Nonlinear Autoregressive Exogenous modeling of a large anaerobic digester producing biogas from cattle waste.

PubMed

Dhussa, Anil K; Sambi, Surinder S; Kumar, Shashi; Kumar, Sandeep; Kumar, Surendra

2014-10-01

In waste-to-energy plants, there is every likelihood of variations in the quantity and characteristics of the feed. Although intermediate storage tanks are used, but many times these are of inadequate capacity to dampen the variations. In such situations an anaerobic digester treating waste slurry operates under dynamic conditions. In this work a special type of dynamic Artificial Neural Network model, called Nonlinear Autoregressive Exogenous model, is used to model the dynamics of anaerobic digesters by using about one year data collected on the operating digesters. The developed model consists of two hidden layers each having 10 neurons, and uses 18days delay. There are five neurons in input layer and one neuron in output layer for a day. Model predictions of biogas production rate are close to plant performance within ±8% deviation. Copyright © 2014 Elsevier Ltd. All rights reserved.

State Space Modeling of Time-Varying Contemporaneous and Lagged Relations in Connectivity Maps

PubMed Central

Molenaar, Peter C. M.; Beltz, Adriene M.; Gates, Kathleen M.; Wilson, Stephen J.

2017-01-01

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. PMID:26546863

AR(p) -based detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Alvarez-Ramirez, J.; Rodriguez, E.

2018-07-01

Autoregressive models are commonly used for modeling time-series from nature, economics and finance. This work explored simple autoregressive AR(p) models to remove long-term trends in detrended fluctuation analysis (DFA). Crude oil prices and bitcoin exchange rate were considered, with the former corresponding to a mature market and the latter to an emergent market. Results showed that AR(p) -based DFA performs similar to traditional DFA. However, the former DFA provides information on stability of long-term trends, which is valuable for understanding and quantifying the dynamics of complex time series from financial systems.

Principal dynamic mode analysis of neural mass model for the identification of epileptic states

NASA Astrophysics Data System (ADS)

Cao, Yuzhen; Jin, Liu; Su, Fei; Wang, Jiang; Deng, Bin

2016-11-01

The detection of epileptic seizures in Electroencephalography (EEG) signals is significant for the diagnosis and treatment of epilepsy. In this paper, in order to obtain characteristics of various epileptiform EEGs that may differentiate different states of epilepsy, the concept of Principal Dynamic Modes (PDMs) was incorporated to an autoregressive model framework. First, the neural mass model was used to simulate the required intracerebral EEG signals of various epileptiform activities. Then, the PDMs estimated from the nonlinear autoregressive Volterra models, as well as the corresponding Associated Nonlinear Functions (ANFs), were used for the modeling of epileptic EEGs. The efficient PDM modeling approach provided physiological interpretation of the system. Results revealed that the ANFs of the 1st and 2nd PDMs for the auto-regressive input exhibited evident differences among different states of epilepsy, where the ANFs of the sustained spikes' activity encountered at seizure onset or during a seizure were the most differentiable from that of the normal state. Therefore, the ANFs may be characteristics for the classification of normal and seizure states in the clinical detection of seizures and thus provide assistance for the diagnosis of epilepsy.

Dynamic RSA: Examining parasympathetic regulatory dynamics via vector-autoregressive modeling of time-varying RSA and heart period.

PubMed

Fisher, Aaron J; Reeves, Jonathan W; Chi, Cyrus

2016-07-01

Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment-to-moment basis. We estimated second-to-second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time-lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t-n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI-referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control-specifically, a method by which parasympathetic regulatory effects can be measured in process. © 2016 Society for Psychophysiological Research.

Comparisons of Four Methods for Estimating a Dynamic Factor Model

ERIC Educational Resources Information Center

Zhang, Zhiyong; Hamaker, Ellen L.; Nesselroade, John R.

2008-01-01

Four methods for estimating a dynamic factor model, the direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based on its state space model representation. The second one employs the maximum likelihood estimation method based on the construction of a…

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model.

PubMed

Bringmann, Laura F; Ferrer, Emilio; Hamaker, Ellen L; Borsboom, Denny; Tuerlinckx, Francis

2018-01-01

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.

Granger causality revisited

PubMed Central

Friston, Karl J.; Bastos, André M.; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir

2014-01-01

This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling. PMID:25003817

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps.

PubMed

Molenaar, Peter C M; Beltz, Adriene M; Gates, Kathleen M; Wilson, Stephen J

2016-01-15

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. Published by Elsevier Inc.

Three essays on price dynamics and causations among energy markets and macroeconomic information

NASA Astrophysics Data System (ADS)

Hong, Sung Wook

This dissertation examines three important issues in energy markets: price dynamics, information flow, and structural change. We discuss each issue in detail, building empirical time series models, analyzing the results, and interpreting the findings. First, we examine the contemporaneous interdependencies and information flows among crude oil, natural gas, and electricity prices in the United States (US) through the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, Directed Acyclic Graph (DAG) for contemporaneous causal structures and Bernanke factorization for price dynamic processes. Test results show that the DAG from residuals of out-of-sample-forecast is consistent with the DAG from residuals of within-sample-fit. The result supports innovation accounting analysis based on DAGs using residuals of out-of-sample-forecast. Second, we look at the effects of the federal fund rate and/or WTI crude oil price shock on US macroeconomic and financial indicators by using a Factor Augmented Vector Autoregression (FAVAR) model and a graphical model without any deductive assumption. The results show that, in contemporaneous time, the federal fund rate shock is exogenous as the identifying assumption in the Vector Autoregression (VAR) framework of the monetary shock transmission mechanism, whereas the WTI crude oil price return is not exogenous. Third, we examine price dynamics and contemporaneous causality among the price returns of WTI crude oil, gasoline, corn, and the S&P 500. We look for structural break points and then build an econometric model to find the consistent sub-periods having stable parameters in a given VAR framework and to explain recent movements and interdependency among returns. We found strong evidence of two structural breaks and contemporaneous causal relationships among the residuals, but also significant differences between contemporaneous causal structures for each sub-period.

Maximum Likelihood Dynamic Factor Modeling for Arbitrary "N" and "T" Using SEM

ERIC Educational Resources Information Center

Voelkle, Manuel C.; Oud, Johan H. L.; von Oertzen, Timo; Lindenberger, Ulman

2012-01-01

This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary "T" and "N" by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time…

Gaussian Process Autoregression for Simultaneous Proportional Multi-Modal Prosthetic Control With Natural Hand Kinematics.

PubMed

Xiloyannis, Michele; Gavriel, Constantinos; Thomik, Andreas A C; Faisal, A Aldo

2017-10-01

Matching the dexterity, versatility, and robustness of the human hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 joints of the hand in a continuous manner-much like we control our natural hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the hand in the forearm, while simultaneously monitoring 11 joints of hand and fingers using a sensorized data glove that tracked the joints of the hand. Instead of learning just a direct mapping from current muscle activity to intended hand movement, we formulated a novel autoregressive approach that combines the context of previous hand movements with instantaneous muscle activity to predict future hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process ( ) autoregressive framework to learn the continuous mapping from hand joint dynamics and muscle activity to decode intended hand movement. Our approach achieves high levels of performance (RMSE of 8°/s and ). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a hand. This novel undersensored control is enabled through the combination of nonlinear autoregressive continuous mapping between muscle activity and joint angles. The system evaluates the muscle signals in the context of previous natural hand movements. This enables us to resolve ambiguities in situations, where muscle signals alone cannot determine the correct action as we evaluate the muscle signals in their context of natural hand movements. autoregression is a particularly powerful approach which makes not only a prediction based on the context but also represents the associated uncertainty of its predictions, thus enabling the novel notion of risk-based control in neuroprosthetics. Our results suggest that autoregressive approaches with exogenous inputs lend themselves for natural, intuitive, and continuous control in neurotechnology, with the particular focus on prosthetic restoration of natural limb function, where high dexterity is required for complex movements.

North American oriented strand board markets, arbitrage activity, and market price dynamics: A smooth transition approach

Treesearch

Barry Goodwin; Matthew Holt; Jeffrey P. Prestemon

2011-01-01

Price dynamics for North American oriented strand board markets are examined. The role of transactions costs are explored vis-à-vis the law of one price. Nonlinearities induced by unobservable transactions costs are modeled by estimating time-varying smooth transition autoregressions (TV-STARs). Results indicate that nonlinearity and structural change are important...

Reconstructing latent dynamical noise for better forecasting observables

NASA Astrophysics Data System (ADS)

Hirata, Yoshito

2018-03-01

I propose a method for reconstructing multi-dimensional dynamical noise inspired by the embedding theorem of Muldoon et al. [Dyn. Stab. Syst. 13, 175 (1998)] by regarding multiple predictions as different observables. Then, applying the embedding theorem by Stark et al. [J. Nonlinear Sci. 13, 519 (2003)] for a forced system, I produce time series forecast by supplying the reconstructed past dynamical noise as auxiliary information. I demonstrate the proposed method on toy models driven by auto-regressive models or independent Gaussian noise.

Time series modelling of increased soil temperature anomalies during long period

NASA Astrophysics Data System (ADS)

Shirvani, Amin; Moradi, Farzad; Moosavi, Ali Akbar

2015-10-01

Soil temperature just beneath the soil surface is highly dynamic and has a direct impact on plant seed germination and is probably the most distinct and recognisable factor governing emergence. Autoregressive integrated moving average as a stochastic model was developed to predict the weekly soil temperature anomalies at 10 cm depth, one of the most important soil parameters. The weekly soil temperature anomalies for the periods of January1986-December 2011 and January 2012-December 2013 were taken into consideration to construct and test autoregressive integrated moving average models. The proposed model autoregressive integrated moving average (2,1,1) had a minimum value of Akaike information criterion and its estimated coefficients were different from zero at 5% significance level. The prediction of the weekly soil temperature anomalies during the test period using this proposed model indicated a high correlation coefficient between the observed and predicted data - that was 0.99 for lead time 1 week. Linear trend analysis indicated that the soil temperature anomalies warmed up significantly by 1.8°C during the period of 1986-2011.

Modelling malaria incidence by an autoregressive distributed lag model with spatial component.

PubMed

Laguna, Francisco; Grillet, María Eugenia; León, José R; Ludeña, Carenne

2017-08-01

The influence of climatic variables on the dynamics of human malaria has been widely highlighted. Also, it is known that this mosquito-borne infection varies in space and time. However, when the data is spatially incomplete most popular spatio-temporal methods of analysis cannot be applied directly. In this paper, we develop a two step methodology to model the spatio-temporal dependence of malaria incidence on local rainfall, temperature, and humidity as well as the regional sea surface temperatures (SST) in the northern coast of Venezuela. First, we fit an autoregressive distributed lag model (ARDL) to the weekly data, and then, we adjust a linear separable spacial vectorial autoregressive model (VAR) to the residuals of the ARDL. Finally, the model parameters are tuned using a Markov Chain Monte Carlo (MCMC) procedure derived from the Metropolis-Hastings algorithm. Our results show that the best model to account for the variations of malaria incidence from 2001 to 2008 in 10 endemic Municipalities in North-Eastern Venezuela is a logit model that included the accumulated local precipitation in combination with the local maximum temperature of the preceding month as positive regressors. Additionally, we show that although malaria dynamics is highly heterogeneous in space, a detailed analysis of the estimated spatial parameters in our model yield important insights regarding the joint behavior of the disease incidence across the different counties in our study. Copyright © 2017 Elsevier Ltd. All rights reserved.

A nonlinear autoregressive Volterra model of the Hodgkin-Huxley equations.

PubMed

Eikenberry, Steffen E; Marmarelis, Vasilis Z

2013-02-01

We propose a new variant of Volterra-type model with a nonlinear auto-regressive (NAR) component that is a suitable framework for describing the process of AP generation by the neuron membrane potential, and we apply it to input-output data generated by the Hodgkin-Huxley (H-H) equations. Volterra models use a functional series expansion to describe the input-output relation for most nonlinear dynamic systems, and are applicable to a wide range of physiologic systems. It is difficult, however, to apply the Volterra methodology to the H-H model because is characterized by distinct subthreshold and suprathreshold dynamics. When threshold is crossed, an autonomous action potential (AP) is generated, the output becomes temporarily decoupled from the input, and the standard Volterra model fails. Therefore, in our framework, whenever membrane potential exceeds some threshold, it is taken as a second input to a dual-input Volterra model. This model correctly predicts membrane voltage deflection both within the subthreshold region and during APs. Moreover, the model naturally generates a post-AP afterpotential and refractory period. It is known that the H-H model converges to a limit cycle in response to a constant current injection. This behavior is correctly predicted by the proposed model, while the standard Volterra model is incapable of generating such limit cycle behavior. The inclusion of cross-kernels, which describe the nonlinear interactions between the exogenous and autoregressive inputs, is found to be absolutely necessary. The proposed model is general, non-parametric, and data-derived.

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.

Estimating time-varying conditional correlations between stock and foreign exchange markets

NASA Astrophysics Data System (ADS)

Tastan, Hüseyin

2006-02-01

This study explores the dynamic interaction between stock market returns and changes in nominal exchange rates. Many financial variables are known to exhibit fat tails and autoregressive variance structure. It is well-known that unconditional covariance and correlation coefficients also vary significantly over time and multivariate generalized autoregressive model (MGARCH) is able to capture the time-varying variance-covariance matrix for stock market returns and changes in exchange rates. The model is applied to daily Euro-Dollar exchange rates and two stock market indexes from the US economy: Dow-Jones Industrial Average Index and S&P500 Index. The news impact surfaces are also drawn based on the model estimates to see the effects of idiosyncratic shocks in respective markets.

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

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

Abbas, Nikhar; Tom, Nathan M

2017-06-03

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

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

Abbas, Nikhar; Tom, Nathan

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Nonlinear and Quasi-Simplex Patterns in Latent Growth Models

ERIC Educational Resources Information Center

Bianconcini, Silvia

2012-01-01

In the SEM literature, simplex and latent growth models have always been considered competing approaches for the analysis of longitudinal data, even if they are strongly connected and both of specific importance. General dynamic models, which simultaneously estimate autoregressive structures and latent curves, have been recently proposed in the…

Pathways between Self-Esteem and Depression in Couples

ERIC Educational Resources Information Center

Johnson, Matthew D.; Galambos, Nancy L.; Finn, Christine; Neyer, Franz J.; Horne, Rebecca M.

2017-01-01

Guided by concepts from a relational developmental perspective, this study examined intra- and interpersonal associations between self-esteem and depressive symptoms in a sample of 1,407 couples surveyed annually across 6 years in the Panel Analysis of Intimate Relations and Family Dynamics (pairfam) study. Autoregressive cross-lagged model…

A Novel Modeling Method for Aircraft Engine Using Nonlinear Autoregressive Exogenous (NARX) Models Based on Wavelet Neural Networks

NASA Astrophysics Data System (ADS)

Yu, Bing; Shu, Wenjun; Cao, Can

2018-05-01

A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine's dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.

Principal Dynamic Mode Analysis of the Hodgkin–Huxley Equations

PubMed Central

Eikenberry, Steffen E.; Marmarelis, Vasilis Z.

2015-01-01

We develop an autoregressive model framework based on the concept of Principal Dynamic Modes (PDMs) for the process of action potential (AP) generation in the excitable neuronal membrane described by the Hodgkin–Huxley (H–H) equations. The model's exogenous input is injected current, and whenever the membrane potential output exceeds a specified threshold, it is fed back as a second input. The PDMs are estimated from the previously developed Nonlinear Autoregressive Volterra (NARV) model, and represent an efficient functional basis for Volterra kernel expansion. The PDM-based model admits a modular representation, consisting of the forward and feedback PDM bases as linear filterbanks for the exogenous and autoregressive inputs, respectively, whose outputs are then fed to a static nonlinearity composed of polynomials operating on the PDM outputs and cross-terms of pair-products of PDM outputs. A two-step procedure for model reduction is performed: first, influential subsets of the forward and feedback PDM bases are identified and selected as the reduced PDM bases. Second, the terms of the static nonlinearity are pruned. The first step reduces model complexity from a total of 65 coefficients to 27, while the second further reduces the model coefficients to only eight. It is demonstrated that the performance cost of model reduction in terms of out-of-sample prediction accuracy is minimal. Unlike the full model, the eight coefficient pruned model can be easily visualized to reveal the essential system components, and thus the data-derived PDM model can yield insight into the underlying system structure and function. PMID:25630480

QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin

PubMed Central

Savol, Andrej J.; Burger, Virginia M.; Agarwal, Pratul K.; Ramanathan, Arvind; Chennubhotla, Chakra S.

2011-01-01

Motivation: Molecular dynamics (MD) simulations have dramatically improved the atomistic understanding of protein motions, energetics and function. These growing datasets have necessitated a corresponding emphasis on trajectory analysis methods for characterizing simulation data, particularly since functional protein motions and transitions are often rare and/or intricate events. Observing that such events give rise to long-tailed spatial distributions, we recently developed a higher-order statistics based dimensionality reduction method, called quasi-anharmonic analysis (QAA), for identifying biophysically-relevant reaction coordinates and substates within MD simulations. Further characterization of conformation space should consider the temporal dynamics specific to each identified substate. Results: Our model uses hierarchical clustering to learn energetically coherent substates and dynamic modes of motion from a 0.5 μs ubiqutin simulation. Autoregressive (AR) modeling within and between states enables a compact and generative description of the conformational landscape as it relates to functional transitions between binding poses. Lacking a predictive component, QAA is extended here within a general AR model appreciative of the trajectory's temporal dependencies and the specific, local dynamics accessible to a protein within identified energy wells. These metastable states and their transition rates are extracted within a QAA-derived subspace using hierarchical Markov clustering to provide parameter sets for the second-order AR model. We show the learned model can be extrapolated to synthesize trajectories of arbitrary length. Contact: ramanathana@ornl.gov; chakracs@pitt.edu PMID:21685101

Assessing the effects of pharmacological agents on respiratory dynamics using time-series modeling.

PubMed

Wong, Kin Foon Kevin; Gong, Jen J; Cotten, Joseph F; Solt, Ken; Brown, Emery N

2013-04-01

Developing quantitative descriptions of how stimulant and depressant drugs affect the respiratory system is an important focus in medical research. Respiratory variables-respiratory rate, tidal volume, and end tidal carbon dioxide-have prominent temporal dynamics that make it inappropriate to use standard hypothesis-testing methods that assume independent observations to assess the effects of these pharmacological agents. We present a polynomial signal plus autoregressive noise model for analysis of continuously recorded respiratory variables. We use a cyclic descent algorithm to maximize the conditional log likelihood of the parameters and the corrected Akaike's information criterion to choose simultaneously the orders of the polynomial and the autoregressive models. In an analysis of respiratory rates recorded from anesthetized rats before and after administration of the respiratory stimulant methylphenidate, we use the model to construct within-animal z-tests of the drug effect that take account of the time-varying nature of the mean respiratory rate and the serial dependence in rate measurements. We correct for the effect of model lack-of-fit on our inferences by also computing bootstrap confidence intervals for the average difference in respiratory rate pre- and postmethylphenidate treatment. Our time-series modeling quantifies within each animal the substantial increase in mean respiratory rate and respiratory dynamics following methylphenidate administration. This paradigm can be readily adapted to analyze the dynamics of other respiratory variables before and after pharmacologic treatments.

Dynamic Forecasting of Zika Epidemics Using Google Trends

PubMed Central

Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Feng, Dan; Tong, Yigang

2017-01-01

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks. PMID:28060809

Dynamic Forecasting of Zika Epidemics Using Google Trends.

PubMed

Teng, Yue; Bi, Dehua; Xie, Guigang; Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Feng, Dan; Tong, Yigang

2017-01-01

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.

Using Threshold Autoregressive Models to Study Dyadic Interactions

ERIC Educational Resources Information Center

Hamaker, Ellen L.; Zhang, Zhiyong; van der Maas, Han L. J.

2009-01-01

Considering a dyad as a dynamic system whose current state depends on its past state has allowed researchers to investigate whether and how partners influence each other. Some researchers have also focused on how differences between dyads in their interaction patterns are related to other differences between them. A promising approach in this area…

Disease Mapping of Zero-excessive Mesothelioma Data in Flanders

PubMed Central

Neyens, Thomas; Lawson, Andrew B.; Kirby, Russell S.; Nuyts, Valerie; Watjou, Kevin; Aregay, Mehreteab; Carroll, Rachel; Nawrot, Tim S.; Faes, Christel

2016-01-01

Purpose To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. Methods The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero-inflation and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. Results The results indicate that hurdle models with a random effects term accounting for extra-variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra-variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra-variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. Conclusions Models taking into account zero-inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this. PMID:27908590

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects

PubMed Central

2012-01-01

Background Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with autoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models. Results We illustrate the applicability of our new model using synthetic and real time-course datasets. We show that our model outperforms existing models to provide more reliable and robust clustering of time-course data. Our model provides superior results when genetic profiles are correlated. It also gives comparable results when the correlation between the gene profiles is weak. In the applications to real time-course data, relevant clusters of coregulated genes are obtained, which are supported by gene-function annotation databases. Conclusions Our new model under our extension of the EMMIX-WIRE procedure is more reliable and robust for clustering time-course data because it adopts a random effects model that allows for the correlation among observations at different time points. It postulates gene-specific random effects with an autocorrelation variance structure that models coregulation within the clusters. The developed R package is flexible in its specification of the random effects through user-input parameters that enables improved modelling and consequent clustering of time-course data. PMID:23151154

Disease mapping of zero-excessive mesothelioma data in Flanders.

PubMed

Neyens, Thomas; Lawson, Andrew B; Kirby, Russell S; Nuyts, Valerie; Watjou, Kevin; Aregay, Mehreteab; Carroll, Rachel; Nawrot, Tim S; Faes, Christel

2017-01-01

To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this. Copyright © 2016 Elsevier Inc. All rights reserved.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Characteristics of the transmission of autoregressive sub-patterns in financial time series

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong

2014-09-01

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.

Characteristics of the transmission of autoregressive sub-patterns in financial time series

PubMed Central

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong

2014-01-01

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors. PMID:25189200

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Autoregressive-model-based missing value estimation for DNA microarray time series data.

PubMed

Choong, Miew Keen; Charbit, Maurice; Yan, Hong

2009-01-01

Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.

A High Precision Prediction Model Using Hybrid Grey Dynamic Model

ERIC Educational Resources Information Center

Li, Guo-Dong; Yamaguchi, Daisuke; Nagai, Masatake; Masuda, Shiro

2008-01-01

In this paper, we propose a new prediction analysis model which combines the first order one variable Grey differential equation Model (abbreviated as GM(1,1) model) from grey system theory and time series Autoregressive Integrated Moving Average (ARIMA) model from statistics theory. We abbreviate the combined GM(1,1) ARIMA model as ARGM(1,1)…

The dynamic conditional relationship between stock market returns and implied volatility

NASA Astrophysics Data System (ADS)

Park, Sung Y.; Ryu, Doojin; Song, Jeongseok

2017-09-01

Using the dynamic conditional correlation multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) model, we empirically examine the dynamic relationship between stock market returns (KOSPI200 returns) and implied volatility (VKOSPI), as well as their statistical mechanics, in the Korean market, a representative and leading emerging market. We consider four macroeconomic variables (exchange rates, risk-free rates, term spreads, and credit spreads) as potential determinants of the dynamic conditional correlation between returns and volatility. Of these macroeconomic variables, the change in exchange rates has a significant impact on the dynamic correlation between KOSPI200 returns and the VKOSPI, especially during the recent financial crisis. We also find that the risk-free rate has a marginal effect on this dynamic conditional relationship.

Modeling time-series count data: the unique challenges facing political communication studies.

PubMed

Fogarty, Brian J; Monogan, James E

2014-05-01

This paper demonstrates the importance of proper model specification when analyzing time-series count data in political communication studies. It is common for scholars of media and politics to investigate counts of coverage of an issue as it evolves over time. Many scholars rightly consider the issues of time dependence and dynamic causality to be the most important when crafting a model. However, to ignore the count features of the outcome variable overlooks an important feature of the data. This is particularly the case when modeling data with a low number of counts. In this paper, we argue that the Poisson autoregressive model (Brandt and Williams, 2001) accurately meets the needs of many media studies. We replicate the analyses of Flemming et al. (1997), Peake and Eshbaugh-Soha (2008), and Ura (2009) and demonstrate that models missing some of the assumptions of the Poisson autoregressive model often yield invalid inferences. We also demonstrate that the effect of any of these models can be illustrated dynamically with estimates of uncertainty through a simulation procedure. The paper concludes with implications of these findings for the practical researcher. Copyright © 2013 Elsevier Inc. All rights reserved.

Damage localization of marine risers using time series of vibration signals

NASA Astrophysics Data System (ADS)

Liu, Hao; Yang, Hezhen; Liu, Fushun

2014-10-01

Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average (ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive (AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect.

The dynamic correlation between policy uncertainty and stock market returns in China

NASA Astrophysics Data System (ADS)

Yang, Miao; Jiang, Zhi-Qiang

2016-11-01

The dynamic correlation is examined between government's policy uncertainty and Chinese stock market returns in the period from January 1995 to December 2014. We find that the stock market is significantly correlated to policy uncertainty based on the results of the Vector Auto Regression (VAR) and Structural Vector Auto Regression (SVAR) models. In contrast, the results of the Dynamic Conditional Correlation Generalized Multivariate Autoregressive Conditional Heteroscedasticity (DCC-MGARCH) model surprisingly show a low dynamic correlation coefficient between policy uncertainty and market returns, suggesting that the fluctuations of each variable are greatly influenced by their values in the preceding period. Our analysis highlights the understanding of the dynamical relationship between stock market and fiscal and monetary policy.

A graphical vector autoregressive modelling approach to the analysis of electronic diary data

PubMed Central

2010-01-01

Background In recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied. Methods We propose to analyse the temporal interrelationships among the variables by a structural modelling approach based on graphical vector autoregressive (VAR) models. We give a comprehensive description of the underlying concepts and explain how the dependence structure can be recovered from electronic diary data by a search over suitable constrained (graphical) VAR models. Results The graphical VAR approach is applied to the electronic diary data of 35 obese patients with and without binge eating disorder (BED). The dynamic relationships for the two subgroups between eating behaviour, depression, anxiety and eating control are visualized in two path diagrams. Results show that the two subgroups of obese patients with and without BED are distinguishable by the temporal patterns which influence their respective eating behaviours. Conclusion The use of the graphical VAR approach for the analysis of electronic diary data leads to a deeper insight into patient's dynamics and dependence structures. An increasing use of this modelling approach could lead to a better understanding of complex psychological and physiological mechanisms in different areas of medical care and research. PMID:20359333

Modeling volatility using state space models.

PubMed

Timmer, J; Weigend, A S

1997-08-01

In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude since they do not distinguish between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. Data sets used: Olsen & Associates high frequency DEM/USD foreign exchange rates (8 years). Nikkei 225 index (40 years). Dow Jones Industrial Average (25 years).

Multifractal detrended cross-correlations between crude oil market and Chinese ten sector stock markets

NASA Astrophysics Data System (ADS)

Yang, Liansheng; Zhu, Yingming; Wang, Yudong; Wang, Yiqi

2016-11-01

Based on the daily price data of spot prices of West Texas Intermediate (WTI) crude oil and ten CSI300 sector indices in China, we apply multifractal detrended cross-correlation analysis (MF-DCCA) method to investigate the cross-correlations between crude oil and Chinese sector stock markets. We find that the strength of multifractality between WTI crude oil and energy sector stock market is the highest, followed by the strength of multifractality between WTI crude oil and financial sector market, which reflects a close connection between energy and financial market. Then we do vector autoregression (VAR) analysis to capture the interdependencies among the multiple time series. By comparing the strength of multifractality for original data and residual errors of VAR model, we get a conclusion that vector auto-regression (VAR) model could not be used to describe the dynamics of the cross-correlations between WTI crude oil and the ten sector stock markets.

On the measurement of stability in over-time data.

PubMed

Kenny, D A; Campbell, D T

1989-06-01

In this article, autoregressive models and growth curve models are compared. Autoregressive models are useful because they allow for random change, permit scores to increase or decrease, and do not require strong assumptions about the level of measurement. Three previously presented designs for estimating stability are described: (a) time-series, (b) simplex, and (c) two-wave, one-factor methods. A two-wave, multiple-factor model also is presented, in which the variables are assumed to be caused by a set of latent variables. The factor structure does not change over time and so the synchronous relationships are temporally invariant. The factors do not cause each other and have the same stability. The parameters of the model are the factor loading structure, each variable's reliability, and the stability of the factors. We apply the model to two data sets. For eight cognitive skill variables measured at four times, the 2-year stability is estimated to be .92 and the 6-year stability is .83. For nine personality variables, the 3-year stability is .68. We speculate that for many variables there are two components: one component that changes very slowly (the trait component) and another that changes very rapidly (the state component); thus each variable is a mixture of trait and state. Circumstantial evidence supporting this view is presented.

The relationship between trading volumes, number of transactions, and stock volatility in GARCH models

NASA Astrophysics Data System (ADS)

Takaishi, Tetsuya; Chen, Ting Ting

2016-08-01

We examine the relationship between trading volumes, number of transactions, and volatility using daily stock data of the Tokyo Stock Exchange. Following the mixture of distributions hypothesis, we use trading volumes and the number of transactions as proxy for the rate of information arrivals affecting stock volatility. The impact of trading volumes or number of transactions on volatility is measured using the generalized autoregressive conditional heteroscedasticity (GARCH) model. We find that the GARCH effects, that is, persistence of volatility, is not always removed by adding trading volumes or number of transactions, indicating that trading volumes and number of transactions do not adequately represent the rate of information arrivals.

Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models

PubMed Central

Smith, Jason F.; Chen, Kewei; Pillai, Ajay S.; Horwitz, Barry

2013-01-01

The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. PMID:23717258

Scaling symmetry, renormalization, and time series modeling: the case of financial assets dynamics.

PubMed

Zamparo, Marco; Baldovin, Fulvio; Caraglio, Michele; Stella, Attilio L

2013-12-01

We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest version the model is the product of an endogenous autoregressive component and a random rescaling factor designed to embody also exogenous influences. Mathematical properties like increments' stationarity and ergodicity can be proven. Thanks to the relatively low number of parameters, model calibration can be conveniently based on a method of moments, as exemplified in the case of historical data of the S&P500 index. The calibrated model accounts very well for many stylized facts, like volatility clustering, power-law decay of the volatility autocorrelation function, and multiscaling with time of the aggregated return distribution. In agreement with empirical evidence in finance, the dynamics is not invariant under time reversal, and, with suitable generalizations, skewness of the return distribution and leverage effects can be included. The analytical tractability of the model opens interesting perspectives for applications, for instance, in terms of obtaining closed formulas for derivative pricing. Further important features are the possibility of making contact, in certain limits, with autoregressive models widely used in finance and the possibility of partially resolving the long- and short-memory components of the volatility, with consistent results when applied to historical series.

Scaling symmetry, renormalization, and time series modeling: The case of financial assets dynamics

NASA Astrophysics Data System (ADS)

Zamparo, Marco; Baldovin, Fulvio; Caraglio, Michele; Stella, Attilio L.

2013-12-01

We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest version the model is the product of an endogenous autoregressive component and a random rescaling factor designed to embody also exogenous influences. Mathematical properties like increments’ stationarity and ergodicity can be proven. Thanks to the relatively low number of parameters, model calibration can be conveniently based on a method of moments, as exemplified in the case of historical data of the S&P500 index. The calibrated model accounts very well for many stylized facts, like volatility clustering, power-law decay of the volatility autocorrelation function, and multiscaling with time of the aggregated return distribution. In agreement with empirical evidence in finance, the dynamics is not invariant under time reversal, and, with suitable generalizations, skewness of the return distribution and leverage effects can be included. The analytical tractability of the model opens interesting perspectives for applications, for instance, in terms of obtaining closed formulas for derivative pricing. Further important features are the possibility of making contact, in certain limits, with autoregressive models widely used in finance and the possibility of partially resolving the long- and short-memory components of the volatility, with consistent results when applied to historical series.

Prediction of Muscle Performance During Dynamic Repetitive Exercise

NASA Technical Reports Server (NTRS)

Byerly, D. L.; Byerly, K. A.; Sognier, M. A.; Squires, W. G.

2002-01-01

A method for predicting human muscle performance was developed. Eight test subjects performed a repetitive dynamic exercise to failure using a Lordex spinal machine. Electromyography (EMG) data was collected from the erector spinae. Evaluation of the EMG data using a 5th order Autoregressive (AR) model and statistical regression analysis revealed that an AR parameter, the mean average magnitude of AR poles, can predict performance to failure as early as the second repetition of the exercise. Potential applications to the space program include evaluating on-orbit countermeasure effectiveness, maximizing post-flight recovery, and future real-time monitoring capability during Extravehicular Activity.

Automatic Target Recognition Using Nonlinear Autoregressive Neural Networks

DTIC Science & Technology

2014-03-27

Lee and Chang (2009) employed a NARXNet for studying the thermodynamics in a pulsating heat pipe (PHP), a type of cooling device which contains...in Thermal Dynamics Identifcation of a Pulsating Heat Pipe . Energy Consversion and Management, 1069-1078. Lisboa, P. J. (2002). A review of...function by adjusting the values of the connections between elements. This flexibility allows ANNs to perform complex functions in fields to include

Fast Algorithms for Mining Co-evolving Time Series

DTIC Science & Technology

2011-09-01

Keogh et al., 2001, 2004] and (b) forecasting, like an autoregressive integrated moving average model ( ARIMA ) and related meth- ods [Box et al., 1994...computing hardware? We develop models to mine time series with missing values, to extract compact representation from time sequences, to segment the...sequences, and to do forecasting. For large scale data, we propose algorithms for learning time series models , in particular, including Linear Dynamical

Subliminal semantic priming changes the dynamic causal influence between the left frontal and temporal cortex.

PubMed

Matsumoto, Atsushi; Kakigi, Ryusuke

2014-01-01

Recent neuroimaging experiments have revealed that subliminal priming of a target stimulus leads to the reduction of neural activity in specific regions concerned with processing the target. Such findings lead to questions about the degree to which the subliminal priming effect is based only on decreased activity in specific local brain regions, as opposed to the influence of neural mechanisms that regulate communication between brain regions. To address this question, this study recorded EEG during performance of a subliminal semantic priming task. We adopted an information-based approach that used independent component analysis and multivariate autoregressive modeling. Results indicated that subliminal semantic priming caused significant modulation of alpha band activity in the left inferior frontal cortex and modulation of gamma band activity in the left inferior temporal regions. The multivariate autoregressive approach confirmed significant increases in information flow from the inferior frontal cortex to inferior temporal regions in the early time window that was induced by subliminal priming. In the later time window, significant enhancement of bidirectional causal flow between these two regions underlying subliminal priming was observed. Results suggest that unconscious processing of words influences not only local activity of individual brain regions but also the dynamics of neural communication between those regions.

Development of First-Graders' Word Reading Skills: For Whom Can Dynamic Assessment Tell Us More?

PubMed

Cho, Eunsoo; Compton, Donald L; Gilbert, Jennifer K; Steacy, Laura M; Collins, Alyson A; Lindström, Esther R

2017-01-01

Dynamic assessment (DA) of word reading measures learning potential for early reading development by documenting the amount of assistance needed to learn how to read words with unfamiliar orthography. We examined the additive value of DA for predicting first-grade decoding and word recognition development while controlling for autoregressive effects. Additionally, we examined whether predictive validity of DA would be higher for students who have poor phonological awareness skills. First-grade students (n = 105) were assessed on measures of word reading, phonological awareness, rapid automatized naming, and DA in the fall and again assessed on word reading measures in the spring. A series of planned, moderated multiple regression analyses indicated that DA made a significant and unique contribution in predicting word recognition development above and beyond the autoregressor, particularly for students with poor phonological awareness skills. For these students, DA explained 3.5% of the unique variance in end-of-first-grade word recognition that was not attributable to autoregressive effect. Results suggest that DA provides an important source of individual differences in the development of word recognition skills that cannot be fully captured by merely assessing the present level of reading skills through traditional static assessment, particularly for students at risk for developing reading disabilities. © Hammill Institute on Disabilities 2015.

Incorporating measurement error in n = 1 psychological autoregressive modeling.

PubMed

Schuurman, Noémi K; Houtveen, Jan H; Hamaker, Ellen L

2015-01-01

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30-50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters.

The gene-environmental architecture of the development of adolescent substance use.

PubMed

Vitaro, Frank; Dickson, Daniel J; Brendgen, Mara; Laursen, Brett; Dionne, Ginette; Boivin, Michel

2018-02-19

Using a longitudinal twin design and a latent growth curve/autoregressive approach, this study examined the genetic-environmental architecture of substance use across adolescence. Self-reports of substance use (i.e. alcohol, marijuana) were collected at ages 13, 14, 15, and 17 years from 476 twin pairs (475 boys, 477 girls) living in the Province of Quebec, Canada. Substance use increased linearly across the adolescent years. ACE modeling revealed that genetic, as well as shared and non-shared environmental factors explained the overall level of substance use and that these same factors also partly accounted for growth in substance use from age 13 to 17. Additional genetic factors predicted the growth in substance use. Finally, autoregressive effects revealed age-specific non-shared environmental influences and, to a lesser degree, age-specific genetic influences, which together accounted for the stability of substance use across adolescence. The results support and expand the notion that genetic and environmental influences on substance use during adolescence are both developmentally stable and developmentally dynamic.

Modeling Bivariate Change in Individual Differences: Prospective Associations Between Personality and Life Satisfaction.

PubMed

Hounkpatin, Hilda Osafo; Boyce, Christopher J; Dunn, Graham; Wood, Alex M

2017-09-18

A number of structural equation models have been developed to examine change in 1 variable or the longitudinal association between 2 variables. The most common of these are the latent growth model, the autoregressive cross-lagged model, the autoregressive latent trajectory model, and the latent change score model. The authors first overview each of these models through evaluating their different assumptions surrounding the nature of change and how these assumptions may result in different data interpretations. They then, to elucidate these issues in an empirical example, examine the longitudinal association between personality traits and life satisfaction. In a representative Dutch sample (N = 8,320), with participants providing data on both personality and life satisfaction measures every 2 years over an 8-year period, the authors reproduce findings from previous research. However, some of the structural equation models overviewed have not previously been applied to the personality-life satisfaction relation. The extended empirical examination suggests intraindividual changes in life satisfaction predict subsequent intraindividual changes in personality traits. The availability of data sets with 3 or more assessment waves allows the application of more advanced structural equation models such as the autoregressive latent trajectory or the extended latent change score model, which accounts for the complex dynamic nature of change processes and allows stronger inferences on the nature of the association between variables. However, the choice of model should be determined by theories of change processes in the variables being studied. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data

PubMed Central

de Haan-Rietdijk, Silvia; Kuppens, Peter; Hamaker, Ellen L.

2016-01-01

In recent years there has been a growing interest in the use of intensive longitudinal research designs to study within-person processes. Examples are studies that use experience sampling data and autoregressive modeling to investigate emotion dynamics and between-person differences therein. Such designs often involve multiple measurements per day and multiple days per person, and it is not clear how this nesting of the data should be accounted for: That is, should such data be considered as two-level data (which is common practice at this point), with occasions nested in persons, or as three-level data with beeps nested in days which are nested in persons. We show that a significance test of the day-level variance in an empty three-level model is not reliable when there is autocorrelation. Furthermore, we show that misspecifying the number of levels can lead to spurious or misleading findings, such as inflated variance or autoregression estimates. Throughout the paper we present instructions and R code for the implementation of the proposed models, which includes a novel three-level AR(1) model that estimates moment-to-moment inertia and day-to-day inertia. Based on our simulations we recommend model selection using autoregressive multilevel models in combination with the AIC. We illustrate this method using empirical emotion data from two independent samples, and discuss the implications and the relevance of the existence of a day level for the field. PMID:27378986

What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data.

PubMed

de Haan-Rietdijk, Silvia; Kuppens, Peter; Hamaker, Ellen L

2016-01-01

In recent years there has been a growing interest in the use of intensive longitudinal research designs to study within-person processes. Examples are studies that use experience sampling data and autoregressive modeling to investigate emotion dynamics and between-person differences therein. Such designs often involve multiple measurements per day and multiple days per person, and it is not clear how this nesting of the data should be accounted for: That is, should such data be considered as two-level data (which is common practice at this point), with occasions nested in persons, or as three-level data with beeps nested in days which are nested in persons. We show that a significance test of the day-level variance in an empty three-level model is not reliable when there is autocorrelation. Furthermore, we show that misspecifying the number of levels can lead to spurious or misleading findings, such as inflated variance or autoregression estimates. Throughout the paper we present instructions and R code for the implementation of the proposed models, which includes a novel three-level AR(1) model that estimates moment-to-moment inertia and day-to-day inertia. Based on our simulations we recommend model selection using autoregressive multilevel models in combination with the AIC. We illustrate this method using empirical emotion data from two independent samples, and discuss the implications and the relevance of the existence of a day level for the field.

Model-Based Clustering of Regression Time Series Data via APECM -- An AECM Algorithm Sung to an Even Faster Beat

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

Chen, Wei-Chen; Maitra, Ranjan

2011-01-01

We propose a model-based approach for clustering time series regression data in an unsupervised machine learning framework to identify groups under the assumption that each mixture component follows a Gaussian autoregressive regression model of order p. Given the number of groups, the traditional maximum likelihood approach of estimating the parameters using the expectation-maximization (EM) algorithm can be employed, although it is computationally demanding. The somewhat fast tune to the EM folk song provided by the Alternating Expectation Conditional Maximization (AECM) algorithm can alleviate the problem to some extent. In this article, we develop an alternative partial expectation conditional maximization algorithmmore » (APECM) that uses an additional data augmentation storage step to efficiently implement AECM for finite mixture models. Results on our simulation experiments show improved performance in both fewer numbers of iterations and computation time. The methodology is applied to the problem of clustering mutual funds data on the basis of their average annual per cent returns and in the presence of economic indicators.« less

Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.

PubMed

De Haan-Rietdijk, Silvia; Gottman, John M; Bergeman, Cindy S; Hamaker, Ellen L

2016-03-01

Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions.

Nonlinear System Identification for Aeroelastic Systems with Application to Experimental Data

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2008-01-01

Representation and identification of a nonlinear aeroelastic pitch-plunge system as a model of the Nonlinear AutoRegressive, Moving Average eXogenous (NARMAX) class is considered. A nonlinear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (1) the outputs of the NARMAX model closely match those generated using continuous-time methods, and (2) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.

Robust Semi-Active Ride Control under Stochastic Excitation

DTIC Science & Technology

2014-01-01

broad classes of time-series models which are of practical importance; the Auto-Regressive (AR) models, the Integrated (I) models, and the Moving...Average (MA) models [12]. Combinations of these models result in autoregressive moving average (ARMA) and autoregressive integrated moving average...Down Up 4) Down Down These four cases can be written in compact form as: (20) Where is the Heaviside

Random Process Simulation for stochastic fatigue analysis. Ph.D. Thesis - Rice Univ., Houston, Tex.

NASA Technical Reports Server (NTRS)

Larsen, Curtis E.

1988-01-01

A simulation technique is described which directly synthesizes the extrema of a random process and is more efficient than the Gaussian simulation method. Such a technique is particularly useful in stochastic fatigue analysis because the required stress range moment E(R sup m), is a function only of the extrema of the random stress process. The family of autoregressive moving average (ARMA) models is reviewed and an autoregressive model is presented for modeling the extrema of any random process which has a unimodal power spectral density (psd). The proposed autoregressive technique is found to produce rainflow stress range moments which compare favorably with those computed by the Gaussian technique and to average 11.7 times faster than the Gaussian technique. The autoregressive technique is also adapted for processes having bimodal psd's. The adaptation involves using two autoregressive processes to simulate the extrema due to each mode and the superposition of these two extrema sequences. The proposed autoregressive superposition technique is 9 to 13 times faster than the Gaussian technique and produces comparable values for E(R sup m) for bimodal psd's having the frequency of one mode at least 2.5 times that of the other mode.

Incorporating measurement error in n = 1 psychological autoregressive modeling

PubMed Central

Schuurman, Noémi K.; Houtveen, Jan H.; Hamaker, Ellen L.

2015-01-01

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30–50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters. PMID:26283988

Insight into the Li2CO3-K2CO3 eutectic mixture from classical molecular dynamics: Thermodynamics, structure, and dynamics

NASA Astrophysics Data System (ADS)

Corradini, Dario; Coudert, François-Xavier; Vuilleumier, Rodolphe

2016-03-01

We use molecular dynamics simulations to study the thermodynamics, structure, and dynamics of the Li2CO3-K2CO3 (62:38 mol. %) eutectic mixture. We present a new classical non-polarizable force field for this molten salt mixture, optimized using experimental and first principles molecular dynamics simulations data as reference. This simple force field allows efficient molecular simulations of phenomena at long time scales. We use this optimized force field to describe the behavior of the eutectic mixture in the 900-1100 K temperature range, at pressures between 0 and 5 GPa. After studying the equation of state in these thermodynamic conditions, we present molecular insight into the structure and dynamics of the melt. In particular, we present an analysis of the temperature and pressure dependence of the eutectic mixture's self-diffusion coefficients, viscosity, and ionic conductivity.

Insight into the Li2CO3-K2CO3 eutectic mixture from classical molecular dynamics: Thermodynamics, structure, and dynamics.

PubMed

Corradini, Dario; Coudert, François-Xavier; Vuilleumier, Rodolphe

2016-03-14

We use molecular dynamics simulations to study the thermodynamics, structure, and dynamics of the Li2CO3-K2CO3 (62:38 mol. %) eutectic mixture. We present a new classical non-polarizable force field for this molten salt mixture, optimized using experimental and first principles molecular dynamics simulations data as reference. This simple force field allows efficient molecular simulations of phenomena at long time scales. We use this optimized force field to describe the behavior of the eutectic mixture in the 900-1100 K temperature range, at pressures between 0 and 5 GPa. After studying the equation of state in these thermodynamic conditions, we present molecular insight into the structure and dynamics of the melt. In particular, we present an analysis of the temperature and pressure dependence of the eutectic mixture's self-diffusion coefficients, viscosity, and ionic conductivity.

To center or not to center? Investigating inertia with a multilevel autoregressive model.

PubMed

Hamaker, Ellen L; Grasman, Raoul P P P

2014-01-01

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.

To center or not to center? Investigating inertia with a multilevel autoregressive model

PubMed Central

Hamaker, Ellen L.; Grasman, Raoul P. P. P.

2015-01-01

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model. PMID:25688215

Instantaneous nonlinear assessment of complex cardiovascular dynamics by Laguerre-Volterra point process models.

PubMed

Valenza, Gaetano; Citi, Luca; Barbieri, Riccardo

2013-01-01

We report an exemplary study of instantaneous assessment of cardiovascular dynamics performed using point-process nonlinear models based on Laguerre expansion of the linear and nonlinear Wiener-Volterra kernels. As quantifiers, instantaneous measures such as high order spectral features and Lyapunov exponents can be estimated from a quadratic and cubic autoregressive formulation of the model first order moment, respectively. Here, these measures are evaluated on heartbeat series coming from 16 healthy subjects and 14 patients with Congestive Hearth Failure (CHF). Data were gathered from the on-line repository PhysioBank, which has been taken as landmark for testing nonlinear indices. Results show that the proposed nonlinear Laguerre-Volterra point-process methods are able to track the nonlinear and complex cardiovascular dynamics, distinguishing significantly between CHF and healthy heartbeat series.

Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models.

PubMed

Ouyang, Yicun; Yin, Hujun

2018-05-01

Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.

Adsorption properties of biologically active derivatives of quaternary ammonium surfactants and their mixtures at aqueous/air interface II. Dynamics of adsorption, micelles dissociation and cytotoxicity of QDLS.

PubMed

Rojewska, Monika; Prochaska, Krystyna; Olejnik, Anna; Rychlik, Joanna

2014-07-01

The main aim of our study was analysis of adsorption dynamics of mixtures containing quaternary derivatives of lysosomotropic substance (QDLS). Two types of equimolar mixtures were considered: the ones containing two derivatives of lysosomotropic substances (DMALM-12 and DMGM-12) as well as the catanionic mixtures i.e. the systems containing QDLS and DBSNa. Dynamic surface tension measurements of surfactant mixtures were made. The results suggested that the diffusivity of the mixed system could be treated as the average value of rates of diffusion of individual components, micelles and ion pairs, which are present in the mixtures studied. Moreover, an attempt was made to explain the influence of the presence of micelles in the mixtures on their adsorption dynamics. The compounds examined show interesting biological properties which can be useful, especially for drug delivery in medical treatment. In vitro cytotoxic activities of the mixtures studied towards human cancer cells were evaluated. Most of the mixtures showed a high antiproliferative potential, especially the ones containing DMALM-12. Each cancer cell line used demonstrated different sensitivity to the same dose of the mixtures tested. Copyright © 2014 Elsevier B.V. All rights reserved.

Linear and nonlinear trending and prediction for AVHRR time series data

NASA Technical Reports Server (NTRS)

Smid, J.; Volf, P.; Slama, M.; Palus, M.

1995-01-01

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Autoregressive-moving-average hidden Markov model for vision-based fall prediction-An application for walker robot.

PubMed

Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro

2017-01-01

Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Cosolvent effect on the dynamics of water in aqueous binary mixtures

NASA Astrophysics Data System (ADS)

Zhang, Xia; Zhang, Lu; Jin, Tan; Zhang, Qiang; Zhuang, Wei

2018-04-01

Water rotational dynamics in the mixtures of water and amphiphilic molecules, such as acetone and dimethyl sulfoxide (DMSO), measured by femtosecond infrared, often vary non-monotonically as the amphiphilic molecule's molar fraction changes from 0 to 1. Recent study has attributed the non-ideal water rotation with concentration in DMSO-water mixtures to different microscopic hydrophilic-hydrophobic segregation structure in water-rich and water-poor mixtures. Interestingly, the acetone molecule has very similar molecular structure to DMSO, but the extremum of the water rotational time in the DMSO-water mixtures significantly shifts to lower concentration and the rotation of water is much faster than those in acetone-water mixtures. The simulation results here shows that the non-ideal rotational dynamics of water in both mixtures are due to the frame rotation during the interval of hydrogen bond (HB) switchings. A turnover of the frame rotation with concentration takes place as the structure transition of mixture from the hydrogen bond percolation structure to the hydrophobic percolation structure. The weak acetone-water hydrogen bond strengthens the hydrophobic aggregation and accelerates the relaxation of the hydrogen bond, so that the structure transition takes places at lower concentration and the rotation of water is faster in acetone-water mixture than in DMSO-water mixture. A generally microscopic picture on the mixing effect on the water dynamics in binary aqueous mixtures is presented here.

Photonic single nonlinear-delay dynamical node for information processing

NASA Astrophysics Data System (ADS)

Ortín, Silvia; San-Martín, Daniel; Pesquera, Luis; Gutiérrez, José Manuel

2012-06-01

An electro-optical system with a delay loop based on semiconductor lasers is investigated for information processing by performing numerical simulations. This system can replace a complex network of many nonlinear elements for the implementation of Reservoir Computing. We show that a single nonlinear-delay dynamical system has the basic properties to perform as reservoir: short-term memory and separation property. The computing performance of this system is evaluated for two prediction tasks: Lorenz chaotic time series and nonlinear auto-regressive moving average (NARMA) model. We sweep the parameters of the system to find the best performance. The results achieved for the Lorenz and the NARMA-10 tasks are comparable to those obtained by other machine learning methods.

Effect of CMC addition on steady and dynamic shear rheological properties of binary systems of xanthan gum and guar gum.

PubMed

Bak, J H; Yoo, B

2018-04-12

The effect of CMC on the steady and dynamic shear rheological properties of binary mixtures of XG and GG was examined at different mixing ratios. All XG-GG-CMC ternary mixtures had high shear-thinning behavior and the n value of the sample with 5% CMC was the smallest compared with those of other samples. A marked increase in K and η a,50 values was observed for ternary mixtures at a lower content (5%) of CMC, indicating that the synergistic interactions of the XG-GG binary mixture were affected by the content of CMC. The effect of temperature on the η a,50 was well described by the Arrhenius equation for all samples. The activation energy values of all ternary gum mixtures are higher than that of binary gum mixture, and these values also decreased with an increase in CMC content from 5 to 15%. The dynamic moduli of ternary gum mixtures decreased with an increase in CMC content. The tan δ value of the ternary gum mixture with 5% CMC was much lower than those of other ternary mixtures. In general, these results suggest that the flow and dynamic shear rheological properties of XG-GG binary mixtures are strongly influenced by a small addition of CMC. Copyright © 2018. Published by Elsevier B.V.

