we used analytical posteriors with features as they often occur in real posteriors, namely high dimensionality, strong non-linear correlations or multimodality. For posteriors with non-linear features, standard convergence diagnostics based on sample means can be insufficient. Therefore, we resorted to an entropy-based convergence measure. We assessed the samplers by means of their convergence speed, robustness and effective sample sizes. For posteriors with strongly non-linear features, we found that the stretch move outperforms the differential evolution move, w.r.t. all three aspects.

Reduced-order dynamic output feedback control of uncertain discrete-time Markov jump linear systems

NASA Astrophysics Data System (ADS)

Morais, Cecília F.; Braga, Márcio F.; Oliveira, Ricardo C. L. F.; Peres, Pedro L. D.

2017-11-01

This paper deals with the problem of designing reduced-order robust dynamic output feedback controllers for discrete-time Markov jump linear systems (MJLS) with polytopic state space matrices and uncertain transition probabilities. Starting from a full order, mode-dependent and polynomially parameter-dependent dynamic output feedback controller, sufficient linear matrix inequality based conditions are provided for the existence of a robust reduced-order dynamic output feedback stabilising controller with complete, partial or none mode dependency assuring an upper bound to the ? or the ? norm of the closed-loop system. The main advantage of the proposed method when compared to the existing approaches is the fact that the dynamic controllers are exclusively expressed in terms of the decision variables of the problem. In other words, the matrices that define the controller realisation do not depend explicitly on the state space matrices associated with the modes of the MJLS. As a consequence, the method is specially suitable to handle order reduction or cluster availability constraints in the context of ? or ? dynamic output feedback control of discrete-time MJLS. Additionally, as illustrated by means of numerical examples, the proposed approach can provide less conservative results than other conditions in the literature.

A reversible-jump Markov chain Monte Carlo algorithm for 1D inversion of magnetotelluric data

NASA Astrophysics Data System (ADS)

Mandolesi, Eric; Ogaya, Xenia; Campanyà, Joan; Piana Agostinetti, Nicola

2018-04-01

This paper presents a new computer code developed to solve the 1D magnetotelluric (MT) inverse problem using a Bayesian trans-dimensional Markov chain Monte Carlo algorithm. MT data are sensitive to the depth-distribution of rock electric conductivity (or its reciprocal, resistivity). The solution provided is a probability distribution - the so-called posterior probability distribution (PPD) for the conductivity at depth, together with the PPD of the interface depths. The PPD is sampled via a reversible-jump Markov Chain Monte Carlo (rjMcMC) algorithm, using a modified Metropolis-Hastings (MH) rule to accept or discard candidate models along the chains. As the optimal parameterization for the inversion process is generally unknown a trans-dimensional approach is used to allow the dataset itself to indicate the most probable number of parameters needed to sample the PPD. The algorithm is tested against two simulated datasets and a set of MT data acquired in the Clare Basin (County Clare, Ireland). For the simulated datasets the correct number of conductive layers at depth and the associated electrical conductivity values is retrieved, together with reasonable estimates of the uncertainties on the investigated parameters. Results from the inversion of field measurements are compared with results obtained using a deterministic method and with well-log data from a nearby borehole. The PPD is in good agreement with the well-log data, showing as a main structure a high conductive layer associated with the Clare Shale formation. In this study, we demonstrate that our new code go beyond algorithms developend using a linear inversion scheme, as it can be used: (1) to by-pass the subjective choices in the 1D parameterizations, i.e. the number of horizontal layers in the 1D parameterization, and (2) to estimate realistic uncertainties on the retrieved parameters. The algorithm is implemented using a simple MPI approach, where independent chains run on isolated CPU, to take full advantage of parallel computer architectures. In case of a large number of data, a master/slave appoach can be used, where the master CPU samples the parameter space and the slave CPUs compute forward solutions.

An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces.

PubMed

Descombes, Xavier; Kruggel, Frithjof; Wollny, Gert; Gertz, Hermann Josef

2004-02-01

This paper is concerned with the detection of multiple small brain lesions from magnetic resonance imaging (MRI) data. A model based on the marked point process framework is designed to detect Virchow-Robin spaces (VRSs). These tubular shaped spaces are due to retraction of the brain parenchyma from its supplying arteries. VRS are described by simple geometrical objects that are introduced as small tubular structures. Their radiometric properties are embedded in a data term. A prior model includes interactions describing the clustering property of VRS. A Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC) optimizes the proposed model, obtained by multiplying the prior and the data model. Example results are shown on T1-weighted MRI datasets of elderly subjects.

Stability and performance analysis of a jump linear control system subject to digital upsets

NASA Astrophysics Data System (ADS)

Wang, Rui; Sun, Hui; Ma, Zhen-Yang

2015-04-01

This paper focuses on the methodology analysis for the stability and the corresponding tracking performance of a closed-loop digital jump linear control system with a stochastic switching signal. The method is applied to a flight control system. A distributed recoverable platform is implemented on the flight control system and subject to independent digital upsets. The upset processes are used to stimulate electromagnetic environments. Specifically, the paper presents the scenarios that the upset process is directly injected into the distributed flight control system, which is modeled by independent Markov upset processes and independent and identically distributed (IID) processes. A theoretical performance analysis and simulation modelling are both presented in detail for a more complete independent digital upset injection. The specific examples are proposed to verify the methodology of tracking performance analysis. The general analyses for different configurations are also proposed. Comparisons among different configurations are conducted to demonstrate the availability and the characteristics of the design. Project supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61403395), the Natural Science Foundation of Tianjin, China (Grant No. 13JCYBJC39000), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, China, the Tianjin Key Laboratory of Civil Aircraft Airworthiness and Maintenance in Civil Aviation of China (Grant No. 104003020106), and the Fund for Scholars of Civil Aviation University of China (Grant No. 2012QD21x).

Annealed Importance Sampling Reversible Jump MCMC algorithms

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

Karagiannis, Georgios; Andrieu, Christophe

2013-03-20

It will soon be 20 years since reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithms have been proposed. They have significantly extended the scope of Markov chain Monte Carlo simulation methods, offering the promise to be able to routinely tackle transdimensional sampling problems, as encountered in Bayesian model selection problems for example, in a principled and flexible fashion. Their practical efficient implementation, however, still remains a challenge. A particular difficulty encountered in practice is in the choice of the dimension matching variables (both their nature and their distribution) and the reversible transformations which allow one to define the one-to-one mappingsmore » underpinning the design of these algorithms. Indeed, even seemingly sensible choices can lead to algorithms with very poor performance. The focus of this paper is the development and performance evaluation of a method, annealed importance sampling RJ-MCMC (aisRJ), which addresses this problem by mitigating the sensitivity of RJ-MCMC algorithms to the aforementioned poor design. As we shall see the algorithm can be understood as being an “exact approximation” of an idealized MCMC algorithm that would sample from the model probabilities directly in a model selection set-up. Such an idealized algorithm may have good theoretical convergence properties, but typically cannot be implemented, and our algorithms can approximate the performance of such idealized algorithms to an arbitrary degree while not introducing any bias for any degree of approximation. Our approach combines the dimension matching ideas of RJ-MCMC with annealed importance sampling and its Markov chain Monte Carlo implementation. We illustrate the performance of the algorithm with numerical simulations which indicate that, although the approach may at first appear computationally involved, it is in fact competitive.« less

Extracting Lane Geometry and Topology Information from Vehicle Fleet Trajectories in Complex Urban Scenarios Using a Reversible Jump Mcmc Method

NASA Astrophysics Data System (ADS)

Roeth, O.; Zaum, D.; Brenner, C.

2017-05-01

Highly automated driving (HAD) requires maps not only of high spatial precision but also of yet unprecedented actuality. Traditionally small highly specialized fleets of measurement vehicles are used to generate such maps. Nevertheless, for achieving city-wide or even nation-wide coverage, automated map update mechanisms based on very large vehicle fleet data gain importance since highly frequent measurements are only to be obtained using such an approach. Furthermore, the processing of imprecise mass data in contrast to few dedicated highly accurate measurements calls for a high degree of automation. We present a method for the generation of lane-accurate road network maps from vehicle trajectory data (GPS or better). Our approach therefore allows for exploiting today's connected vehicle fleets for the generation of HAD maps. The presented algorithm is based on elementary building blocks which guarantees useful lane models and uses a Reversible Jump Markov chain Monte Carlo method to explore the models parameters in order to reconstruct the one most likely emitting the input data. The approach is applied to a challenging urban real-world scenario of different trajectory accuracy levels and is evaluated against a LIDAR-based ground truth map.

Distributed fault detection over sensor networks with Markovian switching topologies

NASA Astrophysics Data System (ADS)

Ge, Xiaohua; Han, Qing-Long

2014-05-01

This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via a communication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filters is derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.

Continuous-time discrete-space models for animal movement

USGS Publications Warehouse

Hanks, Ephraim M.; Hooten, Mevin B.; Alldredge, Mat W.

2015-01-01

The processes influencing animal movement and resource selection are complex and varied. Past efforts to model behavioral changes over time used Bayesian statistical models with variable parameter space, such as reversible-jump Markov chain Monte Carlo approaches, which are computationally demanding and inaccessible to many practitioners. We present a continuous-time discrete-space (CTDS) model of animal movement that can be fit using standard generalized linear modeling (GLM) methods. This CTDS approach allows for the joint modeling of location-based as well as directional drivers of movement. Changing behavior over time is modeled using a varying-coefficient framework which maintains the computational simplicity of a GLM approach, and variable selection is accomplished using a group lasso penalty. We apply our approach to a study of two mountain lions (Puma concolor) in Colorado, USA.

Computing the Length of the Shortest Telomere in the Nucleus

NASA Astrophysics Data System (ADS)

Dao Duc, K.; Holcman, D.

2013-11-01

The telomere length can either be shortened or elongated by an enzyme called telomerase after each cell division. Interestingly, the shortest telomere is involved in controlling the ability of a cell to divide. Yet, its dynamics remains elusive. We present here a stochastic approach where we model this dynamics using a Markov jump process. We solve the forward Fokker-Planck equation to obtain the steady state distribution and the statistical moments of telomere lengths. We focus specifically on the shortest one and we estimate its length difference with the second shortest telomere. After extracting key parameters such as elongation and shortening dynamics from experimental data, we compute the length of telomeres in yeast and obtain as a possible prediction the minimum concentration of telomerase required to ensure a proper cell division.

Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains.

PubMed

Dettmer, Jan; Dosso, Stan E

2012-10-01

This paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are addressed by sampling statistical error-distribution parameters, including correlated errors (covariance), by applying a hierarchical autoregressive error model. The well-known difficulty of low acceptance rates for trans-dimensional jumps is addressed with interacting Markov chains, resulting in a substantial increase in efficiency. The trans-dimensional seabed model and the hierarchical error model relax the degree of prior assumptions required in the inversion, resulting in substantially improved (more realistic) uncertainty estimates and a more automated algorithm. In particular, the approach gives seabed parameter uncertainty estimates that account for uncertainty due to prior model choice (layering and data error statistics). The approach is applied to data measured on a vertical array in the Mediterranean Sea.

Current fluctuations in periodically driven systems

NASA Astrophysics Data System (ADS)

Barato, Andre C.; Chetrite, Raphael

2018-05-01

Small nonequelibrium systems driven by an external periodic protocol can be described by Markov processes with time-periodic transition rates. In general, current fluctuations in such small systems are large and may play a crucial role. We develop a theoretical formalism to evaluate the rate of such large deviations in periodically driven systems. We show that the scaled cumulant generating function that characterizes current fluctuations is given by a maximal Floquet exponent. Comparing deterministic protocols with stochastic protocols, we show that, with respect to large deviations, systems driven by a stochastic protocol with an infinitely large number of jumps are equivalent to systems driven by deterministic protocols. Our results are illustrated with three case studies: a two-state model for a heat engine, a three-state model for a molecular pump, and a biased random walk with a time-periodic affinity.

Extracting volatility signal using maximum a posteriori estimation

NASA Astrophysics Data System (ADS)

Neto, David

2016-11-01

This paper outlines a methodology to estimate a denoised volatility signal for foreign exchange rates using a hidden Markov model (HMM). For this purpose a maximum a posteriori (MAP) estimation is performed. A double exponential prior is used for the state variable (the log-volatility) in order to allow sharp jumps in realizations and then log-returns marginal distributions with heavy tails. We consider two routes to choose the regularization and we compare our MAP estimate to realized volatility measure for three exchange rates.

A compositional framework for Markov processes

NASA Astrophysics Data System (ADS)

Baez, John C.; Fong, Brendan; Pollard, Blake S.

2016-03-01

We define the concept of an "open" Markov process, or more precisely, continuous-time Markov chain, which is one where probability can flow in or out of certain states called "inputs" and "outputs." One can build up a Markov process from smaller open pieces. This process is formalized by making open Markov processes into the morphisms of a dagger compact category. We show that the behavior of a detailed balanced open Markov process is determined by a principle of minimum dissipation, closely related to Prigogine's principle of minimum entropy production. Using this fact, we set up a functor mapping open detailed balanced Markov processes to open circuits made of linear resistors. We also describe how to "black box" an open Markov process, obtaining the linear relation between input and output data that holds in any steady state, including nonequilibrium steady states with a nonzero flow of probability through the system. We prove that black boxing gives a symmetric monoidal dagger functor sending open detailed balanced Markov processes to Lagrangian relations between symplectic vector spaces. This allows us to compute the steady state behavior of an open detailed balanced Markov process from the behaviors of smaller pieces from which it is built. We relate this black box functor to a previously constructed black box functor for circuits.

Event-triggered H∞ state estimation for semi-Markov jumping discrete-time neural networks with quantization.

PubMed

Rakkiyappan, R; Maheswari, K; Velmurugan, G; Park, Ju H

2018-05-17

This paper investigates H ∞ state estimation problem for a class of semi-Markovian jumping discrete-time neural networks model with event-triggered scheme and quantization. First, a new event-triggered communication scheme is introduced to determine whether or not the current sampled sensor data should be broad-casted and transmitted to the quantizer, which can save the limited communication resource. Second, a novel communication framework is employed by the logarithmic quantizer that quantifies and reduces the data transmission rate in the network, which apparently improves the communication efficiency of networks. Third, a stabilization criterion is derived based on the sufficient condition which guarantees a prescribed H ∞ performance level in the estimation error system in terms of the linear matrix inequalities. Finally, numerical simulations are given to illustrate the correctness of the proposed scheme. Copyright © 2018 Elsevier Ltd. All rights reserved.

Open Markov Processes and Reaction Networks

ERIC Educational Resources Information Center

Swistock Pollard, Blake Stephen

2017-01-01

We begin by defining the concept of "open" Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain "boundary" states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow…

Nonlinear Markov Control Processes and Games

DTIC Science & Technology

2012-11-15

the analysis of a new class of stochastic games , nonlinear Markov games , as they arise as a ( competitive ) controlled version of nonlinear Markov... competitive interests) a nonlinear Markov game that we are investigating. I 0. :::tUt::JJt:.l.. I I t:t11VI;:, nonlinear Markov game , nonlinear Markov...corresponding stochastic game Γ+(T, h). In a slightly different setting one can assume that changes in a competitive control process occur as a

The explicit form of the rate function for semi-Markov processes and its contractions

NASA Astrophysics Data System (ADS)

Sughiyama, Yuki; Kobayashi, Testuya J.

2018-03-01

We derive the explicit form of the rate function for semi-Markov processes. Here, the ‘random time change trick’ plays an essential role. Also, by exploiting the contraction principle of large deviation theory to the explicit form, we show that the fluctuation theorem (Gallavotti-Cohen symmetry) holds for semi-Markov cases. Furthermore, we elucidate that our rate function is an extension of the level 2.5 rate function for Markov processes to semi-Markov cases.

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

Duncan, Andrew, E-mail: a.duncan@imperial.ac.uk; Erban, Radek, E-mail: erban@maths.ox.ac.uk; Zygalakis, Konstantinos, E-mail: k.zygalakis@ed.ac.uk

Stochasticity plays a fundamental role in various biochemical processes, such as cell regulatory networks and enzyme cascades. Isothermal, well-mixed systems can be modelled as Markov processes, typically simulated using the Gillespie Stochastic Simulation Algorithm (SSA) [25]. While easy to implement and exact, the computational cost of using the Gillespie SSA to simulate such systems can become prohibitive as the frequency of reaction events increases. This has motivated numerous coarse-grained schemes, where the “fast” reactions are approximated either using Langevin dynamics or deterministically. While such approaches provide a good approximation when all reactants are abundant, the approximation breaks down when onemore » or more species exist only in small concentrations and the fluctuations arising from the discrete nature of the reactions become significant. This is particularly problematic when using such methods to compute statistics of extinction times for chemical species, as well as simulating non-equilibrium systems such as cell-cycle models in which a single species can cycle between abundance and scarcity. In this paper, a hybrid jump-diffusion model for simulating well-mixed stochastic kinetics is derived. It acts as a bridge between the Gillespie SSA and the chemical Langevin equation. For low reactant reactions the underlying behaviour is purely discrete, while purely diffusive when the concentrations of all species are large, with the two different behaviours coexisting in the intermediate region. A bound on the weak error in the classical large volume scaling limit is obtained, and three different numerical discretisations of the jump-diffusion model are described. The benefits of such a formalism are illustrated using computational examples.« less

Modeling of dialogue regimes of distance robot control

NASA Astrophysics Data System (ADS)

Larkin, E. V.; Privalov, A. N.

2017-02-01

Process of distance control of mobile robots is investigated. Petri-Markov net for modeling of dialogue regime is worked out. It is shown, that sequence of operations of next subjects: a human operator, a dialogue computer and an onboard computer may be simulated with use the theory of semi-Markov processes. From the semi-Markov process of the general form Markov process was obtained, which includes only states of transaction generation. It is shown, that a real transaction flow is the result of «concurrency» in states of Markov process. Iteration procedure for evaluation of transaction flow parameters, which takes into account effect of «concurrency», is proposed.

NonMarkov Ito Processes with 1- state memory

NASA Astrophysics Data System (ADS)

McCauley, Joseph L.

2010-08-01

A Markov process, by definition, cannot depend on any previous state other than the last observed state. An Ito process implies the Fokker-Planck and Kolmogorov backward time partial differential eqns. for transition densities, which in turn imply the Chapman-Kolmogorov eqn., but without requiring the Markov condition. We present a class of Ito process superficially resembling Markov processes, but with 1-state memory. In finance, such processes would obey the efficient market hypothesis up through the level of pair correlations. These stochastic processes have been mislabeled in recent literature as 'nonlinear Markov processes'. Inspired by Doob and Feller, who pointed out that the ChapmanKolmogorov eqn. is not restricted to Markov processes, we exhibit a Gaussian Ito transition density with 1-state memory in the drift coefficient that satisfies both of Kolmogorov's partial differential eqns. and also the Chapman-Kolmogorov eqn. In addition, we show that three of the examples from McKean's seminal 1966 paper are also nonMarkov Ito processes. Last, we show that the transition density of the generalized Black-Scholes type partial differential eqn. describes a martingale, and satisfies the ChapmanKolmogorov eqn. This leads to the shortest-known proof that the Green function of the Black-Scholes eqn. with variable diffusion coefficient provides the so-called martingale measure of option pricing.

On a Result for Finite Markov Chains

ERIC Educational Resources Information Center

Kulathinal, Sangita; Ghosh, Lagnojita

2006-01-01

In an undergraduate course on stochastic processes, Markov chains are discussed in great detail. Textbooks on stochastic processes provide interesting properties of finite Markov chains. This note discusses one such property regarding the number of steps in which a state is reachable or accessible from another state in a finite Markov chain with M…

On a numerical method for solving integro-differential equations with variable coefficients with applications in finance

NASA Astrophysics Data System (ADS)

Kudryavtsev, O.; Rodochenko, V.

2018-03-01

We propose a new general numerical method aimed to solve integro-differential equations with variable coefficients. The problem under consideration arises in finance where in the context of pricing barrier options in a wide class of stochastic volatility models with jumps. To handle the effect of the correlation between the price and the variance, we use a suitable substitution for processes. Then we construct a Markov-chain approximation for the variation process on small time intervals and apply a maturity randomization technique. The result is a system of boundary problems for integro-differential equations with constant coefficients on the line in each vertex of the chain. We solve the arising problems using a numerical Wiener-Hopf factorization method. The approximate formulae for the factors are efficiently implemented by means of the Fast Fourier Transform. Finally, we use a recurrent procedure that moves backwards in time on the variance tree. We demonstrate the convergence of the method using Monte-Carlo simulations and compare our results with the results obtained by the Wiener-Hopf method with closed-form expressions of the factors.

Semi-Markov adjunction to the Computer-Aided Markov Evaluator (CAME)

NASA Technical Reports Server (NTRS)

Rosch, Gene; Hutchins, Monica A.; Leong, Frank J.; Babcock, Philip S., IV

1988-01-01

The rule-based Computer-Aided Markov Evaluator (CAME) program was expanded in its ability to incorporate the effect of fault-handling processes into the construction of a reliability model. The fault-handling processes are modeled as semi-Markov events and CAME constructs and appropriate semi-Markov model. To solve the model, the program outputs it in a form which can be directly solved with the Semi-Markov Unreliability Range Evaluator (SURE) program. As a means of evaluating the alterations made to the CAME program, the program is used to model the reliability of portions of the Integrated Airframe/Propulsion Control System Architecture (IAPSA 2) reference configuration. The reliability predictions are compared with a previous analysis. The results bear out the feasibility of utilizing CAME to generate appropriate semi-Markov models to model fault-handling processes.

Unsupervised Detection of Planetary Craters by a Marked Point Process

NASA Technical Reports Server (NTRS)

Troglio, G.; Benediktsson, J. A.; Le Moigne, J.; Moser, G.; Serpico, S. B.

2011-01-01

With the launch of several planetary missions in the last decade, a large amount of planetary images is being acquired. Preferably, automatic and robust processing techniques need to be used for data analysis because of the huge amount of the acquired data. Here, the aim is to achieve a robust and general methodology for crater detection. A novel technique based on a marked point process is proposed. First, the contours in the image are extracted. The object boundaries are modeled as a configuration of an unknown number of random ellipses, i.e., the contour image is considered as a realization of a marked point process. Then, an energy function is defined, containing both an a priori energy and a likelihood term. The global minimum of this function is estimated by using reversible jump Monte-Carlo Markov chain dynamics and a simulated annealing scheme. The main idea behind marked point processes is to model objects within a stochastic framework: Marked point processes represent a very promising current approach in the stochastic image modeling and provide a powerful and methodologically rigorous framework to efficiently map and detect objects and structures in an image with an excellent robustness to noise. The proposed method for crater detection has several feasible applications. One such application area is image registration by matching the extracted features.

Bayesian selection of Markov models for symbol sequences: application to microsaccadic eye movements.

PubMed

Bettenbühl, Mario; Rusconi, Marco; Engbert, Ralf; Holschneider, Matthias

2012-01-01

Complex biological dynamics often generate sequences of discrete events which can be described as a Markov process. The order of the underlying Markovian stochastic process is fundamental for characterizing statistical dependencies within sequences. As an example for this class of biological systems, we investigate the Markov order of sequences of microsaccadic eye movements from human observers. We calculate the integrated likelihood of a given sequence for various orders of the Markov process and use this in a Bayesian framework for statistical inference on the Markov order. Our analysis shows that data from most participants are best explained by a first-order Markov process. This is compatible with recent findings of a statistical coupling of subsequent microsaccade orientations. Our method might prove to be useful for a broad class of biological systems.

Efficient hierarchical trans-dimensional Bayesian inversion of magnetotelluric data

NASA Astrophysics Data System (ADS)

Xiang, Enming; Guo, Rongwen; Dosso, Stan E.; Liu, Jianxin; Dong, Hao; Ren, Zhengyong

2018-06-01

This paper develops an efficient hierarchical trans-dimensional (trans-D) Bayesian algorithm to invert magnetotelluric (MT) data for subsurface geoelectrical structure, with unknown geophysical model parameterization (the number of conductivity-layer interfaces) and data-error models parameterized by an auto-regressive (AR) process to account for potential error correlations. The reversible-jump Markov-chain Monte Carlo algorithm, which adds/removes interfaces and AR parameters in birth/death steps, is applied to sample the trans-D posterior probability density for model parameterization, model parameters, error variance and AR parameters, accounting for the uncertainties of model dimension and data-error statistics in the uncertainty estimates of the conductivity profile. To provide efficient sampling over the multiple subspaces of different dimensions, advanced proposal schemes are applied. Parameter perturbations are carried out in principal-component space, defined by eigen-decomposition of the unit-lag model covariance matrix, to minimize the effect of inter-parameter correlations and provide effective perturbation directions and length scales. Parameters of new layers in birth steps are proposed from the prior, instead of focused distributions centred at existing values, to improve birth acceptance rates. Parallel tempering, based on a series of parallel interacting Markov chains with successively relaxed likelihoods, is applied to improve chain mixing over model dimensions. The trans-D inversion is applied in a simulation study to examine the resolution of model structure according to the data information content. The inversion is also applied to a measured MT data set from south-central Australia.

The behavior of Metropolis-coupled Markov chains when sampling rugged phylogenetic distributions.

PubMed

Brown, Jeremy M; Thomson, Robert C

2018-02-15

Bayesian phylogenetic inference involves sampling from posterior distributions of trees, which sometimes exhibit local optima, or peaks, separated by regions of low posterior density. Markov chain Monte Carlo (MCMC) algorithms are the most widely used numerical method for generating samples from these posterior distributions, but they are susceptible to entrapment on individual optima in rugged distributions when they are unable to easily cross through or jump across regions of low posterior density. Ruggedness of posterior distributions can result from a variety of factors, including unmodeled variation in evolutionary processes and unrecognized variation in the true topology across sites or genes. Ruggedness can also become exaggerated when constraints are placed on topologies that require the presence or absence of particular bipartitions (often referred to as positive or negative constraints, respectively). These types of constraints are frequently employed when conducting tests of topological hypotheses (Bergsten et al. 2013; Brown and Thomson 2017). Negative constraints can lead to particularly rugged distributions when the data strongly support a forbidden clade, because monophyly of the clade can be disrupted by inserting outgroup taxa in many different ways. However, topological moves between the alternative disruptions are very difficult, because they require swaps between the inserted outgroup taxa while the data constrain taxa from the forbidden clade to remain close together on the tree. While this precise form of ruggedness is particular to negative constraints, trees with high posterior density can be separated by similarly complicated topological rearrangements, even in the absence of constraints.

Metrics for Labeled Markov Systems

NASA Technical Reports Server (NTRS)

Desharnais, Josee; Jagadeesan, Radha; Gupta, Vineet; Panangaden, Prakash

1999-01-01

Partial Labeled Markov Chains are simultaneously generalizations of process algebra and of traditional Markov chains. They provide a foundation for interacting discrete probabilistic systems, the interaction being synchronization on labels as in process algebra. Existing notions of process equivalence are too sensitive to the exact probabilities of various transitions. This paper addresses contextual reasoning principles for reasoning about more robust notions of "approximate" equivalence between concurrent interacting probabilistic systems. The present results indicate that:We develop a family of metrics between partial labeled Markov chains to formalize the notion of distance between processes. We show that processes at distance zero are bisimilar. We describe a decision procedure to compute the distance between two processes. We show that reasoning about approximate equivalence can be done compositionally by showing that process combinators do not increase distance. We introduce an asymptotic metric to capture asymptotic properties of Markov chains; and show that parallel composition does not increase asymptotic distance.

Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees.

PubMed

Rabosky, Daniel L

2014-01-01

A number of methods have been developed to infer differential rates of species diversification through time and among clades using time-calibrated phylogenetic trees. However, we lack a general framework that can delineate and quantify heterogeneous mixtures of dynamic processes within single phylogenies. I developed a method that can identify arbitrary numbers of time-varying diversification processes on phylogenies without specifying their locations in advance. The method uses reversible-jump Markov Chain Monte Carlo to move between model subspaces that vary in the number of distinct diversification regimes. The model assumes that changes in evolutionary regimes occur across the branches of phylogenetic trees under a compound Poisson process and explicitly accounts for rate variation through time and among lineages. Using simulated datasets, I demonstrate that the method can be used to quantify complex mixtures of time-dependent, diversity-dependent, and constant-rate diversification processes. I compared the performance of the method to the MEDUSA model of rate variation among lineages. As an empirical example, I analyzed the history of speciation and extinction during the radiation of modern whales. The method described here will greatly facilitate the exploration of macroevolutionary dynamics across large phylogenetic trees, which may have been shaped by heterogeneous mixtures of distinct evolutionary processes.

Automatic Detection of Key Innovations, Rate Shifts, and Diversity-Dependence on Phylogenetic Trees

PubMed Central

Rabosky, Daniel L.

2014-01-01

A number of methods have been developed to infer differential rates of species diversification through time and among clades using time-calibrated phylogenetic trees. However, we lack a general framework that can delineate and quantify heterogeneous mixtures of dynamic processes within single phylogenies. I developed a method that can identify arbitrary numbers of time-varying diversification processes on phylogenies without specifying their locations in advance. The method uses reversible-jump Markov Chain Monte Carlo to move between model subspaces that vary in the number of distinct diversification regimes. The model assumes that changes in evolutionary regimes occur across the branches of phylogenetic trees under a compound Poisson process and explicitly accounts for rate variation through time and among lineages. Using simulated datasets, I demonstrate that the method can be used to quantify complex mixtures of time-dependent, diversity-dependent, and constant-rate diversification processes. I compared the performance of the method to the MEDUSA model of rate variation among lineages. As an empirical example, I analyzed the history of speciation and extinction during the radiation of modern whales. The method described here will greatly facilitate the exploration of macroevolutionary dynamics across large phylogenetic trees, which may have been shaped by heterogeneous mixtures of distinct evolutionary processes. PMID:24586858

Analysing grouping of nucleotides in DNA sequences using lumped processes constructed from Markov chains.

PubMed

Guédon, Yann; d'Aubenton-Carafa, Yves; Thermes, Claude

2006-03-01

The most commonly used models for analysing local dependencies in DNA sequences are (high-order) Markov chains. Incorporating knowledge relative to the possible grouping of the nucleotides enables to define dedicated sub-classes of Markov chains. The problem of formulating lumpability hypotheses for a Markov chain is therefore addressed. In the classical approach to lumpability, this problem can be formulated as the determination of an appropriate state space (smaller than the original state space) such that the lumped chain defined on this state space retains the Markov property. We propose a different perspective on lumpability where the state space is fixed and the partitioning of this state space is represented by a one-to-many probabilistic function within a two-level stochastic process. Three nested classes of lumped processes can be defined in this way as sub-classes of first-order Markov chains. These lumped processes enable parsimonious reparameterizations of Markov chains that help to reveal relevant partitions of the state space. Characterizations of the lumped processes on the original transition probability matrix are derived. Different model selection methods relying either on hypothesis testing or on penalized log-likelihood criteria are presented as well as extensions to lumped processes constructed from high-order Markov chains. The relevance of the proposed approach to lumpability is illustrated by the analysis of DNA sequences. In particular, the use of lumped processes enables to highlight differences between intronic sequences and gene untranslated region sequences.

Markov-modulated Markov chains and the covarion process of molecular evolution.

PubMed

Galtier, N; Jean-Marie, A

2004-01-01

The covarion (or site specific rate variation, SSRV) process of biological sequence evolution is a process by which the evolutionary rate of a nucleotide/amino acid/codon position can change in time. In this paper, we introduce time-continuous, space-discrete, Markov-modulated Markov chains as a model for representing SSRV processes, generalizing existing theory to any model of rate change. We propose a fast algorithm for diagonalizing the generator matrix of relevant Markov-modulated Markov processes. This algorithm makes phylogeny likelihood calculation tractable even for a large number of rate classes and a large number of states, so that SSRV models become applicable to amino acid or codon sequence datasets. Using this algorithm, we investigate the accuracy of the discrete approximation to the Gamma distribution of evolutionary rates, widely used in molecular phylogeny. We show that a relatively large number of classes is required to achieve accurate approximation of the exact likelihood when the number of analyzed sequences exceeds 20, both under the SSRV and among site rate variation (ASRV) models.

Machine learning in sentiment reconstruction of the simulated stock market

NASA Astrophysics Data System (ADS)

Goykhman, Mikhail; Teimouri, Ali

2018-02-01

In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to reconstruct the transition probabilities matrix of the Markov sentiment process and recover the underlying sentiment states from the observed stock price behavior. We demonstrate that the Hidden Markov Model can successfully recover the transition probabilities matrix for the hidden sentiment process of the Markov Chain type. We also demonstrate that the Recurrent Neural Network can successfully recover the hidden sentiment states from the observed simulated stock price time series.

Open Markov Processes and Reaction Networks

NASA Astrophysics Data System (ADS)

Swistock Pollard, Blake Stephen

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Quantifying parameter uncertainty in stochastic models using the Box Cox transformation

NASA Astrophysics Data System (ADS)

Thyer, Mark; Kuczera, George; Wang, Q. J.

2002-08-01

The Box-Cox transformation is widely used to transform hydrological data to make it approximately Gaussian. Bayesian evaluation of parameter uncertainty in stochastic models using the Box-Cox transformation is hindered by the fact that there is no analytical solution for the posterior distribution. However, the Markov chain Monte Carlo method known as the Metropolis algorithm can be used to simulate the posterior distribution. This method properly accounts for the nonnegativity constraint implicit in the Box-Cox transformation. Nonetheless, a case study using the AR(1) model uncovered a practical problem with the implementation of the Metropolis algorithm. The use of a multivariate Gaussian jump distribution resulted in unacceptable convergence behaviour. This was rectified by developing suitable parameter transformations for the mean and variance of the AR(1) process to remove the strong nonlinear dependencies with the Box-Cox transformation parameter. Applying this methodology to the Sydney annual rainfall data and the Burdekin River annual runoff data illustrates the efficacy of these parameter transformations and demonstrate the value of quantifying parameter uncertainty.

A hybrid multiview stereo algorithm for modeling urban scenes.

PubMed

Lafarge, Florent; Keriven, Renaud; Brédif, Mathieu; Vu, Hoang-Hiep

2013-01-01

We present an original multiview stereo reconstruction algorithm which allows the 3D-modeling of urban scenes as a combination of meshes and geometric primitives. The method provides a compact model while preserving details: Irregular elements such as statues and ornaments are described by meshes, whereas regular structures such as columns and walls are described by primitives (planes, spheres, cylinders, cones, and tori). We adopt a two-step strategy consisting first in segmenting the initial meshbased surface using a multilabel Markov Random Field-based model and second in sampling primitive and mesh components simultaneously on the obtained partition by a Jump-Diffusion process. The quality of a reconstruction is measured by a multi-object energy model which takes into account both photo-consistency and semantic considerations (i.e., geometry and shape layout). The segmentation and sampling steps are embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation. Experimental results on complex urban structures and large scenes are presented and compared to state-of-the-art multiview stereo meshing algorithms.

Derivation of Markov processes that violate detailed balance

NASA Astrophysics Data System (ADS)

Lee, Julian

2018-03-01

Time-reversal symmetry of the microscopic laws dictates that the equilibrium distribution of a stochastic process must obey the condition of detailed balance. However, cyclic Markov processes that do not admit equilibrium distributions with detailed balance are often used to model systems driven out of equilibrium by external agents. I show that for a Markov model without detailed balance, an extended Markov model can be constructed, which explicitly includes the degrees of freedom for the driving agent and satisfies the detailed balance condition. The original cyclic Markov model for the driven system is then recovered as an approximation at early times by summing over the degrees of freedom for the driving agent. I also show that the widely accepted expression for the entropy production in a cyclic Markov model is actually a time derivative of an entropy component in the extended model. Further, I present an analytic expression for the entropy component that is hidden in the cyclic Markov model.

Evidence of infinite and finite jump processes in commodity futures prices: Crude oil and natural gas

NASA Astrophysics Data System (ADS)

Cao, Wenbin; Guernsey, Scott B.; Linn, Scott C.

2018-07-01

We examine the frequency and character of price jumps in front month oil and natural gas futures prices. Prices are sampled every five seconds over the period 2006-2014. Our test results indicate that jumps in crude oil and natural gas futures prices can be decomposed into an infinite activity jump diffusion process and a less frequent but larger jump process. We also find that we cannot reject the hypothesis that Brownian motion is also present in both return series. The results are based on a battery of tests that are "model free". We further find that jumps account for respectively 36 and 41 percent of the realized variances of the crude oil and the natural gas returns.

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

Cai, H.

In this dissertation we study a procedure which restarts a Markov process when the process is killed by some arbitrary multiplicative functional. The regenerative nature of this revival procedure is characterized through a Markov renewal equation. An interesting duality between the revival procedure and the classical killing operation is found. Under the condition that the multiplicative functional possesses an intensity, the generators of the revival process can be written down explicitly. An intimate connection is also found between the perturbation of the sample path of a Markov process and the perturbation of a generator (in Kato's sense). The applications ofmore » the theory include the study of the processes like piecewise-deterministic Markov process, virtual waiting time process and the first entrance decomposition (taboo probability).« less

Fluctuation theorems for discrete kinetic models of molecular motors

NASA Astrophysics Data System (ADS)

Faggionato, Alessandra; Silvestri, Vittoria

2017-04-01

Motivated by discrete kinetic models for non-cooperative molecular motors on periodic tracks, we consider random walks (also not Markov) on quasi one dimensional (1d) lattices, obtained by gluing several copies of a fundamental graph in a linear fashion. We show that, for a suitable class of quasi-1d lattices, the large deviation rate function associated to the position of the walker satisfies a Gallavotti-Cohen symmetry for any choice of the dynamical parameters defining the stochastic walk. This class includes the linear model considered in Lacoste et al (2008 Phys. Rev. E 78 011915). We also derive fluctuation theorems for the time-integrated cycle currents and discuss how the matrix approach of Lacoste et al (2008 Phys. Rev. E 78 011915) can be extended to derive the above Gallavotti-Cohen symmetry for any Markov random walk on {Z} with periodic jump rates. Finally, we review in the present context some large deviation results of Faggionato and Silvestri (2017 Ann. Inst. Henri Poincaré 53 46-78) and give some specific examples with explicit computations.

Statistical Analysis of the First Passage Path Ensemble of Jump Processes

NASA Astrophysics Data System (ADS)

von Kleist, Max; Schütte, Christof; Zhang, Wei

2018-02-01

The transition mechanism of jump processes between two different subsets in state space reveals important dynamical information of the processes and therefore has attracted considerable attention in the past years. In this paper, we study the first passage path ensemble of both discrete-time and continuous-time jump processes on a finite state space. The main approach is to divide each first passage path into nonreactive and reactive segments and to study them separately. The analysis can be applied to jump processes which are non-ergodic, as well as continuous-time jump processes where the waiting time distributions are non-exponential. In the particular case that the jump processes are both Markovian and ergodic, our analysis elucidates the relations between the study of the first passage paths and the study of the transition paths in transition path theory. We provide algorithms to numerically compute statistics of the first passage path ensemble. The computational complexity of these algorithms scales with the complexity of solving a linear system, for which efficient methods are available. Several examples demonstrate the wide applicability of the derived results across research areas.

An Overview of Markov Chain Methods for the Study of Stage-Sequential Developmental Processes

ERIC Educational Resources Information Center

Kapland, David

2008-01-01

This article presents an overview of quantitative methodologies for the study of stage-sequential development based on extensions of Markov chain modeling. Four methods are presented that exemplify the flexibility of this approach: the manifest Markov model, the latent Markov model, latent transition analysis, and the mixture latent Markov model.…

Stochastic entrainment of a stochastic oscillator.

PubMed

Wang, Guanyu; Peskin, Charles S

2015-01-01

In this work, we consider a stochastic oscillator described by a discrete-state continuous-time Markov chain, in which the states are arranged in a circle, and there is a constant probability per unit time of jumping from one state to the next in a specified direction around the circle. At each of a sequence of equally spaced times, the oscillator has a specified probability of being reset to a particular state. The focus of this work is the entrainment of the oscillator by this periodic but stochastic stimulus. We consider a distinguished limit, in which (i) the number of states of the oscillator approaches infinity, as does the probability per unit time of jumping from one state to the next, so that the natural mean period of the oscillator remains constant, (ii) the resetting probability approaches zero, and (iii) the period of the resetting signal approaches a multiple, by a ratio of small integers, of the natural mean period of the oscillator. In this distinguished limit, we use analytic and numerical methods to study the extent to which entrainment occurs.

SATCHMO-JS: a webserver for simultaneous protein multiple sequence alignment and phylogenetic tree construction.

PubMed

Hagopian, Raffi; Davidson, John R; Datta, Ruchira S; Samad, Bushra; Jarvis, Glen R; Sjölander, Kimmen

2010-07-01

We present the jump-start simultaneous alignment and tree construction using hidden Markov models (SATCHMO-JS) web server for simultaneous estimation of protein multiple sequence alignments (MSAs) and phylogenetic trees. The server takes as input a set of sequences in FASTA format, and outputs a phylogenetic tree and MSA; these can be viewed online or downloaded from the website. SATCHMO-JS is an extension of the SATCHMO algorithm, and employs a divide-and-conquer strategy to jump-start SATCHMO at a higher point in the phylogenetic tree, reducing the computational complexity of the progressive all-versus-all HMM-HMM scoring and alignment. Results on a benchmark dataset of 983 structurally aligned pairs from the PREFAB benchmark dataset show that SATCHMO-JS provides a statistically significant improvement in alignment accuracy over MUSCLE, Multiple Alignment using Fast Fourier Transform (MAFFT), ClustalW and the original SATCHMO algorithm. The SATCHMO-JS webserver is available at http://phylogenomics.berkeley.edu/satchmo-js. The datasets used in these experiments are available for download at http://phylogenomics.berkeley.edu/satchmo-js/supplementary/.

Processing and Conversion of Algae to Bioethanol

NASA Astrophysics Data System (ADS)

Kampfe, Sara Katherine

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

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.

Net Surface Flux Budget Over Tropical Oceans Estimated from the Tropical Rainfall Measuring Mission (TRMM)

NASA Astrophysics Data System (ADS)

Fan, Tai-Fang

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Magneto - Optical Imaging of Superconducting MgB2 Thin Films

NASA Astrophysics Data System (ADS)

Hummert, Stephanie Maria

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Boron Carbide Filled Neutron Shielding Textile Polymers

NASA Astrophysics Data System (ADS)

Manzlak, Derrick Anthony

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Parallel Unstructured Grid Generation for Complex Real-World Aerodynamic Simulations

NASA Astrophysics Data System (ADS)

Zagaris, George

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Polymeric Radiation Shielding for Applications in Space: Polyimide Synthesis and Modeling of Multi-Layered Polymeric Shields

NASA Astrophysics Data System (ADS)

Schiavone, Clinton Cleveland

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

The Development of the CALIPSO LiDAR Simulator

NASA Astrophysics Data System (ADS)

Powell, Kathleen A.

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Exploring a Novel Approach to Technical Nuclear Forensics Utilizing Atomic Force Microscopy

NASA Astrophysics Data System (ADS)

Peeke, Richard Scot

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Modeling of Critically-Stratified Gravity Flows: Application to the Eel River Continental Shelf, Northern California

NASA Astrophysics Data System (ADS)

Scully, Malcolm E.

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Production of Cyclohexylene-Containing Diamines in Pursuit of Novel Radiation Shielding Materials

NASA Astrophysics Data System (ADS)

Bate, Norah G.

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Development of Boron-Containing Polyimide Materials and Poly(arylene Ether)s for Radiation Shielding

NASA Astrophysics Data System (ADS)

Collins, Brittani May

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Magnetization Dynamics and Anisotropy in Ferromagnetic/Antiferromagnetic Ni/NiO Bilayers

NASA Astrophysics Data System (ADS)

Petersen, Andreas

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes

PubMed Central

Duchen, Pablo; Leuenberger, Christoph; Szilágyi, Sándor M.; Harmon, Luke; Eastman, Jonathan; Schweizer, Manuel

2017-01-01

Abstract Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so-called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson’s hypothesis. [evolutionary jump; Lévy process; phenotypic evolution; punctuated equilibrium; quantitative traits. PMID:28204787

Markov and semi-Markov processes as a failure rate

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

Grabski, Franciszek

2016-06-08

In this paper the reliability function is defined by the stochastic failure rate process with a non negative and right continuous trajectories. Equations for the conditional reliability functions of an object, under assumption that the failure rate is a semi-Markov process with an at most countable state space are derived. A proper theorem is presented. The linear systems of equations for the appropriate Laplace transforms allow to find the reliability functions for the alternating, the Poisson and the Furry-Yule failure rate processes.

Prediction and generation of binary Markov processes: Can a finite-state fox catch a Markov mouse?

NASA Astrophysics Data System (ADS)

Ruebeck, Joshua B.; James, Ryan G.; Mahoney, John R.; Crutchfield, James P.

2018-01-01

Understanding the generative mechanism of a natural system is a vital component of the scientific method. Here, we investigate one of the fundamental steps toward this goal by presenting the minimal generator of an arbitrary binary Markov process. This is a class of processes whose predictive model is well known. Surprisingly, the generative model requires three distinct topologies for different regions of parameter space. We show that a previously proposed generator for a particular set of binary Markov processes is, in fact, not minimal. Our results shed the first quantitative light on the relative (minimal) costs of prediction and generation. We find, for instance, that the difference between prediction and generation is maximized when the process is approximately independently, identically distributed.

Time-resolved methods in biophysics. 9. Laser temperature-jump methods for investigating biomolecular dynamics.

PubMed

Kubelka, Jan

2009-04-01

Many important biochemical processes occur on the time-scales of nanoseconds and microseconds. The introduction of the laser temperature-jump (T-jump) to biophysics more than a decade ago opened these previously inaccessible time regimes up to direct experimental observation. Since then, laser T-jump methodology has evolved into one of the most versatile and generally applicable methods for studying fast biomolecular kinetics. This perspective is a review of the principles and applications of the laser T-jump technique in biophysics. A brief overview of the T-jump relaxation kinetics and the historical development of laser T-jump methodology is presented. The physical principles and practical experimental considerations that are important for the design of the laser T-jump experiments are summarized. These include the Raman conversion for generating heating pulses, considerations of size, duration and uniformity of the temperature jump, as well as potential adverse effects due to photo-acoustic waves, cavitation and thermal lensing, and their elimination. The laser T-jump apparatus developed at the NIH Laboratory of Chemical Physics is described in detail along with a brief survey of other laser T-jump designs in use today. Finally, applications of the laser T-jump in biophysics are reviewed, with an emphasis on the broad range of problems where the laser T-jump methodology has provided important new results and insights into the dynamics of the biomolecular processes.

Conditioned Limit Theorems for Some Null Recurrent Markov Processes

DTIC Science & Technology

1976-08-01

Chapter 1 INTRODUCTION 1.1 Summary of Results Let (Vk, k ! 0) be a discrete time Markov process with state space EC(- , ) and let S be...explain our results in some detail. 2 We begin by stating our three basic assumptions: (1) vk s k 2 0 Is a Markov process with state space E C(-o,%); (Ii... 12 n 3. CONDITIONING ON T (, > n.................................1.9 3.1 Preliminary Results

Estimation of under-reporting in epidemics using approximations.

PubMed

Gamado, Kokouvi; Streftaris, George; Zachary, Stan

2017-06-01

Under-reporting in epidemics, when it is ignored, leads to under-estimation of the infection rate and therefore of the reproduction number. In the case of stochastic models with temporal data, a usual approach for dealing with such issues is to apply data augmentation techniques through Bayesian methodology. Departing from earlier literature approaches implemented using reversible jump Markov chain Monte Carlo (RJMCMC) techniques, we make use of approximations to obtain faster estimation with simple MCMC. Comparisons among the methods developed here, and with the RJMCMC approach, are carried out and highlight that approximation-based methodology offers useful alternative inference tools for large epidemics, with a good trade-off between time cost and accuracy.

Supercomputer optimizations for stochastic optimal control applications

NASA Technical Reports Server (NTRS)

Chung, Siu-Leung; Hanson, Floyd B.; Xu, Huihuang

1991-01-01

Supercomputer optimizations for a computational method of solving stochastic, multibody, dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear, multibody dynamical systems, perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian as well as jump Poisson random white noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, loop collapsing, blocking, and compiler directives. These advanced computing techniques and superconducting hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations, by permitting the solution of large multibody problems. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random aerospace fluctuations.

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.

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes.

PubMed

Duchen, Pablo; Leuenberger, Christoph; Szilágyi, Sándor M; Harmon, Luke; Eastman, Jonathan; Schweizer, Manuel; Wegmann, Daniel

2017-11-01

Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so-called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson's hypothesis. [evolutionary jump; Lévy process; phenotypic evolution; punctuated equilibrium; quantitative traits. The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

High-Performance Nanocomposites Designed for Radiation Shielding in Space and an Application of GIS for Analyzing Nanopowder Dispersion in Polymer Matrixes

NASA Astrophysics Data System (ADS)

Auslander, Joseph Simcha

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Time-Resolved Magneto-Optical Imaging of Superconducting YBCO Thin Films in the High-Frequency AC Current Regime

NASA Astrophysics Data System (ADS)

Frey, Alexander

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Use of Remote Sensing to Identify Essential Habitat for Aeschynomene virginica (L.) BSP, a Threatened Tidal Freshwater Wetland Plant

NASA Astrophysics Data System (ADS)

Mountz, Elizabeth M.

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Silver-Polyimide Nanocomposite Films: Single-Stage Synthesis and Analysis of Metalized Partially-Fluorinated Polyimide BTDA/4-BDAF Prepared from Silver(I) Complexes

NASA Astrophysics Data System (ADS)

Abelard, Joshua Erold Robert

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Multifunctional Polymer Synthesis and Incorporation of Gadolinium Compounds and Modified Tungsten Nanoparticles for Improvement of Radiation Shielding for use in Outer Space

NASA Astrophysics Data System (ADS)

Harbert, Emily Grace

We begin by defining the concept of `open' Markov processes, which are continuous-time Markov chains where probability can flow in and out through certain `boundary' states. We study open Markov processes which in the absence of such boundary flows admit equilibrium states satisfying detailed balance, meaning that the net flow of probability vanishes between all pairs of states. External couplings which fix the probabilities of boundary states can maintain such systems in non-equilibrium steady states in which non-zero probability currents flow. We show that these non-equilibrium steady states minimize a quadratic form which we call 'dissipation.' This is closely related to Prigogine's principle of minimum entropy production. We bound the rate of change of the entropy of a driven non-equilibrium steady state relative to the underlying equilibrium state in terms of the flow of probability through the boundary of the process. We then consider open Markov processes as morphisms in a symmetric monoidal category by splitting up their boundary states into certain sets of `inputs' and `outputs.' Composition corresponds to gluing the outputs of one such open Markov process onto the inputs of another so that the probability flowing out of the first process is equal to the probability flowing into the second. Tensoring in this category corresponds to placing two such systems side by side. We construct a `black-box' functor characterizing the behavior of an open Markov process in terms of the space of possible steady state probabilities and probability currents along the boundary. The fact that this is a functor means that the behavior of a composite open Markov process can be computed by composing the behaviors of the open Markov processes from which it is composed. We prove a similar black-boxing theorem for reaction networks whose dynamics are given by the non-linear rate equation. Along the way we describe a more general category of open dynamical systems where composition corresponds to gluing together open dynamical systems.

Bayesian Analysis of Non-Gaussian Long-Range Dependent Processes

NASA Astrophysics Data System (ADS)

Graves, T.; Franzke, C.; Gramacy, R. B.; Watkins, N. W.

2012-12-01

Recent studies have strongly suggested that surface temperatures exhibit long-range dependence (LRD). The presence of LRD would hamper the identification of deterministic trends and the quantification of their significance. It is well established that LRD processes exhibit stochastic trends over rather long periods of time. Thus, accurate methods for discriminating between physical processes that possess long memory and those that do not are an important adjunct to climate modeling. We have used Markov Chain Monte Carlo algorithms to perform a Bayesian analysis of Auto-Regressive Fractionally-Integrated Moving-Average (ARFIMA) processes, which are capable of modeling LRD. Our principal aim is to obtain inference about the long memory parameter, d,with secondary interest in the scale and location parameters. We have developed a reversible-jump method enabling us to integrate over different model forms for the short memory component. We initially assume Gaussianity, and have tested the method on both synthetic and physical time series such as the Central England Temperature. Many physical processes, for example the Faraday time series from Antarctica, are highly non-Gaussian. We have therefore extended this work by weakening the Gaussianity assumption. Specifically, we assume a symmetric α -stable distribution for the innovations. Such processes provide good, flexible, initial models for non-Gaussian processes with long memory. We will present a study of the dependence of the posterior variance σ d of the memory parameter d on the length of the time series considered. This will be compared with equivalent error diagnostics for other measures of d.

Path integrals and large deviations in stochastic hybrid systems.

PubMed

Bressloff, Paul C; Newby, Jay M

2014-04-01

We construct a path-integral representation of solutions to a stochastic hybrid system, consisting of one or more continuous variables evolving according to a piecewise-deterministic dynamics. The differential equations for the continuous variables are coupled to a set of discrete variables that satisfy a continuous-time Markov process, which means that the differential equations are only valid between jumps in the discrete variables. Examples of stochastic hybrid systems arise in biophysical models of stochastic ion channels, motor-driven intracellular transport, gene networks, and stochastic neural networks. We use the path-integral representation to derive a large deviation action principle for a stochastic hybrid system. Minimizing the associated action functional with respect to the set of all trajectories emanating from a metastable state (assuming that such a minimization scheme exists) then determines the most probable paths of escape. Moreover, evaluating the action functional along a most probable path generates the so-called quasipotential used in the calculation of mean first passage times. We illustrate the theory by considering the optimal paths of escape from a metastable state in a bistable neural network.

Markov Modeling of Component Fault Growth over a Derived Domain of Feasible Output Control Effort Modifications

NASA Technical Reports Server (NTRS)

Bole, Brian; Goebel, Kai; Vachtsevanos, George

2012-01-01

This paper introduces a novel Markov process formulation of stochastic fault growth modeling, in order to facilitate the development and analysis of prognostics-based control adaptation. A metric representing the relative deviation between the nominal output of a system and the net output that is actually enacted by an implemented prognostics-based control routine, will be used to define the action space of the formulated Markov process. The state space of the Markov process will be defined in terms of an abstracted metric representing the relative health remaining in each of the system s components. The proposed formulation of component fault dynamics will conveniently relate feasible system output performance modifications to predictions of future component health deterioration.

The application of Markov decision process with penalty function in restaurant delivery robot

NASA Astrophysics Data System (ADS)

Wang, Yong; Hu, Zhen; Wang, Ying

2017-05-01

As the restaurant delivery robot is often in a dynamic and complex environment, including the chairs inadvertently moved to the channel and customers coming and going. The traditional Markov decision process path planning algorithm is not save, the robot is very close to the table and chairs. To solve this problem, this paper proposes the Markov Decision Process with a penalty term called MDPPT path planning algorithm according to the traditional Markov decision process (MDP). For MDP, if the restaurant delivery robot bumps into an obstacle, the reward it receives is part of the current status reward. For the MDPPT, the reward it receives not only the part of the current status but also a negative constant term. Simulation results show that the MDPPT algorithm can plan a more secure path.

Reactions and Transport: Diffusion, Inertia, and Subdiffusion

NASA Astrophysics Data System (ADS)

Méndez, Vicenç; Fedotov, Sergei; Horsthemke, Werner

Particles, such as molecules, atoms, or ions, and individuals, such as cells or animals, move in space driven by various forces or cues. In particular, particles or individuals can move randomly, undergo velocity jump processes or spatial jump processes [333]. The steps of the random walk can be independent or correlated, unbiased or biased. The probability density function (PDF) for the jump length can decay rapidly or exhibit a heavy tail. Similarly, the PDF for the waiting time between successive jumps can decay rapidly or exhibit a heavy tail. We will discuss these various possibilities in detail in Chap. 3. Below we provide an introduction to three transport processes: standard diffusion, transport with inertia, and anomalous diffusion.

Caliber Corrected Markov Modeling (C2M2): Correcting Equilibrium Markov Models.

PubMed

Dixit, Purushottam D; Dill, Ken A

2018-02-13

Rate processes are often modeled using Markov State Models (MSMs). Suppose you know a prior MSM and then learn that your prediction of some particular observable rate is wrong. What is the best way to correct the whole MSM? For example, molecular dynamics simulations of protein folding may sample many microstates, possibly giving correct pathways through them while also giving the wrong overall folding rate when compared to experiment. Here, we describe Caliber Corrected Markov Modeling (C 2 M 2 ), an approach based on the principle of maximum entropy for updating a Markov model by imposing state- and trajectory-based constraints. We show that such corrections are equivalent to asserting position-dependent diffusion coefficients in continuous-time continuous-space Markov processes modeled by a Smoluchowski equation. We derive the functional form of the diffusion coefficient explicitly in terms of the trajectory-based constraints. We illustrate with examples of 2D particle diffusion and an overdamped harmonic oscillator.

Implementation of jump-diffusion algorithms for understanding FLIR scenes

NASA Astrophysics Data System (ADS)

Lanterman, Aaron D.; Miller, Michael I.; Snyder, Donald L.

1995-07-01

Our pattern theoretic approach to the automated understanding of forward-looking infrared (FLIR) images brings the traditionally separate endeavors of detection, tracking, and recognition together into a unified jump-diffusion process. New objects are detected and object types are recognized through discrete jump moves. Between jumps, the location and orientation of objects are estimated via continuous diffusions. An hypothesized scene, simulated from the emissive characteristics of the hypothesized scene elements, is compared with the collected data by a likelihood function based on sensor statistics. This likelihood is combined with a prior distribution defined over the set of possible scenes to form a posterior distribution. The jump-diffusion process empirically generates the posterior distribution. Both the diffusion and jump operations involve the simulation of a scene produced by a hypothesized configuration. Scene simulation is most effectively accomplished by pipelined rendering engines such as silicon graphics. We demonstrate the execution of our algorithm on a silicon graphics onyx/reality engine.

Genotype calling from next-generation sequencing data using haplotype information of reads

PubMed Central

Zhi, Degui; Wu, Jihua; Liu, Nianjun; Zhang, Kui

2012-01-01

Motivation: Low coverage sequencing provides an economic strategy for whole genome sequencing. When sequencing a set of individuals, genotype calling can be challenging due to low sequencing coverage. Linkage disequilibrium (LD) based refinement of genotyping calling is essential to improve the accuracy. Current LD-based methods use read counts or genotype likelihoods at individual potential polymorphic sites (PPSs). Reads that span multiple PPSs (jumping reads) can provide additional haplotype information overlooked by current methods. Results: In this article, we introduce a new Hidden Markov Model (HMM)-based method that can take into account jumping reads information across adjacent PPSs and implement it in the HapSeq program. Our method extends the HMM in Thunder and explicitly models jumping reads information as emission probabilities conditional on the states of adjacent PPSs. Our simulation results show that, compared to Thunder, HapSeq reduces the genotyping error rate by 30%, from 0.86% to 0.60%. The results from the 1000 Genomes Project show that HapSeq reduces the genotyping error rate by 12 and 9%, from 2.24% and 2.76% to 1.97% and 2.50% for individuals with European and African ancestry, respectively. We expect our program can improve genotyping qualities of the large number of ongoing and planned whole genome sequencing projects. Contact: dzhi@ms.soph.uab.edu; kzhang@ms.soph.uab.edu Availability: The software package HapSeq and its manual can be found and downloaded at www.ssg.uab.edu/hapseq/. Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22285565

Outcome and Process in Motor Performance: A Comparison of Jumping by Typically Developing Children and Those with Low Motor Proficiency

ERIC Educational Resources Information Center

Williams, Morgan D.; Saunders, John E.; Maschette, Wayne E.; Wilson, Cameron J.

2013-01-01

The motivation for this study was to explore a conceptual framework to understand the outcomes and processes of motor performance in children. Vertical jumping, a fundamental movement skill, was used to compare children (ages 6-12 years) who were typically developing (TD) and those identified as having low motor proficiency (LMP). Jumps were…

Continuous-Time Semi-Markov Models in Health Economic Decision Making: An Illustrative Example in Heart Failure Disease Management.

PubMed

Cao, Qi; Buskens, Erik; Feenstra, Talitha; Jaarsma, Tiny; Hillege, Hans; Postmus, Douwe

2016-01-01

Continuous-time state transition models may end up having large unwieldy structures when trying to represent all relevant stages of clinical disease processes by means of a standard Markov model. In such situations, a more parsimonious, and therefore easier-to-grasp, model of a patient's disease progression can often be obtained by assuming that the future state transitions do not depend only on the present state (Markov assumption) but also on the past through time since entry in the present state. Despite that these so-called semi-Markov models are still relatively straightforward to specify and implement, they are not yet routinely applied in health economic evaluation to assess the cost-effectiveness of alternative interventions. To facilitate a better understanding of this type of model among applied health economic analysts, the first part of this article provides a detailed discussion of what the semi-Markov model entails and how such models can be specified in an intuitive way by adopting an approach called vertical modeling. In the second part of the article, we use this approach to construct a semi-Markov model for assessing the long-term cost-effectiveness of 3 disease management programs for heart failure. Compared with a standard Markov model with the same disease states, our proposed semi-Markov model fitted the observed data much better. When subsequently extrapolating beyond the clinical trial period, these relatively large differences in goodness-of-fit translated into almost a doubling in mean total cost and a 60-d decrease in mean survival time when using the Markov model instead of the semi-Markov model. For the disease process considered in our case study, the semi-Markov model thus provided a sensible balance between model parsimoniousness and computational complexity. © The Author(s) 2015.

Modeling the within-host dynamics of cholera: bacterial-viral interaction.

PubMed

Wang, Xueying; Wang, Jin

2017-08-01

Novel deterministic and stochastic models are proposed in this paper for the within-host dynamics of cholera, with a focus on the bacterial-viral interaction. The deterministic model is a system of differential equations describing the interaction among the two types of vibrios and the viruses. The stochastic model is a system of Markov jump processes that is derived based on the dynamics of the deterministic model. The multitype branching process approximation is applied to estimate the extinction probability of bacteria and viruses within a human host during the early stage of the bacterial-viral infection. Accordingly, a closed-form expression is derived for the disease extinction probability, and analytic estimates are validated with numerical simulations. The local and global dynamics of the bacterial-viral interaction are analysed using the deterministic model, and the result indicates that there is a sharp disease threshold characterized by the basic reproduction number [Formula: see text]: if [Formula: see text], vibrios ingested from the environment into human body will not cause cholera infection; if [Formula: see text], vibrios will grow with increased toxicity and persist within the host, leading to human cholera. In contrast, the stochastic model indicates, more realistically, that there is always a positive probability of disease extinction within the human host.

Markovian prediction of future values for food grains in the economic survey

NASA Astrophysics Data System (ADS)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

Now-a-days prediction and forecasting are plays a vital role in research. For prediction, regression is useful to predict the future value and current value on production process. In this paper, we assume food grain production exhibit Markov chain dependency and time homogeneity. The economic generative performance evaluation the balance time artificial fertilization different level in Estrusdetection using a daily Markov chain model. Finally, Markov process prediction gives better performance compare with Regression model.

Potential for Non-Contact ACL Injury Between Step-Close-Jump and Hop-Jump Tasks.

PubMed

Wang, Li-I; Gu, Chin-Yi; Chen, Wei-Ling; Chang, Mu-San

2010-01-01

This study aimed to compare the kinematics and kinetics during the landing of hop-jump and step-close-jump movements in order to provide further inferring that the potential risk of ACL injuries. Eleven elite male volleyball players were recruited to perform hop-jump and step-close-jump tasks. Lower extremity kinematics and ground reaction forces during landing in stop-jump tasks were recorded. Lower extremity kinetics was calculated by using an inverse dynamic process. Step-close-jump tasks demonstrated smaller peak proximal tibia anterior shear forces during the landing phase. In step-close-jump tasks, increasing hip joint angular velocity during initial foot-ground contact decreased peak posterior ground reaction force during the landing phase, which theoretically could reduce the risk of ACL injury. Key pointsThe different landing techniques required for these two stop-jump tasks do not necessarily affect the jump height.Hop-jump decreased the hip joint angular velocity at initial foot contact with ground, which could lead to an increasing peak posterior GRF during the landing phase.Hop-jump decreased hip and knee joint angular flexion displacement during the landing, which could increase the peak vertical loading rate during the landing phase.

From quantum mechanics to finance: Microfoundations for jumps, spikes and high volatility phases in diffusion price processes

NASA Astrophysics Data System (ADS)

Henkel, Christof

2017-03-01

We present an agent behavior based microscopic model that induces jumps, spikes and high volatility phases in the price process of a traded asset. We transfer dynamics of thermally activated jumps of an unexcited/excited two state system discussed in the context of quantum mechanics to agent socio-economic behavior and provide microfoundations. After we link the endogenous agent behavior to price dynamics we establish the circumstances under which the dynamics converge to an Itô-diffusion price processes in the large market limit.

Enzyme kinetics above denaturation temperature: a temperature-jump/stopped-flow apparatus.

PubMed

Kintses, Bálint; Simon, Zoltán; Gyimesi, Máté; Tóth, Júlia; Jelinek, Balázs; Niedetzky, Csaba; Kovács, Mihály; Málnási-Csizmadia, András

2006-12-15

We constructed a "temperature-jump/stopped-flow" apparatus that allows us to study fast enzyme reactions at extremely high temperatures. This apparatus is a redesigned stopped-flow which is capable of mixing the reactants on a submillisecond timescale concomitant with a temperature-jump even as large as 60 degrees C. We show that enzyme reactions that are faster than the denaturation process can be investigated above denaturation temperatures. In addition, the temperature-jump/stopped-flow enables us to investigate at physiological temperature the mechanisms of many human enzymes, which was impossible until now because of their heat instability. Furthermore, this technique is extremely useful in studying the progress of heat-induced protein unfolding. The temperature-jump/stopped-flow method combined with the application of structure-specific fluorescence signals provides novel opportunities to study the stability of certain regions of enzymes and identify the unfolding-initiating regions of proteins. The temperature-jump/stopped-flow technique may become a breakthrough in exploring new features of enzymes and the mechanism of unfolding processes.

Fundamental movement skills testing in children with cerebral palsy.

PubMed

Capio, Catherine M; Sit, Cindy H P; Abernethy, Bruce

2011-01-01

To examine the inter-rater reliability and comparative validity of product-oriented and process-oriented measures of fundamental movement skills among children with cerebral palsy (CP). In total, 30 children with CP aged 6 to 14 years (Mean = 9.83, SD = 2.5) and classified in Gross Motor Function Classification System (GMFCS) levels I-III performed tasks of catching, throwing, kicking, horizontal jumping and running. Process-oriented assessment was undertaken using a number of components of the Test of Gross Motor Development (TGMD-2), while product-oriented assessment included measures of time taken, distance covered and number of successful task completions. Cohen's kappa, Spearman's rank correlation coefficient and tests to compare correlated correlation coefficients were performed. Very good inter-rater reliability was found. Process-oriented measures for running and jumping had significant associations with GMFCS, as did seven product-oriented measures for catching, throwing, kicking, running and jumping. Product-oriented measures of catching, kicking and running had stronger associations with GMFCS than the corresponding process-oriented measures. Findings support the validity of process-oriented measures for running and jumping and of product-oriented measures of catching, throwing, kicking, running and jumping. However, product-oriented measures for catching, kicking and running appear to have stronger associations with functional abilities of children with CP, and are thus recommended for use in rehabilitation processes.

Towards Stability Analysis of Jump Linear Systems with State-Dependent and Stochastic Switching

NASA Technical Reports Server (NTRS)

Tejada, Arturo; Gonzalez, Oscar R.; Gray, W. Steven

2004-01-01

This paper analyzes the stability of hierarchical jump linear systems where the supervisor is driven by a Markovian stochastic process and by the values of the supervised jump linear system s states. The stability framework for this class of systems is developed over infinite and finite time horizons. The framework is then used to derive sufficient stability conditions for a specific class of hybrid jump linear systems with performance supervision. New sufficient stochastic stability conditions for discrete-time jump linear systems are also presented.

MARKOV: A methodology for the solution of infinite time horizon MARKOV decision processes

USGS Publications Warehouse

Williams, B.K.

1988-01-01

Algorithms are described for determining optimal policies for finite state, finite action, infinite discrete time horizon Markov decision processes. Both value-improvement and policy-improvement techniques are used in the algorithms. Computing procedures are also described. The algorithms are appropriate for processes that are either finite or infinite, deterministic or stochastic, discounted or undiscounted, in any meaningful combination of these features. Computing procedures are described in terms of initial data processing, bound improvements, process reduction, and testing and solution. Application of the methodology is illustrated with an example involving natural resource management. Management implications of certain hypothesized relationships between mallard survival and harvest rates are addressed by applying the optimality procedures to mallard population models.

HIV Migration Between Blood and Cerebrospinal Fluid or Semen Over Time

PubMed Central

Chaillon, Antoine; Gianella, Sara; Wertheim, Joel O.; Richman, Douglas D.; Mehta, Sanjay R.; Smith, David M.

2014-01-01

Previous studies reported associations between neuropathogenesis and human immunodeficiency virus (HIV) compartmentalization in cerebrospinal fluid (CSF) and between sexual transmission and human immunodeficiency virus type 1 (HIV) compartmentalization in semen. It remains unclear, however, how compartmentalization dynamics change over time. To address this, we used statistical methods and Bayesian phylogenetic approaches to reconstruct temporal dynamics of HIV migration between blood and CSF and between blood and the male genital tract. We investigated 11 HIV-infected individuals with paired semen and blood samples and 4 individuals with paired CSF and blood samples. Aligned partial HIV env sequences were analyzed by (1) phylogenetic reconstruction, using a Bayesian Markov-chain Monte Carlo approach; (2) evaluation of viral compartmentalization, using tree-based and distance-based methods; and (3) analysis of migration events, using a discrete Bayesian asymmetric phylogeographic approach of diffusion with Markov jump counts estimation. Finally, we evaluated potential correlates of viral gene flow across anatomical compartments. We observed bidirectional replenishment of viral compartments and asynchronous peaks of viral migration from and to blood over time, suggesting that disruption of viral compartment is transient and directionally selected. These findings imply that viral subpopulations in anatomical sites are an active part of the whole viral population and that compartmental reservoirs could have implications in future eradication studies. PMID:24302756

Tracking the visual focus of attention for a varying number of wandering people.

PubMed

Smith, Kevin; Ba, Sileye O; Odobez, Jean-Marc; Gatica-Perez, Daniel

2008-07-01

We define and address the problem of finding the visual focus of attention for a varying number of wandering people (VFOA-W), determining where the people's movement is unconstrained. VFOA-W estimation is a new and important problem with mplications for behavior understanding and cognitive science, as well as real-world applications. One such application, which we present in this article, monitors the attention passers-by pay to an outdoor advertisement. Our approach to the VFOA-W problem proposes a multi-person tracking solution based on a dynamic Bayesian network that simultaneously infers the (variable) number of people in a scene, their body locations, their head locations, and their head pose. For efficient inference in the resulting large variable-dimensional state-space we propose a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampling scheme, as well as a novel global observation model which determines the number of people in the scene and localizes them. We propose a Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM)-based VFOA-W model which use head pose and location information to determine people's focus state. Our models are evaluated for tracking performance and ability to recognize people looking at an outdoor advertisement, with results indicating good performance on sequences where a moderate number of people pass in front of an advertisement.

Markov and non-Markov processes in complex systems by the dynamical information entropy

NASA Astrophysics Data System (ADS)

Yulmetyev, R. M.; Gafarov, F. M.

1999-12-01

We consider the Markov and non-Markov processes in complex systems by the dynamical information Shannon entropy (DISE) method. The influence and important role of the two mutually dependent channels of entropy alternation (creation or generation of correlation) and anti-correlation (destroying or annihilation of correlation) have been discussed. The developed method has been used for the analysis of the complex systems of various natures: slow neutron scattering in liquid cesium, psychology (short-time numeral and pattern human memory and effect of stress on the dynamical taping-test), random dynamics of RR-intervals in human ECG (problem of diagnosis of various disease of the human cardio-vascular systems), chaotic dynamics of the parameters of financial markets and ecological systems.

Open Quantum Systems and Classical Trajectories

NASA Astrophysics Data System (ADS)

Rebolledo, Rolando

2004-09-01

A Quantum Markov Semigroup consists of a family { T} = ({ T}t)_{t ∈ B R+} of normal ω*- continuous completely positive maps on a von Neumann algebra 𝔐 which preserve the unit and satisfy the semigroup property. This class of semigroups has been extensively used to represent open quantum systems. This article is aimed at studying the existence of a { T} -invariant abelian subalgebra 𝔄 of 𝔐. When this happens, the restriction of { T}t to 𝔄 defines a classical Markov semigroup T = (Tt)t ∈ ∝ + say, associated to a classical Markov process X = (Xt)t ∈ ∝ +. The structure (𝔄, T, X) unravels the quantum Markov semigroup { T} , providing a bridge between open quantum systems and classical stochastic processes.

Markov chains for testing redundant software

NASA Technical Reports Server (NTRS)

White, Allan L.; Sjogren, Jon A.

1988-01-01

A preliminary design for a validation experiment has been developed that addresses several problems unique to assuring the extremely high quality of multiple-version programs in process-control software. The procedure uses Markov chains to model the error states of the multiple version programs. The programs are observed during simulated process-control testing, and estimates are obtained for the transition probabilities between the states of the Markov chain. The experimental Markov chain model is then expanded into a reliability model that takes into account the inertia of the system being controlled. The reliability of the multiple version software is computed from this reliability model at a given confidence level using confidence intervals obtained for the transition probabilities during the experiment. An example demonstrating the method is provided.

Envelopes of Sets of Measures, Tightness, and Markov Control Processes

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

Gonzalez-Hernandez, J.; Hernandez-Lerma, O.

1999-11-15

We introduce upper and lower envelopes for sets of measures on an arbitrary topological space, which are then used to give a tightness criterion. These concepts are applied to show the existence of optimal policies for a class of Markov control processes.

Irreversible Local Markov Chains with Rapid Convergence towards Equilibrium.

PubMed

Kapfer, Sebastian C; Krauth, Werner

2017-12-15

We study the continuous one-dimensional hard-sphere model and present irreversible local Markov chains that mix on faster time scales than the reversible heat bath or Metropolis algorithms. The mixing time scales appear to fall into two distinct universality classes, both faster than for reversible local Markov chains. The event-chain algorithm, the infinitesimal limit of one of these Markov chains, belongs to the class presenting the fastest decay. For the lattice-gas limit of the hard-sphere model, reversible local Markov chains correspond to the symmetric simple exclusion process (SEP) with periodic boundary conditions. The two universality classes for irreversible Markov chains are realized by the totally asymmetric SEP (TASEP), and by a faster variant (lifted TASEP) that we propose here. We discuss how our irreversible hard-sphere Markov chains generalize to arbitrary repulsive pair interactions and carry over to higher dimensions through the concept of lifted Markov chains and the recently introduced factorized Metropolis acceptance rule.

Irreversible Local Markov Chains with Rapid Convergence towards Equilibrium

NASA Astrophysics Data System (ADS)

Kapfer, Sebastian C.; Krauth, Werner

2017-12-01

We study the continuous one-dimensional hard-sphere model and present irreversible local Markov chains that mix on faster time scales than the reversible heat bath or Metropolis algorithms. The mixing time scales appear to fall into two distinct universality classes, both faster than for reversible local Markov chains. The event-chain algorithm, the infinitesimal limit of one of these Markov chains, belongs to the class presenting the fastest decay. For the lattice-gas limit of the hard-sphere model, reversible local Markov chains correspond to the symmetric simple exclusion process (SEP) with periodic boundary conditions. The two universality classes for irreversible Markov chains are realized by the totally asymmetric SEP (TASEP), and by a faster variant (lifted TASEP) that we propose here. We discuss how our irreversible hard-sphere Markov chains generalize to arbitrary repulsive pair interactions and carry over to higher dimensions through the concept of lifted Markov chains and the recently introduced factorized Metropolis acceptance rule.

A high-fidelity weather time series generator using the Markov Chain process on a piecewise level

NASA Astrophysics Data System (ADS)

Hersvik, K.; Endrerud, O.-E. V.

2017-12-01

A method is developed for generating a set of unique weather time-series based on an existing weather series. The method allows statistically valid weather variations to take place within repeated simulations of offshore operations. The numerous generated time series need to share the same statistical qualities as the original time series. Statistical qualities here refer mainly to the distribution of weather windows available for work, including durations and frequencies of such weather windows, and seasonal characteristics. The method is based on the Markov chain process. The core new development lies in how the Markov Process is used, specifically by joining small pieces of random length time series together rather than joining individual weather states, each from a single time step, which is a common solution found in the literature. This new Markov model shows favorable characteristics with respect to the requirements set forth and all aspects of the validation performed.

VAMPnets for deep learning of molecular kinetics.

PubMed

Mardt, Andreas; Pasquali, Luca; Wu, Hao; Noé, Frank

2018-01-02

There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Carey, Lawerence D.; Schultz, Elise V.; Stano, Geoffery T.; Kozlowski, Danielle M.; Goodman, Steven

2012-01-01

Key points that this analysis will begin to address are: 1)What physically is going on in the cloud when there is a jump in lightning? - Updraft variations, ice fluxes. 2)How do these processes fit in with severe storm conceptual models? 3)What would this information provide an end user (i.e., the forecaster)? - Relate LJA to radar observations, like changes in reflectivity, MESH, VIL, etc. based multi-Doppler derived physical relationships 4) How do we best transistionthis algorithm into the warning decision process. The known relationship between lightning updraft strength/volume and precipitation ice mass production can be extended to the concept of the lightning jump. Examination of the first lightning jump times from 329 storms in Schultz et al. shows an increase in the mean reflectivity profile and mixed phase echo volume during the 10 minutes prior to the lightning jump. Limited dual-Doppler results show that the largest lightning jumps are well correlated in time with increases in updraft strength/volume and precipitation ice mass production; however, the smaller magnitude lightning jumps appear to have more subtle relationships to updraft and ice mass characteristics.

Dynamic strain aging in stress controlled creep-fatigue tests of 316L stainless steel under different loading conditions

NASA Astrophysics Data System (ADS)

Jiang, Huifeng; Chen, Xuedong; Fan, Zhichao; Dong, Jie; Jiang, Heng; Lu, Shouxiang

2009-08-01

Stress controlled fatigue-creep tests were carried out for 316L stainless steel under different loading conditions, i.e. different loading levels at the fixed temperature (loading condition 1, LC1) and different temperatures at the fixed loading level (loading condition 2, LC2). Cyclic deformation behaviors were investigated with respect to the evolutions of strain amplitude and mean strain. Abrupt mean strain jumps were found during cyclic deformation, which was in response to the dynamic strain aging effect. Moreover, as to LC1, when the minimum stress is negative at 550 °C, abrupt mean strain jumps occur at the early stage of cyclic deformation and there are many jumps during the whole process. While the minimum stress is positive, mean strain only jumps once at the end of deformation. Similar results were also found in LC2, when the loading level is fixed at -100 to 385 MPa, at higher temperatures (560, 575 °C), abrupt mean strain jumps occur at the early stage of cyclic deformation and there are many jumps during the whole process. While at lower temperature (540 °C), mean strain only jumps once at the end of deformation.

Biomechanical Analysis of Locust Jumping in a Physically Realistic Virtual Environment

NASA Astrophysics Data System (ADS)

Cofer, David; Cymbalyuk, Gennady; Heitler, William; Edwards, Donald

2008-03-01

The biomechanical and neural components that underlie locust jumping have been extensively studied. Previous research suggested that jump energy is stored primarily in the extensor apodeme, and in a band of cuticle called the semi-lunar process (SLP). As it has thus far proven impossible to experimentally alter the SLP without rendering a locust unable to jump, it has not been possible to test whether the energy stored in the SLP has a significant impact on the jump. To address problems such as this we have developed a software toolkit, AnimatLab, which allows researchers to build and test virtual organisms. We used this software to build a virtual locust, and then asked how the SLP is utilized during jumping. The results show that without the SLP the jump distance was reduced by almost half. Further, the simulations were also able to show that loss of the SLP had a significant impact on the final phase of the jump. We are currently working on postural control mechanisms for targeted jumping in locust.

RJMCMC based Text Placement to Optimize Label Placement and Quantity

NASA Astrophysics Data System (ADS)

Touya, Guillaume; Chassin, Thibaud

2018-05-01

Label placement is a tedious task in map design, and its automation has long been a goal for researchers in cartography, but also in computational geometry. Methods that search for an optimal or nearly optimal solution that satisfies a set of constraints, such as label overlapping, have been proposed in the literature. Most of these methods mainly focus on finding the optimal position for a given set of labels, but rarely allow the removal of labels as part of the optimization. This paper proposes to apply an optimization technique called Reversible-Jump Markov Chain Monte Carlo that enables to easily model the removal or addition during the optimization iterations. The method, quite preliminary for now, is tested on a real dataset, and the first results are encouraging.

Bayesian reconstruction of projection reconstruction NMR (PR-NMR).

PubMed

Yoon, Ji Won

2014-11-01

Projection reconstruction nuclear magnetic resonance (PR-NMR) is a technique for generating multidimensional NMR spectra. A small number of projections from lower-dimensional NMR spectra are used to reconstruct the multidimensional NMR spectra. In our previous work, it was shown that multidimensional NMR spectra are efficiently reconstructed using peak-by-peak based reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. We propose an extended and generalized RJMCMC algorithm replacing a simple linear model with a linear mixed model to reconstruct close NMR spectra into true spectra. This statistical method generates samples in a Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of a protein HasA. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Large Deviations Analysis of Certain Qualitative Properties of Parallel Tempering and Infinite Swapping Algorithms

DOE PAGES

Doll, J.; Dupuis, P.; Nyquist, P.

2017-02-08

Parallel tempering, or replica exchange, is a popular method for simulating complex systems. The idea is to run parallel simulations at different temperatures, and at a given swap rate exchange configurations between the parallel simulations. From the perspective of large deviations it is optimal to let the swap rate tend to infinity and it is possible to construct a corresponding simulation scheme, known as infinite swapping. In this paper we propose a novel use of large deviations for empirical measures for a more detailed analysis of the infinite swapping limit in the setting of continuous time jump Markov processes. Usingmore » the large deviations rate function and associated stochastic control problems we consider a diagnostic based on temperature assignments, which can be easily computed during a simulation. We show that the convergence of this diagnostic to its a priori known limit is a necessary condition for the convergence of infinite swapping. The rate function is also used to investigate the impact of asymmetries in the underlying potential landscape, and where in the state space poor sampling is most likely to occur.« less

Structure and Randomness of Continuous-Time, Discrete-Event Processes

NASA Astrophysics Data System (ADS)

Marzen, Sarah E.; Crutchfield, James P.

2017-10-01

Loosely speaking, the Shannon entropy rate is used to gauge a stochastic process' intrinsic randomness; the statistical complexity gives the cost of predicting the process. We calculate, for the first time, the entropy rate and statistical complexity of stochastic processes generated by finite unifilar hidden semi-Markov models—memoryful, state-dependent versions of renewal processes. Calculating these quantities requires introducing novel mathematical objects (ɛ -machines of hidden semi-Markov processes) and new information-theoretic methods to stochastic processes.

Bayesian dynamic regression models for interval censored survival data with application to children dental health.

PubMed

Wang, Xiaojing; Chen, Ming-Hui; Yan, Jun

2013-07-01

Cox models with time-varying coefficients offer great flexibility in capturing the temporal dynamics of covariate effects on event times, which could be hidden from a Cox proportional hazards model. Methodology development for varying coefficient Cox models, however, has been largely limited to right censored data; only limited work on interval censored data has been done. In most existing methods for varying coefficient models, analysts need to specify which covariate coefficients are time-varying and which are not at the time of fitting. We propose a dynamic Cox regression model for interval censored data in a Bayesian framework, where the coefficient curves are piecewise constant but the number of pieces and the jump points are covariate specific and estimated from the data. The model automatically determines the extent to which the temporal dynamics is needed for each covariate, resulting in smoother and more stable curve estimates. The posterior computation is carried out via an efficient reversible jump Markov chain Monte Carlo algorithm. Inference of each coefficient is based on an average of models with different number of pieces and jump points. A simulation study with three covariates, each with a coefficient of different degree in temporal dynamics, confirmed that the dynamic model is preferred to the existing time-varying model in terms of model comparison criteria through conditional predictive ordinate. When applied to a dental health data of children with age between 7 and 12 years, the dynamic model reveals that the relative risk of emergence of permanent tooth 24 between children with and without an infected primary predecessor is the highest at around age 7.5, and that it gradually reduces to one after age 11. These findings were not seen from the existing studies with Cox proportional hazards models.

Predictive Rate-Distortion for Infinite-Order Markov Processes

NASA Astrophysics Data System (ADS)

Marzen, Sarah E.; Crutchfield, James P.

2016-06-01

Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on finite sequences cannot capture all of their past dependencies. Spectral arguments confirm a popular intuition: algorithms that cluster finite-length sequences fail dramatically when the underlying process has long-range temporal correlations and can fail even for processes generated by finite-memory hidden Markov models. We circumvent the curse of dimensionality in rate-distortion analysis of finite- and infinite-order processes by casting predictive rate-distortion objective functions in terms of the forward- and reverse-time causal states of computational mechanics. Examples demonstrate that the resulting algorithms yield substantial improvements.

Many roads to synchrony: natural time scales and their algorithms.

PubMed

James, Ryan G; Mahoney, John R; Ellison, Christopher J; Crutchfield, James P

2014-04-01

We consider two important time scales-the Markov and cryptic orders-that monitor how an observer synchronizes to a finitary stochastic process. We show how to compute these orders exactly and that they are most efficiently calculated from the ε-machine, a process's minimal unifilar model. Surprisingly, though the Markov order is a basic concept from stochastic process theory, it is not a probabilistic property of a process. Rather, it is a topological property and, moreover, it is not computable from any finite-state model other than the ε-machine. Via an exhaustive survey, we close by demonstrating that infinite Markov and infinite cryptic orders are a dominant feature in the space of finite-memory processes. We draw out the roles played in statistical mechanical spin systems by these two complementary length scales.

How coalescing droplets jump.

PubMed

Enright, Ryan; Miljkovic, Nenad; Sprittles, James; Nolan, Kevin; Mitchell, Robert; Wang, Evelyn N

2014-10-28

Surface engineering at the nanoscale is a rapidly developing field that promises to impact a range of applications including energy production, water desalination, self-cleaning and anti-icing surfaces, thermal management of electronics, microfluidic platforms, and environmental pollution control. As the area advances, more detailed insights of dynamic wetting interactions on these surfaces are needed. In particular, the coalescence of two or more droplets on ultra-low adhesion surfaces leads to droplet jumping. Here we show, through detailed measurements of jumping droplets during water condensation coupled with numerical simulations of binary droplet coalescence, that this process is fundamentally inefficient with only a small fraction of the available excess surface energy (≲ 6%) convertible into translational kinetic energy. These findings clarify the role of internal fluid dynamics during the jumping droplet coalescence process and underpin the development of systems that can harness jumping droplets for a wide range of applications.

Using the Pearson Distribution for Synthesis of the Suboptimal Algorithms for Filtering Multi-Dimensional Markov Processes

NASA Astrophysics Data System (ADS)

Mit'kin, A. S.; Pogorelov, V. A.; Chub, E. G.

2015-08-01

We consider the method of constructing the suboptimal filter on the basis of approximating the a posteriori probability density of the multidimensional Markov process by the Pearson distributions. The proposed method can efficiently be used for approximating asymmetric, excessive, and finite densities.

A Comparison of Mechanical Parameters Between the Counter Movement Jump and Drop Jump in Biathletes

PubMed Central

Król, Henryk; Mynarski, Władysław

2012-01-01

The main objective of the study was to determine to what degree higher muscular activity, achieved by increased load in the extension phase (eccentric muscle action) of the vertical jump, affects the efficiency of the vertical jump. Sixteen elite biathletes participated in this investigation. The biathletes performed tests that consisted of five, single “maximal” vertical jumps (counter movement jump – CMJ) and five, single vertical jumps, in which the task was to touch a bar placed over the jumping biathletes (specific task counter movement jump – SCMJ). Then, they performed five, single drop jumps from an elevation of 0.4m (DJ). Ground reaction forces were registered using the KISTLER 9182C force platform. MVJ software was used for signal processing (Król, 1999) and enabling calculations for kinematic and kinetic parameters of the subject’s jump movements (on-line system). The results indicate that only height of the jump (h) and mean power (Pmean) during the takeoff are statistically significant. Both h and Pmean are higher in the DJ. The results of this study may indicate that elite biathletes are well adapted to eccentric work of the lower limbs, thus reaching greater values of power during the drop jump. These neuromuscular adaptive changes may allow for a more dynamic and efficient running technique. PMID:23487157

Markov Processes: Exploring the Use of Dynamic Visualizations to Enhance Student Understanding

ERIC Educational Resources Information Center

Pfannkuch, Maxine; Budgett, Stephanie

2016-01-01

Finding ways to enhance introductory students' understanding of probability ideas and theory is a goal of many first-year probability courses. In this article, we explore the potential of a prototype tool for Markov processes using dynamic visualizations to develop in students a deeper understanding of the equilibrium and hitting times…

A Survey of Bioinspired Jumping Robot: Takeoff, Air Posture Adjustment, and Landing Buffer

PubMed Central

2017-01-01

A bioinspired jumping robot has a strong ability to overcome obstacles. It can be applied to the occasion with complex and changeable environment, such as detection of planet surface, postdisaster relief, and military reconnaissance. So the bioinspired jumping robot has broad application prospect. The jumping process of the robot can be divided into three stages: takeoff, air posture adjustment, and landing buffer. The motivation of this review is to investigate the research results of the most published bioinspired jumping robots for these three stages. Then, the movement performance of the bioinspired jumping robots is analyzed and compared quantitatively. Then, the limitation of the research on bioinspired jumping robots is discussed, such as the research on the mechanism of biological motion is not thorough enough, the research method about structural design, material applications, and control are still traditional, and energy utilization is low, which make the robots far from practical applications. Finally, the development trend is summarized. This review provides a reference for further research of bioinspired jumping robots. PMID:29311756

Scalable approximate policies for Markov decision process models of hospital elective admissions.

PubMed

Zhu, George; Lizotte, Dan; Hoey, Jesse

2014-05-01

To demonstrate the feasibility of using stochastic simulation methods for the solution of a large-scale Markov decision process model of on-line patient admissions scheduling. The problem of admissions scheduling is modeled as a Markov decision process in which the states represent numbers of patients using each of a number of resources. We investigate current state-of-the-art real time planning methods to compute solutions to this Markov decision process. Due to the complexity of the model, traditional model-based planners are limited in scalability since they require an explicit enumeration of the model dynamics. To overcome this challenge, we apply sample-based planners along with efficient simulation techniques that given an initial start state, generate an action on-demand while avoiding portions of the model that are irrelevant to the start state. We also propose a novel variant of a popular sample-based planner that is particularly well suited to the elective admissions problem. Results show that the stochastic simulation methods allow for the problem size to be scaled by a factor of almost 10 in the action space, and exponentially in the state space. We have demonstrated our approach on a problem with 81 actions, four specialities and four treatment patterns, and shown that we can generate solutions that are near-optimal in about 100s. Sample-based planners are a viable alternative to state-based planners for large Markov decision process models of elective admissions scheduling. Copyright © 2014 Elsevier B.V. All rights reserved.

Operational Markov Condition for Quantum Processes

NASA Astrophysics Data System (ADS)

Pollock, Felix A.; Rodríguez-Rosario, César; Frauenheim, Thomas; Paternostro, Mauro; Modi, Kavan

2018-01-01

We derive a necessary and sufficient condition for a quantum process to be Markovian which coincides with the classical one in the relevant limit. Our condition unifies all previously known definitions for quantum Markov processes by accounting for all potentially detectable memory effects. We then derive a family of measures of non-Markovianity with clear operational interpretations, such as the size of the memory required to simulate a process or the experimental falsifiability of a Markovian hypothesis.

Fuzzy Markov random fields versus chains for multispectral image segmentation.

PubMed

Salzenstein, Fabien; Collet, Christophe

2006-11-01

This paper deals with a comparison of recent statistical models based on fuzzy Markov random fields and chains for multispectral image segmentation. The fuzzy scheme takes into account discrete and continuous classes which model the imprecision of the hidden data. In this framework, we assume the dependence between bands and we express the general model for the covariance matrix. A fuzzy Markov chain model is developed in an unsupervised way. This method is compared with the fuzzy Markovian field model previously proposed by one of the authors. The segmentation task is processed with Bayesian tools, such as the well-known MPM (Mode of Posterior Marginals) criterion. Our goal is to compare the robustness and rapidity for both methods (fuzzy Markov fields versus fuzzy Markov chains). Indeed, such fuzzy-based procedures seem to be a good answer, e.g., for astronomical observations when the patterns present diffuse structures. Moreover, these approaches allow us to process missing data in one or several spectral bands which correspond to specific situations in astronomy. To validate both models, we perform and compare the segmentation on synthetic images and raw multispectral astronomical data.

Generalization of Faustmann's Formula for Stochastic Forest Growth and Prices with Markov Decision Process Models

Treesearch

Joseph Buongiorno

2001-01-01

Faustmann's formula gives the land value, or the forest value of land with trees, under deterministic assumptions regarding future stand growth and prices, over an infinite horizon. Markov decision process (MDP) models generalize Faustmann's approach by recognizing that future stand states and prices are known only as probabilistic distributions. The...

Dynamic Bandwidth Provisioning Using Markov Chain Based on RSVP

DTIC Science & Technology

2013-09-01

AUTHOR(S) Yavuz Sagir 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES) Naval Postgraduate School Monterey, CA 93943-5000 8. PERFORMING...ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME(S) AND ADDRESS (ES) N/A 10. SPONSORING/MONITORING AGENCY REPORT NUMBER 11...is finite or countable. A Markov process is basically a stochastic process in which the past history of the process is irrelevant if the current

The Embedding Problem for Markov Models of Nucleotide Substitution

PubMed Central

Verbyla, Klara L.; Yap, Von Bing; Pahwa, Anuj; Shao, Yunli; Huttley, Gavin A.

2013-01-01

Continuous-time Markov processes are often used to model the complex natural phenomenon of sequence evolution. To make the process of sequence evolution tractable, simplifying assumptions are often made about the sequence properties and the underlying process. The validity of one such assumption, time-homogeneity, has never been explored. Violations of this assumption can be found by identifying non-embeddability. A process is non-embeddable if it can not be embedded in a continuous time-homogeneous Markov process. In this study, non-embeddability was demonstrated to exist when modelling sequence evolution with Markov models. Evidence of non-embeddability was found primarily at the third codon position, possibly resulting from changes in mutation rate over time. Outgroup edges and those with a deeper time depth were found to have an increased probability of the underlying process being non-embeddable. Overall, low levels of non-embeddability were detected when examining individual edges of triads across a diverse set of alignments. Subsequent phylogenetic reconstruction analyses demonstrated that non-embeddability could impact on the correct prediction of phylogenies, but at extremely low levels. Despite the existence of non-embeddability, there is minimal evidence of violations of the local time homogeneity assumption and consequently the impact is likely to be minor. PMID:23935949

Continuous-time safety-first portfolio selection with jump-diffusion processes

NASA Astrophysics Data System (ADS)

Yan, Wei

2012-04-01

This article is concerned with continuous-time portfolio selection based on a safety-first criterion under discontinuous price processes (jump-diffusion processes). The solution of the corresponding Hamilton-Jacobi-Bellman equation of the problem is demonstrated. The analytical solutions are presented when there does not exist any riskless asset. Moreover, the problem is also discussed while there exists one riskless asset.

Self-jumping Mechanism of Melting Frost on Superhydrophobic Surfaces.

PubMed

Liu, Xiaolin; Chen, Huawei; Zhao, Zehui; Wang, Yamei; Liu, Hong; Zhang, Deyuan

2017-11-07

Frost accretion on surfaces may cause severe problems and the high-efficiency defrosting methods are still urgently needed in many application fields like heat transfer, optical and electric power system, etc. In this study, a nano-needle superhydrophobic surface is prepared and the frosting/defrosting experiments are conducted on it. Three steps are found in the defrosting process: melting frost shrinking and splitting, instantaneous self-triggered deforming followed by deformation-induced movements (namely, in-situ shaking, rotating, rolling, and self-jumping). The self-jumping performance of the melting frost is extremely fascinating and worth studying due to its capability of evidently shortening the defrosting process and reducing (even avoiding) residual droplets after defrosting. The study on the melting frost self-jumping phenomena demonstrates that the kinetic energy transformed from instantaneous superficial area change in self-triggered deforming step is the intrinsic reason for various melting frost self-propelled movements, and when the transformed energy reaches a certain amount, the self-jumping phenomena occur. And some facilitating conditions for melting frost self-jumping phenomena are also discussed. This work will provide an efficient way for defrosting or an inspiration for further research on defrosting.

Measurement-based reliability/performability models

NASA Technical Reports Server (NTRS)

Hsueh, Mei-Chen

1987-01-01

Measurement-based models based on real error-data collected on a multiprocessor system are described. Model development from the raw error-data to the estimation of cumulative reward is also described. A workload/reliability model is developed based on low-level error and resource usage data collected on an IBM 3081 system during its normal operation in order to evaluate the resource usage/error/recovery process in a large mainframe system. Thus, both normal and erroneous behavior of the system are modeled. The results provide an understanding of the different types of errors and recovery processes. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model the system behavior. A sensitivity analysis is performed to investigate the significance of using a semi-Markov process, as opposed to a Markov process, to model the measured system.

Approximation of epidemic models by diffusion processes and their statistical inference.

PubMed

Guy, Romain; Larédo, Catherine; Vergu, Elisabeta

2015-02-01

Multidimensional continuous-time Markov jump processes [Formula: see text] on [Formula: see text] form a usual set-up for modeling [Formula: see text]-like epidemics. However, when facing incomplete epidemic data, inference based on [Formula: see text] is not easy to be achieved. Here, we start building a new framework for the estimation of key parameters of epidemic models based on statistics of diffusion processes approximating [Formula: see text]. First, previous results on the approximation of density-dependent [Formula: see text]-like models by diffusion processes with small diffusion coefficient [Formula: see text], where [Formula: see text] is the population size, are generalized to non-autonomous systems. Second, our previous inference results on discretely observed diffusion processes with small diffusion coefficient are extended to time-dependent diffusions. Consistent and asymptotically Gaussian estimates are obtained for a fixed number [Formula: see text] of observations, which corresponds to the epidemic context, and for [Formula: see text]. A correction term, which yields better estimates non asymptotically, is also included. Finally, performances and robustness of our estimators with respect to various parameters such as [Formula: see text] (the basic reproduction number), [Formula: see text], [Formula: see text] are investigated on simulations. Two models, [Formula: see text] and [Formula: see text], corresponding to single and recurrent outbreaks, respectively, are used to simulate data. The findings indicate that our estimators have good asymptotic properties and behave noticeably well for realistic numbers of observations and population sizes. This study lays the foundations of a generic inference method currently under extension to incompletely observed epidemic data. Indeed, contrary to the majority of current inference techniques for partially observed processes, which necessitates computer intensive simulations, our method being mostly an analytical approach requires only the classical optimization steps.

Monte Carlo Simulation of Markov, Semi-Markov, and Generalized Semi- Markov Processes in Probabilistic Risk Assessment

NASA Technical Reports Server (NTRS)

English, Thomas

2005-01-01

A standard tool of reliability analysis used at NASA-JSC is the event tree. An event tree is simply a probability tree, with the probabilities determining the next step through the tree specified at each node. The nodal probabilities are determined by a reliability study of the physical system at work for a particular node. The reliability study performed at a node is typically referred to as a fault tree analysis, with the potential of a fault tree existing.for each node on the event tree. When examining an event tree it is obvious why the event tree/fault tree approach has been adopted. Typical event trees are quite complex in nature, and the event tree/fault tree approach provides a systematic and organized approach to reliability analysis. The purpose of this study was two fold. Firstly, we wanted to explore the possibility that a semi-Markov process can create dependencies between sojourn times (the times it takes to transition from one state to the next) that can decrease the uncertainty when estimating time to failures. Using a generalized semi-Markov model, we studied a four element reliability model and were able to demonstrate such sojourn time dependencies. Secondly, we wanted to study the use of semi-Markov processes to introduce a time variable into the event tree diagrams that are commonly developed in PRA (Probabilistic Risk Assessment) analyses. Event tree end states which change with time are more representative of failure scenarios than are the usual static probability-derived end states.

Coalescence-Induced Jumping of Multiple Condensate Droplets on Hierarchical Superhydrophobic Surfaces

PubMed Central

Chen, Xuemei; Patel, Ravi S.; Weibel, Justin A.; Garimella, Suresh V.

2016-01-01

Coalescence-induced jumping of condensate droplets from a superhydrophobic surface with hierarchical micro/nanoscale roughness is quantitatively characterized. Experimental observations show that the condensate droplet jumping is induced by coalescence of multiple droplets of different sizes, and that the coalesced droplet trajectories typically deviate from the surface normal. A depth-from-defocus image processing technique is developed to track the out-of-plane displacement of the jumping droplets, so as to accurately measure the droplet size and velocity. The results demonstrate that the highest jumping velocity is achieved when two droplets coalesce. The jumping velocity decreases gradually with an increase in the number of coalescing droplets, despite the greater potential surface energy released upon coalescence. A general theoretical model that accounts for viscous dissipation, surface adhesion, line tension, the initial droplet wetting states, and the number and sizes of the coalescing droplets is developed to explain the trends of droplet jumping velocity observed in the experiments. PMID:26725512

Reversal time of jump-noise magnetization dynamics in nanomagnets via Monte Carlo simulations

NASA Astrophysics Data System (ADS)

Parthasarathy, Arun; Rakheja, Shaloo

2018-06-01

The jump-noise is a nonhomogeneous Poisson process which models thermal effects in magnetization dynamics, with special applications in low temperature escape rate phenomena. In this work, we develop improved numerical methods for Monte Carlo simulation of the jump-noise dynamics and validate the method by comparing the stationary distribution obtained empirically against the Boltzmann distribution. In accordance with the Néel-Brown theory, the jump-noise dynamics display an exponential relaxation toward equilibrium with a characteristic reversal time, which we extract for nanomagnets with uniaxial and cubic anisotropy. We relate the jump-noise dynamics to the equivalent Landau-Lifshitz dynamics up to second order correction for a general energy landscape and obtain the analogous Néel-Brown theory's solution of the reversal time. We find that the reversal time of jump-noise dynamics is characterized by Néel-Brown theory's solution at the energy saddle point for small noise. For large noise, the magnetization reversal due to jump-noise dynamics phenomenologically represents macroscopic tunneling of magnetization.

Markovian Interpretations of Dual Retrieval Processes

PubMed Central

Gomes, C. F. A.; Nakamura, K.; Reyna, V. F.

2013-01-01

A half-century ago, at the dawn of the all-or-none learning era, Estes showed that finite Markov chains supply a tractable, comprehensive framework for discrete-change data of the sort that he envisioned for shifts in conditioning states in stimulus sampling theory. Shortly thereafter, such data rapidly accumulated in many spheres of human learning and animal conditioning, and Estes’ work stimulated vigorous development of Markov models to handle them. A key outcome was that the data of the workhorse paradigms of episodic memory, recognition and recall, proved to be one- and two-stage Markovian, respectively, to close approximations. Subsequently, Markov modeling of recognition and recall all but disappeared from the literature, but it is now reemerging in the wake of dual-process conceptions of episodic memory. In recall, in particular, Markov models are being used to measure two retrieval operations (direct access and reconstruction) and a slave familiarity operation. In the present paper, we develop this family of models and present the requisite machinery for fit evaluation and significance testing. Results are reviewed from selected experiments in which the recall models were used to understand dual memory processes. PMID:24948840

Path-integral methods for analyzing the effects of fluctuations in stochastic hybrid neural networks.

PubMed

Bressloff, Paul C

2015-01-01

We consider applications of path-integral methods to the analysis of a stochastic hybrid model representing a network of synaptically coupled spiking neuronal populations. The state of each local population is described in terms of two stochastic variables, a continuous synaptic variable and a discrete activity variable. The synaptic variables evolve according to piecewise-deterministic dynamics describing, at the population level, synapses driven by spiking activity. The dynamical equations for the synaptic currents are only valid between jumps in spiking activity, and the latter are described by a jump Markov process whose transition rates depend on the synaptic variables. We assume a separation of time scales between fast spiking dynamics with time constant [Formula: see text] and slower synaptic dynamics with time constant τ. This naturally introduces a small positive parameter [Formula: see text], which can be used to develop various asymptotic expansions of the corresponding path-integral representation of the stochastic dynamics. First, we derive a variational principle for maximum-likelihood paths of escape from a metastable state (large deviations in the small noise limit [Formula: see text]). We then show how the path integral provides an efficient method for obtaining a diffusion approximation of the hybrid system for small ϵ. The resulting Langevin equation can be used to analyze the effects of fluctuations within the basin of attraction of a metastable state, that is, ignoring the effects of large deviations. We illustrate this by using the Langevin approximation to analyze the effects of intrinsic noise on pattern formation in a spatially structured hybrid network. In particular, we show how noise enlarges the parameter regime over which patterns occur, in an analogous fashion to PDEs. Finally, we carry out a [Formula: see text]-loop expansion of the path integral, and use this to derive corrections to voltage-based mean-field equations, analogous to the modified activity-based equations generated from a neural master equation.

Lithospheric Structure of the Yamato Basin Inferred from Trans-dimensional Inversion of Receiver Functions

NASA Astrophysics Data System (ADS)

Akuhara, T.; Nakahigashi, K.; Shinohara, M.; Yamada, T.; Yamashita, Y.; Shiobara, H.; Mochizuki, K.

2017-12-01

The Yamato Basin, located at the southeast of the Japan Sea, has been formed by the back-arc opening of the Japan Sea. Wide-angle reflection surveys have revealed that the basin has anomalously thickened crust compared with a normal oceanic crust [e.g., Nakahigashi et al., 2013] while deeper lithospheric structure has not known so far. Revealing the lithospheric structure of the Yamato Basin will lead to better understanding of the formation process of the Japan Sea and thus the Japanese island. In this study, as a first step toward understanding the lithospheric structure, we aim to detect the lithosphere-asthenosphere boundary (LAB) using receiver functions (RFs). We use teleseismic P waveforms recorded by broad-band ocean-bottom seismometers (BBOBS) deployed at the Yamato Basin. We calculated radial-component RFs using the data with the removal of water reverberations from the vertical-component records [Akuhara et al., 2016]. The resultant RFs are more complicated than those calculated at an on-land station, most likely due to sediment-related reverberations. This complexity does not allow either direct detection of a Ps conversion from the LAB or forward modeling by a simple structure composed of a handful number of layers. To overcome this difficulty, we conducted trans-dimensional Markov Chain Monte Carlo inversion of RFs, where we do not need to assume the number of layers in advance [e.g., Bodin et al., 2012; Sambridge et al., 2014]. Our preliminary results show abrupt velocity reduction at 70 km depth, far greater depth than the expected LAB depth from the age of the lithosphere ( 20 Ma, although still debated). If this low-velocity jump truly reflects the LAB, the anomalously thickened lithosphere will provide a new constraint on the complex formation history of the Japan Sea. Further study, however, is required to deny the possibility that the obtained velocity jump is an artificial brought by the overfitting of noisy data.

Markov switching multinomial logit model: An application to accident-injury severities.

PubMed

Malyshkina, Nataliya V; Mannering, Fred L

2009-07-01

In this study, two-state Markov switching multinomial logit models are proposed for statistical modeling of accident-injury severities. These models assume Markov switching over time between two unobserved states of roadway safety as a means of accounting for potential unobserved heterogeneity. The states are distinct in the sense that in different states accident-severity outcomes are generated by separate multinomial logit processes. To demonstrate the applicability of the approach, two-state Markov switching multinomial logit models are estimated for severity outcomes of accidents occurring on Indiana roads over a four-year time period. Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for model estimation. The estimated Markov switching models result in a superior statistical fit relative to the standard (single-state) multinomial logit models for a number of roadway classes and accident types. It is found that the more frequent state of roadway safety is correlated with better weather conditions and that the less frequent state is correlated with adverse weather conditions.

Grey-Markov prediction model based on background value optimization and central-point triangular whitenization weight function

NASA Astrophysics Data System (ADS)

Ye, Jing; Dang, Yaoguo; Li, Bingjun

2018-01-01

Grey-Markov forecasting model is a combination of grey prediction model and Markov chain which show obvious optimization effects for data sequences with characteristics of non-stationary and volatility. However, the state division process in traditional Grey-Markov forecasting model is mostly based on subjective real numbers that immediately affects the accuracy of forecasting values. To seek the solution, this paper introduces the central-point triangular whitenization weight function in state division to calculate possibilities of research values in each state which reflect preference degrees in different states in an objective way. On the other hand, background value optimization is applied in the traditional grey model to generate better fitting data. By this means, the improved Grey-Markov forecasting model is built. Finally, taking the grain production in Henan Province as an example, it verifies this model's validity by comparing with GM(1,1) based on background value optimization and the traditional Grey-Markov forecasting model.