Bayesian dynamic mediation analysis.

PubMed

Huang, Jing; Yuan, Ying

2017-12-01

Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

PubMed

Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

2010-09-13

Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

Analysis of the Westland Data Set

NASA Technical Reports Server (NTRS)

Wen, Fang; Willett, Peter; Deb, Somnath

2001-01-01

The "Westland" set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters call improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior oil training data and is thus able to quantify probability of error in all exact manner, such that features may be discarded or coarsened appropriately.

Environmental filtering and land-use history drive patterns in biomass accumulation in a mediterranean-type landscape.

PubMed

Dahlin, Kyla M; Asner, Gregory P; Field, Christopher B

2012-01-01

Aboveground biomass (AGB) reflects multiple and often undetermined ecological and land-use processes, yet detailed landscape-level studies of AGB are uncommon due to the difficulty in making consistent measurements at ecologically relevant scales. Working in a protected mediterranean-type landscape (Jasper Ridge Biological Preserve, California, USA), we combined field measurements with remotely sensed data from the Carnegie Airborne Observatory's light detection and ranging (lidar) system to create a detailed AGB map. We then developed a predictive model using a maximum of 56 explanatory variables derived from geologic and historic-ownership maps, a digital elevation model, and geographic coordinates to evaluate possible controls over currently observed AGB patterns. We tested both ordinary least-squares regression (OLS) and autoregressive approaches. OLS explained 44% of the variation in AGB, and simultaneous autoregression with a 100-m neighborhood improved the fit to an r2 = 0.72, while reducing the number of significant predictor variables from 27 variables in the OLS model to 11 variables in the autoregressive model. We also compared the results from these approaches to a more typical field-derived data set; we randomly sampled 5% of the data 1000 times and used the same OLS approach each time. Environmental filters including incident solar radiation, substrate type, and topographic position were significant predictors of AGB in all models. Past ownership was a minor but significant predictor, despite the long history of conservation at the site. The weak predictive power of these environmental variables, and the significant improvement when spatial autocorrelation was incorporated, highlight the importance of land-use history, disturbance regime, and population dynamics as controllers of AGB.

Parametric system identification of catamaran for improving controller design

NASA Astrophysics Data System (ADS)

Timpitak, Surasak; Prempraneerach, Pradya; Pengwang, Eakkachai

2018-01-01

This paper presents an estimation of simplified dynamic model for only surge- and yaw- motions of catamaran by using system identification (SI) techniques to determine associated unknown parameters. These methods will enhance the performance of designing processes for the motion control system of Unmanned Surface Vehicle (USV). The simulation results demonstrate an effective way to solve for damping forces and to determine added masses by applying least-square and AutoRegressive Exogenous (ARX) methods. Both methods are then evaluated according to estimated parametric errors from the vehicle’s dynamic model. The ARX method, which yields better estimated accuracy, can then be applied to identify unknown parameters as well as to help improving a controller design of a real unmanned catamaran.

On neural networks in identification and control of dynamic systems

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Hyland, David C.

1993-01-01

This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation

NASA Astrophysics Data System (ADS)

Urata, Kengo; Inoue, Masaki; Murayama, Dai; Adachi, Shuichi

2016-09-01

We propose a statistical modeling method of wind power output for very short-term prediction. The modeling method with a nonlinear model has cascade structure composed of two parts. One is a linear dynamic part that is driven by a Gaussian white noise and described by an autoregressive model. The other is a nonlinear static part that is driven by the output of the linear part. This nonlinear part is designed for output distribution matching: we shape the distribution of the model output to match with that of the wind power output. The constructed model is utilized for one-step ahead prediction of the wind power output. Furthermore, we study the relation between the prediction accuracy and the prediction horizon.

Relaxation dynamics in a binary hard-ellipse liquid.

PubMed

Xu, Wen-Sheng; Sun, Zhao-Yan; An, Li-Jia

2015-01-21

Structural relaxation in binary hard spherical particles has been shown recently to exhibit a wealth of remarkable features when size disparity or mixture composition is varied. In this paper, we test whether or not similar dynamical phenomena occur in glassy systems composed of binary hard ellipses. We demonstrate via event-driven molecular dynamics simulation that a binary hard-ellipse mixture with an aspect ratio of two and moderate size disparity displays characteristic glassy dynamics upon increasing density in both the translational and the rotational degrees of freedom. The rotational glass transition density is found to be close to the translational one for the binary mixtures investigated. More importantly, we assess the influence of size disparity and mixture composition on the relaxation dynamics. We find that an increase of size disparity leads, both translationally and rotationally, to a speed up of the long-time dynamics in the supercooled regime so that both the translational and the rotational glass transition shift to higher densities. By increasing the number concentration of the small particles, the time evolution of both translational and rotational relaxation dynamics at high densities displays two qualitatively different scenarios, i.e., both the initial and the final part of the structural relaxation slow down for small size disparity, while the short-time dynamics still slows down but the final decay speeds up in the binary mixture with large size disparity. These findings are reminiscent of those observed in binary hard spherical particles. Therefore, our results suggest a universal mechanism for the influence of size disparity and mixture composition on the structural relaxation in both isotropic and anisotropic particle systems.

Adaptive spline autoregression threshold method in forecasting Mitsubishi car sales volume at PT Srikandi Diamond Motors

NASA Astrophysics Data System (ADS)

Susanti, D.; Hartini, E.; Permana, A.

2017-01-01

Sale and purchase of the growing competition between companies in Indonesian, make every company should have a proper planning in order to win the competition with other companies. One of the things that can be done to design the plan is to make car sales forecast for the next few periods, it’s required that the amount of inventory of cars that will be sold in proportion to the number of cars needed. While to get the correct forecasting, on of the methods that can be used is the method of Adaptive Spline Threshold Autoregression (ASTAR). Therefore, this time the discussion will focus on the use of Adaptive Spline Threshold Autoregression (ASTAR) method in forecasting the volume of car sales in PT.Srikandi Diamond Motors using time series data.In the discussion of this research, forecasting using the method of forecasting value Adaptive Spline Threshold Autoregression (ASTAR) produce approximately correct.

Effects of Reclaimed Asphalt Pavement (RAP) Contents and Sources on Dynamic Modulus (|E*|) and Fatigue Performance of Asphalt Mixtures in Georgia

DOT National Transportation Integrated Search

2016-09-01

This report presents the effect of RAP contents and sources on the dynamic modulus and the performance of Georgia asphalt concrete mixtures. Asphalt concrete mixtures were prepared based on two Job Mix Formulas from North and South with 12.5mm nomina...

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

NASA Astrophysics Data System (ADS)

Zhang, Rui; Schweizer, Kenneth

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

Insight into the Li{sub 2}CO{sub 3}–K{sub 2}CO{sub 3} eutectic mixture from classical molecular dynamics: Thermodynamics, structure, and dynamics

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

Corradini, Dario; Vuilleumier, Rodolphe, E-mail: rodolphe.vuilleumier@ens.fr; Sorbonne Universités, UPMC Univ. Paris 06, PASTEUR, 75005 Paris

We use molecular dynamics simulations to study the thermodynamics, structure, and dynamics of the Li{sub 2}CO{sub 3}–K{sub 2}CO{sub 3} (62:38 mol. %) eutectic mixture. We present a new classical non-polarizable force field for this molten salt mixture, optimized using experimental and first principles molecular dynamics simulations data as reference. This simple force field allows efficient molecular simulations of phenomena at long time scales. We use this optimized force field to describe the behavior of the eutectic mixture in the 900–1100 K temperature range, at pressures between 0 and 5 GPa. After studying the equation of state in these thermodynamic conditions, wemore » present molecular insight into the structure and dynamics of the melt. In particular, we present an analysis of the temperature and pressure dependence of the eutectic mixture’s self-diffusion coefficients, viscosity, and ionic conductivity.« less

Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling.

PubMed

Perdikaris, P; Raissi, M; Damianou, A; Lawrence, N D; Karniadakis, G E

2017-02-01

Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the low- and high-fidelity models exhibit strong correlations, and can lead to significant computational gains over approaches that solely rely on high-fidelity models. However, in many cases of practical interest, low-fidelity models can only be well correlated to their high-fidelity counterparts for a specific range of input parameters, and potentially return wrong trends and erroneous predictions if probed outside of their validity regime. Here we put forth a probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends. This introduces a new class of multi-fidelity information fusion algorithms that provide a fundamental extension to the existing linear autoregressive methodologies, while still maintaining the same algorithmic complexity and overall computational cost. The performance of the proposed methods is tested in several benchmark problems involving both synthetic and real multi-fidelity datasets from computational fluid dynamics simulations.

Fault detection using a two-model test for changes in the parameters of an autoregressive time series

NASA Technical Reports Server (NTRS)

Scholtz, P.; Smyth, P.

1992-01-01

This article describes an investigation of a statistical hypothesis testing method for detecting changes in the characteristics of an observed time series. The work is motivated by the need for practical automated methods for on-line monitoring of Deep Space Network (DSN) equipment to detect failures and changes in behavior. In particular, on-line monitoring of the motor current in a DSN 34-m beam waveguide (BWG) antenna is used as an example. The algorithm is based on a measure of the information theoretic distance between two autoregressive models: one estimated with data from a dynamic reference window and one estimated with data from a sliding reference window. The Hinkley cumulative sum stopping rule is utilized to detect a change in the mean of this distance measure, corresponding to the detection of a change in the underlying process. The basic theory behind this two-model test is presented, and the problem of practical implementation is addressed, examining windowing methods, model estimation, and detection parameter assignment. Results from the five fault-transition simulations are presented to show the possible limitations of the detection method, and suggestions for future implementation are given.

Using Google Trends and ambient temperature to predict seasonal influenza outbreaks.

PubMed

Zhang, Yuzhou; Bambrick, Hilary; Mengersen, Kerrie; Tong, Shilu; Hu, Wenbiao

2018-05-16

The discovery of the dynamics of seasonal and non-seasonal influenza outbreaks remains a great challenge. Previous internet-based surveillance studies built purely on internet or climate data do have potential error. We collected influenza notifications, temperature and Google Trends (GT) data between January 1st, 2011 and December 31st, 2016. We performed time-series cross correlation analysis and temporal risk analysis to discover the characteristics of influenza epidemics in the period. Then, the seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to track influenza epidemics using GT and climate data. Influenza infection was significantly corrected with GT at lag of 1-7 weeks in Brisbane and Gold Coast, and temperature at lag of 1-10 weeks for the two study settings. SARIMA models with GT and temperature data had better predictive performance. We identified autoregression (AR) for influenza was the most important determinant for influenza occurrence in both Brisbane and Gold Coast. Our results suggested internet search metrics in conjunction with temperature can be used to predict influenza outbreaks, which can be considered as a pre-requisite for constructing early warning systems using search and temperature data. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fuzzy neural network technique for system state forecasting.

PubMed

Li, Dezhi; Wang, Wilson; Ismail, Fathy

2013-10-01

In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

PubMed

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

PubMed Central

Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

Estimating long-run equilibrium real exchange rates: short-lived shocks with long-lived impacts on Pakistan.

PubMed

Zardad, Asma; Mohsin, Asma; Zaman, Khalid

2013-12-01

The purpose of this study is to investigate the factors that affect real exchange rate volatility for Pakistan through the co-integration and error correction model over a 30-year time period, i.e. between 1980 and 2010. The study employed the autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH) and Vector Error Correction model (VECM) to estimate the changes in the volatility of real exchange rate series, while an error correction model was used to determine the short-run dynamics of the system. The study is limited to a few variables i.e., productivity differential (i.e., real GDP per capita relative to main trading partner); terms of trade; trade openness and government expenditures in order to manage robust data. The result indicates that real effective exchange rate (REER) has been volatile around its equilibrium level; while, the speed of adjustment is relatively slow. VECM results confirm long run convergence of real exchange rate towards its equilibrium level. Results from ARCH and GARCH estimation shows that real shocks volatility persists, so that shocks die out rather slowly, and lasting misalignment seems to have occurred.

Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

NASA Astrophysics Data System (ADS)

Leite, Argentina; Paula Rocha, Ana; Eduarda Silva, Maria

2013-06-01

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.

Forecasting coconut production in the Philippines with ARIMA model

NASA Astrophysics Data System (ADS)

Lim, Cristina Teresa

2015-02-01

The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.

Memory and betweenness preference in temporal networks induced from time series

NASA Astrophysics Data System (ADS)

Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

2017-02-01

We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems.

Heterogeneous structure and solvation dynamics of DME/water binary mixtures: A combined spectroscopic and simulation investigation

NASA Astrophysics Data System (ADS)

Das Mahanta, Debasish; Rana, Debkumar; Patra, Animesh; Mukherjee, Biswaroop; Mitra, Rajib Kumar

2018-05-01

Water is often found in (micro)-heterogeneous environments and therefore it is necessary to understand their H-bonded network structure in such altered environments. We explore the structure and dynamics of water in its binary mixture with relatively less polar small biocompatible amphiphilic molecule 1,2-Dimethoxyethane (DME) by a combined spectroscopic and molecular dynamics (MD) simulation study. Picosecond (ps) resolved fluorescence spectroscopy using coumarin 500 as the fluorophore establishes a non-monotonic behaviour of the mixture. Simulation studies also explore the various possible H-bond formations between water and DME. The relative abundance of such different water species manifests the heterogeneity in the mixture.

A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity.

PubMed

Samdin, S Balqis; Ting, Chee-Ming; Ombao, Hernando; Salleh, Sh-Hussain

2017-04-01

This paper addresses the critical problem of estimating time-evolving effective brain connectivity. Current approaches based on sliding window analysis or time-varying coefficient models do not simultaneously capture both slow and abrupt changes in causal interactions between different brain regions. To overcome these limitations, we develop a unified framework based on a switching vector autoregressive (SVAR) model. Here, the dynamic connectivity regimes are uniquely characterized by distinct vector autoregressive (VAR) processes and allowed to switch between quasi-stationary brain states. The state evolution and the associated directed dependencies are defined by a Markov process and the SVAR parameters. We develop a three-stage estimation algorithm for the SVAR model: 1) feature extraction using time-varying VAR (TV-VAR) coefficients, 2) preliminary regime identification via clustering of the TV-VAR coefficients, 3) refined regime segmentation by Kalman smoothing and parameter estimation via expectation-maximization algorithm under a state-space formulation, using initial estimates from the previous two stages. The proposed framework is adaptive to state-related changes and gives reliable estimates of effective connectivity. Simulation results show that our method provides accurate regime change-point detection and connectivity estimates. In real applications to brain signals, the approach was able to capture directed connectivity state changes in functional magnetic resonance imaging data linked with changes in stimulus conditions, and in epileptic electroencephalograms, differentiating ictal from nonictal periods. The proposed framework accurately identifies state-dependent changes in brain network and provides estimates of connectivity strength and directionality. The proposed approach is useful in neuroscience studies that investigate the dynamics of underlying brain states.

Assessing artificial neural networks and statistical methods for infilling missing soil moisture records

NASA Astrophysics Data System (ADS)

Dumedah, Gift; Walker, Jeffrey P.; Chik, Li

2014-07-01

Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.

Robust image modeling techniques with an image restoration application

NASA Astrophysics Data System (ADS)

Kashyap, Rangasami L.; Eom, Kie-Bum

1988-08-01

A robust parameter-estimation algorithm for a nonsymmetric half-plane (NSHP) autoregressive model, where the driving noise is a mixture of a Gaussian and an outlier process, is presented. The convergence of the estimation algorithm is proved. An algorithm to estimate parameters and original image intensity simultaneously from the impulse-noise-corrupted image, where the model governing the image is not available, is also presented. The robustness of the parameter estimates is demonstrated by simulation. Finally, an algorithm to restore realistic images is presented. The entire image generally does not obey a simple image model, but a small portion (e.g., 8 x 8) of the image is assumed to obey an NSHP model. The original image is divided into windows and the robust estimation algorithm is applied for each window. The restoration algorithm is tested by comparing it to traditional methods on several different images.

Workshop on Algorithms for Time-Series Analysis

NASA Astrophysics Data System (ADS)

Protopapas, Pavlos

2012-04-01

abstract-type="normal">SummaryThis Workshop covered the four major subjects listed below in two 90-minute sessions. Each talk or tutorial allowed questions, and concluded with a discussion. Classification: Automatic classification using machine-learning methods is becoming a standard in surveys that generate large datasets. Ashish Mahabal (Caltech) reviewed various methods, and presented examples of several applications. Time-Series Modelling: Suzanne Aigrain (Oxford University) discussed autoregressive models and multivariate approaches such as Gaussian Processes. Meta-classification/mixture of expert models: Karim Pichara (Pontificia Universidad Católica, Chile) described the substantial promise which machine-learning classification methods are now showing in automatic classification, and discussed how the various methods can be combined together. Event Detection: Pavlos Protopapas (Harvard) addressed methods of fast identification of events with low signal-to-noise ratios, enlarging on the characterization and statistical issues of low signal-to-noise ratios and rare events.

GIS-based analysis and modelling with empirical and remotely-sensed data on coastline advance and retreat

NASA Astrophysics Data System (ADS)

Ahmad, Sajid Rashid

With the understanding that far more research remains to be done on the development and use of innovative and functional geospatial techniques and procedures to investigate coastline changes this thesis focussed on the integration of remote sensing, geographical information systems (GIS) and modelling techniques to provide meaningful insights on the spatial and temporal dynamics of coastline changes. One of the unique strengths of this research was the parameterization of the GIS with long-term empirical and remote sensing data. Annual empirical data from 1941--2007 were analyzed by the GIS, and then modelled with statistical techniques. Data were also extracted from Landsat TM and ETM+ images. The band ratio method was used to extract the coastlines. Topographic maps were also used to extract digital map data. All data incorporated into ArcGIS 9.2 were analyzed with various modules, including Spatial Analyst, 3D Analyst, and Triangulated Irregular Networks. The Digital Shoreline Analysis System was used to analyze and predict rates of coastline change. GIS results showed the spatial locations along the coast that will either advance or retreat over time. The linear regression results highlighted temporal changes which are likely to occur along the coastline. Box-Jenkins modelling procedures were utilized to determine statistical models which best described the time series (1941--2007) of coastline change data. After several iterations and goodness-of-fit tests, second-order spatial cyclic autoregressive models, first-order autoregressive models and autoregressive moving average models were identified as being appropriate for describing the deterministic and random processes operating in Guyana's coastal system. The models highlighted not only cyclical patterns in advance and retreat of the coastline, but also the existence of short and long-term memory processes. Long-term memory processes could be associated with mudshoal propagation and stabilization while short-term memory processes were indicative of transitory hydrodynamic and other processes. An innovative framework for a spatio-temporal information-based system (STIBS) was developed. STIBS incorporated diverse datasets within a GIS, dynamic computer-based simulation models, and a spatial information query and graphical subsystem. Tests of the STIBS proved that it could be used to simulate and visualize temporal variability in shifting morphological states of the coastline.

Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Ouyang, Huei-Tau

2017-07-01

Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.

The complex frequencies of long-period seismic events as probes of fluid composition beneath volcanoes

USGS Publications Warehouse

Kumagai, H.; Chouet, B.A.

1999-01-01

Long-period (LP) events have been widely observed in relation to magmatic and hydrothermal activities in volcanic areas. LP waveforms characterized by their harmonic signature have been interpreted as oscillations of a fluid-filled resonator, and mixtures of liquid and gas in the form of bubbly liquids have been mainly assumed for the fluid. To investigate the characteristic properties of the resonator system, we analyse waveforms of LP events observed at four different volcanoes in Hawaii, Alaska, Colombia and Japan using a newly developed spectral method. This method allows an estimation of the complex frequencies of decaying sinusoids based on an autoregressive model. The results of our analysis show a wide variety of Q factors, ranging from tens to several hundred. We compare these complex frequencies with those predicted by the fluid-filled crack model for various mixtures of liquid, gas and ash. Although the oscillations of LP events with Q smaller than 50 can be explained by various combinations of liquids and gases, we find that ash-laden gases are required to explain long-lasting oscillations with Q larger than 100. The complex frequencies of LP events yield useful information on the types of fluids. Temporal and spatial variations of the complex frequencies can be used as probes of fluid composition beneath volcanoes.

Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting

NASA Astrophysics Data System (ADS)

Li, Ming; Wang, Q. J.; Bennett, James C.; Robertson, David E.

2016-09-01

This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.

Detection of ɛ-ergodicity breaking in experimental data—A study of the dynamical functional sensibility

NASA Astrophysics Data System (ADS)

Loch-Olszewska, Hanna; Szwabiński, Janusz

2018-05-01

The ergodicity breaking phenomenon has already been in the area of interest of many scientists, who tried to uncover its biological and chemical origins. Unfortunately, testing ergodicity in real-life data can be challenging, as sample paths are often too short for approximating their asymptotic behaviour. In this paper, the authors analyze the minimal lengths of empirical trajectories needed for claiming the ɛ-ergodicity based on two commonly used variants of an autoregressive fractionally integrated moving average model. The dependence of the dynamical functional on the parameters of the process is studied. The problem of choosing proper ɛ for ɛ-ergodicity testing is discussed with respect to especially the variation of the innovation process and the data sample length, with a presentation on two real-life examples.

Detection of ε-ergodicity breaking in experimental data-A study of the dynamical functional sensibility.

PubMed

Loch-Olszewska, Hanna; Szwabiński, Janusz

2018-05-28

The ergodicity breaking phenomenon has already been in the area of interest of many scientists, who tried to uncover its biological and chemical origins. Unfortunately, testing ergodicity in real-life data can be challenging, as sample paths are often too short for approximating their asymptotic behaviour. In this paper, the authors analyze the minimal lengths of empirical trajectories needed for claiming the ε-ergodicity based on two commonly used variants of an autoregressive fractionally integrated moving average model. The dependence of the dynamical functional on the parameters of the process is studied. The problem of choosing proper ε for ε-ergodicity testing is discussed with respect to especially the variation of the innovation process and the data sample length, with a presentation on two real-life examples.

Nonideality in diffusion of ionic and hydrophobic solutes and pair dynamics in water-acetone mixtures of varying composition.

PubMed

Gupta, Rini; Chandra, Amalendu

2007-07-14

We have performed a series of molecular dynamics simulations of water-acetone mixtures containing either an ionic solute or a neutral hydrophobic solute to study the extent of nonideality in the dynamics of these solutes with variation of composition of the mixtures. The diffusion coefficients of the charged solutes, both cationic and anionic, are found to change nonmonotonically with the composition of the mixtures showing strong nonideality of their dynamics. Also, the extent of nonideality in the diffusion of these charged solutes is found to be similar to the nonideality that is observed for the diffusion and orientational relaxation of water and acetone molecules in these mixtures which show a somewhat similar changes in the solvation characteristics of charged and dipolar solutes with changes of composition of water-acetone mixtures. The diffusion of the hydrophobic solute, however, shows a monotonic increase with increase of acetone concentration showing its different solvation characteristics as compared to the charged and dipolar solutes. The links between the nonideality in diffusion and solvation structures are further confirmed through calculations of the relevant solute-solvent and solvent-solvent radial distribution functions for both ionic and hydrophobic solutes. We have also calculated various pair dynamical properties such as the relaxation of water-water and acetone-water hydrogen bonds and residence dynamics of water molecules in water and acetone hydration shells. The lifetimes of both water-water and acetone-water hydrogen bonds and also the residence times of water molecules are found to increase steadily with increase in acetone concentration. No maximum or minimum was found in the composition dependence of these pair dynamical quantities. The lifetimes of water-water hydrogen bonds are always found to be longer than that of acetone-water hydrogen bonds in these mixtures. The residence times of water molecules are also found to follow a similar trend.

Multiscale analysis of information dynamics for linear multivariate processes.

PubMed

Faes, Luca; Montalto, Alessandro; Stramaglia, Sebastiano; Nollo, Giandomenico; Marinazzo, Daniele

2016-08-01

In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving average (MA) component, we describe how to represent the resulting VARMA process using statespace (SS) models and how to exploit the SS model parameters to compute analytical measures of information storage and information transfer for the original and rescaled processes. The framework is then used to quantify multiscale information dynamics for simulated unidirectionally and bidirectionally coupled VAR processes, showing that rescaling may lead to insightful patterns of information storage and transfer but also to potentially misleading behaviors.

The Disparate Labor Market Impacts of Monetary Policy

ERIC Educational Resources Information Center

Carpenter, Seth B.; Rodgers, William M., III

2004-01-01

Employing two widely used approaches to identify the effects of monetary policy, this paper explores the differential impact of policy on the labor market outcomes of teenagers, minorities, out-of-school youth, and less-skilled individuals. Evidence from recursive vector autoregressions and autoregressive distributed lag models that use…

Spatial Autocorrelation And Autoregressive Models In Ecology

Treesearch

Jeremy W. Lichstein; Theodore R. Simons; Susan A. Shriner; Kathleen E. Franzreb

2003-01-01

Abstract. Recognition and analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern can lead to insights that would have been otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. We used Gaussian spatial autoregressive models, fit with widely available...

Reservoir optimisation using El Niño information. Case study of Daule Peripa (Ecuador)

NASA Astrophysics Data System (ADS)

Gelati, Emiliano; Madsen, Henrik; Rosbjerg, Dan

2010-05-01

The optimisation of water resources systems requires the ability to produce runoff scenarios that are consistent with available climatic information. We approach stochastic runoff modelling with a Markov-modulated autoregressive model with exogenous input, which belongs to the class of Markov-switching models. The model assumes runoff parameterisation to be conditioned on a hidden climatic state following a Markov chain, whose state transition probabilities depend on climatic information. This approach allows stochastic modeling of non-stationary runoff, as runoff anomalies are described by a mixture of autoregressive models with exogenous input, each one corresponding to a climate state. We calibrate the model on the inflows of the Daule Peripa reservoir located in western Ecuador, where the occurrence of El Niño leads to anomalously heavy rainfall caused by positive sea surface temperature anomalies along the coast. El Niño - Southern Oscillation (ENSO) information is used to condition the runoff parameterisation. Inflow predictions are realistic, especially at the occurrence of El Niño events. The Daule Peripa reservoir serves a hydropower plant and a downstream water supply facility. Using historical ENSO records, synthetic monthly inflow scenarios are generated for the period 1950-2007. These scenarios are used as input to perform stochastic optimisation of the reservoir rule curves with a multi-objective Genetic Algorithm (MOGA). The optimised rule curves are assumed to be the reservoir base policy. ENSO standard indices are currently forecasted at monthly time scale with nine-month lead time. These forecasts are used to perform stochastic optimisation of reservoir releases at each monthly time step according to the following procedure: (i) nine-month inflow forecast scenarios are generated using ENSO forecasts; (ii) a MOGA is set up to optimise the upcoming nine monthly releases; (iii) the optimisation is carried out by simulating the releases on the inflow forecasts, and by applying the base policy on a subsequent synthetic inflow scenario in order to account for long-term costs; (iv) the optimised release for the first month is implemented; (v) the state of the system is updated and (i), (ii), (iii), and (iv) are iterated for the following time step. The results highlight the advantages of using a climate-driven stochastic model to produce inflow scenarios and forecasts for reservoir optimisation, showing potential improvements with respect to the current management. Dynamic programming was used to find the best possible release time series given the inflow observations, in order to benchmark any possible operational improvement.

Reversible formation of aminals: a new strategy to control the release of bioactive volatiles from dynamic mixtures.

PubMed

Godin, Guillaume; Levrand, Barbara; Trachsel, Alain; Lehn, Jean-Marie; Herrmann, Andreas

2010-05-14

Dynamic mixtures generated by reversible aminal formation of fragrance aldehydes with N,N-dibenzyl alkyldiamines in aqueous systems were found to be suitable delivery systems for the controlled release of bioactive volatiles.

Mathematical model with autoregressive process for electrocardiogram signals

NASA Astrophysics Data System (ADS)

Evaristo, Ronaldo M.; Batista, Antonio M.; Viana, Ricardo L.; Iarosz, Kelly C.; Szezech, José D., Jr.; Godoy, Moacir F. de

2018-04-01

The cardiovascular system is composed of the heart, blood and blood vessels. Regarding the heart, cardiac conditions are determined by the electrocardiogram, that is a noninvasive medical procedure. In this work, we propose autoregressive process in a mathematical model based on coupled differential equations in order to obtain the tachograms and the electrocardiogram signals of young adults with normal heartbeats. Our results are compared with experimental tachogram by means of Poincaré plot and dentrended fluctuation analysis. We verify that the results from the model with autoregressive process show good agreement with experimental measures from tachogram generated by electrical activity of the heartbeat. With the tachogram we build the electrocardiogram by means of coupled differential equations.

Application of multivariate autoregressive spectrum estimation to ULF waves

NASA Technical Reports Server (NTRS)

Ioannidis, G. A.

1975-01-01

The estimation of the power spectrum of a time series by fitting a finite autoregressive model to the data has recently found widespread application in the physical sciences. The extension of this method to the analysis of vector time series is presented here through its application to ULF waves observed in the magnetosphere by the ATS 6 synchronous satellite. Autoregressive spectral estimates of the power and cross-power spectra of these waves are computed with computer programs developed by the author and are compared with the corresponding Blackman-Tukey spectral estimates. The resulting spectral density matrices are then analyzed to determine the direction of propagation and polarization of the observed waves.

Vector Autoregression, Structural Equation Modeling, and Their Synthesis in Neuroimaging Data Analysis

PubMed Central

Chen, Gang; Glen, Daniel R.; Saad, Ziad S.; Hamilton, J. Paul; Thomason, Moriah E.; Gotlib, Ian H.; Cox, Robert W.

2011-01-01

Vector autoregression (VAR) and structural equation modeling (SEM) are two popular brain-network modeling tools. VAR, which is a data-driven approach, assumes that connected regions exert time-lagged influences on one another. In contrast, the hypothesis-driven SEM is used to validate an existing connectivity model where connected regions have contemporaneous interactions among them. We present the two models in detail and discuss their applicability to FMRI data, and interpretational limits. We also propose a unified approach that models both lagged and contemporaneous effects. The unifying model, structural vector autoregression (SVAR), may improve statistical and explanatory power, and avoids some prevalent pitfalls that can occur when VAR and SEM are utilized separately. PMID:21975109

Projecting county pulpwood production with historical production and macro-economic variables

Treesearch

Consuelo Brandeis; Dayton M. Lambert

2014-01-01

We explored forecasting of county roundwood pulpwood produc-tion with county-vector autoregressive (CVAR) and spatial panelvector autoregressive (SPVAR) methods. The analysis used timberproducts output data for the state of Florida, together with a set ofmacro-economic variables. Overall, we found the SPVAR specifica-tion produced forecasts with lower error rates...

Volatility in GARCH Models of Business Tendency Index

NASA Astrophysics Data System (ADS)

Wahyuni, Dwi A. S.; Wage, Sutarman; Hartono, Ateng

2018-01-01

This paper aims to obtain a model of business tendency index by considering volatility factor. Volatility factor detected by ARCH (Autoregressive Conditional Heteroscedasticity). The ARCH checking was performed using the Lagrange multiplier test. The modeling is Generalized Autoregressive Conditional Heteroscedasticity (GARCH) are able to overcome volatility problems by incorporating past residual elements and residual variants.

Functional MRI and Multivariate Autoregressive Models

PubMed Central

Rogers, Baxter P.; Katwal, Santosh B.; Morgan, Victoria L.; Asplund, Christopher L.; Gore, John C.

2010-01-01

Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays, and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series, and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI. PMID:20444566

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.

PubMed

Johansson, Michael A; Reich, Nicholas G; Hota, Aditi; Brownstein, John S; Santillana, Mauricio

2016-09-26

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

PubMed Central

Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

2016-01-01

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model. PMID:27665707

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud.

PubMed

Zia Ullah, Qazi; Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers' data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers.

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud

PubMed Central

Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers' data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers. PMID:28811819

Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

NASA Astrophysics Data System (ADS)

Li, Gang; McDonald, Geoff L.; Zhao, Qing

2017-01-01

This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method.

Mass transport properties of Pu/DT mixtures from orbital free molecular dynamics simulations

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

Kress, Joel David; Ticknor, Christopher; Collins, Lee A.

2015-09-16

Mass transport properties (shear viscosity and diffusion coefficients) for Pu/DT mixtures were calculated with Orbital Free Molecular Dynamics (OFMD). The results were fitted to simple functions of mass density (for ρ=10.4 to 62.4 g/cm 3) and temperature (for T=100 up to 3,000 eV) for Pu/DT mixtures consisting of 100/0, 25/75, 50/50, and 75/25 by number.

Kepler AutoRegressive Planet Search: Motivation & Methodology

NASA Astrophysics Data System (ADS)

Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian

2015-08-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. We also illustrate the efficient coding in R.

A Stimulus-Locked Vector Autoregressive Model for Slow Event-Related fMRI Designs

PubMed Central

Siegle, Greg

2009-01-01

Summary Neuroscientists have become increasingly interested in exploring dynamic relationships among brain regions. Such a relationship, when directed from one region toward another, is denoted by “effective connectivity.” An fMRI experimental paradigm which is well-suited for examination of effective connectivity is the slow event-related design. This design presents stimuli at sufficient temporal spacing for determining within-trial trajectories of BOLD activation, allowing for the analysis of stimulus-locked temporal covariation of brain responses in multiple regions. This may be especially important for emotional stimuli processing, which can evolve over the course of several seconds, if not longer. However, while several methods have been devised for determining fMRI effective connectivity, few are adapted to event-related designs, which include non-stationary BOLD responses and multiple levels of nesting. We propose a model tailored for exploring effective connectivity of multiple brain regions in event-related fMRI designs - a semi-parametric adaptation of vector autoregressive (VAR) models, termed “stimulus-locked VAR” (SloVAR). Connectivity coefficients vary as a function of time relative to stimulus onset, are regularized via basis expansions, and vary randomly across subjects. SloVAR obtains flexible, data-driven estimates of effective connectivity and hence is useful for building connectivity models when prior information on dynamic regional relationships is sparse. Indices derived from the coefficient estimates can also be used to relate effective connectivity estimates to behavioral or clinical measures. We demonstrate the SloVAR model on a sample of clinically depressed and normal controls, showing that early but not late cortico-amygdala connectivity appears crucial to emotional control and early but not late cortico-cortico connectivity predicts depression severity in the depressed group, relationships that would have been missed in a more traditional VAR analysis. PMID:19236927

Time Series Modeling of Nano-Gold Immunochromatographic Assay via Expectation Maximization Algorithm.

PubMed

Zeng, Nianyin; Wang, Zidong; Li, Yurong; Du, Min; Cao, Jie; Liu, Xiaohui

2013-12-01

In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

PubMed Central

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P.

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis. PMID:23586021

Statistical modelling of subdiffusive dynamics in the cytoplasm of living cells: A FARIMA approach

NASA Astrophysics Data System (ADS)

Burnecki, K.; Muszkieta, M.; Sikora, G.; Weron, A.

2012-04-01

Golding and Cox (Phys. Rev. Lett., 96 (2006) 098102) tracked the motion of individual fluorescently labelled mRNA molecules inside live E. coli cells. They found that in the set of 23 trajectories from 3 different experiments, the automatically recognized motion is subdiffusive and published an intriguing microscopy video. Here, we extract the corresponding time series from this video by image segmentation method and present its detailed statistical analysis. We find that this trajectory was not included in the data set already studied and has different statistical properties. It is best fitted by a fractional autoregressive integrated moving average (FARIMA) process with the normal-inverse Gaussian (NIG) noise and the negative memory. In contrast to earlier studies, this shows that the fractional Brownian motion is not the best model for the dynamics documented in this video.

Tire-road friction coefficient estimation based on the resonance frequency of in-wheel motor drive system

NASA Astrophysics Data System (ADS)

Chen, Long; Bian, Mingyuan; Luo, Yugong; Qin, Zhaobo; Li, Keqiang

2016-01-01

In this paper, a resonance frequency-based tire-road friction coefficient (TRFC) estimation method is proposed by considering the dynamics performance of the in-wheel motor drive system under small slip ratio conditions. A frequency response function (FRF) is deduced for the drive system that is composed of a dynamic tire model and a simplified motor model. A linear relationship between the squared system resonance frequency and the TFRC is described with the FRF. Furthermore, the resonance frequency is identified by the Auto-Regressive eXogenous model using the information of the motor torque and the wheel speed, and the TRFC is estimated thereafter by a recursive least squares filter with the identified resonance frequency. Finally, the effectiveness of the proposed approach is demonstrated through simulations and experimental tests on different road surfaces.

Time series expression analyses using RNA-seq: a statistical approach.

PubMed

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

Correction to the Dynamic Tensile Strength of Ice and Ice-Silicate Mixtures (Lange & Ahrens 1983)

NASA Astrophysics Data System (ADS)

Stewart, S. T.; Ahrens, T. J.

1999-03-01

We present a correction to the Weibull parameters for ice and ice-silicate mixtures (Lange & Ahrens 1983). These parameters relate the dynamic tensile strength to the strain rate. These data are useful for continuum fracture models of ice.

Molecular dynamics simulations of polyethers and a quaternary ammonium ionic liquid as CO2 absorbers

NASA Astrophysics Data System (ADS)

Cardoso, Piercarlo Fortunato; Fernandez, Juan S. L. C.; Lepre, Luiz Fernando; Ando, Rômulo Augusto; Costa Gomes, Margarida F.; Siqueira, Leonardo J. A.

2018-04-01

The properties of mixtures of butyltrimethylammonium bis(trifluoromethylsulfonyl)imide, [N4111][NTf2], with poly(ethyleneglycol) dimethyl ether, PEO, were described as a function of PEO chain size by molecular dynamics simulations. Both PEO chain size and mixture composition revealed to play a significant role in determining the structure and the dynamics of the fluids. The remarkably higher viscosity observed for mixtures composed by 0.25 mole fraction of PEO was attributed to the increase in the gauche population of OCCO dihedral of the polyether of longer chains. The negative solvation enthalpy (ΔsolH < 0) and entropy (ΔsolS < 0) revealed a favorable CO2 absorption by the neat and mixture systems. The CO2 absorption was higher in neat PEO, particularly considering longer chains. The gas solubility in the mixtures presented intermediate values in comparison to the neat PEO and neat ionic liquid. The CO2 solutions had their structures discussed in the light of the calculated radial and spatial distribution functions.

Molecular dynamics simulations of polyethers and a quaternary ammonium ionic liquid as CO2 absorbers.

PubMed

Cardoso, Piercarlo Fortunato; Fernandez, Juan S L C; Lepre, Luiz Fernando; Ando, Rômulo Augusto; Costa Gomes, Margarida F; Siqueira, Leonardo J A

2018-04-07

The properties of mixtures of butyltrimethylammonium bis(trifluoromethylsulfonyl)imide, [N 4111 ][NTf 2 ], with poly(ethyleneglycol) dimethyl ether, PEO, were described as a function of PEO chain size by molecular dynamics simulations. Both PEO chain size and mixture composition revealed to play a significant role in determining the structure and the dynamics of the fluids. The remarkably higher viscosity observed for mixtures composed by 0.25 mole fraction of PEO was attributed to the increase in the gauche population of OCCO dihedral of the polyether of longer chains. The negative solvation enthalpy (Δ sol H < 0) and entropy (Δ sol S < 0) revealed a favorable CO 2 absorption by the neat and mixture systems. The CO 2 absorption was higher in neat PEO, particularly considering longer chains. The gas solubility in the mixtures presented intermediate values in comparison to the neat PEO and neat ionic liquid. The CO 2 solutions had their structures discussed in the light of the calculated radial and spatial distribution functions.

A stochastic evolutionary model generating a mixture of exponential distributions

NASA Astrophysics Data System (ADS)

Fenner, Trevor; Levene, Mark; Loizou, George

2016-02-01

Recent interest in human dynamics has stimulated the investigation of the stochastic processes that explain human behaviour in various contexts, such as mobile phone networks and social media. In this paper, we extend the stochastic urn-based model proposed in [T. Fenner, M. Levene, G. Loizou, J. Stat. Mech. 2015, P08015 (2015)] so that it can generate mixture models, in particular, a mixture of exponential distributions. The model is designed to capture the dynamics of survival analysis, traditionally employed in clinical trials, reliability analysis in engineering, and more recently in the analysis of large data sets recording human dynamics. The mixture modelling approach, which is relatively simple and well understood, is very effective in capturing heterogeneity in data. We provide empirical evidence for the validity of the model, using a data set of popular search engine queries collected over a period of 114 months. We show that the survival function of these queries is closely matched by the exponential mixture solution for our model.

Maxwell-Stefan diffusion and dynamical correlation in molten LiF-KF: A molecular dynamics study

NASA Astrophysics Data System (ADS)

Jain, Richa Naja; Chakraborty, Brahmananda; Ramaniah, Lavanya M.

2016-05-01

In this work our main objective is to compute Dynamical correlations, Onsager coefficients and Maxwell-Stefan (MS) diffusivities for molten salt LiF-KF mixture at various thermodynamic states through Green-Kubo formalism for the first time. The equilibrium molecular dynamics (MD) simulations were performed using BHM potential for LiF-KF mixture. The velocity autocorrelations functions involving Li ions reflect the endurance of cage dynamics or backscattering with temperature. The magnitude of Onsager coefficients for all pairs increases with increase in temperature. Interestingly most of the Onsager coefficients has almost maximum magnitude at the eutectic composition indicating the most dynamic character of the eutectic mixture. MS diffusivity hence diffusion for all ion pairs increases in the system with increasing temperature. Smooth variation of the diffusivity values denies any network formation in the mixture. Also, the striking feature is the noticeable concentration dependence of MS diffusivity between cation-cation pair, ĐLi-K which remains negative for most of the concentration range but changes sign to become positive for higher LiF concentration. The negative MS diffusivity is acceptable as it satisfies the non-negative entropy constraint governed by 2nd law of thermodynamics. This high diffusivity also vouches the candidature of molten salt as a coolant.

Parameter prediction based on Improved Process neural network and ARMA error compensation in Evaporation Process

NASA Astrophysics Data System (ADS)

Qian, Xiaoshan

2018-01-01

The traditional model of evaporation process parameters have continuity and cumulative characteristics of the prediction error larger issues, based on the basis of the process proposed an adaptive particle swarm neural network forecasting method parameters established on the autoregressive moving average (ARMA) error correction procedure compensated prediction model to predict the results of the neural network to improve prediction accuracy. Taking a alumina plant evaporation process to analyze production data validation, and compared with the traditional model, the new model prediction accuracy greatly improved, can be used to predict the dynamic process of evaporation of sodium aluminate solution components.

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

NASA Astrophysics Data System (ADS)

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

2013-08-01

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

Forecasting daily meteorological time series using ARIMA and regression models

NASA Astrophysics Data System (ADS)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

Asphalt mixture performance characterization using small-scale cylindrical specimens.

DOT National Transportation Integrated Search

2015-06-01

The results of dynamic modulus testing have become one of the primarily used performance criteria to evaluate the : laboratory properties of asphalt mixtures. This test is commonly conducted to characterize asphalt mixtures mechanistically : using an...

The Performance of Multilevel Growth Curve Models under an Autoregressive Moving Average Process

ERIC Educational Resources Information Center

Murphy, Daniel L.; Pituch, Keenan A.

2009-01-01

The authors examined the robustness of multilevel linear growth curve modeling to misspecification of an autoregressive moving average process. As previous research has shown (J. Ferron, R. Dailey, & Q. Yi, 2002; O. Kwok, S. G. West, & S. B. Green, 2007; S. Sivo, X. Fan, & L. Witta, 2005), estimates of the fixed effects were unbiased, and Type I…

Testing the Causal Links between School Climate, School Violence, and School Academic Performance: A Cross-Lagged Panel Autoregressive Model

ERIC Educational Resources Information Center

Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.

2016-01-01

The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…

Time to burn: Modeling wildland arson as an autoregressive crime function

Treesearch

Jeffrey P. Prestemon; David T. Butry

2005-01-01

Six Poisson autoregressive models of order p [PAR(p)] of daily wildland arson ignition counts are estimated for five locations in Florida (1994-2001). In addition, a fixed effects time-series Poisson model of annual arson counts is estimated for all Florida counties (1995-2001). PAR(p) model estimates reveal highly significant arson ignition autocorrelation, lasting up...

Theoretical results on fractionally integrated exponential generalized autoregressive conditional heteroskedastic processes

NASA Astrophysics Data System (ADS)

Lopes, Sílvia R. C.; Prass, Taiane S.

2014-05-01

Here we present a theoretical study on the main properties of Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedastic (FIEGARCH) processes. We analyze the conditions for the existence, the invertibility, the stationarity and the ergodicity of these processes. We prove that, if { is a FIEGARCH(p,d,q) process then, under mild conditions, { is an ARFIMA(q,d,0) with correlated innovations, that is, an autoregressive fractionally integrated moving average process. The convergence order for the polynomial coefficients that describes the volatility is presented and results related to the spectral representation and to the covariance structure of both processes { and { are discussed. Expressions for the kurtosis and the asymmetry measures for any stationary FIEGARCH(p,d,q) process are also derived. The h-step ahead forecast for the processes {, { and { are given with their respective mean square error of forecast. The work also presents a Monte Carlo simulation study showing how to generate, estimate and forecast based on six different FIEGARCH models. The forecasting performance of six models belonging to the class of autoregressive conditional heteroskedastic models (namely, ARCH-type models) and radial basis models is compared through an empirical application to Brazilian stock market exchange index.

Medium- and Long-term Prediction of LOD Change with the Leap-step Autoregressive Model

NASA Astrophysics Data System (ADS)

Liu, Q. B.; Wang, Q. J.; Lei, M. F.

2015-09-01

It is known that the accuracies of medium- and long-term prediction of changes of length of day (LOD) based on the combined least-square and autoregressive (LS+AR) decrease gradually. The leap-step autoregressive (LSAR) model is more accurate and stable in medium- and long-term prediction, therefore it is used to forecast the LOD changes in this work. Then the LOD series from EOP 08 C04 provided by IERS (International Earth Rotation and Reference Systems Service) is used to compare the effectiveness of the LSAR and traditional AR methods. The predicted series resulted from the two models show that the prediction accuracy with the LSAR model is better than that from AR model in medium- and long-term prediction.

Trans-dimensional joint inversion of seabed scattering and reflection data.

PubMed

Steininger, Gavin; Dettmer, Jan; Dosso, Stan E; Holland, Charles W

2013-03-01

This paper examines joint inversion of acoustic scattering and reflection data to resolve seabed interface roughness parameters (spectral strength, exponent, and cutoff) and geoacoustic profiles. Trans-dimensional (trans-D) Bayesian sampling is applied with both the number of sediment layers and the order (zeroth or first) of auto-regressive parameters in the error model treated as unknowns. A prior distribution that allows fluid sediment layers over an elastic basement in a trans-D inversion is derived and implemented. Three cases are considered: Scattering-only inversion, joint scattering and reflection inversion, and joint inversion with the trans-D auto-regressive error model. Including reflection data improves the resolution of scattering and geoacoustic parameters. The trans-D auto-regressive model further improves scattering resolution and correctly differentiates between strongly and weakly correlated residual errors.

Non-monotonic dynamics of water in its binary mixture with 1,2-dimethoxy ethane: A combined THz spectroscopic and MD simulation study.

PubMed

Das Mahanta, Debasish; Patra, Animesh; Samanta, Nirnay; Luong, Trung Quan; Mukherjee, Biswaroop; Mitra, Rajib Kumar

2016-10-28

A combined experimental (mid- and far-infrared FTIR spectroscopy and THz time domain spectroscopy (TTDS) (0.3-1.6 THz)) and molecular dynamics (MD) simulation technique are used to understand the evolution of the structure and dynamics of water in its binary mixture with 1,2-dimethoxy ethane (DME) over the entire concentration range. The cooperative hydrogen bond dynamics of water obtained from Debye relaxation of TTDS data reveals a non-monotonous behaviour in which the collective dynamics is much faster in the low X w region (where X w is the mole fraction of water in the mixture), whereas in X w ∼ 0.8 region, the dynamics gets slower than that of pure water. The concentration dependence of the reorientation times of water, calculated from the MD simulations, also captures this non-monotonous character. The MD simulation trajectories reveal presence of large amplitude angular jumps, which dominate the orientational relaxation. We rationalize the non-monotonous, concentration dependent orientational dynamics by identifying two different physical mechanisms which operate at high and low water concentration regimes.

Bayesian spatiotemporal crash frequency models with mixture components for space-time interactions.

PubMed

Cheng, Wen; Gill, Gurdiljot Singh; Zhang, Yongping; Cao, Zhong

2018-03-01

The traffic safety research has developed spatiotemporal models to explore the variations in the spatial pattern of crash risk over time. Many studies observed notable benefits associated with the inclusion of spatial and temporal correlation and their interactions. However, the safety literature lacks sufficient research for the comparison of different temporal treatments and their interaction with spatial component. This study developed four spatiotemporal models with varying complexity due to the different temporal treatments such as (I) linear time trend; (II) quadratic time trend; (III) Autoregressive-1 (AR-1); and (IV) time adjacency. Moreover, the study introduced a flexible two-component mixture for the space-time interaction which allows greater flexibility compared to the traditional linear space-time interaction. The mixture component allows the accommodation of global space-time interaction as well as the departures from the overall spatial and temporal risk patterns. This study performed a comprehensive assessment of mixture models based on the diverse criteria pertaining to goodness-of-fit, cross-validation and evaluation based on in-sample data for predictive accuracy of crash estimates. The assessment of model performance in terms of goodness-of-fit clearly established the superiority of the time-adjacency specification which was evidently more complex due to the addition of information borrowed from neighboring years, but this addition of parameters allowed significant advantage at posterior deviance which subsequently benefited overall fit to crash data. The Base models were also developed to study the comparison between the proposed mixture and traditional space-time components for each temporal model. The mixture models consistently outperformed the corresponding Base models due to the advantages of much lower deviance. For cross-validation comparison of predictive accuracy, linear time trend model was adjudged the best as it recorded the highest value of log pseudo marginal likelihood (LPML). Four other evaluation criteria were considered for typical validation using the same data for model development. Under each criterion, observed crash counts were compared with three types of data containing Bayesian estimated, normal predicted, and model replicated ones. The linear model again performed the best in most scenarios except one case of using model replicated data and two cases involving prediction without including random effects. These phenomena indicated the mediocre performance of linear trend when random effects were excluded for evaluation. This might be due to the flexible mixture space-time interaction which can efficiently absorb the residual variability escaping from the predictable part of the model. The comparison of Base and mixture models in terms of prediction accuracy further bolstered the superiority of the mixture models as the mixture ones generated more precise estimated crash counts across all four models, suggesting that the advantages associated with mixture component at model fit were transferable to prediction accuracy. Finally, the residual analysis demonstrated the consistently superior performance of random effect models which validates the importance of incorporating the correlation structures to account for unobserved heterogeneity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Granger Causality Testing with Intensive Longitudinal Data.

PubMed

Molenaar, Peter C M

2018-06-01

The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

Failure monitoring in dynamic systems: Model construction without fault training data

NASA Technical Reports Server (NTRS)

Smyth, P.; Mellstrom, J.

1993-01-01

Advances in the use of autoregressive models, pattern recognition methods, and hidden Markov models for on-line health monitoring of dynamic systems (such as DSN antennas) have recently been reported. However, the algorithms described in previous work have the significant drawback that data acquired under fault conditions are assumed to be available in order to train the model used for monitoring the system under observation. This article reports that this assumption can be relaxed and that hidden Markov monitoring models can be constructed using only data acquired under normal conditions and prior knowledge of the system characteristics being measured. The method is described and evaluated on data from the DSS 13 34-m beam wave guide antenna. The primary conclusion from the experimental results is that the method is indeed practical and holds considerable promise for application at the 70-m antenna sites where acquisition of fault data under controlled conditions is not realistic.

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

PubMed

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

The compass rose pattern in electricity prices.

PubMed

Batten, Jonathan A; Hamada, Mahmoud

2009-12-01

The "compass rose pattern" is known to appear in the phase portraits, or scatter diagrams, of the high-frequency returns of financial series. We first show that this pattern is also present in the returns of spot electricity prices. Early researchers investigating these phenomena hoped that these patterns signaled the presence of rich dynamics, possibly chaotic or fractal in nature. Although there is a definite autoregressive and conditional heteroscedasticity structure in electricity returns, we find that after simple filtering no pattern remains. While the series is non-normal in terms of their distribution and statistical tests fail to identify significant chaos, there is evidence of fractal structures in periodic price returns when measured over the trading day. The phase diagram of the filtered returns provides a useful visual check on independence, a property necessary for pricing and trading derivatives and portfolio construction, as well as providing useful insights into the market dynamics.

Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

NASA Astrophysics Data System (ADS)

Gu, Huaying; Liu, Zhixue; Weng, Yingliang

2017-04-01

The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.

Texture classification using autoregressive filtering

NASA Technical Reports Server (NTRS)

Lawton, W. M.; Lee, M.

1984-01-01

A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.

Determining factors affecting tourism demand for Malaysia using ARDL modeling: A case of Europe countries

NASA Astrophysics Data System (ADS)

Borhan, Nurbaizura; Arsad, Zainudin

2016-10-01

Tourism industry is the second largest foreign exchange earner after manufacturing in Malaysia. With regards to the importance of tourism industry in Malaysia, any factors that influence tourism demand should be considered cautiously by the government and tourism authorities in order to attract more international tourists in the near future. The purpose of this study is to investigate the dynamic long-run and short-run relationship between the number of international tourist arrivals from six European countries and four selected economic variables. The economic variables used in this study are exchange rate, gross domestic product, relative price and substitute relative price. This study also examines the impact of the European Sovereign crisis on the number of arrivals from the selected European countries to Malaysia. The data covers the period from quarter 1 (Q1) of 1999 to quarter 3 (Q3) of 2014 and employs the autoregressive distributed lag (ARDL) bounds testing approach proposed by Pesaran et al. (2001). The results of unit root test show a mixture of integrated at level and order one, I(0) and I(1). The results show that there exist long-run cointegration between the number of international tourist arrivals and exchange rate, level of income, tourism price and substitute tourism price for all countries. Generally, the results show that level of income is in line with the economic theory and Thailand is a competing destination for the tourism industry in Malaysia. Surprisingly, relative price is found to have positive impact on the number of arrivals to Malaysia and this suggests that an increase in the price level in Malaysia is unexpectedly increase the number of international tourist arrivals to Malaysia. Therefore the Malaysian government and tourism authorities should continue the efforts to withstand the growth of the tourism industry.

Molecular dynamics simulation for the test of calibrated OPLS-AA force field for binary liquid mixture of tri-iso-amyl phosphate and n-dodecane.

PubMed

Das, Arya; Ali, Sk Musharaf

2018-02-21

Tri-isoamyl phosphate (TiAP) has been proposed to be an alternative for tri-butyl phosphate (TBP) in the Plutonium Uranium Extraction (PUREX) process. Recently, we have successfully calibrated and tested all-atom optimized potentials for liquid simulations using Mulliken partial charges for pure TiAP, TBP, and dodecane by performing molecular dynamics (MD) simulation. It is of immense importance to extend this potential for the various molecular properties of TiAP and TiAP/n-dodecane binary mixtures using MD simulation. Earlier, efforts were devoted to find out a suitable force field which can explain both structural and dynamical properties by empirical parameterization. Therefore, the present MD study reports the structural, dynamical, and thermodynamical properties with different mole fractions of TiAP-dodecane mixtures at the entire range of mole fraction of 0-1 employing our calibrated Mulliken embedded optimized potentials for liquid simulation (OPLS) force field. The calculated electric dipole moment of TiAP was seen to be almost unaffected by the TiAP concentration in the dodecane diluent. The calculated liquid densities of the TiAP-dodecane mixture are in good agreement with the experimental data. The mixture densities at different temperatures are also studied which was found to be reduced with temperature as expected. The plot of diffusivities for TiAP and dodecane against mole fraction in the binary mixture intersects at a composition in the range of 25%-30% of TiAP in dodecane, which is very much closer to the TBP/n-dodecane composition used in the PUREX process. The excess volume of mixing was found to be positive for the entire range of mole fraction and the excess enthalpy of mixing was shown to be endothermic for the TBP/n-dodecane mixture as well as TiAP/n-dodecane mixture as reported experimentally. The spatial pair correlation functions are evaluated between TiAP-TiAP and TiAP-dodecane molecules. Further, shear viscosity has been computed by performing the non-equilibrium molecular dynamics employing the periodic perturbation method. The calculated shear viscosity of the binary mixture is found to be in excellent agreement with the experimental values. The use of the newly calibrated OPLS force field embedding Mulliken charges is shown to be equally reliable in predicting the structural and dynamical properties for the mixture without incorporating any arbitrary scaling in the force field or Lennard-Jones parameters. Further, the present MD simulation results demonstrate that the Stokes-Einstein relation breaks down at the molecular level. The present methodology might be adopted to evaluate the liquid state properties of an aqueous-organic biphasic system, which is of great significance in the interfacial science and technology.

Molecular dynamics simulation for the test of calibrated OPLS-AA force field for binary liquid mixture of tri-iso-amyl phosphate and n-dodecane

NASA Astrophysics Data System (ADS)

Das, Arya; Ali, Sk. Musharaf

2018-02-01

Tri-isoamyl phosphate (TiAP) has been proposed to be an alternative for tri-butyl phosphate (TBP) in the Plutonium Uranium Extraction (PUREX) process. Recently, we have successfully calibrated and tested all-atom optimized potentials for liquid simulations using Mulliken partial charges for pure TiAP, TBP, and dodecane by performing molecular dynamics (MD) simulation. It is of immense importance to extend this potential for the various molecular properties of TiAP and TiAP/n-dodecane binary mixtures using MD simulation. Earlier, efforts were devoted to find out a suitable force field which can explain both structural and dynamical properties by empirical parameterization. Therefore, the present MD study reports the structural, dynamical, and thermodynamical properties with different mole fractions of TiAP-dodecane mixtures at the entire range of mole fraction of 0-1 employing our calibrated Mulliken embedded optimized potentials for liquid simulation (OPLS) force field. The calculated electric dipole moment of TiAP was seen to be almost unaffected by the TiAP concentration in the dodecane diluent. The calculated liquid densities of the TiAP-dodecane mixture are in good agreement with the experimental data. The mixture densities at different temperatures are also studied which was found to be reduced with temperature as expected. The plot of diffusivities for TiAP and dodecane against mole fraction in the binary mixture intersects at a composition in the range of 25%-30% of TiAP in dodecane, which is very much closer to the TBP/n-dodecane composition used in the PUREX process. The excess volume of mixing was found to be positive for the entire range of mole fraction and the excess enthalpy of mixing was shown to be endothermic for the TBP/n-dodecane mixture as well as TiAP/n-dodecane mixture as reported experimentally. The spatial pair correlation functions are evaluated between TiAP-TiAP and TiAP-dodecane molecules. Further, shear viscosity has been computed by performing the non-equilibrium molecular dynamics employing the periodic perturbation method. The calculated shear viscosity of the binary mixture is found to be in excellent agreement with the experimental values. The use of the newly calibrated OPLS force field embedding Mulliken charges is shown to be equally reliable in predicting the structural and dynamical properties for the mixture without incorporating any arbitrary scaling in the force field or Lennard-Jones parameters. Further, the present MD simulation results demonstrate that the Stokes-Einstein relation breaks down at the molecular level. The present methodology might be adopted to evaluate the liquid state properties of an aqueous-organic biphasic system, which is of great significance in the interfacial science and technology.

Conventional and advanced time series estimation: application to the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database, 1993-2006.

PubMed

Moran, John L; Solomon, Patricia J

2011-02-01

Time series analysis has seen limited application in the biomedical Literature. The utility of conventional and advanced time series estimators was explored for intensive care unit (ICU) outcome series. Monthly mean time series, 1993-2006, for hospital mortality, severity-of-illness score (APACHE III), ventilation fraction and patient type (medical and surgical), were generated from the Australia and New Zealand Intensive Care Society adult patient database. Analyses encompassed geographical seasonal mortality patterns, series structural time changes, mortality series volatility using autoregressive moving average and Generalized Autoregressive Conditional Heteroscedasticity models in which predicted variances are updated adaptively, and bivariate and multivariate (vector error correction models) cointegrating relationships between series. The mortality series exhibited marked seasonality, declining mortality trend and substantial autocorrelation beyond 24 lags. Mortality increased in winter months (July-August); the medical series featured annual cycling, whereas the surgical demonstrated long and short (3-4 months) cycling. Series structural breaks were apparent in January 1995 and December 2002. The covariance stationary first-differenced mortality series was consistent with a seasonal autoregressive moving average process; the observed conditional-variance volatility (1993-1995) and residual Autoregressive Conditional Heteroscedasticity effects entailed a Generalized Autoregressive Conditional Heteroscedasticity model, preferred by information criterion and mean model forecast performance. Bivariate cointegration, indicating long-term equilibrium relationships, was established between mortality and severity-of-illness scores at the database level and for categories of ICUs. Multivariate cointegration was demonstrated for {log APACHE III score, log ICU length of stay, ICU mortality and ventilation fraction}. A system approach to understanding series time-dependence may be established using conventional and advanced econometric time series estimators. © 2010 Blackwell Publishing Ltd.

Effects of variation in chain length on ternary polymer electrolyte - Ionic liquid mixture - A molecular dynamics simulation study

NASA Astrophysics Data System (ADS)

Raju, S. G.; Hariharan, Krishnan S.; Park, Da-Hye; Kang, HyoRang; Kolake, Subramanya Mayya

2015-10-01

Molecular dynamics (MD) simulations of ternary polymer electrolyte - ionic liquid mixtures are conducted using an all-atom model. N-alkyl-N-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide ([CnMPy][TFSI], n = 1, 3, 6, 9) and polyethylene oxide (PEO) are used. Microscopic structure, energetics and dynamics of ionic liquid (IL) in these ternary mixtures are studied. Properties of these four pure IL are also calculated and compared to that in ternary mixtures. Interaction between pyrrolidinium cation and TFSI is stronger and there is larger propensity of ion-pair formation in ternary mixtures. Unlike the case in imidazolium IL, near neighbor structural correlation between TFSI reduces with increase in chain length on cation in both pure IL and ternary mixtures. Using spatial density maps, regions where PEO and TFSI interact with pyrrolidinium cation are identified. Oxygens of PEO are above and below the pyrrolidinium ring and away from the bulky alkyl groups whereas TFSI is present close to nitrogen atom of CnMPy. In pure IL, diffusion coefficient (D) of C3MPy is larger than of TFSI but D of C9MPy and C6MPy are larger than that of TFSI. The reasons for alkyl chain dependent phenomena are explored.

Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method

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

Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng

This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA)more » models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.« less

Habitat Use and Selection by Giant Pandas.

PubMed

Hull, Vanessa; Zhang, Jindong; Huang, Jinyan; Zhou, Shiqiang; Viña, Andrés; Shortridge, Ashton; Li, Rengui; Liu, Dian; Xu, Weihua; Ouyang, Zhiyun; Zhang, Hemin; Liu, Jianguo

2016-01-01

Animals make choices about where to spend their time in complex and dynamic landscapes, choices that reveal information about their biology that in turn can be used to guide their conservation. Using GPS collars, we conducted a novel individual-based analysis of habitat use and selection by the elusive and endangered giant pandas (Ailuropoda melanoleuca). We constructed spatial autoregressive resource utilization functions (RUF) to model the relationship between the pandas' utilization distributions and various habitat characteristics over a continuous space across seasons. Results reveal several new insights, including use of a broader range of habitat characteristics than previously understood for the species, particularly steep slopes and non-forest areas. We also used compositional analysis to analyze habitat selection (use with respect to availability of habitat types) at two selection levels. Pandas selected against low terrain position and against the highest clumped forest at the at-home range level, but no significant factors were identified at the within-home range level. Our results have implications for modeling and managing the habitat of this endangered species by illustrating how individual pandas relate to habitat and make choices that differ from assumptions made in broad scale models. Our study also highlights the value of using a spatial autoregressive RUF approach on animal species for which a complete picture of individual-level habitat use and selection across space is otherwise lacking.

Habitat Use and Selection by Giant Pandas

PubMed Central

Hull, Vanessa; Zhang, Jindong; Huang, Jinyan; Zhou, Shiqiang; Viña, Andrés; Shortridge, Ashton; Li, Rengui; Liu, Dian; Xu, Weihua; Ouyang, Zhiyun; Zhang, Hemin; Liu, Jianguo

2016-01-01

Animals make choices about where to spend their time in complex and dynamic landscapes, choices that reveal information about their biology that in turn can be used to guide their conservation. Using GPS collars, we conducted a novel individual-based analysis of habitat use and selection by the elusive and endangered giant pandas (Ailuropoda melanoleuca). We constructed spatial autoregressive resource utilization functions (RUF) to model the relationship between the pandas' utilization distributions and various habitat characteristics over a continuous space across seasons. Results reveal several new insights, including use of a broader range of habitat characteristics than previously understood for the species, particularly steep slopes and non-forest areas. We also used compositional analysis to analyze habitat selection (use with respect to availability of habitat types) at two selection levels. Pandas selected against low terrain position and against the highest clumped forest at the at-home range level, but no significant factors were identified at the within-home range level. Our results have implications for modeling and managing the habitat of this endangered species by illustrating how individual pandas relate to habitat and make choices that differ from assumptions made in broad scale models. Our study also highlights the value of using a spatial autoregressive RUF approach on animal species for which a complete picture of individual-level habitat use and selection across space is otherwise lacking. PMID:27627805

Analysis of pelagic species decline in the upper San Francisco Estuary using multivariate autoregressive modeling (MAR)

USGS Publications Warehouse

Mac Nally, Ralph; Thomson, James R.; Kimmerer, Wim J.; Feyrer, Frederick; Newman, Ken B.; Sih, Andy; Bennett, William A.; Brown, Larry; Fleishman, Erica; Culberson, Steven D.; Castillo, Gonzalo

2010-01-01

Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since the late 1960s with extensive collection of data on the fishes, their pelagic prey, phytoplankton biomass, invasive species, and physical factors. We used multivariate autoregressive (MAR) modeling to discern the main factors responsible for the declines. An expert-elicited model was built to describe the system. Fifty-four relationships were built into the model, only one of which was of uncertain direction a priori. Twenty-eight of the proposed relationships were strongly supported by or consistent with the data, while 26 were close to zero (not supported by the data but not contrary to expectations). The position of the 2‰ isohaline (a measure of the physical response of the estuary to freshwater flow) and increased water clarity over the period of analyses were two factors affecting multiple declining taxa (including fishes and the fishes' main zooplankton prey). Our results were relatively robust with respect to the form of stock–recruitment model used and to inclusion of subsidiary covariates but may be enhanced by using detailed state–space models that describe more fully the life-history dynamics of the declining species.

Analysis of pelagic species decline in the upper San Francisco Estuary using multivariate autoregressive modeling (MAR).

PubMed

Mac Nally, Ralph; Thomson, James R; Kimmerer, Wim J; Feyrer, Frederick; Newman, Ken B; Sih, Andy; Bennett, William A; Brown, Larry; Fleishman, Erica; Culberson, Steven D; Castillo, Gonzalo

2010-07-01

Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since the late 1960s with extensive collection of data on the fishes, their pelagic prey, phytoplankton biomass, invasive species, and physical factors. We used multivariate autoregressive (MAR) modeling to discern the main factors responsible for the declines. An expert-elicited model was built to describe the system. Fifty-four relationships were built into the model, only one of which was of uncertain direction a priori. Twenty-eight of the proposed relationships were strongly supported by or consistent with the data, while 26 were close to zero (not supported by the data but not contrary to expectations). The position of the 2 per thousand isohaline (a measure of the physical response of the estuary to freshwater flow) and increased water clarity over the period of analyses were two factors affecting multiple declining taxa (including fishes and the fishes' main zooplankton prey): Our results were relatively robust with respect to the form of stock-recruitment model used and to inclusion of subsidiary covariates but may be enhanced by using detailed state-space models that describe more fully the life-history dynamics of the declining species.

Stochastic Parametrization for the Impact of Neglected Variability Patterns

NASA Astrophysics Data System (ADS)

Kaiser, Olga; Hien, Steffen; Achatz, Ulrich; Horenko, Illia

2017-04-01

An efficient description of the gravity wave variability and the related spontaneous emission processes requires an empirical stochastic closure for the impact of neglected variability patterns (subgridscales or SGS). In particular, we focus on the analysis of the IGW emission within a tangent linear model which requires a stochastic SGS parameterization for taking the self interaction of the ageostrophic flow components into account. For this purpose, we identify the best SGS model in terms of exactness and simplicity by deploying a wide range of different data-driven model classes, including standard stationary regression models, autoregression and artificial neuronal networks models - as well as the family of nonstationary models like FEM-BV-VARX model class (Finite Element based vector autoregressive time series analysis with bounded variation of the model parameters). The models are used to investigate the main characteristics of the underlying dynamics and to explore the significant spatial and temporal neighbourhood dependencies. The best SGS model in terms of exactness and simplicity is obtained for the nonstationary FEM-BV-VARX setting, determining only direct spatial and temporal neighbourhood as significant - and allowing to drastically reduce the number of informations that are required for the optimal SGS. Additionally, the models are characterized by sets of vector- and matrix-valued parameters that must be inferred from big data sets provided by simulations - making it a task that can not be solved without deploying high-performance computing facilities (HPC).

The quadriceps muscle of knee joint modelling Using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN)

NASA Astrophysics Data System (ADS)

Kamaruddin, Saadi Bin Ahmad; Marponga Tolos, Siti; Hee, Pah Chin; Ghani, Nor Azura Md; Ramli, Norazan Mohamed; Nasir, Noorhamizah Binti Mohamed; Ksm Kader, Babul Salam Bin; Saiful Huq, Mohammad

2017-03-01

Neural framework has for quite a while been known for its ability to handle a complex nonlinear system without a logical model and can learn refined nonlinear associations gives. Theoretically, the most surely understood computation to set up the framework is the backpropagation (BP) count which relies on upon the minimization of the mean square error (MSE). However, this algorithm is not totally efficient in the presence of outliers which usually exist in dynamic data. This paper exhibits the modelling of quadriceps muscle model by utilizing counterfeit smart procedures named consolidated backpropagation neural network nonlinear autoregressive (BPNN-NAR) and backpropagation neural network nonlinear autoregressive moving average (BPNN-NARMA) models in view of utilitarian electrical incitement (FES). We adapted particle swarm optimization (PSO) approach to enhance the performance of backpropagation algorithm. In this research, a progression of tests utilizing FES was led. The information that is gotten is utilized to build up the quadriceps muscle model. 934 preparing information, 200 testing and 200 approval information set are utilized as a part of the improvement of muscle model. It was found that both BPNN-NAR and BPNN-NARMA performed well in modelling this type of data. As a conclusion, the neural network time series models performed reasonably efficient for non-linear modelling such as active properties of the quadriceps muscle with one input, namely output namely muscle force.

Study of thermite mixture consolidated by the cold gas dynamic spray process

NASA Astrophysics Data System (ADS)

Bacciochini, A.; Maines, G.; Poupart, C.; Akbarnejad, H.; Radulescu, M.; Jodoin, B.; Zhang, F.; Lee, J. J.

2014-05-01

The present study focused on the cold gas dynamic spray process for manufacturing porosity free, finely structured energetic materials with high reactivity and structural integrity. The experiments have focused the reaction between the aluminium and metal oxide, such as Al-CuO system. The consolidation of the materials used the cold gas dynamic spray technique, where the particles are accelerated to high speeds and consolidated via plastic deformation upon impact. Reactive composites are formed in arbitrary shapes with close to zero porosity and without any reactions during the consolidation phase. Reactivity of mixtures has been investigated through flame propagation analysis on cold sprayed samples and compacted powder mixture. Deflagration tests showed the influence of porosity on the reactivity.

Molecular dynamics simulation of a needle-sphere binary mixture

NASA Astrophysics Data System (ADS)

Raghavan, Karthik

This paper investigates the dynamic behaviour of a hard needle-sphere binary system using a novel numerical technique called the Newton homotopy continuation (NHC) method. This mixture is representative of a polymer melt where both long chain molecules and monomers coexist. Since the intermolecular forces are generated from hard body interactions, the consequence of missed collisions or incorrect collision sequences have a significant bearing on the dynamic properties of the fluid. To overcome this problem, in earlier work NHC was chosen over traditional Newton-Raphson methods to solve the hard body dynamics of a needle fluid in random media composed of overlapping spheres. Furthermore, the simplicity of interactions and dynamics allows us to focus our research directly on the effects of particle shape and density on the transport behaviour of the mixture. These studies are also compared with earlier works that examined molecular chains in porous media primarily to understand the differences in molecular transport in the bulk versus porous systems.

Preferential solvation of lysozyme in dimethyl sulfoxide/water binary mixture probed by terahertz spectroscopy.

PubMed

Das, Dipak Kumar; Patra, Animesh; Mitra, Rajib Kumar

2016-09-01

We report the changes in the hydration dynamics around a model protein hen egg white lysozyme (HEWL) in water-dimethyl sulfoxide (DMSO) binary mixture using THz time domain spectroscopy (TTDS) technique. DMSO molecules get preferentially solvated at the protein surface, as indicated by circular dichroism (CD) and Fourier transform infrared (FTIR) study in the mid-infrared region, resulting in a conformational change in the protein, which consequently modifies the associated hydration dynamics. As a control we also study the collective hydration dynamics of water-DMSO binary mixture and it is found that it follows a non-ideal behavior owing to the formation of DMSO-water clusters. It is observed that the cooperative dynamics of water at the protein surface does follow the DMSO-mediated conformational modulation of the protein. Copyright © 2016 Elsevier B.V. All rights reserved.

Ion-water wires in imidazolium-based ionic liquid/water solutions induce unique trends in density.

PubMed

Ghoshdastidar, Debostuti; Senapati, Sanjib

2016-03-28

Ionic liquid/water binary mixtures are rapidly gaining popularity as solvents for dissolution of cellulose, nucleobases, and other poorly water-soluble biomolecules. Hence, several studies have focused on measuring the thermophysical properties of these versatile mixtures. Among these, 1-ethyl-3-methylimidazolium ([emim]) cation-based ILs containing different anions exhibit unique density behaviours upon addition of water. While [emim][acetate]/water binary mixtures display an unusual rise in density with the addition of low-to-moderate amounts of water, those containing the [trifluoroacetate] ([Tfa]) anion display a sluggish decrease in density. The density of [emim][tetrafluoroborate] ([emim][BF4])/water mixtures, on the other hand, declines rapidly in close accordance with the experimental reports. Here, we unravel the structural basis underlying this unique density behavior of [emim]-based IL/water mixtures using all-atom molecular dynamics (MD) simulations. The results revealed that the distinct nature of anion-water hydrogen bonded networks in the three systems was a key in modulating the observed unique density behaviour. Vast expanses of uninterrupted anion-water-anion H-bonded stretches, denoted here as anion-water wires, induced significant structuring in [emim][Ac]/water mixtures that resulted in the density rise. Conversely, the presence of intermittent large water clusters disintegrated the anion-water wires in [emim][Tfa]/water and [emim][BF4]/water mixtures to cause a monotonic density decrease. The differential nanostructuring affected the dynamics of the solutions proportionately, with the H-bond making and breaking dynamics found to be greatly retarded in [emim][Ac]/water mixtures, while it exhibited a faster relaxation in the other two binary solutions.

Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

NASA Astrophysics Data System (ADS)

Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou

2018-06-01

Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.

Hedonic price models with omitted variables and measurement errors: a constrained autoregression-structural equation modeling approach with application to urban Indonesia

NASA Astrophysics Data System (ADS)

Suparman, Yusep; Folmer, Henk; Oud, Johan H. L.

2014-01-01

Omitted variables and measurement errors in explanatory variables frequently occur in hedonic price models. Ignoring these problems leads to biased estimators. In this paper, we develop a constrained autoregression-structural equation model (ASEM) to handle both types of problems. Standard panel data models to handle omitted variables bias are based on the assumption that the omitted variables are time-invariant. ASEM allows handling of both time-varying and time-invariant omitted variables by constrained autoregression. In the case of measurement error, standard approaches require additional external information which is usually difficult to obtain. ASEM exploits the fact that panel data are repeatedly measured which allows decomposing the variance of a variable into the true variance and the variance due to measurement error. We apply ASEM to estimate a hedonic housing model for urban Indonesia. To get insight into the consequences of measurement error and omitted variables, we compare the ASEM estimates with the outcomes of (1) a standard SEM, which does not account for omitted variables, (2) a constrained autoregression model, which does not account for measurement error, and (3) a fixed effects hedonic model, which ignores measurement error and time-varying omitted variables. The differences between the ASEM estimates and the outcomes of the three alternative approaches are substantial.

Effective time closures: quantifying the conservation benefits of input control for the Pacific chub mackerel fishery.

PubMed

Ichinokawa, Momoko; Okamura, Hiroshi; Watanabe, Chikako; Kawabata, Atsushi; Oozeki, Yoshioki

2015-09-01

Restricting human access to a specific wildlife species, community, or ecosystem, i.e., input control, is one of the most popular tools to control human impacts for natural resource management and wildlife conservation. However, quantitative evaluations of input control are generally difficult, because it is unclear how much human impacts can actually be reduced by the control. We present a model framework to quantify the effectiveness of input control using day closures to reduce actual fishing impact by considering the observed fishery dynamics. The model framework was applied to the management of the Pacific stock of the chub mackerel (Scomber japonicus) fishery, in which fishing was suspended for one day following any day when the total mackerel catch exceeded a threshold level. We evaluated the management measure according to the following steps: (1) we fitted the daily observed catch and fishing effort data to a generalized linear model (GLM) or generalized autoregressive state-space model (GASSM), (2) we conducted population dynamics simulations based on annual catches randomly generated from the parameters estimated in the first step, (3) we quantified the effectiveness of day closures by comparing the results of two simulation scenarios with and without day closures, and (4) we conducted additional simulations based on different sets of explanatory variables and statistical models (sensitivity analysis). In the first step, we found that the GASSM explained the observed data far better than the simple GLM. The model parameterized with the estimates from the GASSM demonstrated that the day closures implemented from 2004 to 2009 would have decreased exploitation fractions by ~10% every year and increased the 2009 stock biomass by 37-46% (median), relative to the values without day closures. The sensitivity analysis revealed that the effectiveness of day closures was particularly influenced by autoregressive processes in the fishery data and by positive relationships between fishing effort and total biomass. Those results indicated the importance of human behavioral dynamics under input control in quantifying the conservation benefit of natural resource management and the applicability of our model framework to the evaluation of the input controls that are actually implemented.

Solubility and conversion of carbamazepine polymorphs in supercritical carbon dioxide.

PubMed

Bettini, R; Bonassi, L; Castoro, V; Rossi, A; Zema, L; Gazzaniga, A; Giordano, F

2001-06-01

The aim of this work was to investigate whether mixtures of carbamazepine polymorphs could be processed in supercritical (SC) CO(2) in order to obtain the pure stable crystalline phase. To accomplish this goal the solubility of carbamazepine polymorphs I and III in supercritical CO(2) was first assessed using a low solvent flux dynamic method. Mixtures of Form I and Form III were processed in dynamic or static conditions in SC-CO(2). Differential scanning calorimetry, Fourier transformed infrared spectroscopy, and powder X-ray diffractometry were used to analyse solid samples in terms of polymorph composition. It was found that Form I and Form III of carbamazepine have different solubility in supercritical CO(2) at 55 degrees C above 300 bar. Due to the transformation of the metastable form, conversion of Form I into Form III can be carried out on a binary mixture of the two polymorphs by treating the mixture at 55 degrees C and 350 bar, under both static and dynamic conditions, via its solubilization in supercritical CO(2).

Adiabatic Coupling Constant of Nitrobenzene- n-Alkane Critical Mixtures. Evidence from Ultrasonic Spectra and Thermodynamic Data

NASA Astrophysics Data System (ADS)

Mirzaev, Sirojiddin Z.; Kaatze, Udo

2016-09-01

Ultrasonic spectra of mixtures of nitrobenzene with n-alkanes, from n-hexane to n-nonane, are analyzed. They feature up to two Debye-type relaxation terms with discrete relaxation times and, near the critical point, an additional relaxation term due to the fluctuations in the local concentration. The latter can be well represented by the dynamic scaling theory. Its amplitude parameter reveals the adiabatic coupling constant of the mixtures of critical composition. The dependence of this thermodynamic parameter upon the length of the n-alkanes corresponds to that of the slope in the pressure dependence of the critical temperature and is thus taken another confirmation of the dynamic scaling model. The change in the variation of the coupling constant and of several other mixture parameters with alkane length probably reflects a structural change in the nitrobenzene- n-alkane mixtures when the number of carbon atoms per alkane exceeds eight.

Rheological characterizations of concentrated binary gum mixtures with xanthan gum and galactomannans.

PubMed

Jo, Wonjun; Bak, June Ha; Yoo, Byoungseung

2018-03-20

The steady and dynamic shear rheological properties of binary gum mixtures with xanthan gum (XG) and galactomannans (guar gum (GG) and locust bean gum (LBG)) were examined in a concentrated solution (1% w/w) as a function of gum mixing ratio (100/0, 75/25, 50/50, and 0/100). All samples, except for individual GG and LBG, showed high shear-thinning behavior with yield stress. The values of flow (K, η a,50 , and σ oc ) and dynamic rheological parameters (G' and G″) of XG-GG and XG-LBG mixtures were significantly higher compared to XG alone, indicating that the flow and viscoelastic properties of binary gum mixtures were greatly affected by the addition of GG and LBG. The maximum elasticity synergistic interaction for XG-galactomannans mixtures was observed at a mixing ratio of 50/50, showing a greatly positive deviation between measured and calculated values of G'. These results suggest that the synergistic effect of GG and LBG addition on rheological properties of XG appears to be due to intermolecular interaction occurred between XG and galactomannans, as confirmed by dynamic rheological properties. Copyright © 2018 Elsevier B.V. All rights reserved.

Competitive adsorption of ibuprofen and amoxicillin mixtures from aqueous solution on activated carbons.

PubMed

Mansouri, Hayet; Carmona, Rocio J; Gomis-Berenguer, Alicia; Souissi-Najar, Souad; Ouederni, Abdelmottaleb; Ania, Conchi O

2015-07-01

This work investigates the competitive adsorption under dynamic and equilibrium conditions of ibuprofen (IBU) and amoxicillin (AMX), two widely consumed pharmaceuticals, on nanoporous carbons of different characteristics. Batch adsorption experiments of pure components in water and their binary mixtures were carried out to measure both adsorption equilibrium and kinetics, and dynamic tests were performed to validate the simultaneous removal of the mixtures in breakthrough experiments. The equilibrium adsorption capacities evaluated from pure component solutions were higher than those measured in dynamic conditions, and were found to depend on the porous features of the adsorbent and the nature of the specific/dispersive interactions that are controlled by the solution pH, density of surface change on the carbon and ionization of the pollutant. A marked roll-up effect was observed for AMX retention on the hydrophobic carbons, not seen for the functionalized adsorbent likely due to the lower affinity of amoxicillin towards the carbon adsorbent. Dynamic adsorption of binary mixtures from wastewater of high salinity and alkalinity showed a slight increase in IBU uptake and a reduced adsorption of AMX, demonstrating the feasibility of the simultaneous removal of both compounds from complex water matrices. Copyright © 2014 Elsevier Inc. All rights reserved.

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

Jain, Richa Naja, E-mail: ltprichanaja@gmail.com; Chakraborty, Brahmananda; Ramaniah, Lavanya M.

In this work our main objective is to compute Dynamical correlations, Onsager coefficients and Maxwell-Stefan (MS) diffusivities for molten salt LiF-KF mixture at various thermodynamic states through Green–Kubo formalism for the first time. The equilibrium molecular dynamics (MD) simulations were performed using BHM potential for LiF–KF mixture. The velocity autocorrelations functions involving Li ions reflect the endurance of cage dynamics or backscattering with temperature. The magnitude of Onsager coefficients for all pairs increases with increase in temperature. Interestingly most of the Onsager coefficients has almost maximum magnitude at the eutectic composition indicating the most dynamic character of the eutectic mixture.more » MS diffusivity hence diffusion for all ion pairs increases in the system with increasing temperature. Smooth variation of the diffusivity values denies any network formation in the mixture. Also, the striking feature is the noticeable concentration dependence of MS diffusivity between cation-cation pair, Đ{sub Li-K} which remains negative for most of the concentration range but changes sign to become positive for higher LiF concentration. The negative MS diffusivity is acceptable as it satisfies the non-negative entropy constraint governed by 2{sup nd} law of thermodynamics. This high diffusivity also vouches the candidature of molten salt as a coolant.« less

Graph-based structural change detection for rotating machinery monitoring

NASA Astrophysics Data System (ADS)

Lu, Guoliang; Liu, Jie; Yan, Peng

2018-01-01

Detection of structural changes is critically important in operational monitoring of a rotating machine. This paper presents a novel framework for this purpose, where a graph model for data modeling is adopted to represent/capture statistical dynamics in machine operations. Meanwhile we develop a numerical method for computing temporal anomalies in the constructed graphs. The martingale-test method is employed for the change detection when making decisions on possible structural changes, where excellent performance is demonstrated outperforming exciting results such as the autoregressive-integrated-moving average (ARIMA) model. Comprehensive experimental results indicate good potentials of the proposed algorithm in various engineering applications. This work is an extension of a recent result (Lu et al., 2017).

A statistical model of the human core-temperature circadian rhythm

NASA Technical Reports Server (NTRS)

Brown, E. N.; Choe, Y.; Luithardt, H.; Czeisler, C. A.

2000-01-01

We formulate a statistical model of the human core-temperature circadian rhythm in which the circadian signal is modeled as a van der Pol oscillator, the thermoregulatory response is represented as a first-order autoregressive process, and the evoked effect of activity is modeled with a function specific for each circadian protocol. The new model directly links differential equation-based simulation models and harmonic regression analysis methods and permits statistical analysis of both static and dynamical properties of the circadian pacemaker from experimental data. We estimate the model parameters by using numerically efficient maximum likelihood algorithms and analyze human core-temperature data from forced desynchrony, free-run, and constant-routine protocols. By representing explicitly the dynamical effects of ambient light input to the human circadian pacemaker, the new model can estimate with high precision the correct intrinsic period of this oscillator ( approximately 24 h) from both free-run and forced desynchrony studies. Although the van der Pol model approximates well the dynamical features of the circadian pacemaker, the optimal dynamical model of the human biological clock may have a harmonic structure different from that of the van der Pol oscillator.

Modified Confidence Intervals for the Mean of an Autoregressive Process.

DTIC Science & Technology

1985-08-01

Validity of the method 45 3.6 Theorem 47 4 Derivation of corrections 48 Introduction 48 The zero order pivot 50 4.1 Algorithm 50 CONTENTS The first...of standard confidence intervals. There are several standard methods of setting confidence intervals in simulations, including the regener- ative... method , batch means, and time series methods . We-will focus-s on improved confidence intervals for the mean of an autoregressive process, and as such our

Autoregressive Methods for Spectral Estimation from Interferograms.

DTIC Science & Technology

1986-09-19

RL83 6?6 AUTOREGRESSIVE METHODS FOR SPECTRAL. ESTIMTION FROM / SPACE ENGINEERING E N RICHARDS ET AL. 19 SEPINEFRGAS.()UA TT NV GNCNE O C: 31SSF...was AUG1085 performed under subcontract to . Center for Space Engineering Utah State University Logan, UT 84322-4140 4 4 Scientific Report No. 17 AFGL...MONITORING ORGANIZATION Center for Space Engineering (iapplicable) Air Force Geophysics Laboratory e. AORESS (City. State and ZIP Code) 7b. AOORESS (City

Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model

NASA Astrophysics Data System (ADS)

Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman

2017-08-01

Seismic data is usually modelled using autoregressive processes. The aim of this paper is to find the arrival times of the seismic waves of Mt. Rinjani in Indonesia. Kitagawa algorithm's is used to detect the seismic P and S-wave. Householder transformation used in the algorithm made it effectively finding the number of change points and parameters of the autoregressive models. The results show that the use of Box-Cox transformation on the variable selection level makes the algorithm works well in detecting the change points. Furthermore, when the basic span of the subinterval is set 200 seconds and the maximum AR order is 20, there are 8 change points which occur at 1601, 2001, 7401, 7601,7801, 8001, 8201 and 9601. Finally, The P and S-wave arrival times are detected at time 1671 and 2045 respectively using a precise detection algorithm.

Spatially explicit dynamic N-mixture models

USGS Publications Warehouse

Zhao, Qing; Royle, Andy; Boomer, G. Scott

2017-01-01

Knowledge of demographic parameters such as survival, reproduction, emigration, and immigration is essential to understand metapopulation dynamics. Traditionally the estimation of these demographic parameters requires intensive data from marked animals. The development of dynamic N-mixture models makes it possible to estimate demographic parameters from count data of unmarked animals, but the original dynamic N-mixture model does not distinguish emigration and immigration from survival and reproduction, limiting its ability to explain important metapopulation processes such as movement among local populations. In this study we developed a spatially explicit dynamic N-mixture model that estimates survival, reproduction, emigration, local population size, and detection probability from count data under the assumption that movement only occurs among adjacent habitat patches. Simulation studies showed that the inference of our model depends on detection probability, local population size, and the implementation of robust sampling design. Our model provides reliable estimates of survival, reproduction, and emigration when detection probability is high, regardless of local population size or the type of sampling design. When detection probability is low, however, our model only provides reliable estimates of survival, reproduction, and emigration when local population size is moderate to high and robust sampling design is used. A sensitivity analysis showed that our model is robust against the violation of the assumption that movement only occurs among adjacent habitat patches, suggesting wide applications of this model. Our model can be used to improve our understanding of metapopulation dynamics based on count data that are relatively easy to collect in many systems.

Molecular dynamics simulations of the structure and single-particle dynamics of mixtures of divalent salts and ionic liquids

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

Gómez-González, Víctor; Docampo-Álvarez, Borja; Gallego, Luis J.

2015-09-28

We report a molecular dynamics study of the structure and single-particle dynamics of mixtures of a protic (ethylammonium nitrate) and an aprotic (1-butyl-3-methylimidazolium hexaflurophosphate [BMIM][PF{sub 6}]) room-temperature ionic liquids doped with magnesium and calcium salts with a common anion at 298.15 K and 1 atm. The solvation of these divalent cations in dense ionic environments is analyzed by means of apparent molar volumes of the mixtures, radial distribution functions, and coordination numbers. For the protic mixtures, the effect of salt concentration on the network of hydrogen bonds is also considered. Moreover, single-particle dynamics of the salt cations is studied by means ofmore » their velocity autocorrelation functions and vibrational densities of states, explicitly analyzing the influence of salt concentration, and cation charge and mass on these magnitudes. The effect of the valency of the salt cation on these properties is considered comparing the results with those for the corresponding mixtures with lithium salts. We found that the main structural and dynamic features of the local solvation of divalent cations in ionic liquids are similar to those of monovalent salts, with cations being localized in the polar nanoregions of the bulk mixture coordinated in monodentate and bidentate coordination modes by the [NO{sub 3}]{sup −} and [PF{sub 6}]{sup −} anions. However, stronger electrostatic correlations of these polar nanoregions than in mixtures with salts with monovalent cations are found. The vibrational modes of the ionic liquid (IL) are seen to be scarcely affected by the addition of the salt, and the effect of mass and charge on the vibrational densities of states of the dissolved cations is reported. Cation mass is seen to exert a deeper influence than charge on the low-frequency vibrational spectra, giving a red shift of the vibrational modes and a virtual suppression of the higher energy vibrational modes for the heavier Ca{sup 2+} cations. No qualitative difference with monovalent cations was found in what solvation is concerned, which suggests that no enhanced reduction of the mobility of these cations and their complexes in ILs respective to those of monovalent cations is to be expected.« less

Using dynamic N-mixture models to test cavity limitation on northern flying squirrel demographic parameters using experimental nest box supplementation.

PubMed

Priol, Pauline; Mazerolle, Marc J; Imbeau, Louis; Drapeau, Pierre; Trudeau, Caroline; Ramière, Jessica

2014-06-01

Dynamic N-mixture models have been recently developed to estimate demographic parameters of unmarked individuals while accounting for imperfect detection. We propose an application of the Dail and Madsen (2011: Biometrics, 67, 577-587) dynamic N-mixture model in a manipulative experiment using a before-after control-impact design (BACI). Specifically, we tested the hypothesis of cavity limitation of a cavity specialist species, the northern flying squirrel, using nest box supplementation on half of 56 trapping sites. Our main purpose was to evaluate the impact of an increase in cavity availability on flying squirrel population dynamics in deciduous stands in northwestern Québec with the dynamic N-mixture model. We compared abundance estimates from this recent approach with those from classic capture-mark-recapture models and generalized linear models. We compared apparent survival estimates with those from Cormack-Jolly-Seber (CJS) models. Average recruitment rate was 6 individuals per site after 4 years. Nevertheless, we found no effect of cavity supplementation on apparent survival and recruitment rates of flying squirrels. Contrary to our expectations, initial abundance was not affected by conifer basal area (food availability) and was negatively affected by snag basal area (cavity availability). Northern flying squirrel population dynamics are not influenced by cavity availability at our deciduous sites. Consequently, we suggest that this species should not be considered an indicator of old forest attributes in our study area, especially in view of apparent wide population fluctuations across years. Abundance estimates from N-mixture models were similar to those from capture-mark-recapture models, although the latter had greater precision. Generalized linear mixed models produced lower abundance estimates, but revealed the same relationship between abundance and snag basal area. Apparent survival estimates from N-mixture models were higher and less precise than those from CJS models. However, N-mixture models can be particularly useful to evaluate management effects on animal populations, especially for species that are difficult to detect in situations where individuals cannot be uniquely identified. They also allow investigating the effects of covariates at the site level, when low recapture rates would require restricting classic CMR analyses to a subset of sites with the most captures.

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

PubMed

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

Molecular Approach to the Synergistic Effect on Astringency Elicited by Mixtures of Flavanols.

PubMed

Ramos-Pineda, Alba María; García-Estévez, Ignacio; Brás, Natércia F; Martín Del Valle, Eva M; Dueñas, Montserrat; Escribano Bailón, María Teresa

2017-08-09

The interactions between salivary proteins and wine flavanols (catechin, epicatechin, and mixtures thereof) have been studied by HPLC-DAD, isothermal titration microcalorimetry, and molecular dynamics simulations. Chromatographic results suggest that the presence of these flavanol mixtures could facilitate the formation of precipitates to the detriment of soluble aggregates. Comparison between the thermodynamic parameters obtained showed remarkably higher negative values of ΔG in the system containing the mixture of both flavanols in comparison to the systems containing individual flavanols, indicating a more favorable scenario in the mixing system. Also, the apparent binding constants were higher in this system. Furthermore, molecular dynamics simulations suggested a faster and greater cooperative binding of catechin and epicatechin to IB7 14 peptides when both types of flavanols are present simultaneously in solution.

SI-traceable and dynamic reference gas mixtures for water vapour at polar and high troposphere atmospheric levels

NASA Astrophysics Data System (ADS)

Guillevic, Myriam; Pascale, Céline; Mutter, Daniel; Wettstein, Sascha; Niederhauser, Bernhard

2017-04-01

In the framework of METAS' AtmoChem-ECV project, new facilities are currently being developed to generate reference gas mixtures for water vapour at concentrations measured in the high troposphere and polar regions, in the range 1-20 µmol/mol (ppm). The generation method is dynamic (the mixture is produced continuously over time) and SI-traceable (i.e. the amount of substance fraction in mole per mole is traceable to the definition of SI-units). The generation process is composed of three successive steps. The first step is to purify the matrix gas, nitrogen or synthetic air. Second, this matrix gas is spiked with the pure substance using a permeation technique: a permeation device contains a few grams of pure water in liquid form and loses it linearly over time by permeation through a membrane. In a third step, to reach the desired concentration, the first, high concentration mixture exiting the permeation chamber is then diluted with a chosen flow of matrix gas with one or two subsequent dilution steps. All flows are piloted by mass flow controllers. All parts in contact with the gas mixture are passivated using coated surfaces, to reduce adsorption/desorption processes as much as possible. The mixture can eventually be directly used to calibrate an analyser. The standard mixture produced by METAS' dynamic setup was injected into a chilled mirror from MBW Calibration AG, the designated institute for absolute humidity calibration in Switzerland. The used chilled mirror, model 373LX, is able to measure frost point and sample pressure and therefore calculate the water vapour concentration. This intercomparison of the two systems was performed in the range 4-18 ppm water vapour in synthetic air, at two different pressure levels, 1013.25 hPa and 2000 hPa. We present here METAS' dynamic setup, its uncertainty budget and the first results of the intercomparison with MBW's chilled mirror.

Anomalous Fluctuations in Autoregressive Models with Long-Term Memory

NASA Astrophysics Data System (ADS)

Sakaguchi, Hidetsugu; Honjo, Haruo

2015-10-01

An autoregressive model with a power-law type memory kernel is studied as a stochastic process that exhibits a self-affine-fractal-like behavior for a small time scale. We find numerically that the root-mean-square displacement Δ(m) for the time interval m increases with a power law as mα with α < 1/2 for small m but saturates at sufficiently large m. The exponent α changes with the power exponent of the memory kernel.

On the Stationarity of Multiple Autoregressive Approximants: Theory and Algorithms

DTIC Science & Technology

1976-08-01

a I (3.4) Hannan and Terrell (1972) consider problems of a similar nature. Efficient estimates A(1),... , A(p) , and i of A(1)... ,A(p) and...34Autoregressive model fitting for control, Ann . Inst. Statist. Math., 23, 163-180. Hannan, E. J. (1970), Multiple Time Series, New York, John Wiley...Hannan, E. J. and Terrell , R. D. (1972), "Time series regression with linear constraints, " International Economic Review, 13, 189-200. Masani, P

Forecasting Instability Indicators in the Horn of Africa

DTIC Science & Technology

2008-03-01

further than 2 (Makridakis, et al, 1983, 359). 2-32 Autoregressive Integrated Moving Average ( ARIMA ) Model . Similar to the ARMA model except for...stationary process. ARIMA models are described as ARIMA (p,d,q), where p is the order of the autoregressive process, d is the degree of the...differential process, and q is the order of the moving average process. The ARMA (1,1) model shown above is equivalent to an ARIMA (1,0,1) model . An ARIMA

Equilibrium Policy Proposals with Abstentions.

DTIC Science & Technology

1981-05-01

David M. Kreps. 262. ’Autoregressive Modelling and Money Income (ajusality Detection." by (heng lisiao. 263. "Measurement IError in a Dynamiic...34Autoregressive Modeling of"Canadian Money and Income Data," by Cheng Ilsjao. 277. "We Can’t Disagree IForever," by John 1). Geanakoplos and Heraklis...34*Optimal & Voluntary Income Distribution," by K. J. Arrow. 289. "’Asymptotic Values mif Mixed Gaime,.," by Abraham Neymnan. 290. "Tinie Series Modelling

Time series models on analysing mortality rates and acute childhood lymphoid leukaemia.

PubMed

Kis, Maria

2005-01-01

In this paper we demonstrate applying time series models on medical research. The Hungarian mortality rates were analysed by autoregressive integrated moving average models and seasonal time series models examined the data of acute childhood lymphoid leukaemia.The mortality data may be analysed by time series methods such as autoregressive integrated moving average (ARIMA) modelling. This method is demonstrated by two examples: analysis of the mortality rates of ischemic heart diseases and analysis of the mortality rates of cancer of digestive system. Mathematical expressions are given for the results of analysis. The relationships between time series of mortality rates were studied with ARIMA models. Calculations of confidence intervals for autoregressive parameters by tree methods: standard normal distribution as estimation and estimation of the White's theory and the continuous time case estimation. Analysing the confidence intervals of the first order autoregressive parameters we may conclude that the confidence intervals were much smaller than other estimations by applying the continuous time estimation model.We present a new approach to analysing the occurrence of acute childhood lymphoid leukaemia. We decompose time series into components. The periodicity of acute childhood lymphoid leukaemia in Hungary was examined using seasonal decomposition time series method. The cyclic trend of the dates of diagnosis revealed that a higher percent of the peaks fell within the winter months than in the other seasons. This proves the seasonal occurrence of the childhood leukaemia in Hungary.

EEG data reduction by means of autoregressive representation and discriminant analysis procedures.

PubMed

Blinowska, K J; Czerwosz, L T; Drabik, W; Franaszczuk, P J; Ekiert, H

1981-06-01

A program for automatic evaluation of EEG spectra, providing considerable reduction of data, was devised. Artefacts were eliminated in two steps: first, the longer duration eye movement artefacts were removed by a fast and simple 'moving integral' methods, then occasional spikes were identified by means of a detection function defined in the formalism of the autoregressive (AR) model. The evaluation of power spectra was performed by means of an FFT and autoregressive representation, which made possible the comparison of both methods. The spectra obtained by means of the AR model had much smaller statistical fluctuations and better resolution, enabling us to follow the time changes of the EEG pattern. Another advantage of the autoregressive approach was the parametric description of the signal. This last property appeared to be essential in distinguishing the changes in the EEG pattern. In a drug study the application of the coefficients of the AR model as input parameters in the discriminant analysis, instead of arbitrary chosen frequency bands, brought a significant improvement in distinguishing the effects of the medication. The favourable properties of the AR model are connected with the fact that the above approach fulfils the maximum entropy principle. This means that the method describes in a maximally consistent way the available information and is free from additional assumptions, which is not the case for the FFT estimate.

Prediction of global ionospheric VTEC maps using an adaptive autoregressive model

NASA Astrophysics Data System (ADS)

Wang, Cheng; Xin, Shaoming; Liu, Xiaolu; Shi, Chuang; Fan, Lei

2018-02-01

In this contribution, an adaptive autoregressive model is proposed and developed to predict global ionospheric vertical total electron content maps (VTEC). Specifically, the spherical harmonic (SH) coefficients are predicted based on the autoregressive model, and the order of the autoregressive model is determined adaptively using the F-test method. To test our method, final CODE and IGS global ionospheric map (GIM) products, as well as altimeter TEC data during low and mid-to-high solar activity period collected by JASON, are used to evaluate the precision of our forecasting products. Results indicate that the predicted products derived from the model proposed in this paper have good consistency with the final GIMs in low solar activity, where the annual mean of the root-mean-square value is approximately 1.5 TECU. However, the performance of predicted vertical TEC in periods of mid-to-high solar activity has less accuracy than that during low solar activity periods, especially in the equatorial ionization anomaly region and the Southern Hemisphere. Additionally, in comparison with forecasting products, the final IGS GIMs have the best consistency with altimeter TEC data. Future work is needed to investigate the performance of forecasting products using the proposed method in an operational environment, rather than using the SH coefficients from the final CODE products, to understand the real-time applicability of the method.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Predicting the shock compression response of heterogeneous powder mixtures

NASA Astrophysics Data System (ADS)

Fredenburg, D. A.; Thadhani, N. N.

2013-06-01

A model framework for predicting the dynamic shock-compression response of heterogeneous powder mixtures using readily obtained measurements from quasi-static tests is presented. Low-strain-rate compression data are first analyzed to determine the region of the bulk response over which particle rearrangement does not contribute to compaction. This region is then fit to determine the densification modulus of the mixture, σD, an newly defined parameter describing the resistance of the mixture to yielding. The measured densification modulus, reflective of the diverse yielding phenomena that occur at the meso-scale, is implemented into a rate-independent formulation of the P-α model, which is combined with an isobaric equation of state to predict the low and high stress dynamic compression response of heterogeneous powder mixtures. The framework is applied to two metal + metal-oxide (thermite) powder mixtures, and good agreement between the model and experiment is obtained for all mixtures at stresses near and above those required to reach full density. At lower stresses, rate-dependencies of the constituents, and specifically those of the matrix constituent, determine the ability of the model to predict the measured response in the incomplete compaction regime.

Dynamics of dense granular flows of small-and-large-grain mixtures in an ambient fluid.