Phasic Triplet Markov Chains.

PubMed

El Yazid Boudaren, Mohamed; Monfrini, Emmanuel; Pieczynski, Wojciech; Aïssani, Amar

2014-11-01

Hidden Markov chains have been shown to be inadequate for data modeling under some complex conditions. In this work, we address the problem of statistical modeling of phenomena involving two heterogeneous system states. Such phenomena may arise in biology or communications, among other fields. Namely, we consider that a sequence of meaningful words is to be searched within a whole observation that also contains arbitrary one-by-one symbols. Moreover, a word may be interrupted at some site to be carried on later. Applying plain hidden Markov chains to such data, while ignoring their specificity, yields unsatisfactory results. The Phasic triplet Markov chain, proposed in this paper, overcomes this difficulty by means of an auxiliary underlying process in accordance with the triplet Markov chains theory. Related Bayesian restoration techniques and parameters estimation procedures according to the new model are then described. Finally, to assess the performance of the proposed model against the conventional hidden Markov chain model, experiments are conducted on synthetic and real data.

A mathematical approach for evaluating Markov models in continuous time without discrete-event simulation.

PubMed

van Rosmalen, Joost; Toy, Mehlika; O'Mahony, James F

2013-08-01

Markov models are a simple and powerful tool for analyzing the health and economic effects of health care interventions. These models are usually evaluated in discrete time using cohort analysis. The use of discrete time assumes that changes in health states occur only at the end of a cycle period. Discrete-time Markov models only approximate the process of disease progression, as clinical events typically occur in continuous time. The approximation can yield biased cost-effectiveness estimates for Markov models with long cycle periods and if no half-cycle correction is made. The purpose of this article is to present an overview of methods for evaluating Markov models in continuous time. These methods use mathematical results from stochastic process theory and control theory. The methods are illustrated using an applied example on the cost-effectiveness of antiviral therapy for chronic hepatitis B. The main result is a mathematical solution for the expected time spent in each state in a continuous-time Markov model. It is shown how this solution can account for age-dependent transition rates and discounting of costs and health effects, and how the concept of tunnel states can be used to account for transition rates that depend on the time spent in a state. The applied example shows that the continuous-time model yields more accurate results than the discrete-time model but does not require much computation time and is easily implemented. In conclusion, continuous-time Markov models are a feasible alternative to cohort analysis and can offer several theoretical and practical advantages.

Mid-ocean ridge jumps associated with hotspot magmatism

NASA Astrophysics Data System (ADS)

Mittelstaedt, Eric; Ito, Garrett; Behn, Mark D.

2008-02-01

Hotspot-ridge interaction produces a wide range of phenomena including excess crustal thickness, geochemical anomalies, off-axis volcanic ridges and ridge relocations or jumps. Ridges are recorded to have jumped toward many hotspots including, Iceland, Discovery, Galápagos, Kerguelen and Tristan de Cuhna. The causes of ridge jumps likely involve a number of interacting processes related to hotspots. One such process is reheating of the lithosphere as magma penetrates it to feed near-axis volcanism. We study this effect by using the hybrid, finite-element code, FLAC, to simulate two-dimensional (2-D, cross-section) viscous mantle flow, elasto-plastic deformation of the lithosphere and heat transport in a ridge setting near an off-axis hotspot. Heating due to magma transport through the lithosphere is implemented within a hotspot region of fixed width. To determine the conditions necessary to initiate a ridge jump, we vary four parameters: hotspot magmatic heating rate, spreading rate, seafloor age at the location of the hotspot and ridge migration rate. Our results indicate that the hotspot magmatic heating rate required to initiate a ridge jump increases non-linearly with increasing spreading rate and seafloor age. Models predict that magmatic heating, itself, is most likely to cause jumps at slow spreading rates such as at the Mid-Atlantic Ridge on Iceland. In contrast, despite the higher magma flux at the Galápagos hotspot, magmatic heating alone is probably insufficient to induce a ridge jump at the present-day due to the intermediate ridge spreading rate of the Galápagos Spreading Center. The time required to achieve a ridge jump, for fixed or migrating ridges, is found to be on the order of 10 5-10 6 years. Simulations that incorporate ridge migration predict that after a ridge jump occurs the hotspot and ridge migrate together for time periods that increase with magma flux. Model results also suggest a mechanism for ridge reorganizations not related to hotspots such as ridge jumps in back-arc settings and ridge segment propagation along the Mid-Atlantic Ridge.

Observation uncertainty in reversible Markov chains.

PubMed

Metzner, Philipp; Weber, Marcus; Schütte, Christof

2010-09-01

In many applications one is interested in finding a simplified model which captures the essential dynamical behavior of a real life process. If the essential dynamics can be assumed to be (approximately) memoryless then a reasonable choice for a model is a Markov model whose parameters are estimated by means of Bayesian inference from an observed time series. We propose an efficient Monte Carlo Markov chain framework to assess the uncertainty of the Markov model and related observables. The derived Gibbs sampler allows for sampling distributions of transition matrices subject to reversibility and/or sparsity constraints. The performance of the suggested sampling scheme is demonstrated and discussed for a variety of model examples. The uncertainty analysis of functions of the Markov model under investigation is discussed in application to the identification of conformations of the trialanine molecule via Robust Perron Cluster Analysis (PCCA+) .

Generalization bounds of ERM-based learning processes for continuous-time Markov chains.

PubMed

Zhang, Chao; Tao, Dacheng

2012-12-01

Many existing results on statistical learning theory are based on the assumption that samples are independently and identically distributed (i.i.d.). However, the assumption of i.i.d. samples is not suitable for practical application to problems in which samples are time dependent. In this paper, we are mainly concerned with the empirical risk minimization (ERM) based learning process for time-dependent samples drawn from a continuous-time Markov chain. This learning process covers many kinds of practical applications, e.g., the prediction for a time series and the estimation of channel state information. Thus, it is significant to study its theoretical properties including the generalization bound, the asymptotic convergence, and the rate of convergence. It is noteworthy that, since samples are time dependent in this learning process, the concerns of this paper cannot (at least straightforwardly) be addressed by existing methods developed under the sample i.i.d. assumption. We first develop a deviation inequality for a sequence of time-dependent samples drawn from a continuous-time Markov chain and present a symmetrization inequality for such a sequence. By using the resultant deviation inequality and symmetrization inequality, we then obtain the generalization bounds of the ERM-based learning process for time-dependent samples drawn from a continuous-time Markov chain. Finally, based on the resultant generalization bounds, we analyze the asymptotic convergence and the rate of convergence of the learning process.

Availability Control for Means of Transport in Decisive Semi-Markov Models of Exploitation Process

NASA Astrophysics Data System (ADS)

Migawa, Klaudiusz

2012-12-01

The issues presented in this research paper refer to problems connected with the control process for exploitation implemented in the complex systems of exploitation for technical objects. The article presents the description of the method concerning the control availability for technical objects (means of transport) on the basis of the mathematical model of the exploitation process with the implementation of the decisive processes by semi-Markov. The presented method means focused on the preparing the decisive for the exploitation process for technical objects (semi-Markov model) and after that specifying the best control strategy (optimal strategy) from among possible decisive variants in accordance with the approved criterion (criteria) of the activity evaluation of the system of exploitation for technical objects. In the presented method specifying the optimal strategy for control availability in the technical objects means a choice of a sequence of control decisions made in individual states of modelled exploitation process for which the function being a criterion of evaluation reaches the extreme value. In order to choose the optimal control strategy the implementation of the genetic algorithm was chosen. The opinions were presented on the example of the exploitation process of the means of transport implemented in the real system of the bus municipal transport. The model of the exploitation process for the means of transports was prepared on the basis of the results implemented in the real transport system. The mathematical model of the exploitation process was built taking into consideration the fact that the model of the process constitutes the homogenous semi-Markov process.

Limiting Distributions of Functionals of Markov Chains.

DTIC Science & Technology

1984-08-01

limiting distributions; periodic * nonhomoger.,!ous Poisson processes . 19 ANS? MACY IConuui oe nonoe’ee if necorglooy and edern thty by block numbers...homogeneous Poisson processes is of interest in itself. The problem considered in this paper is of interest in the theory of partially observable...where we obtain the limiting distribution of the interevent times. Key Words: Markov Chains, Limiting Distributions, Periodic Nonhomogeneous Poisson

Effects of stochastic interest rates in decision making under risk: A Markov decision process model for forest management

Treesearch

Mo Zhou; Joseph Buongiorno

2011-01-01

Most economic studies of forest decision making under risk assume a fixed interest rate. This paper investigated some implications of this stochastic nature of interest rates. Markov decision process (MDP) models, used previously to integrate stochastic stand growth and prices, can be extended to include variable interest rates as well. This method was applied to...

Risk aversion and risk seeking in multicriteria forest management: a Markov decision process approach

Treesearch

Joseph Buongiorno; Mo Zhou; Craig Johnston

2017-01-01

Markov decision process models were extended to reflect some consequences of the risk attitude of forestry decision makers. One approach consisted of maximizing the expected value of a criterion subject to an upper bound on the variance or, symmetrically, minimizing the variance subject to a lower bound on the expected value.  The other method used the certainty...

Neuromechanical simulation of the locust jump

PubMed Central

Cofer, D.; Cymbalyuk, G.; Heitler, W. J.; Edwards, D. H.

2010-01-01

The neural circuitry and biomechanics of kicking in locusts have been studied to understand their roles in the control of both kicking and jumping. It has been hypothesized that the same neural circuit and biomechanics governed both behaviors but this hypothesis was not testable with current technology. We built a neuromechanical model to test this and to gain a better understanding of the role of the semi-lunar process (SLP) in jump dynamics. The jumping and kicking behaviors of the model were tested by comparing them with a variety of published data, and were found to reproduce the results from live animals. This confirmed that the kick neural circuitry can produce the jump behavior. The SLP is a set of highly sclerotized bands of cuticle that can be bent to store energy for use during kicking and jumping. It has not been possible to directly test the effects of the SLP on jump performance because it is an integral part of the joint, and attempts to remove its influence prevent the locust from being able to jump. Simulations demonstrated that the SLP can significantly increase jump distance, power, total energy and duration of the jump impulse. In addition, the geometry of the joint enables the SLP force to assist leg flexion when the leg is flexed, and to assist extension once the leg has begun to extend. PMID:20228342

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems.

PubMed

Winkelmann, Stefanie; Schütte, Christof

2017-09-21

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.

Excess Entropy Production in Quantum System: Quantum Master Equation Approach

NASA Astrophysics Data System (ADS)

Nakajima, Satoshi; Tokura, Yasuhiro

2017-12-01

For open systems described by the quantum master equation (QME), we investigate the excess entropy production under quasistatic operations between nonequilibrium steady states. The average entropy production is composed of the time integral of the instantaneous steady entropy production rate and the excess entropy production. We propose to define average entropy production rate using the average energy and particle currents, which are calculated by using the full counting statistics with QME. The excess entropy production is given by a line integral in the control parameter space and its integrand is called the Berry-Sinitsyn-Nemenman (BSN) vector. In the weakly nonequilibrium regime, we show that BSN vector is described by ln \\breve{ρ }_0 and ρ _0 where ρ _0 is the instantaneous steady state of the QME and \\breve{ρ }_0 is that of the QME which is given by reversing the sign of the Lamb shift term. If the system Hamiltonian is non-degenerate or the Lamb shift term is negligible, the excess entropy production approximately reduces to the difference between the von Neumann entropies of the system. Additionally, we point out that the expression of the entropy production obtained in the classical Markov jump process is different from our result and show that these are approximately equivalent only in the weakly nonequilibrium regime.

Stability and structural properties of gene regulation networks with coregulation rules.

PubMed

Warrell, Jonathan; Mhlanga, Musa

2017-05-07

Coregulation of the expression of groups of genes has been extensively demonstrated empirically in bacterial and eukaryotic systems. Such coregulation can arise through the use of shared regulatory motifs, which allow the coordinated expression of modules (and module groups) of functionally related genes across the genome. Coregulation can also arise through the physical association of multi-gene complexes through chromosomal looping, which are then transcribed together. We present a general formalism for modeling coregulation rules in the framework of Random Boolean Networks (RBN), and develop specific models for transcription factor networks with modular structure (including module groups, and multi-input modules (MIM) with autoregulation) and multi-gene complexes (including hierarchical differentiation between multi-gene complex members). We develop a mean-field approach to analyse the dynamical stability of large networks incorporating coregulation, and show that autoregulated MIM and hierarchical gene-complex models can achieve greater stability than networks without coregulation whose rules have matching activation frequency. We provide further analysis of the stability of small networks of both kinds through simulations. We also characterize several general properties of the transients and attractors in the hierarchical coregulation model, and show using simulations that the steady-state distribution factorizes hierarchically as a Bayesian network in a Markov Jump Process analogue of the RBN model. Copyright © 2017. Published by Elsevier Ltd.

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems

NASA Astrophysics Data System (ADS)

Winkelmann, Stefanie; Schütte, Christof

2017-09-01

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.

Numerical research of the optimal control problem in the semi-Markov inventory model

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

Gorshenin, Andrey K.; Belousov, Vasily V.; Shnourkoff, Peter V.

2015-03-10

This paper is devoted to the numerical simulation of stochastic system for inventory management products using controlled semi-Markov process. The results of a special software for the system’s research and finding the optimal control are presented.

A reward semi-Markov process with memory for wind speed modeling

NASA Astrophysics Data System (ADS)

Petroni, F.; D'Amico, G.; Prattico, F.

2012-04-01

The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [1] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [3], by using two models, first-order Markov chain with different number of states, and Weibull distribution. All this model use Markov chains to generate synthetic wind speed time series but the search for a better model is still open. Approaching this issue, we applied new models which are generalization of Markov models. More precisely we applied semi-Markov models to generate synthetic wind speed time series. The primary goal of this analysis is the study of the time history of the wind in order to assess its reliability as a source of power and to determine the associated storage levels required. In order to assess this issue we use a probabilistic model based on indexed semi-Markov process [4] to which a reward structure is attached. Our model is used to calculate the expected energy produced by a given turbine and its variability expressed by the variance of the process. Our results can be used to compare different wind farms based on their reward and also on the risk of missed production due to the intrinsic variability of the wind speed process. The model is used to generate synthetic time series for wind speed by means of Monte Carlo simulations and backtesting procedure is used to compare results on first and second oder moments of rewards between real and synthetic data. [1] A. Shamshad, M.A. Bawadi, W.M.W. Wan Hussin, T.A. Majid, S.A.M. Sanusi, First and second order Markov chain models for synthetic gen- eration of wind speed time series, Energy 30 (2005) 693-708. [2] H. Nfaoui, H. Essiarab, A.A.M. Sayigh, A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco, Re- newable Energy 29 (2004) 1407-1418. [3] F. Youcef Ettoumi, H. Sauvageot, A.-E.-H. Adane, Statistical bivariate modeling of wind using first-order Markov chain and Weibull distribu- tion, Renewable Energy 28 (2003) 1787-1802. [4]F. Petroni, G. D'Amico, F. Prattico, Indexed semi-Markov process for wind speed modeling. To be submitted.

Stochastic Calculus and Differential Equations for Physics and Finance

NASA Astrophysics Data System (ADS)

McCauley, Joseph L.

2013-02-01

1. Random variables and probability distributions; 2. Martingales, Markov, and nonstationarity; 3. Stochastic calculus; 4. Ito processes and Fokker-Planck equations; 5. Selfsimilar Ito processes; 6. Fractional Brownian motion; 7. Kolmogorov's PDEs and Chapman-Kolmogorov; 8. Non Markov Ito processes; 9. Black-Scholes, martingales, and Feynman-Katz; 10. Stochastic calculus with martingales; 11. Statistical physics and finance, a brief history of both; 12. Introduction to new financial economics; 13. Statistical ensembles and time series analysis; 14. Econometrics; 15. Semimartingales; References; Index.

[Birth and death process of computer viruses].

PubMed

Segawa, Katsunori; Nakano, Tatsuya; Nakata, Kotoko; Hayashi, Yuzuru

2006-01-01

The daily variations in the number of computer viruses found attaching to e-mails and the number of accesses to the home page of a national institute in Japan are examined. The power spectral densities (PSD) of the variation in the computer viruses show a time-correlation characteristic of Markov process, but the daily access number does not (identified as white noise). Like biological viruses, the variation in the computer viruses can be described by the birth-and-death model known as a Markov process.

Image segmentation using hidden Markov Gauss mixture models.

PubMed

Pyun, Kyungsuk; Lim, Johan; Won, Chee Sun; Gray, Robert M

2007-07-01

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

PubMed Central

Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth

2013-01-01

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890

Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model.

PubMed

Sampid, Marius Galabe; Hasim, Haslifah M; Dai, Hongsheng

2018-01-01

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student's-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis.

Repeat ridge jumps associated with plume-ridge interaction, melt transport, and ridge migration

NASA Astrophysics Data System (ADS)

Mittelstaedt, Eric; Ito, Garrett; van Hunen, Jeroen

2011-01-01

Repeated shifts, or jumps, of mid-ocean ridge segments toward nearby hot spots can produce large, long-term changes to the geometry and location of the tectonic plate boundaries. Ridge jumps associated with hot spot-ridge interaction are likely caused by several processes including shear on the base of the plate due to expanding plume material as well as reheating of lithosphere as magma passes through it to feed off-axis volcanism. To study how these processes influence ridge jumps, we use numerical models to simulate 2-D (in cross section) viscous flow of the mantle, viscoplastic deformation of the lithosphere, and melt migration upward from the asthenospheric melting zone, laterally along the base of the lithosphere, and vertically through the lithosphere. The locations and rates that magma penetrates and heats the lithosphere are controlled by the time-varying accumulation of melt beneath the plate and the depth-averaged lithospheric porosity. We examine the effect of four key parameters: magmatic heating rate of the lithosphere, plate spreading rate, age of the seafloor overlying the plume, and the plume-ridge migration rate. Results indicate that the minimum value of the magmatic heating rate needed to initiate a ridge jump increases with plate age and spreading rate. The time required to complete a ridge jump decreases with larger values of magmatic heating rate, younger plate age, and faster spreading rate. For cases with migrating ridges, models predict a range of behaviors including repeating ridge jumps, much like those exhibited on Earth. Repeating ridge jumps occur at moderate magmatic heating rates and are the result of changes in the hot spot magma flux in response to magma migration along the base of an evolving lithosphere. The tendency of slow spreading to promote ridge jumps could help explain the observed clustering of hot spots near the Mid-Atlantic Ridge. Model results also suggest that magmatic heating may significantly thin the lithosphere, as has been suggested at Hawaii and other hot spots.

A hybrid continuous-discrete method for stochastic reaction-diffusion processes.

PubMed

Lo, Wing-Cheong; Zheng, Likun; Nie, Qing

2016-09-01

Stochastic fluctuations in reaction-diffusion processes often have substantial effect on spatial and temporal dynamics of signal transductions in complex biological systems. One popular approach for simulating these processes is to divide the system into small spatial compartments assuming that molecules react only within the same compartment and jump between adjacent compartments driven by the diffusion. While the approach is convenient in terms of its implementation, its computational cost may become prohibitive when diffusive jumps occur significantly more frequently than reactions, as in the case of rapid diffusion. Here, we present a hybrid continuous-discrete method in which diffusion is simulated using continuous approximation while reactions are based on the Gillespie algorithm. Specifically, the diffusive jumps are approximated as continuous Gaussian random vectors with time-dependent means and covariances, allowing use of a large time step, even for rapid diffusion. By considering the correlation among diffusive jumps, the approximation is accurate for the second moment of the diffusion process. In addition, a criterion is obtained for identifying the region in which such diffusion approximation is required to enable adaptive calculations for better accuracy. Applications to a linear diffusion system and two nonlinear systems of morphogens demonstrate the effectiveness and benefits of the new hybrid method.

Analytical and multibody modeling for the power analysis of standing jumps.

PubMed

Palmieri, G; Callegari, M; Fioretti, S

2015-01-01

Two methods for the power analysis of standing jumps are proposed and compared in this article. The first method is based on a simple analytical formulation which requires as input the coordinates of the center of gravity in three specified instants of the jump. The second method is based on a multibody model that simulates the jumps processing the data obtained by a three-dimensional (3D) motion capture system and the dynamometric measurements obtained by the force platforms. The multibody model is developed with OpenSim, an open-source software which provides tools for the kinematic and dynamic analyses of 3D human body models. The study is focused on two of the typical tests used to evaluate the muscular activity of lower limbs, which are the counter movement jump and the standing long jump. The comparison between the results obtained by the two methods confirms that the proposed analytical formulation is correct and represents a simple tool suitable for a preliminary analysis of total mechanical work and the mean power exerted in standing jumps.

Birth of a hydraulic jump

NASA Astrophysics Data System (ADS)

Duchesne, Alexis; Bohr, Tomas; Andersen, Anders

2017-11-01

The hydraulic jump, i.e., the sharp transition between a supercritical and a subcritical free-surface flow, has been extensively studied in the past centuries. However, ever since Leonardo da Vinci asked it for the first time, an important question has been left unanswered: How does a hydraulic jump form? We present an experimental and theoretical study of the formation of stationary hydraulic jumps in centimeter wide channels. Two starting situations are considered: The channel is, respectively, empty or filled with liquid, the liquid level being fixed by the wetting properties and the boundary conditions. We then change the flow-rate abruptly from zero to a constant value. In an empty channel, we observe the formation of a stationary hydraulic jump in a two-stage process: First, the channel fills by the advancing liquid front, which undergoes a transition from supercritical to subcritical at some position in the channel. Later the influence of the downstream boundary conditions makes the jump move slowly upstream to its final position. In the pre-filled channel, the hydraulic jump forms at the injector edge and then moves downstream to its final position.

Symbolic Heuristic Search for Factored Markov Decision Processes

NASA Technical Reports Server (NTRS)

Morris, Robert (Technical Monitor); Feng, Zheng-Zhu; Hansen, Eric A.

2003-01-01

We describe a planning algorithm that integrates two approaches to solving Markov decision processes with large state spaces. State abstraction is used to avoid evaluating states individually. Forward search from a start state, guided by an admissible heuristic, is used to avoid evaluating all states. We combine these two approaches in a novel way that exploits symbolic model-checking techniques and demonstrates their usefulness for decision-theoretic planning.

CTPPL: A Continuous Time Probabilistic Programming Language

DTIC Science & Technology

2009-07-01

recent years there has been a flurry of interest in continuous time models, mostly focused on continuous time Bayesian networks ( CTBNs ) [Nodelman, 2007... CTBNs are built on homogenous Markov processes. A homogenous Markov pro- cess is a finite state, continuous time process, consisting of an initial...q1 : xn()] ... Some state transitions can produce emissions. In a CTBN , each variable has a conditional inten- sity matrix Qu for every combination of

Using Markov Decision Processes with Heterogeneous Queueing Systems to Examine Military MEDEVAC Dispatching Policies

DTIC Science & Technology

2017-03-23

Air Force Institute of Technology AFIT Scholar Theses and Dissertations 3-23-2017 Using Markov Decision Processes with Heterogeneous Queueing Systems... TECHNOLOGY Wright-Patterson Air Force Base, Ohio DISTRIBUTION STATEMENT A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in...POLICIES THESIS Presented to the Faculty Department of Operational Sciences Graduate School of Engineering and Management Air Force Institute of Technology

Generating Impact Maps from Automatically Detected Bomb Craters in Aerial Wartime Images Using Marked Point Processes

NASA Astrophysics Data System (ADS)

Kruse, Christian; Rottensteiner, Franz; Hoberg, Thorsten; Ziems, Marcel; Rebke, Julia; Heipke, Christian

2018-04-01

The aftermath of wartime attacks is often felt long after the war ended, as numerous unexploded bombs may still exist in the ground. Typically, such areas are documented in so-called impact maps which are based on the detection of bomb craters. This paper proposes a method for the automatic detection of bomb craters in aerial wartime images that were taken during the Second World War. The object model for the bomb craters is represented by ellipses. A probabilistic approach based on marked point processes determines the most likely configuration of objects within the scene. Adding and removing new objects to and from the current configuration, respectively, changing their positions and modifying the ellipse parameters randomly creates new object configurations. Each configuration is evaluated using an energy function. High gradient magnitudes along the border of the ellipse are favored and overlapping ellipses are penalized. Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing provides the global energy optimum, which describes the conformance with a predefined model. For generating the impact map a probability map is defined which is created from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively. Our results show the general potential of the method for the automatic detection of bomb craters and its automated generation of an impact map in a heterogeneous image stock.

Analysis of Evolutionary Processes of Species Jump in Waterfowl Parvovirus

PubMed Central

Fan, Wentao; Sun, Zhaoyu; Shen, Tongtong; Xu, Danning; Huang, Kehe; Zhou, Jiyong; Song, Suquan; Yan, Liping

2017-01-01

Waterfowl parvoviruses are classified into goose parvovirus (GPV) and Muscovy duck parvovirus (MDPV) according to their antigenic features and host preferences. A novel duck parvovirus (NDPV), identified as a new variant of GPV, is currently infecting ducks, thus causing considerable economic loss. This study analyzed the molecular evolution and population dynamics of the emerging parvovirus capsid gene to investigate the evolutionary processes concerning the host shift of NDPV. Two important amino acids changes (Asn-489 and Asn-650) were identified in NDPV, which may be responsible for host shift of NDPV. Phylogenetic analysis indicated that the currently circulating NDPV originated from the GPV lineage. The Bayesian Markov chain Monte Carlo tree indicated that the NDPV diverged from GPV approximately 20 years ago. Evolutionary rate analyses demonstrated that GPV evolved with 7.674 × 10-4 substitutions/site/year, and the data for MDPV was 5.237 × 10-4 substitutions/site/year, whereas the substitution rate in NDPV branch was 2.25 × 10-3 substitutions/site/year. Meanwhile, viral population dynamics analysis revealed that the GPV major clade, including NDPV, grew exponentially at a rate of 1.717 year-1. Selection pressure analysis showed that most sites are subject to strong purifying selection and no positively selected sites were found in NDPV. The unique immune-epitopes in waterfowl parvovirus were also estimated, which may be helpful for the prediction of antibody binding sites against NDPV in ducks. PMID:28352261

Dissecting Magnetar Variability with Bayesian Hierarchical Models

NASA Astrophysics Data System (ADS)

Huppenkothen, Daniela; Brewer, Brendon J.; Hogg, David W.; Murray, Iain; Frean, Marcus; Elenbaas, Chris; Watts, Anna L.; Levin, Yuri; van der Horst, Alexander J.; Kouveliotou, Chryssa

2015-09-01

Neutron stars are a prime laboratory for testing physical processes under conditions of strong gravity, high density, and extreme magnetic fields. Among the zoo of neutron star phenomena, magnetars stand out for their bursting behavior, ranging from extremely bright, rare giant flares to numerous, less energetic recurrent bursts. The exact trigger and emission mechanisms for these bursts are not known; favored models involve either a crust fracture and subsequent energy release into the magnetosphere, or explosive reconnection of magnetic field lines. In the absence of a predictive model, understanding the physical processes responsible for magnetar burst variability is difficult. Here, we develop an empirical model that decomposes magnetar bursts into a superposition of small spike-like features with a simple functional form, where the number of model components is itself part of the inference problem. The cascades of spikes that we model might be formed by avalanches of reconnection, or crust rupture aftershocks. Using Markov Chain Monte Carlo sampling augmented with reversible jumps between models with different numbers of parameters, we characterize the posterior distributions of the model parameters and the number of components per burst. We relate these model parameters to physical quantities in the system, and show for the first time that the variability within a burst does not conform to predictions from ideas of self-organized criticality. We also examine how well the properties of the spikes fit the predictions of simplified cascade models for the different trigger mechanisms.

DISSECTING MAGNETAR VARIABILITY WITH BAYESIAN HIERARCHICAL MODELS

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

Huppenkothen, Daniela; Elenbaas, Chris; Watts, Anna L.

Neutron stars are a prime laboratory for testing physical processes under conditions of strong gravity, high density, and extreme magnetic fields. Among the zoo of neutron star phenomena, magnetars stand out for their bursting behavior, ranging from extremely bright, rare giant flares to numerous, less energetic recurrent bursts. The exact trigger and emission mechanisms for these bursts are not known; favored models involve either a crust fracture and subsequent energy release into the magnetosphere, or explosive reconnection of magnetic field lines. In the absence of a predictive model, understanding the physical processes responsible for magnetar burst variability is difficult. Here,more » we develop an empirical model that decomposes magnetar bursts into a superposition of small spike-like features with a simple functional form, where the number of model components is itself part of the inference problem. The cascades of spikes that we model might be formed by avalanches of reconnection, or crust rupture aftershocks. Using Markov Chain Monte Carlo sampling augmented with reversible jumps between models with different numbers of parameters, we characterize the posterior distributions of the model parameters and the number of components per burst. We relate these model parameters to physical quantities in the system, and show for the first time that the variability within a burst does not conform to predictions from ideas of self-organized criticality. We also examine how well the properties of the spikes fit the predictions of simplified cascade models for the different trigger mechanisms.« less

The effect of increasing strength and approach velocity on triple jump performance.

PubMed

Allen, Sam J; Yeadon, M R Fred; King, Mark A

2016-12-08

The triple jump is an athletic event comprising three phases in which the optimal phase ratio (the proportion of each phase to the total distance jumped) is unknown. This study used a planar whole body torque-driven computer simulation model of the ground contact parts of all three phases of the triple jump to investigate the effect of strength and approach velocity on optimal performance. The strength and approach velocity of the simulation model were each increased by up to 30% in 10% increments from baseline data collected from a national standard triple jumper. Increasing strength always resulted in an increased overall jump distance. Increasing approach velocity also typically resulted in an increased overall jump distance but there was a point past which increasing approach velocity without increasing strength did not lead to an increase in overall jump distance. Increasing both strength and approach velocity by 10%, 20%, and 30% led to roughly equivalent increases in overall jump distances. Distances ranged from 14.05m with baseline strength and approach velocity, up to 18.49m with 30% increases in both. Optimal phase ratios were either hop-dominated or balanced, and typically became more balanced when the strength of the model was increased by a greater percentage than its approach velocity. The range of triple jump distances that resulted from the optimisation process suggests that strength and approach velocity are of great importance for triple jump performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Markov reward processes

NASA Technical Reports Server (NTRS)

Smith, R. M.

1991-01-01

Numerous applications in the area of computer system analysis can be effectively studied with Markov reward models. These models describe the behavior of the system with a continuous-time Markov chain, where a reward rate is associated with each state. In a reliability/availability model, upstates may have reward rate 1 and down states may have reward rate zero associated with them. In a queueing model, the number of jobs of certain type in a given state may be the reward rate attached to that state. In a combined model of performance and reliability, the reward rate of a state may be the computational capacity, or a related performance measure. Expected steady-state reward rate and expected instantaneous reward rate are clearly useful measures of the Markov reward model. More generally, the distribution of accumulated reward or time-averaged reward over a finite time interval may be determined from the solution of the Markov reward model. This information is of great practical significance in situations where the workload can be well characterized (deterministically, or by continuous functions e.g., distributions). The design process in the development of a computer system is an expensive and long term endeavor. For aerospace applications the reliability of the computer system is essential, as is the ability to complete critical workloads in a well defined real time interval. Consequently, effective modeling of such systems must take into account both performance and reliability. This fact motivates our use of Markov reward models to aid in the development and evaluation of fault tolerant computer systems.

Operations and support cost modeling using Markov chains

NASA Technical Reports Server (NTRS)

Unal, Resit

1989-01-01

Systems for future missions will be selected with life cycle costs (LCC) as a primary evaluation criterion. This reflects the current realization that only systems which are considered affordable will be built in the future due to the national budget constaints. Such an environment calls for innovative cost modeling techniques which address all of the phases a space system goes through during its life cycle, namely: design and development, fabrication, operations and support; and retirement. A significant portion of the LCC for reusable systems are generated during the operations and support phase (OS). Typically, OS costs can account for 60 to 80 percent of the total LCC. Clearly, OS costs are wholly determined or at least strongly influenced by decisions made during the design and development phases of the project. As a result OS costs need to be considered and estimated early in the conceptual phase. To be effective, an OS cost estimating model needs to account for actual instead of ideal processes by associating cost elements with probabilities. One approach that may be suitable for OS cost modeling is the use of the Markov Chain Process. Markov chains are an important method of probabilistic analysis for operations research analysts but they are rarely used for life cycle cost analysis. This research effort evaluates the use of Markov Chains in LCC analysis by developing OS cost model for a hypothetical reusable space transportation vehicle (HSTV) and suggests further uses of the Markov Chain process as a design-aid tool.

A locust-inspired miniature jumping robot.

PubMed

Zaitsev, Valentin; Gvirsman, Omer; Ben Hanan, Uri; Weiss, Avi; Ayali, Amir; Kosa, Gabor

2015-11-25

Unmanned ground vehicles are mostly wheeled, tracked, or legged. These locomotion mechanisms have a limited ability to traverse rough terrain and obstacles that are higher than the robot's center of mass. In order to improve the mobility of small robots it is necessary to expand the variety of their motion gaits. Jumping is one of nature's solutions to the challenge of mobility in difficult terrain. The desert locust is the model for the presented bio-inspired design of a jumping mechanism for a small mobile robot. The basic mechanism is similar to that of the semilunar process in the hind legs of the locust, and is based on the cocking of a torsional spring by wrapping a tendon-like wire around the shaft of a miniature motor. In this study we present the jumping mechanism design, and the manufacturing and performance analysis of two demonstrator prototypes. The most advanced jumping robot demonstrator is power autonomous, weighs 23 gr, and is capable of jumping to a height of 3.35 m, covering a distance of 1.37 m.

Influence of credit scoring on the dynamics of Markov chain

NASA Astrophysics Data System (ADS)

Galina, Timofeeva

2015-11-01

Markov processes are widely used to model the dynamics of a credit portfolio and forecast the portfolio risk and profitability. In the Markov chain model the loan portfolio is divided into several groups with different quality, which determined by presence of indebtedness and its terms. It is proposed that dynamics of portfolio shares is described by a multistage controlled system. The article outlines mathematical formalization of controls which reflect the actions of the bank's management in order to improve the loan portfolio quality. The most important control is the organization of approval procedure of loan applications. The credit scoring is studied as a control affecting to the dynamic system. Different formalizations of "good" and "bad" consumers are proposed in connection with the Markov chain model.

Dynamic neutron scattering from conformational dynamics. I. Theory and Markov models

NASA Astrophysics Data System (ADS)

Lindner, Benjamin; Yi, Zheng; Prinz, Jan-Hendrik; Smith, Jeremy C.; Noé, Frank

2013-11-01

The dynamics of complex molecules can be directly probed by inelastic neutron scattering experiments. However, many of the underlying dynamical processes may exist on similar timescales, which makes it difficult to assign processes seen experimentally to specific structural rearrangements. Here, we show how Markov models can be used to connect structural changes observed in molecular dynamics simulation directly to the relaxation processes probed by scattering experiments. For this, a conformational dynamics theory of dynamical neutron and X-ray scattering is developed, following our previous approach for computing dynamical fingerprints of time-correlation functions [F. Noé, S. Doose, I. Daidone, M. Löllmann, J. Chodera, M. Sauer, and J. Smith, Proc. Natl. Acad. Sci. U.S.A. 108, 4822 (2011)]. Markov modeling is used to approximate the relaxation processes and timescales of the molecule via the eigenvectors and eigenvalues of a transition matrix between conformational substates. This procedure allows the establishment of a complete set of exponential decay functions and a full decomposition into the individual contributions, i.e., the contribution of every atom and dynamical process to each experimental relaxation process.

Signal Processing for Radar Target Tracking and Identification

DTIC Science & Technology

1996-12-01

Computes the likelihood for various potential jump moves. 12. matrix_mult.m: Parallel implementation of linear algebra ... Elementary Lineary Algebra with Applications, John Wiley k Sons, Inc., New York, 1987. [9] A. K. Bhattacharyya, and D. L. Sengupta, Radar Cross...Miller, ’Target Tracking and Recognition Using Jump-Diffusion Processes," ARO’s 11th Army Conf. on Applied Mathemat- ics and Computing, June 8-11

Transient Properties of Probability Distribution for a Markov Process with Size-dependent Additive Noise

NASA Astrophysics Data System (ADS)

Yamada, Yuhei; Yamazaki, Yoshihiro

2018-04-01

This study considered a stochastic model for cluster growth in a Markov process with a cluster size dependent additive noise. According to this model, the probability distribution of the cluster size transiently becomes an exponential or a log-normal distribution depending on the initial condition of the growth. In this letter, a master equation is obtained for this model, and derivation of the distributions is discussed.

Exact solution of the hidden Markov processes.

PubMed

Saakian, David B

2017-11-01

We write a master equation for the distributions related to hidden Markov processes (HMPs) and solve it using a functional equation. Thus the solution of HMPs is mapped exactly to the solution of the functional equation. For a general case the latter can be solved only numerically. We derive an exact expression for the entropy of HMPs. Our expression for the entropy is an alternative to the ones given before by the solution of integral equations. The exact solution is possible because actually the model can be considered as a generalized random walk on a one-dimensional strip. While we give the solution for the two second-order matrices, our solution can be easily generalized for the L values of the Markov process and M values of observables: We should be able to solve a system of L functional equations in the space of dimension M-1.

Exact solution of the hidden Markov processes

NASA Astrophysics Data System (ADS)

Saakian, David B.

2017-11-01

We write a master equation for the distributions related to hidden Markov processes (HMPs) and solve it using a functional equation. Thus the solution of HMPs is mapped exactly to the solution of the functional equation. For a general case the latter can be solved only numerically. We derive an exact expression for the entropy of HMPs. Our expression for the entropy is an alternative to the ones given before by the solution of integral equations. The exact solution is possible because actually the model can be considered as a generalized random walk on a one-dimensional strip. While we give the solution for the two second-order matrices, our solution can be easily generalized for the L values of the Markov process and M values of observables: We should be able to solve a system of L functional equations in the space of dimension M -1 .

OPTIMIZING OBSERVER EFFORT FOR FIELD DETECTION OF REPRODUCTIVE EFFECTS IN BIRDS

EPA Science Inventory

Avian nest survival is best viewed as a Markov process with two absorbing states, death and fledging. We present a column-stochastic Markov chain from which all major Mayfield formulations of daily nest-survival can be derived contingent upon the degree of observer knowledge of e...

Markov chain Monte Carlo techniques and spatial-temporal modelling for medical EIT.

PubMed

West, Robert M; Aykroyd, Robert G; Meng, Sha; Williams, Richard A

2004-02-01

Many imaging problems such as imaging with electrical impedance tomography (EIT) can be shown to be inverse problems: that is either there is no unique solution or the solution does not depend continuously on the data. As a consequence solution of inverse problems based on measured data alone is unstable, particularly if the mapping between the solution distribution and the measurements is also nonlinear as in EIT. To deliver a practical stable solution, it is necessary to make considerable use of prior information or regularization techniques. The role of a Bayesian approach is therefore of fundamental importance, especially when coupled with Markov chain Monte Carlo (MCMC) sampling to provide information about solution behaviour. Spatial smoothing is a commonly used approach to regularization. In the human thorax EIT example considered here nonlinearity increases the difficulty of imaging, using only boundary data, leading to reconstructions which are often rather too smooth. In particular, in medical imaging the resistivity distribution usually contains substantial jumps at the boundaries of different anatomical regions. With spatial smoothing these boundaries can be masked by blurring. This paper focuses on the medical application of EIT to monitor lung and cardiac function and uses explicit geometric information regarding anatomical structure and incorporates temporal correlation. Some simple properties are assumed known, or at least reliably estimated from separate studies, whereas others are estimated from the voltage measurements. This structural formulation will also allow direct estimation of clinically important quantities, such as ejection fraction and residual capacity, along with assessment of precision.

Stochastic parameterization of shallow cumulus convection estimated from high-resolution model data

NASA Astrophysics Data System (ADS)

Dorrestijn, Jesse; Crommelin, Daan T.; Siebesma, A. Pier.; Jonker, Harm J. J.

2013-02-01

In this paper, we report on the development of a methodology for stochastic parameterization of convective transport by shallow cumulus convection in weather and climate models. We construct a parameterization based on Large-Eddy Simulation (LES) data. These simulations resolve the turbulent fluxes of heat and moisture and are based on a typical case of non-precipitating shallow cumulus convection above sea in the trade-wind region. Using clustering, we determine a finite number of turbulent flux pairs for heat and moisture that are representative for the pairs of flux profiles observed in these simulations. In the stochastic parameterization scheme proposed here, the convection scheme jumps randomly between these pre-computed pairs of turbulent flux profiles. The transition probabilities are estimated from the LES data, and they are conditioned on the resolved-scale state in the model column. Hence, the stochastic parameterization is formulated as a data-inferred conditional Markov chain (CMC), where each state of the Markov chain corresponds to a pair of turbulent heat and moisture fluxes. The CMC parameterization is designed to emulate, in a statistical sense, the convective behaviour observed in the LES data. The CMC is tested in single-column model (SCM) experiments. The SCM is able to reproduce the ensemble spread of the temperature and humidity that was observed in the LES data. Furthermore, there is a good similarity between time series of the fractions of the discretized fluxes produced by SCM and observed in LES.

Geometric analysis of pathways dynamics: Application to versatility of TGF-β receptors.

PubMed

Samal, Satya Swarup; Naldi, Aurélien; Grigoriev, Dima; Weber, Andreas; Théret, Nathalie; Radulescu, Ovidiu

2016-11-01

We propose a new geometric approach to describe the qualitative dynamics of chemical reactions networks. By this method we identify metastable regimes, defined as low dimensional regions of the phase space close to which the dynamics is much slower compared to the rest of the phase space. These metastable regimes depend on the network topology and on the orders of magnitude of the kinetic parameters. Benchmarking of the method on a computational biology model repository suggests that the number of metastable regimes is sub-exponential in the number of variables and equations. The dynamics of the network can be described as a sequence of jumps from one metastable regime to another. We show that a geometrically computed connectivity graph restricts the set of possible jumps. We also provide finite state machine (Markov chain) models for such dynamic changes. Applied to signal transduction models, our approach unravels dynamical and functional capacities of signalling pathways, as well as parameters responsible for specificity of the pathway response. In particular, for a model of TGFβ signalling, we find that the ratio of TGFBR2 to TGFBR1 receptors concentrations can be used to discriminate between metastable regimes. Using expression data from the NCI60 panel of human tumor cell lines, we show that aggressive and non-aggressive tumour cell lines function in different metastable regimes and can be distinguished by measuring the relative concentrations of receptors of the two types. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Markov decision processes in natural resources management: observability and uncertainty

USGS Publications Warehouse

Williams, Byron K.

2015-01-01

The breadth and complexity of stochastic decision processes in natural resources presents a challenge to analysts who need to understand and use these approaches. The objective of this paper is to describe a class of decision processes that are germane to natural resources conservation and management, namely Markov decision processes, and to discuss applications and computing algorithms under different conditions of observability and uncertainty. A number of important similarities are developed in the framing and evaluation of different decision processes, which can be useful in their applications in natural resources management. The challenges attendant to partial observability are highlighted, and possible approaches for dealing with it are discussed.

Resonant Pump-dump Quantum Control of Solvated Dye Molecules with Phase Jumps

NASA Astrophysics Data System (ADS)

Konar, Arkaprabha; Lozovoy, Vadim; Dantus, Marcos

2014-03-01

Quantum coherent control of two photon and multiphoton excitation processes in atomic and condensed phase systems employing phase jumps has been well studied and understood. Here we demonstrate coherent quantum control of a two photon resonant pump-dump process in a complex solvated dye molecule. Phase jump in the frequency domain via a pulse shaper is employed to coherently enhance the stimulated emission by an order of magnitude when compared to transform limited pulses. Red shifted stimulated emission from successive low energy Stokes shifted excited states leading to narrowband emission are observed upon scanning the pi step across the excitation spectrum. A binary search space routine was also employed to investigate the effects of other types of phase jumps on stimulated emission and to determine the optimum phase that maximizes the emission. Understanding the underlying mechanism of this kind of enhancement will guide us in designing pulse shapes for enhancing stimulated emission, which can be further applied in the field of imaging.

Simple approach to superhydrophobic nanostructured Al for practical antifrosting application based on enhanced self-propelled jumping droplets.

PubMed

Kim, Aeree; Lee, Chan; Kim, Hyungmo; Kim, Joonwon

2015-04-08

Frost formation can cause operational difficulty and efficiency loss for many facilities such as aircraft, wind turbines, and outdoor heat exchangers. Self-propelled jumping by condensate droplets on superhydrophobic surfaces delays frost formation, so many attempts have been made to exploit this phenomenon. However, practical application of this phenomenon is currently unfeasible because many processes to fabricate the superhydrophobic surfaces are inefficient and because self-propelled jumping is difficult to be achieved in a humid and low-temperature environment because superhydrophobicity is degraded in these conditions. Here, we achieved significantly effective anti-icing superhydrophobic aluminum. Its extremely low adhesive properties allow self-propelled jumping under highly supersaturated conditions of high humidity or low surface temperature. As a result, this surface helps retard frost formation at that condition. The aluminum was made superhydrophobic by a simple and cost-effective process that is adaptable to any shape. Therefore, it has promise for use in practical and industrial applications.

Space system operations and support cost analysis using Markov chains

NASA Technical Reports Server (NTRS)

Unal, Resit; Dean, Edwin B.; Moore, Arlene A.; Fairbairn, Robert E.

1990-01-01

This paper evaluates the use of Markov chain process in probabilistic life cycle cost analysis and suggests further uses of the process as a design aid tool. A methodology is developed for estimating operations and support cost and expected life for reusable space transportation systems. Application of the methodology is demonstrated for the case of a hypothetical space transportation vehicle. A sensitivity analysis is carried out to explore the effects of uncertainty in key model inputs.

Risk-Sensitive Control of Pure Jump Process on Countable Space with Near Monotone Cost

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

Suresh Kumar, K., E-mail: suresh@math.iitb.ac.in; Pal, Chandan, E-mail: cpal@math.iitb.ac.in

2013-12-15

In this article, we study risk-sensitive control problem with controlled continuous time pure jump process on a countable space as state dynamics. We prove multiplicative dynamic programming principle, elliptic and parabolic Harnack’s inequalities. Using the multiplicative dynamic programing principle and the Harnack’s inequalities, we prove the existence and a characterization of optimal risk-sensitive control under the near monotone condition.

A hybrid continuous-discrete method for stochastic reaction–diffusion processes

PubMed Central

Zheng, Likun; Nie, Qing

2016-01-01

Stochastic fluctuations in reaction–diffusion processes often have substantial effect on spatial and temporal dynamics of signal transductions in complex biological systems. One popular approach for simulating these processes is to divide the system into small spatial compartments assuming that molecules react only within the same compartment and jump between adjacent compartments driven by the diffusion. While the approach is convenient in terms of its implementation, its computational cost may become prohibitive when diffusive jumps occur significantly more frequently than reactions, as in the case of rapid diffusion. Here, we present a hybrid continuous-discrete method in which diffusion is simulated using continuous approximation while reactions are based on the Gillespie algorithm. Specifically, the diffusive jumps are approximated as continuous Gaussian random vectors with time-dependent means and covariances, allowing use of a large time step, even for rapid diffusion. By considering the correlation among diffusive jumps, the approximation is accurate for the second moment of the diffusion process. In addition, a criterion is obtained for identifying the region in which such diffusion approximation is required to enable adaptive calculations for better accuracy. Applications to a linear diffusion system and two nonlinear systems of morphogens demonstrate the effectiveness and benefits of the new hybrid method. PMID:27703710

Application of Markov Models for Analysis of Development of Psychological Characteristics

ERIC Educational Resources Information Center

Kuravsky, Lev S.; Malykh, Sergey B.

2004-01-01

A technique to study combined influence of environmental and genetic factors on the base of changes in phenotype distributions is presented. Histograms are exploited as base analyzed characteristics. A continuous time, discrete state Markov process with piece-wise constant interstate transition rates is associated with evolution of each histogram.…

Modelling Faculty Replacement Strategies Using a Time-Dependent Finite Markov-Chain Process.

ERIC Educational Resources Information Center

Hackett, E. Raymond; Magg, Alexander A.; Carrigan, Sarah D.

1999-01-01

Describes the use of a time-dependent Markov-chain model to develop faculty-replacement strategies within a college at a research university. The study suggests that a stochastic modelling approach can provide valuable insight when planning for personnel needs in the immediate (five-to-ten year) future. (MSE)

Cascade heterogeneous face sketch-photo synthesis via dual-scale Markov Network

NASA Astrophysics Data System (ADS)

Yao, Saisai; Chen, Zhenxue; Jia, Yunyi; Liu, Chengyun

2018-03-01

Heterogeneous face sketch-photo synthesis is an important and challenging task in computer vision, which has widely applied in law enforcement and digital entertainment. According to the different synthesis results based on different scales, this paper proposes a cascade sketch-photo synthesis method via dual-scale Markov Network. Firstly, Markov Network with larger scale is used to synthesise the initial sketches and the local vertical and horizontal neighbour search (LVHNS) method is used to search for the neighbour patches of test patches in training set. Then, the initial sketches and test photos are jointly entered into smaller scale Markov Network. Finally, the fine sketches are obtained after cascade synthesis process. Extensive experimental results on various databases demonstrate the superiority of the proposed method compared with several state-of-the-art methods.

Modeling the coupled return-spread high frequency dynamics of large tick assets

NASA Astrophysics Data System (ADS)

Curato, Gianbiagio; Lillo, Fabrizio

2015-01-01

Large tick assets, i.e. assets where one tick movement is a significant fraction of the price and bid-ask spread is almost always equal to one tick, display a dynamics in which price changes and spread are strongly coupled. We present an approach based on the hidden Markov model, also known in econometrics as the Markov switching model, for the dynamics of price changes, where the latent Markov process is described by the transitions between spreads. We then use a finite Markov mixture of logit regressions on past squared price changes to describe temporal dependencies in the dynamics of price changes. The model can thus be seen as a double chain Markov model. We show that the model describes the shape of the price change distribution at different time scales, volatility clustering, and the anomalous decrease of kurtosis. We calibrate our models based on Nasdaq stocks and we show that this model reproduces remarkably well the statistical properties of real data.

A Comprehensive Model of Electric-Field-Enhanced Jumping-Droplet Condensation on Superhydrophobic Surfaces.

PubMed

Birbarah, Patrick; Li, Zhaoer; Pauls, Alexander; Miljkovic, Nenad

2015-07-21

Superhydrophobic micro/nanostructured surfaces for dropwise condensation have recently received significant attention due to their potential to enhance heat transfer performance by shedding positively charged water droplets via coalescence-induced droplet jumping at length scales below the capillary length and allowing the use of external electric fields to enhance droplet removal and heat transfer, in what has been termed electric-field-enhanced (EFE) jumping-droplet condensation. However, achieving optimal EFE conditions for enhanced heat transfer requires capturing the details of transport processes that is currently lacking. While a comprehensive model has been developed for condensation on micro/nanostructured surfaces, it cannot be applied for EFE condensation due to the dynamic droplet-vapor-electric field interactions. In this work, we developed a comprehensive physical model for EFE condensation on superhydrophobic surfaces by incorporating individual droplet motion, electrode geometry, jumping frequency, field strength, and condensate vapor-flow dynamics. As a first step toward our model, we simulated jumping droplet motion with no external electric field and validated our theoretical droplet trajectories to experimentally obtained trajectories, showing excellent temporal and spatial agreement. We then incorporated the external electric field into our model and considered the effects of jumping droplet size, electrode size and geometry, condensation heat flux, and droplet jumping direction. Our model suggests that smaller jumping droplet sizes and condensation heat fluxes require less work input to be removed by the external fields. Furthermore, the results suggest that EFE electrodes can be optimized such that the work input is minimized depending on the condensation heat flux. To analyze overall efficiency, we defined an incremental coefficient of performance and showed that it is very high (∼10(6)) for EFE condensation. We finally proposed mechanisms for condensate collection which would ensure continuous operation of the EFE system and which can scalably be applied to industrial condensers. This work provides a comprehensive physical model of the EFE condensation process and offers guidelines for the design of EFE systems to maximize heat transfer.

Plume-ridge interaction: Shaping the geometry of mid-ocean ridges

NASA Astrophysics Data System (ADS)

Mittelstaedt, Eric L.

Manifestations of plume-ridge interaction are found across the ocean basins. Currently there are interactions between at least 21 hot spots and nearby ridges along 15--20% of the global mid-ocean ridge network. These interactions produce a number of anomalies including the presence of elevated topography, negative gravity anomalies, and anomalous crustal production. One form of anomalous crustal production is the formation of volcanic lineaments between hotspots and nearby mid-ocean ridges. In addition, observations indicate that mantle plumes tend to "capture" nearby mid-ocean ridges through asymmetric spreading, increased ridge propagation, and discrete shifts of the ridge axis, or ridge jumps. The initiation of ridge jumps and the formation of off-axis volcanic lineaments likely involve similar processes and may be closely related. In the following work, I use theoretical and numerical models to quantify the processes that control the formation of volcanic lineaments (Chapter 2), the initiation of mid-ocean ridge jumps associated with lithospheric heating due to magma passing through the plate (Chapter 3), and the initiation of jumps due to an upwelling mantle plume and magmatic heating governed by melt migration (Chapter 4). Results indicate that lineaments and ridge jumps associated with plume-ridge interaction are most likely to occur on young lithosphere. The shape of lineaments on the seafloor is predicted to be controlled by the pattern of lithospheric stresses associated with a laterally spreading, near-ridge mantle plume. Ridge jumps are likely to occur due to magmatic heating alone only in lithosphere ˜1Myr old, because the heating rate required to jump increases with spreading rate and plate age. The added effect of an upwelling plume introduces competing effects that both promote and inhibit ridge jumps. For models where magmatic heating is controlled by melt migration, repeat ridge jumps are predicted to occur as the plume and ridge separate, but only for restricted values of spreading rate, ridge migration rate, and heating rate. Overall, the results suggest that the combined effect of stresses and magmatism associated with plume-ridge interaction can significantly alter plate geometry over time.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Boostream: a dynamic fluid flow process to assemble nanoparticles at liquid interface

NASA Astrophysics Data System (ADS)

Delléa, Olivier; Lebaigue, Olivier

2017-12-01

CEA-LITEN develops an original process called Boostream® to manipulate, assemble and connect micro- or nanoparticles of various materials, sizes, shapes and functions to obtain monolayer colloidal crystals (MCCs). This process uses the upper surface of a liquid film flowing down a ramp to assemble particles in a manner that is close to the horizontal situation of a Langmuir-Blodgett film construction. In presence of particles at the liquid interface, the film down-flow configuration exhibits an unusual hydraulic jump which results from the fluid flow accommodation to the particle monolayer. In order to master our process, the fluid flow has been modeled and experimentally characterized by optical means, such as with the moiré technique that consists in observing the reflection of a succession of periodic black-and-red fringes on the liquid surface mirror. The fringe images are deformed when reflected by the curved liquid surface associated with the hydraulic jump, the fringe deformation being proportional to the local slope of the surface. This original experimental setup allowed us to get the surface profile in the jump region and to measure it along with the main process parameters (liquid flow rate, slope angle, temperature sensitive fluid properties such as dynamic viscosity or surface tension, particle sizes). This work presents the experimental setup and its simple model, the different experimental characterization techniques used and will focus on the way the hydraulic jump relies on the process parameters.