PubMed

Meruane, C; Tamburrino, A; Roche, O

2012-08-01

Dense grain flows in nature consist of a mixture of solid constituents that are immersed in an ambient fluid. In order to obtain a good representation of these flows, the interaction mechanisms between the different constituents of the mixture should be considered. In this article, we study the dynamics of a dense granular flow composed of a binary mixture of small and large grains immersed in an ambient fluid. In this context, we extend the two-phase approach proposed by Meruane et al. [J. Fluid Mech. 648, 381 (2010)] to the case of flowing dense binary mixtures of solid particles, by including in the momentum equations a constitutive relation that describes the interaction mechanisms between the solid constituents in a dense regime. These coupled equations are solved numerically and validated by comparing the numerical results with experimental measurements of the front speed of gravitational granular flows resulting from the collapse, in ambient air or water, of two-dimensional granular columns that consisted of mixtures of small and large spherical particles of equal mass density. Our results suggest that the model equations include the essential features that describe the dynamics of grains flows of binary mixtures in an ambient fluid. In particular, it is shown that segregation of small and large grains can increase the front speed because of the volumetric expansion of the flow. This increase in flow speed is damped by the interaction forces with the ambient fluid, and this behavior is more pronounced in water than in air.

The excitation and characteristic frequency of the long-period volcanic event: An approach based on an inhomogeneous autoregressive model of a linear dynamic system

USGS Publications Warehouse

Nakano, M.; Kumagai, H.; Kumazawa, M.; Yamaoka, K.; Chouet, B.A.

1998-01-01

We present a method to quantify the source excitation function and characteristic frequencies of long-period volcanic events. The method is based on an inhomogeneous autoregressive (AR) model of a linear dynamic system, in which the excitation is assumed to be a time-localized function applied at the beginning of the event. The tail of an exponentially decaying harmonic waveform is used to determine the characteristic complex frequencies of the event by the Sompi method. The excitation function is then derived by operating an AR filter constructed from the characteristic frequencies to the entire seismogram of the event, including the inhomogeneous part of the signal. We apply this method to three long-period events at Kusatsu-Shirane Volcano, central Japan, whose waveforms display simple decaying monochromatic oscillations except for the beginning of the events. We recover time-localized excitation functions lasting roughly 1 s at the start of each event and find that the estimated functions are very similar to each other at all the stations of the seismic network for each event. The phases of the characteristic oscillations referred to the estimated excitation function fall within a narrow range for almost all the stations. These results strongly suggest that the excitation and mode of oscillation are both dominated by volumetric change components. Each excitation function starts with a pronounced dilatation consistent with a sudden deflation of the volumetric source which may be interpreted in terms of a choked-flow transport mechanism. The frequency and Q of the characteristic oscillation both display a temporal evolution from event to event. Assuming a crack filled with bubbly water as seismic source for these events, we apply the Van Wijngaarden-Papanicolaou model to estimate the acoustic properties of the bubbly liquid and find that the observed changes in the frequencies and Q are consistently explained by a temporal change in the radii of the bubbles characterizing the bubbly water in the crack.

Dynamics of polymerization induced phase separation in reactive polymer blends

NASA Astrophysics Data System (ADS)

Lee, Jaehyung

Mechanisms and dynamics of phase decomposition following polymerization induced phase separation (PIPS) of reactive polymer blends have been investigated experimentally and theoretically. The phenomenon of PIPS is a non-equilibrium and non-linear dynamic process. The mechanism of PIPS has been thought to be a nucleation and growth (NG) type originally, however, newer results indicate spinodal decomposition (SD). In PIPS, the coexistence curve generally passes through the reaction temperature at off-critical compositions, thus phase separation has to be initiated first in the metastable region where nucleation occurs. When the system farther drifts from the metastable to unstable region, the NG structure transforms to the SD bicontinuous morphology. The crossover behavior of PIPS may be called nucleation initiated spinodal decomposition (NISD). The formation of newer domains between the existing ones is responsible for the early stage of PIPS. Since PIPS is non- equilibrium kinetic process, it would not be surprising to discern either or both structures. The phase separation dynamics of DGEBA/CTBN mixtures having various kinds of curing agents from low reactivity to high reactivity and various amount of curing agents were examined at various reaction temperatures. The phase separation behavior was monitored by a quantity of scattered light intensity experimentally and by a quantity of collective structure factor numerically. Prior to the study of phase separation dynamics, a preliminary investigation on the isothermal cure behavior of the mixtures were executed in order to determine reaction kinetics parameters. The cure behavior followed the overall second order reaction kinetics. Next, based on the knowledge obtained from the phase separation dynamics study of DGEBA/CTBN mixtures, the phase separation dynamics of various composition of DGEBA/R45EPI mixtures having MDA as a curing agent were investigated. The phase separation behavior was quite dependent upon the composition variation. R45EPI itself can react with itself or with DGEBA without curing, therefore three-component system was considered in this mixture. For the numerical studies of this three- component mixture, a system that is composed of a reactive component-1 that is miscible with its growing molecules and another reactive component-2 that is not miscible with its growing molecules was considered with crosslinking reaction kinetics of the each component.

Sedimentation of a two-dimensional colloidal mixture exhibiting liquid-liquid and gas-liquid phase separation: a dynamical density functional theory study.

PubMed

Malijevský, Alexandr; Archer, Andrew J

2013-10-14

We present dynamical density functional theory results for the time evolution of the density distribution of a sedimenting model two-dimensional binary mixture of colloids. The interplay between the bulk phase behaviour of the mixture, its interfacial properties at the confining walls, and the gravitational field gives rise to a rich variety of equilibrium and non-equilibrium morphologies. In the fluid state, the system exhibits both liquid-liquid and gas-liquid phase separation. As the system sediments, the phase separation significantly affects the dynamics and we explore situations where the final state is a coexistence of up to three different phases. Solving the dynamical equations in two-dimensions, we find that in certain situations the final density profiles of the two species have a symmetry that is different from that of the external potentials, which is perhaps surprising, given the statistical mechanics origin of the theory. The paper concludes with a discussion on this.

Empirical evaluation of sufficient similarity in dose-response for environmental risk assessment of a mixture of 11 pyrethroids.

EPA Science Inventory

Chemical mixtures in the environment are often the result of a dynamic process. When dose-response data are available on random samples throughout the process, equivalence testing can be used to determine whether the mixtures are sufficiently similar based on a pre-specified biol...

Reinforcement learning for partially observable dynamic processes: adaptive dynamic programming using measured output data.

PubMed

Lewis, F L; Vamvoudakis, Kyriakos G

2011-02-01

Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q -learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.

Explosion hazards of LPG-air mixtures in vented enclosure with obstacles.

PubMed

Zhang, Qi; Wang, Yaxing; Lian, Zhen

2017-07-15

Numerical simulations were performed to study explosion characteristics of liquefied petroleum gas (LPG) explosion in enclosure with a vent. Unlike explosion overpressure and dynamic pressure, explosion temperature of the LPG-air mixture at a given concentration in a vented enclosure has very little variation with obstacle numbers for a given blockage ratio. For an enclosure without obstacle, explosion overpressures for the stoichiometric mixtures and the fuel-lean mixtures reach their maximum within the vent and that for fuel-rich mixture reaches its maximum beyond and near the vent. Dynamic pressures produced by an indoor LPG explosion reach their maximum always beyond the vent no matter obstacles are present or not in the enclosure. A LPG explosion in a vented enclosure with built-in obstacles is strong enough to make the brick and mortar wall with a thickness of 370mm damaged. If there is no obstacle in the enclosure, the lower explosion pressure of several kPa can not break the brick and mortar wall with a thickness of 370mm. For a LPG explosion produced in an enclosure with a vent, main hazards, within the vent, are overpressure and high temperature. However main hazards are dynamic pressure, blast wind, and high temperature beyond the vent. Copyright © 2017 Elsevier B.V. All rights reserved.

Predation and fragmentation portrayed in the statistical structure of prey time series

PubMed Central

Hendrichsen, Ditte K; Topping, Chris J; Forchhammer, Mads C

2009-01-01

Background Statistical autoregressive analyses of direct and delayed density dependence are widespread in ecological research. The models suggest that changes in ecological factors affecting density dependence, like predation and landscape heterogeneity are directly portrayed in the first and second order autoregressive parameters, and the models are therefore used to decipher complex biological patterns. However, independent tests of model predictions are complicated by the inherent variability of natural populations, where differences in landscape structure, climate or species composition prevent controlled repeated analyses. To circumvent this problem, we applied second-order autoregressive time series analyses to data generated by a realistic agent-based computer model. The model simulated life history decisions of individual field voles under controlled variations in predator pressure and landscape fragmentation. Analyses were made on three levels: comparisons between predated and non-predated populations, between populations exposed to different types of predators and between populations experiencing different degrees of habitat fragmentation. Results The results are unambiguous: Changes in landscape fragmentation and the numerical response of predators are clearly portrayed in the statistical time series structure as predicted by the autoregressive model. Populations without predators displayed significantly stronger negative direct density dependence than did those exposed to predators, where direct density dependence was only moderately negative. The effects of predation versus no predation had an even stronger effect on the delayed density dependence of the simulated prey populations. In non-predated prey populations, the coefficients of delayed density dependence were distinctly positive, whereas they were negative in predated populations. Similarly, increasing the degree of fragmentation of optimal habitat available to the prey was accompanied with a shift in the delayed density dependence, from strongly negative to gradually becoming less negative. Conclusion We conclude that statistical second-order autoregressive time series analyses are capable of deciphering interactions within and across trophic levels and their effect on direct and delayed density dependence. PMID:19419539

Evaluation of fatigue life of CRM-reinforced SMA and its relationship to dynamic stiffness.

PubMed

Mashaan, Nuha Salim; Karim, Mohamed Rehan; Abdel Aziz, Mahrez; Ibrahim, Mohd Rasdan; Katman, Herda Yati; Koting, Suhana

2014-01-01

Fatigue cracking is an essential problem of asphalt concrete that contributes to pavement damage. Although stone matrix asphalt (SMA) has significantly provided resistance to rutting failure, its resistance to fatigue failure is yet to be fully addressed. The aim of this study is to evaluate the effect of crumb rubber modifier (CRM) on stiffness and fatigue properties of SMA mixtures at optimum binder content, using four different modification levels, namely, 6%, 8%, 10%, and 12% CRM by weight of the bitumen. The testing undertaken on the asphalt mix comprises the dynamic stiffness (indirect tensile test), dynamic creep (repeated load creep), and fatigue test (indirect tensile fatigue test) at temperature of 25°C. The indirect tensile fatigue test was conducted at three different stress levels (200, 300, and 400 kPa). Experimental results indicate that CRM-reinforced SMA mixtures exhibit significantly higher fatigue life compared to the mixtures without CRM. Further, higher correlation coefficient was obtained between the fatigue life and resilient modulus as compared to permanent strain; thus resilient modulus might be a more reliable indicator in evaluating the fatigue life of asphalt mixture.

Response of macroinvertebrate communities to temporal dynamics of pesticide mixtures: A case study from the Sacramento River watershed, California.

PubMed

Chiu, Ming-Chih; Hunt, Lisa; Resh, Vincent H

2016-12-01

Pesticide pollution from agricultural field run-off or spray drift has been documented to impact river ecosystems worldwide. However, there is limited data on short- and long-term effects of repeated pulses of pesticide mixtures on biotic assemblages in natural systems. We used reported pesticide application data as input to a hydrological fate and transport model (Soil and Water Assessment Tool) to simulate spatiotemporal dynamics of pesticides mixtures in streams on a daily time-step. We then applied regression models to explore the relationship between macroinvertebrate communities and pesticide dynamics in the Sacramento River watershed of California during 2002-2013. We found that both maximum and average pesticide toxic units were important in determining impacts on macroinvertebrates, and that the compositions of macroinvertebrates trended toward taxa having higher resilience and resistance to pesticide exposure, based on the Species at Risk pesticide (SPEAR pesticides ) index. Results indicate that risk-assessment efforts can be improved by considering both short- and long-term effects of pesticide mixtures on macroinvertebrate community composition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Structure and Dynamics of Urea/Water Mixtures Investigated by Vibrational Spectroscopy and Molecular Dynamics Simulation

PubMed Central

Carr, J. K.; Buchanan, L. E.; Schmidt, J. R.; Zanni, M. T.; Skinner, J. L.

2013-01-01

Urea/water is an archetypical “biological” mixture, and is especially well known for its relevance to protein thermodynamics, as urea acts as a protein denaturant at high concentration. This behavior has given rise to an extended debate concerning urea’s influence on water structure. Based on a variety of methods and of definitions of water structure, urea has been variously described as a structure-breaker, a structure-maker, or as remarkably neutral towards water. Because of its sensitivity to microscopic structure and dynamics, vibrational spectroscopy can help resolve these debates. We report experimental and theoretical spectroscopic results for the OD stretch of HOD/H2O/urea mixtures (linear IR, 2DIR, and pump-probe anisotropy decay) and for the CO stretch of urea-D4/D2O mixtures (linear IR only). Theoretical results are obtained using existing approaches for water, and a modification of a frequency map developed for acetamide. All absorption spectra are remarkably insensitive to urea concentration, consistent with the idea that urea only very weakly perturbs water structure. Both this work and experiments by Rezus and Bakker, however, show that water’s rotational dynamics are slowed down by urea. Analysis of the simulations casts doubt on the suggestion that urea immobilizes particular doubly hydrogen bonded water molecules. PMID:23841646

Structural anomaly and dynamic heterogeneity in cycloether/water binary mixtures: Signatures from composition dependent dynamic fluorescence measurements and computer simulations

NASA Astrophysics Data System (ADS)

Indra, Sandipa; Guchhait, Biswajit; Biswas, Ranjit

2016-03-01

We have performed steady state UV-visible absorption and time-resolved fluorescence measurements and computer simulations to explore the cosolvent mole fraction induced changes in structural and dynamical properties of water/dioxane (Diox) and water/tetrahydrofuran (THF) binary mixtures. Diox is a quadrupolar solvent whereas THF is a dipolar one although both are cyclic molecules and represent cycloethers. The focus here is on whether these cycloethers can induce stiffening and transition of water H-bond network structure and, if they do, whether such structural modification differentiates the chemical nature (dipolar or quadrupolar) of the cosolvent molecules. Composition dependent measured fluorescence lifetimes and rotation times of a dissolved dipolar solute (Coumarin 153, C153) suggest cycloether mole-fraction (XTHF/Diox) induced structural transition for both of these aqueous binary mixtures in the 0.1 ≤ XTHF/Diox ≤ 0.2 regime with no specific dependence on the chemical nature. Interestingly, absorption measurements reveal stiffening of water H-bond structure in the presence of both the cycloethers at a nearly equal mole-fraction, XTHF/Diox ˜ 0.05. Measurements near the critical solution temperature or concentration indicate no role for the solution criticality on the anomalous structural changes. Evidences for cycloether aggregation at very dilute concentrations have been found. Simulated radial distribution functions reflect abrupt changes in respective peak heights at those mixture compositions around which fluorescence measurements revealed structural transition. Simulated water coordination numbers (for a dissolved C153) and number of H-bonds also exhibit minima around these cosolvent concentrations. In addition, several dynamic heterogeneity parameters have been simulated for both the mixtures to explore the effects of structural transition and chemical nature of cosolvent on heterogeneous dynamics of these systems. Simulated four-point dynamic susceptibility suggests formation of clusters inducing local heterogeneity in the solution structure.

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

PubMed Central

Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen

2014-01-01

Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922

Tailoring the properties of acetate-based ionic liquids using the tricyanomethanide anion.

PubMed

Lepre, L F; Szala-Bilnik, J; Padua, A A H; Traïkia, M; Ando, R A; Costa Gomes, M F

2016-08-17

The equilibrium and transport properties of mixtures of two ionic liquids - [C4C1Im][OAc] and [C4C1Im][C(CN)3] - were determined and interpreted at the molecular level using vibration spectroscopy, NMR and molecular dynamics simulation. The non-ideality of the mixtures [C4C1Im][OAc](1-x)[C(CN)3]x was characterized by V(E) = +0.28 cm(3) mol(-1) (293 K, x = 0.65) and H(E) = -2.2 kJ mol(-1) for x = 0.5. These values could be explained by a rearrangement of the hydrogen-bond network of the mixture that favours the interaction of the acetate anion with the imidazolium cation at position C2. The dynamic properties of the mixture are also dramatically influenced by the composition with a decrease of the viscosity and an increase of self-diffusion coefficients of the ions when the amount of tricyanomethanide anion increases in the mixture.

Processing on weak electric signals by the autoregressive model

NASA Astrophysics Data System (ADS)

Ding, Jinli; Zhao, Jiayin; Wang, Lanzhou; Li, Qiao

2008-10-01

A model of the autoregressive model of weak electric signals in two plants was set up for the first time. The result of the AR model to forecast 10 values of the weak electric signals is well. It will construct a standard set of the AR model coefficient of the plant electric signal and the environmental factor, and can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on agricultural productions.

Fisher Consistency of AM-Estimates of the Autoregression Parameter Using Hard Rejection Filter Cleaners

DTIC Science & Technology

1987-02-04

U5tr,)! P(U 5-t Since U - F with F RS, we get (3.1). Case b: 0 S 5 k -a Now P([U~t]riM) = P(UZk-a) and P([ Ugt ]rM) = P(US-k-a) S P(US-(k-a)) which again...robustness for autoregressive processes." The Annals of Statistics, 12, 843-863. Mallows, C.L. (1980). "Some theory of nonlinear smoothen." The Annals of

Forecast of severe fever with thrombocytopenia syndrome incidence with meteorological factors.

PubMed

Sun, Ji-Min; Lu, Liang; Liu, Ke-Ke; Yang, Jun; Wu, Hai-Xia; Liu, Qi-Yong

2018-06-01

Severe fever with thrombocytopenia syndrome (SFTS) is emerging and some studies reported that SFTS incidence was associated with meteorological factors, while no report on SFTS forecast models was reported up to date. In this study, we constructed and compared three forecast models using autoregressive integrated moving average (ARIMA) model, negative binomial regression model (NBM), and quasi-Poisson generalized additive model (GAM). The dataset from 2011 to 2015 were used for model construction and the dataset in 2016 were used for external validity assessment. All the three models fitted the SFTS cases reasonably well during the training process and forecast process, while the NBM model forecasted better than other two models. Moreover, we demonstrated that temperature and relative humidity played key roles in explaining the temporal dynamics of SFTS occurrence. Our study contributes to better understanding of SFTS dynamics and provides predictive tools for the control and prevention of SFTS. Copyright © 2018 Elsevier B.V. All rights reserved.

Self-organization in a bimotility mixture of model microswimmers

NASA Astrophysics Data System (ADS)

Agrawal, Adyant; Babu, Sujin B.

2018-02-01

We study the cooperation and segregation dynamics in a bimotility mixture of microorganisms which swim at low Reynolds numbers via periodic deformations along the body. We employ a multiparticle collision dynamics method to simulate a two component mixture of artificial swimmers, termed as Taylor lines, which differ from each other only in the propulsion speed. The analysis reveals that a contribution of slower swimmers towards clustering, on average, is much larger as compared to the faster ones. We notice distinctive self-organizing dynamics, depending on the percentage difference in the speed of the two kinds. If this difference is large, the faster ones fragment the clusters of the slower ones in order to reach the boundary and form segregated clusters. Contrarily, when it is small, both kinds mix together at first, the faster ones usually leading the cluster and then gradually the slower ones slide out thereby also leading to segregation.

Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model

NASA Astrophysics Data System (ADS)

Wang, Qijie

2015-08-01

The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.

Acceleration and Velocity Sensing from Measured Strain

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi; Truax, Roger

2015-01-01

A simple approach for computing acceleration and velocity of a structure from the strain is proposed in this study. First, deflection and slope of the structure are computed from the strain using a two-step theory. Frequencies of the structure are computed from the time histories of strain using a parameter estimation technique together with an autoregressive moving average model. From deflection, slope, and frequencies of the structure, acceleration and velocity of the structure can be obtained using the proposed approach. Simple harmonic motion is assumed for the acceleration computations, and the central difference equation with a linear autoregressive model is used for the computations of velocity. A cantilevered rectangular wing model is used to validate the simple approach. Quality of the computed deflection, acceleration, and velocity values are independent of the number of fibers. The central difference equation with a linear autoregressive model proposed in this study follows the target response with reasonable accuracy. Therefore, the handicap of the backward difference equation, phase shift, is successfully overcome.

Sleep analysis for wearable devices applying autoregressive parametric models.

PubMed

Mendez, M O; Villantieri, O; Bianchi, A; Cerutti, S

2005-01-01

We applied time-variant and time-invariant parametric models in both healthy subjects and patients with sleep disorder recordings in order to assess the skills of those approaches to sleep disorders diagnosis in wearable devices. The recordings present the Obstructive Sleep Apnea (OSA) pathology which is characterized by fluctuations in the heart rate, bradycardia in apneonic phase and tachycardia at the recovery of ventilation. Data come from a web database in www.physionet.org. During OSA the spectral indexes obtained by time-variant lattice filters presented oscillations that correspond to the changes brady-tachycardia of the RR intervals and greater values than healthy ones. Multivariate autoregressive models showed an increment in very low frequency component (PVLF) at each apneic event. Also a rise in high frequency component (PHF) occurred over the breathing restore in the spectrum of both quadratic coherence and cross-spectrum in OSA. These autoregressive parametric approaches could help in the diagnosis of Sleep Disorder inside of the wearable devices.

Water balance models in one-month-ahead streamflow forecasting

USGS Publications Warehouse

Alley, William M.

1985-01-01

Techniques are tested that incorporate information from water balance models in making 1-month-ahead streamflow forecasts in New Jersey. The results are compared to those based on simple autoregressive time series models. The relative performance of the models is dependent on the month of the year in question. The water balance models are most useful for forecasts of April and May flows. For the stations in northern New Jersey, the April and May forecasts were made in order of decreasing reliability using the water-balance-based approaches, using the historical monthly means, and using simple autoregressive models. The water balance models were useful to a lesser extent for forecasts during the fall months. For the rest of the year the improvements in forecasts over those obtained using the simpler autoregressive models were either very small or the simpler models provided better forecasts. When using the water balance models, monthly corrections for bias are found to improve minimum mean-square-error forecasts as well as to improve estimates of the forecast conditional distributions.

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

ReaxFF molecular dynamics simulation of intermolecular structure formation in acetic acid-water mixtures at elevated temperatures and pressures

NASA Astrophysics Data System (ADS)

Sengul, Mert Y.; Randall, Clive A.; van Duin, Adri C. T.

2018-04-01

The intermolecular structure formation in liquid and supercritical acetic acid-water mixtures was investigated using ReaxFF-based molecular dynamics simulations. The microscopic structures of acetic acid-water mixtures with different acetic acid mole fractions (1.0 ≥ xHAc ≥ 0.2) at ambient and critical conditions were examined. The potential energy surface associated with the dissociation of acetic acid molecules was calculated using a metadynamics procedure to optimize the dissociation energy of ReaxFF potential. At ambient conditions, depending on the acetic acid concentration, either acetic acid clusters or water clusters are dominant in the liquid mixture. When acetic acid is dominant (0.4 ≤ xHAc), cyclic dimers and chain structures between acetic acid molecules are present in the mixture. Both structures disappear at increased water content of the mixture. It was found by simulations that the acetic acid molecules released from these dimer and chain structures tend to stay in a dipole-dipole interaction. These structural changes are in agreement with the experimental results. When switched to critical conditions, the long-range interactions (e.g., second or fourth neighbor) disappear and the water-water and acetic acid-acetic acid structural formations become disordered. The simulated radial distribution function for water-water interactions is in agreement with experimental and computational studies. The first neighbor interactions between acetic acid and water molecules are preserved at relatively lower temperatures of the critical region. As higher temperatures are reached in the critical region, these interactions were observed to weaken. These simulations indicate that ReaxFF molecular dynamics simulations are an appropriate tool for studying supercritical water/organic acid mixtures.

Condition Monitoring for Helicopter Data. Appendix A

NASA Technical Reports Server (NTRS)

Wen, Fang; Willett, Peter; Deb, Somnath

2000-01-01

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.

PubMed

Hasenauer, Jan; Hasenauer, Christine; Hucho, Tim; Theis, Fabian J

2014-07-01

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.

Reciprocal Associations between Negative Affect, Binge Eating, and Purging in the Natural Environment in Women with Bulimia Nervosa

PubMed Central

Lavender, Jason M.; Utzinger, Linsey M.; Cao, Li; Wonderlich, Stephen A.; Engel, Scott G.; Mitchell, James E.; Crosby, Ross D.

2016-01-01

Although negative affect (NA) has been identified as a common trigger for bulimic behaviors, findings regarding NA following such behaviors have been mixed. This study examined reciprocal associations between NA and bulimic behaviors using real-time, naturalistic data. Participants were 133 women with DSM-IV bulimia nervosa (BN) who completed a two-week ecological momentary assessment (EMA) protocol in which they recorded bulimic behaviors and provided multiple daily ratings of NA. A multilevel autoregressive cross-lagged analysis was conducted to examine concurrent, first-order autoregressive, and prospective associations between NA, binge eating, and purging across the day. Results revealed positive concurrent associations between all variables across all time points, as well as numerous autoregressive associations. For prospective associations, higher NA predicted subsequent bulimic symptoms at multiple time points; conversely, binge eating predicted lower NA at multiple time points, and purging predicted higher NA at one time point. Several autoregressive and prospective associations were also found between binge eating and purging. This study used a novel approach to examine NA in relation to bulimic symptoms, contributing to the existing literature by directly examining the magnitude of the associations, examining differences in the associations across the day, and controlling for other associations in testing each effect in the model. These findings may have relevance for understanding the etiology and/or maintenance of bulimic symptoms, as well as potentially informing psychological interventions for BN. PMID:26692122

Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800-2014 period in the USA.

PubMed

Bildirici, Melike; Ersin, Özgür Ömer

2018-01-01

The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO 2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO 2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO 2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.

Multifractality and autoregressive processes of dry spell lengths in Europe: an approach to their complexity and predictability

NASA Astrophysics Data System (ADS)

Lana, X.; Burgueño, A.; Serra, C.; Martínez, M. D.

2017-01-01

Dry spell lengths, DSL, defined as the number of consecutive days with daily rain amounts below a given threshold, may provide relevant information about drought regimes. Taking advantage of a daily pluviometric database covering a great extension of Europe, a detailed analysis of the multifractality of the dry spell regimes is achieved. At the same time, an autoregressive process is applied with the aim of predicting DSL. A set of parameters, namely Hurst exponent, H, estimated from multifractal spectrum, f( α), critical Hölder exponent, α 0, for which f( α) reaches its maximum value, spectral width, W, and spectral asymmetry, B, permits a first clustering of European rain gauges in terms of the complexity of their DSL series. This set of parameters also allows distinguishing between time series describing fine- or smooth-structure of the DSL regime by using the complexity index, CI. Results of previous monofractal analyses also permits establishing comparisons between smooth-structures, relatively low correlation dimensions, notable predictive instability and anti-persistence of DSL for European areas, sometimes submitted to long droughts. Relationships are also found between the CI and the mean absolute deviation, MAD, and the optimum autoregressive order, OAO, of an ARIMA( p, d,0) autoregressive process applied to the DSL series. The detailed analysis of the discrepancies between empiric and predicted DSL underlines the uncertainty over predictability of long DSL, particularly for the Mediterranean region.

Dynamic Patterns in Mood Among Newly Diagnosed Patients With Major Depressive Episode or Panic Disorder and Normal Controls

PubMed Central

Katerndahl, David; Ferrer, Robert; Best, Rick; Wang, Chen-Pin

2007-01-01

Objective: The purpose of this pilot study was to compare the dynamic patterns of hourly mood variation among newly diagnosed primary care patients with major depressive disorder or panic disorder with patterns in patients with neither disorder. Method: Five adult patients with major depressive episode, 5 with panic disorder, and 5 with neither disorder were asked to complete hourly self-assessments of anxiety and depression (using 100-mm visual analog scales) for each hour they were awake during a 30-day period. Time series were analyzed using ARIMA (autoregression, integration, moving average) modeling (to assess periodicity), Lyapunov exponents (to assess sensitivity to initial conditions indicative of chaotic patterns), and correlation dimension saturation (to assess whether an attractor is limiting change). The study was conducted from March to June 2003. Results: Controls displayed circadian rhythms with underlying chaotic variability. Depressed patients did not display circadian rhythm, but did show chaotic dynamics. Panic disorder patients showed circadian rhythms, but 2 of the 4 patients completing the self-assessments displayed nonchaotic underlying patterns. Conclusions: Patients with major depressive disorder or panic disorder may differ from controls and from each other in their patterns of mood variability. There is a need for more research on the dynamics of mood among patients with mental disorders. PMID:17632650

Thermodynamic properties and diffusion of water + methane binary mixtures

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

Shvab, I.; Sadus, Richard J., E-mail: rsadus@swin.edu.au

2014-03-14

Thermodynamic and diffusion properties of water + methane mixtures in a single liquid phase are studied using NVT molecular dynamics. An extensive comparison is reported for the thermal pressure coefficient, compressibilities, expansion coefficients, heat capacities, Joule-Thomson coefficient, zero frequency speed of sound, and diffusion coefficient at methane concentrations up to 15% in the temperature range of 298–650 K. The simulations reveal a complex concentration dependence of the thermodynamic properties of water + methane mixtures. The compressibilities, heat capacities, and diffusion coefficients decrease with increasing methane concentration, whereas values of the thermal expansion coefficients and speed of sound increase. Increasing methanemore » concentration considerably retards the self-diffusion of both water and methane in the mixture. These effects are caused by changes in hydrogen bond network, solvation shell structure, and dynamics of water molecules induced by the solvation of methane at constant volume conditions.« less

Eleven years of crop diversification alters decomposition dynamics of litter mixtures incubated with soil

DOE PAGES

McDaniel, M. D.; Grandy, A. S.; Tiemann, L. K.; ...

2016-08-11

Agricultural crop rotations have been shown to increase soil carbon (C), nitrogen (N), and microbial biomass. The mechanisms behind these increases remain unclear, but may be linked to the diversity of crop residue inputs to soil organic matter (SOM). Here, we used a residue mixture incubation to examine how variation in long-term diversity of plant communities in agroecosystems influences decomposition of residue mixtures, thus providing a comparison of the effects of plant diversification on decomposition in the long term (via crop rotation) and short term (via residue mixtures). Three crop residue mixtures, ranging in diversity from two to four species,more » were incubated for 360 d with soils from five crop rotations, ranging from monoculture corn (mC) to a complex five-crop rotation. In response, we measured fundamental soil pools and processes underlying C and N cycling. These included soil respiration, inorganic N, microbial biomass, and extracellular enzymes. We hypothesized that soils with more diverse cropping histories would show greater synergistic mixture effects than mC. For most variables (except extracellular enzymes), crop rotation history, or the long-term history of plant diversity in the field, had a stronger effect on soil processes than mixture composition. In contrast to our hypothesis, the mC soil had nearly three and seven times greater synergistic mixture effects for respiration and microbial biomass N, respectively, compared with soils from crop rotations. This was due to the low response of the mC soils to poor quality residues (corn and wheat), likely resulting from a lack of available C and nutrients to cometabolize these residues. These results indicate that diversifying crop rotations in agricultural systems alter the decomposition dynamics of new residue inputs, which may be linked to the benefits of increasing crop rotation diversity on soil nutrient cycling, SOM dynamics, and yields.« less

Molecular Packing, Hydrogen Bonding, and Fast Dynamics in Lysozyme/Trehalose/Glycerol and Trehalose/Glycerol Glasses at Low Hydration.

PubMed

Lerbret, Adrien; Affouard, Frédéric

2017-10-12

Water and glycerol are well-known to facilitate the structural relaxation of amorphous protein matrices. However, several studies evidenced that they may also limit fast (∼picosecond-nanosecond, ps-ns) and small-amplitude (∼Å) motions of proteins, which govern their stability in freeze-dried sugar mixtures. To determine how they interact with proteins and sugars in glassy matrices and, thereby, modulate their fast dynamics, we performed molecular dynamics (MD) simulations of lysozyme/trehalose/glycerol (LTG) and trehalose/glycerol (TG) mixtures at low glycerol and water concentrations. Upon addition of glycerol and/or water, the glass transition temperature, T g , of LTG and TG mixtures decreases, the molecular packing of glasses is improved, and the mean-square displacements (MSDs) of lysozyme and trehalose either decrease or increase, depending on the time scale and on the temperature considered. A detailed analysis of the hydrogen bonds (HBs) formed between species reveals that water and glycerol may antiplasticize the fast dynamics of lysozyme and trehalose by increasing the total number and/or the strength of the HBs they form in glassy matrices.

Nanostructural organization in carbon disulfide/ionic liquid mixtures: Molecular dynamics simulations and optical Kerr effect spectroscopy

NASA Astrophysics Data System (ADS)

Yang, Peng; Voth, Gregory A.; Xiao, Dong; Hines, Larry G.; Bartsch, Richard A.; Quitevis, Edward L.

2011-07-01

In this paper, the nanostructural organization and subpicosecond intermolecular dynamics in the mixtures of CS2 and the room temperature ionic liquid (IL) 1-pentyl-3-methylimidazolium bis{(trifluoromethane)sulfonyl}amide ([C5mim][NTf2]) were studied as a function of concentration using molecular dynamics (MD) simulations and optical heterodyne-detected Raman-induced Kerr effect spectroscopy. At low CS2 concentrations (<10 mol.% CS2/IL), the MD simulations indicate that the CS2 molecules are localized in the nonpolar domains. In contrast, at higher concentrations (≥10 mol.% CS2/IL), the MD simulations show aggregation of the CS2 molecules. The optical Kerr effect (OKE) spectra of the mixtures are interpreted in terms of an additivity model with the components arising from the subpicosecond dynamics of CS2 and the IL. Comparison of the CS2-component with the OKE spectra of CS2 in alkane solvents is consistent with CS2 mainly being localized in the nonpolar domains, even at high CS2 concentrations, and the local CS2 concentration being higher than the bulk CS2 concentration.

Collapse and revival of the Fermi sea in a Bose-Fermi mixture

NASA Astrophysics Data System (ADS)

Iyer, Deepak; Will, Sebastian; Rigol, Marcos

2014-05-01

The collapse and revival of quantum fields is one of the most pristine forms of coherent quantum dynamics far from equilibrium. Until now, it has only been observed in the dynamical evolution of bosonic systems. We report on the first observation of the boson mediated collapse and revival of the Fermi sea in a Bose-Fermi mixture. Specifically, we present a simple model which captures the experimental observations shown in the talk titled Observation of Collapse and Revival Dynamics in the Fermionic Component of a Lattice Bose-Fermi Mixture by Sebastian Will. Our theoretical analysis shows why the results are robust to the presence of harmonic traps during the loading or the time evolution phase. It also makes apparent that the fermionic dynamics is independent of whether the bosonic component consists of a coherent state or localized Fock states with random occupation numbers. Because of the robustness of the experimental results, we argue that this kind of collapse and revival experiment can be used to accurately characterize interactions between bosons and fermions in a lattice.

A fractal approach to dynamic inference and distribution analysis

PubMed Central

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

2013-01-01

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

On hydrodynamic phase field models for binary fluid mixtures

NASA Astrophysics Data System (ADS)

Yang, Xiaogang; Gong, Yuezheng; Li, Jun; Zhao, Jia; Wang, Qi

2018-05-01

Two classes of thermodynamically consistent hydrodynamic phase field models have been developed for binary fluid mixtures of incompressible viscous fluids of possibly different densities and viscosities. One is quasi-incompressible, while the other is incompressible. For the same binary fluid mixture of two incompressible viscous fluid components, which one is more appropriate? To answer this question, we conduct a comparative study in this paper. First, we visit their derivation, conservation and energy dissipation properties and show that the quasi-incompressible model conserves both mass and linear momentum, while the incompressible one does not. We then show that the quasi-incompressible model is sensitive to the density deviation of the fluid components, while the incompressible model is not in a linear stability analysis. Second, we conduct a numerical investigation on coarsening or coalescent dynamics of protuberances using the two models. We find that they can predict quite different transient dynamics depending on the initial conditions and the density difference although they predict essentially the same quasi-steady results in some cases. This study thus cast a doubt on the applicability of the incompressible model to describe dynamics of binary mixtures of two incompressible viscous fluids especially when the two fluid components have a large density deviation.

An improved molecular dynamics algorithm to study thermodiffusion in binary hydrocarbon mixtures

NASA Astrophysics Data System (ADS)

Antoun, Sylvie; Saghir, M. Ziad; Srinivasan, Seshasai

2018-03-01

In multicomponent liquid mixtures, the diffusion flow of chemical species can be induced by temperature gradients, which leads to a separation of the constituent components. This cross effect between temperature and concentration is known as thermodiffusion or the Ludwig-Soret effect. The performance of boundary driven non-equilibrium molecular dynamics along with the enhanced heat exchange (eHEX) algorithm was studied by assessing the thermodiffusion process in n-pentane/n-decane (nC5-nC10) binary mixtures. The eHEX algorithm consists of an extended version of the HEX algorithm with an improved energy conservation property. In addition to this, the transferable potentials for phase equilibria-united atom force field were employed in all molecular dynamics (MD) simulations to precisely model the molecular interactions in the fluid. The Soret coefficients of the n-pentane/n-decane (nC5-nC10) mixture for three different compositions (at 300.15 K and 0.1 MPa) were calculated and compared with the experimental data and other MD results available in the literature. Results of our newly employed MD algorithm showed great agreement with experimental data and a better accuracy compared to other MD procedures.

Evaluation of Fatigue Life of CRM-Reinforced SMA and Its Relationship to Dynamic Stiffness

PubMed Central

Mashaan, Nuha Salim; Karim, Mohamed Rehan; Abdel Aziz, Mahrez; Ibrahim, Mohd Rasdan; Katman, Herda Yati

2014-01-01

Fatigue cracking is an essential problem of asphalt concrete that contributes to pavement damage. Although stone matrix asphalt (SMA) has significantly provided resistance to rutting failure, its resistance to fatigue failure is yet to be fully addressed. The aim of this study is to evaluate the effect of crumb rubber modifier (CRM) on stiffness and fatigue properties of SMA mixtures at optimum binder content, using four different modification levels, namely, 6%, 8%, 10%, and 12% CRM by weight of the bitumen. The testing undertaken on the asphalt mix comprises the dynamic stiffness (indirect tensile test), dynamic creep (repeated load creep), and fatigue test (indirect tensile fatigue test) at temperature of 25°C. The indirect tensile fatigue test was conducted at three different stress levels (200, 300, and 400 kPa). Experimental results indicate that CRM-reinforced SMA mixtures exhibit significantly higher fatigue life compared to the mixtures without CRM. Further, higher correlation coefficient was obtained between the fatigue life and resilient modulus as compared to permanent strain; thus resilient modulus might be a more reliable indicator in evaluating the fatigue life of asphalt mixture. PMID:25050406

Dielectric and spectroscopic study of binary mixture of Acrylonitrile with Chlorobenzene

NASA Astrophysics Data System (ADS)

Deshmukh, Snehal D.; Pattebahadur, K. L.; Mohod, A. G.; Undre, P. B.; Patil, S. S.; Khirade, P. W.

2018-05-01

In this paper, study of binary mixture of Acrylonitrile (ACN) with Chlorobenzene (CBZ) has been carried out at eleven concentrations at room temperature. The determined Dielectric Constant (ɛ0) Density (ρ) and Refractive index (nD) values of binary mixture are used to calculate the excess properties of mixture over the entire composition range and fitted to the Redlich-Kister equation. From the above parameters, intermolecular interaction and dynamics of molecules of binary mixture at molecular level are discussed. The Conformational analysis of the intermolecular interaction between Acrylonitrile and Chlorobenzene is supported by the FTIR spectra.

Numerical limitations in application of vector autoregressive modeling and Granger causality to analysis of EEG time series

NASA Astrophysics Data System (ADS)

Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.

2007-11-01

In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.

Inhomogeneous point-process entropy: An instantaneous measure of complexity in discrete systems

NASA Astrophysics Data System (ADS)

Valenza, Gaetano; Citi, Luca; Scilingo, Enzo Pasquale; Barbieri, Riccardo

2014-05-01

Measures of entropy have been widely used to characterize complexity, particularly in physiological dynamical systems modeled in discrete time. Current approaches associate these measures to finite single values within an observation window, thus not being able to characterize the system evolution at each moment in time. Here, we propose a new definition of approximate and sample entropy based on the inhomogeneous point-process theory. The discrete time series is modeled through probability density functions, which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed measures of entropy are instantaneously defined through probability functions, the novel indices are able to provide instantaneous tracking of the system complexity. The new measures are tested on synthetic data, as well as on real data gathered from heartbeat dynamics of healthy subjects and patients with cardiac heart failure and gait recordings from short walks of young and elderly subjects. Results show that instantaneous complexity is able to effectively track the system dynamics and is not affected by statistical noise properties.

Testing competing forms of the Milankovitch hypothesis: A multivariate approach

NASA Astrophysics Data System (ADS)

Kaufmann, Robert K.; Juselius, Katarina

2016-02-01

We test competing forms of the Milankovitch hypothesis by estimating the coefficients and diagnostic statistics for a cointegrated vector autoregressive model that includes 10 climate variables and four exogenous variables for solar insolation. The estimates are consistent with the physical mechanisms postulated to drive glacial cycles. They show that the climate variables are driven partly by solar insolation, determining the timing and magnitude of glaciations and terminations, and partly by internal feedback dynamics, pushing the climate variables away from equilibrium. We argue that the latter is consistent with a weak form of the Milankovitch hypothesis and that it should be restated as follows: internal climate dynamics impose perturbations on glacial cycles that are driven by solar insolation. Our results show that these perturbations are likely caused by slow adjustment between land ice volume and solar insolation. The estimated adjustment dynamics show that solar insolation affects an array of climate variables other than ice volume, each at a unique rate. This implies that previous efforts to test the strong form of the Milankovitch hypothesis by examining the relationship between solar insolation and a single climate variable are likely to suffer from omitted variable bias.

On fractality and chaos in Moroccan family business stock returns and volatility

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2017-05-01

The purpose of this study is to examine existence of fractality and chaos in returns and volatilities of family business companies listed on the Casablanca Stock Exchange (CSE) in Morocco, and also in returns and volatility of the CSE market index. Detrended fluctuation analysis based Hurst exponent and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) model are used to quantify fractality in returns and volatility time series respectively. Besides, the largest Lyapunov exponent is employed to quantify chaos in both time series. The empirical results from sixteen family business companies follow. For return series, fractality analysis show that most of family business returns listed on CSE exhibit anti-persistent dynamics, whilst market returns have persistent dynamics. Besides, chaos tests show that business family stock returns are not chaotic while market returns exhibit evidence of chaotic behaviour. For volatility series, fractality analysis shows that most of family business stocks and market index exhibit long memory in volatility. Furthermore, results from chaos tests show that volatility of family business returns is not chaotic, whilst volatility of market index is chaotic. These results may help understanding irregularities patterns in Moroccan family business stock returns and volatility, and how they are different from market dynamics.

A Unified Point Process Probabilistic Framework to Assess Heartbeat Dynamics and Autonomic Cardiovascular Control

PubMed Central

Chen, Zhe; Purdon, Patrick L.; Brown, Emery N.; Barbieri, Riccardo

2012-01-01

In recent years, time-varying inhomogeneous point process models have been introduced for assessment of instantaneous heartbeat dynamics as well as specific cardiovascular control mechanisms and hemodynamics. Assessment of the model’s statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR) structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreceptor-cardiac reflex (baroreflex) sensitivity (BRS), are derived within a parametric framework and instantaneously updated with adaptive and local maximum likelihood estimation algorithms. Inclusion of second-order non-linearities, with subsequent bispectral quantification in the frequency domain, further allows for definition of instantaneous metrics of non-linearity. We here present a comprehensive review of the devised methods as applied to experimental recordings from healthy subjects during propofol anesthesia. Collective results reveal interesting dynamic trends across the different pharmacological interventions operated within each anesthesia session, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, non-invasive assessment in clinical practice. We also discuss the limitations and other alternative modeling strategies of our point process approach. PMID:22375120

A molecular dynamics simulation study of dynamic process and mesoscopic structure in liquid mixture systems

NASA Astrophysics Data System (ADS)

Yang, Peng

The focus of this dissertation is the Molecular Dynamics (MD) simulation study of two different systems. In thefirst system, we study the dynamic process of graphene exfoliation, particularly graphene dispersion using ionic surfactants (Chapter 2). In the second system, we investigate the mesoscopic structure of binary solute/ionic liquid (IL) mixtures through the comparison between simulations and corresponding experiments (Chapter 3 and 4). In the graphene exfoliation study, we consider two separation mechanisms: changing the interlayer distance and sliding away the relative distance of two single-layer graphene sheets. By calculating the energy barrier as a function of separation (interlayer or sliding-away) distance and performing sodium dodecyl sulfate (SDS) structure analysis around graphene surface in SDS surfactant/water + bilayer graphene mixture systems, we find that the sliding-away mechanism is the dominant, feasible separation process. In this process, the SDS-graphene interaction gradually replaces the graphene-graphene Van der Waals (VdW) interaction, and decreases the energy barrier until almost zero at critical SDS concentration. In solute/IL study, we investigate nonpolar (CS2) and dipolar (CH 3CN) solute/IL mixture systems. MD simulation shows that at low concentrations, IL is nanosegregated into an ionic network and nonpolar domain. It is also found that CS2 molecules tend to be localized into the nonpolar domain, while CH3CN interacts with nonpolar domain as well as with the charged head groups in the ionic network because of its amphiphilicity. At high concentrations, CH3CN molecules eventually disrupt the nanostructural organization. This dissertation is organized in four chapters: (1) introduction to graphene, ionic liquids and the methodology of MD; (2) MD simulation of graphene exfoliation; (3) Nanostructural organization in acetonitrile/IL mixtures; (4) Nanostructural organization in carbon disulfide/IL mixtures; (5) Conclusions. Results of MD simulations of liquid mixture systems car-ried out in this research explain observed experiments and show the details of nanostructural organizations in small solute molecules/IL mixture. Additionally, the research successfully reveals the correct mechanism of graphene exfoliation process in liquid solution. (This will be summarized in Chapter 5.) The research presented in this dissertation enhances our understanding of the microscopic behaviors in complex liquid systems as well as the theoretical method to explore them.

Structure and component dynamics in binary mixtures of poly(2-(dimethylamino)ethyl methacrylate) with water and tetrahydrofuran: A diffraction, calorimetric, and dielectric spectroscopy study

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

Goracci, G., E-mail: sckgorag@ehu.es; Arbe, A.; Alegría, A.

2016-04-21

We have combined X-ray diffraction, neutron diffraction with polarization analysis, small angle neutron scattering, differential scanning calorimetry, and broad band dielectric spectroscopy to investigate the structure and dynamics of binary mixtures of poly (2-(dimethylamino)ethyl methacrylate) with either water or tetrahydrofuran (THF) at different concentrations. Aqueous mixtures are characterized by a highly heterogeneous structure where water clusters coexist with an underlying nano-segregation of main chains and side groups of the polymeric matrix. THF molecules are homogeneously distributed among the polymeric nano-domains for concentrations of one THF molecule/monomer or lower. A more heterogeneous situation is found for higher THF amounts, but withoutmore » evidences for solvent clusters. In THF-mixtures, we observe a remarkable reduction of the glass-transition temperature which is enhanced with increasing amount of solvent but seems to reach saturation at high THF concentrations. Adding THF markedly reduces the activation energy of the polymer β-relaxation. The presence of THF molecules seemingly hinders a slow component of this process which is active in the dry state. The aqueous mixtures present a strikingly broad glass-transition feature, revealing a highly heterogeneous behavior in agreement with the structural study. Regarding the solvent dynamics, deep in the glassy state all data can be described by an Arrhenius temperature dependence with a rather similar activation energy. However, the values of the characteristic times are about three orders of magnitude smaller for THF than for water. Water dynamics display a crossover toward increasingly higher apparent activation energies in the region of the onset of the glass transition, supporting its interpretation as a consequence of the freezing of the structural relaxation of the surrounding matrix. The absence of such a crossover (at least in the wide dynamic window here accessed) in THF is attributed to the lack of cooperativity effects in the relaxation of these molecules within the polymeric matrix.« less

Molecular Dynamics Simulation Study of the Capacitive Performance of a Binary Mixture of Ionic Liquids near an Onion-like Carbon Electrode.

PubMed

Li, Song; Feng, Guang; Fulvio, Pasquale F; Hillesheim, Patrick C; Liao, Chen; Dai, Sheng; Cummings, Peter T

2012-09-06

An equimolar mixture of 1-methyl-1-propylpyrrolidinium bis(trifluoromethylsulfonyl)imide ([C3mpy][Tf2N]), 1-methyl-1-butylpiperidinium bis(trifluoromethylsulfonyl)imide ([C4mpip][Tf2N]) was investigated by classic molecular dynamics (MD) simulation. Differential scanning calorimetry (DSC) measurements verified that the binary mixture exhibited lower glass transition temperature than either of the pure room-temperature ionic liquids (RTILs). Moreover, the binary mixture gave rise to higher conductivity than the neat RTILs at lower temperature range. In order to study its capacitive performance in supercapacitors, simulations were performed of the mixture, and the neat RTILs used as electrolytes near an onion-like carbon (OLC) electrode at varying temperatures. The differential capacitance exhibited independence of the electrical potential applied for three electrolytes, which is in agreement with previous work on OLC electrodes in a different RTILs. Positive temperature dependence of the differential capacitance was observed, and it was dominated by the electrical double layer (EDL) thickness, which is for the first time substantiated in MD simulation.

Radial artery pulse waveform analysis based on curve fitting using discrete Fourier series.

PubMed

Jiang, Zhixing; Zhang, David; Lu, Guangming

2018-04-19

Radial artery pulse diagnosis has been playing an important role in traditional Chinese medicine (TCM). For its non-invasion and convenience, the pulse diagnosis has great significance in diseases analysis of modern medicine. The practitioners sense the pulse waveforms in patients' wrist to make diagnoses based on their non-objective personal experience. With the researches of pulse acquisition platforms and computerized analysis methods, the objective study on pulse diagnosis can help the TCM to keep up with the development of modern medicine. In this paper, we propose a new method to extract feature from pulse waveform based on discrete Fourier series (DFS). It regards the waveform as one kind of signal that consists of a series of sub-components represented by sine and cosine (SC) signals with different frequencies and amplitudes. After the pulse signals are collected and preprocessed, we fit the average waveform for each sample using discrete Fourier series by least squares. The feature vector is comprised by the coefficients of discrete Fourier series function. Compared with the fitting method using Gaussian mixture function, the fitting errors of proposed method are smaller, which indicate that our method can represent the original signal better. The classification performance of proposed feature is superior to the other features extracted from waveform, liking auto-regression model and Gaussian mixture model. The coefficients of optimized DFS function, who is used to fit the arterial pressure waveforms, can obtain better performance in modeling the waveforms and holds more potential information for distinguishing different psychological states. Copyright © 2018 Elsevier B.V. All rights reserved.

Using chromatography – desorption method of manufacturing gas mixtures for analytical instruments calibration

NASA Astrophysics Data System (ADS)

Platonov, I. A.; Kolesnichenko, I. N.; Lange, P. K.

2018-05-01

In this paper, the chromatography desorption method of obtaining gas mixtures of known compositions stable for a time sufficient to calibrate analytical instruments is considered. The comparative analysis results of the preparation accuracy of gas mixtures with volatile organic compounds using diffusion, polyabarbotage and chromatography desorption methods are presented. It is shown that the application of chromatography desorption devices allows one to obtain gas mixtures that are stable for 10...60 hours in a dynamic condition. These gas mixtures contain volatile aliphatic and aromatic hydrocarbons with a concentration error of no more than 7%. It is shown that it is expedient to use such gas mixtures for analytical instruments calibration (chromatographs, spectrophotometers, etc.)

ODE Constrained Mixture Modelling: A Method for Unraveling Subpopulation Structures and Dynamics

PubMed Central

Hasenauer, Jan; Hasenauer, Christine; Hucho, Tim; Theis, Fabian J.

2014-01-01

Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity. PMID:24992156

Thermophysical Properties of Energetic Ionic Liquids/Nitric Acid Mixtures: Insights from Molecular Dynamics Simulations

DTIC Science & Technology

2013-01-01

W L. Physical properties of concentrated nitric acid . UNT Digital Library. http://digital.library.unt.edu/ark:/67531/metadc56640/.) 23 M. Engelmann... Nitric Acid Mixtures: Insights from Molecular Dynamics Simulations 5a. CONTRACT NUMBER FA9300-11-C-3012 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...Rev. 8-98) Prescribed by ANSI Std. 239.18 1     Thermophysical  Properties  of  Energetic  Ionic  Liquids/ Nitric   Acid

Population mixture model for nonlinear telomere dynamics

NASA Astrophysics Data System (ADS)

Itzkovitz, Shalev; Shlush, Liran I.; Gluck, Dan; Skorecki, Karl

2008-12-01

Telomeres are DNA repeats protecting chromosomal ends which shorten with each cell division, eventually leading to cessation of cell growth. We present a population mixture model that predicts an exponential decrease in telomere length with time. We analytically solve the dynamics of the telomere length distribution. The model provides an excellent fit to available telomere data and accounts for the previously unexplained observation of telomere elongation following stress and bone marrow transplantation, thereby providing insight into the nature of the telomere clock.