Biomechanics of jumping in the flea.

PubMed

Sutton, Gregory P; Burrows, Malcolm

2011-03-01

It has long been established that fleas jump by storing and releasing energy in a cuticular spring, but it is not known how forces from that spring are transmitted to the ground. One hypothesis is that the recoil of the spring pushes the trochanter onto the ground, thereby generating the jump. A second hypothesis is that the recoil of the spring acts through a lever system to push the tibia and tarsus onto the ground. To decide which of these two hypotheses is correct, we built a kinetic model to simulate the different possible velocities and accelerations produced by each proposed process and compared those simulations with the kinematics measured from high-speed images of natural jumping. The in vivo velocity and acceleration kinematics are consistent with the model that directs ground forces through the tibia and tarsus. Moreover, in some natural jumps there was no contact between the trochanter and the ground. There were also no observable differences between the kinematics of jumps that began with the trochanter on the ground and jumps that did not. Scanning electron microscopy showed that the tibia and tarsus have spines appropriate for applying forces to the ground, whereas no such structures were seen on the trochanter. Based on these observations, we discount the hypothesis that fleas use their trochantera to apply forces to the ground and conclude that fleas jump by applying forces to the ground through the end of the tibiae.

Developing a statistically powerful measure for quartet tree inference using phylogenetic identities and Markov invariants.

PubMed

Sumner, Jeremy G; Taylor, Amelia; Holland, Barbara R; Jarvis, Peter D

2017-12-01

Recently there has been renewed interest in phylogenetic inference methods based on phylogenetic invariants, alongside the related Markov invariants. Broadly speaking, both these approaches give rise to polynomial functions of sequence site patterns that, in expectation value, either vanish for particular evolutionary trees (in the case of phylogenetic invariants) or have well understood transformation properties (in the case of Markov invariants). While both approaches have been valued for their intrinsic mathematical interest, it is not clear how they relate to each other, and to what extent they can be used as practical tools for inference of phylogenetic trees. In this paper, by focusing on the special case of binary sequence data and quartets of taxa, we are able to view these two different polynomial-based approaches within a common framework. To motivate the discussion, we present three desirable statistical properties that we argue any invariant-based phylogenetic method should satisfy: (1) sensible behaviour under reordering of input sequences; (2) stability as the taxa evolve independently according to a Markov process; and (3) explicit dependence on the assumption of a continuous-time process. Motivated by these statistical properties, we develop and explore several new phylogenetic inference methods. In particular, we develop a statistically bias-corrected version of the Markov invariants approach which satisfies all three properties. We also extend previous work by showing that the phylogenetic invariants can be implemented in such a way as to satisfy property (3). A simulation study shows that, in comparison to other methods, our new proposed approach based on bias-corrected Markov invariants is extremely powerful for phylogenetic inference. The binary case is of particular theoretical interest as-in this case only-the Markov invariants can be expressed as linear combinations of the phylogenetic invariants. A wider implication of this is that, for models with more than two states-for example DNA sequence alignments with four-state models-we find that methods which rely on phylogenetic invariants are incapable of satisfying all three of the stated statistical properties. This is because in these cases the relevant Markov invariants belong to a class of polynomials independent from the phylogenetic invariants.

Markov Analysis of Sleep Dynamics

NASA Astrophysics Data System (ADS)

Kim, J. W.; Lee, J.-S.; Robinson, P. A.; Jeong, D.-U.

2009-05-01

A new approach, based on a Markov transition matrix, is proposed to explain frequent sleep and wake transitions during sleep. The matrix is determined by analyzing hypnograms of 113 obstructive sleep apnea patients. Our approach shows that the statistics of sleep can be constructed via a single Markov process and that durations of all states have modified exponential distributions, in contrast to recent reports of a scale-free form for the wake stage and an exponential form for the sleep stage. Hypnograms of the same subjects, but treated with Continuous Positive Airway Pressure, are analyzed and compared quantitatively with the pretreatment ones, suggesting potential clinical applications.

Illusion Of Defeat: Egyptian Strategic Thinking And The 1973 Yom Kippur War

DTIC Science & Technology

2016-06-04

jump-start a stalled political process, reclaim the Sinai Peninsula, and ultimately achieve peace with Israel. Egypt had lost tactically, but won...Israeli vulnerability, jump-start a stalled political process, reclaim the Sinai Peninsula, and ultimately achieve peace with Israel. Egypt had lost...New York: Free Press, 1990), 95. 2 William Burr, ed., “The October War and U.S. Policy,” The National Security Archive at The George Washington

Optimal estimation for discrete time jump processes

NASA Technical Reports Server (NTRS)

Vaca, M. V.; Tretter, S. A.

1978-01-01

Optimum estimates of nonobservable random variables or random processes which influence the rate functions of a discrete time jump process (DTJP) are derived. The approach used is based on the a posteriori probability of a nonobservable event expressed in terms of the a priori probability of that event and of the sample function probability of the DTJP. Thus a general representation is obtained for optimum estimates, and recursive equations are derived for minimum mean-squared error (MMSE) estimates. In general, MMSE estimates are nonlinear functions of the observations. The problem is considered of estimating the rate of a DTJP when the rate is a random variable with a beta probability density function and the jump amplitudes are binomially distributed. It is shown that the MMSE estimates are linear. The class of beta density functions is rather rich and explains why there are insignificant differences between optimum unconstrained and linear MMSE estimates in a variety of problems.

Jump-Diffusion models and structural changes for asset forecasting in hydrology

NASA Astrophysics Data System (ADS)

Tranquille Temgoua, André Guy; Martel, Richard; Chang, Philippe J. J.; Rivera, Alfonso

2017-04-01

Impacts of climate change on surface water and groundwater are of concern in many regions of the world since water is an essential natural resource. Jump-Diffusion models are generally used in economics and other related fields but not in hydrology. The potential application could be made for hydrologic data series analysis and forecast. The present study uses Jump-Diffusion models by adding structural changes to detect fluctuations in hydrologic processes in relationship with climate change. The model implicitly assumes that modifications in rivers' flowrates can be divided into three categories: (a) normal changes due to irregular precipitation events especially in tropical regions causing major disturbance in hydrologic processes (this component is modelled by a discrete Brownian motion); (b) abnormal, sudden and non-persistent modifications in hydrologic proceedings are handled by Poisson processes; (c) the persistence of hydrologic fluctuations characterized by structural changes in hydrological data related to climate variability. The objective of this paper is to add structural changes in diffusion models with jumps, in order to capture the persistence of hydrologic fluctuations. Indirectly, the idea is to observe if there are structural changes of discharge/recharge over the study area, and to find an efficient and flexible model able of capturing a wide variety of hydrologic processes. Structural changes in hydrological data are estimated using the method of nonlinear discrete filters via Method of Simulated Moments (MSM). An application is given using sensitive parameters such as baseflow index and recession coefficient to capture discharge/recharge. Historical dataset are examined by the Volume Spread Analysis (VSA) to detect real time and random perturbations in hydrologic processes. The application of the method allows establishing more accurate hydrologic parameters. The impact of this study is perceptible in forecasting floods and groundwater recession. Keywords: hydrologic processes, Jump-Diffusion models, structural changes, forecast, climate change

Bayesian Analysis of Non-Gaussian Long-Range Dependent Processes

NASA Astrophysics Data System (ADS)

Graves, Timothy; Watkins, Nicholas; Franzke, Christian; Gramacy, Robert

2013-04-01

Recent studies [e.g. the Antarctic study of Franzke, J. Climate, 2010] have strongly suggested that surface temperatures exhibit long-range dependence (LRD). The presence of LRD would hamper the identification of deterministic trends and the quantification of their significance. It is well established that LRD processes exhibit stochastic trends over rather long periods of time. Thus, accurate methods for discriminating between physical processes that possess long memory and those that do not are an important adjunct to climate modeling. As we briefly review, the LRD idea originated at the same time as H-selfsimilarity, so it is often not realised that a model does not have to be H-self similar to show LRD [e.g. Watkins, GRL Frontiers, 2013]. We have used Markov Chain Monte Carlo algorithms to perform a Bayesian analysis of Auto-Regressive Fractionally-Integrated Moving-Average ARFIMA(p,d,q) processes, which are capable of modeling LRD. Our principal aim is to obtain inference about the long memory parameter, d, with secondary interest in the scale and location parameters. We have developed a reversible-jump method enabling us to integrate over different model forms for the short memory component. We initially assume Gaussianity, and have tested the method on both synthetic and physical time series. Many physical processes, for example the Faraday Antarctic time series, are significantly non-Gaussian. We have therefore extended this work by weakening the Gaussianity assumption, assuming an alpha-stable distribution for the innovations, and performing joint inference on d and alpha. Such a modified FARIMA(p,d,q) process is a flexible, initial model for non-Gaussian processes with long memory. We will present a study of the dependence of the posterior variance of the memory parameter d on the length of the time series considered. This will be compared with equivalent error diagnostics for other measures of d.

Jump-Down Performance Alterations after Space Flight

NASA Technical Reports Server (NTRS)

Reschke, M. F.; Kofman, I. S.; Cerisano, J. M.; Fisher, E. A.; Peters, B. T.; Miller, C. A.; Harm, D. L.; Bloomberg, J. J.

2011-01-01

INTRODUCTION: Successful jump performance requires functional coordination of visual, vestibular, and somatosensory systems, which are affected by prolonged exposure to microgravity. Astronauts returning from space flight exhibit impaired ability to coordinate effective landing strategies when jumping from a platform to the ground. This study compares jump strategies used by astronauts before and after flight, changes to those strategies within a test session, and recoveries in jump-down performance parameters across several postflight test sessions. These data were obtained as part of an ongoing interdisciplinary study (Functional Task Test, FTT) designed to evaluate both astronaut postflight functional performance and related physiological changes. METHODS: Seven astronauts from short-duration (Shuttle) and three from long-duration (International Space Station) flights performed 3 two-footed jumps from a platform 30 cm high onto a force plate that measured the ground reaction forces and center-of-pressure displacement from the landings. Neuromuscular activation data were collected from the medial gastrocnemius and anterior tibialis of both legs using surface electromyography electrodes. Two load cells in the platform measured the load exerted by each foot during the takeoff phase of the jump. Data were collected in 2 preflight sessions, on landing day (Shuttle only), and 1, 6, and 30 days after flight. RESULTS: Postural settling time was significantly increased on the first postflight test session and many of the astronauts tested were unable to maintain balance on their first jump landing but recovered by the third jump, showing a learning progression in which performance improvements could be attributed to adjustments in takeoff or landing strategy. Jump strategy changes were evident in reduced air time (time between takeoff and landing) and also in increased asymmetry in foot latencies on takeoff. CONCLUSIONS: The test results revealed significant decrements in astronauts abilities to maintain balance and achieve a postural stability upon landing from a jump early after flight. However, the jump landing adaptation process often begins after the first jump with full recovery of most performance parameters within days after space flight. As expected, performance of ISS astronauts on the first day after flight was similar to that of Shuttle crewmembers on landing day.

Students' Progress throughout Examination Process as a Markov Chain

ERIC Educational Resources Information Center

Hlavatý, Robert; Dömeová, Ludmila

2014-01-01

The paper is focused on students of Mathematical methods in economics at the Czech university of life sciences (CULS) in Prague. The idea is to create a model of students' progress throughout the whole course using the Markov chain approach. Each student has to go through various stages of the course requirements where his success depends on the…

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Carey, Lawrence D.; Schultz, Elise V.; Stano, Geoffrey T.; Gatlin, Patrick N.

2013-01-01

The presence and rates of total lightning are both correlated to and physically dependent upon storm updraft strength, mixed phase precipitation volume and the size of the charging zone. The updraft modulates the ingredients necessary for electrification within a thunderstorm, while the updraft also plays a critical role in the development of severe and hazardous weather. Therefore utilizing this relationship, the monitoring of lightning rates and jumps provides an additional piece of information on the evolution of a thunderstorm, more often than not, at higher temporal resolution than current operational radar systems. This correlation is the basis for the total lightning jump algorithm that has been developed in recent years. In order to become a viable option for operational forecasters to incorporate into their severe storm monitoring process, the total lightning jump must be placed into the framework of several severe storm conceptual models (e.g., radar evolution, storm morphology) which forecasters have built through training and experience. Thus, one of the goals of this study is to examine and relate the lightning jump concept to often used radar parameters (e.g., dBZ vertical structure, VIL, MESH, MESO/shear) in the warning environment. Tying lightning trends and lightning jump occurrences to these radar based parameters will provide forecasters with an additional tool that they can use to build an accurate realtime depiction as to what is going on in a given environment. Furthermore, relating the lightning jump concept to these parameters could also increase confidence in a warning decision they have already made, help tip the scales on whether or not to warn on a given storm, or to draw the forecaster s attention to a particular storm that is rapidly developing. Furthermore the lightning information will add vital storm scale information in regions that are not well covered by radar, or when radar failures occur. The physical basis for the lightning jump algorithm in relation to severe storm dynamics and microphysics is a key component that must be further explored. Many radar studies have examined flash rates and their relation to updraft strength, updraft volume, precipitation -sized ice mass, etc.; however, very few have related the concept of the lightning jump and manifestation of severe weather to storm dynamics and microphysics using multi -Doppler and polarimetric radar techniques. Therefore, the second half of this study will combine the lightning jump algorithm and these radar techniques in order to place the lightning jump concept into a physical and dynamical framework. This analysis includes examining such parameters as mixed phase precipitation volume, charging zone, updraft strength and updraft volume. Such a study should provide increased understanding of and confidence in the strengths and limitations of the lightning jump algorithm in the storm warning process.

Markov Chains for Investigating and Predicting Migration: A Case from Southwestern China

NASA Astrophysics Data System (ADS)

Qin, Bo; Wang, Yiyu; Xu, Haoming

2018-03-01

In order to accurately predict the population’s happiness, this paper conducted two demographic surveys on a new district of a city in western China, and carried out a dynamic analysis using related mathematical methods. This paper argues that the migration of migrants in the city will change the pattern of spatial distribution of human resources in the city and thus affect the social and economic development in all districts. The migration status of the population will change randomly with the passage of time, so it can be predicted and analyzed through the Markov process. The Markov process provides the local government and decision-making bureau a valid basis for the dynamic analysis of the mobility of migrants in the city as well as the ways for promoting happiness of local people’s lives.

Parameters estimation using the first passage times method in a jump-diffusion model

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

Khaldi, K., E-mail: kkhaldi@umbb.dz; LIMOSE Laboratory, Boumerdes University, 35000; Meddahi, S., E-mail: samia.meddahi@gmail.com

2016-06-02

The main purposes of this paper are two contributions: (1) it presents a new method, which is the first passage time (FPT method) generalized for all passage times (GPT method), in order to estimate the parameters of stochastic Jump-Diffusion process. (2) it compares in a time series model, share price of gold, the empirical results of the estimation and forecasts obtained with the GPT method and those obtained by the moments method and the FPT method applied to the Merton Jump-Diffusion (MJD) model.

An open Markov chain scheme model for a credit consumption portfolio fed by ARIMA and SARMA processes

NASA Astrophysics Data System (ADS)

Esquível, Manuel L.; Fernandes, José Moniz; Guerreiro, Gracinda R.

2016-06-01

We introduce a schematic formalism for the time evolution of a random population entering some set of classes and such that each member of the population evolves among these classes according to a scheme based on a Markov chain model. We consider that the flow of incoming members is modeled by a time series and we detail the time series structure of the elements in each of the classes. We present a practical application to data from a credit portfolio of a Cape Verdian bank; after modeling the entering population in two different ways - namely as an ARIMA process and as a deterministic sigmoid type trend plus a SARMA process for the residues - we simulate the behavior of the population and compare the results. We get that the second method is more accurate in describing the behavior of the populations when compared to the observed values in a direct simulation of the Markov chain.

On the Mathematical Consequences of Binning Spike Trains.

PubMed

Cessac, Bruno; Le Ny, Arnaud; Löcherbach, Eva

2017-01-01

We initiate a mathematical analysis of hidden effects induced by binning spike trains of neurons. Assuming that the original spike train has been generated by a discrete Markov process, we show that binning generates a stochastic process that is no longer Markov but is instead a variable-length Markov chain (VLMC) with unbounded memory. We also show that the law of the binned raster is a Gibbs measure in the DLR (Dobrushin-Lanford-Ruelle) sense coined in mathematical statistical mechanics. This allows the derivation of several important consequences on statistical properties of binned spike trains. In particular, we introduce the DLR framework as a natural setting to mathematically formalize anticipation, that is, to tell "how good" our nervous system is at making predictions. In a probabilistic sense, this corresponds to condition a process by its future, and we discuss how binning may affect our conclusions on this ability. We finally comment on the possible consequences of binning in the detection of spurious phase transitions or in the detection of incorrect evidence of criticality.

Estimation in a semi-Markov transformation model

PubMed Central

Dabrowska, Dorota M.

2012-01-01

Multi-state models provide a common tool for analysis of longitudinal failure time data. In biomedical applications, models of this kind are often used to describe evolution of a disease and assume that patient may move among a finite number of states representing different phases in the disease progression. Several authors developed extensions of the proportional hazard model for analysis of multi-state models in the presence of covariates. In this paper, we consider a general class of censored semi-Markov and modulated renewal processes and propose the use of transformation models for their analysis. Special cases include modulated renewal processes with interarrival times specified using transformation models, and semi-Markov processes with with one-step transition probabilities defined using copula-transformation models. We discuss estimation of finite and infinite dimensional parameters of the model, and develop an extension of the Gaussian multiplier method for setting confidence bands for transition probabilities. A transplant outcome data set from the Center for International Blood and Marrow Transplant Research is used for illustrative purposes. PMID:22740583

Large deviations and mixing for dissipative PDEs with unbounded random kicks

NASA Astrophysics Data System (ADS)

Jakšić, V.; Nersesyan, V.; Pillet, C.-A.; Shirikyan, A.

2018-02-01

We study the problem of exponential mixing and large deviations for discrete-time Markov processes associated with a class of random dynamical systems. Under some dissipativity and regularisation hypotheses for the underlying deterministic dynamics and a non-degeneracy condition for the driving random force, we discuss the existence and uniqueness of a stationary measure and its exponential stability in the Kantorovich-Wasserstein metric. We next turn to the large deviations principle (LDP) and establish its validity for the occupation measures of the Markov processes in question. The proof is based on Kifer’s criterion for non-compact spaces, a result on large-time asymptotics for generalised Markov semigroup, and a coupling argument. These tools combined together constitute a new approach to LDP for infinite-dimensional processes without strong Feller property in a non-compact space. The results obtained can be applied to the two-dimensional Navier-Stokes system in a bounded domain and to the complex Ginzburg-Landau equation.

Multiscale implementation of infinite-swap replica exchange molecular dynamics.

PubMed

Yu, Tang-Qing; Lu, Jianfeng; Abrams, Cameron F; Vanden-Eijnden, Eric

2016-10-18

Replica exchange molecular dynamics (REMD) is a popular method to accelerate conformational sampling of complex molecular systems. The idea is to run several replicas of the system in parallel at different temperatures that are swapped periodically. These swaps are typically attempted every few MD steps and accepted or rejected according to a Metropolis-Hastings criterion. This guarantees that the joint distribution of the composite system of replicas is the normalized sum of the symmetrized product of the canonical distributions of these replicas at the different temperatures. Here we propose a different implementation of REMD in which (i) the swaps obey a continuous-time Markov jump process implemented via Gillespie's stochastic simulation algorithm (SSA), which also samples exactly the aforementioned joint distribution and has the advantage of being rejection free, and (ii) this REMD-SSA is combined with the heterogeneous multiscale method to accelerate the rate of the swaps and reach the so-called infinite-swap limit that is known to optimize sampling efficiency. The method is easy to implement and can be trivially parallelized. Here we illustrate its accuracy and efficiency on the examples of alanine dipeptide in vacuum and C-terminal β-hairpin of protein G in explicit solvent. In this latter example, our results indicate that the landscape of the protein is a triple funnel with two folded structures and one misfolded structure that are stabilized by H-bonds.

A stochastic model for transmission, extinction and outbreak of Escherichia coli O157:H7 in cattle as affected by ambient temperature and cleaning practices.

PubMed

Wang, Xueying; Gautam, Raju; Pinedo, Pablo J; Allen, Linda J S; Ivanek, Renata

2014-08-01

Many infectious agents transmitting through a contaminated environment are able to persist in the environment depending on the temperature and sanitation determined rates of their replication and clearance, respectively. There is a need to elucidate the effect of these factors on the infection transmission dynamics in terms of infection outbreaks and extinction while accounting for the random nature of the process. Also, it is important to distinguish between the true and apparent extinction, where the former means pathogen extinction in both the host and the environment while the latter means extinction only in the host population. This study proposes a stochastic-differential equation model as an approximation to a Markov jump process model, using Escherichia coli O157:H7 in cattle as a model system. In the model, the host population infection dynamics are described using the standard susceptible-infected-susceptible framework, and the E. coli O157:H7 population in the environment is represented by an additional variable. The backward Kolmogorov equations that determine the probability distribution and the expectation of the first passage time are provided in a general setting. The outbreak and apparent extinction of infection are investigated by numerically solving the Kolmogorov equations for the probability density function of the associated process and the expectation of the associated stopping time. The results provide insight into E. coli O157:H7 transmission and apparent extinction, and suggest ways for controlling the spread of infection in a cattle herd. Specifically, this study highlights the importance of ambient temperature and sanitation, especially during summer.

A novel framework to simulating non-stationary, non-linear, non-Normal hydrological time series using Markov Switching Autoregressive Models

NASA Astrophysics Data System (ADS)

Birkel, C.; Paroli, R.; Spezia, L.; Tetzlaff, D.; Soulsby, C.

2012-12-01

In this paper we present a novel model framework using the class of Markov Switching Autoregressive Models (MSARMs) to examine catchments as complex stochastic systems that exhibit non-stationary, non-linear and non-Normal rainfall-runoff and solute dynamics. Hereby, MSARMs are pairs of stochastic processes, one observed and one unobserved, or hidden. We model the unobserved process as a finite state Markov chain and assume that the observed process, given the hidden Markov chain, is conditionally autoregressive, which means that the current observation depends on its recent past (system memory). The model is fully embedded in a Bayesian analysis based on Markov Chain Monte Carlo (MCMC) algorithms for model selection and uncertainty assessment. Hereby, the autoregressive order and the dimension of the hidden Markov chain state-space are essentially self-selected. The hidden states of the Markov chain represent unobserved levels of variability in the observed process that may result from complex interactions of hydroclimatic variability on the one hand and catchment characteristics affecting water and solute storage on the other. To deal with non-stationarity, additional meteorological and hydrological time series along with a periodic component can be included in the MSARMs as covariates. This extension allows identification of potential underlying drivers of temporal rainfall-runoff and solute dynamics. We applied the MSAR model framework to streamflow and conservative tracer (deuterium and oxygen-18) time series from an intensively monitored 2.3 km2 experimental catchment in eastern Scotland. Statistical time series analysis, in the form of MSARMs, suggested that the streamflow and isotope tracer time series are not controlled by simple linear rules. MSARMs showed that the dependence of current observations on past inputs observed by transport models often in form of the long-tailing of travel time and residence time distributions can be efficiently explained by non-stationarity either of the system input (climatic variability) and/or the complexity of catchment storage characteristics. The statistical model is also capable of reproducing short (event) and longer-term (inter-event) and wet and dry dynamical "hydrological states". These reflect the non-linear transport mechanisms of flow pathways induced by transient climatic and hydrological variables and modified by catchment characteristics. We conclude that MSARMs are a powerful tool to analyze the temporal dynamics of hydrological data, allowing for explicit integration of non-stationary, non-linear and non-Normal characteristics.

Balance disorders caused by running and jumping occurring in young basketball players.

PubMed

Struzik, Artur; Zawadzki, Jerzy; Pietraszewski, Bogdan

2015-01-01

Body balance, as one of the coordination abilities,is a desirable variable for basketball players as regards the necessity of efficient responses in constantly changing situations on a basketball court. The aim of this study was to check whether physical activity in the form of running and jumping influences variables characterizing the process of keeping body balance of a basketball player in the standing position. The research was conducted on 11 young basketball players. The measurements were taken with a Kistler force plate. Apart from commonly registered COP displacements, an additional variable describing the process of keeping body balance by a basketball player was ankle joint stiffness on the basis of which an "Index of Balance-Stiffness" (IB-S) was created. Statistically significant differences were obtained for the maximum COP displacements and ankle joint stiffness between measurements of balance in the standing position before and after the employed movement tasks whereas there were no statistically significant differences for the aforementioned variables describing the process of keeping balance between measurements after running and after jumping. The research results indicate that the employed movement activities brought about significant changes in the process of keeping balance of basketball player in the standing position which, after the run performed, remain on a similar level to the series of jumps being performed. The authors attempted to establish an index based on the stiffness which yields a possibility to perceive each basketball player as an individual person in the process of keeping balance.

Atomic clocks and the continuous-time random-walk

NASA Astrophysics Data System (ADS)

Formichella, Valerio; Camparo, James; Tavella, Patrizia

2017-11-01

Atomic clocks play a fundamental role in many fields, most notably they generate Universal Coordinated Time and are at the heart of all global navigation satellite systems. Notwithstanding their excellent timekeeping performance, their output frequency does vary: it can display deterministic frequency drift; diverse continuous noise processes result in nonstationary clock noise (e.g., random-walk frequency noise, modelled as a Wiener process), and the clock frequency may display sudden changes (i.e., "jumps"). Typically, the clock's frequency instability is evaluated by the Allan or Hadamard variances, whose functional forms can identify the different operative noise processes. Here, we show that the Allan and Hadamard variances of a particular continuous-time random-walk, the compound Poisson process, have the same functional form as for a Wiener process with drift. The compound Poisson process, introduced as a model for observed frequency jumps, is an alternative to the Wiener process for modelling random walk frequency noise. This alternate model fits well the behavior of the rubidium clocks flying on GPS Block-IIR satellites. Further, starting from jump statistics, the model can be improved by considering a more general form of continuous-time random-walk, and this could bring new insights into the physics of atomic clocks.

Hydraulic jumps in 'viscous' accretion disks. [in astronomical models

NASA Technical Reports Server (NTRS)

Michel, F. C.

1984-01-01

It is proposed that the dissipative process necessary for rapid accretion disk evolution is driven by hydraulic jump waves on the surface of the disk. These waves are excited by the asymmetric nature of the central rotator (e.g., neutron star magnetosphere) and spiral out into the disk to form a pattern corotating with the central object. Disk matter in turn is slowed slightly at each encounter with the jump and spirals inward. In this process, the disk is heated by true turbulence produced in the jumps. Additional effects, such as a systematic misalignment of the magnetic moment of the neutron star until it is nearly orthogonal, and systematic distortion of the magnetosphere in such a way as to form an even more asymmetric central 'paddle wheel', may enhance the interaction with inflowing matter. The application to X-ray sources corresponds to the 'slow' solutions of Ghosh and Lamb, and therefore to rms magnetic fields of about 4 x 10 to the 10th gauss. Analogous phenomena have been proposed to act in the formation of galactic spiral structure.

Spontaneous jumping, bouncing and trampolining of hydrogel drops on a heated plate.

PubMed

Pham, Jonathan T; Paven, Maxime; Wooh, Sanghyuk; Kajiya, Tadashi; Butt, Hans-Jürgen; Vollmer, Doris

2017-10-13

The contact between liquid drops and hot solid surfaces is of practical importance for industrial processes, such as thermal spraying and spray cooling. The contact and bouncing of solid spheres is also an important event encountered in ball milling, powder processing, and everyday activities, such as ball sports. Using high speed video microscopy, we demonstrate that hydrogel drops, initially at rest on a surface, spontaneously jump upon rapid heating and continue to bounce with increasing amplitudes. Jumping is governed by the surface wettability, surface temperature, hydrogel elasticity, and adhesion. A combination of low-adhesion impact behavior and fast water vapor formation supports continuous bouncing and trampolining. Our results illustrate how the interplay between solid and liquid characteristics of hydrogels results in intriguing dynamics, as reflected by spontaneous jumping, bouncing, trampolining, and extremely short contact times.Drops of liquid on a hot surface can exhibit fascinating behaviour such as the Leidenfrost effect in which drops hover on a vapour layer. Here Pham et al. show that when hydrogel drops are placed on a rapidly heated plate they bounce to increasing heights even if they were initially at rest.

Analysis of single-molecule fluorescence spectroscopic data with a Markov-modulated Poisson process.

PubMed

Jäger, Mark; Kiel, Alexander; Herten, Dirk-Peter; Hamprecht, Fred A

2009-10-05

We present a photon-by-photon analysis framework for the evaluation of data from single-molecule fluorescence spectroscopy (SMFS) experiments using a Markov-modulated Poisson process (MMPP). A MMPP combines a discrete (and hidden) Markov process with an additional Poisson process reflecting the observation of individual photons. The algorithmic framework is used to automatically analyze the dynamics of the complex formation and dissociation of Cu2+ ions with the bidentate ligand 2,2'-bipyridine-4,4'dicarboxylic acid in aqueous media. The process of association and dissociation of Cu2+ ions is monitored with SMFS. The dcbpy-DNA conjugate can exist in two or more distinct states which influence the photon emission rates. The advantage of a photon-by-photon analysis is that no information is lost in preprocessing steps. Different model complexities are investigated in order to best describe the recorded data and to determine transition rates on a photon-by-photon basis. The main strength of the method is that it allows to detect intermittent phenomena which are masked by binning and that are difficult to find using correlation techniques when they are short-lived.

Markov Chains For Testing Redundant Software

NASA Technical Reports Server (NTRS)

White, Allan L.; Sjogren, Jon A.

1990-01-01

Preliminary design developed for validation experiment that addresses problems unique to assuring extremely high quality of multiple-version programs in process-control software. Approach takes into account inertia of controlled system in sense it takes more than one failure of control program to cause controlled system to fail. Verification procedure consists of two steps: experimentation (numerical simulation) and computation, with Markov model for each step.

Towards automatic Markov reliability modeling of computer architectures

NASA Technical Reports Server (NTRS)

Liceaga, C. A.; Siewiorek, D. P.

1986-01-01

The analysis and evaluation of reliability measures using time-varying Markov models is required for Processor-Memory-Switch (PMS) structures that have competing processes such as standby redundancy and repair, or renewal processes such as transient or intermittent faults. The task of generating these models is tedious and prone to human error due to the large number of states and transitions involved in any reasonable system. Therefore model formulation is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model formulation. This paper presents an overview of the Automated Reliability Modeling (ARM) program, under development at NASA Langley Research Center. ARM will accept as input a description of the PMS interconnection graph, the behavior of the PMS components, the fault-tolerant strategies, and the operational requirements. The output of ARM will be the reliability of availability Markov model formulated for direct use by evaluation programs. The advantages of such an approach are (a) utility to a large class of users, not necessarily expert in reliability analysis, and (b) a lower probability of human error in the computation.

Copula-based prediction of economic movements

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Hirsh, I. D.

2016-06-01

In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

Stochastic-shielding approximation of Markov chains and its application to efficiently simulate random ion-channel gating.

PubMed

Schmandt, Nicolaus T; Galán, Roberto F

2012-09-14

Markov chains provide realistic models of numerous stochastic processes in nature. We demonstrate that in any Markov chain, the change in occupation number in state A is correlated to the change in occupation number in state B if and only if A and B are directly connected. This implies that if we are only interested in state A, fluctuations in B may be replaced with their mean if state B is not directly connected to A, which shortens computing time considerably. We show the accuracy and efficacy of our approximation theoretically and in simulations of stochastic ion-channel gating in neurons.

Combination of Markov state models and kinetic networks for the analysis of molecular dynamics simulations of peptide folding.

PubMed

Radford, Isolde H; Fersht, Alan R; Settanni, Giovanni

2011-06-09

Atomistic molecular dynamics simulations of the TZ1 beta-hairpin peptide have been carried out using an implicit model for the solvent. The trajectories have been analyzed using a Markov state model defined on the projections along two significant observables and a kinetic network approach. The Markov state model allowed for an unbiased identification of the metastable states of the system, and provided the basis for commitment probability calculations performed on the kinetic network. The kinetic network analysis served to extract the main transition state for folding of the peptide and to validate the results from the Markov state analysis. The combination of the two techniques allowed for a consistent and concise characterization of the dynamics of the peptide. The slowest relaxation process identified is the exchange between variably folded and denatured species, and the second slowest process is the exchange between two different subsets of the denatured state which could not be otherwise identified by simple inspection of the projected trajectory. The third slowest process is the exchange between a fully native and a partially folded intermediate state characterized by a native turn with a proximal backbone H-bond, and frayed side-chain packing and termini. The transition state for the main folding reaction is similar to the intermediate state, although a more native like side-chain packing is observed.

Markov Tracking for Agent Coordination

NASA Technical Reports Server (NTRS)

Washington, Richard; Lau, Sonie (Technical Monitor)

1998-01-01

Partially observable Markov decision processes (POMDPs) axe an attractive representation for representing agent behavior, since they capture uncertainty in both the agent's state and its actions. However, finding an optimal policy for POMDPs in general is computationally difficult. In this paper we present Markov Tracking, a restricted problem of coordinating actions with an agent or process represented as a POMDP Because the actions coordinate with the agent rather than influence its behavior, the optimal solution to this problem can be computed locally and quickly. We also demonstrate the use of the technique on sequential POMDPs, which can be used to model a behavior that follows a linear, acyclic trajectory through a series of states. By imposing a "windowing" restriction that restricts the number of possible alternatives considered at any moment to a fixed size, a coordinating action can be calculated in constant time, making this amenable to coordination with complex agents.

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.

Constructing 1/omegaalpha noise from reversible Markov chains.

PubMed

Erland, Sveinung; Greenwood, Priscilla E

2007-09-01

This paper gives sufficient conditions for the output of 1/omegaalpha noise from reversible Markov chains on finite state spaces. We construct several examples exhibiting this behavior in a specified range of frequencies. We apply simple representations of the covariance function and the spectral density in terms of the eigendecomposition of the probability transition matrix. The results extend to hidden Markov chains. We generalize the results for aggregations of AR1-processes of C. W. J. Granger [J. Econometrics 14, 227 (1980)]. Given the eigenvalue function, there is a variety of ways to assign values to the states such that the 1/omegaalpha condition is satisfied. We show that a random walk on a certain state space is complementary to the point process model of 1/omega noise of B. Kaulakys and T. Meskauskas [Phys. Rev. E 58, 7013 (1998)]. Passing to a continuous state space, we construct 1/omegaalpha noise which also has a long memory.

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.

Reduced equations of motion for quantum systems driven by diffusive Markov processes.

PubMed

Sarovar, Mohan; Grace, Matthew D

2012-09-28

The expansion of a stochastic Liouville equation for the coupled evolution of a quantum system and an Ornstein-Uhlenbeck process into a hierarchy of coupled differential equations is a useful technique that simplifies the simulation of stochastically driven quantum systems. We expand the applicability of this technique by completely characterizing the class of diffusive Markov processes for which a useful hierarchy of equations can be derived. The expansion of this technique enables the examination of quantum systems driven by non-Gaussian stochastic processes with bounded range. We present an application of this extended technique by simulating Stark-tuned Förster resonance transfer in Rydberg atoms with nonperturbative position fluctuations.

A Langevin equation for the rates of currency exchange based on the Markov analysis

NASA Astrophysics Data System (ADS)

Farahpour, F.; Eskandari, Z.; Bahraminasab, A.; Jafari, G. R.; Ghasemi, F.; Sahimi, Muhammad; Reza Rahimi Tabar, M.

2007-11-01

We propose a method for analyzing the data for the rates of exchange of various currencies versus the U.S. dollar. The method analyzes the return time series of the data as a Markov process, and develops an effective equation which reconstructs it. We find that the Markov time scale, i.e., the time scale over which the data are Markov-correlated, is one day for the majority of the daily exchange rates that we analyze. We derive an effective Langevin equation to describe the fluctuations in the rates. The equation contains two quantities, D and D, representing the drift and diffusion coefficients, respectively. We demonstrate how the two coefficients are estimated directly from the data, without using any assumptions or models for the underlying stochastic time series that represent the daily rates of exchange of various currencies versus the U.S. dollar.

Hybrid Discrete-Continuous Markov Decision Processes

NASA Technical Reports Server (NTRS)

Feng, Zhengzhu; Dearden, Richard; Meuleau, Nicholas; Washington, Rich

2003-01-01

This paper proposes a Markov decision process (MDP) model that features both discrete and continuous state variables. We extend previous work by Boyan and Littman on the mono-dimensional time-dependent MDP to multiple dimensions. We present the principle of lazy discretization, and piecewise constant and linear approximations of the model. Having to deal with several continuous dimensions raises several new problems that require new solutions. In the (piecewise) linear case, we use techniques from partially- observable MDPs (POMDPS) to represent value functions as sets of linear functions attached to different partitions of the state space.

Mathematical model of the loan portfolio dynamics in the form of Markov chain considering the process of new customers attraction

NASA Astrophysics Data System (ADS)

Bozhalkina, Yana

2017-12-01

Mathematical model of the loan portfolio structure change in the form of Markov chain is explored. This model considers in one scheme both the process of customers attraction, their selection based on the credit score, and loans repayment. The model describes the structure and volume of the loan portfolio dynamics, which allows to make medium-term forecasts of profitability and risk. Within the model corrective actions of bank management in order to increase lending volumes or to reduce the risk are formalized.

A Bayesian model for visual space perception

NASA Technical Reports Server (NTRS)

Curry, R. E.

1972-01-01

A model for visual space perception is proposed that contains desirable features in the theories of Gibson and Brunswik. This model is a Bayesian processor of proximal stimuli which contains three important elements: an internal model of the Markov process describing the knowledge of the distal world, the a priori distribution of the state of the Markov process, and an internal model relating state to proximal stimuli. The universality of the model is discussed and it is compared with signal detection theory models. Experimental results of Kinchla are used as a special case.

On spatial mutation-selection models

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

Kondratiev, Yuri, E-mail: kondrat@math.uni-bielefeld.de; Kutoviy, Oleksandr, E-mail: kutoviy@math.uni-bielefeld.de, E-mail: kutovyi@mit.edu; Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139

2013-11-15

We discuss the selection procedure in the framework of mutation models. We study the regulation for stochastically developing systems based on a transformation of the initial Markov process which includes a cost functional. The transformation of initial Markov process by cost functional has an analytic realization in terms of a Kimura-Maruyama type equation for the time evolution of states or in terms of the corresponding Feynman-Kac formula on the path space. The state evolution of the system including the limiting behavior is studied for two types of mutation-selection models.

Effects of Cerebellar Disease on Sequences of Rapid Eye Movements

PubMed Central

King, Susan; Chen, Athena L.; Joshi, Anand; Serra, Alessandro; Leigh, R. John

2011-01-01

Summary Studying saccades can illuminate the more complex decision-making processes required for everyday movements. The double-step task, in which a target jumps to two successive locations before the subject has time to react, has proven a powerful research tool to investigate the brain’s ability to program sequential responses. We asked how patients with a range of cerebellar disorders responded to the double-step task, specifically, whether the initial saccadic response made to a target is affected by the appearance of a second target jump. We also sought to determine whether cerebellar patients were able to make corrective saccades towards the remembered second target location, if it were turned off soon after presentation. We tested saccades to randomly interleaved single- and double-step target jumps to eight locations on a circle. Patient’s initial responses to double-step stimuli showed 50% more error than saccades to single target jumps, and often, they failed to make a saccade to the first target jump. The presence of a second target jump had similar, but smaller effects in control subjects (error increased by 18%). During memory-guided double-step trials, both patients and controls made corrective saccades in darkness to the remembered location of the second jump. We conclude that in cerebellar patients, the second target jump interferes with programming of the saccade to the first target jump of a double-step stimulus; this defect highlights patients’ impaired ability to respond appropriately to sudden, conflicting changes in their environment. Conversely, since cerebellar patients can make corrective memory-guided saccades in darkness, they retain the ability to remember spatial locations, possibly due to non-retinal neural signals (corollary discharge) from cerebral hemispheric areas concerned with spatial localization. PMID:21385592

Saccade selection when reward probability is dynamically manipulated using Markov chains

PubMed Central

Lovejoy, Lee P.; Krauzlis, Richard J.

2012-01-01

Markov chains (stochastic processes where probabilities are assigned based on the previous outcome) are commonly used to examine the transitions between behavioral states, such as those that occur during foraging or social interactions. However, relatively little is known about how well primates can incorporate knowledge about Markov chains into their behavior. Saccadic eye movements are an example of a simple behavior influenced by information about probability, and thus are good candidates for testing whether subjects can learn Markov chains. In addition, when investigating the influence of probability on saccade target selection, the use of Markov chains could provide an alternative method that avoids confounds present in other task designs. To investigate these possibilities, we evaluated human behavior on a task in which stimulus reward probabilities were assigned using a Markov chain. On each trial, the subject selected one of four identical stimuli by saccade; after selection, feedback indicated the rewarded stimulus. Each session consisted of 200–600 trials, and on some sessions, the reward magnitude varied. On sessions with a uniform reward, subjects (n = 6) learned to select stimuli at a frequency close to reward probability, which is similar to human behavior on matching or probability classification tasks. When informed that a Markov chain assigned reward probabilities, subjects (n = 3) learned to select the greatest reward probability more often, bringing them close to behavior that maximizes reward. On sessions where reward magnitude varied across stimuli, subjects (n = 6) demonstrated preferences for both greater reward probability and greater reward magnitude, resulting in a preference for greater expected value (the product of reward probability and magnitude). These results demonstrate that Markov chains can be used to dynamically assign probabilities that are rapidly exploited by human subjects during saccade target selection. PMID:18330552

Saccade selection when reward probability is dynamically manipulated using Markov chains.

PubMed

Nummela, Samuel U; Lovejoy, Lee P; Krauzlis, Richard J

2008-05-01

Markov chains (stochastic processes where probabilities are assigned based on the previous outcome) are commonly used to examine the transitions between behavioral states, such as those that occur during foraging or social interactions. However, relatively little is known about how well primates can incorporate knowledge about Markov chains into their behavior. Saccadic eye movements are an example of a simple behavior influenced by information about probability, and thus are good candidates for testing whether subjects can learn Markov chains. In addition, when investigating the influence of probability on saccade target selection, the use of Markov chains could provide an alternative method that avoids confounds present in other task designs. To investigate these possibilities, we evaluated human behavior on a task in which stimulus reward probabilities were assigned using a Markov chain. On each trial, the subject selected one of four identical stimuli by saccade; after selection, feedback indicated the rewarded stimulus. Each session consisted of 200-600 trials, and on some sessions, the reward magnitude varied. On sessions with a uniform reward, subjects (n = 6) learned to select stimuli at a frequency close to reward probability, which is similar to human behavior on matching or probability classification tasks. When informed that a Markov chain assigned reward probabilities, subjects (n = 3) learned to select the greatest reward probability more often, bringing them close to behavior that maximizes reward. On sessions where reward magnitude varied across stimuli, subjects (n = 6) demonstrated preferences for both greater reward probability and greater reward magnitude, resulting in a preference for greater expected value (the product of reward probability and magnitude). These results demonstrate that Markov chains can be used to dynamically assign probabilities that are rapidly exploited by human subjects during saccade target selection.

A temperature-jump NMR probe setup using rf heating optimized for the analysis of temperature-induced biomacromolecular kinetic processes

NASA Astrophysics Data System (ADS)

Rinnenthal, Jörg; Wagner, Dominic; Marquardsen, Thorsten; Krahn, Alexander; Engelke, Frank; Schwalbe, Harald

2015-02-01

A novel temperature jump (T-jump) probe operational at B0 fields of 600 MHz (14.1 Tesla) with an integrated cage radio-frequency (rf) coil for rapid (<1 s) heating in high-resolution (HR) liquid-state NMR-spectroscopy is presented and its performance investigated. The probe consists of an inner 2.5 mm "heating coil" designed for generating rf-electric fields of 190-220 MHz across a lossy dielectric sample and an outer two coil assembly for 1H-, 2H- and 15N-nuclei. High B0 field homogeneities (0.7 Hz at 600 MHz) are combined with high heating rates (20-25 K/s) and only small temperature gradients (<±1.5 K, 3 s after 20 K T-jump). The heating coil is under control of a high power rf-amplifier within the NMR console and can therefore easily be accessed by the pulse programmer. Furthermore, implementation of a real-time setup including synchronization of the NMR spectrometer's air flow heater with the rf-heater used to maintain the temperature of the sample is described. Finally, the applicability of the real-time T-jump setup for the investigation of biomolecular kinetic processes in the second-to-minute timescale is demonstrated for samples of a model 14mer DNA hairpin and a 15N-selectively labeled 40nt hsp17-RNA thermometer.

Maxwell boundary condition and velocity dependent accommodation coefficient

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

Struchtrup, Henning, E-mail: struchtr@uvic.ca

2013-11-15

A modification of Maxwell's boundary condition for the Boltzmann equation is developed that allows to incorporate velocity dependent accommodation coefficients into the microscopic description. As a first example, it is suggested to consider the wall-particle interaction as a thermally activated process with three parameters. A simplified averaging procedure leads to jump and slip boundary conditions for hydrodynamics. Coefficients for velocity slip, temperature jump, and thermal transpiration flow are identified and compared with those resulting from the original Maxwell model and the Cercignani-Lampis model. An extension of the model leads to temperature dependent slip and jump coefficients.

Heart rate variability as determinism with jump stochastic parameters.

PubMed

Zheng, Jiongxuan; Skufca, Joseph D; Bollt, Erik M

2013-08-01

We use measured heart rate information (RR intervals) to develop a one-dimensional nonlinear map that describes short term deterministic behavior in the data. Our study suggests that there is a stochastic parameter with persistence which causes the heart rate and rhythm system to wander about a bifurcation point. We propose a modified circle map with a jump process noise term as a model which can qualitatively capture such this behavior of low dimensional transient determinism with occasional (stochastically defined) jumps from one deterministic system to another within a one parameter family of deterministic systems.

A renewal jump-diffusion process with threshold dividend strategy

NASA Astrophysics Data System (ADS)

Li, Bo; Wu, Rong; Song, Min

2009-06-01

In this paper, we consider a jump-diffusion risk process with the threshold dividend strategy. Both the distributions of the inter-arrival times and the claims are assumed to be in the class of phase-type distributions. The expected discounted dividend function and the Laplace transform of the ruin time are discussed. Motivated by Asmussen [S. Asmussen, Stationary distributions for fluid flow models with or without Brownian noise, Stochastic Models 11 (1) (1995) 21-49], instead of studying the original process, we study the constructed fluid flow process and their closed-form formulas are obtained in terms of matrix expression. Finally, numerical results are provided to illustrate the computation.

Sliding Mode Control for Discrete-Time Systems With Markovian Packet Dropouts.

PubMed

Song, Heran; Chen, Shih-Chi; Yam, Yeung

2017-11-01

This paper presents the design of a sliding mode controller for networked control systems subject to successive Markovian packet dropouts. This paper adopts the Gilbert-Elliott channel model to describe the temporal correlation among packet losses, and proposes an update scheme to select the assumed available states for use in a sliding mode control law. A technique used in the theory of discrete-time Markov jump linear systems is applied to tackle the effect of the packet losses. This involves introducing a couple of Lyapunov functions dependent on the indicator functions of the instantaneous packet loss, and proving that the sliding mode controller is able to drive the system state trajectories into the neighborhood of the designed integral sliding surface in mean-square sense given that the corresponding Lyapunov inequalities are satisfied. The system is guaranteed thereafter to remain inside the neighborhood of the sliding surface. Simulated case studies are presented to illustrate the effectiveness of the control law.

Dual gait generative models for human motion estimation from a single camera.

PubMed

Zhang, Xin; Fan, Guoliang

2010-08-01

This paper presents a general gait representation framework for video-based human motion estimation. Specifically, we want to estimate the kinematics of an unknown gait from image sequences taken by a single camera. This approach involves two generative models, called the kinematic gait generative model (KGGM) and the visual gait generative model (VGGM), which represent the kinematics and appearances of a gait by a few latent variables, respectively. The concept of gait manifold is proposed to capture the gait variability among different individuals by which KGGM and VGGM can be integrated together, so that a new gait with unknown kinematics can be inferred from gait appearances via KGGM and VGGM. Moreover, a new particle-filtering algorithm is proposed for dynamic gait estimation, which is embedded with a segmental jump-diffusion Markov Chain Monte Carlo scheme to accommodate the gait variability in a long observed sequence. The proposed algorithm is trained from the Carnegie Mellon University (CMU) Mocap data and tested on the Brown University HumanEva data with promising results.

Water movement in glass bead porous media: 1. Experiments of capillary rise and hysteresis

NASA Astrophysics Data System (ADS)

Lu, T. X.; Biggar, J. W.; Nielsen, D. R.

1994-12-01

Experimental observations of capillary rise and hysteresis of water or ethanol in glass beads are presented to improve our understanding of those physical processes in porous media. The results provide evidence that capillary rise into porous media cannot be fully explained by a model of cylinders. They further demonstrate that the "Ink bottle" model does not provide an adequate explanation of hysteresis. Glass beads serving as a model for ideal soil are enclosed in a rectangular glass chamber model. A TV camera associated with a microscope was used to record the processes of capillary rise and drainage. It is clearly shown during capillary rise that the fluid exhibits a "jump" behavior at the neck of the pores in an initially dry profile or at the bottom of the water film in an initially wet profile. Under an initially dry condition, the jump initiates at the particle with smallest diameter. The jump process continues to higher elevations until at equilibrium the surface tensile force is balanced by the hydrostatic force. The wetting front at that time is readily observed as flat and saturated. Under an initially wet condition, capillary rise occurs as a water film thickening process associated with the jump process. Trapped air behind the wetting front renders the wetting front irregular and unsaturated. The capillary rise into an initially wet porous medium can be higher than that into an initially dry profile. During the drying process, large surface areas associated with the gas-liquid interface develop, allowing the porous medium to retain more water than during the wetting process at the same pressure. That mechanism explains better the hysteresis phenomenon in porous media in contrast to other mechanisms that now prevail.

Signal processing of white-light interferometric low-finesse fiber-optic Fabry-Perot sensors.

PubMed

Ma, Cheng; Wang, Anbo

2013-01-10

Signal processing for low-finesse fiber-optic Fabry-Perot sensors based on white-light interferometry is investigated. The problem is demonstrated as analogous to the parameter estimation of a noisy, real, discrete harmonic of finite length. The Cramer-Rao bounds for the estimators are given, and three algorithms are evaluated and proven to approach the bounds. A long-standing problem with these types of sensors is the unpredictable jumps in the phase estimation. Emphasis is made on the property and mechanism of the "total phase" estimator in reducing the estimation error, and a varying phase term in the total phase is identified to be responsible for the unwanted demodulation jumps. The theories are verified by simulation and experiment. A solution to reducing the probability of jump is demonstrated. © 2013 Optical Society of America

Indexed semi-Markov process for wind speed modeling.

NASA Astrophysics Data System (ADS)

Petroni, F.; D'Amico, G.; Prattico, F.

2012-04-01

The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [1] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [3], by using two models, first-order Markov chain with different number of states, and Weibull distribution. All this model use Markov chains to generate synthetic wind speed time series but the search for a better model is still open. Approaching this issue, we applied new models which are generalization of Markov models. More precisely we applied semi-Markov models to generate synthetic wind speed time series. In a previous work we proposed different semi-Markov models, showing their ability to reproduce the autocorrelation structures of wind speed data. In that paper we showed also that the autocorrelation is higher with respect to the Markov model. Unfortunately this autocorrelation was still too small compared to the empirical one. In order to overcome the problem of low autocorrelation, in this paper we propose an indexed semi-Markov model. More precisely we assume that wind speed is described by a discrete time homogeneous semi-Markov process. We introduce a memory index which takes into account the periods of different wind activities. With this model the statistical characteristics of wind speed are faithfully reproduced. The wind is a very unstable phenomenon characterized by a sequence of lulls and sustained speeds, and a good wind generator must be able to reproduce such sequences. To check the validity of the predictive semi-Markovian model, the persistence of synthetic winds were calculated, then averaged and computed. The model is used to generate synthetic time series for wind speed by means of Monte Carlo simulations and the time lagged autocorrelation is used to compare statistical properties of the proposed models with those of real data and also with a time series generated though a simple Markov chain. [1] A. Shamshad, M.A. Bawadi, W.M.W. Wan Hussin, T.A. Majid, S.A.M. Sanusi, First and second order Markov chain models for synthetic generation of wind speed time series, Energy 30 (2005) 693-708. [2] H. Nfaoui, H. Essiarab, A.A.M. Sayigh, A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco, Renewable Energy 29 (2004) 1407-1418. [3] F. Youcef Ettoumi, H. Sauvageot, A.-E.-H. Adane, Statistical bivariate modeling of wind using first-order Markov chain and Weibull distribution, Renewable Energy 28 (2003) 1787-1802.

An abstract specification language for Markov reliability models

NASA Technical Reports Server (NTRS)

Butler, R. W.

1985-01-01

Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.

An abstract language for specifying Markov reliability models

NASA Technical Reports Server (NTRS)

Butler, Ricky W.

1986-01-01

Markov models can be used to compute the reliability of virtually any fault tolerant system. However, the process of delineating all of the states and transitions in a model of complex system can be devastatingly tedious and error-prone. An approach to this problem is presented utilizing an abstract model definition language. This high level language is described in a nonformal manner and illustrated by example.

Modeling Dyadic Processes Using Hidden Markov Models: A Time Series Approach to Mother-Infant Interactions during Infant Immunization

ERIC Educational Resources Information Center

Stifter, Cynthia A.; Rovine, Michael

2015-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at 2 and 6?months of age, used hidden Markov modelling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a…

A Systematic Approach to Determining the Identifiability of Multistage Carcinogenesis Models.