A deeper insight into an intriguing acetonitrile-water binary mixture: synergistic effect, dynamic Stokes shift, fluorescence correlation spectroscopy, and NMR studies.

PubMed

Koley, Somnath; Ghosh, Subhadip

2016-11-30

An insight study reveals the strong synergistic solvation behaviours from reporter dye molecules within the acetonitrile (ACN)-water (WT) binary mixture. Synergism of a binary mixture refers to some unique changes of the physical and thermodynamic properties of the solvent mixture, originating from the interactions among its cosolvents, which are absent within the pure cosolvents. Synergistic solvation of a binary mixture is likely to be fundamental for greater stabilization of an excited state solute dipole; at least to some extent greater as compared to one stabilized by any of its cosolvents alone. A dynamic Stokes shift due to the solvation of an excited dipole in the ACN-WT binary mixture is found to be highly relevant to the ground state physical properties of the solute molecule (polarity, hydrophilicity, acidity, etc.). Largely different solvation times in the ACN-WT mixture are observed from different dye molecules with widely varying polarities. However, earlier study shows that dye molecules, irrespective of their varying polarities, exhibit very similar solvation times within a pure solvent (J. Phys. Chem. B, 2014, 118, 7577-7785). On further study with fluorescence correlation spectroscopy (FCS) we observed that, unlike the translational diffusion coefficient (D t ) of a dye molecule within a pure solvent, which remains the same irrespective of the location of the dye molecule inside the solvent, a broad distribution among the D t values of a dye molecule is obtained from different locations within the ACN-WT binary mixture. Lastly our 1 H NMR study in the ACN-WT binary mixture shows the existence of strong hydrogen bond interactions among the cosolvents in the ACN-WT mixture.

Solvation dynamics of tryptophan in water-dimethyl sulfoxide binary mixture: in search of molecular origin of composition dependent multiple anomalies.

PubMed

Roy, Susmita; Bagchi, Biman

2013-07-21

Experimental and simulation studies have uncovered at least two anomalous concentration regimes in water-dimethyl sulfoxide (DMSO) binary mixture whose precise origin has remained a subject of debate. In order to facilitate time domain experimental investigation of the dynamics of such binary mixtures, we explore strength or extent of influence of these anomalies in dipolar solvation dynamics by carrying out long molecular dynamics simulations over a wide range of DMSO concentration. The solvation time correlation function so calculated indeed displays strong composition dependent anomalies, reflected in pronounced non-exponential kinetics and non-monotonous composition dependence of the average solvation time constant. In particular, we find remarkable slow-down in the solvation dynamics around 10%-20% and 35%-50% mole percentage. We investigate microscopic origin of these two anomalies. The population distribution analyses of different structural morphology elucidate that these two slowing down are reflections of intriguing structural transformations in water-DMSO mixture. The structural transformations themselves can be explained in terms of a change in the relative coordination number of DMSO and water molecules, from 1DMSO:2H2O to 1H2O:1DMSO and 1H2O:2DMSO complex formation. Thus, while the emergence of first slow down (at 15% DMSO mole percentage) is due to the percolation among DMSO molecules supported by the water molecules (whose percolating network remains largely unaffected), the 2nd anomaly (centered on 40%-50%) is due to the formation of the network structure where the unit of 1DMSO:1H2O and 2DMSO:1H2O dominates to give rise to rich dynamical features. Through an analysis of partial solvation dynamics an interesting negative cross-correlation between water and DMSO is observed that makes an important contribution to relaxation at intermediate to longer times.

Reciprocal associations between negative affect, binge eating, and purging in the natural environment in women with bulimia nervosa.

PubMed

Lavender, Jason M; Utzinger, Linsey M; Cao, Li; Wonderlich, Stephen A; Engel, Scott G; Mitchell, James E; Crosby, Ross D

2016-04-01

Although negative affect (NA) has been identified as a common trigger for bulimic behaviors, findings regarding NA following such behaviors have been mixed. This study examined reciprocal associations between NA and bulimic behaviors using real-time, naturalistic data. Participants were 133 women with bulimia nervosa (BN) according to the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders who completed a 2-week ecological momentary assessment protocol in which they recorded bulimic behaviors and provided multiple daily ratings of NA. A multilevel autoregressive cross-lagged analysis was conducted to examine concurrent, first-order autoregressive, and prospective associations between NA, binge eating, and purging across the day. Results revealed positive concurrent associations between all variables across all time points, as well as numerous autoregressive associations. For prospective associations, higher NA predicted subsequent bulimic symptoms at multiple time points; conversely, binge eating predicted lower NA at multiple time points, and purging predicted higher NA at 1 time point. Several autoregressive and prospective associations were also found between binge eating and purging. This study used a novel approach to examine NA in relation to bulimic symptoms, contributing to the existing literature by directly examining the magnitude of the associations, examining differences in the associations across the day, and controlling for other associations in testing each effect in the model. These findings may have relevance for understanding the etiology and/or maintenance of bulimic symptoms, as well as potentially informing psychological interventions for BN. (c) 2016 APA, all rights reserved).

Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China.

PubMed

Yu, Lijing; Zhou, Lingling; Tan, Li; Jiang, Hongbo; Wang, Ying; Wei, Sheng; Nie, Shaofa

2014-01-01

Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic. In this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012. The best-fitted hybrid model was combined with seasonal ARIMA [Formula: see text] and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively -965.03, -1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend. The model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.

Methodology for the AutoRegressive Planet Search (ARPS) Project

NASA Astrophysics Data System (ADS)

Feigelson, Eric; Caceres, Gabriel; ARPS Collaboration

2018-01-01

The detection of periodic signals of transiting exoplanets is often impeded by the presence of aperiodic photometric variations. This variability is intrinsic to the host star in space-based observations (typically arising from magnetic activity) and from observational conditions in ground-based observations. The most common statistical procedures to remove stellar variations are nonparametric, such as wavelet decomposition or Gaussian Processes regression. However, many stars display variability with autoregressive properties, wherein later flux values are correlated with previous ones. Providing the time series is evenly spaced, parametric autoregressive models can prove very effective. Here we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) models to treat a wide variety of stochastic short-memory processes, as well as nonstationarity. Additionally, we introduce a planet-search algorithm to detect periodic transits in the time-series residuals after application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), replaces the traditional box-fitting step. We construct a periodogram based on the TCF to concentrate the signal of these periodic spikes. Various features of the original light curves, the ARIMA fits, the TCF periodograms, and folded light curves at peaks of the TCF periodogram can then be collected to provide constraints for planet detection. These features provide input into a multivariate classifier when a training set is available. The ARPS procedure has been applied NASA's Kepler mission observations of ~200,000 stars (Caceres, Dissertation Talk, this meeting) and will be applied in the future to other datasets.

Molecular dynamics study of thermodynamic stability and dynamics of [Li(glyme)]+ complex in lithium-glyme solvate ionic liquids

NASA Astrophysics Data System (ADS)

Shinoda, Wataru; Hatanaka, Yuta; Hirakawa, Masashi; Okazaki, Susumu; Tsuzuki, Seiji; Ueno, Kazuhide; Watanabe, Masayoshi

2018-05-01

Equimolar mixtures of glymes and organic lithium salts are known to produce solvate ionic liquids, in which the stability of the [Li(glyme)]+ complex plays an important role in determining the ionic dynamics. Since these mixtures have attractive physicochemical properties for application as electrolytes, it is important to understand the dependence of the stability of the [Li(glyme)]+ complex on the ion dynamics. A series of microsecond molecular dynamics simulations has been conducted to investigate the dynamic properties of these solvate ionic liquids. Successful solvate ionic liquids with high stability of the [Li(glyme)]+ complex have been shown to have enhanced ion dynamics. Li-glyme pair exchange rarely occurs: its characteristic time is longer than that of ion diffusion by one or two orders of magnitude. Li-glyme pair exchange most likely occurs through cluster formation involving multiple [Li(glyme)]+ pairs. In this process, multiple exchanges likely take place in a concerted manner without the production of energetically unfavorable free glyme or free Li+ ions.

Evaluation of Permanent Deformation of CRM-Reinforced SMA and Its Correlation with Dynamic Stiffness and Dynamic Creep

PubMed Central

Mashaan, Nuha Salim; Karim, Mohamed Rehan

2013-01-01

Today, rapid economic and industrial growth generates increasing amounts of waste materials such as waste tyre rubber. Attempts to inspire a green technology which is more environmentally friendly that can produce economic value are a major consideration in the utilization of waste materials. The aim of this study is to evaluate the effect of waste tyre rubber (crumb rubber modifier (CRM)), in stone mastic asphalt (SMA 20) performance. The virgin bitumen (80/100) penetration grade was used, modified with crumb rubber at four different modification levels, namely, 6%, 12%, 16%, and 20% by weight of the bitumen. The testing undertaken on the asphalt mix comprises the indirect tensile (dynamic stiffness), dynamic creep, and wheel tracking tests. By the experimentation, the appropriate amount of CRM was found to be 16% by weight of bitumen. The results show that the addition of CRM into the mixture has an obvious significant effect on the performance properties of SMA which could improve the mixture's resistance against permanent deformation. Further, higher correlation coefficient was obtained between the rut depth and permanent strain as compared to resilient modulus; thus dynamic creep test might be a more reliable test in evaluating the rut resistance of asphalt mixture. PMID:24302883

Evaluation of permanent deformation of CRM-reinforced SMA and its correlation with dynamic stiffness and dynamic creep.

PubMed

Mashaan, Nuha Salim; Karim, Mohamed Rehan

2013-01-01

Today, rapid economic and industrial growth generates increasing amounts of waste materials such as waste tyre rubber. Attempts to inspire a green technology which is more environmentally friendly that can produce economic value are a major consideration in the utilization of waste materials. The aim of this study is to evaluate the effect of waste tyre rubber (crumb rubber modifier (CRM)), in stone mastic asphalt (SMA 20) performance. The virgin bitumen (80/100) penetration grade was used, modified with crumb rubber at four different modification levels, namely, 6%, 12%, 16%, and 20% by weight of the bitumen. The testing undertaken on the asphalt mix comprises the indirect tensile (dynamic stiffness), dynamic creep, and wheel tracking tests. By the experimentation, the appropriate amount of CRM was found to be 16% by weight of bitumen. The results show that the addition of CRM into the mixture has an obvious significant effect on the performance properties of SMA which could improve the mixture's resistance against permanent deformation. Further, higher correlation coefficient was obtained between the rut depth and permanent strain as compared to resilient modulus; thus dynamic creep test might be a more reliable test in evaluating the rut resistance of asphalt mixture.

Optimal HRF and smoothing parameters for fMRI time series within an autoregressive modeling framework.

PubMed

Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru

2010-12-01

The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.

Spectral Analysis of Ultrasound Radiofrequency Backscatter for the Detection of Intercostal Blood Vessels.

PubMed

Klingensmith, Jon D; Haggard, Asher; Fedewa, Russell J; Qiang, Beidi; Cummings, Kenneth; DeGrande, Sean; Vince, D Geoffrey; Elsharkawy, Hesham

2018-04-19

Spectral analysis of ultrasound radiofrequency backscatter has the potential to identify intercostal blood vessels during ultrasound-guided placement of paravertebral nerve blocks and intercostal nerve blocks. Autoregressive models were used for spectral estimation, and bandwidth, autoregressive order and region-of-interest size were evaluated. Eight spectral parameters were calculated and used to create random forests. An autoregressive order of 10, bandwidth of 6 dB and region-of-interest size of 1.0 mm resulted in the minimum out-of-bag error. An additional random forest, using these chosen values, was created from 70% of the data and evaluated independently from the remaining 30% of data. The random forest achieved a predictive accuracy of 92% and Youden's index of 0.85. These results suggest that spectral analysis of ultrasound radiofrequency backscatter has the potential to identify intercostal blood vessels. (jokling@siue.edu) © 2018 World Federation for Ultrasound in Medicine and Biology. Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions.

PubMed

Ou, Lu; Chow, Sy-Miin; Ji, Linying; Molenaar, Peter C M

2017-01-01

The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.

Kepler AutoRegressive Planet Search (KARPS)

NASA Astrophysics Data System (ADS)

Caceres, Gabriel

2018-01-01

One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.

Monthly streamflow forecasting with auto-regressive integrated moving average

NASA Astrophysics Data System (ADS)

Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

2017-09-01

Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

Molecular dynamics simulations of a DMSO/water mixture using the AMBER force field.

PubMed

Stachura, Slawomir S; Malajczuk, Chris J; Mancera, Ricardo L

2018-06-25

Due to its protective properties of biological samples at low temperatures and under desiccation, dimethyl sulfoxide (DMSO) in aqueous solutions has been studied widely by many experimental approaches and molecular dynamics (MD) simulations. In the case of the latter, AMBER is among the most commonly used force fields for simulations of biomolecular systems; however, the parameters for DMSO published by Fox and Kollman in 1998 have only been tested for pure liquid DMSO. We have conducted an MD simulation study of DMSO in a water mixture and computed several structural and dynamical properties such as of the mean density, self-diffusion coefficient, hydrogen bonding and DMSO and water ordering. The AMBER force field of DMSO is seen to reproduce well most of the experimental properties of DMSO in water, with the mixture displaying strong and specific water ordering, as observed in experiments and multiple other MD simulations with other non-polarizable force fields. Graphical abstract Hydration structure within hydrogen-bonding distance around a DMSOmolecule.

Characterizing Touch Using Pressure Data and Auto Regressive Models

PubMed Central

Laufer, Shlomi; Pugh, Carla M.; Van Veen, Barry D.

2014-01-01

Palpation plays a critical role in medical physical exams. Despite the wide range of exams, there are several reproducible and subconscious sets of maneuvers that are common to examination by palpation. Previous studies by our group demonstrated the use of manikins and pressure sensors for measuring and quantifying how physicians palpate during different physical exams. In this study we develop mathematical models that describe some of these common maneuvers. Dynamic pressure data was measured using a simplified testbed and different autoregressive models were used to describe the motion of interest. The frequency, direction and type of motion used were identified from the models. We believe these models can a provide better understanding of how humans explore objects in general and more specifically give insights to understand medical physical exams. PMID:25570335

Primed for death: Law enforcement-citizen homicides, social media, and retaliatory violence.

PubMed

Bejan, Vladimir; Hickman, Matthew; Parkin, William S; Pozo, Veronica F

2018-01-01

We examine whether retaliatory violence exists between law enforcement and citizens while controlling for any social media contagion effect related to prior fatal encounters. Analyzed using a trivariate dynamic structural vector-autoregressive model, daily time-series data over a 21-month period captured the frequencies of police killed in the line of duty, police deadly use of force incidents, and social media coverage. The results support a significant retaliatory violence effect against minorities by police, yet there is no evidence of retaliatory violence against law enforcement officers by minorities. Also, social media coverage of the Black Lives Matter movement increases the risk of fatal victimization to both law enforcement officers and minorities. Possible explanations for these results are based in rational choice and terror management theories.

Primed for death: Law enforcement-citizen homicides, social media, and retaliatory violence

PubMed Central

Bejan, Vladimir; Hickman, Matthew; Pozo, Veronica F.

2018-01-01

We examine whether retaliatory violence exists between law enforcement and citizens while controlling for any social media contagion effect related to prior fatal encounters. Analyzed using a trivariate dynamic structural vector-autoregressive model, daily time-series data over a 21-month period captured the frequencies of police killed in the line of duty, police deadly use of force incidents, and social media coverage. The results support a significant retaliatory violence effect against minorities by police, yet there is no evidence of retaliatory violence against law enforcement officers by minorities. Also, social media coverage of the Black Lives Matter movement increases the risk of fatal victimization to both law enforcement officers and minorities. Possible explanations for these results are based in rational choice and terror management theories. PMID:29320548

GPC-Based Stable Reconfigurable Control

NASA Technical Reports Server (NTRS)

Soloway, Don; Shi, Jian-Jun; Kelkar, Atul

2004-01-01

This paper presents development of multi-input multi-output (MIMO) Generalized Pre-dictive Control (GPC) law and its application to reconfigurable control design in the event of actuator saturation. A Controlled Auto-Regressive Integrating Moving Average (CARIMA) model is used to describe the plant dynamics. The control law is derived using input-output description of the system and is also related to the state-space form of the model. The stability of the GPC control law without reconfiguration is first established using Riccati-based approach and state-space formulation. A novel reconfiguration strategy is developed for the systems which have actuator redundancy and are faced with actuator saturation type failure. An elegant reconfigurable control design is presented with stability proof. Several numerical examples are presented to demonstrate the application of various results.

Molecular simulation of fluid mixtures in bulk and at solid-liquid interfaces

NASA Astrophysics Data System (ADS)

Kern, Jesse L.

The properties of a diverse range of mixture systems at interfaces are investigated using a variety of computational techniques. Molecular simulation is used to examine the thermodynamic, structural, and transport properties of heterogeneous systems of theoretical and practical importance. The study of binary hard-sphere mixtures at a hard wall demonstrates the high accuracy of recently developed classical-density functionals. The study of aluminum--gallium solid--liquid heterogeneous interfaces predicts a significant amount of prefreezing of the liquid by adopting the structure of the solid surface. The study of ethylene-expanded methanol within model silica mesopores shows the effect of confinement and surface functionalzation on the mixture composition and transport inside of the pores. From our molecular-dynamics study of binary hard-sphere fluid mixtures at a hard wall, we obtained high-precision calculations of the wall-fluid interfacial free energies, gamma. We have considered mixtures of varying diameter ratio, alpha = 0.7,0.8,0.9; mole fraction, x 1 = 0.25,0.50,0.75; and packing fraction, eta < 0.50. Using Gibbs-Cahn Integration, gamma is calculated from the system pressure, chemical potentials, and density profiles. Recent classical density-functional theory predictions agree very well with our results. Structural, thermodynamic, and transport properties of the aluminum--gallium solid--liquid interface at 368 K are obtained for the (100), (110), and (111) orientations using molecular dynamics. Density, potential energy, stress, and diffusion profiles perpendicular to the interface are calculated. The layers of Ga that form on the Al surface are strongly adsorbed and take the in-plane structure of the underlying crystal layers for all orientations, which results in significant compressive stress on the Ga atoms. Bulk methanol--ethylene mixtures under vapor-liquid equilibrium conditions have been characterized using Monte Carlo and molecular dynamics. The simulated vapor-liquid coexistence curves for the pure-component and binary mixtures agree well with experiment, as do the mixture volumetric expansion results. Using chemical potentials obtained from the bulk simulations, the filling of a number of model silica mesopores with ethylene and methanol is simulated. We report the compositions of the confined fluid mixtures over a range of pressures and for three degrees of nominal pore hydrophobicity.

Investigation of intermolecular interaction of binary mixture of acrylonitrile with bromobenzene

NASA Astrophysics Data System (ADS)

Deshmukh, S. D.; Pattebahadur, K. L.; Mohod, A. G.; Patil, S. S.; Khirade, P. W.

2018-04-01

In this paper, study of binary mixture of Acrylonitrile (ACN)with Bromobenzene(BB) has been carried out at eleven concentrations at room temperature. The determined density(ρ) and refractive index (nD) values of binary mixture are used to calculate the excess properties of mixture over the entire composition range. The aforesaid parameters are used to calculate excess parameters and fitted to the Redlich-Kister equation to determine the bj coefficients. From the above parameters, intermolecular interaction and dynamics of molecules of binary mixture at molecular level are discussed. The Conformational analysis of the intermolecular interaction between Acrylonitrile and Bromobenzene is supported by the FTIR spectra.

Species Composition and Fire: Non-Additive Mixture Effects on Ground Fuel Flammability

PubMed Central

van Altena, Cassandra; van Logtestijn, Richard S. P.; Cornwell, William K.; Cornelissen, Johannes H. C.

2012-01-01

Diversity effects on many aspects of ecosystem function have been well documented. However, fire is an exception: fire experiments have mainly included single species, bulk litter, or vegetation, and, as such, the role of diversity as a determinant of flammability, a crucial aspect of ecosystem function, is poorly understood. This study is the first to experimentally test whether flammability characteristics of two-species mixtures are non-additive, i.e., differ from expected flammability based on the component species in monospecific fuel. In standardized fire experiments on ground fuels, including monospecific fuels and mixtures of five contrasting subarctic plant fuel types in a controlled laboratory environment, we measured flame speed, flame duration, and maximum temperature. Broadly half of the mixture combinations showed non-additive effects for these flammability indicators; these were mainly enhanced dominance effects for temporal dynamics – fire speed and duration. Fuel types with the more flammable value for a characteristic determined the rate of fire speed and duration of the whole mixture; in contrast, maximum temperature of the fire was determined by the biomass-weighted mean of the mixture. These results suggest that ecological invasions by highly flammable species may have effects on ground-fire dynamics well out of proportion to their biomass. PMID:22639656

Unravelling the Composition Dependent Anomalies of Pair Hydrophobicity in Water-Ethanol Binary Mixtures.

PubMed

Halder, Ritaban; Jana, Biman

2018-06-05

Aqueous binary mixtures have received immense attention in recent years because of their extensive application in several biological and industrial processes. Water-ethanol binary mixture serves as a unique system because it exhibits composition dependent alteration of dynamic and thermodynamic properties. Our present work demonstrates how different compositions of water-ethanol binary mixtures affect the pair hydrophobicity of different hydrophobes. Pair hydrophobicity is measured by the depth of the first minimum (contact minima) of potential of mean force (PMF) profile between two hydrophobes. The pair hydrophobicity is found to be increased with addition of ethanol to water up to mole fraction of 0.10 and decreased with further addition of ethanol. This observation is shown to be true for three different pairs of hydrophobes. Decomposition of PMF into enthalpic and entropic contribution indicates a switch from entropic to enthalpic stabilization of the contact minimum upon addition of ethanol to water. The gain in mixing enthalpy of the binary solvent system upon association of two hydrophobes is found to be the determining factor for the stabilization of contact minimum. Several static/dynamics quantities (average composition fluctuations, diffusion coefficients, fluctuations in total dipole moment, propensity of ethyl-ethyl association, etc) of the ethanol-water binary mixture also show irregularities around xEtOH =0.10-0.15. We have also discovered that the hydrogen bonding pattern of ethanol rather than water reveals a change in trend near the similar composition range. As the anomalous behaviour of the physical/dynamical properties along with the pair hydrophobicity in aqueous binary mixture of amphiphilic solutes is common phenomena, our results may provide a general viewpoint on these aspects.

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks

PubMed Central

Courellis, Hristos; Mullen, Tim; Poizner, Howard; Cauwenberghs, Gert; Iversen, John R.

2017-01-01

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a “reach/saccade to spatial target” cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI. PMID:28566997

An Approach for Peptide Identification by De Novo Sequencing of Mixture Spectra.

PubMed

Liu, Yi; Ma, Bin; Zhang, Kaizhong; Lajoie, Gilles

2017-01-01

Mixture spectra occur quite frequently in a typical wet-lab mass spectrometry experiment, which result from the concurrent fragmentation of multiple precursors. The ability to efficiently and confidently identify mixture spectra is essential to alleviate the existent bottleneck of low mass spectra identification rate. However, most of the traditional computational methods are not suitable for interpreting mixture spectra, because they still take the assumption that the acquired spectra come from the fragmentation of a single precursor. In this manuscript, we formulate the mixture spectra de novo sequencing problem mathematically, and propose a dynamic programming algorithm for the problem. Additionally, we use both simulated and real mixture spectra data sets to verify the merits of the proposed algorithm.

Computational problems in autoregressive moving average (ARMA) models

NASA Technical Reports Server (NTRS)

Agarwal, G. C.; Goodarzi, S. M.; Oneill, W. D.; Gottlieb, G. L.

1981-01-01

The choice of the sampling interval and the selection of the order of the model in time series analysis are considered. Band limited (up to 15 Hz) random torque perturbations are applied to the human ankle joint. The applied torque input, the angular rotation output, and the electromyographic activity using surface electrodes from the extensor and flexor muscles of the ankle joint are recorded. Autoregressive moving average models are developed. A parameter constraining technique is applied to develop more reliable models. The asymptotic behavior of the system must be taken into account during parameter optimization to develop predictive models.

Molecular Dynamics Evaluation of Dielectric-Constant Mixing Rules for H2O-CO2 at Geologic Conditions

PubMed Central

Mountain, Raymond D.; Harvey, Allan H.

2015-01-01

Modeling of mineral reaction equilibria and aqueous-phase speciation of C-O-H fluids requires the dielectric constant of the fluid mixture, which is not known from experiment and is typically estimated by some rule for mixing pure-component values. In order to evaluate different proposed mixing rules, we use molecular dynamics simulation to calculate the dielectric constant of a model H2O–CO2 mixture at temperatures of 700 K and 1000 K at pressures up to 3 GPa. We find that theoretically based mixing rules that depend on combining the molar polarizations of the pure fluids systematically overestimate the dielectric constant of the mixture, as would be expected for mixtures of nonpolar and strongly polar components. The commonly used semiempirical mixing rule due to Looyenga works well for this system at the lower pressures studied, but somewhat underestimates the dielectric constant at higher pressures and densities, especially at the water-rich end of the composition range. PMID:26664009

Molecular Dynamics Evaluation of Dielectric-Constant Mixing Rules for H2O-CO2 at Geologic Conditions.

PubMed

Mountain, Raymond D; Harvey, Allan H

2015-10-01

Modeling of mineral reaction equilibria and aqueous-phase speciation of C-O-H fluids requires the dielectric constant of the fluid mixture, which is not known from experiment and is typically estimated by some rule for mixing pure-component values. In order to evaluate different proposed mixing rules, we use molecular dynamics simulation to calculate the dielectric constant of a model H 2 O-CO 2 mixture at temperatures of 700 K and 1000 K at pressures up to 3 GPa. We find that theoretically based mixing rules that depend on combining the molar polarizations of the pure fluids systematically overestimate the dielectric constant of the mixture, as would be expected for mixtures of nonpolar and strongly polar components. The commonly used semiempirical mixing rule due to Looyenga works well for this system at the lower pressures studied, but somewhat underestimates the dielectric constant at higher pressures and densities, especially at the water-rich end of the composition range.

Thermophysical properties of energetic ionic liquids/nitric acid mixtures: Insights from molecular dynamics simulationsa)

NASA Astrophysics Data System (ADS)

Hooper, Justin B.; Smith, Grant D.; Bedrov, Dmitry

2013-09-01

Molecular dynamics (MD) simulations of mixtures of the room temperature ionic liquids (ILs) 1-butyl-4-methyl imidazolium [BMIM]/dicyanoamide [DCA] and [BMIM][NO3-] with HNO3 have been performed utilizing the polarizable, quantum chemistry based APPLE&P® potential. Experimentally it has been observed that [BMIM][DCA] exhibits hypergolic behavior when mixed with HNO3 while [BMIM][NO3-] does not. The structural, thermodynamic, and transport properties of the IL/HNO3 mixtures have been determined from equilibrium MD simulations over the entire composition range (pure IL to pure HNO3) based on bulk simulations. Additional (non-equilibrium) simulations of the composition profile for IL/HNO3 interfaces as a function of time have been utilized to estimate the composition dependent mutual diffusion coefficients for the mixtures. The latter have been employed in continuum-level simulations in order to examine the nature (composition and width) of the IL/HNO3 interfaces on the millisecond time scale.

Dynamics of Uncrystallized Water, Ice, and Hydrated Protein in Partially Crystallized Gelatin-Water Mixtures Studied by Broadband Dielectric Spectroscopy.

PubMed

Sasaki, Kaito; Panagopoulou, Anna; Kita, Rio; Shinyashiki, Naoki; Yagihara, Shin; Kyritsis, Apostolos; Pissis, Polycarpos

2017-01-12

The glass transition of partially crystallized gelatin-water mixtures was investigated using broadband dielectric spectroscopy (BDS) over a wide range of frequencies (10 mHz to 10 MHz), temperatures (113-298 K), and concentrations (10-45 wt %). Three dielectric relaxation processes (processes I, II, and III) were clearly observed. Processes I, II, and III originate from uncrystallized water (UCW) in the hydration shells of gelatin, ice, and hydrated gelatin, respectively. A dynamic crossover, called the Arrhenius to non-Arrhenius transition of UCW, was observed at the glass transition temperature of the relaxation process of hydrated gelatin for all mixtures. The amount of UCW increases with increasing gelatin content. However, above 35 wt % gelatin, the amount of UCW became more dependent on the gelatin concentration. This increase in UCW causes a decrease in the glass transition temperature of the cooperative motion of gelatin and UCW, which appears to result from a change in the aggregation structure of gelatin in the mixture at a gelatin concentration of approximately 35 wt %. The temperature dependence of the relaxation time of process II has nearly the same activation energy as pure ice made by slow crystallization of ice Ih. This implies that process II originates from the dynamics of slowly crystallized ice Ih.

Coarse-grained modelling of triglyceride crystallisation: a molecular insight into tripalmitin tristearin binary mixtures by molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Pizzirusso, Antonio; Brasiello, Antonio; De Nicola, Antonio; Marangoni, Alejandro G.; Milano, Giuseppe

2015-12-01

The first simulation study of the crystallisation of a binary mixture of triglycerides using molecular dynamics simulations is reported. Coarse-grained models of tristearin (SSS) and tripalmitin (PPP) molecules have been considered. The models have been preliminarily tested in the crystallisation of pure SSS and PPP systems. Two different quenching procedures have been tested and their performances have been analysed. The structures obtained from the crystallisation procedures show a high orientation order and a high content of molecules in the tuning fork conformation, comparable with the crystalline α phase. The behaviour of melting temperatures for the α phase of the mixture SSS/PPP obtained from the simulations is in qualitative agreement with the behaviour that was experimentally determined.

Hydrophobic fluorine mediated switching of the hydrogen bonding site as well as orientation of water molecules in the aqueous mixture of monofluoroethanol: IR, molecular dynamics and quantum chemical studies.

PubMed

Mondal, Saptarsi; Biswas, Biswajit; Nandy, Tonima; Singh, Prashant Chandra

2017-09-20

The local structures between water-water, alcohol-water and alcohol-alcohol have been investigated for aqueous mixtures of ethanol (ETH) and monofluoroethanol (MFE) by the deconvolution of IR bands in the OH stretching region, molecular dynamics simulation and quantum chemical calculations. It has been found that the addition of a small amount of ETH into the aqueous medium increases the strength of the hydrogen bonds between water molecules. In an aqueous mixture of MFE, the substitution of a single fluorine induces a change in the orientation as well as the hydrogen bonding site of water molecules from the oxygen to the fluorine terminal of MFE. The switching of the hydrogen bonding site of water in the aqueous mixture of MFE results in comparatively strong hydrogen bonds between MFE and water molecules as well as less clustering of water molecules, unlike the case of the aqueous mixture of ETH. These findings about the modification of a hydrogen bond network by the hydrophobic fluorine group probably make fluorinated molecules useful for pharmaceutical as well as biological applications.

Detonation Shock Dynamics Calibration for Non-Ideal He: Anfo

NASA Astrophysics Data System (ADS)

Short, Mark; Salyer, Terry R.; Aslam, Tariq D.; Kiyanda, Charles B.; Morris, John S.; Zimmerly, Tony

2009-12-01

Linear Dn-κ detonation shock dynamics (DSD) fitting forms are obtained for four ammonium nitrate-fuel oil (ANFO) mixtures involving variations in the ammonium nitrate prill properties and ANFO stoichiometries.

Hot mix asphalt (HMA) characterization for the 2002 AASHTO design guide.

DOT National Transportation Integrated Search

2008-09-30

The two study objectives were to conduct dynamic modulus and APA rutting tests of selected Mississippi HMA mixtures. A total of twenty-five mixtures were tested including aggregate combinations of gravel and gravel/limestone; 9.5mm, 12.5mm and 19.0mm...

Kepler AutoRegressive Planet Search

NASA Astrophysics Data System (ADS)

Feigelson, Eric

NASA's Kepler mission is the source of more exoplanets than any other instrument, but the discovery depends on complex statistical analysis procedures embedded in the Kepler pipeline. A particular challenge is mitigating irregular stellar variability without loss of sensitivity to faint periodic planetary transits. This proposal presents a two-stage alternative analysis procedure. First, parametric autoregressive ARFIMA models, commonly used in econometrics, remove most of the stellar variations. Second, a novel matched filter is used to create a periodogram from which transit-like periodicities are identified. This analysis procedure, the Kepler AutoRegressive Planet Search (KARPS), is confirming most of the Kepler Objects of Interest and is expected to identify additional planetary candidates. The proposed research will complete application of the KARPS methodology to the prime Kepler mission light curves of 200,000: stars, and compare the results with Kepler Objects of Interest obtained with the Kepler pipeline. We will then conduct a variety of astronomical studies based on the KARPS results. Important subsamples will be extracted including Habitable Zone planets, hot super-Earths, grazing-transit hot Jupiters, and multi-planet systems. Groundbased spectroscopy of poorly studied candidates will be performed to better characterize the host stars. Studies of stellar variability will then be pursued based on KARPS analysis. The autocorrelation function and nonstationarity measures will be used to identify spotted stars at different stages of autoregressive modeling. Periodic variables with folded light curves inconsistent with planetary transits will be identified; they may be eclipsing or mutually-illuminating binary star systems. Classification of stellar variables with KARPS-derived statistical properties will be attempted. KARPS procedures will then be applied to archived K2 data to identify planetary transits and characterize stellar variability.

Nonrandom variability in respiratory cycle parameters of humans during stage 2 sleep.

PubMed

Modarreszadeh, M; Bruce, E N; Gothe, B

1990-08-01

We analyzed breath-to-breath inspiratory time (TI), expiratory time (TE), inspiratory volume (VI), and minute ventilation (Vm) from 11 normal subjects during stage 2 sleep. The analysis consisted of 1) fitting first- and second-order autoregressive models (AR1 and AR2) and 2) obtaining the power spectra of the data by fast-Fourier transform. For the AR2 model, the only coefficients that were statistically different from zero were the average alpha 1 (a1) for TI, VI, and Vm (a1 = 0.19, 0.29, and 0.15, respectively). However, the power spectra of all parameters often exhibited peaks at low frequency (less than 0.2 cycles/breath) and/or at high frequency (greater than 0.2 cycles/breath), indicative of periodic oscillations. After accounting for the corrupting effects of added oscillations on the a1 estimates, we conclude that 1) breath-to-breath fluctuations of VI, and to a lesser extent TI and Vm, exhibit a first-order autoregressive structure such that fluctuations of each breath are positively correlated with those of immediately preceding breaths and 2) the correlated components of variability in TE are mostly due to discrete high- and/or low-frequency oscillations with no underlying autoregressive structure. We propose that the autoregressive structure of VI, TI, and Vm during spontaneous breathing in stage 2 sleep may reflect either a central neural mechanism or the effects of noise in respiratory chemical feedback loops; the presence of low-frequency oscillations, seen more often in Vm, suggests possible instability in the chemical feedback loops. Mechanisms of high-frequency periodicities, seen more often in TE, are unknown.

Autoregressive Processes in Homogenization of GNSS Tropospheric Data

NASA Astrophysics Data System (ADS)

Klos, A.; Bogusz, J.; Teferle, F. N.; Bock, O.; Pottiaux, E.; Van Malderen, R.

2016-12-01

Offsets due to changes in hardware equipment or any other artificial event are all a subject of a task of homogenization of tropospheric data estimated within a processing of Global Navigation Satellite System (GNSS) observables. This task is aimed at identifying exact epochs of offsets and estimate their magnitudes since they may artificially under- or over-estimate trend and its uncertainty delivered from tropospheric data and used in climate studies. In this research, we analysed a common data set of differences of Integrated Water Vapour (IWV) from GPS and ERA-Interim (1995-2010) provided for a homogenization group working within ES1206 COST Action GNSS4SWEC. We analysed daily IWV records of GPS and ERA-Interim in terms of trend, seasonal terms and noise model with Maximum Likelihood Estimation in Hector software. We found that this data has a character of autoregressive process (AR). Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different noise types: white as well as combination of white and autoregressive and also added few strictly defined offsets. This synthetic data set of exactly the same character as IWV from GPS and ERA-Interim was then subjected to a task of manual and automatic/statistical homogenization. We made blind tests and detected possible epochs of offsets manually. We found that simulated offsets were easily detected in series with white noise, no influence of seasonal signal was noticed. The autoregressive series were much more problematic when offsets had to be determined. We found few epochs, for which no offset was simulated. This was mainly due to strong autocorrelation of data, which brings an artificial trend within. Due to regime-like behaviour of AR it is difficult for statistical methods to properly detect epochs of offsets, which was previously reported by climatologists.

Reconstruction of missing daily streamflow data using dynamic regression models

NASA Astrophysics Data System (ADS)

Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault

2015-12-01

River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

PubMed

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-05-30

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

PubMed Central

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-01-01

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control. PMID:28556817

The dynamics of radical right-wing populist party preferences and perceived group threat: A comparative panel analysis of three competing hypotheses in the Netherlands and Germany.

PubMed

Berning, Carl C; Schlueter, Elmar

2016-01-01

Existing cross-sectional research considers citizens' preferences for radical right-wing populist (RRP) parties to be centrally driven by their perception that immigrants threaten the well-being of the national ingroup. However, longitudinal evidence for this relationship is largely missing. To remedy this gap in the literature, we developed three competing hypotheses to investigate: (a) whether perceived group threat is temporally prior to RRP party preferences, (b) whether RRP party preferences are temporally prior to perceived group threat, or (c) whether the relation between perceived group threat and RRP party preferences is bidirectional. Based on multiwave panel data from the Netherlands for the years 2008-2013 and from Germany spanning the period 1994-2002, we examined the merits of these hypotheses using autoregressive cross-lagged structural equation models. The results show that perceptions of threatened group interests precipitate rather than follow citizens' preferences for RRP parties. These findings help to clarify our knowledge of the dynamic structure underlying RRP party preferences. Copyright © 2015 Elsevier Inc. All rights reserved.

Does preprocessing change nonlinear measures of heart rate variability?

PubMed

Gomes, Murilo E D; Guimarães, Homero N; Ribeiro, Antônio L P; Aguirre, Luis A

2002-11-01

This work investigated if methods used to produce a uniformly sampled heart rate variability (HRV) time series significantly change the deterministic signature underlying the dynamics of such signals and some nonlinear measures of HRV. Two methods of preprocessing were used: the convolution of inverse interval function values with a rectangular window and the cubic polynomial interpolation. The HRV time series were obtained from 33 Wistar rats submitted to autonomic blockade protocols and from 17 healthy adults. The analysis of determinism was carried out by the method of surrogate data sets and nonlinear autoregressive moving average modelling and prediction. The scaling exponents alpha, alpha(1) and alpha(2) derived from the detrended fluctuation analysis were calculated from raw HRV time series and respective preprocessed signals. It was shown that the technique of cubic interpolation of HRV time series did not significantly change any nonlinear characteristic studied in this work, while the method of convolution only affected the alpha(1) index. The results suggested that preprocessed time series may be used to study HRV in the field of nonlinear dynamics.

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems

NASA Astrophysics Data System (ADS)

Liu, Haopeng; Zhu, Yunpeng; Luo, Zhong; Han, Qingkai

2017-09-01

In response to the identification problem concerning multi-degree of freedom (MDOF) nonlinear systems, this study presents the extended forward orthogonal regression (EFOR) based on predicted residual sums of squares (PRESS) to construct a nonlinear dynamic parametrical model. The proposed parametrical model is based on the non-linear autoregressive with exogenous inputs (NARX) model and aims to explicitly reveal the physical design parameters of the system. The PRESS-based EFOR algorithm is proposed to identify such a model for MDOF systems. By using the algorithm, we built a common-structured model based on the fundamental concept of evaluating its generalization capability through cross-validation. The resulting model aims to prevent over-fitting with poor generalization performance caused by the average error reduction ratio (AERR)-based EFOR algorithm. Then, a functional relationship is established between the coefficients of the terms and the design parameters of the unified model. Moreover, a 5-DOF nonlinear system is taken as a case to illustrate the modeling of the proposed algorithm. Finally, a dynamic parametrical model of a cantilever beam is constructed from experimental data. Results indicate that the dynamic parametrical model of nonlinear systems, which depends on the PRESS-based EFOR, can accurately predict the output response, thus providing a theoretical basis for the optimal design of modeling methods for MDOF nonlinear systems.

The cortical focus in childhood absence epilepsy; evidence from nonlinear analysis of scalp EEG recordings.

PubMed

Sarrigiannis, Ptolemaios G; Zhao, Yifan; He, Fei; Billings, Stephen A; Baster, Kathleen; Rittey, Chris; Yianni, John; Zis, Panagiotis; Wei, Hualiang; Hadjivassiliou, Marios; Grünewald, Richard

2018-03-01

To determine the origin and dynamic characteristics of the generalised hyper-synchronous spike and wave (SW) discharges in childhood absence epilepsy (CAE). We applied nonlinear methods, the error reduction ratio (ERR) causality test and cross-frequency analysis, with a nonlinear autoregressive exogenous (NARX) model, to electroencephalograms (EEGs) from CAE, selected with stringent electro-clinical criteria (17 cases, 42 absences). We analysed the pre-ictal and ictal strength of association between homologous and heterologous EEG derivations and estimated the direction of synchronisation and corresponding time lags. A frontal/fronto-central onset of the absences is detected in 13 of the 17 cases with the highest ictal strength of association between homologous frontal followed by centro-temporal and fronto-central areas. Delays consistently in excess of 4 ms occur at the very onset between these regions, swiftly followed by the emergence of "isochronous" (0-2 ms) synchronisation but dynamic time lag changes occur during SW discharges. In absences an initial cortico-cortical spread leads to dynamic lag changes to include periods of isochronous interhemispheric synchronisation, which we hypothesize is mediated by the thalamus. Absences from CAE show ictal epileptic network dynamics remarkably similar to those observed in WAG/Rij rats which guided the formulation of the cortical focus theory. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Forecasting financial asset processes: stochastic dynamics via learning neural networks.

PubMed

Giebel, S; Rainer, M

2010-01-01

Models for financial asset dynamics usually take into account their inherent unpredictable nature by including a suitable stochastic component into their process. Unknown (forward) values of financial assets (at a given time in the future) are usually estimated as expectations of the stochastic asset under a suitable risk-neutral measure. This estimation requires the stochastic model to be calibrated to some history of sufficient length in the past. Apart from inherent limitations, due to the stochastic nature of the process, the predictive power is also limited by the simplifying assumptions of the common calibration methods, such as maximum likelihood estimation and regression methods, performed often without weights on the historic time series, or with static weights only. Here we propose a novel method of "intelligent" calibration, using learning neural networks in order to dynamically adapt the parameters of the stochastic model. Hence we have a stochastic process with time dependent parameters, the dynamics of the parameters being themselves learned continuously by a neural network. The back propagation in training the previous weights is limited to a certain memory length (in the examples we consider 10 previous business days), which is similar to the maximal time lag of autoregressive processes. We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts for the EURTRY and EUR-HUF exchange rates each.

Olive Fruit Fly (Bactrocera oleae) Population Dynamics in the Eastern Mediterranean: Influence of Exogenous Uncertainty on a Monophagous Frugivorous Insect

PubMed Central

Ordano, Mariano; Engelhard, Izhar; Rempoulakis, Polychronis; Nemny-Lavy, Esther; Blum, Moshe; Yasin, Sami; Lensky, Itamar M.; Papadopoulos, Nikos T.; Nestel, David

2015-01-01

Despite of the economic importance of the olive fly (Bactrocera oleae) and the large amount of biological and ecological studies on the insect, the factors driving its population dynamics (i.e., population persistence and regulation) had not been analytically investigated until the present study. Specifically, our study investigated the autoregressive process of the olive fly populations, and the joint role of intrinsic and extrinsic factors molding the population dynamics of the insect. Accounting for endogenous dynamics and the influences of exogenous factors such as olive grove temperature, the North Atlantic Oscillation and the presence of potential host fruit, we modeled olive fly populations in five locations in the Eastern Mediterranean region. Our models indicate that the rate of population change is mainly shaped by first and higher order non-monotonic, endogenous dynamics (i.e., density-dependent population feedback). The olive grove temperature was the main exogenous driver, while the North Atlantic Oscillation and fruit availability acted as significant exogenous factors in one of the five populations. Seasonal influences were also relevant for three of the populations. In spite of exogenous effects, the rate of population change was fairly stable along time. We propose that a special reproductive mechanism, such as reproductive quiescence, allows populations of monophagous fruit flies such as the olive fly to remain stable. Further, we discuss how weather factors could impinge constraints on the population dynamics at the local level. Particularly, local temperature dynamics could provide forecasting cues for management guidelines. Jointly, our results advocate for establishing monitoring programs and for a major focus of research on the relationship between life history traits and populations dynamics. PMID:26010332

Sound velocity in five-component air mixtures of various densities

NASA Astrophysics Data System (ADS)

Bogdanova, N. V.; Rydalevskaya, M. A.

2018-05-01

The local equilibrium flows of five-component air mixtures are considered. Gas dynamic equations are derived from the kinetic equations for aggregate values of collision invariants. It is shown that the traditional formula for sound velocity is true in air mixtures considered with the chemical reactions and the internal degrees of freedom. This formula connects the square of sound velocity with pressure and density. However, the adiabatic coefficient is not constant under existing conditions. The analytical expression for this coefficient is obtained. The examples of its calculation in air mixtures of various densities are presented.

DEM study of the size-induced segregation dynamics of a ternary-size granular mixture in the rolling-regime rotating drum

NASA Astrophysics Data System (ADS)

Yang, Shiliang; Zhang, Liangqi; Luo, Kun; Chew, Jia Wei

2017-12-01

Segregation induced by size, shape, or density difference of the granular material is inevitable in both natural and industrial processes; unfortunately, the underlying mechanism is still not fully understood. In view of the ubiquitous continuous particle size distributions, this study builds on the considerable knowledge gained so far from binary-size mixtures and extends it to a ternary-size mixture to understand the impact of the presence of a third particle size in the three-dimensional rotating drum operating in the rolling flow regime. The discrete element method is employed. The evolution of segregation, the active-passive interface, and the dynamical response of the particle-scale characteristics of the different particle types in the two regions are investigated. The results reveal that the medium particles are spatially sandwiched in between the large and small particles in both the radial and axial directions and therefore exhibit behaviors intermediate to the other two particle types. Compared to the binary-size mixture, the presence of the medium particles leads to (i) higher purity of small particles in the innermost of the radial core, causing a decrease of the translational velocity of small particles; (ii) decrease and increase of the collision forces exerted on, respectively, the large and small particles in both regions; and (iii) increase in the relative ratio of the active-passive exchange rates of small to large particles. The results obtained in the current study therefore provide valuable insights regarding the size-segregation dynamics of granular mixtures with constituents of different sizes.

Simple views on critical binary liquid mixtures in porous glass

NASA Astrophysics Data System (ADS)

Tremblay, L.; Socol, S. M.; Lacelle, S.

2000-01-01

A simple scenario, different from previous attempts, is proposed to resolve the problem of the slow phase separation dynamics of binary liquid mixtures confined in porous Vycor glass. We demonstrate that simply mutual diffusion, renormalized by critical composition fluctuations and geometrical hindrance of the porous glass, accounts for the slow phase separation kinetics. Capillary invasion studies of porous Vycor glass by the critical isobutyric acid-water mixture, close to the consolute solution temperature, corroborate our analysis.

Nonlinear Dynamical Modes as a Basis for Short-Term Forecast of Climate Variability

NASA Astrophysics Data System (ADS)

Feigin, A. M.; Mukhin, D.; Gavrilov, A.; Seleznev, A.; Loskutov, E.

2017-12-01

We study abilities of data-driven stochastic models constructed by nonlinear dynamical decomposition of spatially distributed data to quantitative (short-term) forecast of climate characteristics. We compare two data processing techniques: (i) widely used empirical orthogonal function approach, and (ii) nonlinear dynamical modes (NDMs) framework [1,2]. We also make comparison of two kinds of the prognostic models: (i) traditional autoregression (linear) model and (ii) model in the form of random ("stochastic") nonlinear dynamical system [3]. We apply all combinations of the above-mentioned data mining techniques and kinds of models to short-term forecasts of climate indices based on sea surface temperature (SST) data. We use NOAA_ERSST_V4 dataset (monthly SST with space resolution 20 × 20) covering the tropical belt and starting from the year 1960. We demonstrate that NDM-based nonlinear model shows better prediction skill versus EOF-based linear and nonlinear models. Finally we discuss capability of NDM-based nonlinear model for long-term (decadal) prediction of climate variability. [1] D. Mukhin, A. Gavrilov, E. Loskutov , A.Feigin, J.Kurths, 2015: Principal nonlinear dynamical modes of climate variability, Scientific Reports, rep. 5, 15510; doi: 10.1038/srep15510. [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J., 2016: Method for reconstructing nonlinear modes with adaptive structure from multidimensional data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 26(12), 123101. [3] Ya. Molkov, D. Mukhin, E. Loskutov, A. Feigin, 2012: Random dynamical models from time series. Phys. Rev. E, Vol. 85, n.3.

Dynamics of Aqueous Foam Drops

NASA Technical Reports Server (NTRS)

Akhatov, Iskander; McDaniel, J. Gregory; Holt, R. Glynn

2001-01-01

We develop a model for the nonlinear oscillations of spherical drops composed of aqueous foam. Beginning with a simple mixture law, and utilizing a mass-conserving bubble-in-cell scheme, we obtain a Rayleigh-Plesset-like equation for the dynamics of bubbles in a foam mixture. The dispersion relation for sound waves in a bubbly liquid is then coupled with a normal modes expansion to derive expressions for the frequencies of eigenmodal oscillations. These eigenmodal (breathing plus higher-order shape modes) frequencies are elicited as a function of the void fraction of the foam. A Mathieu-like equation is obtained for the dynamics of the higher-order shape modes and their parametric coupling to the breathing mode. The proposed model is used to explain recently obtained experimental data.

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.

PubMed

Epskamp, Sacha; Waldorp, Lourens J; Mõttus, René; Borsboom, Denny

2018-04-16

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.

Selective distillation phenomenon in two-species Bose-Einstein condensates in open boundary optical lattices.

PubMed

Bai, Xiao-Dong; Zhang, Mei; Xiong, Jun; Yang, Guo-Jian; Deng, Fu-Guo

2015-11-24

We investigate the formation of discrete breathers (DBs) and the dynamics of the mixture of two-species Bose-Einstein condensates (BECs) in open boundary optical lattices using the discrete nonlinear Schrödinger equations. The results show that the coupling of intra- and interspecies interaction can lead to the existence of pure single-species DBs and symbiotic DBs (i.e., two-species DBs). Furthermore, we find that there is a selective distillation phenomenon in the dynamics of the mixture of two-species BECs. One can selectively distil one species from the mixture of two-species BECs and can even control dominant species fraction by adjusting the intra- and interspecies interaction in optical lattices. Our selective distillation mechanism may find potential application in quantum information storage and quantum information processing based on multi-species atoms.

Selective distillation phenomenon in two-species Bose-Einstein condensates in open boundary optical lattices

PubMed Central

Bai, Xiao-Dong; Zhang, Mei; Xiong, Jun; Yang, Guo-Jian; Deng, Fu-Guo

2015-01-01

We investigate the formation of discrete breathers (DBs) and the dynamics of the mixture of two-species Bose-Einstein condensates (BECs) in open boundary optical lattices using the discrete nonlinear Schrödinger equations. The results show that the coupling of intra- and interspecies interaction can lead to the existence of pure single-species DBs and symbiotic DBs (i.e., two-species DBs). Furthermore, we find that there is a selective distillation phenomenon in the dynamics of the mixture of two-species BECs. One can selectively distil one species from the mixture of two-species BECs and can even control dominant species fraction by adjusting the intra- and interspecies interaction in optical lattices. Our selective distillation mechanism may find potential application in quantum information storage and quantum information processing based on multi-species atoms. PMID:26597592

Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Barrett, Adam B.; Seth, Anil K.

2009-12-01

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.

iVAR: a program for imputing missing data in multivariate time series using vector autoregressive models.