PubMed

Brouwer, Andrew F; Meza, Rafael; Eisenberg, Marisa C

2017-07-01

Multistage clonal expansion (MSCE) models of carcinogenesis are continuous-time Markov process models often used to relate cancer incidence to biological mechanism. Identifiability analysis determines what model parameter combinations can, theoretically, be estimated from given data. We use a systematic approach, based on differential algebra methods traditionally used for deterministic ordinary differential equation (ODE) models, to determine identifiable combinations for a generalized subclass of MSCE models with any number of preinitation stages and one clonal expansion. Additionally, we determine the identifiable combinations of the generalized MSCE model with up to four clonal expansion stages, and conjecture the results for any number of clonal expansion stages. The results improve upon previous work in a number of ways and provide a framework to find the identifiable combinations for further variations on the MSCE models. Finally, our approach, which takes advantage of the Kolmogorov backward equations for the probability generating functions of the Markov process, demonstrates that identifiability methods used in engineering and mathematics for systems of ODEs can be applied to continuous-time Markov processes. © 2016 Society for Risk Analysis.

Modeling dyadic processes using Hidden Markov Models: A time series approach to mother-infant interactions during infant immunization.

PubMed

Stifter, Cynthia A; Rovine, Michael

2015-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at two and six months of age, used hidden Markov modeling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a 4-state model for the dyadic responses to a two-month inoculation whereas a 6-state model best described the dyadic process at six months. Two of the states at two months and three of the states at six months suggested a progression from high intensity crying to no crying with parents using vestibular and auditory soothing methods. The use of feeding and/or pacifying to soothe the infant characterized one two-month state and two six-month states. These data indicate that with maturation and experience, the mother-infant dyad is becoming more organized around the soothing interaction. Using hidden Markov modeling to describe individual differences, as well as normative processes, is also presented and discussed.

GPU-powered Shotgun Stochastic Search for Dirichlet process mixtures of Gaussian Graphical Models

PubMed Central

Mukherjee, Chiranjit; Rodriguez, Abel

2016-01-01

Gaussian graphical models are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of Gaussian graphical models extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous sub-groups. In this paper we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable Gaussian graphical models. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the Markov chain Monte Carlo algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which Markov chain Monte Carlo algorithms are too slow to be practically useful. PMID:28626348

GPU-powered Shotgun Stochastic Search for Dirichlet process mixtures of Gaussian Graphical Models.

PubMed

Mukherjee, Chiranjit; Rodriguez, Abel

2016-01-01

Gaussian graphical models are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of Gaussian graphical models extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous sub-groups. In this paper we present a novel stochastic search algorithm for finding the posterior mode of high-dimensional Dirichlet process mixtures of decomposable Gaussian graphical models. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the Markov chain Monte Carlo algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which Markov chain Monte Carlo algorithms are too slow to be practically useful.

Modeling dyadic processes using Hidden Markov Models: A time series approach to mother-infant interactions during infant immunization

PubMed Central

Stifter, Cynthia A.; Rovine, Michael

2016-01-01

The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at two and six months of age, used hidden Markov modeling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a 4-state model for the dyadic responses to a two-month inoculation whereas a 6-state model best described the dyadic process at six months. Two of the states at two months and three of the states at six months suggested a progression from high intensity crying to no crying with parents using vestibular and auditory soothing methods. The use of feeding and/or pacifying to soothe the infant characterized one two-month state and two six-month states. These data indicate that with maturation and experience, the mother-infant dyad is becoming more organized around the soothing interaction. Using hidden Markov modeling to describe individual differences, as well as normative processes, is also presented and discussed. PMID:27284272

Multiscale hidden Markov models for photon-limited imaging

NASA Astrophysics Data System (ADS)

Nowak, Robert D.

1999-06-01

Photon-limited image analysis is often hindered by low signal-to-noise ratios. A novel Bayesian multiscale modeling and analysis method is developed in this paper to assist in these challenging situations. In addition to providing a very natural and useful framework for modeling an d processing images, Bayesian multiscale analysis is often much less computationally demanding compared to classical Markov random field models. This paper focuses on a probabilistic graph model called the multiscale hidden Markov model (MHMM), which captures the key inter-scale dependencies present in natural image intensities. The MHMM framework presented here is specifically designed for photon-limited imagin applications involving Poisson statistics, and applications to image intensity analysis are examined.

Computer modeling of lung cancer diagnosis-to-treatment process

PubMed Central

Ju, Feng; Lee, Hyo Kyung; Osarogiagbon, Raymond U.; Yu, Xinhua; Faris, Nick

2015-01-01

We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed. PMID:26380181

Computing rates of Markov models of voltage-gated ion channels by inverting partial differential equations governing the probability density functions of the conducting and non-conducting states.

PubMed

Tveito, Aslak; Lines, Glenn T; Edwards, Andrew G; McCulloch, Andrew

2016-07-01

Markov models are ubiquitously used to represent the function of single ion channels. However, solving the inverse problem to construct a Markov model of single channel dynamics from bilayer or patch-clamp recordings remains challenging, particularly for channels involving complex gating processes. Methods for solving the inverse problem are generally based on data from voltage clamp measurements. Here, we describe an alternative approach to this problem based on measurements of voltage traces. The voltage traces define probability density functions of the functional states of an ion channel. These probability density functions can also be computed by solving a deterministic system of partial differential equations. The inversion is based on tuning the rates of the Markov models used in the deterministic system of partial differential equations such that the solution mimics the properties of the probability density function gathered from (pseudo) experimental data as well as possible. The optimization is done by defining a cost function to measure the difference between the deterministic solution and the solution based on experimental data. By evoking the properties of this function, it is possible to infer whether the rates of the Markov model are identifiable by our method. We present applications to Markov model well-known from the literature. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

First and second order semi-Markov chains for wind speed modeling

NASA Astrophysics Data System (ADS)

Prattico, F.; Petroni, F.; D'Amico, G.

2012-04-01

The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [3] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [1], by using two models, first-order Markov chain with different number of states, and Weibull distribution. All this model use Markov chains to generate synthetic wind speed time series but the search for a better model is still open. Approaching this issue, we applied new models which are generalization of Markov models. More precisely we applied semi-Markov models to generate synthetic wind speed time series. Semi-Markov processes (SMP) are a wide class of stochastic processes which generalize at the same time both Markov chains and renewal processes. Their main advantage is that of using whatever type of waiting time distribution for modeling the time to have a transition from one state to another one. This major flexibility has a price to pay: availability of data to estimate the parameters of the model which are more numerous. Data availability is not an issue in wind speed studies, therefore, semi-Markov models can be used in a statistical efficient way. In this work we present three different semi-Markov chain models: the first one is a first-order SMP where the transition probabilities from two speed states (at time Tn and Tn-1) depend on the initial state (the state at Tn-1), final state (the state at Tn) and on the waiting time (given by t=Tn-Tn-1), the second model is a second order SMP where we consider the transition probabilities as depending also on the state the wind speed was before the initial state (which is the state at Tn-2) and the last one is still a second order SMP where the transition probabilities depends on the three states at Tn-2,Tn-1 and Tn and on the waiting times t_1=Tn-1-Tn-2 and t_2=Tn-Tn-1. The three models are used to generate synthetic time series for wind speed by means of Monte Carlo simulations and the time lagged autocorrelation is used to compare statistical properties of the proposed models with those of real data and also with a time series generated though a simple Markov chain. [1] F. Youcef Ettoumi, H. Sauvageot, A.-E.-H. Adane, Statistical bivariate modeling of wind using first-order Markov chain and Weibull distribution, Renewable Energy, 28/2003 1787-1802. [2] A. Shamshad, M.A. Bawadi, W.M.W. Wan Hussin, T.A. Majid, S.A.M. Sanusi, First and second order Markov chain models for synthetic generation of wind speed time series, Energy 30/2005 693-708. [3] H. Nfaoui, H. Essiarab, A.A.M. Sayigh, A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco, Renewable Energy 29/2004, 1407-1418.

77 FR 1079 - Notice of Intent To Prepare an Environmental Impact Statement To Address Grazing Permit Renewals...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-09

... Jump Creek, Succor Creek, and Cow Creek Watersheds in the Owyhee Field Office of the Boise District, ID... may submit comments and issues related to the Jump Creek, Succor Creek, and Cow Creek Watersheds... Creek, Succor Creek, and Cow Creek areas, and announces the beginning of the scoping process. The area...

Estimation and prediction under local volatility jump-diffusion model

NASA Astrophysics Data System (ADS)

Kim, Namhyoung; Lee, Younhee

2018-02-01

Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.

Liquid film on a circular plate formed by a droplet train impingement

NASA Astrophysics Data System (ADS)

Sanada, Toshiyuki; Yamamoto, Shoya

2017-11-01

Droplet impingement phenomena are found in the wide variety of industrial processes, however the detail of liquid film structure formed by the continuous impact of droplets is not clarified. In this study, we experimentally investigated behavior of liquid film which was formed by a droplet train impact. Especially, we focus on the diameter of hydraulic jump formed on a circular plate. The effects of nozzle diameter, liquid surface tension and liquid flow rate on the jump diameter were investigated. In addition, we compared the liquid film by the droplet train impact with that by a liquid column impact. As a result, the hydraulic jump was observed under the smaller water flow rate condition compare to the liquid column impact. And the jump diameters for the case of droplet train impact were greater than that of liquid column impact. However, the jump diameters for the small surface tension liquid for the case of droplet train impact were smaller than that of liquid column impact. We consider that this phenomenon is related to both high speed lateral flow after droplet impact and splash formation. In addition, the liquid film heights after hydraulic jump on a small circular plate were sensitive to either the droplet train impact or liquid column impact.

Theory and Applications of Weakly Interacting Markov Processes

DTIC Science & Technology

2018-02-03

Moderate deviation principles for stochastic dynamical systems. Boston University, Math Colloquium, March 27, 2015. • Moderate Deviation Principles for...Markov chain approximation method. Submitted. [8] E. Bayraktar and M. Ludkovski. Optimal trade execution in illiquid markets. Math . Finance, 21(4):681...701, 2011. [9] E. Bayraktar and M. Ludkovski. Liquidation in limit order books with controlled intensity. Math . Finance, 24(4):627–650, 2014. [10] P.D

A method of hidden Markov model optimization for use with geophysical data sets

NASA Technical Reports Server (NTRS)

Granat, R. A.

2003-01-01

Geophysics research has been faced with a growing need for automated techniques with which to process large quantities of data. A successful tool must meet a number of requirements: it should be consistent, require minimal parameter tuning, and produce scientifically meaningful results in reasonable time. We introduce a hidden Markov model (HMM)-based method for analysis of geophysical data sets that attempts to address these issues.

Semi-Markov Models for Degradation-Based Reliability

DTIC Science & Technology

2010-01-01

standard analysis techniques for Markov processes can be employed (cf. Whitt (1984), Altiok (1985), Perros (1994), and Osogami and Harchol-Balter...We want to approximate X by a PH random variable, sayY, with c.d.f. Ĥ. Marie (1980), Altiok (1985), Johnson (1993), Perros (1994), and Osogami and...provides a minimal representation when matching only two moments. By considering the guidance provided by Marie (1980), Whitt (1984), Altiok (1985), Perros

Semi-Markov Approach to the Shipping Safety Modelling

NASA Astrophysics Data System (ADS)

Guze, Sambor; Smolarek, Leszek

2012-02-01

In the paper the navigational safety model of a ship on the open area has been studied under conditions of incomplete information. Moreover the structure of semi-Markov processes is used to analyse the stochastic ship safety according to the subjective acceptance of risk by the navigator. In addition, the navigator’s behaviour can be analysed by using the numerical simulation to estimate the probability of collision in the safety model.

Mean-Variance Hedging on Uncertain Time Horizon in a Market with a Jump

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

Kharroubi, Idris, E-mail: kharroubi@ceremade.dauphine.fr; Lim, Thomas, E-mail: lim@ensiie.fr; Ngoupeyou, Armand, E-mail: armand.ngoupeyou@univ-paris-diderot.fr

2013-12-15

In this work, we study the problem of mean-variance hedging with a random horizon T∧τ, where T is a deterministic constant and τ is a jump time of the underlying asset price process. We first formulate this problem as a stochastic control problem and relate it to a system of BSDEs with a jump. We then provide a verification theorem which gives the optimal strategy for the mean-variance hedging using the solution of the previous system of BSDEs. Finally, we prove that this system of BSDEs admits a solution via a decomposition approach coming from filtration enlargement theory.

Modeling and estimating the jump risk of exchange rates: Applications to RMB

NASA Astrophysics Data System (ADS)

Wang, Yiming; Tong, Hanfei

2008-11-01

In this paper we propose a new type of continuous-time stochastic volatility model, SVDJ, for the spot exchange rate of RMB, and other foreign currencies. In the model, we assume that the change of exchange rate can be decomposed into two components. One is the normally small-cope innovation driven by the diffusion motion; the other is a large drop or rise engendered by the Poisson counting process. Furthermore, we develop a MCMC method to estimate our model. Empirical results indicate the significant existence of jumps in the exchange rate. Jump components explain a large proportion of the exchange rate change.

Coalescence-Induced Jumping of Nanodroplets on Textured Surfaces.

PubMed

Gao, Shan; Liao, Quanwen; Liu, Wei; Liu, Zhichun

2018-01-04

Conducting experimental studies on nanoscale droplet coalescence using traditional microscopes is a challenging research topic, and views differ as to whether the spontaneous removal can occur in the coalescing nanodroplets. Here, a molecular dynamics simulation is carried out to investigate the coalescence process of two equally sized nanodroplets. On the basis of atomic coordinates, we compute the liquid bridge radii for various cases, which is described by a power law of spreading time, and these nanodroplets undergo coalescence in the inertially limited-viscous regime. Moreover, coalescence-induced jumping is also possible for the nanodroplets, and the attraction force between surface and water molecules plays a crucial role in this process, where the merged nanodroplets prefer to jump away from those surfaces with lower attraction force. When the solid-liquid interaction intensity and surface structure parameters are varied, the attraction force is shown to decrease with decreasing surface wettability intensity and solid fraction.

Markov Decision Process Measurement Model.

PubMed

LaMar, Michelle M

2018-03-01

Within-task actions can provide additional information on student competencies but are challenging to model. This paper explores the potential of using a cognitive model for decision making, the Markov decision process, to provide a mapping between within-task actions and latent traits of interest. Psychometric properties of the model are explored, and simulation studies report on parameter recovery within the context of a simple strategy game. The model is then applied to empirical data from an educational game. Estimates from the model are found to correlate more strongly with posttest results than a partial-credit IRT model based on outcome data alone.

A Stable Clock Error Model Using Coupled First and Second Order Gauss-Markov Processes

NASA Technical Reports Server (NTRS)

Carpenter, Russell; Lee, Taesul

2008-01-01

Long data outages may occur in applications of global navigation satellite system technology to orbit determination for missions that spend significant fractions of their orbits above the navigation satellite constellation(s). Current clock error models based on the random walk idealization may not be suitable in these circumstances, since the covariance of the clock errors may become large enough to overflow flight computer arithmetic. A model that is stable, but which approximates the existing models over short time horizons is desirable. A coupled first- and second-order Gauss-Markov process is such a model.

Increased muscular volume and cuticular specialisations enhance jump velocity in solitarious compared with gregarious desert locusts, Schistocerca gregaria.

PubMed

Rogers, Stephen M; Riley, Joanna; Brighton, Caroline; Sutton, Gregory P; Cullen, Darron A; Burrows, Malcolm

2016-03-01

The desert locust, Schistocerca gregaria, shows a strong phenotypic plasticity. It can develop, depending upon population density, into either a solitarious or gregarious phase that differs in many aspects of behaviour, physiology and morphology. Prominent amongst these differences is that solitarious locusts have proportionately longer hind femora than gregarious locusts. The hind femora contain the muscles and energy-storing cuticular structures that propel powerful jumps using a catapult-like mechanism. We show that solitarious locusts jump on average 23% faster and 27% further than gregarious locusts, and attribute this improved performance to three sources: first, a 17.5% increase in the relative volume of their hind femur, and hence muscle volume; second, a 24.3% decrease in the stiffness of the energy-storing semi-lunar processes of the distal femur; and third, a 4.5% decrease in the stiffness of the tendon of the extensor tibiae muscle. These differences mean that solitarious locusts can generate more power and store more energy in preparation for a jump than can gregarious locusts. This improved performance comes at a cost: solitarious locusts expend nearly twice the energy of gregarious locusts during a single jump and the muscular co-contraction that energises the cuticular springs takes twice as long. There is thus a trade-off between achieving maximum jump velocity in the solitarious phase against the ability to engage jumping rapidly and repeatedly in the gregarious phase. © 2016. Published by The Company of Biologists Ltd.

Electron heating and the potential jump across fast mode shocks. [in interplanetary space

NASA Technical Reports Server (NTRS)

Schwartz, Steven J.; Thomsen, Michelle F.; Bame, S. J.; Stansberry, John

1988-01-01

Two different methods were applied to determine the cross-shock potential jump in the de Hoffmann-Teller reference frame, using a data set that represented 66 crossings of the terrestrial bow shock and 14 interplanetary shocks observed by various ISEE spacecraft, and one crossing each of the Jovian bow shock and the Uranian bow shock made by the Voyager spacecraft. Results for estimates of the electrostatic potential based on an estimate of the jump in electron enthalpy correlated well with estimates based on Liouville's theorem, although the Liouville-determined values were systematically the higher of the two, suggesting that significant irreversible processes contribute to the shape of the downstream distribution. The potential jump corresponds to approximately 12-15 percent of the incident ion ram kinetic energy, and was found not to be controlled by the Mach number, plasma beta, shock geometry, or electron to ion temperature ratios.

Birth-jump processes and application to forest fire spotting.

PubMed

Hillen, T; Greese, B; Martin, J; de Vries, G

2015-01-01

Birth-jump models are designed to describe population models for which growth and spatial spread cannot be decoupled. A birth-jump model is a nonlinear integro-differential equation. We present two different derivations of this equation, one based on a random walk approach and the other based on a two-compartmental reaction-diffusion model. In the case that the redistribution kernels are highly concentrated, we show that the integro-differential equation can be approximated by a reaction-diffusion equation, in which the proliferation rate contributes to both the diffusion term and the reaction term. We completely solve the corresponding critical domain size problem and the minimal wave speed problem. Birth-jump models can be applied in many areas in mathematical biology. We highlight an application of our results in the context of forest fire spread through spotting. We show that spotting increases the invasion speed of a forest fire front.

Sprayable superhydrophobic nano-chains coating with continuous self-jumping of dew and melting frost

PubMed Central

Wang, Shanlin; Zhang, Wenwen; Yu, Xinquan; Liang, Caihua; Zhang, Youfa

2017-01-01

Spontaneous movement of condensed matter provides a new insight to efficiently improve condensation heat transfer on superhydrophobic surface. However, very few reports have shown the jumping behaviors on the sprayable superhydrophobic coatings. Here, we developed a sprayable silica nano-porous coating assembled by fluorinated nano-chains to survey the condensates’ dynamics. The dewdrops were continuously removed by self- and/or trigger-propelling motion due to abundant nano-pores from random multilayer stacking of nano-chains. In comparison, the dewdrops just could be slipped under the gravity effect on lack of nano-pores coatings stacked by silica nano-spheres and nano-aggregates. More interestingly, the spontaneous jumping effect also occurred on micro-scale frost crystals under the defrosting process on nano-chains coating surfaces. Different from self-jumping of dewdrops motion, the propelling force of frost crystals were provided by a sudden increase of the pressure under the frost crystal. PMID:28074938

Enhanced Coalescence-Induced Droplet-Jumping on Nanostructured Superhydrophobic Surfaces in the Absence of Microstructures.

PubMed

Zhang, Peng; Maeda, Yota; Lv, Fengyong; Takata, Yasuyuki; Orejon, Daniel

2017-10-11

Superhydrophobic surfaces are receiving increasing attention due to the enhanced condensation heat transfer, self-cleaning, and anti-icing properties by easing droplet self-removal. Despite the extensive research carried out on this topic, the presence or absence of microstructures on droplet adhesion during condensation has not been fully addressed yet. In this work we, therefore, study the condensation behavior on engineered superhydrophobic copper oxide surfaces with different structural finishes. More specifically, we investigate the coalescence-induced droplet-jumping performance on superhydrophobic surfaces with structures varying from the micro- to the nanoscale. The different structural roughness is possible due to the specific etching parameters adopted during the facile low-cost dual-scale fabrication process. A custom-built optical microscopy setup inside a temperature and relative humidity controlled environmental chamber was used for the experimental observations. By varying the structural roughness, from the micro- to the nanoscale, important differences on the number of droplets involved in the jumps, on the frequency of the jumps, and on the size distribution of the jumping droplets were found. In the absence of microstructures, we report an enhancement of the droplet-jumping performance of small droplets with sizes in the same order of magnitude as the microstructures. Microstructures induce further droplet adhesion, act as a structural barrier for the coalescence between droplets growing on the same microstructure, and cause the droplet angular deviation from the main surface normal. As a consequence, upon coalescence, there is a decrease in the net momentum in the out-of-plane direction, and the jump does not ensue. We demonstrate that the absence of microstructures has therefore a positive impact on the coalescence-induced droplet-jumping of micrometer droplets for antifogging, anti-icing, and condensation heat transfer applications.

Constructing 1/ωα noise from reversible Markov chains

NASA Astrophysics Data System (ADS)

Erland, Sveinung; Greenwood, Priscilla E.

2007-09-01

This paper gives sufficient conditions for the output of 1/ωα noise from reversible Markov chains on finite state spaces. We construct several examples exhibiting this behavior in a specified range of frequencies. We apply simple representations of the covariance function and the spectral density in terms of the eigendecomposition of the probability transition matrix. The results extend to hidden Markov chains. We generalize the results for aggregations of AR1-processes of C. W. J. Granger [J. Econometrics 14, 227 (1980)]. Given the eigenvalue function, there is a variety of ways to assign values to the states such that the 1/ωα condition is satisfied. We show that a random walk on a certain state space is complementary to the point process model of 1/ω noise of B. Kaulakys and T. Meskauskas [Phys. Rev. E 58, 7013 (1998)]. Passing to a continuous state space, we construct 1/ωα noise which also has a long memory.

Semi-Markov models for interval censored transient cognitive states with back transitions and a competing risk

PubMed Central

Wei, Shaoceng; Kryscio, Richard J.

2015-01-01

Continuous-time multi-state stochastic processes are useful for modeling the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient, cognitive states and death as a competing risk (Figure 1). Each subject's cognition is assessed periodically resulting in interval censoring for the cognitive states while death without dementia is not interval censored. Since back transitions among the transient states are possible, Markov chains are often applied to this type of panel data. In this manuscript we apply a Semi-Markov process in which we assume that the waiting times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and in which we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for likelihood estimation. We apply our model to a real dataset, the Nun Study, a cohort of 461 participants. PMID:24821001

Semi-Markov models for interval censored transient cognitive states with back transitions and a competing risk.

PubMed

Wei, Shaoceng; Kryscio, Richard J

2016-12-01

Continuous-time multi-state stochastic processes are useful for modeling the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient cognitive states and death as a competing risk. Each subject's cognition is assessed periodically resulting in interval censoring for the cognitive states while death without dementia is not interval censored. Since back transitions among the transient states are possible, Markov chains are often applied to this type of panel data. In this manuscript, we apply a semi-Markov process in which we assume that the waiting times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and in which we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for likelihood estimation. We apply our model to a real dataset, the Nun Study, a cohort of 461 participants. © The Author(s) 2014.

Genetic correlations between dressage, show jumping and studbook-entry inspection traits in a process of specialization in Dutch Warmblood horses.

PubMed

Rovere, G; Ducro, B J; van Arendonk, J A M; Norberg, E; Madsen, P

2017-04-01

Sport performance in dressage and show jumping are two important traits in the breeding goals of many studbooks. To determine the optimum selection scheme for jumping and dressage, knowledge is needed on the genetic correlation between both disciplines and between traits measured early in life and performance in competition in each discipline. This study aimed to estimate genetic parameters to support decision-making on specialization of breeding horses for dressage and show jumping in Dutch warmblood horses. Genetic correlations between performance of horses in dressage and show jumping were estimated as well as the genetic correlation between traits recorded during studbook-entry inspections and performance in dressage and show jumping competitions. The information on competition comprised the performance of 82 694 horses in dressage and 62 072 horses in show jumping, recorded in the period 1993-2012. For 26 056 horses, information was available for both disciplines. The information on traits recorded at studbook-entry inspections comprised 62 628 horses, recorded in the period 1992-2013. Genetic parameters were estimated from the whole dataset and from a subset without horses recorded in both disciplines. Additionally, the genetic parameters were estimated in three different time periods defined by horses' birth year. The genetic correlation between dressage and show jumping in the whole dataset was -0.23, and it was -0.03 when it was estimated from horses recorded in only one discipline. The genetic correlation between dressage and show jumping was more negative in the most recent time period in all the cases. The more negative correlation between disciplines in more recent time periods was not reflected in changes in the correlations between competitions traits and the traits recorded in the studbook-first inspection. These results suggest that a breeding programme under specialization might be most effective defining two separate aggregate breeding goals for each of the disciplines. © 2016 Blackwell Verlag GmbH.

On Semiotics and Jumping Frogs: The Role of Gesture in the Teaching of Subtraction

ERIC Educational Resources Information Center

Farrugia, Marie Therese

2017-01-01

In this article, I describe a research/teaching experience I undertook with a class of 5-year-old children in Malta. The topic was subtraction on the number line. I interpret the teaching/learning process through a semiotic perspective. In particular, I highlight the role played by the gesture of forming "frog jumps" on the number line.…

Large Deviations for Stationary Probabilities of a Family of Continuous Time Markov Chains via Aubry-Mather Theory

NASA Astrophysics Data System (ADS)

Lopes, Artur O.; Neumann, Adriana

2015-05-01

In the present paper, we consider a family of continuous time symmetric random walks indexed by , . For each the matching random walk take values in the finite set of states ; notice that is a subset of , where is the unitary circle. The infinitesimal generator of such chain is denoted by . The stationary probability for such process converges to the uniform distribution on the circle, when . Here we want to study other natural measures, obtained via a limit on , that are concentrated on some points of . We will disturb this process by a potential and study for each the perturbed stationary measures of this new process when . We disturb the system considering a fixed potential and we will denote by the restriction of to . Then, we define a non-stochastic semigroup generated by the matrix , where is the infinifesimal generator of . From the continuous time Perron's Theorem one can normalized such semigroup, and, then we get another stochastic semigroup which generates a continuous time Markov Chain taking values on . This new chain is called the continuous time Gibbs state associated to the potential , see (Lopes et al. in J Stat Phys 152:894-933, 2013). The stationary probability vector for such Markov Chain is denoted by . We assume that the maximum of is attained in a unique point of , and from this will follow that . Thus, here, our main goal is to analyze the large deviation principle for the family , when . The deviation function , which is defined on , will be obtained from a procedure based on fixed points of the Lax-Oleinik operator and Aubry-Mather theory. In order to obtain the associated Lax-Oleinik operator we use the Varadhan's Lemma for the process . For a careful analysis of the problem we present full details of the proof of the Large Deviation Principle, in the Skorohod space, for such family of Markov Chains, when . Finally, we compute the entropy of the invariant probabilities on the Skorohod space associated to the Markov Chains we analyze.

Discrete time Markov chains (DTMC) susceptible infected susceptible (SIS) epidemic model with two pathogens in two patches

NASA Astrophysics Data System (ADS)

Lismawati, Eka; Respatiwulan; Widyaningsih, Purnami

2017-06-01

The SIS epidemic model describes the pattern of disease spread with characteristics that recovered individuals can be infected more than once. The number of susceptible and infected individuals every time follows the discrete time Markov process. It can be represented by the discrete time Markov chains (DTMC) SIS. The DTMC SIS epidemic model can be developed for two pathogens in two patches. The aims of this paper are to reconstruct and to apply the DTMC SIS epidemic model with two pathogens in two patches. The model was presented as transition probabilities. The application of the model obtain that the number of susceptible individuals decreases while the number of infected individuals increases for each pathogen in each patch.

An 'adding' algorithm for the Markov chain formalism for radiation transfer

NASA Technical Reports Server (NTRS)

Esposito, L. W.

1979-01-01

An adding algorithm is presented, that extends the Markov chain method and considers a preceding calculation as a single state of a new Markov chain. This method takes advantage of the description of the radiation transport as a stochastic process. Successive application of this procedure makes calculation possible for any optical depth without increasing the size of the linear system used. It is determined that the time required for the algorithm is comparable to that for a doubling calculation for homogeneous atmospheres. For an inhomogeneous atmosphere the new method is considerably faster than the standard adding routine. It is concluded that the algorithm is efficient, accurate, and suitable for smaller computers in calculating the diffuse intensity scattered by an inhomogeneous planetary atmosphere.

A stochastic model for tumor geometry evolution during radiation therapy in cervical cancer

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

Liu, Yifang; Lee, Chi-Guhn; Chan, Timothy C. Y., E-mail: tcychan@mie.utoronto.ca

2014-02-15

Purpose: To develop mathematical models to predict the evolution of tumor geometry in cervical cancer undergoing radiation therapy. Methods: The authors develop two mathematical models to estimate tumor geometry change: a Markov model and an isomorphic shrinkage model. The Markov model describes tumor evolution by investigating the change in state (either tumor or nontumor) of voxels on the tumor surface. It assumes that the evolution follows a Markov process. Transition probabilities are obtained using maximum likelihood estimation and depend on the states of neighboring voxels. The isomorphic shrinkage model describes tumor shrinkage or growth in terms of layers of voxelsmore » on the tumor surface, instead of modeling individual voxels. The two proposed models were applied to data from 29 cervical cancer patients treated at Princess Margaret Cancer Centre and then compared to a constant volume approach. Model performance was measured using sensitivity and specificity. Results: The Markov model outperformed both the isomorphic shrinkage and constant volume models in terms of the trade-off between sensitivity (target coverage) and specificity (normal tissue sparing). Generally, the Markov model achieved a few percentage points in improvement in either sensitivity or specificity compared to the other models. The isomorphic shrinkage model was comparable to the Markov approach under certain parameter settings. Convex tumor shapes were easier to predict. Conclusions: By modeling tumor geometry change at the voxel level using a probabilistic model, improvements in target coverage and normal tissue sparing are possible. Our Markov model is flexible and has tunable parameters to adjust model performance to meet a range of criteria. Such a model may support the development of an adaptive paradigm for radiation therapy of cervical cancer.« less

Revisiting Temporal Markov Chains for Continuum modeling of Transport in Porous Media

NASA Astrophysics Data System (ADS)

Delgoshaie, A. H.; Jenny, P.; Tchelepi, H.

2017-12-01

The transport of fluids in porous media is dominated by flow­-field heterogeneity resulting from the underlying permeability field. Due to the high uncertainty in the permeability field, many realizations of the reference geological model are used to describe the statistics of the transport phenomena in a Monte Carlo (MC) framework. There has been strong interest in working with stochastic formulations of the transport that are different from the standard MC approach. Several stochastic models based on a velocity process for tracer particle trajectories have been proposed. Previous studies have shown that for high variances of the log-conductivity, the stochastic models need to account for correlations between consecutive velocity transitions to predict dispersion accurately. The correlated velocity models proposed in the literature can be divided into two general classes of temporal and spatial Markov models. Temporal Markov models have been applied successfully to tracer transport in both the longitudinal and transverse directions. These temporal models are Stochastic Differential Equations (SDEs) with very specific drift and diffusion terms tailored for a specific permeability correlation structure. The drift and diffusion functions devised for a certain setup would not necessarily be suitable for a different scenario, (e.g., a different permeability correlation structure). The spatial Markov models are simple discrete Markov chains that do not require case specific assumptions. However, transverse spreading of contaminant plumes has not been successfully modeled with the available correlated spatial models. Here, we propose a temporal discrete Markov chain to model both the longitudinal and transverse dispersion in a two-dimensional domain. We demonstrate that these temporal Markov models are valid for different correlation structures without modification. Similar to the temporal SDEs, the proposed model respects the limited asymptotic transverse spreading of the plume in two-dimensional problems.

A Nonstationary Markov Model Detects Directional Evolution in Hymenopteran Morphology.

PubMed

Klopfstein, Seraina; Vilhelmsen, Lars; Ronquist, Fredrik

2015-11-01

Directional evolution has played an important role in shaping the morphological, ecological, and molecular diversity of life. However, standard substitution models assume stationarity of the evolutionary process over the time scale examined, thus impeding the study of directionality. Here we explore a simple, nonstationary model of evolution for discrete data, which assumes that the state frequencies at the root differ from the equilibrium frequencies of the homogeneous evolutionary process along the rest of the tree (i.e., the process is nonstationary, nonreversible, but homogeneous). Within this framework, we develop a Bayesian approach for testing directional versus stationary evolution using a reversible-jump algorithm. Simulations show that when only data from extant taxa are available, the success in inferring directionality is strongly dependent on the evolutionary rate, the shape of the tree, the relative branch lengths, and the number of taxa. Given suitable evolutionary rates (0.1-0.5 expected substitutions between root and tips), accounting for directionality improves tree inference and often allows correct rooting of the tree without the use of an outgroup. As an empirical test, we apply our method to study directional evolution in hymenopteran morphology. We focus on three character systems: wing veins, muscles, and sclerites. We find strong support for a trend toward loss of wing veins and muscles, while stationarity cannot be ruled out for sclerites. Adding fossil and time information in a total-evidence dating approach, we show that accounting for directionality results in more precise estimates not only of the ancestral state at the root of the tree, but also of the divergence times. Our model relaxes the assumption of stationarity and reversibility by adding a minimum of additional parameters, and is thus well suited to studying the nature of the evolutionary process in data sets of limited size, such as morphology and ecology. © The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

Exploring the WTI crude oil price bubble process using the Markov regime switching model

NASA Astrophysics Data System (ADS)

Zhang, Yue-Jun; Wang, Jing

2015-03-01

The sharp volatility of West Texas Intermediate (WTI) crude oil price in the past decade triggers us to investigate the price bubbles and their evolving process. Empirical results indicate that the fundamental price of WTI crude oil appears relatively more stable than that of the market-trading price, which verifies the existence of oil price bubbles during the sample period. Besides, by allowing the WTI crude oil price bubble process to switch between two states (regimes) according to a first-order Markov chain, we are able to statistically discriminate upheaval from stable states in the crude oil price bubble process; and in most of time, the stable state dominates the WTI crude oil price bubbles while the upheaval state usually proves short-lived and accompanies unexpected market events.

Nonequilibrium thermodynamic potentials for continuous-time Markov chains.

PubMed

Verley, Gatien

2016-01-01

We connect the rare fluctuations of an equilibrium (EQ) process and the typical fluctuations of a nonequilibrium (NE) stationary process. In the framework of large deviation theory, this observation allows us to introduce NE thermodynamic potentials. For continuous-time Markov chains, we identify the relevant pairs of conjugated variables and propose two NE ensembles: one with fixed dynamics and fluctuating time-averaged variables, and another with fixed time-averaged variables, but a fluctuating dynamics. Accordingly, we show that NE processes are equivalent to conditioned EQ processes ensuring that NE potentials are Legendre dual. We find a variational principle satisfied by the NE potentials that reach their maximum in the NE stationary state and whose first derivatives produce the NE equations of state and second derivatives produce the NE Maxwell relations generalizing the Onsager reciprocity relations.

Time since maximum of Brownian motion and asymmetric Lévy processes

NASA Astrophysics Data System (ADS)

Martin, R. J.; Kearney, M. J.

2018-07-01

Motivated by recent studies of record statistics in relation to strongly correlated time series, we consider explicitly the drawdown time of a Lévy process, which is defined as the time since it last achieved its running maximum when observed over a fixed time period . We show that the density function of this drawdown time, in the case of a completely asymmetric jump process, may be factored as a function of t multiplied by a function of T  ‑  t. This extends a known result for the case of pure Brownian motion. We state the factors explicitly for the cases of exponential down-jumps with drift, and for the downward inverse Gaussian Lévy process with drift.

Jumping acoustic bubbles on lipid bilayers.

PubMed

Der Loughian, Christelle; Muleki Seya, Pauline; Pirat, Christophe; Inserra, Claude; Béra, Jean-Christophe; Rieu, Jean-Paul

2015-05-07

In the context of sonoporation, we use supported lipid bilayers as a model for biological membranes and investigate the interactions between the bilayer and microbubbles induced by ultrasound. Among the various types of damage caused by bubbles on the surface, our experiments exhibit a singular dynamic interaction process where bubbles are jumping on the bilayer, forming a necklace pattern of alteration on the membrane. This phenomenon was explored with different time and space resolutions and, based on our observations, we propose a model for a microbubble subjected to the combined action of van der Waals, acoustic and hydrodynamic forces. Describing the repeated jumps of the bubble, this model explains the lipid exchanges between the bubble and bilayer.

Conditioning exercises in ski jumping: biomechanical relationship of squat jumps, imitation jumps, and hill jumps.

PubMed

Lorenzetti, Silvio; Ammann, Fabian; Windmüller, Sabrina; Häberle, Ramona; Müller, Sören; Gross, Micah; Plüss, Michael; Plüss, Stefan; Schödler, Berni; Hübner, Klaus

2017-11-22

As hill jumps are very time-consuming, ski jumping athletes often perform various imitation jumps during training. The performed jumps should be similar to hill jumps, but a direct comparison of the kinetic and kinematic parameters has not been performed yet. Therefore, this study aimed to correlate 11 common parameters during hill jumps (Oberstdorf Germany), squat jumps (wearing indoor shoes), and various imitation jumps (rolling 4°, rolling flat, static; jumping equipment or indoor shoes) on a custom-built instrumented vehicle with a catch by the coach. During the performed jumps, force and video data of the take-off of 10 athletes were measured. The imitation and squat jumps were then ranked. The main difference between the hill jumps and the imitation and squat jumps is the higher maximal force loading rate during the hill jumps. Imitation jumps performed on a rolling platform, on flat ground were the most similar to hill jumps in terms of the force-time, and leg joint kinematic properties. Thus, non-hill jumps with a technical focus should be performed from a rolling platform with a flat inrun with normal indoor shoes or jumping equipment, and high normal force loading rates should be the main focus of imitation training.

Patchwork sampling of stochastic differential equations

NASA Astrophysics Data System (ADS)

Kürsten, Rüdiger; Behn, Ulrich

2016-03-01

We propose a method to sample stationary properties of solutions of stochastic differential equations, which is accurate and efficient if there are rarely visited regions or rare transitions between distinct regions of the state space. The method is based on a complete, nonoverlapping partition of the state space into patches on which the stochastic process is ergodic. On each of these patches we run simulations of the process strictly truncated to the corresponding patch, which allows effective simulations also in rarely visited regions. The correct weight for each patch is obtained by counting the attempted transitions between all different patches. The results are patchworked to cover the whole state space. We extend the concept of truncated Markov chains which is originally formulated for processes which obey detailed balance to processes not fulfilling detailed balance. The method is illustrated by three examples, describing the one-dimensional diffusion of an overdamped particle in a double-well potential, a system of many globally coupled overdamped particles in double-well potentials subject to additive Gaussian white noise, and the overdamped motion of a particle on the circle in a periodic potential subject to a deterministic drift and additive noise. In an appendix we explain how other well-known Markov chain Monte Carlo algorithms can be related to truncated Markov chains.

Markov-chain model of classified atomistic transition states for discrete kinetic Monte Carlo simulations.

PubMed

Numazawa, Satoshi; Smith, Roger

2011-10-01

Classical harmonic transition state theory is considered and applied in discrete lattice cells with hierarchical transition levels. The scheme is then used to determine transitions that can be applied in a lattice-based kinetic Monte Carlo (KMC) atomistic simulation model. The model results in an effective reduction of KMC simulation steps by utilizing a classification scheme of transition levels for thermally activated atomistic diffusion processes. Thermally activated atomistic movements are considered as local transition events constrained in potential energy wells over certain local time periods. These processes are represented by Markov chains of multidimensional Boolean valued functions in three-dimensional lattice space. The events inhibited by the barriers under a certain level are regarded as thermal fluctuations of the canonical ensemble and accepted freely. Consequently, the fluctuating system evolution process is implemented as a Markov chain of equivalence class objects. It is shown that the process can be characterized by the acceptance of metastable local transitions. The method is applied to a problem of Au and Ag cluster growth on a rippled surface. The simulation predicts the existence of a morphology-dependent transition time limit from a local metastable to stable state for subsequent cluster growth by accretion. Excellent agreement with observed experimental results is obtained.

Impulsive Control for Continuous-Time Markov Decision Processes: A Linear Programming Approach

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

Dufour, F., E-mail: dufour@math.u-bordeaux1.fr; Piunovskiy, A. B., E-mail: piunov@liv.ac.uk

2016-08-15

In this paper, we investigate an optimization problem for continuous-time Markov decision processes with both impulsive and continuous controls. We consider the so-called constrained problem where the objective of the controller is to minimize a total expected discounted optimality criterion associated with a cost rate function while keeping other performance criteria of the same form, but associated with different cost rate functions, below some given bounds. Our model allows multiple impulses at the same time moment. The main objective of this work is to study the associated linear program defined on a space of measures including the occupation measures ofmore » the controlled process and to provide sufficient conditions to ensure the existence of an optimal control.« less

Modeling treatment of ischemic heart disease with partially observable Markov decision processes.

PubMed

Hauskrecht, M; Fraser, H

1998-01-01

Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead they are very often dependent and interleaved over time, mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different diagnostic (investigative) and treatment procedures. The framework of Partially observable Markov decision processes (POMDPs) developed and used in operations research, control theory and artificial intelligence communities is particularly suitable for modeling such a complex decision process. In the paper, we show how the POMDP framework could be used to model and solve the problem of the management of patients with ischemic heart disease, and point out modeling advantages of the framework over standard decision formalisms.

Pelvic Floor Dynamics During High-Impact Athletic Activities: A Computational Modeling Study

PubMed Central

Dias, Nicholas; Peng, Yun; Khavari, Rose; Nakib, Nissrine A.; Sweet, Robert M.; Timm, Gerald W.; Erdman, Arthur G.; Boone, Timothy B.

2017-01-01

Background Stress urinary incontinence is a significant problem in young female athletes, but the pathophysiology remains unclear because of the limited knowledge of the pelvic floor support function and limited capability of currently available assessment tools. The aim of our study is to develop an advanced computer modeling tool to better understand the dynamics of the internal pelvic floor during highly transient athletic activities. Methods Apelvic model was developed based on high-resolution MRI scans of a healthy nulliparous young female. A jump-landing process was simulated using realistic boundary conditions captured from jumping experiments. Hypothesized alterations of the function of pelvic floor muscles were simulated by weakening or strengthening the levator ani muscle stiffness at different levels. Intra-abdominal pressures and corresponding deformations of pelvic floor structures were monitored at different levels of weakness or enhancement. Findings Results show that pelvic floor deformations generated during a jump-landing process differed greatly from those seen in a Valsalva maneuver which is commonly used for diagnosis in clinic. The urethral mobility was only slightly influenced by the alterations of the levator ani muscle stiffness. Implications for risk factors and treatment strategies were also discussed. Interpretation Results suggest that clinical diagnosis should make allowances for observed differences in pelvic floor deformations between a Valsalva maneuver and a jump-landing process to ensure accuracy. Urethral hypermobility may be a less contributing factor than the intrinsic sphincteric closure system to the incontinence of young female athletes. PMID:27886590

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, Chris; Carey, Larry; Schultz, Elise V.; Stano, Geoffrey; Gatlin, Patrick N.; Kozlowski, Danielle M.; Blakeslee, Rich J.; Goodman, Steve

2013-01-01

Key points this analysis will address: 1) What physically is going on in the cloud when there is a jump in lightning? -- Updraft variations, Ice fluxes 2) How do these processes fit in with severe storm conceptual models? 3) What would this information provide an end user? --Relate LJA to radar observations, like changes in reflectivity, MESH, VIL, etc. based multi -Doppler derived physical relationships

Convexity of Ruin Probability and Optimal Dividend Strategies for a General Lévy Process

PubMed Central

Yuen, Kam Chuen; Shen, Ying

2015-01-01

We consider the optimal dividends problem for a company whose cash reserves follow a general Lévy process with certain positive jumps and arbitrary negative jumps. The objective is to find a policy which maximizes the expected discounted dividends until the time of ruin. Under appropriate conditions, we use some recent results in the theory of potential analysis of subordinators to obtain the convexity properties of probability of ruin. We present conditions under which the optimal dividend strategy, among all admissible ones, takes the form of a barrier strategy. PMID:26351655

Throughput, latency and cost comparisons of microcontroller-based implementations of wireless sensor network (WSN) in high jump sports

NASA Astrophysics Data System (ADS)

Ahmad, Afandi; Roslan, Muhammad Faris; Amira, Abbes

2017-09-01

In high jump sports, approach take-off speed and force during the take-off are two (2) main important parts to gain maximum jump. To measure both parameters, wireless sensor network (WSN) that contains microcontroller and sensor are needed to describe the results of speed and force for jumpers. Most of the microcontroller exhibit transmission issues in terms of throughput, latency and cost. Thus, this study presents the comparison of wireless microcontrollers in terms of throughput, latency and cost, and the microcontroller that have best performances and cost will be implemented in high jump wearable device. In the experiments, three (3) parts have been integrated - input, process and output. Force (for ankle) and global positioning system (GPS) sensor (for body waist) acts as an input for data transmission. These data were then being processed by both microcontrollers, ESP8266 and Arduino Yun Mini to transmit the data from sensors to the server (host-PC) via message queuing telemetry transport (MQTT) protocol. The server acts as receiver and the results was calculated from the MQTT log files. At the end, results obtained have shown ESP8266 microcontroller had been chosen since it achieved high throughput, low latency and 11 times cheaper in term of prices compared to Arduino Yun Mini microcontroller.

Environmentally transmitted parasites: Host-jumping in a heterogeneous environment.

PubMed

Caraco, Thomas; Cizauskas, Carrie A; Wang, Ing-Nang

2016-05-21

Groups of chronically infected reservoir-hosts contaminate resource patches by shedding a parasite׳s free-living stage. Novel-host groups visit the same patches, where they are exposed to infection. We treat arrival at patches, levels of parasite deposition, and infection of the novel host as stochastic processes, and derive the expected time elapsing until a host-jump (initial infection of a novel host) occurs. At stationarity, mean parasite densities are independent of reservoir-host group size. But within-patch parasite-density variances increase with reservoir group size. The probability of infecting a novel host declines with parasite-density variance; consequently larger reservoir groups extend the mean waiting time for host-jumping. Larger novel-host groups increase the probability of a host-jump during any single patch visit, but also reduce the total number of visits per unit time. Interaction of these effects implies that the waiting time for the first infection increases with the novel-host group size. If the reservoir-host uses resource patches in any non-uniform manner, reduced spatial overlap between host species increases the waiting time for host-jumping. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multi-camera volumetric PIV for the study of jumping fish

NASA Astrophysics Data System (ADS)

Mendelson, Leah; Techet, Alexandra H.

2018-01-01

Archer fish accurately jump multiple body lengths for aerial prey from directly below the free surface. Multiple fins provide combinations of propulsion and stabilization, enabling prey capture success. Volumetric flow field measurements are crucial to characterizing multi-propulsor interactions during this highly three-dimensional maneuver; however, the fish's behavior also drives unique experimental constraints. Measurements must be obtained in close proximity to the water's surface and in regions of the flow field which are partially-occluded by the fish body. Aerial jump trajectories must also be known to assess performance. This article describes experiment setup and processing modifications to the three-dimensional synthetic aperture particle image velocimetry (SAPIV) technique to address these challenges and facilitate experimental measurements on live jumping fish. The performance of traditional SAPIV algorithms in partially-occluded regions is characterized, and an improved non-iterative reconstruction routine for SAPIV around bodies is introduced. This reconstruction procedure is combined with three-dimensional imaging on both sides of the free surface to reveal the fish's three-dimensional wake, including a series of propulsive vortex rings generated by the tail. In addition, wake measurements from the anal and dorsal fins indicate their stabilizing and thrust-producing contributions as the archer fish jumps.

Neyman, Markov processes and survival analysis.

PubMed

Yang, Grace

2013-07-01

J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.

The cutoff phenomenon in finite Markov chains.

PubMed Central

Diaconis, P

1996-01-01

Natural mixing processes modeled by Markov chains often show a sharp cutoff in their convergence to long-time behavior. This paper presents problems where the cutoff can be proved (card shuffling, the Ehrenfests' urn). It shows that chains with polynomial growth (drunkard's walk) do not show cutoffs. The best general understanding of such cutoffs (high multiplicity of second eigenvalues due to symmetry) is explored. Examples are given where the symmetry is broken but the cutoff phenomenon persists. PMID:11607633

Covariate adjustment of event histories estimated from Markov chains: the additive approach.

PubMed

Aalen, O O; Borgan, O; Fekjaer, H

2001-12-01

Markov chain models are frequently used for studying event histories that include transitions between several states. An empirical transition matrix for nonhomogeneous Markov chains has previously been developed, including a detailed statistical theory based on counting processes and martingales. In this article, we show how to estimate transition probabilities dependent on covariates. This technique may, e.g., be used for making estimates of individual prognosis in epidemiological or clinical studies. The covariates are included through nonparametric additive models on the transition intensities of the Markov chain. The additive model allows for estimation of covariate-dependent transition intensities, and again a detailed theory exists based on counting processes. The martingale setting now allows for a very natural combination of the empirical transition matrix and the additive model, resulting in estimates that can be expressed as stochastic integrals, and hence their properties are easily evaluated. Two medical examples will be given. In the first example, we study how the lung cancer mortality of uranium miners depends on smoking and radon exposure. In the second example, we study how the probability of being in response depends on patient group and prophylactic treatment for leukemia patients who have had a bone marrow transplantation. A program in R and S-PLUS that can carry out the analyses described here has been developed and is freely available on the Internet.

Long-range memory and non-Markov statistical effects in human sensorimotor coordination

NASA Astrophysics Data System (ADS)

M. Yulmetyev, Renat; Emelyanova, Natalya; Hänggi, Peter; Gafarov, Fail; Prokhorov, Alexander

2002-12-01

In this paper, the non-Markov statistical processes and long-range memory effects in human sensorimotor coordination are investigated. The theoretical basis of this study is the statistical theory of non-stationary discrete non-Markov processes in complex systems (Phys. Rev. E 62, 6178 (2000)). The human sensorimotor coordination was experimentally studied by means of standard dynamical tapping test on the group of 32 young peoples with tap numbers up to 400. This test was carried out separately for the right and the left hand according to the degree of domination of each brain hemisphere. The numerical analysis of the experimental results was made with the help of power spectra of the initial time correlation function, the memory functions of low orders and the first three points of the statistical spectrum of non-Markovity parameter. Our observations demonstrate, that with the regard to results of the standard dynamic tapping-test it is possible to divide all examinees into five different dynamic types. We have introduced the conflict coefficient to estimate quantitatively the order-disorder effects underlying life systems. The last one reflects the existence of disbalance between the nervous and the motor human coordination. The suggested classification of the neurophysiological activity represents the dynamic generalization of the well-known neuropsychological types and provides the new approach in a modern neuropsychology.

Influence of lumbar spine extension on vertical jump height during maximal squat jumping.

PubMed

Blache, Yoann; Monteil, Karine

2014-01-01

The purpose of this study was to determine the influence of lumbar spine extension and erector spinae muscle activation on vertical jump height during maximal squat jumping. Eight male athletes performed maximal squat jumps. Electromyograms of the erector spinae were recorded during these jumps. A simulation model of the musculoskeletal system was used to simulate maximal squat jumping with and without spine extension. The effect on vertical jump height of changing erector spinae strength was also tested through the simulated jumps. Concerning the participant jumps, the kinematics indicated a spine extension and erector spinae activation. Concerning the simulated jumps, vertical jump height was about 5.4 cm lower during squat jump without trunk extension compared to squat jump. These results were explained by greater total muscle work during squat jump, more especially by the erector spinae work (+119.5 J). The erector spinae may contribute to spine extension during maximal squat jumping. The simulated jumps confirmed this hypothesis showing that vertical jumping was decreased if this muscle was not taken into consideration in the model. Therefore it is concluded that the erector spinae should be considered as a trunk extensor, which enables to enhance total muscle work and consequently vertical jump height.

Effect of the Thermocouple on Measuring the Temperature Discontinuity at a Liquid-Vapor Interface.

PubMed

Kazemi, Mohammad Amin; Nobes, David S; Elliott, Janet A W

2017-07-18

The coupled heat and mass transfer that occurs in evaporation is of interest in a large number of fields such as evaporative cooling, distillation, drying, coating, printing, crystallization, welding, atmospheric processes, and pool fires. The temperature jump that occurs at an evaporating interface is of central importance to understanding this complex process. Over the past three decades, thermocouples have been widely used to measure the interfacial temperature jumps at a liquid-vapor interface during evaporation. However, the reliability of these measurements has not been investigated so far. In this study, a numerical simulation of a thermocouple when it measures the interfacial temperatures at a liquid-vapor interface is conducted to understand the possible effects of the thermocouple on the measured temperature and features in the temperature profile. The differential equations of heat transfer in the solid and fluids as well as the momentum transfer in the fluids are coupled together and solved numerically subject to appropriate boundary conditions between the solid and fluids. The results of the numerical simulation showed that while thermocouples can measure the interfacial temperatures in the liquid correctly, they fail to read the actual interfacial temperatures in the vapor. As the results of our numerical study suggest, the temperature jumps at a liquid-vapor interface measured experimentally by using a thermocouple are larger than what really exists at the interface. For a typical experimental study of evaporation of water at low pressure, it was found that the temperature jumps measured by a thermocouple are overestimated by almost 50%. However, the revised temperature jumps are still in agreement with the statistical rate theory of interfacial transport. As well as addressing the specific application of the liquid-vapor temperature jump, this paper provides significant insight into the role that heat transfer plays in the operation of thermocouples in general.

Drop jumping. I. The influence of jumping technique on the biomechanics of jumping.

PubMed

Bobbert, M F; Huijing, P A; van Ingen Schenau, G J

1987-08-01

In the literature, drop jumping is advocated as an effective exercise for athletes who prepare themselves for explosive activities. When executing drop jumps, different jumping techniques can be used. In this study, the influence of jumping technique on the biomechanics of jumping is investigated. Ten subjects executed drop jumps from a height of 20 cm and counter-movement jumps. For the execution of the drop jumps, two different techniques were adopted. The first technique, referred to as bounce drop jump, required the subjects to reverse the downward velocity into an upward one as soon as possible after landing. The second technique, referred to as counter-movement drop jump, required them to do this more gradually by making a larger downward movement. During jumping, the subjects were filmed, ground reaction forces were registered, and electromyograms were recorded. The results of a biomechanical analysis show that moments and power output about knee and ankle joints reach larger values during the drop jumps than during counter-movement jumps. The largest values were attained during bounce drop jumps. Based on this finding, it was hypothesized that bounce drop jump is better suited than counter-movement drop jump for athletes who seek to improve the mechanical output of knee extensors and plantar flexors. Researchers are, therefore, advised to control jumping technique when investigating training effects of executing drop jumps.

Recursive utility in a Markov environment with stochastic growth

PubMed Central

Hansen, Lars Peter; Scheinkman, José A.

2012-01-01

Recursive utility models that feature investor concerns about the intertemporal composition of risk are used extensively in applied research in macroeconomics and asset pricing. These models represent preferences as the solution to a nonlinear forward-looking difference equation with a terminal condition. In this paper we study infinite-horizon specifications of this difference equation in the context of a Markov environment. We establish a connection between the solution to this equation and to an arguably simpler Perron–Frobenius eigenvalue equation of the type that occurs in the study of large deviations for Markov processes. By exploiting this connection, we establish existence and uniqueness results. Moreover, we explore a substantive link between large deviation bounds for tail events for stochastic consumption growth and preferences induced by recursive utility. PMID:22778428

Markov modeling and reliability analysis of urea synthesis system of a fertilizer plant

NASA Astrophysics Data System (ADS)

Aggarwal, Anil Kr.; Kumar, Sanjeev; Singh, Vikram; Garg, Tarun Kr.

2015-12-01

This paper deals with the Markov modeling and reliability analysis of urea synthesis system of a fertilizer plant. This system was modeled using Markov birth-death process with the assumption that the failure and repair rates of each subsystem follow exponential distribution. The first-order Chapman-Kolmogorov differential equations are developed with the use of mnemonic rule and these equations are solved with Runga-Kutta fourth-order method. The long-run availability, reliability and mean time between failures are computed for various choices of failure and repair rates of subsystems of the system. The findings of the paper are discussed with the plant personnel to adopt and practice suitable maintenance policies/strategies to enhance the performance of the urea synthesis system of the fertilizer plant.

Inferring Markov chains: Bayesian estimation, model comparison, entropy rate, and out-of-class modeling.

PubMed

Strelioff, Christopher C; Crutchfield, James P; Hübler, Alfred W

2007-07-01

Markov chains are a natural and well understood tool for describing one-dimensional patterns in time or space. We show how to infer kth order Markov chains, for arbitrary k , from finite data by applying Bayesian methods to both parameter estimation and model-order selection. Extending existing results for multinomial models of discrete data, we connect inference to statistical mechanics through information-theoretic (type theory) techniques. We establish a direct relationship between Bayesian evidence and the partition function which allows for straightforward calculation of the expectation and variance of the conditional relative entropy and the source entropy rate. Finally, we introduce a method that uses finite data-size scaling with model-order comparison to infer the structure of out-of-class processes.

Recursive utility in a Markov environment with stochastic growth.

PubMed

Hansen, Lars Peter; Scheinkman, José A

2012-07-24

Recursive utility models that feature investor concerns about the intertemporal composition of risk are used extensively in applied research in macroeconomics and asset pricing. These models represent preferences as the solution to a nonlinear forward-looking difference equation with a terminal condition. In this paper we study infinite-horizon specifications of this difference equation in the context of a Markov environment. We establish a connection between the solution to this equation and to an arguably simpler Perron-Frobenius eigenvalue equation of the type that occurs in the study of large deviations for Markov processes. By exploiting this connection, we establish existence and uniqueness results. Moreover, we explore a substantive link between large deviation bounds for tail events for stochastic consumption growth and preferences induced by recursive utility.

Option pricing for stochastic volatility model with infinite activity Lévy jumps

NASA Astrophysics Data System (ADS)

Gong, Xiaoli; Zhuang, Xintian

2016-08-01

The purpose of this paper is to apply the stochastic volatility model driven by infinite activity Lévy processes to option pricing which displays infinite activity jumps behaviors and time varying volatility that is consistent with the phenomenon observed in underlying asset dynamics. We specially pay attention to three typical Lévy processes that replace the compound Poisson jumps in Bates model, aiming to capture the leptokurtic feature in asset returns and volatility clustering effect in returns variance. By utilizing the analytical characteristic function and fast Fourier transform technique, the closed form formula of option pricing can be derived. The intelligent global optimization search algorithm called Differential Evolution is introduced into the above highly dimensional models for parameters calibration so as to improve the calibration quality of fitted option models. Finally, we perform empirical researches using both time series data and options data on financial markets to illustrate the effectiveness and superiority of the proposed method.

Leveraging annotation-based modeling with Jump.

PubMed

Bergmayr, Alexander; Grossniklaus, Michael; Wimmer, Manuel; Kappel, Gerti

2018-01-01

The capability of UML profiles to serve as annotation mechanism has been recognized in both research and industry. Today's modeling tools offer profiles specific to platforms, such as Java, as they facilitate model-based engineering approaches. However, considering the large number of possible annotations in Java, manually developing the corresponding profiles would only be achievable by huge development and maintenance efforts. Thus, leveraging annotation-based modeling requires an automated approach capable of generating platform-specific profiles from Java libraries. To address this challenge, we present the fully automated transformation chain realized by Jump, thereby continuing existing mapping efforts between Java and UML by emphasizing on annotations and profiles. The evaluation of Jump shows that it scales for large Java libraries and generates profiles of equal or even improved quality compared to profiles currently used in practice. Furthermore, we demonstrate the practical value of Jump by contributing profiles that facilitate reverse engineering and forward engineering processes for the Java platform by applying it to a modernization scenario.

Pressure jump relaxation setup with IR detection and millisecond time resolution

NASA Astrophysics Data System (ADS)

Schiewek, Martin; Krumova, Marina; Hempel, Günter; Blume, Alfred

2007-04-01

An instrument is described that allows the use of Fourier transform infrared (FTIR) spectroscopy as a detection system for kinetic processes after a pressure jump of up to 100bars. The pressure is generated using a high performance liquid chromatography (HPLC) pump and water as a pressure transducing medium. A flexible membrane separates the liquid sample in the IR cell from the pressure transducing medium. Two electromagnetic switching valves in the setup enable pressure jumps with a decay time of 4ms. The FTIR spectrometer is configured to measure time resolved spectra in the millisecond time regime using the rapid scan mode. All components are computer controlled. For a demonstration of the capability of the method first results on the kinetics of a phase transition between two lamellar phases of an aqueous phospholipid dispersion are presented. This combination of FTIR spectroscopy with the pressure jump relaxation technique can also be used for other systems which display cooperative transitions with concomitant volume changes.

Chandler wobble: two more large phase jumps revealed

NASA Astrophysics Data System (ADS)

Malkin, Zinovy; Miller, Natalia

2010-12-01

Investigations of the anomalies in the Earth rotation, in particular, the polar motion components, play an important role in our understanding of the processes that drive changes in the Earth's surface, interior, atmosphere, and ocean. This paper is primarily aimed at investigation of the Chandler wobble (CW) at the whole available 163-year interval to search for the major CW amplitude and phase variations. First, the CW signal was extracted from the IERS (International Earth Rotation and Reference Systems Service) Pole coordinates time series using two digital filters: the singular spectrum analysis and Fourier transform. The CW amplitude and phase variations were examined by means of the wavelet transform and Hilbert transform. Results of our analysis have shown that, besides the well-known CW phase jump in the 1920s, two other large phase jumps have been found in the 1850s and 2000s. As in the 1920s, these phase jumps occurred contemporarily with a sharp decrease in the CW amplitude.

Markov modulated Poisson process models incorporating covariates for rainfall intensity.

PubMed

Thayakaran, R; Ramesh, N I

2013-01-01

Time series of rainfall bucket tip times at the Beaufort Park station, Bracknell, in the UK are modelled by a class of Markov modulated Poisson processes (MMPP) which may be thought of as a generalization of the Poisson process. Our main focus in this paper is to investigate the effects of including covariate information into the MMPP model framework on statistical properties. In particular, we look at three types of time-varying covariates namely temperature, sea level pressure, and relative humidity that are thought to be affecting the rainfall arrival process. Maximum likelihood estimation is used to obtain the parameter estimates, and likelihood ratio tests are employed in model comparison. Simulated data from the fitted model are used to make statistical inferences about the accumulated rainfall in the discrete time interval. Variability of the daily Poisson arrival rates is studied.

Inferring phenomenological models of Markov processes from data

NASA Astrophysics Data System (ADS)

Rivera, Catalina; Nemenman, Ilya

Microscopically accurate modeling of stochastic dynamics of biochemical networks is hard due to the extremely high dimensionality of the state space of such networks. Here we propose an algorithm for inference of phenomenological, coarse-grained models of Markov processes describing the network dynamics directly from data, without the intermediate step of microscopically accurate modeling. The approach relies on the linear nature of the Chemical Master Equation and uses Bayesian Model Selection for identification of parsimonious models that fit the data. When applied to synthetic data from the Kinetic Proofreading process (KPR), a common mechanism used by cells for increasing specificity of molecular assembly, the algorithm successfully uncovers the known coarse-grained description of the process. This phenomenological description has been notice previously, but this time it is derived in an automated manner by the algorithm. James S. McDonnell Foundation Grant No. 220020321.

Optimal regulation in systems with stochastic time sampling

NASA Technical Reports Server (NTRS)

Montgomery, R. C.; Lee, P. S.

1980-01-01

An optimal control theory that accounts for stochastic variable time sampling in a distributed microprocessor based flight control system is presented. The theory is developed by using a linear process model for the airplane dynamics and the information distribution process is modeled as a variable time increment process where, at the time that information is supplied to the control effectors, the control effectors know the time of the next information update only in a stochastic sense. An optimal control problem is formulated and solved for the control law that minimizes the expected value of a quadratic cost function. The optimal cost obtained with a variable time increment Markov information update process where the control effectors know only the past information update intervals and the Markov transition mechanism is almost identical to that obtained with a known and uniform information update interval.

Markov Processes in Image Processing

NASA Astrophysics Data System (ADS)

Petrov, E. P.; Kharina, N. L.

2018-05-01

Digital images are used as an information carrier in different sciences and technologies. The aspiration to increase the number of bits in the image pixels for the purpose of obtaining more information is observed. In the paper, some methods of compression and contour detection on the basis of two-dimensional Markov chain are offered. Increasing the number of bits on the image pixels will allow one to allocate fine object details more precisely, but it significantly complicates image processing. The methods of image processing do not concede by the efficiency to well-known analogues, but surpass them in processing speed. An image is separated into binary images, and processing is carried out in parallel with each without an increase in speed, when increasing the number of bits on the image pixels. One more advantage of methods is the low consumption of energy resources. Only logical procedures are used and there are no computing operations. The methods can be useful in processing images of any class and assignment in processing systems with a limited time and energy resources.

Random walk in nonhomogeneous environments: A possible approach to human and animal mobility

NASA Astrophysics Data System (ADS)

Srokowski, Tomasz

2017-03-01

The random walk process in a nonhomogeneous medium, characterized by a Lévy stable distribution of jump length, is discussed. The width depends on a position: either before the jump or after that. In the latter case, the density slope is affected by the variable width and the variance may be finite; then all kinds of the anomalous diffusion are predicted. In the former case, only the time characteristics are sensitive to the variable width. The corresponding Langevin equation with different interpretations of the multiplicative noise is discussed. The dependence of the distribution width on position after jump is interpreted in terms of cognitive abilities and related to such problems as migration in a human population and foraging habits of animals.

Hidden Markov models and other machine learning approaches in computational molecular biology

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

Baldi, P.

1995-12-31

This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In thismore » tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.« less

Decentralized control of Markovian decision processes: Existence Sigma-admissable policies

NASA Technical Reports Server (NTRS)

Greenland, A.

1980-01-01

The problem of formulating and analyzing Markov decision models having decentralized information and decision patterns is examined. Included are basic examples as well as the mathematical preliminaries needed to understand Markov decision models and, further, to superimpose decentralized decision structures on them. The notion of a variance admissible policy for the model is introduced and it is proved that there exist (possibly nondeterministic) optional policies from the class of variance admissible policies. Directions for further research are explored.

A Stochastic Polygons Model for Glandular Structures in Colon Histology Images.

PubMed

Sirinukunwattana, Korsuk; Snead, David R J; Rajpoot, Nasir M

2015-11-01

In this paper, we present a stochastic model for glandular structures in histology images of tissue slides stained with Hematoxylin and Eosin, choosing colon tissue as an example. The proposed Random Polygons Model (RPM) treats each glandular structure in an image as a polygon made of a random number of vertices, where the vertices represent approximate locations of epithelial nuclei. We formulate the RPM as a Bayesian inference problem by defining a prior for spatial connectivity and arrangement of neighboring epithelial nuclei and a likelihood for the presence of a glandular structure. The inference is made via a Reversible-Jump Markov chain Monte Carlo simulation. To the best of our knowledge, all existing published algorithms for gland segmentation are designed to mainly work on healthy samples, adenomas, and low grade adenocarcinomas. One of them has been demonstrated to work on intermediate grade adenocarcinomas at its best. Our experimental results show that the RPM yields favorable results, both quantitatively and qualitatively, for extraction of glandular structures in histology images of normal human colon tissues as well as benign and cancerous tissues, excluding undifferentiated carcinomas.

Chasing maximal performance: a cautionary tale from the celebrated jumping frogs of Calaveras County.

PubMed

Astley, H C; Abbott, E M; Azizi, E; Marsh, R L; Roberts, T J

2013-11-01

Maximal performance is an essential metric for understanding many aspects of an organism's biology, but it can be difficult to determine because a measured maximum may reflect only a peak level of effort, not a physiological limit. We used a unique opportunity provided by a frog jumping contest to evaluate the validity of existing laboratory estimates of maximum jumping performance in bullfrogs (Rana catesbeiana). We recorded video of 3124 bullfrog jumps over the course of the 4-day contest at the Calaveras County Jumping Frog Jubilee, and determined jump distance from these images and a calibration of the jump arena. Frogs were divided into two groups: 'rental' frogs collected by fair organizers and jumped by the general public, and frogs collected and jumped by experienced, 'professional' teams. A total of 58% of recorded jumps surpassed the maximum jump distance in the literature (1.295 m), and the longest jump was 2.2 m. Compared with rental frogs, professionally jumped frogs jumped farther, and the distribution of jump distances for this group was skewed towards long jumps. Calculated muscular work, historical records and the skewed distribution of jump distances all suggest that the longest jumps represent the true performance limit for this species. Using resampling, we estimated the probability of observing a given jump distance for various sample sizes, showing that large sample sizes are required to detect rare maximal jumps. These results show the importance of sample size, animal motivation and physiological conditions for accurate maximal performance estimates.

Relationships Between Countermovement Jump Ground Reaction Forces and Jump Height, Reactive Strength Index, and Jump Time.

PubMed

Barker, Leland A; Harry, John R; Mercer, John A

2018-01-01

Barker, LA, Harry, JR, and Mercer, JA. Relationships between countermovement jump ground reaction forces and jump height, reactive strength index, and jump time. J Strength Cond Res 32(1): 248-254, 2018-The purpose of this study was to determine the relationship between ground reaction force (GRF) variables to jump height, jump time, and the reactive strength index (RSI). Twenty-six, Division-I, male, soccer players performed 3 maximum effort countermovement jumps (CMJs) on a dual-force platform system that measured 3-dimensional kinetic data. The trial producing peak jump height was used for analysis. Vertical GRF (Fz) variables were divided into unloading, eccentric, amortization, and concentric phases and correlated with jump height, RSI (RSI = jump height/jump time), and jump time (from start to takeoff). Significant correlations were observed between jump height and RSI, concentric kinetic energy, peak power, concentric work, and concentric displacement. Significant correlations were observed between RSI and jump time, peak power, unload Fz, eccentric work, eccentric rate of force development (RFD), amortization Fz, amortization time, second Fz peak, average concentric Fz, and concentric displacement. Significant correlations were observed between jump time and unload Fz, eccentric work, eccentric RFD, amortization Fz, amortization time, average concentric Fz, and concentric work. In conclusion, jump height correlated with variables derived from the concentric phase only (work, power, and displacement), whereas Fz variables from the unloading, eccentric, amortization, and concentric phases correlated highly with RSI and jump time. These observations demonstrate the importance of countermovement Fz characteristics for time-sensitive CMJ performance measures. Researchers and practitioners should include RSI and jump time with jump height to improve their assessment of jump performance.

A valid and reliable method to measure jump-specific training and competition load in elite volleyball players.

PubMed

Skazalski, C; Whiteley, R; Hansen, C; Bahr, R

2018-05-01

Use of a commercially available wearable device to monitor jump load with elite volleyball players has become common practice. The purpose of this study was to evaluate the validity and reliability of this device, the Vert, to count jumps and measure jump height with professional volleyball players. Jump count accuracy was determined by comparing jumps recorded by the device to jumps observed through systematic video analysis of three practice sessions and two league matches performed by a men's professional volleyball team. Jumps performed by 14 players were each coded for time and jump type and individually matched to device recorded jumps. Jump height validity of the device was examined against reference standards as participants performed countermovement jumps on a force plate and volleyball-specific jumps with a Vertec. The Vert device accurately counted 99.3% of the 3637 jumps performed during practice and match play. The device showed excellent jump height interdevice reliability for two devices placed in the same pouch during volleyball jumps (r = .99, 95% CI 0.98-0.99). The device had a minimum detectable change (MDC) of 9.7 cm and overestimated jump height by an average of 5.5 cm (95% CI 4.5-6.5) across all volleyball jumps. The Vert device demonstrates excellent accuracy counting volleyball-specific jumps during training and competition. While the device is not recommended to measure maximal jumping ability when precision is needed, it provides an acceptable measure of on-court jump height that can be used to monitor athlete jump load. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Relative net vertical impulse determines jumping performance.

PubMed

Kirby, Tyler J; McBride, Jeffrey M; Haines, Tracie L; Dayne, Andrea M

2011-08-01

The purpose of this investigation was to determine the relationship between relative net vertical impulse and jump height in a countermovement jump and static jump performed to varying squat depths. Ten college-aged males with 2 years of jumping experience participated in this investigation (age: 23.3 ± 1.5 years; height: 176.7 ± 4.5 cm; body mass: 84.4 ± 10.1 kg). Subjects performed a series of static jumps and countermovement jumps in a randomized fashion to a depth of 0.15, 0.30, 0.45, 0.60, and 0.75 m and a self-selected depth (static jump depth = 0.38 ± 0.08 m, countermovement jump depth = 0.49 ± 0.06 m). During the concentric phase of each jump, peak force, peak velocity, peak power, jump height, and net vertical impulse were recorded and analyzed. Net vertical impulse was divided by body mass to produce relative net vertical impulse. Increasing squat depth corresponded to a decrease in peak force and an increase in jump height and relative net vertical impulse for both static jump and countermovement jump. Across all depths, relative net vertical impulse was statistically significantly correlated to jump height in the static jump (r = .9337, p < .0001, power = 1.000) and countermovement jump (r = .925, p < .0001, power = 1.000). Across all depths, peak force was negatively correlated to jump height in the static jump (r = -0.3947, p = .0018, power = 0.8831) and countermovement jump (r = -0.4080, p = .0012, power = 0.9050). These results indicate that relative net vertical impulse can be used to assess vertical jump performance, regardless of initial squat depth, and that peak force may not be the best measure to assess vertical jump performance.

The application of Markov decision process in restaurant delivery robot

NASA Astrophysics Data System (ADS)

Wang, Yong; Hu, Zhen; Wang, Ying

2017-05-01

As the restaurant delivery robot is often in a dynamic and complex environment, including the chairs inadvertently moved to the channel and customers coming and going. The traditional path planning algorithm is not very ideal. To solve this problem, this paper proposes the Markov dynamic state immediate reward (MDR) path planning algorithm according to the traditional Markov decision process. First of all, it uses MDR to plan a global path, then navigates along this path. When the sensor detects there is no obstructions in front state, increase its immediate state reward value; when the sensor detects there is an obstacle in front, plan a global path that can avoid obstacle with the current position as the new starting point and reduce its state immediate reward value. This continues until the target is reached. When the robot learns for a period of time, it can avoid those places where obstacles are often present when planning the path. By analyzing the simulation experiment, the algorithm has achieved good results in the global path planning under the dynamic environment.

Radiative transfer calculated from a Markov chain formalism

NASA Technical Reports Server (NTRS)

Esposito, L. W.; House, L. L.

1978-01-01

The theory of Markov chains is used to formulate the radiative transport problem in a general way by modeling the successive interactions of a photon as a stochastic process. Under the minimal requirement that the stochastic process is a Markov chain, the determination of the diffuse reflection or transmission from a scattering atmosphere is equivalent to the solution of a system of linear equations. This treatment is mathematically equivalent to, and thus has many of the advantages of, Monte Carlo methods, but can be considerably more rapid than Monte Carlo algorithms for numerical calculations in particular applications. We have verified the speed and accuracy of this formalism for the standard problem of finding the intensity of scattered light from a homogeneous plane-parallel atmosphere with an arbitrary phase function for scattering. Accurate results over a wide range of parameters were obtained with computation times comparable to those of a standard 'doubling' routine. The generality of this formalism thus allows fast, direct solutions to problems that were previously soluble only by Monte Carlo methods. Some comparisons are made with respect to integral equation methods.

Markov Chain Monte Carlo in the Analysis of Single-Molecule Experimental Data

NASA Astrophysics Data System (ADS)

Kou, S. C.; Xie, X. Sunney; Liu, Jun S.

2003-11-01

This article provides a Bayesian analysis of the single-molecule fluorescence lifetime experiment designed to probe the conformational dynamics of a single DNA hairpin molecule. The DNA hairpin's conformational change is initially modeled as a two-state Markov chain, which is not observable and has to be indirectly inferred. The Brownian diffusion of the single molecule, in addition to the hidden Markov structure, further complicates the matter. We show that the analytical form of the likelihood function can be obtained in the simplest case and a Metropolis-Hastings algorithm can be designed to sample from the posterior distribution of the parameters of interest and to compute desired estiamtes. To cope with the molecular diffusion process and the potentially oscillating energy barrier between the two states of the DNA hairpin, we introduce a data augmentation technique to handle both the Brownian diffusion and the hidden Ornstein-Uhlenbeck process associated with the fluctuating energy barrier, and design a more sophisticated Metropolis-type algorithm. Our method not only increases the estimating resolution by several folds but also proves to be successful for model discrimination.

Local Composite Quantile Regression Smoothing for Harris Recurrent Markov Processes

PubMed Central

Li, Degui; Li, Runze

2016-01-01

In this paper, we study the local polynomial composite quantile regression (CQR) smoothing method for the nonlinear and nonparametric models under the Harris recurrent Markov chain framework. The local polynomial CQR regression method is a robust alternative to the widely-used local polynomial method, and has been well studied in stationary time series. In this paper, we relax the stationarity restriction on the model, and allow that the regressors are generated by a general Harris recurrent Markov process which includes both the stationary (positive recurrent) and nonstationary (null recurrent) cases. Under some mild conditions, we establish the asymptotic theory for the proposed local polynomial CQR estimator of the mean regression function, and show that the convergence rate for the estimator in nonstationary case is slower than that in stationary case. Furthermore, a weighted type local polynomial CQR estimator is provided to improve the estimation efficiency, and a data-driven bandwidth selection is introduced to choose the optimal bandwidth involved in the nonparametric estimators. Finally, we give some numerical studies to examine the finite sample performance of the developed methodology and theory. PMID:27667894

Block-accelerated aggregation multigrid for Markov chains with application to PageRank problems

NASA Astrophysics Data System (ADS)

Shen, Zhao-Li; Huang, Ting-Zhu; Carpentieri, Bruno; Wen, Chun; Gu, Xian-Ming

2018-06-01

Recently, the adaptive algebraic aggregation multigrid method has been proposed for computing stationary distributions of Markov chains. This method updates aggregates on every iterative cycle to keep high accuracies of coarse-level corrections. Accordingly, its fast convergence rate is well guaranteed, but often a large proportion of time is cost by aggregation processes. In this paper, we show that the aggregates on each level in this method can be utilized to transfer the probability equation of that level into a block linear system. Then we propose a Block-Jacobi relaxation that deals with the block system on each level to smooth error. Some theoretical analysis of this technique is presented, meanwhile it is also adapted to solve PageRank problems. The purpose of this technique is to accelerate the adaptive aggregation multigrid method and its variants for solving Markov chains and PageRank problems. It also attempts to shed some light on new solutions for making aggregation processes more cost-effective for aggregation multigrid methods. Numerical experiments are presented to illustrate the effectiveness of this technique.

Planning treatment of ischemic heart disease with partially observable Markov decision processes.

PubMed

Hauskrecht, M; Fraser, H

2000-03-01

Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead, they are very often dependent and interleaved over time. This is mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different diagnostic (investigative) and treatment procedures. The framework of partially observable Markov decision processes (POMDPs) developed and used in the operations research, control theory and artificial intelligence communities is particularly suitable for modeling such a complex decision process. In this paper, we show how the POMDP framework can be used to model and solve the problem of the management of patients with ischemic heart disease (IHD), and demonstrate the modeling advantages of the framework over standard decision formalisms.

Evaluation methodologies for an advanced information processing system

NASA Technical Reports Server (NTRS)

Schabowsky, R. S., Jr.; Gai, E.; Walker, B. K.; Lala, J. H.; Motyka, P.

1984-01-01

The system concept and requirements for an Advanced Information Processing System (AIPS) are briefly described, but the emphasis of this paper is on the evaluation methodologies being developed and utilized in the AIPS program. The evaluation tasks include hardware reliability, maintainability and availability, software reliability, performance, and performability. Hardware RMA and software reliability are addressed with Markov modeling techniques. The performance analysis for AIPS is based on queueing theory. Performability is a measure of merit which combines system reliability and performance measures. The probability laws of the performance measures are obtained from the Markov reliability models. Scalar functions of this law such as the mean and variance provide measures of merit in the AIPS performability evaluations.

Recombination Processes and Nonlinear Markov Chains.

PubMed

Pirogov, Sergey; Rybko, Alexander; Kalinina, Anastasia; Gelfand, Mikhail

2016-09-01

Bacteria are known to exchange genetic information by horizontal gene transfer. Since the frequency of homologous recombination depends on the similarity between the recombining segments, several studies examined whether this could lead to the emergence of subspecies. Most of them simulated fixed-size Wright-Fisher populations, in which the genetic drift should be taken into account. Here, we use nonlinear Markov processes to describe a bacterial population evolving under mutation and recombination. We consider a population structure as a probability measure on the space of genomes. This approach implies the infinite population size limit, and thus, the genetic drift is not assumed. We prove that under these conditions, the emergence of subspecies is impossible.

Multiscale Hy3S: hybrid stochastic simulation for supercomputers.

PubMed

Salis, Howard; Sotiropoulos, Vassilios; Kaznessis, Yiannis N

2006-02-24

Stochastic simulation has become a useful tool to both study natural biological systems and design new synthetic ones. By capturing the intrinsic molecular fluctuations of "small" systems, these simulations produce a more accurate picture of single cell dynamics, including interesting phenomena missed by deterministic methods, such as noise-induced oscillations and transitions between stable states. However, the computational cost of the original stochastic simulation algorithm can be high, motivating the use of hybrid stochastic methods. Hybrid stochastic methods partition the system into multiple subsets and describe each subset as a different representation, such as a jump Markov, Poisson, continuous Markov, or deterministic process. By applying valid approximations and self-consistently merging disparate descriptions, a method can be considerably faster, while retaining accuracy. In this paper, we describe Hy3S, a collection of multiscale simulation programs. Building on our previous work on developing novel hybrid stochastic algorithms, we have created the Hy3S software package to enable scientists and engineers to both study and design extremely large well-mixed biological systems with many thousands of reactions and chemical species. We have added adaptive stochastic numerical integrators to permit the robust simulation of dynamically stiff biological systems. In addition, Hy3S has many useful features, including embarrassingly parallelized simulations with MPI; special discrete events, such as transcriptional and translation elongation and cell division; mid-simulation perturbations in both the number of molecules of species and reaction kinetic parameters; combinatorial variation of both initial conditions and kinetic parameters to enable sensitivity analysis; use of NetCDF optimized binary format to quickly read and write large datasets; and a simple graphical user interface, written in Matlab, to help users create biological systems and analyze data. We demonstrate the accuracy and efficiency of Hy3S with examples, including a large-scale system benchmark and a complex bistable biochemical network with positive feedback. The software itself is open-sourced under the GPL license and is modular, allowing users to modify it for their own purposes. Hy3S is a powerful suite of simulation programs for simulating the stochastic dynamics of networks of biochemical reactions. Its first public version enables computational biologists to more efficiently investigate the dynamics of realistic biological systems.

Performance analysis of jump-gliding locomotion for miniature robotics.

PubMed

Vidyasagar, A; Zufferey, Jean-Christohphe; Floreano, Dario; Kovač, M

2015-03-26

Recent work suggests that jumping locomotion in combination with a gliding phase can be used as an effective mobility principle in robotics. Compared to pure jumping without a gliding phase, the potential benefits of hybrid jump-gliding locomotion includes the ability to extend the distance travelled and reduce the potentially damaging impact forces upon landing. This publication evaluates the performance of jump-gliding locomotion and provides models for the analysis of the relevant dynamics of flight. It also defines a jump-gliding envelope that encompasses the range that can be achieved with jump-gliding robots and that can be used to evaluate the performance and improvement potential of jump-gliding robots. We present first a planar dynamic model and then a simplified closed form model, which allow for quantification of the distance travelled and the impact energy on landing. In order to validate the prediction of these models, we validate the model with experiments using a novel jump-gliding robot, named the 'EPFL jump-glider'. It has a mass of 16.5 g and is able to perform jumps from elevated positions, perform steered gliding flight, land safely and traverse on the ground by repetitive jumping. The experiments indicate that the developed jump-gliding model fits very well with the measured flight data using the EPFL jump-glider, confirming the benefits of jump-gliding locomotion to mobile robotics. The jump-glide envelope considerations indicate that the EPFL jump-glider, when traversing from a 2 m height, reaches 74.3% of optimal jump-gliding distance compared to pure jumping without a gliding phase which only reaches 33.4% of the optimal jump-gliding distance. Methods of further improving flight performance based on the models and inspiration from biological systems are presented providing mechanical design pathways to future jump-gliding robot designs.

Intertime jump statistics of state-dependent Poisson processes.

PubMed

Daly, Edoardo; Porporato, Amilcare

2007-01-01

A method to obtain the probability distribution of the interarrival times of jump occurrences in systems driven by state-dependent Poisson noise is proposed. Such a method uses the survivor function obtained by a modified version of the master equation associated to the stochastic process under analysis. A model for the timing of human activities shows the capability of state-dependent Poisson noise to generate power-law distributions. The application of the method to a model for neuron dynamics and to a hydrological model accounting for land-atmosphere interaction elucidates the origin of characteristic recurrence intervals and possible persistence in state-dependent Poisson models.

Cauchy flights in confining potentials

NASA Astrophysics Data System (ADS)

Garbaczewski, Piotr

2010-03-01

We analyze confining mechanisms for Lévy flights evolving under an influence of external potentials. Given a stationary probability density function (pdf), we address the reverse engineering problem: design a jump-type stochastic process whose target pdf (eventually asymptotic) equals the preselected one. To this end, dynamically distinct jump-type processes can be employed. We demonstrate that one “targeted stochasticity” scenario involves Langevin systems with a symmetric stable noise. Another derives from the Lévy-Schrödinger semigroup dynamics (closely linked with topologically induced super-diffusions), which has no standard Langevin representation. For computational and visualization purposes, the Cauchy driver is employed to exemplify our considerations.

The development of an imaging informatics-based multi-institutional platform to support sports performance and injury prevention in track and field

NASA Astrophysics Data System (ADS)

Liu, Joseph; Wang, Ximing; Verma, Sneha; McNitt-Gray, Jill; Liu, Brent

2018-03-01

The main goal of sports science and performance enhancement is to collect video and image data, process them, and quantify the results, giving insight to help athletes improve technique. For long jump in track and field, the processed output of video with force vector overlays and force calculations allow coaches to view specific stages of the hop, step, and jump, and identify how each stage can be improved to increase jump distance. Outputs also provide insight into how athletes can better maneuver to prevent injury. Currently, each data collection site collects and stores data with their own methods. There is no standard for data collection, formats, or storage. Video files and quantified results are stored in different formats, structures, and locations such as Dropbox and hard drives. Using imaging informatics-based principles we can develop a platform for multiple institutions that promotes the standardization of sports performance data. In addition, the system will provide user authentication and privacy as in clinical trials, with specific user access rights. Long jump data collected from different field sites will be standardized into specified formats before database storage. Quantified results from image-processing algorithms are stored similar to CAD algorithm results. The system will streamline the current sports performance data workflow and provide a user interface for athletes and coaches to view results of individual collections and also longitudinally across different collections. This streamlined platform and interface is a tool for coaches and athletes to easily access and review data to improve sports performance and prevent injury.

Motor expertise modulates the unconscious processing of human body postures.

PubMed

Güldenpenning, Iris; Koester, Dirk; Kunde, Wilfried; Weigelt, Matthias; Schack, Thomas

2011-09-01

Little is known about the cognitive background of unconscious visuomotor control of complex sports movements. Therefore, we investigated the extent to which novices and skilled high-jump athletes are able to identify visually presented body postures of the high jump unconsciously. We also asked whether or not the manner of processing differs (qualitatively or quantitatively) between these groups as a function of their motor expertise. A priming experiment with not consciously perceivable stimuli was designed to determine whether subliminal priming of movement phases (same vs. different movement phases) or temporal order (i.e. natural vs. reversed movement order) affects target processing. Participants had to decide which phase of the high jump (approach vs. flight phase) a target photograph was taken from. We found a main effect of temporal order for skilled athletes, that is, faster reaction times for prime-target pairs that reflected the natural movement order as opposed to the reversed movement order. This result indicates that temporal-order information pertaining to the domain of expertise plays a critical role in athletes' perceptual capacities. For novices, data analyses revealed an interaction between temporal order and movement phases. That is, only the reversed movement order of flight-approach pictures increased processing time. Taken together, the results suggest that the structure of cognitive movement representation modulates unconscious processing of movement pictures and points to a functional role of motor representations in visual perception.

The interaction of affective with psychotic processes: A test of the effects of worrying on working memory, jumping to conclusions, and anomalies of experience in patients with persecutory delusions

PubMed Central

Freeman, Daniel; Startup, Helen; Dunn, Graham; Černis, Emma; Wingham, Gail; Pugh, Katherine; Cordwell, Jacinta; Kingdon, David

2013-01-01

Worry has traditionally been considered in the study of common emotional disorders such as anxiety and depression, but recent studies indicate that worry may be a causal factor in the occurrence and persistence of persecutory delusions. The effect of worry on processes traditionally associated with psychosis has not been tested. The aim of the study was to examine the short-term effects of a bout of worry on three cognitive processes typically considered markers of psychosis: working memory, jumping to conclusions, and anomalous internal experience. Sixty-seven patients with persecutory delusions in the context of a non-affective psychotic disorder were randomised to a worry induction, a worry reduction, or a neutral control condition. They completed tests of the cognitive processes before and after the randomisation condition. The worry induction procedure led to a significant increase in worry. The induction of worry did not affect working memory or jumping to conclusions, but it did increase a range of mild anomalous experiences including feelings of unreality, perceptual alterations, and temporal disintegration. Worry did not affect the occurrence of hallucinations. The study shows that a period of worry causes a range of subtle odd perceptual disturbances that are known to increase the likelihood of delusions. It demonstrates an interaction between affective and psychotic processes in patients with delusions. PMID:23871449

Hierarchical Superhydrophobic Surfaces with Micropatterned Nanowire Arrays for High-Efficiency Jumping Droplet Condensation.

PubMed

Wen, Rongfu; Xu, Shanshan; Zhao, Dongliang; Lee, Yung-Cheng; Ma, Xuehu; Yang, Ronggui

2017-12-27

Self-propelled droplet jumping on nanostructured superhydrophobic surfaces is of interest for a variety of industrial applications including self-cleaning, water harvesting, power generation, and thermal management systems. However, the uncontrolled nucleation-induced Wenzel state of condensed droplets at large surface subcooling (high heat flux) leads to the formation of unwanted large pinned droplets, which results in the flooding phenomenon and greatly degrades the heat transfer performance. In this work, we present a novel strategy to manipulate droplet behaviors during the process from the droplet nucleation to growth and departure through a combination of spatially controlling initial nucleation for mobile droplets by closely spaced nanowires and promoting the spontaneous outward movement of droplets for rapid removal using micropatterned nanowire arrays. Through the optical visualization experiments and heat transfer tests, we demonstrate greatly improved condensation heat transfer characteristics on the hierarchical superhydrophobic surface including the higher density of microdroplets, smaller droplet departure radius, 133% wider range of surface subcooling for droplet jumping, and 37% enhancement in critical heat flux for jumping droplet condensation, compared to the-state-of-art jumping droplet condensation on nanostructured superhydrophobic surfaces. The excellent water repellency of such hierarchical superhydrophobic surfaces can be promising for many potential applications, such as anti-icing, antifogging, water desalination, and phase-change heat transfer.

Hierarchical Superhydrophobic Surfaces with Micropatterned Nanowire Arrays for High-Efficiency Jumping Droplet Condensation

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

Wen, Rongfu; Xu, Shanshan; Zhao, Dongliang

Self-propelled droplet jumping on nanostructured superhydrophobic surfaces is of interest for a variety of industrial applications including self-cleaning, water harvesting, power generation, and thermal management systems. However, the uncontrolled nucleation-induced Wenzel state of condensed droplets at large surface subcooling (high heat flux) leads to the formation of unwanted large pinned droplets, which results in the flooding phenomenon and greatly degrades the heat transfer performance. In this work, we present a novel strategy to manipulate droplet behaviors during the process from the droplet nucleation to growth and departure through a combination of spatially controlling initial nucleation for mobile droplets by closelymore » spaced nanowires and promoting the spontaneous outward movement of droplets for rapid removal using micropatterned nanowire arrays. Through the optical visualization experiments and heat transfer tests, we demonstrate greatly improved condensation heat transfer characteristics on the hierarchical superhydrophobic surface including the higher density of microdroplets, smaller droplet departure radius, 133% wider range of surface subcooling for droplet jumping, and 37% enhancement in critical heat flux for jumping droplet condensation, compared to the-state-of-art jumping droplet condensation on nanostructured superhydrophobic surfaces. The excellent water repellency of such hierarchical superhydrophobic surfaces can be promising for many potential applications, such as anti-icing, antifogging, water desalination, and phase-change heat transfer.« less

Hierarchical Superhydrophobic Surfaces with Micropatterned Nanowire Arrays for High-Efficiency Jumping Droplet Condensation

DOE PAGES

Wen, Rongfu; Xu, Shanshan; Zhao, Dongliang; ...

2017-12-07

Self-propelled droplet jumping on nanostructured superhydrophobic surfaces is of interest for a variety of industrial applications including self-cleaning, water harvesting, power generation, and thermal management systems. However, the uncontrolled nucleation-induced Wenzel state of condensed droplets at large surface subcooling (high heat flux) leads to the formation of unwanted large pinned droplets, which results in the flooding phenomenon and greatly degrades the heat transfer performance. In this work, we present a novel strategy to manipulate droplet behaviors during the process from the droplet nucleation to growth and departure through a combination of spatially controlling initial nucleation for mobile droplets by closelymore » spaced nanowires and promoting the spontaneous outward movement of droplets for rapid removal using micropatterned nanowire arrays. Through the optical visualization experiments and heat transfer tests, we demonstrate greatly improved condensation heat transfer characteristics on the hierarchical superhydrophobic surface including the higher density of microdroplets, smaller droplet departure radius, 133% wider range of surface subcooling for droplet jumping, and 37% enhancement in critical heat flux for jumping droplet condensation, compared to the-state-of-art jumping droplet condensation on nanostructured superhydrophobic surfaces. The excellent water repellency of such hierarchical superhydrophobic surfaces can be promising for many potential applications, such as anti-icing, antifogging, water desalination, and phase-change heat transfer.« less

Diffusion in Ordered Alloys, Symposium Held in Chicago, Illinois on November 3 - 4, 1992

DTIC Science & Technology

1992-11-04

calculation of transport proneres The essence of an atomistic theory of diffusion within the linear approximation of the Onsager formalism is to derive...the pair model may be extended to the low temperature range and that this linear behavior exists nearly over the whole temperature range where SRO...being the concentration of the component X. The successive jumps of vacancies are considered to be the elementary process of orde- ring. The jump

ASSIST user manual

NASA Technical Reports Server (NTRS)

Johnson, Sally C.; Boerschlein, David P.

1995-01-01

Semi-Markov models can be used to analyze the reliability of virtually any fault-tolerant system. However, the process of delineating all the states and transitions in a complex system model can be devastatingly tedious and error prone. The Abstract Semi-Markov Specification Interface to the SURE Tool (ASSIST) computer program allows the user to describe the semi-Markov model in a high-level language. Instead of listing the individual model states, the user specifies the rules governing the behavior of the system, and these are used to generate the model automatically. A few statements in the abstract language can describe a very large, complex model. Because no assumptions are made about the system being modeled, ASSIST can be used to generate models describing the behavior of any system. The ASSIST program and its input language are described and illustrated by examples.

Effective degree Markov-chain approach for discrete-time epidemic processes on uncorrelated networks.

PubMed

Cai, Chao-Ran; Wu, Zhi-Xi; Guan, Jian-Yue

2014-11-01

Recently, Gómez et al. proposed a microscopic Markov-chain approach (MMCA) [S. Gómez, J. Gómez-Gardeñes, Y. Moreno, and A. Arenas, Phys. Rev. E 84, 036105 (2011)PLEEE81539-375510.1103/PhysRevE.84.036105] to the discrete-time susceptible-infected-susceptible (SIS) epidemic process and found that the epidemic prevalence obtained by this approach agrees well with that by simulations. However, we found that the approach cannot be straightforwardly extended to a susceptible-infected-recovered (SIR) epidemic process (due to its irreversible property), and the epidemic prevalences obtained by MMCA and Monte Carlo simulations do not match well when the infection probability is just slightly above the epidemic threshold. In this contribution we extend the effective degree Markov-chain approach, proposed for analyzing continuous-time epidemic processes [J. Lindquist, J. Ma, P. Driessche, and F. Willeboordse, J. Math. Biol. 62, 143 (2011)JMBLAJ0303-681210.1007/s00285-010-0331-2], to address discrete-time binary-state (SIS) or three-state (SIR) epidemic processes on uncorrelated complex networks. It is shown that the final epidemic size as well as the time series of infected individuals obtained from this approach agree very well with those by Monte Carlo simulations. Our results are robust to the change of different parameters, including the total population size, the infection probability, the recovery probability, the average degree, and the degree distribution of the underlying networks.

The Influence of Hydroxylation on Maintaining CpG Methylation Patterns: A Hidden Markov Model Approach.

PubMed

Giehr, Pascal; Kyriakopoulos, Charalampos; Ficz, Gabriella; Wolf, Verena; Walter, Jörn

2016-05-01

DNA methylation and demethylation are opposing processes that when in balance create stable patterns of epigenetic memory. The control of DNA methylation pattern formation by replication dependent and independent demethylation processes has been suggested to be influenced by Tet mediated oxidation of 5mC. Several alternative mechanisms have been proposed suggesting that 5hmC influences either replication dependent maintenance of DNA methylation or replication independent processes of active demethylation. Using high resolution hairpin oxidative bisulfite sequencing data, we precisely determine the amount of 5mC and 5hmC and model the contribution of 5hmC to processes of demethylation in mouse ESCs. We develop an extended hidden Markov model capable of accurately describing the regional contribution of 5hmC to demethylation dynamics. Our analysis shows that 5hmC has a strong impact on replication dependent demethylation, mainly by impairing methylation maintenance.

Hetero-diffusion of Au epitaxy on stepped Ag(110) surface: Study of the jump rate and diffusion coefficient

NASA Astrophysics Data System (ADS)

Benlattar, M.; El koraychy, E.; Kotri, A.; Mazroui, M.

2017-12-01

We have used molecular dynamics simulations combined with an interatomic potential derived from the embedded atom method, to investigate the hetero-diffusion of Au adatom near a stepped Ag(110) surface with the height of one monoatomic layer. The activation energies for different diffusion processes, which occur on the terrace and near the step edge, are calculated both by molecular statics and molecular dynamics simulations. Static energies are found by the drag method, whereas the dynamic barriers are computed at high temperature from the Arrhenius plots. Our numerical results reveal that the jump process requires very high activation energy compared to the exchange process either on the terrace or near the step edge. In this work, other processes, such as upward and downward diffusion at step edges, have also been discussed.

Direct Observation of Insulin Association Dynamics with Time-Resolved X-ray Scattering

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

Rimmerman, Dolev; Leshchev, Denis; Hsu, Darren J.

Biological functions frequently require protein-protein interactions that involve secondary and tertiary structural perturbation. Here we study protein-protein dissociation and reassociation dynamics in insulin, a model system for protein oligomerization. Insulin dimer dissociation into monomers was induced by a nanosecond temperature-jump (T-jump) of ~8 °C in aqueous solution, and the resulting protein and solvent dynamics were tracked by time-resolved X-ray solution scattering (TRXSS) on time scales of 10 ns to 100 ms. The protein scattering signals revealed the formation of five distinguishable transient species during the association process that deviate from simple two state kinetics. Our results show that the combinationmore » of T-jump pump coupled to TRXSS probe allows for direct tracking of structural dynamics in nonphotoactive proteins.« less

Zero-state Markov switching count-data models: an empirical assessment.

PubMed

Malyshkina, Nataliya V; Mannering, Fred L

2010-01-01

In this study, a two-state Markov switching count-data model is proposed as an alternative to zero-inflated models to account for the preponderance of zeros sometimes observed in transportation count data, such as the number of accidents occurring on a roadway segment over some period of time. For this accident-frequency case, zero-inflated models assume the existence of two states: one of the states is a zero-accident count state, which has accident probabilities that are so low that they cannot be statistically distinguished from zero, and the other state is a normal-count state, in which counts can be non-negative integers that are generated by some counting process, for example, a Poisson or negative binomial. While zero-inflated models have come under some criticism with regard to accident-frequency applications - one fact is undeniable - in many applications they provide a statistically superior fit to the data. The Markov switching approach we propose seeks to overcome some of the criticism associated with the zero-accident state of the zero-inflated model by allowing individual roadway segments to switch between zero and normal-count states over time. An important advantage of this Markov switching approach is that it allows for the direct statistical estimation of the specific roadway-segment state (i.e., zero-accident or normal-count state) whereas traditional zero-inflated models do not. To demonstrate the applicability of this approach, a two-state Markov switching negative binomial model (estimated with Bayesian inference) and standard zero-inflated negative binomial models are estimated using five-year accident frequencies on Indiana interstate highway segments. It is shown that the Markov switching model is a viable alternative and results in a superior statistical fit relative to the zero-inflated models.

The effect of dropping height on jumping performance in trained and untrained prepubertal boys and girls.

PubMed

Bassa, Eleni I; Patikas, Dimitrios A; Panagiotidou, Aikaterini I; Papadopoulou, Sophia D; Pylianidis, Theofilos C; Kotzamanidis, Christos M

2012-08-01

Plyometric training in children, including different types of jumps, has become common practice during the last few years in different sports, although there is limited information about the adaptability of children with respect to different loads and the differences in performance between various jump types. The purpose of this study was to examine the effect of gender and training background on the optimal drop jump height of 9- to 11-year-old children. Sixty prepubertal (untrained and track and field athletes, boys and girls, equally distributed in each group [n = 15]), performed the following in random order: 3 squat jumps, 3 countermovement jumps (CMJs) and 3 drop jumps from heights of 10, 20, 30, 40, and 50 cm. The trial with the best performance in jump height of each test was used for further analysis. The jump type significantly affected the jump height. The jump height during the CMJ was the highest among all other jump types, resulting in advanced performance for both trained and untrained prepubertal boys and girls. However, increasing the dropping height did not change the jumping height or contact time during the drop jump. This possibly indicates an inability of prepubertal children to use their stored elastic energy to increase jumping height during drop jumps, irrespective of their gender or training status. This indicates that children, independent of gender and training status, have no performance gain during drop jumps from heights up to 50 cm, and therefore, it is recommended that only low drop jump heights be included in plyometric training to limit the probability of sustaining injuries.

On Markov parameters in system identification

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Longman, Richard W.

1991-01-01

A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest.

A solid-state [sup 13]C NMR study of the molecular motion of ethylene adsorbed on a silver surface

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

Jianxin Wang; Ellis, P.D.

1993-01-13

The reorientation of ethylene on a silver catalyst surface has been studied by solid-state [sup 13]C NMR. The static cross-polarization spectra at different temperatures have been measured. Different jump site models are proposed to simulate the experimental results. It was found that the models involving a low number of jump sites are more sensitive to the experimental details. By comparison of the simulated and experimental results, the 6- and 4-site jump models are chosen as the most satisfactory model to fit the experimental spectra. On the basis of this representation, the activation energy derived for the jump process is 4.3more » kJ/mol. From the simulated results, it was concluded that the symmetry axis for the motion of the ethylene at low temperatures ([minus]173 to ca. [minus]45[degrees]C) is perpendicular to the plane of the ethylene molecule. At higher temperatures motion about other axes is initiated such that at room temperature a nearly isotropically averaged [sup 13]C shielding tensor is observed. 20 refs., 9 figs.« less

[The role of the jumping to conclusion bias in delusions formation].

PubMed

Rózycka, Jagoda; Prochwicz, Katarzyna

2013-01-01

The results of many researches indicate that individuals with delusions reveal the reasoning bias. In probabilistic reasoning tasks they reveal hastiness in decision-making. The individuals with delusions request less information than non-deluded individuals, even if additional data is easily available. What is more, they also prove to be convinced to a greater extend of having made the right decision. This finding has been replicated by a number of studies. However, the previous researches have not confirmed the origins of 'jumping to conclusion' bias, and its role in the process of forming delusions has not been yet confirmed. The article in question contains the review of the results of the jumping to conclusion bias in people with delusions. It discusses the main hypotheses explaining the relations between the hasty decision making and the delusions formation. The article also deals with the specifics of 'jumping to conclusion' bias in case of individuals with delusions, as well as summarizes its relation to factors such as the level of intelligence or the intensity of delusion.

Trajectory and chirality of vortex domain walls in ferromagnetic nanowires with an asymmetric Y-branch

NASA Astrophysics Data System (ADS)

Brandão, J.; Mello, A.; Garcia, F.; Sampaio, L. C.

2017-03-01

The motion and trajectory of vortex domain walls (VDWs) driven by magnetic field were investigated in Fe80Ni20 nanowires with an asymmetric Y-shape branch. By using the focused magneto-optical Kerr effect, we have probed the injection, pinning, and propagation of VDWs in the branch and in the wire beyond the branch entrance. Hysteresis cycles measured at these points show 3 and 4 jumps in the magnetization reversal, respectively. Micromagnetic simulations were carried out to obtain the number of jumps in the hysteresis cycles, and the magnetization process involved in each jump. Based on simulations and from the size of the jumps in the measured hysteresis cycles, one obtains the histogram of the domain wall type probability. While in the branch domain walls of different types are equiprobable, in the nanowire vortex domain walls with counter clockwise and clockwise chiralities and transverse-down domain walls are measured with probabilities of 65%, 25%, and 10%, respectively. These results provide an additional route to select the trajectory and chirality of VDWs in magnetic nanostructures.

Faster proton transfer dynamics of water on SnO2 compared to TiO2.

PubMed

Kumar, Nitin; Kent, Paul R C; Bandura, Andrei V; Kubicki, James D; Wesolowski, David J; Cole, David R; Sofo, Jorge O

2011-01-28

Proton jump processes in the hydration layer on the iso-structural TiO(2) rutile (110) and SnO(2) cassiterite (110) surfaces were studied with density functional theory molecular dynamics. We find that the proton jump rate is more than three times faster on cassiterite compared with rutile. A local analysis based on the correlation between the stretching band of the O-H vibrations and the strength of H-bonds indicates that the faster proton jump activity on cassiterite is produced by a stronger H-bond formation between the surface and the hydration layer above the surface. The origin of the increased H-bond strength on cassiterite is a combined effect of stronger covalent bonding and stronger electrostatic interactions due to differences of its electronic structure. The bridging oxygens form the strongest H-bonds between the surface and the hydration layer. This higher proton jump rate is likely to affect reactivity and catalytic activity on the surface. A better understanding of its origins will enable methods to control these rates.

Multiscale Modeling of Diffusion in a Crowded Environment.

PubMed

Meinecke, Lina

2017-11-01

We present a multiscale approach to model diffusion in a crowded environment and its effect on the reaction rates. Diffusion in biological systems is often modeled by a discrete space jump process in order to capture the inherent noise of biological systems, which becomes important in the low copy number regime. To model diffusion in the crowded cell environment efficiently, we compute the jump rates in this mesoscopic model from local first exit times, which account for the microscopic positions of the crowding molecules, while the diffusing molecules jump on a coarser Cartesian grid. We then extract a macroscopic description from the resulting jump rates, where the excluded volume effect is modeled by a diffusion equation with space-dependent diffusion coefficient. The crowding molecules can be of arbitrary shape and size, and numerical experiments demonstrate that those factors together with the size of the diffusing molecule play a crucial role on the magnitude of the decrease in diffusive motion. When correcting the reaction rates for the altered diffusion we can show that molecular crowding either enhances or inhibits chemical reactions depending on local fluctuations of the obstacle density.

Valuing options in shot noise market

NASA Astrophysics Data System (ADS)

Laskin, Nick

2018-07-01

A new exactly solvable option pricing model has been introduced and elaborated. It is assumed that a stock price follows a Geometric shot noise process. An arbitrage-free integro-differential option pricing equation has been obtained and solved. The new Greeks have been analytically calculated. It has been shown that in diffusion approximation the developed option pricing model incorporates the well-known Black-Scholes equation and its solution. The stochastic dynamic origin of the Black-Scholes volatility has been uncovered. To model the observed market stock price patterns consisting of high frequency small magnitude and low frequency large magnitude jumps, the superposition of two Geometric shot noises has been implemented. A new generalized option pricing equation has been obtained and its exact solution was found. Merton's jump-diffusion formula for option price was recovered in diffusion approximation. Despite the non-Gaussian nature of probability distributions involved, the new option pricing model has the same degree of analytical tractability as the Black-Scholes model and the Merton jump-diffusion model. This attractive feature allows one to derive exact formulas to value options and option related instruments in the market with jump-like price patterns.