PubMed

Liu, Siwei; Molenaar, Peter C M

2014-12-01

This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.

Maximum likelihood estimation for periodic autoregressive moving average models

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

A useful class of models for seasonal time series that cannot be filtered or standardized to achieve second-order stationarity is that of periodic autoregressive moving average (PARMA) models, which are extensions of ARMA models that allow periodic (seasonal) parameters. An approximation to the exact likelihood for Gaussian PARMA processes is developed, and a straightforward algorithm for its maximization is presented. The algorithm is tested on several periodic ARMA(1, 1) models through simulation studies and is compared to moment estimation via the seasonal Yule-Walker equations. Applicability of the technique is demonstrated through an analysis of a seasonal stream-flow series from the Rio Caroni River in Venezuela.

TaiWan Ionospheric Model (TWIM) prediction based on time series autoregressive analysis

NASA Astrophysics Data System (ADS)

Tsai, L. C.; Macalalad, Ernest P.; Liu, C. H.

2014-10-01

As described in a previous paper, a three-dimensional ionospheric electron density (Ne) model has been constructed from vertical Ne profiles retrieved from the FormoSat3/Constellation Observing System for Meteorology, Ionosphere, and Climate GPS radio occultation measurements and worldwide ionosonde foF2 and foE data and named the TaiWan Ionospheric Model (TWIM). The TWIM exhibits vertically fitted α-Chapman-type layers with distinct F2, F1, E, and D layers, and surface spherical harmonic approaches for the fitted layer parameters including peak density, peak density height, and scale height. To improve the TWIM into a real-time model, we have developed a time series autoregressive model to forecast short-term TWIM coefficients. The time series of TWIM coefficients are considered as realizations of stationary stochastic processes within a processing window of 30 days. These autocorrelation coefficients are used to derive the autoregressive parameters and then forecast the TWIM coefficients, based on the least squares method and Lagrange multiplier technique. The forecast root-mean-square relative TWIM coefficient errors are generally <30% for 1 day predictions. The forecast TWIM values of foE and foF2 values are also compared and evaluated using worldwide ionosonde data.

Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes

NASA Astrophysics Data System (ADS)

Assaad, Bassel; Eltabach, Mario; Antoni, Jérôme

2014-01-01

This paper proposes a model-based technique for detecting wear in a multistage planetary gearbox used by lifting cranes. The proposed method establishes a vibration signal model which deals with cyclostationary and autoregressive models. First-order cyclostationarity is addressed by the analysis of the time synchronous average (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores a number of methods commonly used in vibration monitoring of planetary gearboxes, in order to make comparisons. In the experimental part of this study, these techniques are applied to accelerated lifetime test bench data for the lifting winch. After processing raw signals recorded with an accelerometer mounted on the outside of the gearbox, a number of condition indicators (CIs) are derived from the TSA signal, the residual autoregressive signal and other signals derived using standard signal processing methods. The goal is to check the evolution of the CIs during the accelerated lifetime test (ALT). Clarity and fluctuation level of the historical trends are finally considered as a criteria for comparing between the extracted CIs.

Exploring the Mechanisms of Ecological Land Change Based on the Spatial Autoregressive Model: A Case Study of the Poyang Lake Eco-Economic Zone, China

PubMed Central

Xie, Hualin; Liu, Zhifei; Wang, Peng; Liu, Guiying; Lu, Fucai

2013-01-01

Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran’s I value is 0.1646 during the 1990 to 2005 time period and indicated significant positive spatial correlation (p < 0.05). The results also imply that the clustering trend of ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model. PMID:24384778

Comparison of six methods for the detection of causality in a bivariate time series

NASA Astrophysics Data System (ADS)

Krakovská, Anna; Jakubík, Jozef; Chvosteková, Martina; Coufal, David; Jajcay, Nikola; Paluš, Milan

2018-04-01

In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20 000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation.

Modeling Polio Data Using the First Order Non-Negative Integer-Valued Autoregressive, INAR(1), Model

NASA Astrophysics Data System (ADS)

Vazifedan, Turaj; Shitan, Mahendran

Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.

Analysis of Factors Influencing PM2.5 in Beijing: A Microcosmic and Dynamic Perspective for Sustainable Development

NASA Astrophysics Data System (ADS)

Wang, Yani; Wang, Jun; Tao, Guiping

2017-12-01

Haze pollution has become a hot issue concerned with the process of modernization and one serious problem requiring urgent solution, especially in Beijing. PM2.5 is the main reason causing haze and its harm. Although there has been research centering on factors affecting PM2.5, little attention has been devoted to the microcosmic and dynamic effects on it. Vector auto-regression (VAR) mode is applied in this study to explore the interaction between PM2.5, PM10, SO2, CO and NO2. Results of Granger causality tests tell that there exists causal relationship between PM10, SO2, CO, NO2 and PM2.5. Impulse response functions (IRFs) show that the response of PM2.5 to a shock in CO is positive and large in the short period, while the reaction of PM2.5 to a shock in SO2 increases over time. Meanwhile, variance decomposition indicate that PM2.5 is more closely related to CO in the short term while SO2’ influence accounts for a higher proportion in the long run. The findings provide a novel perspective to analyze the factors influencing PM2.5 dynamically and contribute to a better understanding of haze and its relationship with sustainable development.

Prediction of muscle performance during dynamic repetitive movement

NASA Technical Reports Server (NTRS)

Byerly, D. L.; Byerly, K. A.; Sognier, M. A.; Squires, W. G.

2003-01-01

BACKGROUND: During long-duration spaceflight, astronauts experience progressive muscle atrophy and often perform strenuous extravehicular activities. Post-flight, there is a lengthy recovery period with an increased risk for injury. Currently, there is a critical need for an enabling tool to optimize muscle performance and to minimize the risk of injury to astronauts while on-orbit and during post-flight recovery. Consequently, these studies were performed to develop a method to address this need. METHODS: Eight test subjects performed a repetitive dynamic exercise to failure at 65% of their upper torso weight using a Lordex spinal machine. Surface electromyography (SEMG) data was collected from the erector spinae back muscle. The SEMG data was evaluated using a 5th order autoregressive (AR) model and linear regression analysis. RESULTS: The best predictor found was an AR parameter, the mean average magnitude of AR poles, with r = 0.75 and p = 0.03. This parameter can predict performance to failure as early as the second repetition of the exercise. CONCLUSION: A method for predicting human muscle performance early during dynamic repetitive exercise was developed. The capability to predict performance to failure has many potential applications to the space program including evaluating countermeasure effectiveness on-orbit, optimizing post-flight recovery, and potential future real-time monitoring capability during extravehicular activity.

Dynamic Cerebral Autoregulation Changes during Sub-Maximal Handgrip Maneuver

PubMed Central

Nogueira, Ricardo C.; Bor-Seng-Shu, Edson; Santos, Marcelo R.; Negrão, Carlos E.; Teixeira, Manoel J.; Panerai, Ronney B.

2013-01-01

Purpose We investigated the effect of handgrip (HG) maneuver on time-varying estimates of dynamic cerebral autoregulation (CA) using the autoregressive moving average technique. Methods Twelve healthy subjects were recruited to perform HG maneuver during 3 minutes with 30% of maximum contraction force. Cerebral blood flow velocity, end-tidal CO2 pressure (PETCO2), and noninvasive arterial blood pressure (ABP) were continuously recorded during baseline, HG and recovery. Critical closing pressure (CrCP), resistance area-product (RAP), and time-varying autoregulation index (ARI) were obtained. Results PETCO2 did not show significant changes during HG maneuver. Whilst ABP increased continuously during the maneuver, to 27% above its baseline value, CBFV raised to a plateau approximately 15% above baseline. This was sustained by a parallel increase in RAP, suggestive of myogenic vasoconstriction, and a reduction in CrCP that could be associated with metabolic vasodilation. The time-varying ARI index dropped at the beginning and end of the maneuver (p<0.005), which could be related to corresponding alert reactions or to different time constants of the myogenic, metabolic and/or neurogenic mechanisms. Conclusion Changes in dynamic CA during HG suggest a complex interplay of regulatory mechanisms during static exercise that should be considered when assessing the determinants of cerebral blood flow and metabolism. PMID:23967113

Cannabinoids disrupt memory encoding by functionally isolating hippocampal CA1 from CA3.

PubMed

Sandler, Roman A; Fetterhoff, Dustin; Hampson, Robert E; Deadwyler, Sam A; Marmarelis, Vasilis Z

2017-07-01

Much of the research on cannabinoids (CBs) has focused on their effects at the molecular and synaptic level. However, the effects of CBs on the dynamics of neural circuits remains poorly understood. This study aims to disentangle the effects of CBs on the functional dynamics of the hippocampal Schaffer collateral synapse by using data-driven nonparametric modeling. Multi-unit activity was recorded from rats doing an working memory task in control sessions and under the influence of exogenously administered tetrahydrocannabinol (THC), the primary CB found in marijuana. It was found that THC left firing rate unaltered and only slightly reduced theta oscillations. Multivariate autoregressive models, estimated from spontaneous spiking activity, were then used to describe the dynamical transformation from CA3 to CA1. They revealed that THC served to functionally isolate CA1 from CA3 by reducing feedforward excitation and theta information flow. The functional isolation was compensated by increased feedback excitation within CA1, thus leading to unaltered firing rates. Finally, both of these effects were shown to be correlated with memory impairments in the working memory task. By elucidating the circuit mechanisms of CBs, these results help close the gap in knowledge between the cellular and behavioral effects of CBs.

Cannabinoids disrupt memory encoding by functionally isolating hippocampal CA1 from CA3

PubMed Central

Fetterhoff, Dustin; Hampson, Robert E.; Deadwyler, Sam A.; Marmarelis, Vasilis Z.

2017-01-01

Much of the research on cannabinoids (CBs) has focused on their effects at the molecular and synaptic level. However, the effects of CBs on the dynamics of neural circuits remains poorly understood. This study aims to disentangle the effects of CBs on the functional dynamics of the hippocampal Schaffer collateral synapse by using data-driven nonparametric modeling. Multi-unit activity was recorded from rats doing an working memory task in control sessions and under the influence of exogenously administered tetrahydrocannabinol (THC), the primary CB found in marijuana. It was found that THC left firing rate unaltered and only slightly reduced theta oscillations. Multivariate autoregressive models, estimated from spontaneous spiking activity, were then used to describe the dynamical transformation from CA3 to CA1. They revealed that THC served to functionally isolate CA1 from CA3 by reducing feedforward excitation and theta information flow. The functional isolation was compensated by increased feedback excitation within CA1, thus leading to unaltered firing rates. Finally, both of these effects were shown to be correlated with memory impairments in the working memory task. By elucidating the circuit mechanisms of CBs, these results help close the gap in knowledge between the cellular and behavioral effects of CBs. PMID:28686594

Dynamic Granger-Geweke causality modeling with application to interictal spike propagation

PubMed Central

Lin, Fa-Hsuan; Hara, Keiko; Solo, Victor; Vangel, Mark; Belliveau, John W.; Stufflebeam, Steven M.; Hamalainen, Matti S.

2010-01-01

A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using Structural Equation Modeling and, more recently, with Granger-Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger-Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger-Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger-Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain. PMID:19378280

Onset of hydrogen bonded collective network of water in 1,4-dioxane.

PubMed

Luong, Trung Quan; Verma, Pramod Kumar; Mitra, Rajib Kumar; Havenith, Martina

2011-12-22

We have studied the evolution of water hydrogen bonded collective network dynamics in mixtures of 1,4-dioxane (Dx) as the mole fraction of water (X(w)) increases from 0.005 to 0.54. The inter- and intramolecular vibrations of water have been observed using terahertz time domain spectroscopy (THz-TDS) in the frequency range 0.4-1.4 THz (13-47 cm(-1)) and Fourier transform infrared (FTIR) spectroscopy in the far-infrared (30-650 cm(-1)) and mid-infrared (3000-3700 cm(-1)) regions. These results have been correlated with the reactivity of water in these mixtures as determined by kinetic studies of the solvolysis reaction of benzoyl chloride (BzCl). Our studies show an onset of intermolecular hydrogen bonded water network dynamics beyond X(w) ≥ 0.1. At the same concentration, we observe a rapid increase of the rate constant of solvolysis of BzCl in water-Dx mixtures. Our results establish a correlation between the onset of collective hydrogen bonded network with the solvation dynamics and the activity of clustered water.

Transfer Entropy as a Log-Likelihood Ratio

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Bossomaier, Terry

2012-09-01

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Transfer entropy as a log-likelihood ratio.

PubMed

Barnett, Lionel; Bossomaier, Terry

2012-09-28

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Real Time Data Management for Estimating Probabilities of Incidents and Near Misses

NASA Astrophysics Data System (ADS)

Stanitsas, P. D.; Stephanedes, Y. J.

2011-08-01

Advances in real-time data collection, data storage and computational systems have led to development of algorithms for transport administrators and engineers that improve traffic safety and reduce cost of road operations. Despite these advances, problems in effectively integrating real-time data acquisition, processing, modelling and road-use strategies at complex intersections and motorways remain. These are related to increasing system performance in identification, analysis, detection and prediction of traffic state in real time. This research develops dynamic models to estimate the probability of road incidents, such as crashes and conflicts, and incident-prone conditions based on real-time data. The models support integration of anticipatory information and fee-based road use strategies in traveller information and management. Development includes macroscopic/microscopic probabilistic models, neural networks, and vector autoregressions tested via machine vision at EU and US sites.

Discrete approach to stochastic parametrization and dimension reduction in nonlinear dynamics.

PubMed

Chorin, Alexandre J; Lu, Fei

2015-08-11

Many physical systems are described by nonlinear differential equations that are too complicated to solve in full. A natural way to proceed is to divide the variables into those that are of direct interest and those that are not, formulate solvable approximate equations for the variables of greater interest, and use data and statistical methods to account for the impact of the other variables. In the present paper we consider time-dependent problems and introduce a fully discrete solution method, which simplifies both the analysis of the data and the numerical algorithms. The resulting time series are identified by a NARMAX (nonlinear autoregression moving average with exogenous input) representation familiar from engineering practice. The connections with the Mori-Zwanzig formalism of statistical physics are discussed, as well as an application to the Lorenz 96 system.

Thermal signature identification system (TheSIS): a spread spectrum temperature cycling method

NASA Astrophysics Data System (ADS)

Merritt, Scott

2015-03-01

NASA GSFC's Thermal Signature Identification System (TheSIS) 1) measures the high order dynamic responses of optoelectronic components to direct sequence spread-spectrum temperature cycling, 2) estimates the parameters of multiple autoregressive moving average (ARMA) or other models the of the responses, 3) and selects the most appropriate model using the Akaike Information Criterion (AIC). Using the AIC-tested model and parameter vectors from TheSIS, one can 1) select high-performing components on a multivariate basis, i.e., with multivariate Figures of Merit (FOMs), 2) detect subtle reversible shifts in performance, and 3) investigate irreversible changes in component or subsystem performance, e.g. aging. We show examples of the TheSIS methodology for passive and active components and systems, e.g. fiber Bragg gratings (FBGs) and DFB lasers with coupled temperature control loops, respectively.

Income-environment relationship in Sub-Saharan African countries: Further evidence with trade openness.

PubMed

Zerbo, Eléazar

2017-07-01

This paper examines the dynamic relationship between energy consumption, income growth, carbon emissions and trade openness in fourteen Sub-Saharan African (SSA) countries. The autoregressive distributed lag (ARDL) approach to cointegration and the Toda-Yamamoto causality test were used to investigate the long-run and short-run properties, respectively. The long-run estimations give evidence against the environmental Kuznets curve (EKC) hypothesis in SSA countries. In contrast, the results highlight the significant and monotonically contribution of income growth and energy consumption in explaining carbon emissions in the long-run and short-run in several countries. Furthermore, the results show that trade openness enhances economic growth and is not linked to causing carbon emissions in these countries. Hence, a trade incentive policy may be implemented without harmful effect on the quality of the environment.

Ionic liquid/water mixtures: from hostility to conciliation.

PubMed

Kohno, Yuki; Ohno, Hiroyuki

2012-07-21

Water was originally inimical to ionic liquids (ILs) especially in the analysis of their detailed properties. Various data on the properties of ILs indicate that there are two ways to design functions of ionic liquids. The first is to change the structure of component ions, to provide "task-specific ILs". The second is to mix ILs with other components, such as other ILs, organic solvents or water. Mixing makes it easy to control the properties of the solution. In this strategy, water is now a very important partner. Below, we summarise our recent results on the properties of IL/water mixtures. Stable phase separation is an effective method in some separation processes. Conversely, a dynamic phase change between a homogeneous mixture and separation of phases is important in many fields. Analysis of the relation between phase behaviour and the hydration state of the component ions indicates that the pattern of phase separation is governed by the hydrophilicity of the ions. Sufficiently hydrophilic ions yielded ILs that are miscible with water, and hydrophobic ions gave stable phase separation with water. ILs composed of hydrophobic but hydrated ions undergo a dynamic phase change between a homogeneous mixture and separate phases according to temperature. ILs having more than seven water molecules per ion pair undergo this phase transition. These dynamic phase changes are considered, with some examples, and application is made to the separation of water-soluble proteins.

Dynamics of Nafion membrane swelling in H2O/D2O mixtures as studied using FTIR technique

NASA Astrophysics Data System (ADS)

Bunkin, Nikolai F.; Kozlov, Valeriy A.; Shkirin, Alexey V.; Ninham, Barry W.; Balashov, Anatoliy A.; Gudkov, Sergey V.

2018-03-01

Experiments with Fourier transform spectrometry of Nafion, a water-swollen polymeric membrane, are described. The transmittance spectra of liquid samples and Nafion, soaked in these samples, were studied, depending on the deuterium content in water in the spectral range 1.8-2.15 μm. The experiments were carried out using two protocols: in the first protocol we studied the dynamics of Nafion swelling in H2O + D2O mixtures for the deuterium concentrations 3 < C < 104 ppm, and in the second protocol we studied the dynamics of swelling in pure heavy water (C = 106 ppm). For liquid mixtures in the concentration range 3 < C < 104 ppm, the transmittance spectra are the same, but for Nafion soaked in these fluids, the corresponding spectra are different. It is shown that, in the range of deuterium contents C = 90-500 ppm, the behavior of transmittance of the polymer membrane is non-monotonic. In experiments using the second protocol, the dynamics of diffusion replacement of residual water, which is always present in the bulk of the polymer membrane inside closed cavities (i.e., without access to atmospheric air), were studied. The experimentally estimated diffusion coefficient for this process is ≈6.10-11 cm2/s.

Effects of non-condensable gas on the dynamic oscillations of cavitation bubbles

NASA Astrophysics Data System (ADS)

Zhang, Yuning

2016-11-01

Cavitation is an essential topic of multiphase flow with a broad range of applications. Generally, there exists non-condensable gas in the liquid and a complex vapor/gas mixture bubble will be formed. A rigorous prediction of the dynamic behavior of the aforementioned mixture bubble is essential for the development of a complete cavitation model. In the present paper, effects of non-condensable gas on the dynamic oscillations of the vapor/gas mixture bubble are numerically investigated in great detail. For the completeness, a large parameter zone (e.g. bubble radius, frequency and ratio between gas and vapor) is investigated with many demonstrating examples. The mechanisms of mass diffusion are categorized into different groups with their characteristics and dominated regions given. Influences of non-condensable gas on the wave propagation (e.g. wave speed and attenuation) in the bubbly liquids are also briefly discussed. Specifically, the minimum wave speed is quantitatively predicted in order to close the pressure-density coupling relationship usually employed for the cavitation modelling. Finally, the application of the present finding on the development of cavitation model is demonstrated with a brief discussion of its influence on the cavitation dynamics. This work was financially supported by the National Natural Science Foundation of China (Project No.: 51506051).

LTPP Computed Parameter: Dynamic Modulus

DOT National Transportation Integrated Search

2011-09-01

The dynamic modulus, |E*|, is a fundamental property that defines the stiffness characteristics of hot mix asphalt (HMA) mixtures as a function of loading rate and temperature. In spite of the demonstrated significance of |E*|, it is not included in ...

Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

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

Sakaguchi, Kaori; Nagatsuma, Tsutomu; Reeves, Geoffrey D.

The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. In order to provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraftmore » were used as the predictors. Furthermore, we selected model explanatory parameters from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L≥4.8 and L ≥5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≤ L ≤ 6, while for the GEO flux prediction, the K P index is better than Dst. Finally, a test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.« less

Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

DOE PAGES

Sakaguchi, Kaori; Nagatsuma, Tsutomu; Reeves, Geoffrey D.; ...

2015-12-22

The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. In order to provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraftmore » were used as the predictors. Furthermore, we selected model explanatory parameters from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L≥4.8 and L ≥5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≤ L ≤ 6, while for the GEO flux prediction, the K P index is better than Dst. Finally, a test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.« less

Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

NASA Astrophysics Data System (ADS)

Sakaguchi, Kaori; Nagatsuma, Tsutomu; Reeves, Geoffrey D.; Spence, Harlan E.

2015-12-01

The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. To provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraft were used as the predictors. Model explanatory parameters were selected from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L ≧ 4.8 and L ≧ 5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≦ L ≦ 6, while for the GEO flux prediction, the KP index is better than Dst. A test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.

Protein dynamics in organic media at varying water activity studied by molecular dynamics simulation.

PubMed

Wedberg, Rasmus; Abildskov, Jens; Peters, Günther H

2012-03-01

In nonaqueous enzymology, control of enzyme hydration is commonly approached by fixing the thermodynamic water activity of the medium. In this work, we present a strategy for evaluating the water activity in molecular dynamics simulations of proteins in water/organic solvent mixtures. The method relies on determining the water content of the bulk phase and uses a combination of Kirkwood-Buff theory and free energy calculations to determine corresponding activity coefficients. We apply the method in a molecular dynamics study of Candida antarctica lipase B in pure water and the organic solvents methanol, tert-butyl alcohol, methyl tert-butyl ether, and hexane, each mixture at five different water activities. It is shown that similar water activity yields similar enzyme hydration in the different solvents. However, both solvent and water activity are shown to have profound effects on enzyme structure and flexibility.

Dynamic Infinite Mixed-Membership Stochastic Blockmodel.

PubMed

Fan, Xuhui; Cao, Longbing; Xu, Richard Yi Da

2015-09-01

Directional and pairwise measurements are often used to model interactions in a social network setting. The mixed-membership stochastic blockmodel (MMSB) was a seminal work in this area, and its ability has been extended. However, models such as MMSB face particular challenges in modeling dynamic networks, for example, with the unknown number of communities. Accordingly, this paper proposes a dynamic infinite mixed-membership stochastic blockmodel, a generalized framework that extends the existing work to potentially infinite communities inside a network in dynamic settings (i.e., networks are observed over time). Additional model parameters are introduced to reflect the degree of persistence among one's memberships at consecutive time stamps. Under this framework, two specific models, namely mixture time variant and mixture time invariant models, are proposed to depict two different time correlation structures. Two effective posterior sampling strategies and their results are presented, respectively, using synthetic and real-world data.

Molecular Dynamics Analysis of Lysozyme Protein in Ethanol- Water Mixed Solvent

DTIC Science & Technology

2012-01-01

molecular dynamics simulations of solvent effect on lysozyme protein, using water, ethanol, and different concentrations of water-ethanol mixtures as...understood. This work focuses on detailed molecular dynamics simulations of solvent effect on lysozyme protein, using water, ethanol, and different...using GROMACS molecular dynamics simulation (MD) code. Compared to water environment, the lysozyme structure showed remarkable changes in water

Reservoir Computing Beyond Memory-Nonlinearity Trade-off.

PubMed

Inubushi, Masanobu; Yoshimura, Kazuyuki

2017-08-31

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

Ecology of West Nile virus across four European countries: empirical modelling of the Culex pipiens abundance dynamics as a function of weather.

PubMed

Groen, Thomas A; L'Ambert, Gregory; Bellini, Romeo; Chaskopoulou, Alexandra; Petric, Dusan; Zgomba, Marija; Marrama, Laurence; Bicout, Dominique J

2017-10-26

Culex pipiens is the major vector of West Nile virus in Europe, and is causing frequent outbreaks throughout the southern part of the continent. Proper empirical modelling of the population dynamics of this species can help in understanding West Nile virus epidemiology, optimizing vector surveillance and mosquito control efforts. But modelling results may differ from place to place. In this study we look at which type of models and weather variables can be consistently used across different locations. Weekly mosquito trap collections from eight functional units located in France, Greece, Italy and Serbia for several years were combined. Additionally, rainfall, relative humidity and temperature were recorded. Correlations between lagged weather conditions and Cx. pipiens dynamics were analysed. Also seasonal autoregressive integrated moving-average (SARIMA) models were fitted to describe the temporal dynamics of Cx. pipiens and to check whether the weather variables could improve these models. Correlations were strongest between mean temperatures at short time lags, followed by relative humidity, most likely due to collinearity. Precipitation alone had weak correlations and inconsistent patterns across sites. SARIMA models could also make reasonable predictions, especially when longer time series of Cx. pipiens observations are available. Average temperature was a consistently good predictor across sites. When only short time series (~ < 4 years) of observations are available, average temperature can therefore be used to model Cx. pipiens dynamics. When longer time series (~ > 4 years) are available, SARIMAs can provide better statistical descriptions of Cx. pipiens dynamics, without the need for further weather variables. This suggests that density dependence is also an important determinant of Cx. pipiens dynamics.

Kirkwood–Buff integrals for ideal solutions

PubMed Central

Ploetz, Elizabeth A.; Bentenitis, Nikolaos; Smith, Paul E.

2010-01-01

The Kirkwood–Buff (KB) theory of solutions is a rigorous theory of solution mixtures which relates the molecular distributions between the solution components to the thermodynamic properties of the mixture. Ideal solutions represent a useful reference for understanding the properties of real solutions. Here, we derive expressions for the KB integrals, the central components of KB theory, in ideal solutions of any number of components corresponding to the three main concentration scales. The results are illustrated by use of molecular dynamics simulations for two binary solutions mixtures, benzene with toluene, and methanethiol with dimethylsulfide, which closely approach ideal behavior, and a binary mixture of benzene and methanol which is nonideal. Simulations of a quaternary mixture containing benzene, toluene, methanethiol, and dimethylsulfide suggest this system displays ideal behavior and that ideal behavior is not limited to mixtures containing a small number of components. PMID:20441282

On the nanosecond proton dynamics in phosphoric acid–benzimidazole and phosphoric acid–water mixtures

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

Melchior, Jan-Patrick; Frick, Bernhard

Combining 1H-NMR, 17O-NMR, and high-resolution backscattering QENS hydrodynamic and structural proton transport in phosphoric acid is separated. The rate limiting steps for structural proton diffusion in mixtures of acid with Brønsted bases are found to occur below the nanosecond timescale.

On the nanosecond proton dynamics in phosphoric acid–benzimidazole and phosphoric acid–water mixtures

DOE PAGES

Melchior, Jan-Patrick; Frick, Bernhard

2017-09-22

Combining 1H-NMR, 17O-NMR, and high-resolution backscattering QENS hydrodynamic and structural proton transport in phosphoric acid is separated. The rate limiting steps for structural proton diffusion in mixtures of acid with Brønsted bases are found to occur below the nanosecond timescale.

Multicomponent Diffusion of Penetrant Mixtures in Rubbery Polymers: A Molecular Dynamics Study

NASA Astrophysics Data System (ADS)

Bringuier, Stefan; Varady, Mark; Knox, Craig; Cabalo, Jerry; Pearl, Thomas; Mantooth, Brent

The importance of understanding transport of chemical species across liquid-solid boundaries is of particular interest in the decontamination of harmful chemicals absorbed within polymeric materials. To characterize processes associated with liquid-phase extraction of absorbed species from polymers, it is necessary to determine an appropriate physical description of species transport in multicomponent systems. The Maxwell-Stefan (M-S) formulation is a rigorous description of mass transport in multicomponent solutions, in which, mutual diffusivities determine the degree of relative motion between interacting molecules in response to a chemical potential gradient. The work presented focuses on the determination of M-S diffusivities from molecular dynamics (MD) simulations of nerve agent O-ethyl S-[2(diisopropylamino)ethyl] methylphosphonothioate (VX), water, and methanol mixtures within a poly(dimethylsiloxane) matrix. We investigate the composition dependence of M-S diffusivities and compare the results to values predicted using empirical relations for binary and ternary mixtures. Finally, we highlight the pertinent differences in molecular mechanisms associated with species transport and employ non-equilibrium MD to probe transport across the mixture-polymer interface.

Stabilizing the hexagonal close packed structure of hard spheres with polymers: Phase diagram, structure, and dynamics

NASA Astrophysics Data System (ADS)

Edison, John R.; Dasgupta, Tonnishtha; Dijkstra, Marjolein

2016-08-01

We study the phase behaviour of a binary mixture of colloidal hard spheres and freely jointed chains of beads using Monte Carlo simulations. Recently Panagiotopoulos and co-workers predicted [Nat. Commun. 5, 4472 (2014)] that the hexagonal close packed (HCP) structure of hard spheres can be stabilized in such a mixture due to the interplay between polymer and the void structure in the crystal phase. Their predictions were based on estimates of the free-energy penalty for adding a single hard polymer chain in the HCP and the competing face centered cubic (FCC) phase. Here we calculate the phase diagram using free-energy calculations of the full binary mixture and find a broad fluid-solid coexistence region and a metastable gas-liquid coexistence region. For the colloid-monomer size ratio considered in this work, we find that the HCP phase is only stable in a small window at relatively high polymer reservoir packing fractions, where the coexisting HCP phase is nearly close packed. Additionally we investigate the structure and dynamic behaviour of these mixtures.

Viscosity minima in binary mixtures of ionic liquids + molecular solvents.

PubMed

Tariq, M; Shimizu, K; Esperança, J M S S; Canongia Lopes, J N; Rebelo, L P N

2015-05-28

The viscosity (η) of four binary mixtures (ionic liquids plus molecular solvents, ILs+MSs) was measured in the 283.15 < T/K < 363.15 temperature range. Different IL/MS combinations were selected in such a way that the corresponding η(T) functions exhibit crossover temperatures at which both pure components present identical viscosity values. Consequently, most of the obtained mixture isotherms, η(x), exhibit clear viscosity minima in the studied T-x range. The results are interpreted using auxiliary molecular dynamics (MD) simulation data in order to correlate the observed η(T,x) trends with the interactions in each mixture, including the balance between electrostatic forces and hydrogen bonding.

Molecular dynamics investigation of separation of hydrogen sulfide from acidic gas mixtures inside metal-doped graphite micropores.

PubMed

Huang, Pei-Hsing

2015-09-21

The separation of poisonous compounds from various process fluids has long been highly intractable, motivating the present study on the dynamic separation of H2S in acidic-gas-mixture-filled micropores. The molecular dynamics approach, coupled with the isothermal-isochoric ensemble, was used to model the molecular interactions and adsorption of H2S/CO2/CO/H2O mixtures inside metal-doped graphite slits. Due to the difference in the adsorption characteristics between the two distinct adsorbent materials, the metal dopant in the graphitic micropores leads to competitive adsorption, i.e. the Au and graphite walls compete to capture free adsorbates. The effects of competitive adsorption, coupled with changes in the gas temperature, concentration, constituent ratio and slit width on the constituent separation of mixtures were systematically studied. The molecule-wall binding energies calculated in this work (those of H2S, H2O and CO on Au walls and those of H2O, CO and CO2 on graphite walls) show good agreement with those obtained using density functional theory (DFT) and experimental results. The z-directional self-diffusivities (Dz) for adsorbates inside the slit ranged from 10(-9) to 10(-7) m(2) s(-1) as the temperature was increased from 10 to 500 K. The values are comparable with those for a typical microporous fluid (10(-8)-10(-9) m(2) s(-1) in a condensed phase and 10(-6)-10(-7) m(2) s(-1) in the gaseous state). The formation of H-bonding networks and hydrates of H2S is disadvantageous for the separation of mixtures. The results indicate that H2S can be efficiently separated from acidic gas mixtures onto the Au(111) surface by (i) reducing the mole fraction of H2S and H2O in the mixtures, (ii) raising the gas temperature to the high temperature limit (≥400 K), and (iii) lowering the slit width to below the threshold dimension (≤23.26 Å).

Flow of variably fluidized granular masses across three-dimensional terrain I. Coulomb mixture theory

USGS Publications Warehouse

Iverson, R.M.; Denlinger, R.P.

2001-01-01

Rock avalanches, debris flows, and related phenomena consist of grain-fluid mixtures that move across three-dimensional terrain. In all these phenomena the same basic forces, govern motion, but differing mixture compositions, initial conditions, and boundary conditions yield varied dynamics and deposits. To predict motion of diverse grain-fluid masses from initiation to deposition, we develop a depth-averaged, threedimensional mathematical model that accounts explicitly for solid- and fluid-phase forces and interactions. Model input consists of initial conditions, path topography, basal and internal friction angles of solid grains, viscosity of pore fluid, mixture density, and a mixture diffusivity that controls pore pressure dissipation. Because these properties are constrained by independent measurements, the model requires little or no calibration and yields readily testable predictions. In the limit of vanishing Coulomb friction due to persistent high fluid pressure the model equations describe motion of viscous floods, and in the limit of vanishing fluid stress they describe one-phase granular avalanches. Analysis of intermediate phenomena such as debris flows and pyroclastic flows requires use of the full mixture equations, which can simulate interaction of high-friction surge fronts with more-fluid debris that follows. Special numerical methods (described in the companion paper) are necessary to solve the full equations, but exact analytical solutions of simplified equations provide critical insight. An analytical solution for translational motion of a Coulomb mixture accelerating from rest and descending a uniform slope demonstrates that steady flow can occur only asymptotically. A solution for the asymptotic limit of steady flow in a rectangular channel explains why shear may be concentrated in narrow marginal bands that border a plug of translating debris. Solutions for static equilibrium of source areas describe conditions of incipient slope instability, and other static solutions show that nonuniform distributions of pore fluid pressure produce bluntly tapered vertical profiles at the margins of deposits. Simplified equations and solutions may apply in additional situations identified by a scaling analysis. Assessment of dimensionless scaling parameters also reveals that miniature laboratory experiments poorly simulate the dynamics of full-scale flows in which fluid effects are significant. Therefore large geophysical flows can exhibit dynamics not evident at laboratory scales.

Influences of Quantum Mechanically Mixed Electronic and Vibrational Pigment States in 2D Electronic Spectra of Photosynthetic Systems: Strong Electronic Coupling Cases

DOE PAGES

Fujihashi, Yuta; Fleming, Graham R.; Ishizaki, Akihito

2015-09-07

In 2D electronic spectroscopy studies, long-lived quantum beats have recently been observed in photosynthetic systems, and several theoretical studies have suggested that the beats are produced by quantum mechanically mixed electronic and vibrational states. Concerning the electronic-vibrational quantum mixtures, the impact of protein-induced fluctuations was examined by calculating the 2D electronic spectra of a weakly coupled dimer with the Franck-Condon active vibrational modes in the resonant condition. This analysis demonstrated that quantum mixtures of the vibronic resonance are rather robust under the influence of the fluctuations at cryogenic temperatures, whereas the mixtures are eradicated by the fluctuations at physiological temperatures.more » However, this conclusion cannot be generalized because the magnitude of the coupling inducing the quantum mixtures is proportional to the inter-pigment electronic coupling. In this paper, we explore the impact of the fluctuations on electronic-vibrational quantum mixtures in a strongly coupled dimer with an off-resonant vibrational mode. Toward this end, we calculate energy transfer dynamics and 2D electronic spectra of a model dimer that corresponds to the most strongly coupled bacteriochlorophyll molecules in the Fenna-Matthews-Olson complex in a numerically accurate manner. The quantum mixtures are found to be robust under the exposure of protein-induced fluctuations at cryogenic temperatures, irrespective of the resonance. At 300 K, however, the quantum mixing is disturbed more strongly by the fluctuations, and therefore, the beats in the 2D spectra become obscure even in a strongly coupled dimer with a resonant vibrational mode. Further, the overall behaviors of the energy transfer dynamics are demonstrated to be dominated by the environment and coupling between the 0 0 vibronic transitions as long as the Huang-Rhys factor of the vibrational mode is small. Finally, the electronic-vibrational quantum mixtures do not necessarily play a significant role in electronic energy transfer dynamics despite contributing to the enhancement of long-lived quantum beating in the 2D spectra.« less

Flow of variably fluidized granular masses across three-dimensional terrain: 1. Coulomb mixture theory

NASA Astrophysics Data System (ADS)

Iverson, Richard M.; Denlinger, Roger P.

2001-01-01

Rock avalanches, debris flows, and related phenomena consist of grain-fluid mixtures that move across three-dimensional terrain. In all these phenomena the same basic forces govern motion, but differing mixture compositions, initial conditions, and boundary conditions yield varied dynamics and deposits. To predict motion of diverse grain-fluid masses from initiation to deposition, we develop a depth-averaged, three-dimensional mathematical model that accounts explicitly for solid- and fluid-phase forces and interactions. Model input consists of initial conditions, path topography, basal and internal friction angles of solid grains, viscosity of pore fluid, mixture density, and a mixture diffusivity that controls pore pressure dissipation. Because these properties are constrained by independent measurements, the model requires little or no calibration and yields readily testable predictions. In the limit of vanishing Coulomb friction due to persistent high fluid pressure the model equations describe motion of viscous floods, and in the limit of vanishing fluid stress they describe one-phase granular avalanches. Analysis of intermediate phenomena such as debris flows and pyroclastic flows requires use of the full mixture equations, which can simulate interaction of high-friction surge fronts with more-fluid debris that follows. Special numerical methods (described in the companion paper) are necessary to solve the full equations, but exact analytical solutions of simplified equations provide critical insight. An analytical solution for translational motion of a Coulomb mixture accelerating from rest and descending a uniform slope demonstrates that steady flow can occur only asymptotically. A solution for the asymptotic limit of steady flow in a rectangular channel explains why shear may be concentrated in narrow marginal bands that border a plug of translating debris. Solutions for static equilibrium of source areas describe conditions of incipient slope instability, and other static solutions show that nonuniform distributions of pore fluid pressure produce bluntly tapered vertical profiles at the margins of deposits. Simplified equations and solutions may apply in additional situations identified by a scaling analysis. Assessment of dimensionless scaling parameters also reveals that miniature laboratory experiments poorly simulate the dynamics of full-scale flows in which fluid effects are significant. Therefore large geophysical flows can exhibit dynamics not evident at laboratory scales.

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models.

PubMed

Yu, Kezi; Quirk, J Gerald; Djurić, Petar M

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting.

Structural and energetic properties of La3+ in water/DMSO mixtures

NASA Astrophysics Data System (ADS)

Montagna, Maria; Spezia, Riccardo; Bodo, Enrico

2017-11-01

By using molecular dynamics based on a custom polarizable force field, we have studied the solvation of La3+ in an equimolar mixture of dimethylsulfoxide (DMSO) with water. An extended structural analysis has been performed to provide a complete picture of the physical properties at the basis of the interaction of La3+ with both solvents. Through our simulations we found that, very likely, the first solvation shell in the mixture is not unlike the one found in pure water or pure DMSO and contains 9 solvent molecules. We have also found that the solvation is preferentially due to DMSO molecules with the water initially present in first shell quickly leaving to the bulk. The dehydration process of the first shell has been analyzed by both plain MD simulations and a constrained dynamics approach; the free energy profiles for the extraction of water from first shell have also been computed.

Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models

PubMed Central

Yu, Kezi; Quirk, J. Gerald

2017-01-01

In this paper, we propose an application of non-parametric Bayesian (NPB) models for classification of fetal heart rate (FHR) recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP) and the Chinese restaurant process with finite capacity (CRFC). Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR) recordings in a real-time setting. PMID:28953927

RADIAL COMBUSTION DYNAMICS IN Fe{sub 2}O{sub 3}/Al THERMITE: VARIABILITY OF THE FLAME PROPAGATION PROFILES

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

Duraes, L.; Portugal, A.; Plaksin, I.

2009-12-28

In this work, the radial combustion in thin circular samples of stoichiometric and over aluminized Fe{sub 2}O{sub 3}/Al mixtures is studied. Two confinement materials are tested: stainless steel and PVC. The combustion front profiles are registered by digital video-crono-photography. The radial geometry allows an easy detection of sample heterogeneities, via the circularity distortions of the combustion front profiles. The influence of the Al content in the mixtures and the type of confinement on the combustion propagation dynamics is analyzed. Additionally, an asymmetry parameter of the combustion front profiles is defined and statistically treated via ANOVA. Although the type of confinementmore » contributes more than the mixture composition to the variability of the asymmetry parameter, they both have a weak influence. The main source of variability is the intrinsic variations of the samples, which are due to their heterogeneous character.« less

Study of thermite mixtures consolidated by cold gas dynamic spray process

NASA Astrophysics Data System (ADS)

Bacciochini, Antoine; Maines, Geoffrey; Poupart, Christian; Radulescu, Matei; Jodoin, Bertrand; Lee, Julian

2013-06-01

The present study focused on the cold gas dynamic spray process for manufacturing finely structured energetic materials with high reactivity, vanishing porosity, as well as structural integrity and arbitrary shape. The experiments have focused the reaction between the aluminum and metal oxides, such as Al-CuO and Al-MoO3 systems. To increase the reactivity, an initial mechanical activation was achieved through interrupted ball milling. The consolidation of the materials used the supersonic cold gas spray technique, where the particles are accelerated to high speeds and consolidated via plastic deformation upon impact, forming activated nano-composites in arbitrary shapes with close to zero porosity. This technique permits to retain the feedstock powder micro-structure and prevents any reactions during the consolidation phase. Reactivity of mixtures has been investigated through flame propagation analysis on cold sprayed samples and compacted powder mixture. Deflagration tests showed the influence of porosity on the reactivity.

Do group 1 metal salts form deep eutectic solvents?

PubMed

Abbott, A P; D'Agostino, C; Davis, S J; Gladden, L F; Mantle, M D

2016-09-14

Mixtures of metal salts such as ZnCl 2 , AlCl 3 and CrCl 3 ·6H 2 O form eutectic mixtures with complexing agents, such as urea. The aim of this research was to see if alkali metal salts also formed eutectics in the same way. It is shown that only a limited number of sodium salts form homogeneous liquids at ambient temperatures and then only with glycerol. None of these mixtures showed eutectic behaviour but the liquids showed the physical properties similar to the group of mixtures classified as deep eutectic solvents. This study focussed on four sodium salts: NaBr, NaOAc, NaOAc·3H 2 O and Na 2 B 4 O 7 ·10H 2 O. The ionic conductivity and viscosity of these salts with glycerol were studied, and it was found that unlike previous studies of quaternary ammonium salts with glycerol, where the salt decreased the viscosity, most of the sodium salts increased the viscosity. This suggests that sodium salts have a structure making effect on glycerol. This phenomenon is probably due to the high charge density of Na + , which coordinates to the glycerol. 1 H and 23 Na NMR diffusion and relaxation methods have been used to understand the molecular dynamics in the glycerol-salt mixtures, and probe the effect of water on some of these systems. The results reveal a complex dynamic behaviour of the different species within these liquids. Generally, the translational dynamics of the 1 H species, probed by means of PFG NMR diffusion coefficients, is in line with the viscosity of these liquids. However, 1 H and 23 Na T 1 relaxation measurements suggest that the Na-containing species also play a crucial role in the structure of the liquids.

Molecular dynamics study of polysaccharides in binary solvent mixtures of an ionic liquid and water.

PubMed

Liu, Hanbin; Sale, Kenneth L; Simmons, Blake A; Singh, Seema

2011-09-01

Some ionic liquids (ILs) have great promise as effective solvents for biomass pretreatment, and there are several that have been reported that can dissolve large amounts of cellulose. The solubilized cellulose can then be recovered by addition of antisolvents, such as water or ethanol, and this regeneration process plays an important role in the subsequent enzymatic saccharification reactions and in the recovery of the ionic liquid. To date, little is known about the fundamental intermolecular interactions that drive the dissolution and subsequent regeneration of cellulose in complex mixtures of ionic liquids, water, and cellulose. To investigate these interactions, in this work, molecular dynamics (MD) simulations were carried out to study binary and ternary mixtures of the ionic liquid 1-ethyl-3-methylimidazolium acetate ([C2mim][OAc]) with water and a cellulose oligomer. Simulations of a cellulose oligomer dissolved in three concentrations of binary mixtures of [C2mim][OAc] and water were used to represent the ternary system in the dissolution phase (high [C2mim][OAc] concentration) and present during the initial phase of the regeneration step (intermediate and low [C2mim][OAc] concentrations). The MD analysis of the structure and dynamics that exist in these binary and ternary mixtures provides information on the key intermolecular interactions between cellulose and [C2mim][OAc] that lead to dissolution of cellulose and the key intermolecular interactions in the intermediate states of cellulose precipitation as a function of water content in the cellulose/IL/water system. The analysis of this intermediate state provides new insight into the molecular driving forces present in this ternary system. © 2011 American Chemical Society

The dynamic behavior of a liquid ethanol-water mixture: a perspective from quantum chemical topology.

PubMed

Mejía, Sol M; Mills, Matthew J L; Shaik, Majeed S; Mondragon, Fanor; Popelier, Paul L A

2011-05-07

Quantum Chemical Topology (QCT) is used to reveal the dynamics of atom-atom interactions in a liquid. A molecular dynamics simulation was carried out on an ethanol-water liquid mixture at its azeotropic concentration (X(ethanol)=0.899), using high-rank multipolar electrostatics. A thousand (ethanol)(9)-water heterodecamers, respecting the water-ethanol ratio of the azeotropic mixture, were extracted from the simulation. Ab initio electron densities were computed at the B3LYP/6-31+G(d) level for these molecular clusters. A video shows the dynamical behavior of a pattern of bond critical points and atomic interaction lines, fluctuating over 1 ns. A bond critical point distribution revealed the fluctuating behavior of water and ethanol molecules in terms of O-H···O, C-H···O and H···H interactions. Interestingly, the water molecule formed one to six C-H···O and one to four O-H···O interactions as a proton acceptor. We found that the more localized a dynamical bond critical point distribution, the higher the average electron density at its bond critical points. The formation of multiple C-H···O interactions affected the shape of the oxygen basin of the water molecule, which is shown in three dimensions. The hydrogen atoms of water strongly preferred to form H···H interactions with ethanol's alkyl hydrogen atoms over its hydroxyl hydrogen. This journal is © the Owner Societies 2011

Asymptotically stable phase synchronization revealed by autoregressive circle maps

NASA Astrophysics Data System (ADS)

Drepper, F. R.

2000-11-01

A specially designed of nonlinear time series analysis is introduced based on phases, which are defined as polar angles in spaces spanned by a finite number of delayed coordinates. A canonical choice of the polar axis and a related implicit estimation scheme for the potentially underlying autoregressive circle map (next phase map) guarantee the invertibility of reconstructed phase space trajectories to the original coordinates. The resulting Fourier approximated, invertibility enforcing phase space map allows us to detect conditional asymptotic stability of coupled phases. This comparatively general synchronization criterion unites two existing generalizations of the old concept and can successfully be applied, e.g., to phases obtained from electrocardiogram and airflow recordings characterizing cardiorespiratory interaction.

A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction

PubMed Central

Lehman, Li-Wei H.; Adams, Ryan P.; Mayaud, Louis; Moody, George B.; Malhotra, Atul; Mark, Roger G.; Nemati, Shamim

2015-01-01

Cardiovascular variables such as heart rate (HR) and blood pressure (BP) are regulated by an underlying control system, and therefore, the time series of these vital signs exhibit rich dynamical patterns of interaction in response to external perturbations (e.g., drug administration), as well as pathological states (e.g., onset of sepsis and hypotension). A question of interest is whether “similar” dynamical patterns can be identified across a heterogeneous patient cohort, and be used for prognosis of patients’ health and progress. In this paper, we used a switching vector autoregressive framework to systematically learn and identify a collection of vital sign time series dynamics, which are possibly recurrent within the same patient and may be shared across the entire cohort. We show that these dynamical behaviors can be used to characterize the physiological “state” of a patient. We validate our technique using simulated time series of the cardiovascular system, and human recordings of HR and BP time series from an orthostatic stress study with known postural states. Using the HR and BP dynamics of an intensive care unit (ICU) cohort of over 450 patients from the MIMIC II database, we demonstrate that the discovered cardiovascular dynamics are significantly associated with hospital mortality (dynamic modes 3 and 9, p = 0.001, p = 0.006 from logistic regression after adjusting for the APACHE scores). Combining the dynamics of BP time series and SAPS-I or APACHE-III provided a more accurate assessment of patient survival/mortality in the hospital than using SAPS-I and APACHE-III alone (p = 0.005 and p = 0.045). Our results suggest that the discovered dynamics of vital sign time series may contain additional prognostic value beyond that of the baseline acuity measures, and can potentially be used as an independent predictor of outcomes in the ICU. PMID:25014976

Dynamical Approach to Multiequilibria Problems for Mixtures of Acids and Their Conjugated Bases

ERIC Educational Resources Information Center

Glaser, Rainer E.; Delarosa, Marco A.; Salau, Ahmed Olasunkanmi; Chicone, Carmen

2014-01-01

Mathematical methods are described for the determination of steady-state concentrations of all species in multiequilibria systems consisting of several acids and their conjugated bases in aqueous solutions. The main example consists of a mixture of a diprotic acid H[subscript 2]A, a monoprotic acid HB, and their conjugate bases. The reaction…

Multipoint Ignition of a Gas Mixture by a Microwave Subcritical Discharge with an Extended Streamer Structure

NASA Astrophysics Data System (ADS)

Aleksandrov, K. V.; Busleev, N. I.; Grachev, L. P.; Esakov, I. I.; Ravaev, A. A.

2018-02-01

The results of experimental studies on using an electrical discharge with an extended streamer structure in a quasioptical microwave beam in the multipoint ignition of a propane-air mixture have been reported. The pulsed microwave discharge was initiated at the interior surface of a quartz tube that was filled with the mentioned flammable mixture and introduced into a microwave beam with a subbreakdown initial field. Gas breakdown was initiated by an electromagnetic vibrator. The dependence of the type of discharge on the microwave field strength was examined, the lower concentration threshold of ignition of the propane-air mixture by the studied discharge was determined, and the dynamics of combustion of the flammable mixture with local and multipoint ignition were compared.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

PubMed Central

Havlicek, Martin; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2015-01-01

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided. PMID:20561919

Spatial and temporal synchrony in reptile population dynamics in variable environments.

PubMed

Greenville, Aaron C; Wardle, Glenda M; Nguyen, Vuong; Dickman, Chris R

2016-10-01

Resources are seldom distributed equally across space, but many species exhibit spatially synchronous population dynamics. Such synchrony suggests the operation of large-scale external drivers, such as rainfall or wildfire, or the influence of oasis sites that provide water, shelter, or other resources. However, testing the generality of these factors is not easy, especially in variable environments. Using a long-term dataset (13-22 years) from a large (8000 km(2)) study region in arid Central Australia, we tested firstly for regional synchrony in annual rainfall and the dynamics of six reptile species across nine widely separated sites. For species that showed synchronous spatial dynamics, we then used multivariate follow a multivariate auto-regressive state-space (MARSS) models to predict that regional rainfall would be positively associated with their populations. For asynchronous species, we used MARSS models to explore four other possible population structures: (1) populations were asynchronous, (2) differed between oasis and non-oasis sites, (3) differed between burnt and unburnt sites, or (4) differed between three sub-regions with different rainfall gradients. Only one species showed evidence of spatial population synchrony and our results provide little evidence that rainfall synchronizes reptile populations. The oasis or the wildfire hypotheses were the best-fitting models for the other five species. Thus, our six study species appear generally to be structured in space into one or two populations across the study region. Our findings suggest that for arid-dwelling reptile populations, spatial and temporal dynamics are structured by abiotic events, but individual responses to covariates at smaller spatial scales are complex and poorly understood.