RANDOM EVOLUTIONS, MARKOV CHAINS, AND SYSTEMS OF PARTIAL DIFFERENTIAL EQUATIONS

PubMed Central

Griego, R. J.; Hersh, R.

1969-01-01

Several authors have considered Markov processes defined by the motion of a particle on a fixed line with a random velocity1, 6, 8, 10 or a random diffusivity.5, 12 A “random evolution” is a natural but apparently new generalization of this notion. In this note we hope to show that this concept leads to simple and powerful applications of probabilistic tools to initial-value problems of both parabolic and hyperbolic type. We obtain existence theorems, representation theorems, and asymptotic formulas, both old and new. PMID:16578690

ASSIST: User's manual

NASA Technical Reports Server (NTRS)

Johnson, S. C.

1986-01-01

Semi-Markov models can be used to compute the reliability of virtually any fault-tolerant system. However, the process of delineating all of the states and transitions in a model of a complex system can be devastingly tedious and error-prone. The ASSIST program allows the user to describe the semi-Markov model in a high-level language. Instead of specifying the individual states of the model, the user specifies the rules governing the behavior of the system and these are used by ASSIST to automatically generate the model. The ASSIST program is described and illustrated by examples.

Dynamic Programming for Structured Continuous Markov Decision Problems

NASA Technical Reports Server (NTRS)

Dearden, Richard; Meuleau, Nicholas; Washington, Richard; Feng, Zhengzhu

2004-01-01

We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamically partitioned into regions where the value function is the same throughout the region. We first describe the algorithm for piecewise constant representations. We then extend it to piecewise linear representations, using techniques from POMDPs to represent and reason about linear surfaces efficiently. We show that for complex, structured problems, our approach exploits the natural structure so that optimal solutions can be computed efficiently.

Upper and lower bounds for semi-Markov reliability models of reconfigurable systems

NASA Technical Reports Server (NTRS)

White, A. L.

1984-01-01

This paper determines the information required about system recovery to compute the reliability of a class of reconfigurable systems. Upper and lower bounds are derived for these systems. The class consists of those systems that satisfy five assumptions: the components fail independently at a low constant rate, fault occurrence and system reconfiguration are independent processes, the reliability model is semi-Markov, the recovery functions which describe system configuration have small means and variances, and the system is well designed. The bounds are easy to compute, and examples are included.

Does trampoline or hard surface jumping influence lower extremity alignment?

PubMed

Akasaka, Kiyokazu; Tamura, Akihiro; Katsuta, Aoi; Sagawa, Ayako; Otsudo, Takahiro; Okubo, Yu; Sawada, Yutaka; Hall, Toby

2017-12-01

[Purpose] To determine whether repetitive trampoline or hard surface jumping affects lower extremity alignment on jump landing. [Subjects and Methods] Twenty healthy females participated in this study. All subjects performed a drop vertical jump before and after repeated maximum effort trampoline or hard surface jumping. A three-dimensional motion analysis system and two force plates were used to record lower extremity angles, moments, and vertical ground reaction force during drop vertical jumps. [Results] Knee extensor moment after trampoline jumping was greater than that after hard surface jumping. There were no significant differences between trials in vertical ground reaction force and lower extremity joint angles following each form of exercise. Repeated jumping on a trampoline increased peak vertical ground reaction force, hip extensor, knee extensor moments, and hip adduction angle, while decreasing hip flexion angle during drop vertical jumps. In contrast, repeated jumping on a hard surface increased peak vertical ground reaction force, ankle dorsiflexion angle, and hip extensor moment during drop vertical jumps. [Conclusion] Repeated jumping on the trampoline compared to jumping on a hard surface has different effects on lower limb kinetics and kinematics. Knowledge of these effects may be useful in designing exercise programs for different clinical presentations.

Does trampoline or hard surface jumping influence lower extremity alignment?

PubMed Central

Akasaka, Kiyokazu; Tamura, Akihiro; Katsuta, Aoi; Sagawa, Ayako; Otsudo, Takahiro; Okubo, Yu; Sawada, Yutaka; Hall, Toby

2017-01-01

[Purpose] To determine whether repetitive trampoline or hard surface jumping affects lower extremity alignment on jump landing. [Subjects and Methods] Twenty healthy females participated in this study. All subjects performed a drop vertical jump before and after repeated maximum effort trampoline or hard surface jumping. A three-dimensional motion analysis system and two force plates were used to record lower extremity angles, moments, and vertical ground reaction force during drop vertical jumps. [Results] Knee extensor moment after trampoline jumping was greater than that after hard surface jumping. There were no significant differences between trials in vertical ground reaction force and lower extremity joint angles following each form of exercise. Repeated jumping on a trampoline increased peak vertical ground reaction force, hip extensor, knee extensor moments, and hip adduction angle, while decreasing hip flexion angle during drop vertical jumps. In contrast, repeated jumping on a hard surface increased peak vertical ground reaction force, ankle dorsiflexion angle, and hip extensor moment during drop vertical jumps. [Conclusion] Repeated jumping on the trampoline compared to jumping on a hard surface has different effects on lower limb kinetics and kinematics. Knowledge of these effects may be useful in designing exercise programs for different clinical presentations. PMID:29643592

Numerical investigations on the characteristics of thermomagnetic instability in MgB2 bulks

NASA Astrophysics Data System (ADS)

Xia, Jing; Li, Maosheng; Zhou, Youhe

2017-07-01

This paper presents the characteristics of thermomagnetic instability in MgB2 bulks by numerically solving the macroscopic dynamics of thermomagnetic interaction governed by the coupled magnetic and heat diffusion equations in association with a modified E-J power-law relationship. The finite element method is used to discretize the system of partial differential equations. The calculated magnetization loops with flux jumps are consistent with the experimental results for MgB2 slabs bathed in a wide range of ambient temperatures. We reveal the evolution process of the thermomagnetic instability and present the distributions of the magnetic field, temperature, and current density before and after flux jumps. A 2D axisymmetric model is used to study the thermomagnetic instability in cylindrical MgB2 bulks. It is found that the number of flux jumps monotonously reduces as the ambient temperature rises and no flux jump appears when the ambient temperature exceeds a certain value. Moreover, the flux-jump phenomenon exists in a wide range of the ramp rate of the applied external field, i.e. 10-2-102 T s-1. Furthermore, the dependences of the first flux-jump field on the ambient temperature, ramp rate, and bulk thickness are investigated. The critical bulk thicknesses for stability are obtained for different ambient temperatures and sample radii. In addition, the influence of the capability of the interfacial heat transfer on the temporal response of the bulk temperature is discussed. We also find that the prediction of thermomagnetic instability is sensitive to the employment of the flux creep exponent in the simulations.

Electrical conductivity of the Earth's mantle after one year of SWARM magnetic field measurements

NASA Astrophysics Data System (ADS)

Civet, François; Thebault, Erwan; Verhoeven, Olivier; Langlais, Benoit; Saturnino, Diana

2015-04-01

We present a global EM induction study using L1b Swarm satellite magnetic field measurements data down to a depth of 2000 km. Starting from raw measurements, we first derive a model for the main magnetic field, correct the data for a lithospheric field model, and further select the data to reduce the contributions of the ionospheric field. These computations allowed us to keep a full control on the data processes. We correct residual field from outliers and estimate the spherical harmonic coefficients of the transient field for periods between 2 and 256 days. We used full latitude range and all local times to keep a maximum amount of data. We perform a Bayesian inversion and construct a Markov chain during which model parameters are randomly updated at each iteration. We first consider regular layers of equal thickness and extra layers are added where conductivity contrast between successive layers exceed a threshold value. The mean and maximum likelihood of the electrical conductivity profile is then estimated from the probability density function. The obtained profile particularly shows a conductivity jump in the 600-700 km depth range, consistent with the olivine phase transition at 660 km depth. Our study is the first one to show such a conductivity increase in this depth range without any a priori informations on the internal strucutres. Assuming a pyrolitic mantle composition, this profile is interpreted in terms of temperature variations in the depth range where the probability density function is the narrowest. We finally obtained a temperature gradient in the lower mantle close to adiabatic.

Pitch angle scattering of relativistic electrons from stationary magnetic waves: Continuous Markov process and quasilinear theory

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

Lemons, Don S.

2012-01-15

We develop a Markov process theory of charged particle scattering from stationary, transverse, magnetic waves. We examine approximations that lead to quasilinear theory, in particular the resonant diffusion approximation. We find that, when appropriate, the resonant diffusion approximation simplifies the result of the weak turbulence approximation without significant further restricting the regime of applicability. We also explore a theory generated by expanding drift and diffusion rates in terms of a presumed small correlation time. This small correlation time expansion leads to results valid for relatively small pitch angle and large wave energy density - a regime that may govern pitchmore » angle scattering of high-energy electrons into the geomagnetic loss cone.« less

Resolvent-Techniques for Multiple Exercise Problems

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

Christensen, Sören, E-mail: christensen@math.uni-kiel.de; Lempa, Jukka, E-mail: jukka.lempa@hioa.no

2015-02-15

We study optimal multiple stopping of strong Markov processes with random refraction periods. The refraction periods are assumed to be exponentially distributed with a common rate and independent of the underlying dynamics. Our main tool is using the resolvent operator. In the first part, we reduce infinite stopping problems to ordinary ones in a general strong Markov setting. This leads to explicit solutions for wide classes of such problems. Starting from this result, we analyze problems with finitely many exercise rights and explain solution methods for some classes of problems with underlying Lévy and diffusion processes, where the optimal characteristicsmore » of the problems can be identified more explicitly. We illustrate the main results with explicit examples.« less

Mobile Jump Assessment (mJump): A Descriptive and Inferential Study.

PubMed

Mateos-Angulo, Alvaro; Galán-Mercant, Alejandro; Cuesta-Vargas, Antonio

2015-08-26

Vertical jump tests are used in athletics and rehabilitation to measure physical performance in people of different age ranges and fitness. Jumping ability can be analyzed through different variables, and the most commonly used are fly time and jump height. They can be obtained by a variety of measuring devices, but most are limited to laboratory use only. The current generation of smartphones contains inertial sensors that are able to record kinematic variables for human motion analysis, since they are tools for easy access and portability for clinical use. The aim of this study was to describe and analyze the kinematics characteristics using the inertial sensor incorporated in the iPhone 4S, the lower limbs strength through a manual dynamometer, and the jump variables obtained with a contact mat in the squat jump and countermovement jump tests (fly time and jump height) from a cohort of healthy people. A cross sectional study was conducted on a population of healthy young adults. Twenty-seven participants performed three trials (n=81 jumps) of squat jump and countermovement jump tests. Acceleration variables were measured through a smartphone's inertial sensor. Additionally, jump variables from a contact mat and lower limbs dynamometry were collected. In the present study, the kinematic variables derived from acceleration through the inertial sensor of a smartphone iPhone 4S, dynamometry of lower limbs with a handheld dynamometer, and the height and flight time with a contact mat have been described in vertical jump tests from a cohort of young healthy subjects. The development of the execution has been described, examined and identified in a squat jump test and countermovement jump test under acceleration variables that were obtained with the smartphone. The built-in iPhone 4S inertial sensor is able to measure acceleration variables while performing vertical jump tests for the squat jump and countermovement jump in healthy young adults. The acceleration kinematics variables derived from the smartphone's inertial sensor are higher in the countermovement jump test than the squat jump test. ©Alvaro Mateos-Angulo, Alejandro Galán-Mercant, Antonio Cuesta-Vargas. Originally published in JMIR Rehabilitation and Assistive Technology (http://rehab.jmir.org), 26.08.2015.

Multivariate longitudinal data analysis with mixed effects hidden Markov models.

PubMed

Raffa, Jesse D; Dubin, Joel A

2015-09-01

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. © 2015, The International Biometric Society.

Quasi- and pseudo-maximum likelihood estimators for discretely observed continuous-time Markov branching processes

PubMed Central

Chen, Rui; Hyrien, Ollivier

2011-01-01

This article deals with quasi- and pseudo-likelihood estimation in a class of continuous-time multi-type Markov branching processes observed at discrete points in time. “Conventional” and conditional estimation are discussed for both approaches. We compare their properties and identify situations where they lead to asymptotically equivalent estimators. Both approaches possess robustness properties, and coincide with maximum likelihood estimation in some cases. Quasi-likelihood functions involving only linear combinations of the data may be unable to estimate all model parameters. Remedial measures exist, including the resort either to non-linear functions of the data or to conditioning the moments on appropriate sigma-algebras. The method of pseudo-likelihood may also resolve this issue. We investigate the properties of these approaches in three examples: the pure birth process, the linear birth-and-death process, and a two-type process that generalizes the previous two examples. Simulations studies are conducted to evaluate performance in finite samples. PMID:21552356

Generalized species sampling priors with latent Beta reinforcements

PubMed Central

Airoldi, Edoardo M.; Costa, Thiago; Bassetti, Federico; Leisen, Fabrizio; Guindani, Michele

2014-01-01

Many popular Bayesian nonparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, exchangeability may not be appropriate. We introduce a novel and probabilistically coherent family of non-exchangeable species sampling sequences characterized by a tractable predictive probability function with weights driven by a sequence of independent Beta random variables. We compare their theoretical clustering properties with those of the Dirichlet Process and the two parameters Poisson-Dirichlet process. The proposed construction provides a complete characterization of the joint process, differently from existing work. We then propose the use of such process as prior distribution in a hierarchical Bayes modeling framework, and we describe a Markov Chain Monte Carlo sampler for posterior inference. We evaluate the performance of the prior and the robustness of the resulting inference in a simulation study, providing a comparison with popular Dirichlet Processes mixtures and Hidden Markov Models. Finally, we develop an application to the detection of chromosomal aberrations in breast cancer by leveraging array CGH data. PMID:25870462

Zipf exponent of trajectory distribution in the hidden Markov model

NASA Astrophysics Data System (ADS)

Bochkarev, V. V.; Lerner, E. Yu

2014-03-01

This paper is the first step of generalization of the previously obtained full classification of the asymptotic behavior of the probability for Markov chain trajectories for the case of hidden Markov models. The main goal is to study the power (Zipf) and nonpower asymptotics of the frequency list of trajectories of hidden Markov frequencys and to obtain explicit formulae for the exponent of the power asymptotics. We consider several simple classes of hidden Markov models. We prove that the asymptotics for a hidden Markov model and for the corresponding Markov chain can be essentially different.

Analysis of the Vertical Ground Reaction Forces and Temporal Factors in the Landing Phase of a Countermovement Jump

PubMed Central

Ortega, Daniel Rojano; Rodríguez Bíes, Elisabeth C.; Berral de la Rosa, Francisco J.

2010-01-01

In most common bilateral landings of vertical jumps, there are two peak forces (F1 and F2) in the force-time curve. The combination of these peak forces and the high frequency of jumps during sports produce a large amount of stress in the joints of the lower limbs which can be determinant of injury. The aim of this study was to find possible relationships between the jump height and F1 and F2, between F1 and F2 themselves, and between F1, F2, the time they appear (T1 and T2, respectively) and the length of the impact absorption phase (T). Thirty semi-professional football players made five countermovement jumps and the highest jump of each player was analyzed. They were instructed to perform the jumps with maximum effort and to land first with the balls of their feet and then with their heels. All the data were collected using a Kistler Quattro Jump force plate with a sample rate of 500 Hz. Quattro Jump Software, v.1.0.9.0., was used. There was neither significant correlation between T1 and F1 nor between T1 and F2. There was a significant positive correlation between flight height (FH) and F1 (r = 0.584, p = 0.01) but no significant correlation between FH and F2. A significant positive correlation between F1 and T2 (r = 0.418, p < 0.05) and a significant negative correlation between F2 and T2 (r = -0.406, p < 0.05) were also found. There is a significant negative correlation between T2 and T (r = -0. 443, p < 0.05). T1 has a little effect in the impact absorption process. F1 increases with increasing T2 but F2 decreases with increasing T2. Besides, increasing T2, with the objective of decreasing F2, makes the whole impact absorption shorter and the jump landing faster. Key points In the landing phase of a jump there are always sev-eral peak forces. The combination of these peaks forces and the high frequency of jumps during sports produces a large amount of stress in the joints of the lower limbs which can be determinant of injury. In the most common two-footed landings usually appear two peak forces (F1 and F2) in the force-time curve and the second one is usually related to injury’s risk. In this article it is shown that increasing the time F2 appears decrease F2. Increasing landing times could be counterproductive with respect to the goals of the sport. In this article it is shown that increasing the time F2 appears makes, however, the whole impact absorption shorter in du-ration. PMID:24149697

Vertical jumping tests in volleyball: reliability, validity, and playing-position specifics.

PubMed

Sattler, Tine; Sekulic, Damir; Hadzic, Vedran; Uljevic, Ognjen; Dervisevic, Edvin

2012-06-01

Vertical jumping is known to be important in volleyball, and jumping performance tests are frequently studied for their reliability and validity. However, most studies concerning jumping in volleyball have dealt with standard rather than sport-specific jumping procedures and tests. The aims of this study, therefore, were (a) to determine the reliability and factorial validity of 2 volleyball-specific jumping tests, the block jump (BJ) test and the attack jump (AJ) test, relative to 2 frequently used and systematically validated jumping tests, the countermovement jump test and the squat jump test and (b) to establish volleyball position-specific differences in the jumping tests and simple anthropometric indices (body height [BH], body weight, and body mass index [BMI]). The BJ was performed from a defensive volleyball position, with the hands positioned in front of the chest. During an AJ, the players used a 2- to 3-step approach and performed a drop jump with an arm swing followed by a quick vertical jump. A total of 95 high-level volleyball players (all men) participated in this study. The reliability of the jumping tests ranged from 0.97 to 0.99 for Cronbach's alpha coefficients, from 0.93 to 0.97 for interitem correlation coefficients and from 2.1 to 2.8 for coefficients of variation. The highest reliability was found for the specific jumping tests. The factor analysis extracted one significant component, and all of the tests were highly intercorrelated. The analysis of variance with post hoc analysis showed significant differences between 5 playing positions in some of the jumping tests. In general, receivers had a greater jumping capacity, followed by libero players. The differences in jumping capacities should be emphasized vis-a-vis differences in the anthropometric measures of players, where middle hitters had higher BH and body weight, followed by opposite hitters and receivers, with no differences in the BMI between positions.

Noise can speed convergence in Markov chains.

PubMed

Franzke, Brandon; Kosko, Bart

2011-10-01

A new theorem shows that noise can speed convergence to equilibrium in discrete finite-state Markov chains. The noise applies to the state density and helps the Markov chain explore improbable regions of the state space. The theorem ensures that a stochastic-resonance noise benefit exists for states that obey a vector-norm inequality. Such noise leads to faster convergence because the noise reduces the norm components. A corollary shows that a noise benefit still occurs if the system states obey an alternate norm inequality. This leads to a noise-benefit algorithm that requires knowledge of the steady state. An alternative blind algorithm uses only past state information to achieve a weaker noise benefit. Simulations illustrate the predicted noise benefits in three well-known Markov models. The first model is a two-parameter Ehrenfest diffusion model that shows how noise benefits can occur in the class of birth-death processes. The second model is a Wright-Fisher model of genotype drift in population genetics. The third model is a chemical reaction network of zeolite crystallization. A fourth simulation shows a convergence rate increase of 64% for states that satisfy the theorem and an increase of 53% for states that satisfy the corollary. A final simulation shows that even suboptimal noise can speed convergence if the noise applies over successive time cycles. Noise benefits tend to be sharpest in Markov models that do not converge quickly and that do not have strong absorbing states.

The algebra of the general Markov model on phylogenetic trees and networks.

PubMed

Sumner, J G; Holland, B R; Jarvis, P D

2012-04-01

It is known that the Kimura 3ST model of sequence evolution on phylogenetic trees can be extended quite naturally to arbitrary split systems. However, this extension relies heavily on mathematical peculiarities of the associated Hadamard transformation, and providing an analogous augmentation of the general Markov model has thus far been elusive. In this paper, we rectify this shortcoming by showing how to extend the general Markov model on trees to include incompatible edges; and even further to more general network models. This is achieved by exploring the algebra of the generators of the continuous-time Markov chain together with the “splitting” operator that generates the branching process on phylogenetic trees. For simplicity, we proceed by discussing the two state case and then show that our results are easily extended to more states with little complication. Intriguingly, upon restriction of the two state general Markov model to the parameter space of the binary symmetric model, our extension is indistinguishable from the Hadamard approach only on trees; as soon as any incompatible splits are introduced the two approaches give rise to differing probability distributions with disparate structure. Through exploration of a simple example, we give an argument that our extension to more general networks has desirable properties that the previous approaches do not share. In particular, our construction allows for convergent evolution of previously divergent lineages; a property that is of significant interest for biological applications.

Effects of timing of signal indicating jump directions on knee biomechanics in jump-landing-jump tasks.

PubMed

Stephenson, Mitchell L; Hinshaw, Taylour J; Wadley, Haley A; Zhu, Qin; Wilson, Margaret A; Byra, Mark; Dai, Boyi

2018-03-01

A variety of the available time to react (ATR) has been utilised to study knee biomechanics during reactive jump-landing tasks. The purpose was to quantify knee kinematics and kinetics during a jump-land-jump task of three possible directions as the ATR was reduced. Thirty-four recreational athletes performed 45 trials of a jump-land-jump task, during which the direction of the second jump (lateral, medial or vertical) was indicated before they initiated the first jump, the instant they initiated the first jump, 300 ms before landing, 150 ms before landing or at the instant of landing. Knee joint angles and moments close to the instant of landing were significantly different when the ATR was equal to or more than 300 ms before landing, but became similar when the ATR was 150 ms or 0 ms before landing. As the ATR was decreased, knee moments decreased for the medial jump direction, but increased for the lateral jump direction. When the ATR is shorter than an individual's reaction time, the movement pattern cannot be pre-planned before landing. Knee biomechanics are dependent on the timing of the signal and the subsequent jump direction. Precise control of timing and screening athletes with low ATR are suggested.

Validation of an inertial measurement unit for the measurement of jump count and height.

PubMed

MacDonald, Kerry; Bahr, Roald; Baltich, Jennifer; Whittaker, Jackie L; Meeuwisse, Willem H

2017-05-01

To validate the use of an inertial measurement unit (IMU) for the collection of total jump count and assess the validity of an IMU for the measurement of jump height against 3-D motion analysis. Cross sectional validation study. 3D motion-capture laboratory and field based settings. Thirteen elite adolescent volleyball players. Participants performed structured drills, played a 4 set volleyball match and performed twelve counter movement jumps. Jump counts from structured drills and match play were validated against visual count from recorded video. Jump height during the counter movement jumps was validated against concurrent 3-D motion-capture data. The IMU device captured more total jumps (1032) than visual inspection (977) during match play. During structured practice, device jump count sensitivity was strong (96.8%) while specificity was perfect (100%). The IMU underestimated jump height compared to 3D motion-capture with mean differences for maximal and submaximal jumps of 2.5 cm (95%CI: 1.3 to 3.8) and 4.1 cm (3.1-5.1), respectively. The IMU offers a valid measuring tool for jump count. Although the IMU underestimates maximal and submaximal jump height, our findings demonstrate its practical utility for field-based measurement of jump load. Copyright © 2016 Elsevier Ltd. All rights reserved.

Kinematics and Kinetics of Squats, Drop Jumps and Imitation Jumps of Ski Jumpers.

PubMed

Pauli, Carole A; Keller, Melanie; Ammann, Fabian; Hübner, Klaus; Lindorfer, Julia; Taylor, William R; Lorenzetti, Silvio

2016-03-01

Squats, drop jumps, and imitation jumps are commonly used training exercises in ski jumping to enhance maximum force, explosive force, and sport-specific skills. The purpose of this study was to evaluate the kinetics and kinematics of training exercises in ski jumping and to find objective parameters in training exercises that most correlate with the competition performance of ski jumpers. To this end, barbell squats, drop jumps, and imitation jumps were measured in a laboratory environment for 10 elite ski jumpers. Force and motion data were captured, and the influence of maximum vertical force, force difference, vertical take-off velocity, knee moments, knee joint power, and a knee valgus/varus index was evaluated and correlated with their season jump performance. The results indicate that, especially for the imitation jumps, a good correlation exists between the vertical take-off velocity and the personal jump performance on the hill (R = 0.718). Importantly, however, the more the athletes tended toward a valgus knee alignment during the measured movements, the worse their performance (R = 0.729 imitation jumps; R = 0.685 squats). Although an evaluation of the athletes' lower limb alignment during competitive jumping on the hill is still required, these preliminary data suggest that performance training should additionally concentrate on improving knee alignment to increase ski jumping performance.

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

NASA Astrophysics Data System (ADS)

Li, Zhiqiang; Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-04-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.

Validity of Hip-worn Inertial Measurement Unit Compared to Jump Mat for Jump Height Measurement in Adolescents.

PubMed

Rantalainen, T; Hesketh, K D; Rodda, C; Duckham, R L

2018-06-16

Jump tests assess lower body power production capacity, and can be used to evaluate athletic ability and development during growth. Wearable inertial measurement units (IMU) seem to offer a feasible alternative to laboratory-based equipment for jump height assessments. Concurrent validity of these devices for jump height assessments has only been established in adults. Therefore, the purpose of this study was to evaluate the concurrent validity of IMU-based jump height estimate compared to contact mat-based jump height estimate in adolescents. Ninety-five adolescents (10-13 years-of-age; girls N=41, height = 154 (SD 9) cm, weight = 44 (11) kg; boys N=54, height=156 (10) cm, weight = 46 (13) kg) completed three counter-movement jumps for maximal jump height on a contact mat. Inertial recordings (accelerations, rotations) were concurrently recorded with a hip-worn IMU (sampling at 256 Hz). Jump height was evaluated based on flight time. The mean IMU-derived jump height was 27.1 (SD 3.8) cm, and the corresponding mean jump-mat-derived value was 21.5 (3.4) cm. While a significant 26% mean difference was observed between the methods (5.5 [95% limits of agreement 2.2 to 8.9] cm, p = 0.006), the correspondence between methods was excellent (ICC = 0.89). The difference between methods was weakly positively associated with jump height (r = 0.28, P = 0.007). Take-off velocity derived jump height was also explored but produced only fair congruence. In conclusion, IMU-derived jump height exhibited excellent congruence to contact mat-based jump height and therefore presents a feasible alternative for jump height assessments in adolescents. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Spatiotemporal characteristics of motor actions by blind long jump athletes.

PubMed

Torralba, Miguel Angel; Padullés, José María; Losada, Jose Luis; López, Jose Luis

2017-01-01

Blind people depend on spatial and temporal representations to perform activities of daily living and compete in sport. The aim of this study is to determine the spatiotemporal characteristics of long jumps performed by blind athletes and compare findings with those reported for sighted athletes. We analysed a sample of 12 male athletes competing in the F11 Long Jump Finals at the Paralympic Games in London 2012. Performances were recorded using four high-speed cameras, and speeds were measured using a radar speed gun. The images were processed using validated image analysis software. The long jump run-up is shorter in blind athletes than in sighted athletes. We observed statistically significant differences for body centre of mass velocity and an increase in speed over the last three strides prior to take-off, contrasting with reports for sighted athletes and athletes with less severe visual impairment, who maintain or reduce their speed during the last stride. Stride length for the last three strides was the only spatial characteristic that was not significantly associated with effective jump distance. Blind long jumpers extend rather than shorten their last stride. Contact time with the take-off board is longer than that reported for sighted athletes. The actions of blind long jumpers, unlike those without disabilities, do not vary their leg actions during the final runway approach for optimal placement on the take-off board.

Evidence of behavioral co-option from context-dependent variation in mandible use in trap-jaw ants ( Odontomachus spp.)

NASA Astrophysics Data System (ADS)

Spagna, Joseph C.; Schelkopf, Adam; Carrillo, Tiana; Suarez, Andrew V.

2009-02-01

Evolutionary co-option of existing structures for new functions is a powerful yet understudied mechanism for generating novelty. Trap-jaw ants of the predatory genus Odontomachus are capable of some of the fastest self-propelled appendage movements ever recorded; their devastating strikes are not only used to disable and capture prey, but produce enough force to launch the ants into the air. We tested four Odontomachus species in a variety of behavioral contexts to examine if their mandibles have been co-opted for an escape mechanism through ballistic propulsion. We found that nest proximity makes no difference in interactions with prey, but that prey size has a strong influence on the suite of behaviors employed by the ants. In trials involving a potential threat (another trap-jaw ant species), vertical jumps were significantly more common in ants acting as intruders than in residents (i.e. a dangerous context), while horizontal jumps occurred at the same rate in both contexts. Additionally, horizontal jump trajectories were heavily influenced by the angle at which the substrate was struck and appear to be under little control by the ant. We conclude that while horizontal jumps may be accidental side-effects of strikes against hard surfaces, vertical escape jumps are likely intentional defensive behaviors that have been co-opted from the original prey-gathering and food-processing functions of Odontomachus jaws.

On the source of stochastic volatility: Evidence from CAC40 index options during the subprime crisis

NASA Astrophysics Data System (ADS)

Slim, Skander

2016-12-01

This paper investigates the performance of time-changed Lévy processes with distinct sources of return volatility variation for modeling cross-sectional option prices on the CAC40 index during the subprime crisis. Specifically, we propose a multi-factor stochastic volatility model: one factor captures the diffusion component dynamics and two factors capture positive and negative jump variations. In-sample and out-of-sample tests show that our full-fledged model significantly outperforms nested lower-dimensional specifications. We find that all three sources of return volatility variation, with different persistence, are needed to properly account for market pricing dynamics across moneyness, maturity and volatility level. Besides, the model estimation reveals negative risk premium for both diffusive volatility and downward jump intensity whereas a positive risk premium is found to be attributed to upward jump intensity.

Kinematic and Microphysical Significance of Lightning Jumps versus Non-Jump Increases in Total Flash Rate

PubMed Central

Schultz, Christopher J.; Carey, Lawrence D.; Schultz, Elise V.; Blakeslee, Richard J.

2017-01-01

Thirty-nine thunderstorms are examined using multiple-Doppler, polarimetric and total lightning observations to understand the role of mixed phase kinematics and microphysics in the development of lightning jumps. This sample size is larger than those of previous studies on this topic. The principal result of this study is that lightning jumps are a result of mixed phase updraft intensification. Larger increases in intense updraft volume (≥ 10 m s−1) and larger changes in peak updraft speed are observed prior to lightning jump occurrence when compared to other non-jump increases in total flash rate. Wilcoxon-Mann-Whitney Rank Sum testing yields p-values ≤0.05, indicating statistical independence between lightning jump and non-jump distributions for these two parameters. Similar changes in mixed phase graupel mass magnitude are observed prior to lightning jumps and non-jump increases in total flash rate. The p-value for graupel mass change is p=0.096, so jump and non-jump distributions for graupel mass change are not found statistically independent using the p=0.05 significance level. Timing of updraft volume, speed and graupel mass increases are found to be 4 to 13 minutes in advance of lightning jump occurrence. Also, severe storms without lightning jumps lack robust mixed phase updrafts, demonstrating that mixed phase updrafts are not always a requirement for severe weather occurrence. Therefore, the results of this study show that lightning jump occurrences are coincident with larger increases in intense mixed phase updraft volume and peak updraft speed than smaller non-jump increases in total flash rate. PMID:29158622

Kinematic and Microphysical Significance of Lightning Jumps versus Non-Jump Increases in Total Flash Rate.

PubMed

Schultz, Christopher J; Carey, Lawrence D; Schultz, Elise V; Blakeslee, Richard J

2017-02-01

Thirty-nine thunderstorms are examined using multiple-Doppler, polarimetric and total lightning observations to understand the role of mixed phase kinematics and microphysics in the development of lightning jumps. This sample size is larger than those of previous studies on this topic. The principal result of this study is that lightning jumps are a result of mixed phase updraft intensification. Larger increases in intense updraft volume (≥ 10 m s -1 ) and larger changes in peak updraft speed are observed prior to lightning jump occurrence when compared to other non-jump increases in total flash rate. Wilcoxon-Mann-Whitney Rank Sum testing yields p-values ≤0.05, indicating statistical independence between lightning jump and non-jump distributions for these two parameters. Similar changes in mixed phase graupel mass magnitude are observed prior to lightning jumps and non-jump increases in total flash rate. The p-value for graupel mass change is p=0.096, so jump and non-jump distributions for graupel mass change are not found statistically independent using the p=0.05 significance level. Timing of updraft volume, speed and graupel mass increases are found to be 4 to 13 minutes in advance of lightning jump occurrence. Also, severe storms without lightning jumps lack robust mixed phase updrafts, demonstrating that mixed phase updrafts are not always a requirement for severe weather occurrence. Therefore, the results of this study show that lightning jump occurrences are coincident with larger increases in intense mixed phase updraft volume and peak updraft speed than smaller non-jump increases in total flash rate.

Kinematic and Microphysical Significance of Lightning Jumps Versus Non-Jump Increases in Total Flash Rate

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Carey, Lawrence D.; Schultz, Elise V.; Blakeslee, Richard J.

2017-01-01

Thirty-nine thunderstorms are examined using multiple-Doppler, polarimetric and total lightning observations to understand the role of mixed phase kinematics and microphysics in the development of lightning jumps. This sample size is larger than those of previous studies on this topic. The principal result of this study is that lightning jumps are a result of mixed phase updraft intensification. Larger increases in intense updraft volume greater than or equal to 10 m(sup -1) and larger changes in peak updraft speed are observed prior to lightning jump occurrence when compared to other non-jump increases in total ash rate. Wilcoxon-Mann-Whitney Rank Sum testing yields p-values 0.05, indicating statistical independence between lightning jump and non-jump distributions for these two parameters. Similar changes in mixed phase graupel mass magnitude are observed prior to lightning jumps and non-jump increases in total ash rate. The p-value for graupel mass change is p=0.096, so jump and non-jump distributions for graupel mass change are not found statistically independent using the p=0.05 significance level. Timing of updraft volume, speed and graupel mass increases are found to be 4 to 13 minutes in advance of lightning jump occurrence. Also, severe storms without lightning jumps lack robust mixed phase updrafts, demonstrating that mixed phase updrafts are not always a requirement for severe weather occurrence. Therefore, the results of this study show that lightning jump occurrences are coincident with larger increases in intense mixed phase updraft volume and peak updraft speed than smaller non-jump increases in total ash rate.

Do Bilateral Vertical Jumps With Reactive Jump Landings Achieve Osteogenic Thresholds With and Without Instruction in Premenopausal Women?

PubMed

Clissold, Tracey L; Winwood, Paul W; Cronin, John B; De Souza, Mary Jane

2018-04-01

Jumps have been investigated as a stimulus for bone development; however, effects of instruction, jump type, and jump-landing techniques need investigation. This study sought to identify whether ground reaction forces (GRFs) for bilateral vertical jumps (countermovement jumps and drop jumps) with reactive jump-landings (ie, jumping immediately after initial jump-landing), with instruction and with instruction withdrawn, achieve magnitudes and rates of strain previously shown to improve bone mass among premenopausal women. Twenty-one women (Mean ± SD: 43.3 ± 5.9 y; 69.4 ± 9.6 kg; 167 ± 5.5 cm; 27.5 ± 8.7% body fat) performed a testing session 'with instruction' followed by a testing session performed 1 week later with 'instruction withdrawn.' The magnitudes (4.59 to 5.49 body weight [BW]) and rates of strain (263 to 359 BW·s -1 ) for the jump-landings, performed on an AMTI force plate, exceeded previously determined thresholds (>3 BWs and >43 BW·s -1 ). Interestingly, significantly larger peak resultant forces, (↑10%; P = .002) and peak rates of force development (↑20%; P < .001) values (in relation to BW and BW·s -1 , respectively) were observed for the second jump-landing (postreactive jump). Small increases (ES = 0.22-0.42) in all landing forces were observed in the second jump-landing with 'instruction withdrawn.' These jumps represent a unique training stimulus for premenopausal women and achieve osteogenic thresholds thought prerequisite for bone growth.

Kinematics and Kinetics of Squats, Drop Jumps and Imitation Jumps of Ski Jumpers

PubMed Central

Pauli, Carole A.; Keller, Melanie; Ammann, Fabian; Hübner, Klaus; Lindorfer, Julia; Taylor, William R.

2016-01-01

Abstract Pauli, CA, Keller, M, Ammann, F, Hübner, K, Lindorfer, J, Taylor, WR, and Lorenzetti, S. Kinematics and kinetics of squats, drop jumps and imitation jumps of ski jumpers. J Strength Cond Res 30(3): 643–652, 2016—Squats, drop jumps, and imitation jumps are commonly used training exercises in ski jumping to enhance maximum force, explosive force, and sport-specific skills. The purpose of this study was to evaluate the kinetics and kinematics of training exercises in ski jumping and to find objective parameters in training exercises that most correlate with the competition performance of ski jumpers. To this end, barbell squats, drop jumps, and imitation jumps were measured in a laboratory environment for 10 elite ski jumpers. Force and motion data were captured, and the influence of maximum vertical force, force difference, vertical take-off velocity, knee moments, knee joint power, and a knee valgus/varus index was evaluated and correlated with their season jump performance. The results indicate that, especially for the imitation jumps, a good correlation exists between the vertical take-off velocity and the personal jump performance on the hill (R = 0.718). Importantly, however, the more the athletes tended toward a valgus knee alignment during the measured movements, the worse their performance (R = 0.729 imitation jumps; R = 0.685 squats). Although an evaluation of the athletes' lower limb alignment during competitive jumping on the hill is still required, these preliminary data suggest that performance training should additionally concentrate on improving knee alignment to increase ski jumping performance. PMID:26418370

Diffusion mechanism of non-interacting Brownian particles through a deformed substrate

NASA Astrophysics Data System (ADS)

Arfa, Lahcen; Ouahmane, Mehdi; El Arroum, Lahcen

2018-02-01

We study the diffusion mechanism of non-interacting Brownian particles through a deformed substrate. The study is done at low temperature for different values of the friction. The deformed substrate is represented by a periodic Remoissenet-Peyrard potential with deformability parameter s. In this potential, the particles (impurity, adatoms…) can diffuse. We ignore the interactions between these mobile particles consider them merely as non-interacting Brownian particles and this system is described by a Fokker-Planck equation. We solve this equation numerically using the matrix continued fraction method to calculate the dynamic structure factor S(q , ω) . From S(q , ω) some relevant correlation functions are also calculated. In particular, we determine the half-width line λ(q) of the peak of the quasi-elastic dynamic structure factor S(q , ω) and the diffusion coefficient D. Our numerical results show that the diffusion mechanism is described, depending on the structure of the potential, either by a simple jump diffusion process with jump length close to the lattice constant a or by a combination of a jump diffusion model with jump length close to lattice constant a and a liquid-like motion inside the unit cell. It shows also that, for different friction regimes and various potential shapes, the friction attenuates the diffusion mechanism. It is found that, in the high friction regime, the diffusion process is more important through a deformed substrate than through a non-deformed one.

Performance Development in Adolescent Track and Field Athletes According to Age, Sex and Sport Discipline

PubMed Central

Tønnessen, Espen; Svendsen, Ida Siobhan; Olsen, Inge Christoffer; Guttormsen, Atle; Haugen, Thomas

2015-01-01

Introduction Sex-specific differences that arise during puberty have a pronounced effect on the training process. However, the consequences this should have for goal-setting, planning and implementation of training for boys and girls of different ages remains poorly understood. The aim of this study was to quantify performance developments in athletic running and jumping disciplines in the age range 11-18 and identify progression differences as a function of age, discipline and sex. Methods The 100 all-time best Norwegian male and female 60-m, 800-m, long jump and high jump athletes in each age category from 11 to 18 years were analysed using mixed models with random intercept according to athlete. Results Male and female athletes perform almost equally in running and jumping events up to the age of 12. Beyond this age, males outperform females. Relative annual performance development in females gradually decreases throughout the analyzed age period. In males, annual relative performance development accelerates up to the age of 13 (for running events) or 14 (for jumping events) and then gradually declines when approaching 18 years of age. The relative improvement from age 11 to 18 was twice as high in jumping events compared to running events. For all of the analyzed disciplines, overall improvement rates were >50% higher for males than for females. The performance sex difference evolves from < 5% to 10-18% in all the analyzed disciplines from age 11 to 18 yr. Conclusion To the authors’ knowledge, this is the first study to present absolute and relative annual performance developments in running and jumping events for competitive athletes from early to late adolescence. These results allow coaches and athletes to set realistic goals and prescribe conditioning programs that take into account sex-specific differences in the rate of performance development at different stages of maturation. PMID:26043192

A data-driven wavelet-based approach for generating jumping loads

NASA Astrophysics Data System (ADS)

Chen, Jun; Li, Guo; Racic, Vitomir

2018-06-01

This paper suggests an approach to generate human jumping loads using wavelet transform and a database of individual jumping force records. A total of 970 individual jumping force records of various frequencies were first collected by three experiments from 147 test subjects. For each record, every jumping pulse was extracted and decomposed into seven levels by wavelet transform. All the decomposition coefficients were stored in an information database. Probability distributions of jumping cycle period, contact ratio and energy of the jumping pulse were statistically analyzed. Inspired by the theory of DNA recombination, an approach was developed by interchanging the wavelet coefficients between different jumping pulses. To generate a jumping force time history with N pulses, wavelet coefficients were first selected randomly from the database at each level. They were then used to reconstruct N pulses by the inverse wavelet transform. Jumping cycle periods and contract ratios were then generated randomly based on their probabilistic functions. These parameters were assigned to each of the N pulses which were in turn scaled by the amplitude factors βi to account for energy relationship between successive pulses. The final jumping force time history was obtained by linking all the N cycles end to end. This simulation approach can preserve the non-stationary features of the jumping load force in time-frequency domain. Application indicates that this approach can be used to generate jumping force time history due to single people jumping and also can be extended further to stochastic jumping loads due to groups and crowds.

Analysis of the association between isokinetic knee strength with offensive and defensive jumping capacity in high-level female volleyball athletes.

PubMed

Sattler, Tine; Sekulic, Damir; Esco, Michael R; Mahmutovic, Ifet; Hadzic, Vedran

2015-09-01

Isokinetic-knee-strength was hypothesized to be an important factor related to jumping performance. However, studies examining this relation among elite female athletes and sport-specific jumps are lacking. This investigation determined the influence of isokinetic-knee flexor/extensor strength measures on spike-jump (offensive) and block-jump (defensive) performance among high-level female volleyball players. Cross-sectional laboratory study. Eighty-two female volleyball athletes (age = 21.3 ± 3.8 years, height = 175.4 ± 6.76 cm, and weight = 68.29 ± 8.53 kg) volunteered to participate in this study. The studied variables included spike-jump and block-jump performance and a set of isokinetic tests to evaluate the eccentric and concentric strength capacities of the knee extensors (quadriceps - Q), and flexors (hamstring - H) for both legs. Both jumping tests showed high intra-session reliability (ICC of 0.87 and 0.95 for spike-jump and block-jump, respectively). The athletes were clustered into three achievement-groups based on their spike-jump and block-jump performances. For the block-jump, ANOVA identified significant differences between achievement-groups for all isokinetic variables except the Right-Q-Eccentric-Strength. When observed for spike-jump, achievement-groups differed significantly in all tests but Right-H-Concentric-Strength. Discriminant canonical analysis showed that the isokinetic-strength variables were more associated with block-jump then spike-jump-performance. The eccentric isokinetic measures were relatively less important determinants of block-jump than for the spike-jump performance. Data support the hypothesis of the importance of isokinetic strength measures for the expression of rapid muscular performance in volleyball. The results point to the necessity of the differential approach in sport training for defensive and offensive duties. Copyright © 2014 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Determination of K-shell absorption jump factors and jump ratios for La2O3, Ce and Gd using two different methods

NASA Astrophysics Data System (ADS)

Akman, Ferdi; Durak, Rıdvan; Kaçal, Mustafa Recep; Turhan, Mehmet Fatih; Akdemir, Fatma

2015-02-01

The K shell absorption jump factors and jump ratios for La2O3, Ce and Gd samples have been determined using the gamma or X-ray attenuation and EDXRF methods. It is the first time that the K shell absorption jump factor and jump ratio have been discussed for present elements using two different methods. To detect K X-rays, a high resolution Si(Li) detector was used. The experimental results of K shell absorption jump factors and jump ratios were compared with the theoretically calculated ones.

The impact of temporal sampling resolution on parameter inference for biological transport models.

PubMed

Harrison, Jonathan U; Baker, Ruth E

2018-06-25

Imaging data has become an essential tool to explore key biological questions at various scales, for example the motile behaviour of bacteria or the transport of mRNA, and it has the potential to transform our understanding of important transport mechanisms. Often these imaging studies require us to compare biological species or mutants, and to do this we need to quantitatively characterise their behaviour. Mathematical models offer a quantitative description of a system that enables us to perform this comparison, but to relate mechanistic mathematical models to imaging data, we need to estimate their parameters. In this work we study how collecting data at different temporal resolutions impacts our ability to infer parameters of biological transport models; performing exact inference for simple velocity jump process models in a Bayesian framework. The question of how best to choose the frequency with which data is collected is prominent in a host of studies because the majority of imaging technologies place constraints on the frequency with which images can be taken, and the discrete nature of observations can introduce errors into parameter estimates. In this work, we mitigate such errors by formulating the velocity jump process model within a hidden states framework. This allows us to obtain estimates of the reorientation rate and noise amplitude for noisy observations of a simple velocity jump process. We demonstrate the sensitivity of these estimates to temporal variations in the sampling resolution and extent of measurement noise. We use our methodology to provide experimental guidelines for researchers aiming to characterise motile behaviour that can be described by a velocity jump process. In particular, we consider how experimental constraints resulting in a trade-off between temporal sampling resolution and observation noise may affect parameter estimates. Finally, we demonstrate the robustness of our methodology to model misspecification, and then apply our inference framework to a dataset that was generated with the aim of understanding the localization of RNA-protein complexes.

Jump events in a 3D Edwards-Anderson spin glass

NASA Astrophysics Data System (ADS)

Mártin, Daniel A.; Iguain, José Luis

2017-11-01

The statistical properties of infrequent particle displacements, greater than a certain distance, are known as jump dynamics in the context of structural glass formers. We generalize the concept of a jump to the case of a spin glass, by dividing the system into small boxes, and considering the infrequent cooperative spin flips in each box. Jumps defined this way share similarities with jumps in structural glasses. We perform numerical simulations for the 3D Edwards-Anderson model, and study how the properties of these jumps depend on the waiting time after a quench. Similar to the results for structural glasses, we find that while jump frequency depends strongly on time, the jump duration and jump length are roughly stationary. At odds with some results reported on studies of structural glass formers, at long enough times, the rest time between jumps varies as the inverse of jump frequency. We give a possible explanation for this discrepancy. We also find that our results are qualitatively reproduced by a fully-connected trap model.

Does gymnastics practice improve vertical jump reliability from the age of 8 to 10 years?

PubMed

Marina, Michel; Torrado, Priscila

2013-01-01

The objective of this study was to confirm whether gymnastics practice from a young age can induce greater vertical jump reliability. Fifty young female gymnasts (8.84 ± 0.62 years) and 42 females in the control group (8.58 ± 0.92 years) performed the following jump tests on a contact mat: squat jump, countermovement jump, countermovement jump with arm swing and drop jump from heights of 40 and 60 cm. The two testing sessions had three trials each and were separated by one week. A 2 (groups) × 2 (sessions) × 3 (trials) repeated measures analysis of variance (ANOVA) and a test-retest correlation analysis were used to study the reliability. There was no systematic source of error in either group for non-plyometric jumps such as squat jump, countermovement jump, and countermovement jump with arm swing. A significant group per trial interaction revealed a learning effect in gymnasts' drop jumps from 40 cm height. Additionally, the test-retest correlation analysis and the higher minimum detectable error suggest that the quick drop jump technique was not fully consolidated in either group. At an introductory level of gymnastics and between the ages of 8-10 years, the condition of being a gymnast did not lead to conclusively higher reliability, aside from better overall vertical jump performance.

Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes

PubMed Central

Nakamura, Tomoaki; Nagai, Takayuki; Mochihashi, Daichi; Kobayashi, Ichiro; Asoh, Hideki; Kaneko, Masahide

2017-01-01

Humans divide perceived continuous information into segments to facilitate recognition. For example, humans can segment speech waves into recognizable morphemes. Analogously, continuous motions are segmented into recognizable unit actions. People can divide continuous information into segments without using explicit segment points. This capacity for unsupervised segmentation is also useful for robots, because it enables them to flexibly learn languages, gestures, and actions. In this paper, we propose a Gaussian process-hidden semi-Markov model (GP-HSMM) that can divide continuous time series data into segments in an unsupervised manner. Our proposed method consists of a generative model based on the hidden semi-Markov model (HSMM), the emission distributions of which are Gaussian processes (GPs). Continuous time series data is generated by connecting segments generated by the GP. Segmentation can be achieved by using forward filtering-backward sampling to estimate the model's parameters, including the lengths and classes of the segments. In an experiment using the CMU motion capture dataset, we tested GP-HSMM with motion capture data containing simple exercise motions; the results of this experiment showed that the proposed GP-HSMM was comparable with other methods. We also conducted an experiment using karate motion capture data, which is more complex than exercise motion capture data; in this experiment, the segmentation accuracy of GP-HSMM was 0.92, which outperformed other methods. PMID:29311889

Pavement maintenance optimization model using Markov Decision Processes

NASA Astrophysics Data System (ADS)

Mandiartha, P.; Duffield, C. F.; Razelan, I. S. b. M.; Ismail, A. b. H.

2017-09-01

This paper presents an optimization model for selection of pavement maintenance intervention using a theory of Markov Decision Processes (MDP). There are some particular characteristics of the MDP developed in this paper which distinguish it from other similar studies or optimization models intended for pavement maintenance policy development. These unique characteristics include a direct inclusion of constraints into the formulation of MDP, the use of an average cost method of MDP, and the policy development process based on the dual linear programming solution. The limited information or discussions that are available on these matters in terms of stochastic based optimization model in road network management motivates this study. This paper uses a data set acquired from road authorities of state of Victoria, Australia, to test the model and recommends steps in the computation of MDP based stochastic optimization model, leading to the development of optimum pavement maintenance policy.

Asymptotic inference in system identification for the atom maser.

PubMed

Catana, Catalin; van Horssen, Merlijn; Guta, Madalin

2012-11-28

System identification is closely related to control theory and plays an increasing role in quantum engineering. In the quantum set-up, system identification is usually equated to process tomography, i.e. estimating a channel by probing it repeatedly with different input states. However, for quantum dynamical systems such as quantum Markov processes, it is more natural to consider the estimation based on continuous measurements of the output, with a given input that may be stationary. We address this problem using asymptotic statistics tools, for the specific example of estimating the Rabi frequency of an atom maser. We compute the Fisher information of different measurement processes as well as the quantum Fisher information of the atom maser, and establish the local asymptotic normality of these statistical models. The statistical notions can be expressed in terms of spectral properties of certain deformed Markov generators, and the connection to large deviations is briefly discussed.

Measuring the impact of final demand on global production system based on Markov process

NASA Astrophysics Data System (ADS)

Xing, Lizhi; Guan, Jun; Wu, Shan

2018-07-01

Input-output table is a comprehensive and detailed in describing the national economic systems, consisting of supply and demand information among various industrial sectors. The complex network, a theory and method for measuring the structure of complex system, can depict the structural properties of social and economic systems, and reveal the complicated relationships between the inner hierarchies and the external macroeconomic functions. This paper tried to measure the globalization degree of industrial sectors on the global value chain. Firstly, it constructed inter-country input-output network models to reproduce the topological structure of global economic system. Secondly, it regarded the propagation of intermediate goods on the global value chain as Markov process and introduced counting first passage betweenness to quantify the added processing amount when globally final demand stimulates this production system. Thirdly, it analyzed the features of globalization at both global and country-sector level

Sorting processes with energy-constrained comparisons*

NASA Astrophysics Data System (ADS)

Geissmann, Barbara; Penna, Paolo

2018-05-01

We study very simple sorting algorithms based on a probabilistic comparator model. In this model, errors in comparing two elements are due to (1) the energy or effort put in the comparison and (2) the difference between the compared elements. Such algorithms repeatedly compare and swap pairs of randomly chosen elements, and they correspond to natural Markovian processes. The study of these Markov chains reveals an interesting phenomenon. Namely, in several cases, the algorithm that repeatedly compares only adjacent elements is better than the one making arbitrary comparisons: in the long-run, the former algorithm produces sequences that are "better sorted". The analysis of the underlying Markov chain poses interesting questions as the latter algorithm yields a nonreversible chain, and therefore its stationary distribution seems difficult to calculate explicitly. We nevertheless provide bounds on the stationary distributions and on the mixing time of these processes in several restrictions.

Renormalization group theory for percolation in time-varying networks.

PubMed

Karschau, Jens; Zimmerling, Marco; Friedrich, Benjamin M

2018-05-22

Motivated by multi-hop communication in unreliable wireless networks, we present a percolation theory for time-varying networks. We develop a renormalization group theory for a prototypical network on a regular grid, where individual links switch stochastically between active and inactive states. The question whether a given source node can communicate with a destination node along paths of active links is equivalent to a percolation problem. Our theory maps the temporal existence of multi-hop paths on an effective two-state Markov process. We show analytically how this Markov process converges towards a memoryless Bernoulli process as the hop distance between source and destination node increases. Our work extends classical percolation theory to the dynamic case and elucidates temporal correlations of message losses. Quantification of temporal correlations has implications for the design of wireless communication and control protocols, e.g. in cyber-physical systems such as self-organized swarms of drones or smart traffic networks.

Analyzing Single-Molecule Protein Transportation Experiments via Hierarchical Hidden Markov Models

PubMed Central

Chen, Yang; Shen, Kuang

2017-01-01

To maintain proper cellular functions, over 50% of proteins encoded in the genome need to be transported to cellular membranes. The molecular mechanism behind such a process, often referred to as protein targeting, is not well understood. Single-molecule experiments are designed to unveil the detailed mechanisms and reveal the functions of different molecular machineries involved in the process. The experimental data consist of hundreds of stochastic time traces from the fluorescence recordings of the experimental system. We introduce a Bayesian hierarchical model on top of hidden Markov models (HMMs) to analyze these data and use the statistical results to answer the biological questions. In addition to resolving the biological puzzles and delineating the regulating roles of different molecular complexes, our statistical results enable us to propose a more detailed mechanism for the late stages of the protein targeting process. PMID:28943680

Neuromuscular function during drop jumps in young and elderly males.