Structure investigations on assembled astaxanthin molecules

NASA Astrophysics Data System (ADS)

Köpsel, Christian; Möltgen, Holger; Schuch, Horst; Auweter, Helmut; Kleinermanns, Karl; Martin, Hans-Dieter; Bettermann, Hans

2005-08-01

The carotenoid r,r-astaxanthin (3R,3‧R-dihydroxy-4,4‧-diketo-β-carotene) forms different types of aggregates in acetone-water mixtures. H-type aggregates were found in mixtures with a high part of water (e.g. 1:9 acetone-water mixture) whereas two different types of J-aggregates were identified in mixtures with a lower part of water (3:7 acetone-water mixture). These aggregates were characterized by recording UV/vis-absorption spectra, CD-spectra and fluorescence emissions. The sizes of the molecular assemblies were determined by dynamic light scattering experiments. The hydrodynamic diameter of the assemblies amounts 40 nm in 1:9 acetone-water mixtures and exceeds up to 1 μm in 3:7 acetone-water mixtures. Scanning tunneling microscopy monitored astaxanthin aggregates on graphite surfaces. The structure of the H-aggregate was obtained by molecular modeling calculations. The structure was confirmed by calculating the electronic absorption spectrum and the CD-spectrum where the molecular modeling structure was used as input.

Dynamic regulation of heart rate during acute hypotension: new insight into baroreflex function

NASA Technical Reports Server (NTRS)

Zhang, R.; Behbehani, K.; Crandall, C. G.; Zuckerman, J. H.; Levine, B. D.; Blomqvist, C. G. (Principal Investigator)

2001-01-01

To examine the dynamic properties of baroreflex function, we measured beat-to-beat changes in arterial blood pressure (ABP) and heart rate (HR) during acute hypotension induced by thigh cuff deflation in 10 healthy subjects under supine resting conditions and during progressive lower body negative pressure (LBNP). The quantitative, temporal relationship between ABP and HR was fitted by a second-order autoregressive (AR) model. The frequency response was evaluated by transfer function analysis. Results: HR changes during acute hypotension appear to be controlled by an ABP error signal between baseline and induced hypotension. The quantitative relationship between changes in ABP and HR is characterized by a second-order AR model with a pure time delay of 0.75 s containing low-pass filter properties. During LBNP, the change in HR/change in ABP during induced hypotension significantly decreased, as did the numerator coefficients of the AR model and transfer function gain. Conclusions: 1) Beat-to-beat HR responses to dynamic changes in ABP may be controlled by an error signal rather than directional changes in pressure, suggesting a "set point" mechanism in short-term ABP control. 2) The quantitative relationship between dynamic changes in ABP and HR can be described by a second-order AR model with a pure time delay. 3) The ability of the baroreflex to evoke a HR response to transient changes in pressure was reduced during LBNP, which was due primarily to a reduction of the static gain of the baroreflex.

Oil prices, fiscal policy, and economic growth in oil-exporting countries

NASA Astrophysics Data System (ADS)

El-Anshasy, Amany A.

This dissertation argues that in oil-exporting countries fiscal policy could play an important role in transmitting the oil shocks to the economy and that the indirect effects of the changes in oil prices via the fiscal channel could be quite significant. The study comprises three distinct, yet related, essays. In the first essay, I try to study the fiscal policy response to the changes in oil prices and to their growing volatility. In a dynamic general equilibrium framework, a fiscal policy reaction function is derived and is empirically tested for a panel of 15 oil-exporters covering the period 1970--2000. After the link between oil price shocks and fiscal policy is established, the second essay tries to investigate the impact of the highly volatile oil prices on economic growth for the same sample, controlling for the fiscal channel. In both essays the study employs recent dynamic panel-data estimation techniques: System GMM. This approach has the potential advantages of minimizing the bias resulting from estimating dynamic panel models, exploiting the time series properties of the data, controlling for the unobserved country-specific effects, and correcting for any simultaneity bias. In the third essay, I focus on the case of Venezuela for the period 1950--2001. The recent developments in the cointegrating vector autoregression, CVAR technique is applied to provide a suitable framework for analyzing the short-run dynamics and the long-run relationships among oil prices, government revenues, government consumption, investment, and output.

Large signal-to-noise ratio quantification in MLE for ARARMAX models

NASA Astrophysics Data System (ADS)

Zou, Yiqun; Tang, Xiafei

2014-06-01

It has been shown that closed-loop linear system identification by indirect method can be generally transferred to open-loop ARARMAX (AutoRegressive AutoRegressive Moving Average with eXogenous input) estimation. For such models, the gradient-related optimisation with large enough signal-to-noise ratio (SNR) can avoid the potential local convergence in maximum likelihood estimation. To ease the application of this condition, the threshold SNR needs to be quantified. In this paper, we build the amplitude coefficient which is an equivalence to the SNR and prove the finiteness of the threshold amplitude coefficient within the stability region. The quantification of threshold is achieved by the minimisation of an elaborately designed multi-variable cost function which unifies all the restrictions on the amplitude coefficient. The corresponding algorithm based on two sets of physically realisable system input-output data details the minimisation and also points out how to use the gradient-related method to estimate ARARMAX parameters when local minimum is present as the SNR is small. Then, the algorithm is tested on a theoretical AutoRegressive Moving Average with eXogenous input model for the derivation of the threshold and a gas turbine engine real system for model identification, respectively. Finally, the graphical validation of threshold on a two-dimensional plot is discussed.

[Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example].

PubMed

Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi

2018-04-01

Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.

Self-esteem Is Mostly Stable Across Young Adulthood: Evidence from Latent STARTS Models.

PubMed

Wagner, Jenny; Lüdtke, Oliver; Trautwein, Ulrich

2016-08-01

How stable is self-esteem? This long-standing debate has led to different conclusions across different areas of psychology. Longitudinal data and up-to-date statistical models have recently indicated that self-esteem has stable and autoregressive trait-like components and state-like components. We applied latent STARTS models with the goal of replicating previous findings in a longitudinal sample of young adults (N = 4,532; Mage  = 19.60, SD = 0.85; 55% female). In addition, we applied multigroup models to extend previous findings on different patterns of stability for men versus women and for people with high versus low levels of depressive symptoms. We found evidence for the general pattern of a major proportion of stable and autoregressive trait variance and a smaller yet substantial amount of state variance in self-esteem across 10 years. Furthermore, multigroup models suggested substantial differences in the variance components: Females showed more state variability than males. Individuals with higher levels of depressive symptoms showed more state and less autoregressive trait variance in self-esteem. Results are discussed with respect to the ongoing trait-state debate and possible implications of the group differences that we found in the stability of self-esteem. © 2015 Wiley Periodicals, Inc.

A MISO-ARX-Based Method for Single-Trial Evoked Potential Extraction.

PubMed

Yu, Nannan; Wu, Lingling; Zou, Dexuan; Chen, Ying; Lu, Hanbing

2017-01-01

In this paper, we propose a novel method for solving the single-trial evoked potential (EP) estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished according to their respective latencies and amplitudes and are extracted simultaneously by multiple-input single-output autoregressive modeling with exogenous input (MISO-ARX). The extraction process is performed in three stages: first, we use a reference EP as a template and decompose it into a set of components, which serve as subtemplates for the remaining steps. Then, a dictionary is constructed with these subtemplates, and EPs are preliminarily extracted by sparse coding in order to roughly estimate the latency of each component. Finally, the single-trial measurement is parametrically modeled by MISO-ARX while characterizing spontaneous electroencephalographic activity as an autoregression model driven by white noise and with each component of the EP modeled by autoregressive-moving-average filtering of the subtemplates. Once optimized, all components of the EP can be extracted. Compared with ARX, our method has greater tracking capabilities of specific components of the EP complex as each component is modeled individually in MISO-ARX. We provide exhaustive experimental results to show the effectiveness and feasibility of our method.

Autoregressive modelling of species richness in the Brazilian Cerrado.

PubMed

Vieira, C M; Blamires, D; Diniz-Filho, J A F; Bini, L M; Rangel, T F L V B

2008-05-01

Spatial autocorrelation is the lack of independence between pairs of observations at given distances within a geographical space, a phenomenon commonly found in ecological data. Taking into account spatial autocorrelation when evaluating problems in geographical ecology, including gradients in species richness, is important to describe both the spatial structure in data and to correct the bias in Type I errors of standard statistical analyses. However, to effectively solve these problems it is necessary to establish the best way to incorporate the spatial structure to be used in the models. In this paper, we applied autoregressive models based on different types of connections and distances between 181 cells covering the Cerrado region of Central Brazil to study the spatial variation in mammal and bird species richness across the biome. Spatial structure was stronger for birds than for mammals, with R(2) values ranging from 0.77 to 0.94 for mammals and from 0.77 to 0.97 for birds, for models based on different definitions of spatial structures. According to the Akaike Information Criterion (AIC), the best autoregressive model was obtained by using the rook connection. In general, these results furnish guidelines for future modelling of species richness patterns in relation to environmental predictors and other variables expressing human occupation in the biome.

Modeling and roles of meteorological factors in outbreaks of highly pathogenic avian influenza H5N1.

PubMed

Biswas, Paritosh K; Islam, Md Zohorul; Debnath, Nitish C; Yamage, Mat

2014-01-01

The highly pathogenic avian influenza A virus subtype H5N1 (HPAI H5N1) is a deadly zoonotic pathogen. Its persistence in poultry in several countries is a potential threat: a mutant or genetically reassorted progenitor might cause a human pandemic. Its world-wide eradication from poultry is important to protect public health. The global trend of outbreaks of influenza attributable to HPAI H5N1 shows a clear seasonality. Meteorological factors might be associated with such trend but have not been studied. For the first time, we analyze the role of meteorological factors in the occurrences of HPAI outbreaks in Bangladesh. We employed autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to assess the roles of different meteorological factors in outbreaks of HPAI. Outbreaks were modeled best when multiplicative seasonality was incorporated. Incorporation of any meteorological variable(s) as inputs did not improve the performance of any multivariable models, but relative humidity (RH) was a significant covariate in several ARIMA and SARIMA models with different autoregressive and moving average orders. The variable cloud cover was also a significant covariate in two SARIMA models, but air temperature along with RH might be a predictor when moving average (MA) order at lag 1 month is considered.

Formation of a nanobubble and its effect on the structural ordering of water in a CH4-N2-CO2-H2O mixture.

PubMed

Kaur, Surinder Pal; Sujith, K S; Ramachandran, C N

2018-04-04

The replacement of methane (CH4) from its hydrate by a mixture of nitrogen (N2) and carbon dioxide (CO2) involves the dissociation of methane hydrate leading to the formation of a CH4-N2-CO2-H2O mixture that can significantly influence the subsequent steps of the replacement process. In the present work, we study the evolution of dissolved gas molecules in this mixture by applying classical molecular dynamics simulations. Our study shows that a higher CO2 : N2 ratio in the mixture enhances the formation of nanobubbles composed of N2, CH4 and CO2 molecules. To understand how the CO2 : N2 ratio affects nanobubble nucleation, the distribution of molecules in the bubble formed is examined. It is observed that unlike N2 and CH4, the density of CO2 in the bubble reaches a maximum at the surface of the bubble. The accumulation of CO2 molecules at the surface makes the bubble more stable by decreasing the excess pressure inside the bubble as well as surface tension at its interface with water. It is found that a frequent exchange of gas molecules takes place between the bubble and the surrounding liquid and an increase in concentration of CO2 in the mixture leads to a decrease in the number of such exchanges. The effect of nanobubbles on the structural ordering of water molecules is examined by determining the number of water rings formed per unit volume in the mixture. The role of nanobubbles in water structuring is correlated to the dynamic nature of the bubble arising from the exchange of gas molecules between the bubble and the liquid.

Structure and dynamics of propylammonium nitrate-acetonitrile mixtures: An intricate multi-scale system probed with experimental and theoretical techniques.

PubMed

Campetella, Marco; Mariani, Alessandro; Sadun, Claudia; Wu, Boning; Castner, Edward W; Gontrani, Lorenzo

2018-04-07

In this article, we report the study of structural and dynamical properties for a series of acetonitrile/propylammonium nitrate mixtures as a function of their composition. These systems display an unusual increase in intensity in their X-ray diffraction patterns in the low-q regime, and their 1 H-NMR diffusion-ordered NMR spectroscopy (DOSY) spectra display unusual diffusivities. However, the magnitude of both phenomena for mixtures of propylammonium nitrate is smaller than those observed for ethylammonium nitrate mixtures with the same cosolvent, suggesting that the cation alkyl tail plays an important role in these observations. The experimental X-ray scattering data are compared with the results of molecular dynamics simulations, including both ab initio studies used to interpret short-range interactions and classical simulations to describe longer range interactions. The higher level calculations highlight the presence of a strong hydrogen bond network within the ionic liquid, only slightly perturbed even at high acetonitrile concentration. These strong interactions lead to the symmetry breaking of the NO 3 - vibrations, with a splitting of about 88 cm -1 in the ν 3 antisymmetric stretch. The classical force field simulations use a greater number of ion pairs, but are not capable of fully describing the longest range interactions, although they do successfully account for the observed concentration trend, and the analysis of the models confirms the nano-inhomogeneity of these kinds of samples.

Structure and dynamics of propylammonium nitrate-acetonitrile mixtures: An intricate multi-scale system probed with experimental and theoretical techniques

NASA Astrophysics Data System (ADS)

Campetella, Marco; Mariani, Alessandro; Sadun, Claudia; Wu, Boning; Castner, Edward W.; Gontrani, Lorenzo

2018-04-01

In this article, we report the study of structural and dynamical properties for a series of acetonitrile/propylammonium nitrate mixtures as a function of their composition. These systems display an unusual increase in intensity in their X-ray diffraction patterns in the low-q regime, and their 1H-NMR diffusion-ordered NMR spectroscopy (DOSY) spectra display unusual diffusivities. However, the magnitude of both phenomena for mixtures of propylammonium nitrate is smaller than those observed for ethylammonium nitrate mixtures with the same cosolvent, suggesting that the cation alkyl tail plays an important role in these observations. The experimental X-ray scattering data are compared with the results of molecular dynamics simulations, including both ab initio studies used to interpret short-range interactions and classical simulations to describe longer range interactions. The higher level calculations highlight the presence of a strong hydrogen bond network within the ionic liquid, only slightly perturbed even at high acetonitrile concentration. These strong interactions lead to the symmetry breaking of the NO3 - vibrations, with a splitting of about 88 cm-1 in the ν3 antisymmetric stretch. The classical force field simulations use a greater number of ion pairs, but are not capable of fully describing the longest range interactions, although they do successfully account for the observed concentration trend, and the analysis of the models confirms the nano-inhomogeneity of these kinds of samples.

Dissipative particle dynamics parameterization and simulations to predict negative volume excess and structure of PEG and water mixtures

NASA Astrophysics Data System (ADS)

Kacar, Gokhan

2017-12-01

We report the results of dissipative particle dynamics (DPD) parameterization and simulations of a mixture of hydrophilic polymer, PEG 400, and water which are known to exhibit negative volume excess property upon mixing. The addition of a Morse potential to the conventional DPD potential mimics the hydrogen bond attraction, where the parameterization takes the internal chemistry of the beads into account. The results indicate that the mixing of PEG and water are maintained by the influence of hydrogen bonds, and the mesoscopic structure is characterized by the trade-off of enthalpic and entropic effects.

Dynamic-Stark-effect-induced coherent mixture of virtual paths in laser-dressed helium: energetic electron impact excitation

NASA Astrophysics Data System (ADS)

Agueny, Hicham; Makhoute, Abdelkader; Dubois, Alain

2017-06-01

We theoretically investigate quantum virtual path interference caused by the dynamic Stark effect in bound-bound electronic transitions. The effect is studied in an intermediate resonant region and in connection with the energetic electron impact excitation of a helium atom embedded in a weak low-frequency laser field. The process under investigation is dealt with via a Born-Floquet approach. Numerical calculations show a resonant feature in laser-assisted cross sections. The latter is found to be sensitive to the intensity of the laser field dressing. We show that this feature is a signature of quantum beats which result from the coherent mixture of different quantum virtual pathways, and that excitation may follow in order to end up with a common final channel. This mixture arises from the dynamic Stark effect, which produces a set of avoided crossings in laser-dressed states. The effect allows one to coherently control quantum virtual path interference by varying the intensity of the laser field dressing. Our findings suggest that the combination of an energetic electron and a weak laser field is a useful tool for the coherent control of nonadiabatic transitions in an intermediate resonant region.

Nonnormality and Divergence in Posttreatment Alcohol Use

PubMed Central

Witkiewitz, Katie; van der Maas, Han L. J.; Hufford, Michael R.; Marlatt, G. Alan

2007-01-01

Alcohol lapses are the modal outcome following treatment for alcohol use disorders, yet many alcohol researchers have encountered limited success in the prediction and prevention of relapse. One hypothesis is that lapses are unpredictable, but another possibility is the complexity of the relapse process is not captured by traditional statistical methods. Data from Project Matching Alcohol Treatments to Client Heterogeneity (Project MATCH), a multisite alcohol treatment study, were reanalyzed with 2 statistical methodologies: catastrophe and 2-part growth mixture modeling. Drawing on previous investigations of self-efficacy as a dynamic predictor of relapse, the current study revisits the self-efficacy matching hypothesis, which was not statistically supported in Project MATCH. Results from both the catastrophe and growth mixture analyses demonstrated a dynamic relationship between self-efficacy and drinking outcomes. The growth mixture analyses provided evidence in support of the original matching hypothesis: Individuals with lower self-efficacy who received cognitive behavior therapy drank far less frequently than did those with low self-efficacy who received motivational therapy. These results highlight the dynamical nature of the relapse process and the importance of the use of methodologies that accommodate this complexity when evaluating treatment outcomes. PMID:17516769

Solvent Dependent Dynamics of Salicylidene Aniline in Binary Mixtures of Supercritical CO2 with 1-Propanol or Cyclohexane.

PubMed

Kieda, Ryan D; Dunkelberger, Adam D; Case, Amanda S; Crim, F Fleming

2017-02-02

The role of different solvent environments in determining the behavior of molecules in solution is a fundamental aspect of chemical reactivity. We present an approach for exploring the influence of solvent properties on condensed-phase dynamics using ultrafast transient absorption spectroscopy in supercritical CO 2 . Using supercritical CO 2 permits adjustment of the density, by varying the temperature and pressure, whereas varying the concentration or identity of a second solvent, the cosolvent, in a binary mixture allows for adjustments of the degree of interaction between the solute and the solvent. Salicylidene aniline, a prototypical excited-state intramolecular proton-transfer system, is the subject of this study. In this system, the decay rate of the transient absorption signal decreases as the fraction of the cosolvent (for both 1-propanol and cyclohexane) increases. The decay rate also decreases with an increase in the viscosity of the mixture, but the effect is much larger for the 1-propanol cosolvent than for cyclohexane. These observations illustrate that the decay rate of the photoexcited salicylidene aniline depends on more than just the solvent viscosity, suggesting that properties such as polarity also play a role in the dynamics.

On the validity of Stokes-Einstein and Stokes-Einstein-Debye relations in ionic liquids and ionic-liquid mixtures.

PubMed

Köddermann, Thorsten; Ludwig, Ralf; Paschek, Dietmar

2008-09-15

Stokes-Einstein (SE) and Stokes-Einstein-Debye (SED) relations in the neat ionic liquid (IL) [C(2)mim][NTf(2)] and IL/chloroform mixtures are studied by means of molecular dynamics (MD) simulations. For this purpose, we simulate the translational diffusion coefficients of the cations and anions, the rotational correlation times of the C(2)--H bond in the cation C(2)mim(+), and the viscosities of the whole system. We find that the SE and SED relations are not valid for the pure ionic liquid, nor for IL/chloroform mixtures down to the miscibility gap (at 50 wt % IL). The deviations from both relations could be related to dynamical heterogeneities described by the non-Gaussian parameter alpha(t). If alpha(t) is close to zero, at a concentration of 1 wt % IL in chloroform, both relations become valid. Then, the effective radii and volumes calculated from the SE and SED equations can be related to the structures found in the MD simulations, such as aggregates of ion pairs. Overall, similarities are observed between the dynamical properties of supercooled water and those of ionic liquids.

Comparison of detailed and reduced kinetics mechanisms of silane oxidation in the basis of detonation wave structure problem

NASA Astrophysics Data System (ADS)

Fedorov, A. V.; Tropin, D. A.; Fomin, P. A.

2018-03-01

The paper deals with the problem of the structure of detonation waves in the silane-air mixture within the framework of mathematical model of a nonequilibrium gas dynamics. Detailed kinetic scheme of silane oxidation as well as the newly developed reduced kinetic model of detonation combustion of silane are used. On its basis the detonation wave (DW) structure in stoichiometric silane - air mixture and dependences of Chapman-Jouguet parameters of mixture on stoichiometric ratio between the fuel (silane) and an oxidizer (air) were obtained.

Pollution source localization in an urban water supply network based on dynamic water demand.

PubMed

Yan, Xuesong; Zhu, Zhixin; Li, Tian

2017-10-27

Urban water supply networks are susceptible to intentional, accidental chemical, and biological pollution, which pose a threat to the health of consumers. In recent years, drinking-water pollution incidents have occurred frequently, seriously endangering social stability and security. The real-time monitoring for water quality can be effectively implemented by placing sensors in the water supply network. However, locating the source of pollution through the data detection obtained by water quality sensors is a challenging problem. The difficulty lies in the limited number of sensors, large number of water supply network nodes, and dynamic user demand for water, which leads the pollution source localization problem to an uncertainty, large-scale, and dynamic optimization problem. In this paper, we mainly study the dynamics of the pollution source localization problem. Previous studies of pollution source localization assume that hydraulic inputs (e.g., water demand of consumers) are known. However, because of the inherent variability of urban water demand, the problem is essentially a fluctuating dynamic problem of consumer's water demand. In this paper, the water demand is considered to be stochastic in nature and can be described using Gaussian model or autoregressive model. On this basis, an optimization algorithm is proposed based on these two dynamic water demand change models to locate the pollution source. The objective of the proposed algorithm is to find the locations and concentrations of pollution sources that meet the minimum between the analogue and detection values of the sensor. Simulation experiments were conducted using two different sizes of urban water supply network data, and the experimental results were compared with those of the standard genetic algorithm.

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

PubMed

Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

2018-01-01

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

Excited-state proton transfer dynamics of firefly's chromophore D-luciferin in DMSO-water binary mixture.

PubMed

Kuchlyan, Jagannath; Banik, Debasis; Roy, Arpita; Kundu, Niloy; Sarkar, Nilmoni

2014-12-04

In this article we have investigated intermolecular excited-state proton transfer (ESPT) of firefly's chromophore D-luciferin in DMSO-water binary mixtures using steady-state and time-resolved fluorescence spectroscopy. The unusual behavior of DMSO-water binary mixture as reported by Bagchi et al. (J. Phys. Chem. B 2010, 114, 12875-12882) was also found using D-luciferin as intermolecular ESPT probe. The binary mixture has given evidence of its anomalous nature at low mole fractions of DMSO (below XD = 0.4) in our systematic investigation. Upon excitation of neutral D-luciferin molecule, dual fluorescence emissions (protonated and deprotonated form) are observed in DMSO-water binary mixture. A clear isoemissive point in the time-resolved area normalized emission spectra further indicates two emissive species in the excited state of D-luciferin in DMSO-water binary mixture. DMSO-water binary mixtures of different compositions are fascinating hydrogen bonding systems. Therefore, we have observed unusual changes in the fluorescence emission intensity, fluorescence quantum yield, and fluorescence lifetime of more hydrogen bonding sensitive anionic form of D-luciferin in low DMSO content of DMSO-water binary mixture.

New views of granular mass flows

USGS Publications Warehouse

Iverson, R.M.; Vallance, J.W.

2001-01-01

Concentrated grain-fluid mixtures in rock avalanches, debris flows, and pyroclastic flows do not behave as simple materials with fixed rheologies. Instead, rheology evolves as mixture agitation, grain concentration, and fluid-pressure change during flow initiation, transit, and deposition. Throughout a flow, however, normal forces on planes parallel to the free upper surface approximately balance the weight of the superincumbent mixture, and the Coulomb friction rule describes bulk intergranular shear stresses on such planes. Pore-fluid pressure can temporarily or locally enhance mixture mobility by reducing Coulomb friction and transferring shear stress to the fluid phase. Initial conditions, boundary conditions, and grain comminution and sorting can influence pore-fluid pressures and cause variations in flow dynamics and deposits.

Mixtures of amino-acid based ionic liquids and water.

PubMed

Chaban, Vitaly V; Fileti, Eudes Eterno

2015-09-01

New ionic liquids (ILs) involving increasing numbers of organic and inorganic ions are continuously being reported. We recently developed a new force field; in the present work, we applied that force field to investigate the structural properties of a few novel imidazolium-based ILs in aqueous mixtures via molecular dynamics (MD) simulations. Using cluster analysis, radial distribution functions, and spatial distribution functions, we argue that organic ions (imidazolium, deprotonated alanine, deprotonated methionine, deprotonated tryptophan) are well dispersed in aqueous media, irrespective of the IL content. Aqueous dispersions exhibit desirable properties for chemical engineering. The ILs exist as ion pairs in relatively dilute aqueous mixtures (10 mol%), while more concentrated mixtures feature a certain amount of larger ionic aggregates.

Effects of solvent concentration and composition on protein dynamics: 13C MAS NMR studies of elastin in glycerol-water mixtures.

PubMed

Demuth, Dominik; Haase, Nils; Malzacher, Daniel; Vogel, Michael

2015-08-01

We use (13)C CP MAS NMR to investigate the dependence of elastin dynamics on the concentration and composition of the solvent at various temperatures. For elastin in pure glycerol, line-shape analysis shows that larger-scale fluctuations of the protein backbone require a minimum glycerol concentration of ~0.6 g/g at ambient temperature, while smaller-scale fluctuations are activated at lower solvation levels of ~0.2 g/g. Immersing elastin in various glycerol-water mixtures, we observe at room temperature that the protein mobility is higher for lower glycerol fractions in the solvent and, thus, lower solvent viscosity. When decreasing the temperature, the elastin spectra approach the line shape for the rigid protein at 245 K for all studied samples, indicating that the protein ceases to be mobile on the experimental time scale of ~10(-5) s. Our findings yield evidence for a strong coupling between elastin fluctuations and solvent dynamics and, hence, such interaction is not restricted to the case of protein-water mixtures. Spectral resolution of different carbon species reveals that the protein-solvent couplings can, however, be different for side chain and backbone units. We discuss these results against the background of the slaving model for protein dynamics. Copyright © 2015 Elsevier B.V. All rights reserved.

Detection of "noisy" chaos in a time series

NASA Technical Reports Server (NTRS)

Chon, K. H.; Kanters, J. K.; Cohen, R. J.; Holstein-Rathlou, N. H.

1997-01-01

Time series from biological system often displays fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". The output from most biological systems is probably the result of both the internal dynamics of the systems, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

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

PubMed

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

2012-12-01

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

Spectral analysis based on fast Fourier transformation (FFT) of surveillance data: the case of scarlet fever in China.

PubMed

Zhang, T; Yang, M; Xiao, X; Feng, Z; Li, C; Zhou, Z; Ren, Q; Li, X

2014-03-01

Many infectious diseases exhibit repetitive or regular behaviour over time. Time-domain approaches, such as the seasonal autoregressive integrated moving average model, are often utilized to examine the cyclical behaviour of such diseases. The limitations for time-domain approaches include over-differencing and over-fitting; furthermore, the use of these approaches is inappropriate when the assumption of linearity may not hold. In this study, we implemented a simple and efficient procedure based on the fast Fourier transformation (FFT) approach to evaluate the epidemic dynamic of scarlet fever incidence (2004-2010) in China. This method demonstrated good internal and external validities and overcame some shortcomings of time-domain approaches. The procedure also elucidated the cycling behaviour in terms of environmental factors. We concluded that, under appropriate circumstances of data structure, spectral analysis based on the FFT approach may be applicable for the study of oscillating diseases.

The log-periodic-AR(1)-GARCH(1,1) model for financial crashes

NASA Astrophysics Data System (ADS)

Gazola, L.; Fernandes, C.; Pizzinga, A.; Riera, R.

2008-02-01

This paper intends to meet recent claims for the attainment of more rigorous statistical methodology within the econophysics literature. To this end, we consider an econometric approach to investigate the outcomes of the log-periodic model of price movements, which has been largely used to forecast financial crashes. In order to accomplish reliable statistical inference for unknown parameters, we incorporate an autoregressive dynamic and a conditional heteroskedasticity structure in the error term of the original model, yielding the log-periodic-AR(1)-GARCH(1,1) model. Both the original and the extended models are fitted to financial indices of U. S. market, namely S&P500 and NASDAQ. Our analysis reveal two main points: (i) the log-periodic-AR(1)-GARCH(1,1) model has residuals with better statistical properties and (ii) the estimation of the parameter concerning the time of the financial crash has been improved.

Combined non-parametric and parametric approach for identification of time-variant systems

NASA Astrophysics Data System (ADS)

Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

2018-03-01

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.

Children's representations of multiple family relationships: organizational structure and development in early childhood.

PubMed

Schermerhorn, Alice C; Cummings, E Mark; Davies, Patrick T

2008-02-01

The authors examine mutual family influence processes at the level of children's representations of multiple family relationships, as well as the structure of those representations. From a community sample with 3 waves, each spaced 1 year apart, kindergarten-age children (105 boys and 127 girls) completed a story-stem completion task, tapping representations of multiple family relationships. Structural equation modeling with autoregressive controls indicated that representational processes involving different family relationships were interrelated over time, including links between children's representations of marital conflict and reactions to conflict, between representations of security about marital conflict and parent-child relationships, and between representations of security in father-child and mother-child relationships. Mixed support was found for notions of increasing stability in representations during this developmental period. Results are discussed in terms of notions of transactional family dynamics, including family-wide perspectives on mutual influence processes attributable to multiple family relationships.

Simulation of high-energy radiation belt electron fluxes using NARMAX-VERB coupled codes

PubMed Central

Pakhotin, I P; Drozdov, A Y; Shprits, Y Y; Boynton, R J; Subbotin, D A; Balikhin, M A

2014-01-01

This study presents a fusion of data-driven and physics-driven methodologies of energetic electron flux forecasting in the outer radiation belt. Data-driven NARMAX (Nonlinear AutoRegressive Moving Averages with eXogenous inputs) model predictions for geosynchronous orbit fluxes have been used as an outer boundary condition to drive the physics-based Versatile Electron Radiation Belt (VERB) code, to simulate energetic electron fluxes in the outer radiation belt environment. The coupled system has been tested for three extended time periods totalling several weeks of observations. The time periods involved periods of quiet, moderate, and strong geomagnetic activity and captured a range of dynamics typical of the radiation belts. The model has successfully simulated energetic electron fluxes for various magnetospheric conditions. Physical mechanisms that may be responsible for the discrepancies between the model results and observations are discussed. PMID:26167432

Toddlers’ transition to out-of-home day care: Settling into a new care environment

PubMed Central

Datler, Wilfried; Ereky-Stevens, Katharina; Hover-Reisner, Nina; Malmberg, Lars-Erik

2012-01-01

This study investigates toddlers’ initial reaction to day care entry and their behaviour change over the first few months in care. One hundred and four toddlers (10–33 months of age) in Viennese childcare centres participated in the study. One-hour video observations were carried out at 3 time points during the first 4 months in the setting and coded into a total of 36 5-min observation segments. Multilevel models (observation segments nested within children) with an autoregressive error structure fitted data well. Two weeks after entry into care, toddlers’ levels of affect and interaction were low. Overall, changes in all areas of observed behaviour were less than expected. There were considerable individual differences in change over time, mostly unrelated to child characteristics. Significant associations between children's positive affect, their dynamic interactions and their explorative and investigative interest were found. PMID:22721743

Economic and Sociological Correlates of Suicides: Multilevel Analysis of the Time Series Data in the United Kingdom.

PubMed

Sun, Bruce Qiang; Zhang, Jie

2016-03-01

For the effects of social integration on suicides, there have been different and even contradictive conclusions. In this study, the selected economic and social risks of suicide for different age groups and genders in the United Kingdom were identified and the effects were estimated by the multilevel time series analyses. To our knowledge, there exist no previous studies that estimated a dynamic model of suicides on the time series data together with multilevel analysis and autoregressive distributed lags. The investigation indicated that unemployment rate, inflation rate, and divorce rate are all significantly and positively related to the national suicide rates in the United Kingdom from 1981 to 2011. Furthermore, the suicide rates of almost all groups above 40 years are significantly associated with the risk factors of unemployment and inflation rate, in comparison with the younger groups. © 2016 American Academy of Forensic Sciences.

The dynamic relationship between health expenditure and economic growth: is the health-led growth hypothesis valid for Turkey?

PubMed

Atilgan, Emre; Kilic, Dilek; Ertugrul, Hasan Murat

2017-06-01

The well-known health-led growth hypothesis claims a positive correlation between health expenditure and economic growth. The aim of this paper is to empirically investigate the health-led growth hypothesis for the Turkish economy. The bound test approach, autoregressive-distributed lag approach (ARDL) and Kalman filter modeling are employed for the 1975-2013 period to examine the co-integration relationship between economic growth and health expenditure. The ARDL model is employed in order to investigate the long-term and short-term static relationship between health expenditure and economic growth. The results show that a 1 % increase in per-capita health expenditure will lead to a 0.434 % increase in per-capita gross domestic product. These findings are also supported by the Kalman filter model's results. Our findings show that the health-led growth hypothesis is supported for Turkey.

Simulation And Forecasting of Daily Pm10 Concentrations Using Autoregressive Models In Kagithane Creek Valley, Istanbul

NASA Astrophysics Data System (ADS)

Ağaç, Kübra; Koçak, Kasım; Deniz, Ali

2015-04-01

A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built in MATLAB an Eviews programmes. Because of the seasonality of PM10 data SARIMA model was also used. The order of autoregression model was determined according to AIC and BIC criteria. The model performances were evaluated from Fractional Bias, Normalized Mean Square Error (NMSE) and Mean Absolute Percentage Error (MAPE). As expected, the results were encouraging. Keywords: PM10, Autoregression, Forecast Acknowledgement The authors would like to acknowledge the financial support by the Scientific and Technological Research Council of Turkey (TUBITAK, project no:112Y319).

Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models

NASA Astrophysics Data System (ADS)

Dettmer, Jan; Molnar, Sheri; Steininger, Gavin; Dosso, Stan E.; Cassidy, John F.

2012-02-01

This paper applies a general trans-dimensional Bayesian inference methodology and hierarchical autoregressive data-error models to the inversion of microtremor array dispersion data for shear wave velocity (vs) structure. This approach accounts for the limited knowledge of the optimal earth model parametrization (e.g. the number of layers in the vs profile) and of the data-error statistics in the resulting vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the indexing parameter) are considered in the results. The earth model is parametrized in terms of a partition model with interfaces given over a depth-range of interest. In this work, the number of interfaces (layers) in the partition model represents the trans-dimensional model indexing. In addition, serial data-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate data-error statistics, and have no requirement for computing the inverse or determinant of a data-error covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the state space that spans multiple subspaces of different dimensionalities. The order of the autoregressive process required to fit the data is determined here by posterior residual-sample examination and statistical tests. Inference for earth model parameters is carried out on the trans-dimensional posterior probability distribution by considering ensembles of parameter vectors. In particular, vs uncertainty estimates are obtained by marginalizing the trans-dimensional posterior distribution in terms of vs-profile marginal distributions. The methodology is applied to microtremor array dispersion data collected at two sites with significantly different geology in British Columbia, Canada. At both sites, results show excellent agreement with estimates from invasive measurements.

Solvent sensitivity of protein unfolding: dynamical study of chicken villin headpiece subdomain in water-ethanol binary mixture.

PubMed

Ghosh, Rikhia; Roy, Susmita; Bagchi, Biman

2013-12-12

We carry out a series of long atomistic molecular dynamics simulations to study the unfolding of a small protein, chicken villin headpiece (HP-36), in water-ethanol (EtOH) binary mixture. The prime objective of this work is to explore the sensitivity of protein unfolding dynamics toward increasing concentration of the cosolvent and unravel essential features of intermediates formed in search of a dynamical pathway toward unfolding. In water-ethanol binary mixtures, HP-36 is found to unfold partially, under ambient conditions, that otherwise requires temperature as high as ∼600 K to denature in pure aqueous solvent. However, an interesting course of pathway is observed to be followed in the process, guided by the formation of unique intermediates. The first step of unfolding is essentially the separation of the cluster formed by three hydrophobic (phenylalanine) residues, namely, Phe-7, Phe-11, and Phe-18, which constitute the hydrophobic core, thereby initiating melting of helix-2 of the protein. The initial steps are similar to temperature-induced unfolding as well as chemical unfolding using DMSO as cosolvent. Subsequent unfolding steps follow a unique path. As water-ethanol shows composition-dependent anomalies, so do the details of unfolding dynamics. With an increase in cosolvent concentration, different partially unfolded intermediates are found to be formed. This is reflected in a remarkable nonmonotonic composition dependence of several order parameters, including fraction of native contacts and protein-solvent interaction energy. The emergence of such partially unfolded states can be attributed to the preferential solvation of the hydrophobic residues by the ethyl groups of ethanol. We further quantify the local dynamics of unfolding by using a Marcus-type theory.

Hydrogen bonded structure, polarity, molecular motion and frequency fluctuations at liquid-vapor interface of a water-methanol mixture: an ab initio molecular dynamics study.

PubMed

Choudhuri, Jyoti Roy; Chandra, Amalendu

2014-10-07

We have performed ab initio molecular dynamics simulations of a liquid-vapor interfacial system consisting of a mixture of water and methanol molecules. Detailed results are obtained for the structural and dynamical properties of the bulk and interfacial regions of the mixture. Among structural properties, we have looked at the inhomogeneous density profiles of water and methanol molecules, hydrogen bond distributions and also the orientational profiles of bulk and interfacial molecules. The methanol molecules are found to have a higher propensity to be at the interface than water molecules. It is found that the interfacial molecules show preference for specific orientations so as to form water-methanol hydrogen bonds at the interface with the hydrophobic methyl group pointing towards the vapor side. It is also found that for both types of molecules, the dipole moment decreases at the interface. It is also found that the local electric field of water influences the dipole moment of methanol molecules. Among the dynamical properties, we have calculated the diffusion, orientational relaxation, hydrogen bond dynamics, and vibrational frequency fluctuations in bulk and interfacial regions. It is found that the diffusion and orientation relaxation of the interfacial molecules are faster than those of the bulk. However, the hydrogen bond lifetimes are longer at the interface which can be correlated with the time scales found from the decay of frequency time correlations. The slower hydrogen bond dynamics for the interfacial molecules with respect to bulk can be attributed to diminished cooperative effects at the interface due to reduced density and number of hydrogen bonds.

Shear viscosity for dense plasmas by equilibrium molecular dynamics in asymmetric Yukawa ionic mixtures.

PubMed

Haxhimali, Tomorr; Rudd, Robert E; Cabot, William H; Graziani, Frank R

2015-11-01

We present molecular dynamics (MD) calculations of shear viscosity for asymmetric mixed plasma for thermodynamic conditions relevant to astrophysical and inertial confinement fusion plasmas. Specifically, we consider mixtures of deuterium and argon at temperatures of 100-500 eV and a number density of 10^{25} ions/cc. The motion of 30,000-120,000 ions is simulated in which the ions interact via the Yukawa (screened Coulomb) potential. The electric field of the electrons is included in this effective interaction; the electrons are not simulated explicitly. Shear viscosity is calculated using the Green-Kubo approach with an integral of the shear stress autocorrelation function, a quantity calculated in the equilibrium MD simulations. We systematically study different mixtures through a series of simulations with increasing fraction of the minority high-Z element (Ar) in the D-Ar plasma mixture. In the more weakly coupled plasmas, at 500 eV and low Ar fractions, results from MD compare very well with Chapman-Enskog kinetic results. In the more strongly coupled plasmas, the kinetic theory does not agree well with the MD results. We develop a simple model that interpolates between classical kinetic theories at weak coupling and the Murillo Yukawa viscosity model at higher coupling. This hybrid kinetics-MD viscosity model agrees well with the MD results over the conditions simulated, ranging from moderately weakly coupled to moderately strongly coupled asymmetric plasma mixtures.

Shear viscosity for dense plasmas by equilibrium molecular dynamics in asymmetric Yukawa ionic mixtures

NASA Astrophysics Data System (ADS)

Haxhimali, Tomorr; Rudd, Robert E.; Cabot, William H.; Graziani, Frank R.

2015-11-01

We present molecular dynamics (MD) calculations of shear viscosity for asymmetric mixed plasma for thermodynamic conditions relevant to astrophysical and inertial confinement fusion plasmas. Specifically, we consider mixtures of deuterium and argon at temperatures of 100-500 eV and a number density of 1025 ions/cc. The motion of 30 000-120 000 ions is simulated in which the ions interact via the Yukawa (screened Coulomb) potential. The electric field of the electrons is included in this effective interaction; the electrons are not simulated explicitly. Shear viscosity is calculated using the Green-Kubo approach with an integral of the shear stress autocorrelation function, a quantity calculated in the equilibrium MD simulations. We systematically study different mixtures through a series of simulations with increasing fraction of the minority high-Z element (Ar) in the D-Ar plasma mixture. In the more weakly coupled plasmas, at 500 eV and low Ar fractions, results from MD compare very well with Chapman-Enskog kinetic results. In the more strongly coupled plasmas, the kinetic theory does not agree well with the MD results. We develop a simple model that interpolates between classical kinetic theories at weak coupling and the Murillo Yukawa viscosity model at higher coupling. This hybrid kinetics-MD viscosity model agrees well with the MD results over the conditions simulated, ranging from moderately weakly coupled to moderately strongly coupled asymmetric plasma mixtures.

Widom Lines in Binary Mixtures of Supercritical Fluids.

PubMed

Raju, Muralikrishna; Banuti, Daniel T; Ma, Peter C; Ihme, Matthias

2017-06-08

Recent experiments on pure fluids have identified distinct liquid-like and gas-like regimes even under supercritical conditions. The supercritical liquid-gas transition is marked by maxima in response functions that define a line emanating from the critical point, referred to as Widom line. However, the structure of analogous state transitions in mixtures of supercritical fluids has not been determined, and it is not clear whether a Widom line can be identified for binary mixtures. Here, we present first evidence for the existence of multiple Widom lines in binary mixtures from molecular dynamics simulations. By considering mixtures of noble gases, we show that, depending on the phase behavior, mixtures transition from a liquid-like to a gas-like regime via distinctly different pathways, leading to phase relationships of surprising complexity and variety. Specifically, we show that miscible binary mixtures have behavior analogous to a pure fluid and the supercritical state space is characterized by a single liquid-gas transition. In contrast, immiscible binary mixture undergo a phase separation in which the clusters transition separately at different temperatures, resulting in multiple distinct Widom lines. The presence of this unique transition behavior emphasizes the complexity of the supercritical state to be expected in high-order mixtures of practical relevance.

Interactive mixture of inhomogeneous dark fluids driven by dark energy: a dynamical system analysis

NASA Astrophysics Data System (ADS)

Izquierdo, Germán; Blanquet-Jaramillo, Roberto C.; Sussman, Roberto A.

2018-03-01

We examine the evolution of an inhomogeneous mixture of non-relativistic pressureless cold dark matter (CDM), coupled to dark energy (DE) characterised by the equation of state parameter w<-1/3, with the interaction term proportional to the DE density. This coupled mixture is the source of a spherically symmetric Lemaître-Tolman-Bondi (LTB) metric admitting an asymptotic Friedman-Lemaître-Robertson-Walker (FLRW) background. Einstein's equations reduce to a 5-dimensional autonomous dynamical system involving quasi-local variables related to suitable averages of covariant scalars and their fluctuations. The phase space evolution around the critical points (past/future attractors and five saddles) is examined in detail. For all parameter values and both directions of energy flow (CDM to DE and DE to CDM) the phase space trajectories are compatible with a physically plausible early cosmic times behaviour near the past attractor. This result compares favourably with mixtures with interaction driven by the CDM density, whose past evolution is unphysical for DE to CDM energy flow. Numerical examples are provided describing the evolution of an initial profile that can be associated with idealised structure formation scenarios.

Hydrogen bonding in a mixture of protic ionic liquids: a molecular dynamics simulation study.

PubMed

Paschek, Dietmar; Golub, Benjamin; Ludwig, Ralf

2015-04-07

We report results of molecular dynamics (MD) simulations characterising the hydrogen bonding in mixtures of two different protic ionic liquids sharing the same cation: triethylammonium-methylsulfonate (TEAMS) and triethylammonium-triflate (TEATF). The triethylammonium-cation acts as a hydrogen-bond donor, being able to donate a single hydrogen-bond. Both, the methylsulfonate- and the triflate-anions can act as hydrogen-bond acceptors, which can accept multiple hydrogen bonds via their respective SO3-groups. In addition, replacing a methyl-group in the methylsulfonate by a trifluoromethyl-group in the triflate significantly weakens the strength of a hydrogen bond from an adjacent triethylammonium cation to the oxygen-site in the SO3-group of the anion. Our MD simulations show that these subtle differences in hydrogen bond strength significantly affect the formation of differently-sized hydrogen-bonded aggregates in these mixtures as a function of the mixture-composition. Moreover, the reported hydrogen-bonded cluster sizes can be predicted and explained by a simple combinatorial lattice model, based on the approximate coordination number of the ions, and using statistical weights that mostly account for the fact that each anion can only accept three hydrogen bonds.

Structure and Dynamics of Water/Methanol Mixtures at Hydroxylated Silica Interfaces Relevant to Chromatography.

PubMed

Gupta, Prashant Kumar; Meuwly, Markus

2016-09-19

The spectroscopy and dynamics of water/methanol (MeOH) mixtures at hydroxylated silica surfaces is investigated from atomistic simulations. The particular focus is on how the structural dynamics of MeOH changes when comparing surface-bound and MeOH in the bulk. From analyzing the frequency frequency correlation functions it is found that the dynamics on the picosecond time scale differs by almost a factor of two. While the relaxation time is 2.0 ps for MeOH in the bulk solvent it is considerably slowed-down to 3.5 ps for surface-bound MeOH. Surface-adsorbed MeOH molecules reside there for several nanoseconds and their H-bonds are strongly oriented towards the surface-OH groups. These results are of particular relevance for chromatographic systems where the solvent may play a central role in their function. The present simulations suggest that surface-sensitive spectroscopic techniques should be useful in better characterizing such heterogeneous systems and provide detailed insight into solvent dynamics and structure relevant in chromatographic applications. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Equilibrium disorders in workers exposed to mixed solvents.

PubMed

Giorgianni, Concetto; Tanzariello, Mariagiuseppina; De Pasquale, Domenico; Brecciaroli, Renato; Spatari, Giovanna

2018-02-06

Organic solvents cause diseases of the vestibular system. However, little is known regarding the correlation between vestibular damage and exposure to organic solvents below threshold limit values. The best measure by which to evaluate vestibular disorders is static and dynamic posturography. The aim of this study was to evaluate equilibrium disorders via static and dynamic posturography in workers without clear symptoms and exposed to low doses of mixed solvents. 200 subjects were selected. Using an Otometrics device (Madsen, Denmark), all subjects endured static and dynamic posturography testing with both eyes-open and eyes-closed conditions. Results were compared with a control group of unexposed individuals. Based on the obtained data, the following results can be drawn: (a) subjects exposed to mixtures of solvents show highly significant differences regarding all static and dynamic posturography parameters in comparison to the control group; (b) posturography testing has proven to be a valid means by which to detect subliminal equilibrium disorders in subjects exposed to solvents. We can confirm that refinery workers exposed to mixtures of solvents can present subliminal equilibrium disorders. Early diagnosis of the latter is made possible by static and dynamic posturography.

Nonlinear data-driven identification of polymer electrolyte membrane fuel cells for diagnostic purposes: A Volterra series approach

NASA Astrophysics Data System (ADS)

Ritzberger, D.; Jakubek, S.

2017-09-01

In this work, a data-driven identification method, based on polynomial nonlinear autoregressive models with exogenous inputs (NARX) and the Volterra series, is proposed to describe the dynamic and nonlinear voltage and current characteristics of polymer electrolyte membrane fuel cells (PEMFCs). The structure selection and parameter estimation of the NARX model is performed on broad-band voltage/current data. By transforming the time-domain NARX model into a Volterra series representation using the harmonic probing algorithm, a frequency-domain description of the linear and nonlinear dynamics is obtained. With the Volterra kernels corresponding to different operating conditions, information from existing diagnostic tools in the frequency domain such as electrochemical impedance spectroscopy (EIS) and total harmonic distortion analysis (THDA) are effectively combined. Additionally, the time-domain NARX model can be utilized for fault detection by evaluating the difference between measured and simulated output. To increase the fault detectability, an optimization problem is introduced which maximizes this output residual to obtain proper excitation frequencies. As a possible extension it is shown, that by optimizing the periodic signal shape itself that the fault detectability is further increased.

The color of sea level: Importance of spatial variations in spectral shape for assessing the significance of trends

NASA Astrophysics Data System (ADS)

Hughes, Chris W.; Williams, Simon D. P.

2010-10-01

We investigate spatial variations in the shape of the spectrum of sea level variability based on a homogeneously sampled 12 year gridded altimeter data set. We present a method of plotting spectral information as color, focusing on periods between 2 and 24 weeks, which shows that significant spatial variations in the spectral shape exist and contain useful dynamical information. Using the Bayesian Information Criterion, we determine that, typically, a fifth-order autoregressive model is needed to capture the structure in the spectrum. Using this model, we show that statistical errors in fitted local trends range between less than 1 and more than 5 times of what would be calculated assuming "white" noise and that the time needed to detect a 1 mm/yr trend ranges between about 5 years and many decades. For global mean sea level, the statistical error reduces to 0.1 mm/yr over 12 years, with only 2 years needed to detect a 1 mm/yr trend. We find significant regional differences in trend from the global mean. The patterns of these regional differences are indicative of a sea level trend dominated by dynamical ocean processes over this period.

Tuning aggregation of microemulsion droplets and silica nanoparticles using solvent mixtures.

PubMed

Salabat, Alireza; Eastoe, Julian; Mutch, Kevin J; Tabor, Rico F

2008-02-15

The effect of solvent on stability of water-in-oil microemulsions has been studied with AOT (sodium bis(2-ethylhexyl)sulfosuccinate) and different solvent mixtures of n-heptane, toluene and dodecane. Dynamic light scattering DLS was used to monitor the apparent diffusion coefficient D(A) and effective microemulsion droplet diameter on changing composition of the solvent. Interdroplet attractive interactions, as indicated by variations in D(A), can be tuned by formulation of appropriate solvent mixtures using heptane, toluene, and dodecane. In extreme cases, solvent mixtures can be used to induce phase transitions in the microemulsions. Aggregation and stability of model AOT-stabilized silica nanoparticles in different solvents were also investigated to explore further these solvent effects. For both systems the state of aggregation can be correlated with the effective molecular volume of the solvent V(mol)(eff) mixture.

Effect of Glycerol Water Binary Mixtures on the Structure and Dynamics of Protein Solutions

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

Ghattyvenkatakrishna, Pavan K; Carri, Gustavo A.

We have performed 20ns of fully atomistic molecular dynamics simulations of Hen Egg-White Lysozyme in 0, 10, 20, 30 and 100% by weight of glycerol in water to better understand the microscopic physics behind the bioprotection offered by glycerol to naturally occuring biological systems. The sovlent exposure of protein surface residues changes when glycerol is introduced. The dynamic behavior of the protein, as quantified by the Incoherent Intermediate Scattering Function, shows a non-monotonic dependence on glycerol content. The fluctuations of the protein residues with respect to each other were found to be similar in all water containing solvents; but differentmore » from the pure glycerol case. The increase in the number of protein glycerol hydrogen bonds in glycerol water binary mixtures explains the slowing down of protein dynamics as the glycerol content increases. We also explored the dynamic behavior of the hydration layer. We show that the short-length scale dynamics of this layer are insenstive to glycerol concentration. However, the long-length scale behavior shows a significant dependence on glycerol content. We also provide insights into the behavior of bound and mobile water molecules.« less

Dynamics of Model Hydraulic Fracturing Liquid Studied by Two-Dimensional Infrared Spectroscopy

NASA Astrophysics Data System (ADS)

Daley, Kim; Kubarych, Kevin J.

2014-06-01

The technique of two-dimensional infrared (2DIR) spectroscopy is used to expose the chemical dynamics of various concentrations of polymers and their monomers in heterogeneous mixtures. An environmentally relevant heterogeneous mixture, which inspires this study, is hydraulic fracturing liquid (HFL). Hydraulic fracking is a technique used to extract natural gas from shale deposits. HFL consists of mostly water, proppant (sand), an emulsifier (guar), and other chemicals specific to the drilling site. Utilizing a metal carbonyl as a probe, we observe the spectral dynamics of the polymer, guar, and its monomer, mannose, and compare the results to see how hydration dynamics change with varying concentration. Another polymer, Ficoll, and its monomer, sucrose, are also compared to see how polymer size affects hydration dynamics. The two results are as follows: (1) Guar experiences collective hydration at high concentrations, where as mannose experiences independent hydration; (2) no collective hydration is observed for Ficoll in the same concentration range as guar, possibly due to polymer shape and size. HFL experiences extremely high pressure during natural gas removal, so future studies will focus on how increased pressure affects the hydration dynamics of polymers and monomers.