PubMed

Piirainen, Jarmo M; Linnamo, Vesa; Sippola, Niina; Avela, Janne

2012-12-01

The Hoffman reflex (H-reflex), indicating alpha-motoneuron pool activity, has been shown to be task - and in resting conditions - age dependent. How aging affects H-reflex activity during explosive movements is not clear at present. The purpose of this study was to examine the effects of aging on H-reflexes during drop jumps, and its possible role in drop jump performance. Ten young (26.8 ± 2.7 years) and twenty elderly (64.2 ± 2.7 years) subjects participated in the study. Maximal drop jump performance and soleus H-reflex response (H/M jump) 20 ms after ground contact were measured in a sledge ergometer. Maximal H-reflex, maximal M-wave, Hmax/Mmax-ratio and H-reflex excitability curves were measured during standing rest. Although in young the H-reflex response (Hmax/Mmax) was 6.5% higher during relaxed standing and 19.7% higher during drop jumps (H jump/M jump) than in the elderly group, these differences were not statistically significant. In drop jumps, the elderly subjects had lower jumping height (30.4%, p < 0.001), longer braking time (32.4%, p < 0.01), lower push-off force (18.0%, p < 0.05) and longer push-off time (31.0% p < 0.01). H jump/M jump correlated with the average push-off force (r = 0.833, p < 0.05) and with push-off time (r = -0.857, p < 0.01) in young but not in the elderly. Correlations between H-reflex response and jumping parameters in young may indicate different jumping and activation strategies in drop jumps. However, it does not fully explain age related differences in jumping performance, since age related differences in H-reflex activity were non-significant. Copyright © 2012 Elsevier Ltd. All rights reserved.

Accuracy of Jump-Mat Systems for Measuring Jump Height.

PubMed

Pueo, Basilio; Lipinska, Patrycja; Jiménez-Olmedo, José M; Zmijewski, Piotr; Hopkins, Will G

2017-08-01

Vertical-jump tests are commonly used to evaluate lower-limb power of athletes and nonathletes. Several types of equipment are available for this purpose. To compare the error of measurement of 2 jump-mat systems (Chronojump-Boscosystem and Globus Ergo Tester) with that of a motion-capture system as a criterion and to determine the modifying effect of foot length on jump height. Thirty-one young adult men alternated 4 countermovement jumps with 4 squat jumps. Mean jump height and standard deviations representing technical error of measurement arising from each device and variability arising from the subjects themselves were estimated with a novel mixed model and evaluated via standardization and magnitude-based inference. The jump-mat systems produced nearly identical measures of jump height (differences in means and in technical errors of measurement ≤1 mm). Countermovement and squat-jump height were both 13.6 cm higher with motion capture (90% confidence limits ±0.3 cm), but this very large difference was reduced to small unclear differences when adjusted to a foot length of zero. Variability in countermovement and squat-jump height arising from the subjects was small (1.1 and 1.5 cm, respectively, 90% confidence limits ±0.3 cm); technical error of motion capture was similar in magnitude (1.7 and 1.6 cm, ±0.3 and ±0.4 cm), and that of the jump mats was similar or smaller (1.2 and 0.3 cm, ±0.5 and ±0.9 cm). The jump-mat systems provide trustworthy measurements for monitoring changes in jump height. Foot length can explain the substantially higher jump height observed with motion capture.

Costs and benefits of larval jumping behaviour of Bathyplectes anurus.

PubMed

Saeki, Yoriko; Tani, Soichiro; Fukuda, Katsuto; Iwase, Shun-ichiro; Sugawara, Yuma; Tuda, Midori; Takagi, Masami

2016-02-01

Bathyplectes anurus, a parasitoid of the alfalfa weevils, forms a cocoon in the late larval stage and exhibits jumping behaviour. Adaptive significance and costs of the cocoon jumping have not been thoroughly studied. We hypothesised that jumping has the fitness benefits of enabling habitat selection by avoiding unfavourable environments. We conducted laboratory experiments, which demonstrated that jumping frequencies increased in the presence of light, with greater magnitudes of temperature increase and at lower relative humidity. In addition, when B. anurus individuals were allowed to freely jump in an arena with a light gradient, more cocoons were found in the shady area, suggesting microhabitat selection. In a field experiment, mortality of cocoons placed in the sun was significantly higher than for cocoons placed in the shade. B. anurus cocoons respond to environmental stress by jumping, resulting in habitat selection. In the presence of potential predators (ants), jumping frequencies were higher than in the control (no ant) arenas, though jumping frequencies decreased after direct contact with the predators. Body mass of B. anurus cocoons induced to jump significantly decreased over time than cocoons that did not jump, suggesting a cost to jumping. We discuss the benefits and costs of jumping behaviour and potential evolutionary advantages of this peculiar trait, which is present in a limited number of species.

Validity Study of a Jump Mat Compared to the Reference Standard Force Plate.

PubMed

Rogan, Slavko; Radlinger, Lorenz; Imhasly, Caroline; Kneubuehler, Andrea; Hilfiker, Roger

2015-12-01

In the field of vertical jump diagnostics, force plates (FP) are the reference standard. Recently, despite a lack of evidence, jump mats have been used increasingly. Important factors in favor of jumping mats are their low cost and portability. This validity study compared the Haynl-Elektronik jump mat (HE jump mat) with the reference standard force plate. Ten healthy volunteers participated and each participant completed three series of five drop jumps (DJ). The parameters ground contact time (GCT) and vertical jump height (VJH) from the HE jump mat and the FP were used to evaluate the concurrent validity. The following statistical calculations were performed: Pearson's correlation (r), Bland-Altman plots (standard and for adjusted trend), and regression equations. The Bland-Altman plots suggest that the HE jump mat measures shorter contact times and higher jump heights than the FP. The trend-adjusted Bland-Altman plot shows higher mean differences and wider wing-spreads of confidence limits during longer GCT. During the VJH the mean differences and the wing-spreads of the confidence limits throughout the range present as relatively constant. The following regression equations were created, as close as possible to the true value: GCT = 5.920385 + 1.072293 × [value HE jump mat] and VJH = -1.73777 + 1.011156 × [value HE jump mat]. The HE jump mat can be recommended in relation to the validity of constraints. In this study, only a part of the quality criteria were examined. For the final recommendation it is advised to examine the HE jump mat on the other quality criteria (test-retest reliability, sensitivity change).

Jump spillover between oil prices and exchange rates

NASA Astrophysics Data System (ADS)

Li, Xiao-Ping; Zhou, Chun-Yang; Wu, Chong-Feng

2017-11-01

In this paper, we investigate the jump spillover effects between oil prices and exchange rates. To identify the latent historical jumps for exchange rates and oil prices, we use a Bayesian MCMC approach to estimate the stochastic volatility model with correlated jumps in both returns and volatilities for each. We examine the simultaneous jump intensities and the conditional jump spillover probabilities between oil prices and exchange rates, finding strong evidence of jump spillover effects. Further analysis shows that the jump spillovers are mainly due to exogenous events such as financial crises and geopolitical events. Thus, the findings have important implications for financial risk management.

Jump frequency may contribute to risk of jumper's knee: a study of interindividual and sex differences in a total of 11,943 jumps video recorded during training and matches in young elite volleyball players.

PubMed

Bahr, Martin A; Bahr, Roald

2014-09-01

Male sex, total training volume (number of hours per week) and match exposure (number of sets played per week) are risk factors for jumper's knee among young elite volleyball players. However, it is not known whether jump frequency differs among players on the same squad. To examine interindividual and sex differences in jump frequency during training and matches in young elite volleyball players. Observational study. Norwegian elite volleyball boarding school training programme. Student-athletes (26 boys and 18 girls, 16-18 years). Individual jump counts were recorded based on visual analysis of video recordings obtained from 1 week of volleyball training (9 training sessions for boys and 10 for girls, 14.1 h and 17.8 h of training, respectively) and 10 matches (5.9 h for boys (16 sets) and 7.7 h for girls (21 sets). A total of 11,943 jumps were recorded, 4138 during matches and 7805 during training. As training attendance and jump frequency varied substantially between players, the total exposure in training ranged from 50 to 666 jumps/week among boys and from 11 to 251 jumps/week among girls. On average, this corresponded to 35.7 jumps/h for boys and 13.7 jumps/h for girls (Student t test, p=0.002). Total jump exposure during matches ranged between 1 and 339 jumps among boys and between 0 and 379 jumps among girls, corresponding to an average jump frequency of 62.2 jumps/h for boys and 41.9 jumps/h for girls (Student t test, p<0.039). The interindividual differences in jump frequency were substantially greater than any differences observed among player functions. Jump frequency has substantial interindividual and sex differences during training and matches in young elite volleyball players. Total jump volume may represent a more important risk factor for jumper's knee than total training volume, warranting further research attention. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

RELATIONSHIP BETWEEN ISOKINETIC KNEE STRENGTH AND JUMP CHARACTERISTICS FOLLOWING ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION.

PubMed

Laudner, Kevin; Evans, Daniel; Wong, Regan; Allen, Aaron; Kirsch, Tom; Long, Brian; Meister, Keith

2015-06-01

Clinicians are often challenged when making return-to-play decisions following anterior cruciate ligament reconstruction (ACL-R). Isokinetic strength and jump performance testing are common tools used to make this decision. Unfortunately, vertical jump performance standards have not been clearly established and many clinicians do not have access to isokinetic testing equipment. To establish normative jump and strength characteristics in ACL-R patients cleared by an orthopedic physician to return-to-play and to determine if relationships exist between knee isokinetic strength measurements and jump characteristics described using an electronic jump map system. Descriptive laboratory study. Thirty-three ACL-R patients who had been cleared to return to athletic competition participated in this study. Twenty-six of these ACL-R participants were also matched to 26 asymptomatic athletes based on sex, limb, height, and mass to determine isokinetic strength and jump characteristic differences between groups. Jump tests consisted of single leg vertical, double leg vertical, and a 4-jump single leg vertical jump assessed using an electronic jump mat system. Independent t-tests were used to determine differences between groups and multiple regression analyses were used to identify any relationships between jump performance and knee strength (p<0.05). The ACL-R group had lower vertical jump capabilities and some bilateral knee strength deficiencies compared to the matched control group. The ACL-R group also showed several moderate-to-strong positive relationships for both knee extension and flexion strength with several jump performance characteristics, such as single and double leg vertical jump height. The current results indicate that ACL-R patients present with several knee strength and vertical jump differences compared to a matched control group at the time of return-to-play. Also, ACL-R patient's performance on an electronic jump mat system is strongly related to isokinetic knee strength measures. 2b.

Transition-Independent Decentralized Markov Decision Processes

NASA Technical Reports Server (NTRS)

Becker, Raphen; Silberstein, Shlomo; Lesser, Victor; Goldman, Claudia V.; Morris, Robert (Technical Monitor)

2003-01-01

There has been substantial progress with formal models for sequential decision making by individual agents using the Markov decision process (MDP). However, similar treatment of multi-agent systems is lacking. A recent complexity result, showing that solving decentralized MDPs is NEXP-hard, provides a partial explanation. To overcome this complexity barrier, we identify a general class of transition-independent decentralized MDPs that is widely applicable. The class consists of independent collaborating agents that are tied up by a global reward function that depends on both of their histories. We present a novel algorithm for solving this class of problems and examine its properties. The result is the first effective technique to solve optimally a class of decentralized MDPs. This lays the foundation for further work in this area on both exact and approximate solutions.

AN OPTIMAL MAINTENANCE MANAGEMENT MODEL FOR AIRPORT CONCRETE PAVEMENT

NASA Astrophysics Data System (ADS)

Shimomura, Taizo; Fujimori, Yuji; Kaito, Kiyoyuki; Obama, Kengo; Kobayashi, Kiyoshi

In this paper, an optimal management model is formulated for the performance-based rehabilitation/maintenance contract for airport concrete pavement, whereby two types of life cycle cost risks, i.e., ground consolidation risk and concrete depreciation risk, are explicitly considered. The non-homogenous Markov chain model is formulated to represent the deterioration processes of concrete pavement which are conditional upon the ground consolidation processes. The optimal non-homogenous Markov decision model with multiple types of risk is presented to design the optimal rehabilitation/maintenance plans. And the methodology to revise the optimal rehabilitation/maintenance plans based upon the monitoring data by the Bayesian up-to-dating rules. The validity of the methodology presented in this paper is examined based upon the case studies carried out for the H airport.

Accelerated decomposition techniques for large discounted Markov decision processes

NASA Astrophysics Data System (ADS)

Larach, Abdelhadi; Chafik, S.; Daoui, C.

2017-12-01

Many hierarchical techniques to solve large Markov decision processes (MDPs) are based on the partition of the state space into strongly connected components (SCCs) that can be classified into some levels. In each level, smaller problems named restricted MDPs are solved, and then these partial solutions are combined to obtain the global solution. In this paper, we first propose a novel algorithm, which is a variant of Tarjan's algorithm that simultaneously finds the SCCs and their belonging levels. Second, a new definition of the restricted MDPs is presented to ameliorate some hierarchical solutions in discounted MDPs using value iteration (VI) algorithm based on a list of state-action successors. Finally, a robotic motion-planning example and the experiment results are presented to illustrate the benefit of the proposed decomposition algorithms.

Bayesian experimental design for models with intractable likelihoods.

PubMed

Drovandi, Christopher C; Pettitt, Anthony N

2013-12-01

In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo sampling, with approximate Bayesian computation (ABC) to ensure that no likelihood evaluations are required. The utility function considered for precise parameter estimation is based upon the precision of the ABC posterior distribution, which we form efficiently via the ABC rejection algorithm based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology for Markov process models of epidemics and macroparasite population evolution. The macroparasite example involves a multivariate process and we assess the loss of information from not observing all variables. © 2013, The International Biometric Society.

Froghopper-inspired direction-changing concept for miniature jumping robots.

PubMed

Jung, Gwang-Pil; Cho, Kyu-Jin

2016-09-14

To improve the maneuverability and agility of jumping robots, several researchers have studied steerable jumping mechanisms. This steering ability enables robots to reach a particular target by controlling their jumping direction. To this end, we propose a novel direction-changing concept for miniature jumping robots. The proposed concept allows robots to be steerable while exerting minimal effects on jumping performance. The key design principles were adopted from the froghopper's power-producing hind legs and the moment cancellation accomplished by synchronized leg operation. These principles were applied via a pair of symmetrically positioned legs and conventional gears, which were modeled on the froghopper's anatomy. Each leg has its own thrusting energy, which improves jumping performance by allowing the mechanism to thrust itself with both power-producing legs. Conventional gears were utilized to simultaneously operate the legs and cancel out the moments that they induce, which minimizes body spin. A prototype to verify the concept was built and tested by varying the initial jumping posture. Three jumping postures (synchronous, asynchronous, and single-legged) were tested to investigate how synchronization and moment cancelling affect jumping performance. The results show that synchronous jumping allows the mechanism to change direction from -40° to 40°, with an improved take-off speed. The proposed concept can only be steered in a limited range of directions, but it has potential for use in miniature jumping robots that can change jumping direction with a minimal drop in jumping performance.

Markov stochasticity coordinates

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

Eliazar, Iddo, E-mail: iddo.eliazar@intel.com

Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method–termed Markov Stochasticity Coordinates–is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.

Nonequilibrium Dynamics of Arbitrary-Range Ising Models with Decoherence: An Exact Analytic Solution

DTIC Science & Technology

2013-04-03

spontaneous deexcitation, spontaneous excitation, and elastic dephasing, respectively (see Fig. 1). We refer to the spin-changing processes (σ̂±) as Raman ...Series of Raman flips of the spin on site j can be formally accounted for as a magnetic field of strength 2Jjk/N acting for a time τ upj − τ downj . In...2σ̂±j , all Rayleigh jumps can be evaluated at t = 0 (their commutation with Raman jumps only affects the overall sign of the wave function). To the

ModFossa: A library for modeling ion channels using Python.

PubMed

Ferneyhough, Gareth B; Thibealut, Corey M; Dascalu, Sergiu M; Harris, Frederick C

2016-06-01

The creation and simulation of ion channel models using continuous-time Markov processes is a powerful and well-used tool in the field of electrophysiology and ion channel research. While several software packages exist for the purpose of ion channel modeling, most are GUI based, and none are available as a Python library. In an attempt to provide an easy-to-use, yet powerful Markov model-based ion channel simulator, we have developed ModFossa, a Python library supporting easy model creation and stimulus definition, complete with a fast numerical solver, and attractive vector graphics plotting.

Graph transformation method for calculating waiting times in Markov chains.

PubMed

Trygubenko, Semen A; Wales, David J

2006-06-21

We describe an exact approach for calculating transition probabilities and waiting times in finite-state discrete-time Markov processes. All the states and the rules for transitions between them must be known in advance. We can then calculate averages over a given ensemble of paths for both additive and multiplicative properties in a nonstochastic and noniterative fashion. In particular, we can calculate the mean first-passage time between arbitrary groups of stationary points for discrete path sampling databases, and hence extract phenomenological rate constants. We present a number of examples to demonstrate the efficiency and robustness of this approach.

Dynamic Noise and its Role in Understanding Epidemiological Processes

NASA Astrophysics Data System (ADS)

Stollenwerk, Nico; Aguiar, Maíra

2010-09-01

We investigate the role of dynamic noise in understanding epidemiological systems, such as influenza or dengue fever by deriving stochastic ordinary differential equations from markov processes for discrete populations. This approach allows for an easy analysis of dynamical noise transitions between co-existing attractors.

Analyzing a stochastic time series obeying a second-order differential equation.

PubMed

Lehle, B; Peinke, J

2015-06-01

The stochastic properties of a Langevin-type Markov process can be extracted from a given time series by a Markov analysis. Also processes that obey a stochastically forced second-order differential equation can be analyzed this way by employing a particular embedding approach: To obtain a Markovian process in 2N dimensions from a non-Markovian signal in N dimensions, the system is described in a phase space that is extended by the temporal derivative of the signal. For a discrete time series, however, this derivative can only be calculated by a differencing scheme, which introduces an error. If the effects of this error are not accounted for, this leads to systematic errors in the estimation of the drift and diffusion functions of the process. In this paper we will analyze these errors and we will propose an approach that correctly accounts for them. This approach allows an accurate parameter estimation and, additionally, is able to cope with weak measurement noise, which may be superimposed to a given time series.

Using Bayesian Nonparametric Hidden Semi-Markov Models to Disentangle Affect Processes during Marital Interaction

PubMed Central

Griffin, William A.; Li, Xun

2016-01-01

Sequential affect dynamics generated during the interaction of intimate dyads, such as married couples, are associated with a cascade of effects—some good and some bad—on each partner, close family members, and other social contacts. Although the effects are well documented, the probabilistic structures associated with micro-social processes connected to the varied outcomes remain enigmatic. Using extant data we developed a method of classifying and subsequently generating couple dynamics using a Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM). Our findings indicate that several key aspects of existing models of marital interaction are inadequate: affect state emissions and their durations, along with the expected variability differences between distressed and nondistressed couples are present but highly nuanced; and most surprisingly, heterogeneity among highly satisfied couples necessitate that they be divided into subgroups. We review how this unsupervised learning technique generates plausible dyadic sequences that are sensitive to relationship quality and provide a natural mechanism for computational models of behavioral and affective micro-social processes. PMID:27187319

Inter-segmental moment analysis characterises the partial correspondence of jumping and jerking

PubMed Central

Cleather, Daniel J; Goodwin, Jon E; Bull, Anthony MJ

2014-01-01

The aim of this study was to quantify internal joint moments of the lower limb during vertical jumping and the weightlifting jerk in order to improve awareness of the control strategies and correspondence between these activities, and to facilitate understanding of the likely transfer of training effects. Athletic males completed maximal unloaded vertical jumps (n=12) and explosive push jerks at 40 kg (n=9). Kinematic data were collected using optical motion tracking and kinetic data via a force plate, both at 200 Hz. Joint moments were calculated using a previously described biomechanical model of the right lower limb. Peak moment results highlighted that sagittal plane control strategies differed between jumping and jerking (p<0.05) with jerking being a knee dominant task in terms of peak moments as opposed to a more balanced knee and hip strategy in jumping and landing. Jumping and jerking exhibited proximal to distal joint involvement and landing was typically reversed. High variability was seen in non-sagittal moments at the hip and knee. Significant correlations were seen between jump height and hip and knee moments in jumping (p<0.05). Whilst hip and knee moments were correlated between jumping and jerking (p<0.05), joint moments in the jerk were not significantly correlated to jump height (p>0.05) possibly indicating a limit to the direct transferability of jerk performance to jumping. Ankle joint moments were poorly related to jump performance (p>0.05). Peak knee and hip moment generating capacity are important to vertical jump performance. The jerk appears to offer an effective strategy to overload joint moment generation in the knee relative to jumping. However, an absence of hip involvement would appear to make it a general, rather than specific, training modality in relation to jumping. PMID:22362089

Validity and intra-rater reliability of MyJump app on iPhone 6s in jump performance.

PubMed

Stanton, Robert; Wintour, Sally-Anne; Kean, Crystal O

2017-05-01

Smartphone applications are increasingly used by researchers, coaches, athletes and clinicians. The aim of this study was to examine the concurrent validity and intra-rater reliability of the smartphone-based application, MyJump, against laboratory-based force plate measurements. Cross sectional study. Participants completed counter-movement jumps (CMJ) (n=29) and 30cm drop jumps (DJ) (n=27) on a force plate which were simultaneously recorded using MyJump. To assess concurrent validity, jump height, derived from flight time acquired from each device, was compared for each jump type. Intra-rater reliability was determined by replicating data analysis of MyJump recordings on two occasions separated by seven days. CMJ and DJ heights derived from MyJump showed excellent agreement with the force plate (ICC values range from 0.991 for CMJ to 0.993) However mean DJ height from the force plate was significantly higher than MyJump (mean difference: 0.87cm, 95% CI: 0.69-1.04cm). Intra-rater reliability of MyJump for both CMJ and DJ was almost perfect (ICC values range from 0.997 for CMJ to 0.998 for DJ); however, mean CMJ and DJ jump height for Day 1 was significantly higher than Day 2 (CMJ: 0.43cm, 95% CI: 0.23-0.62cm); (DJ: 0.38cm, 95% CI: 0.23-0.53cm). The present study finds MyJump to be a valid and highly reliable tool for researchers, coaches, athletes and clinicians; however, systematic bias should be considered when comparing MyJump outputs to other testing devices. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Generalized master equation via aging continuous-time random walks.

PubMed

Allegrini, Paolo; Aquino, Gerardo; Grigolini, Paolo; Palatella, Luigi; Rosa, Angelo

2003-11-01

We discuss the problem of the equivalence between continuous-time random walk (CTRW) and generalized master equation (GME). The walker, making instantaneous jumps from one site of the lattice to another, resides in each site for extended times. The sojourn times have a distribution density psi(t) that is assumed to be an inverse power law with the power index micro. We assume that the Onsager principle is fulfilled, and we use this assumption to establish a complete equivalence between GME and the Montroll-Weiss CTRW. We prove that this equivalence is confined to the case where psi(t) is an exponential. We argue that is so because the Montroll-Weiss CTRW, as recently proved by Barkai [E. Barkai, Phys. Rev. Lett. 90, 104101 (2003)], is nonstationary, thereby implying aging, while the Onsager principle is valid only in the case of fully aged systems. The case of a Poisson distribution of sojourn times is the only one with no aging associated to it, and consequently with no need to establish special initial conditions to fulfill the Onsager principle. We consider the case of a dichotomous fluctuation, and we prove that the Onsager principle is fulfilled for any form of regression to equilibrium provided that the stationary condition holds true. We set the stationary condition on both the CTRW and the GME, thereby creating a condition of total equivalence, regardless of the nature of the waiting-time distribution. As a consequence of this procedure we create a GME that is a bona fide master equation, in spite of being non-Markov. We note that the memory kernel of the GME affords information on the interaction between system of interest and its bath. The Poisson case yields a bath with infinitely fast fluctuations. We argue that departing from the Poisson form has the effect of creating a condition of infinite memory and that these results might be useful to shed light on the problem of how to unravel non-Markov quantum master equations.

Bending effects and temperature dependence of magnetic properties in a Fe-rich amorphous wire

NASA Astrophysics Data System (ADS)

Bordin, G.; Buttino, G.; Poppi, M.

2001-08-01

Amorphous wires with composition Fe 77.5Si 7.5B 15 exhibit a very peculiar magnetization process characterized by a single and quite large Barkhausen jump. This gives rise to a squared hysteresis loop at a critical magnetic field. The bistable behaviour, widely studied in wires with typical length of 10 cm and diameter of 125 μm, appears above a length of about 7 cm in straight wires and disappears for curvature radius within the range 2-12 cm in bent wires. In this work it is shown that bistability occurs in bent wires, whatever their curvature is, provided the wires are long enough. To this purpose spiral-shaped samples with several turns are considered. However, when the wire length is not a integer number of turns the magnetization reverses through many large Barkhausen jumps. In this condition, varying the measuring temperature can activate the energy barriers for the jumps.

Steel characteristics measurement system using Barkhausen jump sum rate and magnetic field intensity and method of using same

DOEpatents

Kohn, Gabriel; Hicho, George; Swartzendruber, Lydon

1997-01-01

A steel hardness measurement system and method of using same are provided for measuring at least one mechanical or magnetic characteristic of a ferromagnetic sample as a function of at least one magnetic characteristic of the sample. A magnetic field generator subjects the sample to a variable external magnetic field. The magnetic field intensity of the magnetic field generated by the magnetic field generating means is measured and a signal sensor is provided for measuring Barkhausen signals from the sample when the sample is subjected to the external magnetic field. A signal processing unit calculates a jump sum rate first moment as a function of the Barkhausen signals measured by the signal sensor and the magnetic field intensity, and for determining the at least one mechanical or magnetic characteristic as a function of the jump sum rate first moment.

Steel characteristics measurement system using Barkhausen jump sum rate and magnetic field intensity and method of using same

DOEpatents

Kohn, G.; Hicho, G.; Swartzendruber, L.

1997-04-08

A steel hardness measurement system and method of using same are provided for measuring at least one mechanical or magnetic characteristic of a ferromagnetic sample as a function of at least one magnetic characteristic of the sample. A magnetic field generator subjects the sample to a variable external magnetic field. The magnetic field intensity of the magnetic field generated by the magnetic field generating means is measured and a signal sensor is provided for measuring Barkhausen signals from the sample when the sample is subjected to the external magnetic field. A signal processing unit calculates a jump sum rate first moment as a function of the Barkhausen signals measured by the signal sensor and the magnetic field intensity, and for determining the at least one mechanical or magnetic characteristic as a function of the jump sum rate first moment. 7 figs.

Test-retest reliability of jump execution variables using mechanography: A comparison of jump protocols

USDA-ARS?s Scientific Manuscript database

Mechanography during the vertical jump test allows for evaluation of force-time variables reflecting jump execution, which may enhance screening for functional deficits that reduce physical performance and determining mechanistic causes underlying performance changes. However, utility of jump mechan...

Psychophysiological response in parachute jumps, the effect of experience and type of jump.

PubMed

Clemente-Suárez, Vicente Javier; Robles-Pérez, José Juan; Fernández-Lucas, Jesús

2017-10-01

We aimed to analyse the effect of experience and type of parachute jump on the psychophysiological responses of jumpers. We analysed blood oxygen saturation, heart rate, blood glucose, lactate and creatinkinase, leg strength, isometric hand strength, cortical arousal, specific fine motor skills, self-confidence and cognition, and somatic and state anxiety, before and after four different parachute jumps: a sport parachute jump, a manual tactical parachute jump, tandem pilots, and tandem passengers. Independently of the parachute jump, the psychophysiological responses of experienced paratroopers were not affected by the jumps, except for an increase in anaerobic metabolism. Novice parachute jumpers presented a higher psychophysiological stress response than the experienced jumpers, together with a large anticipatory anxiety response before the jump; however, this decreased after the jump, although the high physiological activation was maintained. This information could be used by civil and military paratroopers' instructors to improve their training programmes. Copyright © 2017 Elsevier Inc. All rights reserved.

Markov chain decision model for urinary incontinence procedures.

PubMed

Kumar, Sameer; Ghildayal, Nidhi; Ghildayal, Neha

2017-03-13

Purpose Urinary incontinence (UI) is a common chronic health condition, a problem specifically among elderly women that impacts quality of life negatively. However, UI is usually viewed as likely result of old age, and as such is generally not evaluated or even managed appropriately. Many treatments are available to manage incontinence, such as bladder training and numerous surgical procedures such as Burch colposuspension and Sling for UI which have high success rates. The purpose of this paper is to analyze which of these popular surgical procedures for UI is effective. Design/methodology/approach This research employs randomized, prospective studies to obtain robust cost and utility data used in the Markov chain decision model for examining which of these surgical interventions is more effective in treating women with stress UI based on two measures: number of quality adjusted life years (QALY) and cost per QALY. Treeage Pro Healthcare software was employed in Markov decision analysis. Findings Results showed the Sling procedure is a more effective surgical intervention than the Burch. However, if a utility greater than certain utility value, for which both procedures are equally effective, is assigned to persistent incontinence, the Burch procedure is more effective than the Sling procedure. Originality/value This paper demonstrates the efficacy of a Markov chain decision modeling approach to study the comparative effectiveness analysis of available treatments for patients with UI, an important public health issue, widely prevalent among elderly women in developed and developing countries. This research also improves upon other analyses using a Markov chain decision modeling process to analyze various strategies for treating UI.

Bayesian clustering of DNA sequences using Markov chains and a stochastic partition model.

PubMed

Jääskinen, Väinö; Parkkinen, Ville; Cheng, Lu; Corander, Jukka

2014-02-01

In many biological applications it is necessary to cluster DNA sequences into groups that represent underlying organismal units, such as named species or genera. In metagenomics this grouping needs typically to be achieved on the basis of relatively short sequences which contain different types of errors, making the use of a statistical modeling approach desirable. Here we introduce a novel method for this purpose by developing a stochastic partition model that clusters Markov chains of a given order. The model is based on a Dirichlet process prior and we use conjugate priors for the Markov chain parameters which enables an analytical expression for comparing the marginal likelihoods of any two partitions. To find a good candidate for the posterior mode in the partition space, we use a hybrid computational approach which combines the EM-algorithm with a greedy search. This is demonstrated to be faster and yield highly accurate results compared to earlier suggested clustering methods for the metagenomics application. Our model is fairly generic and could also be used for clustering of other types of sequence data for which Markov chains provide a reasonable way to compress information, as illustrated by experiments on shotgun sequence type data from an Escherichia coli strain.

The reliability of vertical jump tests between the Vertec and My Jump phone application.

PubMed

Yingling, Vanessa R; Castro, Dimitri A; Duong, Justin T; Malpartida, Fiorella J; Usher, Justin R; O, Jenny

2018-01-01

The vertical jump is used to estimate sports performance capabilities and physical fitness in children, elderly, non-athletic and injured individuals. Different jump techniques and measurement tools are available to assess vertical jump height and peak power; however, their use is limited by access to laboratory settings, excessive cost and/or time constraints thus making these tools oftentimes unsuitable for field assessment. A popular field test uses the Vertec and the Sargent vertical jump with countermovement; however, new low cost, easy to use tools are becoming available, including the My Jump iOS mobile application (app). The purpose of this study was to assess the reliability of the My Jump relative to values obtained by the Vertec for the Sargent stand and reach vertical jump (VJ) test. One hundred and thirty-five healthy participants aged 18-39 years (94 males, 41 females) completed three maximal Sargent VJ with countermovement that were simultaneously measured using the Vertec and the My Jump . Jump heights were quantified for each jump and peak power was calculated using the Sayers equation. Four separate ICC estimates and their 95% confidence intervals were used to assess reliability. Two analyses (with jump height and calculated peak power as the dependent variables, respectively) were based on a single rater, consistency, two-way mixed-effects model, while two others (with jump height and calculated peak power as the dependent variables, respectively) were based on a single rater, absolute agreement, two-way mixed-effects model. Moderate to excellent reliability relative to the degree of consistency between the Vertec and My Jump values was found for jump height (ICC = 0.813; 95% CI [0.747-0.863]) and calculated peak power (ICC = 0.926; 95% CI [0.897-0.947]). However, poor to good reliability relative to absolute agreement for VJ height (ICC = 0.665; 95% CI [0.050-0.859]) and poor to excellent reliability relative to absolute agreement for peak power (ICC = 0.851; 95% CI [0.272-0.946]) between the Vertec and My Jump values were found; Vertec VJ height, and thus, Vertec calculated peak power values, were significantly higher than those calculated from My Jump values ( p < 0.0001). The My Jump app may provide a reliable measure of vertical jump height and calculated peak power in multiple field and laboratory settings without the need of costly equipment such as force plates or Vertec. The reliability relative to degree of consistency between the Vertec and My Jump app was moderate to excellent. However, the reliability relative to absolute agreement between Vertec and My Jump values contained significant variation (based on CI values), thus, it is recommended that either the My Jump or the Vertec be used to assess VJ height in repeated measures within subjects' designs; these measurement tools should not be considered interchangeable within subjects or in group measurement designs.

The reliability of vertical jump tests between the Vertec and My Jump phone application

PubMed Central

Castro, Dimitri A.; Duong, Justin T.; Malpartida, Fiorella J.; Usher, Justin R.; O, Jenny

2018-01-01

Background The vertical jump is used to estimate sports performance capabilities and physical fitness in children, elderly, non-athletic and injured individuals. Different jump techniques and measurement tools are available to assess vertical jump height and peak power; however, their use is limited by access to laboratory settings, excessive cost and/or time constraints thus making these tools oftentimes unsuitable for field assessment. A popular field test uses the Vertec and the Sargent vertical jump with countermovement; however, new low cost, easy to use tools are becoming available, including the My Jump iOS mobile application (app). The purpose of this study was to assess the reliability of the My Jump relative to values obtained by the Vertec for the Sargent stand and reach vertical jump (VJ) test. Methods One hundred and thirty-five healthy participants aged 18–39 years (94 males, 41 females) completed three maximal Sargent VJ with countermovement that were simultaneously measured using the Vertec and the My Jump. Jump heights were quantified for each jump and peak power was calculated using the Sayers equation. Four separate ICC estimates and their 95% confidence intervals were used to assess reliability. Two analyses (with jump height and calculated peak power as the dependent variables, respectively) were based on a single rater, consistency, two-way mixed-effects model, while two others (with jump height and calculated peak power as the dependent variables, respectively) were based on a single rater, absolute agreement, two-way mixed-effects model. Results Moderate to excellent reliability relative to the degree of consistency between the Vertec and My Jump values was found for jump height (ICC = 0.813; 95% CI [0.747–0.863]) and calculated peak power (ICC = 0.926; 95% CI [0.897–0.947]). However, poor to good reliability relative to absolute agreement for VJ height (ICC = 0.665; 95% CI [0.050–0.859]) and poor to excellent reliability relative to absolute agreement for peak power (ICC = 0.851; 95% CI [0.272–0.946]) between the Vertec and My Jump values were found; Vertec VJ height, and thus, Vertec calculated peak power values, were significantly higher than those calculated from My Jump values (p < 0.0001). Discussion The My Jump app may provide a reliable measure of vertical jump height and calculated peak power in multiple field and laboratory settings without the need of costly equipment such as force plates or Vertec. The reliability relative to degree of consistency between the Vertec and My Jump app was moderate to excellent. However, the reliability relative to absolute agreement between Vertec and My Jump values contained significant variation (based on CI values), thus, it is recommended that either the My Jump or the Vertec be used to assess VJ height in repeated measures within subjects’ designs; these measurement tools should not be considered interchangeable within subjects or in group measurement designs. PMID:29692955

Comparison of the Hang High-Pull and Loaded Jump Squat for the Development of Vertical Jump and Isometric Force-Time Characteristics.

PubMed

Oranchuk, Dustin J; Robinson, Tracey L; Switaj, Zachary J; Drinkwater, Eric J

2017-04-15

Weightlifting movements have high skill demands and require expert coaching. Loaded jumps have a comparably lower skill demand, but may be similarly effective for improving explosive performance. The purpose of this study was to compare vertical jump performance, isometric force, and rate of force development (RFD) following a ten-week intervention employing the hang high-pull (hang-pull) or trap-bar jump squat (jump-squat). Eighteen NCAA Division II swimmers (8 males, 10 females) with at least one year of resistance training experience volunteered to participate. Testing included the squat jump (SJ), countermovement jump (CMJ) and the isometric mid-thigh pull (IMTP). Vertical ground reaction forces were analyzed to obtain jump height and relative peak power. Relative peak force, peak RFD and relative force at five time bands were obtained from the IMTP. Subjects were randomly assigned to either a hang-pull (n = 9) or jump-squat (n = 9) training group and completed a ten-week, volume-equated, periodized training program. While there was a significant main effect of training for both groups, no statistically significant between-group differences were found (p ≥ 0.17) for any of the dependent variables. However, medium effect sizes in favor of the jump-squat training group were seen in SJ height (d = 0.56) and SJ peak power (d = 0.69). Loaded jumps seem equally effective as weightlifting derivatives for improving lower-body power in experienced athletes. Since loaded jumps require less skill and less coaching expertise than weightlifting, loaded jumps should be considered where coaching complex movements is difficult.

Keeping Your Eye on the Rail: Gaze Behaviour of Horse Riders Approaching a Jump

PubMed Central

Hall, Carol; Varley, Ian; Kay, Rachel; Crundall, David

2014-01-01

The gaze behaviour of riders during their approach to a jump was investigated using a mobile eye tracking device (ASL Mobile Eye). The timing, frequency and duration of fixations on the jump and the percentage of time when their point of gaze (POG) was located elsewhere were assessed. Fixations were identified when the POG remained on the jump for 100 ms or longer. The jumping skill of experienced but non-elite riders (n = 10) was assessed by means of a questionnaire. Their gaze behaviour was recorded as they completed a course of three identical jumps five times. The speed and timing of the approach was calculated. Gaze behaviour throughout the overall approach and during the last five strides before take-off was assessed following frame-by-frame analyses. Differences in relation to both round and jump number were found. Significantly longer was spent fixated on the jump during round 2, both during the overall approach and during the last five strides (p<0.05). Jump 1 was fixated on significantly earlier and more frequently than jump 2 or 3 (p<0.05). Significantly more errors were made with jump 3 than with jump 1 (p = 0.01) but there was no difference in errors made between rounds. Although no significant correlations between gaze behaviour and skill scores were found, the riders who scored higher for jumping skill tended to fixate on the jump earlier (p = 0.07), when the horse was further from the jump (p = 0.09) and their first fixation on the jump was of a longer duration (p = 0.06). Trials with elite riders are now needed to further identify sport-specific visual skills and their relationship with performance. Visual training should be included in preparation for equestrian sports participation, the positive impact of which has been clearly demonstrated in other sports. PMID:24846055

Validity of two alternative systems for measuring vertical jump height.

PubMed

Leard, John S; Cirillo, Melissa A; Katsnelson, Eugene; Kimiatek, Deena A; Miller, Tim W; Trebincevic, Kenan; Garbalosa, Juan C

2007-11-01

Vertical jump height is frequently used by coaches, health care professionals, and strength and conditioning professionals to objectively measure function. The purpose of this study is to determine the concurrent validity of the jump and reach method (Vertec) and the contact mat method (Just Jump) in assessing vertical jump height when compared with the criterion reference 3-camera motion analysis system. Thirty-nine college students, 25 females and 14 males between the ages of 18 and 25 (mean age 20.65 years), were instructed to perform the countermovement jump. Reflective markers were placed at the base of the individual's sacrum for the 3-camera motion analysis system to measure vertical jump height. The subject was then instructed to stand on the Just Jump mat beneath the Vertec and perform the jump. Measurements were recorded from each of the 3 systems simultaneously for each jump. The Pearson r statistic between the video and the jump and reach (Vertec) was 0.906. The Pearson r between the video and contact mat (Just Jump) was 0.967. Both correlations were significant at the 0.01 level. Analysis of variance showed a significant difference among the 3 means F(2,235) = 5.51, p < 0.05. The post hoc analysis showed a significant difference between the criterion reference (M = 0.4369 m) and the Vertec (M = 0.3937 m, p = 0.005) but not between the criterion reference and the Just Jump system (M = 0.4420 m, p = 0.972). The Just Jump method of measuring vertical jump height is a valid measure when compared with the 3-camera system. The Vertec was found to have a high correlation with the criterion reference, but the mean differed significantly. This study indicates that a higher degree of confidence is warranted when comparing Just Jump results with a 3-camera system study.

Pressure-jump induced rapid solidification of melt: a method of preparing amorphous materials

NASA Astrophysics Data System (ADS)

Liu, Xiuru; Jia, Ru; Zhang, Doudou; Yuan, Chaosheng; Shao, Chunguang; Hong, Shiming

2018-04-01

By using a self-designed pressure-jump apparatus, we investigated the melt solidification behavior in rapid compression process for several kinds of materials, such as elementary sulfur, polymer polyether-ether-ketone (PEEK) and poly-ethylene-terephthalate, alloy La68Al10Cu20Co2 and Nd60Cu20Ni10Al10. Experimental results clearly show that their melts could be solidified to be amorphous states through the rapid compression process. Bulk amorphous PEEK with 24 mm in diameter and 12 mm in height was prepared, which exceeds the size obtained by melt quenching method. The bulk amorphous sulfur thus obtained exhibited extraordinarily high thermal stability, and an abnormal exothermic transition to liquid sulfur was observed at around 396 K for the first time. Furthermore, it is suggested that the glass transition pressure and critical compression rate exist to form the amorphous phase. This approach of rapid compression is very attractive not only because it is a new technique of make bulk amorphous materials, but also because novel properties are expected in the amorphous materials solidified by the pressure-jump within milliseconds or microseconds.

Molecular Dynamics Simulation of Salt Diffusion in Polyelectrolyte Assemblies.

PubMed

Zhang, Ran; Duan, Xiaozheng; Ding, Mingming; Shi, Tongfei

2018-06-05

The diffusion of salt ions and charged probe molecules in polyelectrolyte assemblies is often assumed to follow a theoretical hopping model, in which the diffusing ion is hopping between charged sites of chains based on electroneutrality. However, experimental verification of diffusing pathway at such microscales is difficult, and the corresponding molecular mechanisms remain elusive. In this study, we perform all-atom molecular dynamics (MD) simulations of salt diffusion in polyelectrolyte (PE) assembly of poly (sodium 4-styrenesulfonate) (PSS) and poly (diallyldimethylammonium chloride) (PDAC). Besides the ion hopping mode, the diffusing trajectories are found presenting common features of a jump process, i.e., subjecting to PE relaxation, water pockets in the structure open and close, thus the ion can move from one pocket to another. Anomalous subdiffusion of ions and water is observed due to the trapping scenarios in these water pockets. The jump events are much rarer compared with ion hopping but significantly increases salt diffusion with increasing temperature. Our result strongly indicates that salt diffusion in hydrated PDAC/PSS is a combined process of ion hopping and jump motion. This provides new molecular explanation for the coupling of salt motion with chain motion and the nonlinear increase of salt diffusion at glass transition temperature.

The effect of stress and incentive magnetic field on the average volume of magnetic Barkhausen jump in iron

NASA Astrophysics Data System (ADS)

Shu, Di; Guo, Lei; Yin, Liang; Chen, Zhaoyang; Chen, Juan; Qi, Xin

2015-11-01

The average volume of magnetic Barkhausen jump (AVMBJ) v bar generated by magnetic domain wall irreversible displacement under the effect of the incentive magnetic field H for ferromagnetic materials and the relationship between irreversible magnetic susceptibility χirr and stress σ are adopted in this paper to study the theoretical relationship among AVMBJ v bar(magneto-elasticity noise) and the incentive magnetic field H. Then the numerical relationship among AVMBJ v bar, stress σ and the incentive magnetic field H is deduced. Utilizing this numerical relationship, the displacement process of magnetic domain wall for single crystal is analyzed and the effect of the incentive magnetic field H and the stress σ on the AVMBJ v bar (magneto-elasticity noise) is explained from experimental and theoretical perspectives. The saturation velocity of Barkhausen jump characteristic value curve is different when tensile or compressive stress is applied on ferromagnetic materials, because the resistance of magnetic domain wall displacement is different. The idea of critical magnetic field in the process of magnetic domain wall displacement is introduced in this paper, which solves the supersaturated calibration problem of AVMBJ - σ calibration curve.

Spatiotemporal characteristics of motor actions by blind long jump athletes

PubMed Central

Torralba, Miguel Angel; Padullés, José María; Losada, Jose Luis; López, Jose Luis

2017-01-01

Background Blind people depend on spatial and temporal representations to perform activities of daily living and compete in sport. Objective The aim of this study is to determine the spatiotemporal characteristics of long jumps performed by blind athletes and compare findings with those reported for sighted athletes. Methods We analysed a sample of 12 male athletes competing in the F11 Long Jump Finals at the Paralympic Games in London 2012. Performances were recorded using four high-speed cameras, and speeds were measured using a radar speed gun. The images were processed using validated image analysis software. Results The long jump run-up is shorter in blind athletes than in sighted athletes. We observed statistically significant differences for body centre of mass velocity and an increase in speed over the last three strides prior to take-off, contrasting with reports for sighted athletes and athletes with less severe visual impairment, who maintain or reduce their speed during the last stride. Stride length for the last three strides was the only spatial characteristic that was not significantly associated with effective jump distance. Blind long jumpers extend rather than shorten their last stride. Contact time with the take-off board is longer than that reported for sighted athletes. Conclusion The actions of blind long jumpers, unlike those without disabilities, do not vary their leg actions during the final runway approach for optimal placement on the take-off board. PMID:29018542

Numerical study of laminar, standing hydraulic jumps in a planar geometry.

PubMed

Dasgupta, Ratul; Tomar, Gaurav; Govindarajan, Rama

2015-05-01

We solve the two-dimensional, planar Navier-Stokes equations to simulate a laminar, standing hydraulic jump using a Volume-of-Fluid method. The geometry downstream of the jump has been designed to be similar to experimental conditions by including a pit at the edge of the platform over which liquid film flows. We obtain jumps with and without separation. Increasing the inlet Froude number pushes the jump downstream and makes the slope of the jump weaker, consistent with experimental observations of circular jumps, and decreasing the Reynolds number brings the jump upstream while making it steeper. We study the effect of the length of the domain and that of a downstream obstacle on the structure and location of the jump. The transient flow which leads to a final steady jump is described for the first time to our knowledge. In the moderate Reynolds number regime, we obtain steady undular jumps with a separated bubble underneath the first few undulations. Interestingly, surface tension leads to shortening of wavelength of these undulations. We show that the undulations can be explained using the inviscid theory of Benjamin and Lighthill (Proc. R. Soc. London, Ser. A, 1954). We hope this new finding will motivate experimental verification.

Dynamics and stability of directional jumps in the desert locust.

PubMed

Gvirsman, Omer; Kosa, Gabor; Ayali, Amir

2016-01-01

Locusts are known for their ability to jump large distances to avoid predation. The jump also serves to launch the adult locust into the air in order to initiate flight. Various aspects of this important behavior have been studied extensively, from muscle physiology and biomechanics, to the energy storage systems involved in powering the jump, and more. Less well understood are the mechanisms participating in control of the jump trajectory. Here we utilise video monitoring and careful analysis of experimental directional jumps by adult desert locusts, together with dynamic computer simulation, in order to understand how the locusts control the direction and elevation of the jump, the residual angular velocities resulting from the jump and the timing of flapping-flight initiation. Our study confirms and expands early findings regarding the instrumental role of the initial body position and orientation. Both real-jump video analysis and simulations based on our expanded dynamical model demonstrate that the initial body coordinates of position (relative to the hind-legs ground-contact points) are dominant in predicting the jumps' azimuth and elevation angles. We also report a strong linear correlation between the jumps' pitch-angular-velocity and flight initiation timing, such that head downwards rotations lead to earlier wing opening. In addition to offering important insights into the bio-mechanical principles of locust jumping and flight initiation, the findings from this study will be used in designing future prototypes of a bio-inspired miniature jumping robot that will be employed in animal behaviour studies and environmental monitoring applications.

Maximum height and minimum time vertical jumping.

PubMed

Domire, Zachary J; Challis, John H

2015-08-20

The performance criterion in maximum vertical jumping has typically been assumed to simply raise the center of mass as high as possible. In many sporting activities minimizing movement time during the jump is likely also critical to successful performance. The purpose of this study was to examine maximum height jumps performed while minimizing jump time. A direct dynamics model was used to examine squat jump performance, with dual performance criteria: maximize jump height and minimize jump time. The muscle model had activation dynamics, force-length, force-velocity properties, and a series of elastic component representing the tendon. The simulations were run in two modes. In Mode 1 the model was placed in a fixed initial position. In Mode 2 the simulation model selected the initial squat configuration as well as the sequence of muscle activations. The inclusion of time as a factor in Mode 1 simulations resulted in a small decrease in jump height and moderate time savings. The improvement in time was mostly accomplished by taking off from a less extended position. In Mode 2 simulations, more substantial time savings could be achieved by beginning the jump in a more upright posture. However, when time was weighted more heavily in these simulations, there was a more substantial reduction in jump height. Future work is needed to examine the implications for countermovement jumping and to examine the possibility of minimizing movement time as part of the control scheme even when the task is to jump maximally. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluation of different jumping tests in defining position-specific and performance-level differences in high level basketball players

PubMed Central

Pehar, Miran; Sekulic, Damir; Sisic, Nedim; Spasic, Miodrag; Uljevic, Ognjen; Krolo, Ante; Sattler, Tine

2017-01-01

The importance of jumping ability in basketball is well known, but there is an evident lack of studies that have examined different jumping testing protocols in basketball players at advanced levels. The aim of this study was to assess the applicability of different tests of jumping capacity in identifying differences between (i) playing position and (ii) competitive levels of professional players. Participants were 110 male professional basketball players (height: 194.92±8.09 cm; body mass: 89.33±10.91 kg; 21.58±3.92 years of age; Guards, 49; Forwards, 22; Centres, 39) who competed in the first (n = 58) and second division (n = 52). The variables included anthropometrics and jumping test performance. Jumping performances were evaluated by the standing broad jump (SBJ), countermovement jump (CMJ), reactive strength index (RSI), repeated reactive strength ability (RRSA) and four running vertical jumps: maximal jump with (i) take-off from the dominant leg and (ii) non-dominant leg, lay-up shot jump with take-off from the (iii) dominant leg and (iv) non-dominant leg. First-division players were taller (ES: 0.76, 95%CI: 0.35-1.16, moderate differences), heavier (0.69, 0.29-1.10), had higher maximal reach height (0.67, 0.26-1.07, moderate differences), and had lower body fat % (-0.87, -1.27-0.45, moderate differences) than second-division players. The playing positions differed significantly in three of four running jump achievements, RSI and RRSA, with Centres being least successful. The first-division players were superior to second-division players in SBJ (0.63, 0.23-1.03; 0.87, 0.26-1.43; 0.76, 0.11-1.63, all moderate differences, for total sample, Guards, and Forwards, respectively). Running vertical jumps and repeated jumping capacity can be used as valid measures of position-specific jumping ability in basketball. The differences between playing levels in vertical jumping achievement can be observed by assessing vertical jump scores together with differences in anthropometric indices between levels. PMID:29158620

The Effects of Temperature and Body Mass on Jump Performance of the Locust Locusta migratoria

PubMed Central

Snelling, Edward P.; Becker, Christie L.; Seymour, Roger S.

2013-01-01

Locusts jump by rapidly releasing energy from cuticular springs built into the hind femur that deform when the femur muscle contracts. This study is the first to examine the effect of temperature on jump energy at each life stage of any orthopteran. Ballistics and high-speed cinematography were used to quantify the energy, distance, and take-off angle of the jump at 15, 25, and 35°C in the locust Locusta migratoria. Allometric analysis across the five juvenile stages at 35°C reveals that jump distance (D; m) scales with body mass (M; g) according to the power equation D = 0.35M 0.17±0.08 (95% CI), jump take-off angle (A; degrees) scales as A = 52.5M 0.00±0.06, and jump energy (E; mJ per jump) scales as E = 1.91M 1.14±0.09. Temperature has no significant effect on the exponent of these relationships, and only a modest effect on the elevation, with an overall Q10 of 1.08 for jump distance and 1.09 for jump energy. On average, adults jump 87% farther and with 74% more energy than predicted based on juvenile scaling data. The positive allometric scaling of jump distance and jump energy across the juvenile life stages is likely facilitated by the concomitant relative increase in the total length (L f+t; mm) of the femur and tibia of the hind leg, L f+t = 34.9M 0.37±0.02. The weak temperature-dependence of jump performance can be traced to the maximum tension of the hind femur muscle and the energy storage capacity of the femur's cuticular springs. The disproportionately greater jump energy and jump distance of adults is associated with relatively longer (12%) legs and a relatively larger (11%) femur muscle cross-sectional area, which could allow more strain loading into the femur's cuticular springs. Augmented jump performance in volant adult locusts achieves the take-off velocity required to initiate flight. PMID:23967304

A comparison of men's and women's strength to body mass ratio and varus/valgus knee angle during jump landings.

PubMed

Haines, Tracie L; McBride, Jeffrey M; Triplett, N Travis; Skinner, Jared W; Fairbrother, Kimberly R; Kirby, Tyler J

2011-10-01

The purpose of this investigation was to compare valgus/varus knee angles during various jumps and lower body strength between males and females relative to body mass. Seventeen recreationally active females (age: 21.94 ± 2.59 years; height: 1.67 ± 0.05 m; mass: 64.42 ± 8.39 kg; percent body fat: 26.89 ± 6.26%; squat one-repetition maximum: 66.18 ± 19.47 kg; squat to body mass ratio: 1.03 ± 0.28) and 13 recreationally active males (age: 21.69 ± 1.65 years; height: 1.77 ± 0.07 m; mass: 72.39 ± 9.23 kg; percent body fat: 13.15 ± 5.18%; squat one-repetition maximum: 115.77 ± 30.40 kg; squat to body mass ratio: 1.59 ± 0.31) performed a one-repetition maximum in the squat and three of each of the following jumps: countermovement jump, 30 cm drop jump, 45 cm drop jump, and 60 cm drop jump. Knee angles were analysed using videography and body composition was analysed by dual-energy X-ray absorptiometry to allow for squat to body mass ratio and squat to fat free mass ratio to be calculated. Significant differences (P ≤ 0.05) were found between male and female one-repetition maximum, male and female squat to body mass ratio, and male and female squat to fat free mass ratio. Significant differences were found between male and female varus/valgus knee positions during maximum flexion of the right and left leg in the countermovement jump, drop jump from 30 cm, drop jump from 45 cm, and drop jump from 60 cm. Correlations between varus/valgus knee angles and squat to body mass ratio for all jumps displayed moderate, non-significant relationships (countermovement jump: r = 0.445; drop jump from 30 cm: r = 0.448; drop jump from 45 cm: r = 0.449; drop jump from 60 cm: r = 0.439). In conclusion, males and females have significantly different lower body strength and varus/valgus knee position when landing from jumps.