Molecular dynamics study of congruent melting of the equimolar ionic liquid-benzene inclusion crystal [emim][NTf2]•C6H6

NASA Astrophysics Data System (ADS)

Kowsari, M. H.; Alavi, Saman; Ashrafizaadeh, Mahmud; Najafi, Bijan

2010-01-01

We use molecular dynamics simulations to study the structure, dynamics, and details of the mechanism of congruent melting of the equimolar mixture of 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl) imide with benzene, [emim][NTf2]•C6H6. Changes in the molecular arrangement, radial distribution functions, and the dynamic behavior of species are used to detect the solid to liquid transition, show an indication of the formation of polar islands by aggregating of the ions in the liquid phase, and characterize the melting process. The predicted enthalpy of melting ΔHm=38±2 kJ mol-1 for the equimolar inclusion mixture at 290 K is in good agreement with the differential scanning calorimetry experimental results of 42±2 kJ mol-1. The dynamics of the ions and benzene molecules were studied in the solid and liquid states by calculating the mean-square displacement (MSD) and the orientational autocorrelation function. The MSD plots show strong association between ion pairs of the ionic liquid in the inclusion mixture. Indeed, the presence of a stoichiometric number of benzene molecules does not affect the nearest neighbor ionic association between [emim]+ and [NTf2]-, but increases the MSDs of both cations and anions compared to pure liquid [emim][NTf2], showing that second shell ionic associations are weakened. We monitored the rotational motion of the alkyl chain sides of imidazolium cations and also calculated the activation energy for rotation of benzene molecules about their C6 symmetry axes in their lattice sites prior to melting.

Level shift two-components autoregressive conditional heteroscedasticity modelling for WTI crude oil market

NASA Astrophysics Data System (ADS)

Sin, Kuek Jia; Cheong, Chin Wen; Hooi, Tan Siow

2017-04-01

This study aims to investigate the crude oil volatility using a two components autoregressive conditional heteroscedasticity (ARCH) model with the inclusion of abrupt jump feature. The model is able to capture abrupt jumps, news impact, clustering volatility, long persistence volatility and heavy-tailed distributed error which are commonly observed in the crude oil time series. For the empirical study, we have selected the WTI crude oil index from year 2000 to 2016. The results found that by including the multiple-abrupt jumps in ARCH model, there are significant improvements of estimation evaluations as compared with the standard ARCH models. The outcomes of this study can provide useful information for risk management and portfolio analysis in the crude oil markets.

Asymmetric impact of rainfall on India's food grain production: evidence from quantile autoregressive distributed lag model

NASA Astrophysics Data System (ADS)

Pal, Debdatta; Mitra, Subrata Kumar

2018-01-01

This study used a quantile autoregressive distributed lag (QARDL) model to capture asymmetric impact of rainfall on food production in India. It was found that the coefficient corresponding to the rainfall in the QARDL increased till the 75th quantile and started decreasing thereafter, though it remained in the positive territory. Another interesting finding is that at the 90th quantile and above the coefficients of rainfall though remained positive was not statistically significant and therefore, the benefit of high rainfall on crop production was not conclusive. However, the impact of other determinants, such as fertilizer and pesticide consumption, is quite uniform over the whole range of the distribution of food grain production.

On-line algorithms for forecasting hourly loads of an electric utility

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

Vemuri, S.; Huang, W.L.; Nelson, D.J.

A method that lends itself to on-line forecasting of hourly electric loads is presented, and the results of its use are compared to models developed using the Box-Jenkins method. The method consits of processing the historical hourly loads with a sequential least-squares estimator to identify a finite-order autoregressive model which, in turn, is used to obtain a parsimonious autoregressive-moving average model. The method presented has several advantages in comparison with the Box-Jenkins method including much-less human intervention, improved model identification, and better results. The method is also more robust in that greater confidence can be placed in the accuracy ofmore » models based upon the various measures available at the identification stage.« less

Generating Dynamic Persistence in the Time Domain

NASA Astrophysics Data System (ADS)

Guerrero, A.; Smith, L. A.; Smith, L. A.; Kaplan, D. T.

2001-12-01

Many dynamical systems present long-range correlations. Physically, these systems vary from biological to economical, including geological or urban systems. Important geophysical candidates for this type of behaviour include weather (or climate) and earthquake sequences. Persistence is characterised by slowly decaying correlation function; that, in theory, never dies out. The Persistence exponent reflects the degree of memory in the system and much effort has been expended creating and analysing methods that successfully estimate this parameter and model data that exhibits persistence. The most widely used methods for generating long correlated time series are not dynamical systems in the time domain, but instead are derived from a given spectral density. Little attention has been drawn to modelling persistence in the time domain. The time domain approach has the advantage that an observation at certain time can be calculated using previous observations which is particularly suitable when investigating the predictability of a long memory process. We will describe two of these methods in the time domain. One is a traditional approach using fractional ARIMA (autoregressive and moving average) models; the second uses a novel approach to extending a given series using random Fourier basis functions. The statistical quality of the two methods is compared, and they are contrasted with weather data which shows, reportedly, persistence. The suitability of this approach both for estimating predictability and for making predictions is discussed.

Diffusive and subdiffusive dynamics of indoor microclimate: a time series modeling.

PubMed

Maciejewska, Monika; Szczurek, Andrzej; Sikora, Grzegorz; Wyłomańska, Agnieszka

2012-09-01

The indoor microclimate is an issue in modern society, where people spend about 90% of their time indoors. Temperature and relative humidity are commonly used for its evaluation. In this context, the two parameters are usually considered as behaving in the same manner, just inversely correlated. This opinion comes from observation of the deterministic components of temperature and humidity time series. We focus on the dynamics and the dependency structure of the time series of these parameters, without deterministic components. Here we apply the mean square displacement, the autoregressive integrated moving average (ARIMA), and the methodology for studying anomalous diffusion. The analyzed data originated from five monitoring locations inside a modern office building, covering a period of nearly one week. It was found that the temperature data exhibited a transition between diffusive and subdiffusive behavior, when the building occupancy pattern changed from the weekday to the weekend pattern. At the same time the relative humidity consistently showed diffusive character. Also the structures of the dependencies of the temperature and humidity data sets were different, as shown by the different structures of the ARIMA models which were found appropriate. In the space domain, the dynamics and dependency structure of the particular parameter were preserved. This work proposes an approach to describe the very complex conditions of indoor air and it contributes to the improvement of the representative character of microclimate monitoring.

Boussinesq approximation of the Cahn-Hilliard-Navier-Stokes equations.

PubMed

Vorobev, Anatoliy

2010-11-01

We use the Cahn-Hilliard approach to model the slow dissolution dynamics of binary mixtures. An important peculiarity of the Cahn-Hilliard-Navier-Stokes equations is the necessity to use the full continuity equation even for a binary mixture of two incompressible liquids due to dependence of mixture density on concentration. The quasicompressibility of the governing equations brings a short time-scale (quasiacoustic) process that may not affect the slow dynamics but may significantly complicate the numerical treatment. Using the multiple-scale method we separate the physical processes occurring on different time scales and, ultimately, derive the equations with the filtered-out quasiacoustics. The derived equations represent the Boussinesq approximation of the Cahn-Hilliard-Navier-Stokes equations. This approximation can be further employed as a universal theoretical model for an analysis of slow thermodynamic and hydrodynamic evolution of the multiphase systems with strongly evolving and diffusing interfacial boundaries, i.e., for the processes involving dissolution/nucleation, evaporation/condensation, solidification/melting, polymerization, etc.

Effect of Substrate Wetting on the Morphology and Dynamics of Phase Separating Multi-Component Mixture

NASA Astrophysics Data System (ADS)

Goyal, Abheeti; Toschi, Federico; van der Schoot, Paul

2017-11-01

We study the morphological evolution and dynamics of phase separation of multi-component mixture in thin film constrained by a substrate. Specifically, we have explored the surface-directed spinodal decomposition of multicomponent mixture numerically by Free Energy Lattice Boltzmann (LB) simulations. The distinguishing feature of this model over the Shan-Chen (SC) model is that we have explicit and independent control over the free energy functional and EoS of the system. This vastly expands the ambit of physical systems that can be realistically simulated by LB simulations. We investigate the effect of composition, film thickness and substrate wetting on the phase morphology and the mechanism of growth in the vicinity of the substrate. The phase morphology and averaged size in the vicinity of the substrate fluctuate greatly due to the wetting of the substrate in both the parallel and perpendicular directions. Additionally, we also describe how the model presented here can be extended to include an arbitrary number of fluid components.

Ab initio study of the structural properties of acetonitrile-water mixtures

NASA Astrophysics Data System (ADS)

Chen, Jinfan; Sit, Patrick H.-L.

2015-08-01

Structural properties of acetonitrile and acetonitrile-water mixtures are studied using Density Functional Theory (DFT) and ab initio molecular dynamics simulations. Stable molecular clusters consisted of several water and acetonitrile molecules are identified to provide microscopic understanding of the interaction among water and acetonitrile molecules. Ab initio molecular dynamics simulations are performed to study the liquid structure at the finite temperature. Three mixing compositions in which the mole fraction of acetonitrile equals 0.109, 0.5 and 0.891 are studied. These compositions correspond to three distinct structural regimes. At the 0.109 and 0.891 mole fraction of acetonitrile, the majority species are mostly connected among themselves and the minority species are either isolated or forming small clusters without disrupting the network of the majority species. At the 0.5 mole fraction of acetonitrile, large water and acetonitrile clusters persist throughout the simulation, exhibiting the microheterogeneous behavior in acetonitrile-water mixtures in the mid-range mixing ratio.

Experimental setup for investigation of two-phase (water-air) flows in a tube

NASA Astrophysics Data System (ADS)

Kazunin, D. V.; Lashkov, V. A.; Mashek, I. Ch.; Khoronzhuk, R. S.

2018-05-01

A special setup was designed and built at St. Petersburg State University for providing experimental research in flow dynamics of the of air-water mixtures in a pipeline. The test section of the setup allows simulating a wide range of flow regimes of a gas-liquid mixture. The parameters of the experimental setup are given; the initial test results are discussed.

Self-diffusion Coefficient and Structure of Binary n-Alkane Mixtures at the Liquid-Vapor Interfaces.

PubMed

Chilukoti, Hari Krishna; Kikugawa, Gota; Ohara, Taku

2015-10-15

The self-diffusion coefficient and molecular-scale structure of several binary n-alkane liquid mixtures in the liquid-vapor interface regions have been examined using molecular dynamics simulations. It was observed that in hexane-tetracosane mixture hexane molecules are accumulated in the liquid-vapor interface region and the accumulation intensity decreases with increase in a molar fraction of hexane in the examined range. Molecular alignment and configuration in the interface region of the liquid mixture change with a molar fraction of hexane. The self-diffusion coefficient in the direction parallel to the interface of both tetracosane and hexane in their binary mixture increases in the interface region. It was found that the self-diffusion coefficient of both tetracosane and hexane in their binary mixture is considerably higher in the vapor side of the interface region as the molar fraction of hexane goes lower, which is mostly due to the increase in local free volume caused by the local structure of the liquid in the interface region.

Solvent effects on the polar network of ionic liquid solutions

NASA Astrophysics Data System (ADS)

Bernardes, Carlos E. S.; Shimizu, Karina; Canongia Lopes, José N.

2015-05-01

Molecular dynamics simulations were used to probe mixtures of ionic liquids (ILs) with common molecular solvents. Four types of systems were considered: (i) 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide plus benzene, hexafluorobenzene or 1,2-difluorobenzene mixtures; (ii) choline-based ILs plus ether mixtures (iii) choline-based ILs plus n-alkanol mixtures; and (iv) 1-butyl-3-methylimidazolium nitrate and 1-ethyl-3-methylimidazolium ethyl sulfate aqueous mixtures. The results produced a wealth of structural and aggregation information that highlight the resilience of the polar network of the ILs (formed by clusters of alternating ions and counter-ions) to the addition of different types of molecular solvent. The analysis of the MD data also shows that the intricate balance between different types of interaction (electrostatic, van der Waals, H-bond-like) between the different species present in the mixtures has a profound effect on the morphology of the mixtures at a mesoscopic scale. In the case of the IL aqueous solutions, the present results suggest an alternative interpretation for very recently published x-ray and neutron diffraction data on similar systems.

Alcohol consumption and pancreatitis mortality in Russia.

PubMed

Razvodovsky, Yury E

2014-07-28

Pancreatitis is a major public health problem with high associated economic costs. The incidence of pancreatitis has increased in many European countries in recent decade. Accumulated research and empirical evidence suggests that excessive alcohol consumption is a major risk factor for both acute and chronic pancreatitis. The aim of this study was to examine the aggregate-level relation between the alcohol consumption and pancreatitis mortality rates in Russia. Age-standardized sex-specific male and female pancreatitis mortality data for the period 1970-2005 and data on overall alcohol consumption were analyzed by means ARIMA (autoregressive integrated moving average) time series analysis. Alcohol consumption was significantly associated with both male and female pancreatitis mortality rates: a 1 liter increase in overall alcohol consumption would result in a 7.0% increase in the male pancreatitis mortality rate and in 2.3% increase in the female mortality rate. The results of the analysis suggest that 63.1% of all male pancreatitis deaths and 26.8% female deaths in Russia could be attributed to alcohol. Conclusions The outcomes of this study provide indirect support for the hypothesis that unfavorable mixture of higher overall level of alcohol consumption and binge drinking pattern is an important contributor to the pancreatitis mortality rate in Russian Federation.

DNA-Encoded Dynamic Combinatorial Chemical Libraries.

PubMed

Reddavide, Francesco V; Lin, Weilin; Lehnert, Sarah; Zhang, Yixin

2015-06-26

Dynamic combinatorial chemistry (DCC) explores the thermodynamic equilibrium of reversible reactions. Its application in the discovery of protein binders is largely limited by difficulties in the analysis of complex reaction mixtures. DNA-encoded chemical library (DECL) technology allows the selection of binders from a mixture of up to billions of different compounds; however, experimental results often show low a signal-to-noise ratio and poor correlation between enrichment factor and binding affinity. Herein we describe the design and application of DNA-encoded dynamic combinatorial chemical libraries (EDCCLs). Our experiments have shown that the EDCCL approach can be used not only to convert monovalent binders into high-affinity bivalent binders, but also to cause remarkably enhanced enrichment of potent bivalent binders by driving their in situ synthesis. We also demonstrate the application of EDCCLs in DNA-templated chemical reactions. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Glass formation of a DMSO-water mixture probed with a photosynthetic pigment.

PubMed

Huerta-Viga, Adriana; Nguyen, Linh-Lan; Amirjalayer, Saeed; Sim, Jamie H N; Zhang, Zhengyang; Tan, Howe-Siang

2018-06-19

Despite their extensive industrial usage, glass-forming liquids are not fully understood, and methods to investigate their dynamical heterogeneity are sought after. Here we show how the appearance of a second component in the visible absorption spectrum of a photosynthetic pigment upon cooling can be used to probe the glass transition of a dimethylsulfoxide-water mixture. The changes in the relative ratio of the two components with respect to temperature follow a sigmoid curve, and we show that the second component arises due to protonation of the pigment at low temperatures. Furthermore, from visible transient absorption spectra we show that, unlike the first component, the dynamics of the second component slows down significantly at lower temperatures, suggesting that there are two distinct environments with fast and slow fluctuations. Our results therefore enable a new method to characterize the dynamical heterogeneity of glass-forming liquids.

Spectroscopic Analysis of 10MAG/LDAO Reverse Micelles to Determine Characteristic Properties and Behavioral Extrema

NASA Astrophysics Data System (ADS)

Berg, Joshua; Mawson, Cara; Norris, Zach; Nucci, Nathaniel

Reverse micelles are spontaneously organizing complexes of surfactant that encapsulate a nanoscale pool of water in a bulk non-polar solvent. Reverse micelle (RM) mixtures have a wide range of applications, including biophysical investigation of protein systems. A new RM mixture composed of decyl-1-monoglycerol (10MAG) and lauryldimethylammonium-N-oxide (LDAO) was recently described. This mixture has the potential to prove more widely applicable for use of RMs in applications that involve encapsulation of macromolecules, yet little is known about the phase behavior or size of reverse micelles created by this mixture. Data describing such behaviors for this mixture are presented here. We have used dynamic light scattering (DLS) and fluorescence spectroscopy to investigate the size and partitioning behavior of RMs in varying mixtures of 10MAG, LDAO, water, pentane, and hexanol. These data demonstrate that the 10MAG/LDAO RM mixture exhibits markedly different phase and RM size behavior than that of commonly used RM surfactant mixtures. The implications of these findings for use of the 10MAG/LDAO mix for RM applications will also be addressed. Funding provided by Rowan University.

Estimating nitrogen mineralization from cover crop mixtures using the Precision Nitrogen Management model

USDA-ARS?s Scientific Manuscript database

Cover crops influence soil nitrogen (N) mineralization-immobilization-turnover cycles (MIT), thus influencing N availability to a subsequent crop. Dynamic simulation models of the soil/crop system, if properly calibrated and tested, can simulate carbon (C) and N dynamics of a terminated cover crop a...

Equations of state and transport properties of mixtures in the warm dense regime

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

Hou, Yong; Dai, Jiayu; Kang, Dongdong

2015-02-15

We have performed average-atom molecular dynamics to simulate the CH and LiH mixtures in the warm dense regime, and obtained equations of state and the ionic transport properties. The electronic structures are calculated by using the modified average-atom model, which have included the broadening of energy levels, and the ion-ion pair potentials of mixtures are constructed based on the temperature-dependent density functional theory. The ionic transport properties, such as ionic diffusion and shear viscosity, are obtained through the ionic velocity correlation functions. The equations of state and transport properties for carbon, hydrogen and lithium, hydrogen mixtures in a wide regionmore » of density and temperature are calculated. Through our computing the average ionization degree, average ion-sphere diameter and transition properties in the mixture, it is shown that transport properties depend not only on the ionic mass but also on the average ionization degree.« less

Effects of Fuel Distribution on Detonation Tube Performance

NASA Technical Reports Server (NTRS)

Perkins, H. Douglas; Sung, Chih-Jen

2003-01-01

A pulse detonation engine uses a series of high frequency intermittent detonation tubes to generate thrust. The process of filling the detonation tube with fuel and air for each cycle may yield non-uniform mixtures. Uniform mixing is commonly assumed when calculating detonation tube thrust performance. In this study, detonation cycles featuring idealized non-uniform Hz/air mixtures were analyzed using a two-dimensional Navier-Stokes computational fluid dynamics code with detailed chemistry. Mixture non-uniformities examined included axial equivalence ratio gradients, transverse equivalence ratio gradients, and partially fueled tubes. Three different average test section equivalence ratios were studied; one stoichiometric, one fuel lean, and one fuel rich. All mixtures were detonable throughout the detonation tube. Various mixtures representing the same average test section equivalence ratio were shown to have specific impulses within 1% of each other, indicating that good fuel/air mixing is not a prerequisite for optimal detonation tube performance under conditions investigated.

Devices for the Production of Reference Gas Mixtures.

PubMed

Fijało, Cyprian; Dymerski, Tomasz; Gębicki, Jacek; Namieśnik, Jacek

2016-09-02

For many years there has been growing demand for gaseous reference materials, which is connected with development in many fields of science and technology. As a result, new methodological and instrumental solutions appear that can be used for this purpose. Appropriate quality assurance/quality control (QA/QC) must be used to make sure that measurement data are a reliable source of information. Reference materials are a significant element of such systems. In the case of gas samples, such materials are generally called reference gas mixtures. This article presents the application and classification of reference gas mixtures, which are a specific type of reference materials, and the methods for obtaining them are described. Construction solutions of devices for the production of reference gas mixtures are detailed, and a description of a prototype device for dynamic production of reference gas mixtures containing aroma compounds is presented.

Features of the propagation of laminar spherical flames initiated by a spark discharge in mixtures of methane, pentane, and hydrogen with air at atmospheric pressure

NASA Astrophysics Data System (ADS)

Rubtsov, N. M.; Seplyarskii, B. S.; Troshin, K. Ya.; Chernysh, V. I.; Tsvetkov, G. I.

2011-10-01

Using high-speed digital color cinematography, we studied the propagation of a laminar spherical flame in stoichiometric mixtures of hydrogen, methane, and pentane with air in the presence of additives at atmospheric pressure in constant-volume reactors, and derived quantitative data on the time of formation of a stable flame front. Cellular flames caused by gas-dynamic instability attributable to convective flows arising during the afterburning of gas were observed in hydrocarbon-air stoichiometric mixtures diluted with inert additives. It was found that the effect of additives of carbon dioxide and argon (>10%) and minor additives of CCl4 on the combustion of hydrocarbons, and of propylene on the combustion of hydrogen-rich mixtures, lead to periods of delay in the development of a laminar spherical flame; in addition, additives of propylene promote the combustion of hydrogen poor mixtures.

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

NASA Astrophysics Data System (ADS)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Robust Prediction for Stationary Processes. 2D Enriched Version.

DTIC Science & Technology

1987-11-24

the absence of data outliers. Important performance characteristics studied include the breakdown point and the influence function . Included are numerical results, for some autoregressive nominal processes.

Laboratory Experiments of Sand Ripples with Bimodal Size Distributions Under Asymmetric Oscillatory Flows

NASA Astrophysics Data System (ADS)

Calantoni, J.; Landry, B. J.

2010-12-01

The dynamics of sand ripples are vital to understanding numerous coastal processes such as sediment transport, wave attenuation, boundary layer development, and seafloor acoustic properties. Though significant laboratory research has been conducted to elucidate oscillatory flow morphodynamics under various constant and transient forcing conditions, the majority of the previous experiments were conducted only for beds with unimodal size distributions of sediment. Recent oscillatory flow experiments as well as past laboratory observations in uniform flows suggest that the presence of heterogeneous size sand compositions may significantly impact ripple morphology, resulting in a variety of observable effects (e.g., sediment sorting, bed armoring, and altered transport rates). Experimental work was conducted in a small oscillatory flow tunnel at the Sediment Dynamics Laboratory at the Naval Research Laboratory, Stennis Space Center. Three different monochromatic oscillatory forcings having velocity asymmetry were used to study sand ripple dynamics over five bimodal and two unimodal sediment beds. The seven different mixtures were composed using two unimodal sands of different colors (blue/white) and median grain diameters (d=0.31 mm / d=0.65 mm) combined into various mixtures by mass (i.e., 0/100; 10/90; 25/75; 50/50; 75/25; 90/10; and 100/0 which denotes mass percentage of blue/white sand, respectively, within each mixture). High-definition video of the sediment bed profile was acquired in conjunction with sediment trap measurements to resolve differences in ripple geometries, migration and evolution rates due to the different sediment mixtures and flow conditions. Observational findings clearly illustrate sediment stratification within ripple crests and the depth of the active mixing layer in addition to supporting sediment sorting in previous research on symmetric oscillatory flows in which the larger grains collect on top of ripple crests and smaller grains in the troughs. Preliminary quantitative results illuminate variations in equilibrium ripple geometry, ripple migration rates, and transition time scales between equilibrium states, all as functions of the sediment size mixture and flow forcing.

Performance characterizations of asphalt binders and mixtures incorporating silane additive ZycoTherm

NASA Astrophysics Data System (ADS)

Hasan, Mohd Rosli Mohd; Hamzah, Meor Othman; Yee, Teh Sek

2017-10-01

Experimental works were conducted to evaluate the properties of asphalt binders and mixtures produced using a relatively new silane additive, named ZycoTherm. In this study, 0.1wt% ZycoTherm was blended with asphalt binder to enable production of asphalt mixture at lower than normal temperatures, as well as improve mix workability and compactability. Asphalt mixture performances towards pavement distresses in tropical climate region were also investigated. The properties of control asphalt binders (60/70 and 80/10 penetration grade) and asphalt binders incorporating 0.1% ZycoTherm were reported based on the penetration, softening point, rotational viscosity, complex modulus and phase angle. Subsequently, to compare the performance of asphalt mixture incorporating ZycoTherm with the control asphalt mixture, cylindrical samples were prepared at recommended temperatures and air voids depending on the binder types and test requirements. The samples were tested for indirect tensile strength (ITS), resilient modulus, dynamic creep, Hamburg wheel tracking and moisture induced damage. From compaction data using the Servopak gyratory compactor, specimen prepared using ZycoTherm exhibit higher workability and compactability compared to the conventional mixture. From the mixture performance test results, mixtures prepared with ZycoTherm showed comparable if not better performance than the control sample in terms of the resistance to moisture damage, permanent deformation and cracking.

Nonlinear dynamics of cardiovascular ageing

PubMed Central

Shiogai, Y.; Stefanovska, A.; McClintock, P.V.E.

2010-01-01

The application of methods drawn from nonlinear and stochastic dynamics to the analysis of cardiovascular time series is reviewed, with particular reference to the identification of changes associated with ageing. The natural variability of the heart rate (HRV) is considered in detail, including the respiratory sinus arrhythmia (RSA) corresponding to modulation of the instantaneous cardiac frequency by the rhythm of respiration. HRV has been intensively studied using traditional spectral analyses, e.g. by Fourier transform or autoregressive methods, and, because of its complexity, has been used as a paradigm for testing several proposed new methods of complexity analysis. These methods are reviewed. The application of time–frequency methods to HRV is considered, including in particular the wavelet transform which can resolve the time-dependent spectral content of HRV. Attention is focused on the cardio-respiratory interaction by introduction of the respiratory frequency variability signal (RFV), which can be acquired simultaneously with HRV by use of a respiratory effort transducer. Current methods for the analysis of interacting oscillators are reviewed and applied to cardio-respiratory data, including those for the quantification of synchronization and direction of coupling. These reveal the effect of ageing on the cardio-respiratory interaction through changes in the mutual modulation of the instantaneous cardiac and respiratory frequencies. Analyses of blood flow signals recorded with laser Doppler flowmetry are reviewed and related to the current understanding of how endothelial-dependent oscillations evolve with age: the inner lining of the vessels (the endothelium) is shown to be of crucial importance to the emerging picture. It is concluded that analyses of the complex and nonlinear dynamics of the cardiovascular system can illuminate the mechanisms of blood circulation, and that the heart, the lungs and the vascular system function as a single entity in dynamical terms. Clear evidence is found for dynamical ageing. PMID:20396667

Nonlinear dynamics of cardiovascular ageing

NASA Astrophysics Data System (ADS)

Shiogai, Y.; Stefanovska, A.; McClintock, P. V. E.

2010-03-01

The application of methods drawn from nonlinear and stochastic dynamics to the analysis of cardiovascular time series is reviewed, with particular reference to the identification of changes associated with ageing. The natural variability of the heart rate (HRV) is considered in detail, including the respiratory sinus arrhythmia (RSA) corresponding to modulation of the instantaneous cardiac frequency by the rhythm of respiration. HRV has been intensively studied using traditional spectral analyses, e.g. by Fourier transform or autoregressive methods, and, because of its complexity, has been used as a paradigm for testing several proposed new methods of complexity analysis. These methods are reviewed. The application of time-frequency methods to HRV is considered, including in particular the wavelet transform which can resolve the time-dependent spectral content of HRV. Attention is focused on the cardio-respiratory interaction by introduction of the respiratory frequency variability signal (RFV), which can be acquired simultaneously with HRV by use of a respiratory effort transducer. Current methods for the analysis of interacting oscillators are reviewed and applied to cardio-respiratory data, including those for the quantification of synchronization and direction of coupling. These reveal the effect of ageing on the cardio-respiratory interaction through changes in the mutual modulation of the instantaneous cardiac and respiratory frequencies. Analyses of blood flow signals recorded with laser Doppler flowmetry are reviewed and related to the current understanding of how endothelial-dependent oscillations evolve with age: the inner lining of the vessels (the endothelium) is shown to be of crucial importance to the emerging picture. It is concluded that analyses of the complex and nonlinear dynamics of the cardiovascular system can illuminate the mechanisms of blood circulation, and that the heart, the lungs and the vascular system function as a single entity in dynamical terms. Clear evidence is found for dynamical ageing.

Shear viscosity for dense plasmas by equilibrium molecular dynamics in asymmetric Yukawa ionic mixtures

DOE PAGES

Haxhimali, Tomorr; Rudd, Robert E.; Cabot, William H.; ...

2015-11-24

We present molecular dynamics (MD) calculations of shear viscosity for asymmetric mixed plasma for thermodynamic conditions relevant to astrophysical and inertial confinement fusion plasmas. Specifically, we consider mixtures of deuterium and argon at temperatures of 100–500 eV and a number density of 10 25 ions/cc. The motion of 30 000–120 000 ions is simulated in which the ions interact via the Yukawa (screened Coulomb) potential. The electric field of the electrons is included in this effective interaction; the electrons are not simulated explicitly. Shear viscosity is calculated using the Green-Kubo approach with an integral of the shear stress autocorrelation function,more » a quantity calculated in the equilibrium MD simulations. We systematically study different mixtures through a series of simulations with increasing fraction of the minority high- Z element (Ar) in the D-Ar plasma mixture. In the more weakly coupled plasmas, at 500 eV and low Ar fractions, results from MD compare very well with Chapman-Enskog kinetic results. In the more strongly coupled plasmas, the kinetic theory does not agree well with the MD results. Here, we develop a simple model that interpolates between classical kinetic theories at weak coupling and the Murillo Yukawa viscosity model at higher coupling. Finally, this hybrid kinetics-MD viscosity model agrees well with the MD results over the conditions simulated, ranging from moderately weakly coupled to moderately strongly coupled asymmetric plasma mixtures.« less

Development of a primary diffusion source of organic vapors for gas analyzer calibration

NASA Astrophysics Data System (ADS)

Lecuna, M.; Demichelis, A.; Sassi, G.; Sassi, M. P.

2018-03-01

The generation of reference mixtures of volatile organic compounds (VOCs) at trace levels (10 ppt-10 ppb) is a challenge for both environmental and clinical measurements. The calibration of gas analyzers for trace VOC measurements requires a stable and accurate source of the compound of interest. The dynamic preparation of gas mixtures by diffusion is a suitable method for fulfilling these requirements. The estimation of the uncertainty of the molar fraction of the VOC in the mixture is a key step in the metrological characterization of a dynamic generator. The performance of a dynamic generator was monitored over a wide range of operating conditions. The generation system was simulated by a model developed with computational fluid dynamics and validated against experimental data. The vapor pressure of the VOC was found to be one of the main contributors to the uncertainty of the diffusion rate and its influence at 10-70 kPa was analyzed and discussed. The air buoyancy effect and perturbations due to the weighing duration were studied. The gas carrier flow rate and the amount of liquid in the vial were found to play a role in limiting the diffusion rate. The results of sensitivity analyses were reported through an uncertainty budget for the diffusion rate. The roles of each influence quantity were discussed. A set of criteria to minimize the uncertainty contribution to the primary diffusion source (25 µg min-1) were estimated: carrier gas flow rate higher than 37.7 sml min-1, a maximum VOC liquid mass decrease in the vial of 4.8 g, a minimum residual mass of 1 g and vial weighing times of 1-3 min. With this procedure a limit uncertainty of 0.5% in the diffusion rate can be obtained for VOC mixtures at trace levels (10 ppt-10 ppb), making the developed diffusion vials a primary diffusion source with potential to become a new reference material for trace VOC analysis.

Distinctly Different Glass Transition Behaviors of Trehalose Mixed with Na2HPO 4 or NaH 2PO 4: Evidence for its Molecular Origin.

PubMed

Weng, Lindong; Elliott, Gloria D

2015-07-01

The present study is aimed at understanding how the interactions between sugar molecules and phosphate ions affect the glass transition temperature of their mixtures, and the implications for pharmaceutical formulations. The glass transition temperature (Tg) and the α-relaxation temperature (Tα) of dehydrated trehalose/sodium phosphate mixtures (monobasic or dibasic) were determined by differential scanning calorimetry and dynamic mechanical analysis, respectively. Molecular dynamics simulations were also conducted to investigate the microscopic interactions between sugar molecules and phosphate ions. The hydrogen-bonding characteristics and the self-aggregation features of these mixtures were quantified and compared. Thermal analysis measurements demonstrated that the addition of NaH2PO4 decreased both the glass transition temperature and the α-relaxation temperature of the dehydrated trehalose/NaH2PO4 mixture compared to trehalose alone while both Tg and Tα were increased by adding Na2HPO4 to pure trehalose. The hydrogen-bonding interactions between trehalose and HPO4(2-) were found to be stronger than both the trehalose-trehalose hydrogen bonds and those formed between trehalose and H2PO4(-). The HPO4(2-) ions also aggregated into smaller clusters than H2PO4(-) ions. The trehalose/Na2HPO4 mixture yielded a higher T g than pure trehalose because marginally self-aggregated HPO4(2-) ions established a strengthened hydrogen-bonding network with trehalose molecules. In contrast H2PO4(-) ions served only as plasticizers, resulting in a lower Tg of the mixtures than trehalose alone, creating large-sized ionic pockets, weakening interactions, and disrupting the original hydrogen-bonding network amongst trehalose molecules.

Analyzing gene expression time-courses based on multi-resolution shape mixture model.

PubMed

Li, Ying; He, Ye; Zhang, Yu

2016-11-01

Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups. Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein-protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance. A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles. The R and Matlab program is available upon the request. Copyright © 2016 Elsevier Inc. All rights reserved.

Distinctly Different Glass Transition Behaviors of Trehalose Mixed with Na2HPO4 or NaH2PO4: Evidence for its Molecular Origin

PubMed Central

Weng, Lindong; Elliott, Gloria D.

2015-01-01

Purpose The present study is aimed at understanding how the interactions between sugar molecules and phosphate ions affect the glass transition temperature of their mixtures, and the implications for pharmaceutical formulations. Methods The glass transition temperature (Tg) and the α-relaxation temperature (Tα) of dehydrated trehalose/sodium phosphate mixtures (monobasic or dibasic) were determined by differential scanning calorimetry and dynamic mechanical analysis, respectively. Molecular dynamics simulations were also conducted to investigate the microscopic interactions between sugar molecules and phosphate ions. The hydrogen-bonding characteristics and the self-aggregation features of these mixtures were quantified and compared. Results Thermal analysis measurements demonstrated that the addition of NaH2PO4 decreased both the glass transition temperature and the α-relaxation temperature of the dehydrated trehalose/NaH2PO4 mixture compared to trehalose alone while both Tg and Tα were increased by adding Na2HPO4 to pure trehalose. The hydrogen-bonding interactions between trehalose and HPO42− were found to be stronger than both the trehalose-trehalose hydrogen bonds and those formed between trehalose and H2PO4−. The HPO42− ions also aggregated into smaller clusters than H2PO4− ions. Conclusions The trehalose/Na2HPO4 mixture yielded a higher Tg than pure trehalose because marginally self-aggregated HPO42− ions established a strengthened hydrogen-bonding network with trehalose molecules. In contrast H2PO4− ions served only as plasticizers, resulting in a lower Tg of the mixtures than trehalose alone, creating large-sized ionic pockets, weakening interactions, and disrupting the original hydrogen-bonding network amongst trehalose molecules. PMID:25537342

Crystallization-induced dynamic resolution R-epimer from 25-OCH3-PPD epimeric mixture.

PubMed

Zhang, Sainan; Tang, Yun; Cao, Jiaqing; Zhao, Chen; Zhao, Yuqing

2015-11-15

25-OCH3-PPD is a promising antitumor dammarane sapogenin isolated from the total saponin-hydrolyzed extract of Panax ginseng berry and Panax notoginseng leaves. 20(R)-25-OCH3-PPD was more potent as an anti-cancer agent than 20(S)-25-OCH3-PPD and epimeric mixture of 25-OCH3-PPD. This paper describes the rapid separation process of the R-epimer of 25-OCH3-PPD from its epimeric mixture by crystallization-induced dynamic resolution (CIDR). The optimized CIDR process was based on single factor analysis and nine well-planned orthogonal design experiments (OA9 matrix). A rapid and sensitive reverse phase high-performance liquid chromatographic (HPLC) method with evaporative light-scattering detector (ELSD) was developed and validated for the quantitation of 25-OCH3-PPD epimeric mixture and crystalline product. Separation and quantitation were achieved with a silica column using a mobile phase consisting of methanol and water (87:13, v/v) at a flow rate of 1.0mL/min. The ELSD detection was performed at 50°C and 3L/min. Under conditions involving 3mL of 95% ethanol, 8% HCl, and a hermetically sealed environment for 72h, the maximum production of 25(R)-OCH3-PPD was achieved with a chemical purity of 97% and a total yield of 87% through the CIDR process. The 25(R)-OCH3-PPD was nearly completely separated from the 220mg 25-OCH3-PPD epimeric mixture. Overall, a simple and steady small-batch purification process for the large-scale production of 25(R)-OCH3-PPD from 25-OCH3-PPD epimeric mixture was developed. Copyright © 2015 Elsevier B.V. All rights reserved.

Autoregressive models for estimating phylogenetic and environmental effects: accounting for within-species variations.

PubMed

Cornillon, P A; Pontier, D; Rochet, M J

2000-02-21

Comparative methods are used to investigate the attributes of present species or higher taxa. Difficulties arise from the phylogenetic heritage: taxa are not independent and neglecting phylogenetic inertia can lead to inaccurate results. Within-species variations in life-history traits are also not negligible, but most comparative methods are not designed to take them into account. Taxa are generally described by a single value for each trait. We have developed a new model which permits the incorporation of both the phylogenetic relationships among populations and within-species variations. This is an extension of classical autoregressive models. This family of models was used to study the effect of fishing on six demographic traits measured on 77 populations of teleost fishes. Copyright 2000 Academic Press.

Getting It Right Matters: Climate Spectra and Their Estimation

NASA Astrophysics Data System (ADS)

Privalsky, Victor; Yushkov, Vladislav

2018-06-01

In many recent publications, climate spectra estimated with different methods from observed, GCM-simulated, and reconstructed time series contain many peaks at time scales from a few years to many decades and even centuries. However, respective spectral estimates obtained with the autoregressive (AR) and multitapering (MTM) methods showed that spectra of climate time series are smooth and contain no evidence of periodic or quasi-periodic behavior. Four order selection criteria for the autoregressive models were studied and proven sufficiently reliable for 25 time series of climate observations at individual locations or spatially averaged at local-to-global scales. As time series of climate observations are short, an alternative reliable nonparametric approach is Thomson's MTM. These results agree with both the earlier climate spectral analyses and the Markovian stochastic model of climate.

A multivariate auto-regressive combined-harmonics analysis and its application to ozone time series data

NASA Astrophysics Data System (ADS)

Yang, Eun-Su

2001-07-01

A new statistical approach is used to analyze Dobson Umkehr layer-ozone measurements at Arosa for 1979-1996 and Total Ozone Mapping Spectrometer (TOMS) Version 7 zonal mean ozone for 1979-1993, accounting for stratospheric aerosol optical depth (SAOD), quasi-biennial oscillation (QBO), and solar flux effects. A stepwise regression scheme selects statistically significant periodicities caused by season, SAOD, QBO, and solar variations and filters them out. Auto-regressive (AR) terms are included in ozone residuals and time lags are assumed for the residuals of exogenous variables. Then, the magnitudes of responses of ozone to the SAOD, QBO, and solar index (SI) series are derived from the stationary time series of the residuals. These Multivariate Auto-Regressive Combined Harmonics (MARCH) processes possess the following significant advantages: (1)the ozone trends are estimated more precisely than the previous methods; (2)the influences of the exogenous SAOD, QBO, and solar variations are clearly separated at various time lags; (3)the collinearity of the exogenous variables in the regression is significantly reduced; and (4)the probability of obtaining misleading correlations between ozone and exogenous times series is reduced. The MARCH results indicate that the Umkehr ozone response to SAOD (not a real ozone response but rather an optical interference effect), QBO, and solar effects is driven by combined dynamical radiative-chemical processes. These results are independently confirmed using the revised Standard models that include aerosol and solar forcing mechanisms with all possible time lags but not by the Standard model when restricted to a zero time lag in aerosol and solar ozone forcings. As for Dobson Umkehr ozone measurements at Arosa, the aerosol effects are most significant in layers 8, 7, and 6 with no time lag, as is to be expected due to the optical contamination of Umkehr measurements by SAOD. The QBO and solar UV effects appear in all layers 4-8, and in total ozone. In order to account for annual modulation of the equatorial winds that affects ozone at midlatitudes, a new QBO proxy is selected and applied to the Dobson Umkehr measurements at Arosa. The QBO proxy turns out to be more effective to filter the modulated ozone signals at midlatitudes than the mostly used QBO proxy, the Singapore winds at 30 mb. A statistically significant negative phase relationship is found between solar UV variation and ozone response, especially in layer 4, implying dynamical effects of solar variations on ozone at midlatitudes. Linear negative trends in ozone of -7.8 +/- 1.1 and -5.2 +/- 1.4 [%/decade +/- 2σ] are calculated in layers 7 (~35 km) and 8 (~40 km), respectively, for the period of 1979-1996, with smaller trends of -2.2 +/- 1.0, 1.8 +/- 0.9, and -1.4 +/- 1.1 in layers 6 (~30 km), 5 (~25 km), and 4 (~20 km), respectively. A trend in total ozone (layers 1 through 10) of -2.9 +/- 1.2 [%/decade +/- 2σ] is found over this same period. The aerosol effects obtained from the TOMS zonal means become significant at midlatitudes. QBO ozone contributes to the TOMS zonal means by +/-2 to 4% of their means. The negative solar ozone responses are also found at midlatitudes from the TOMS measurements. The most negative trends from TOMS zonal means are about -6.3 +/- 0.6%/decade at 40-50°N.

Molecular mechanisms of cryoprotection in aqueous proline: light scattering and molecular dynamics simulations.

PubMed

Troitzsch, R Z; Vass, H; Hossack, W J; Martyna, G J; Crain, J

2008-04-10

Free proline amino acid is a natural cryoprotectant expressed by numerous organisms under low-temperature stress. Previous reports have suggested that complex assemblies underlie its functional properties. We investigate here aqueous proline solutions as a function of temperature using combinations of Raman spectroscopy, Rayleigh-Brillouin light scattering, and molecular dynamics simulations with the view to revealing the molecular origins of the mixtures' functionality as a cryoprotectant. The evolution of the Brillouin frequency shifts and line widths with temperature shows that, above a critical proline concentration, the water-like dynamics is suppressed and viscoelastic behavior emerges: Here, the Landau-Placzek ratio also shows a temperature-independent maximum arising from concentration fluctuations. Molecular dynamics simulations reveal that the water-water correlations in the mixtures depend much more weakly on temperature than does bulk water. By contrast, the water OH Raman bands exhibit strong red-shifts on cooling similar to those seen in ices; however, no evidence of ice lattice phonons is observed in the low-frequency spectrum. We attribute this primarily to enhanced proline-water hydrogen bonding. In general, the picture that emerges is that aqueous proline is a heterogeneous mixture on molecular length scales (characterized by significant concentration fluctuations rather than well-defined aggregates). Simulations reveal that proline also appears to suppress the normal dependence of water structure on temperature and preserves the ambient-temperature correlations even in very cold solutions. The water structure in cold proline solutions therefore appears to be similar to that at a higher effective temperature. This, coupled with the emergence of glassy dynamics offers a molecular explanation for the functional properties of proline as a cryoprotectant without the need to invoke previously proposed complex aggregates.

A sub-grid, mixture-fraction-based thermodynamic equilibrium model for gas phase combustion in FIRETEC: development and results

Treesearch

M. M. Clark; T. H. Fletcher; R. R. Linn

2010-01-01

The chemical processes of gas phase combustion in wildland fires are complex and occur at length-scales that are not resolved in computational fluid dynamics (CFD) models of landscape-scale wildland fire. A new approach for modelling fire chemistry in HIGRAD/FIRETEC (a landscape-scale CFD wildfire model) applies a mixture– fraction model relying on thermodynamic...

Dynamical Behaviors between the PM10 and the meteorological factor using the detrended cross-correlation analysis method

NASA Astrophysics Data System (ADS)

Kim, Kyungsik; Lee, Dong-In

2013-04-01

There is considerable interest in cross-correlations in collective modes of real data from atmospheric geophysics, seismology, finance, physiology, genomics, and nanodevices. If two systems interact mutually, that interaction gives rise to collective modes. This phenomenon is able to be analyzed using the cross-correlation of traditional methods, random matrix theory, and the detrended cross-correlation analysis method. The detrended cross-correlation analysis method was used in the past to analyze several models such as autoregressive fractionally integrated moving average processes, stock prices and their trading volumes, and taxi accidents. Particulate matter is composed of the organic and inorganic mixtures such as the natural sea salt, soil particle, vehicles exhaust, construction dust, and soot. The PM10 is known as the particle with the aerodynamic diameter (less than 10 microns) that is able to enter the human respiratory system. The PM10 concentration has an effect on the climate change by causing an unbalance of the global radiative equilibrium through the direct effect that blocks the stoma of plants and cuts off the solar radiation, different from the indirect effect that changes the optical property of clouds, cloudiness, and lifetime of clouds. Various factors contribute to the degree of the PM10 concentration. Notable among these are the land-use types, surface vegetation coverage, as well as meteorological factors. In this study, we analyze and simulate cross-correlations in time scales between the PM10 concentration and the meteorological factor (among temperature, wind speed and humidity) using the detrended cross-correlation analysis method through the removal of specific trends at eight cities in the Korean peninsula. We divide time series data into Asian dust events and non-Asian dust events to analyze the change of meteorological factors on the fluctuation of PM10 the concentration during Asian dust events. In particular, our result is compared to analytic findings from references published in all nations. ----------------------------------------------------------------- This work was supported by Center for the ASER (CATER 2012-6110) and by the NRFK through a grant provided by the KMEST(No.K1663000201107900).

Nonlinear effects of climate and density in the dynamics of a fluctuating population of reindeer.

PubMed

Tyler, Nicholas J C; Forchhammer, Mads C; Øritsland, Nils Are

2008-06-01

Nonlinear and irregular population dynamics may arise as a result of phase dependence and coexistence of multiple attractors. Here we explore effects of climate and density in the dynamics of a highly fluctuating population of wild reindeer (Rangifer tarandus platyrhynchus) on Svalbard observed over a period of 29 years. Time series analyses revealed that density dependence and the effects of local climate (measured as the degree of ablation [melting] of snow during winter) on numbers were both highly nonlinear: direct negative density dependence was found when the population was growing (Rt > 0) and during phases of the North Atlantic Oscillation (NAO) characterized by winters with generally high (1979-1995) and low (1996-2007) indices, respectively. A growth-phase-dependent model explained the dynamics of the population best and revealed the influence of density-independent processes on numbers that a linear autoregressive model missed altogether. In particular, the abundance of reindeer was enhanced by ablation during phases of growth (Rt > 0), an observation that contrasts with the view that periods of mild weather in winter are normally deleterious for reindeer owing to icing of the snowpack. Analyses of vital rates corroborated the nonlinearity described in the population time series and showed that both starvation mortality in winter and fecundity were nonlinearly related to fluctuations in density and the level of ablation. The erratic pattern of growth of the population of reindeer in Adventdalen seems, therefore, to result from a combination of the effects of nonlinear density dependence, strong density-dependent mortality, and variable density independence related to ablation in winter.

Dynamics of prebiotic RNA reproduction illuminated by chemical game theory

PubMed Central

Yeates, Jessica A. M.; Hilbe, Christian; Zwick, Martin; Nowak, Martin A.; Lehman, Niles

2016-01-01

Many origins-of-life scenarios depict a situation in which there are common and potentially scarce resources needed by molecules that compete for survival and reproduction. The dynamics of RNA assembly in a complex mixture of sequences is a frequency-dependent process and mimics such scenarios. By synthesizing Azoarcus ribozyme genotypes that differ in their single-nucleotide interactions with other genotypes, we can create molecules that interact among each other to reproduce. Pairwise interplays between RNAs involve both cooperation and selfishness, quantifiable in a 2 × 2 payoff matrix. We show that a simple model of differential equations based on chemical kinetics accurately predicts the outcomes of these molecular competitions using simple rate inputs into these matrices. In some cases, we find that mixtures of different RNAs reproduce much better than each RNA type alone, reflecting a molecular form of reciprocal cooperation. We also demonstrate that three RNA genotypes can stably coexist in a rock–paper–scissors analog. Our experiments suggest a new type of evolutionary game dynamics, called prelife game dynamics or chemical game dynamics. These operate without template-directed replication, illustrating how small networks of RNAs could have developed and evolved in an RNA world. PMID:27091972

Dynamics of prebiotic RNA reproduction illuminated by chemical game theory.

PubMed

Yeates, Jessica A M; Hilbe, Christian; Zwick, Martin; Nowak, Martin A; Lehman, Niles

2016-05-03

Many origins-of-life scenarios depict a situation in which there are common and potentially scarce resources needed by molecules that compete for survival and reproduction. The dynamics of RNA assembly in a complex mixture of sequences is a frequency-dependent process and mimics such scenarios. By synthesizing Azoarcus ribozyme genotypes that differ in their single-nucleotide interactions with other genotypes, we can create molecules that interact among each other to reproduce. Pairwise interplays between RNAs involve both cooperation and selfishness, quantifiable in a 2 × 2 payoff matrix. We show that a simple model of differential equations based on chemical kinetics accurately predicts the outcomes of these molecular competitions using simple rate inputs into these matrices. In some cases, we find that mixtures of different RNAs reproduce much better than each RNA type alone, reflecting a molecular form of reciprocal cooperation. We also demonstrate that three RNA genotypes can stably coexist in a rock-paper-scissors analog. Our experiments suggest a new type of evolutionary game dynamics, called prelife game dynamics or chemical game dynamics. These operate without template-directed replication, illustrating how small networks of RNAs could have developed and evolved in an RNA world.

Performance of the Prognocean Plus system during the El Niño 2015/2016: predictions of sea level anomalies as tools for forecasting El Niño

NASA Astrophysics Data System (ADS)

Świerczyńska-Chlaściak, Małgorzata; Niedzielski, Tomasz; Miziński, Bartłomiej

2017-04-01

The aim of this paper is to present the performance of the Prognocean Plus system, which produces long-term predictions of sea level anomalies, during the El Niño 2015/2016. The main objective of work is to identify such ocean areas in which long-term forecasts of sea level anomalies during El Niño 2015/2016 reveal a considerable accuracy. At present, the system produces prognoses using four data-based models and their combinations: polynomial-harmonic model, autoregressive model, threshold autoregressive model and multivariate autoregressive model. The system offers weekly forecasts, with lead time up to 12 weeks. Several statistics that describe the efficiency of the available prediction models in four seasons used for estimating Oceanic Niño index (ONI) are calculated. The accuracies/skills of the predicting models were computed in the specific locations in the equatorial Pacific, namely the geometrically-determined central points of all Niño regions. For the said locations, we focused on the forecasts which targeted at the local maximum of sea level, driven by the El Niño 2015/2016. As a result, a series of the "spaghetti" graphs (for each point, season and model) as well as plots presenting the prognostic performance of every model - for all lead times, seasons and locations - were created. It is found that the Prognocean Plus system has a potential to become a new solution which may enhance the diagnostic discussions on the El Niño development. The forecasts produced by the threshold autoregressive model, for lead times of 5-6 weeks and 9 weeks, within the Niño1+2 region for the November-to-January (NDJ) season anticipated the culmination of the El Niño 2015/2016. The longest forecasts (8-12 weeks) were found to be the most accurate in the phase of transition from El Niño to normal conditions (the multivariate autoregressive model, central point of Niño1+2 region, the December-to-February season). The study was conducted to verify the ability and usefulness of sea level anomaly forecasts in predicting phenomena that are controlled by the ocean-atmosphere processes, such as El Niño Southern Oscillation or North Atlantic Oscillation. The results may support further investigations into long-term forecasting of the quantitative indices of these oscillations, solely based on prognoses of sea level change. In particular, comparing the accuracies of prognoses of the North Atlantic Oscillation index remains one of the tasks of the research project no. 2016/21/N/ST10/03231, financed by the National Science Center of Poland.

Thermal conductivity of disperse insulation materials and their mixtures

NASA Astrophysics Data System (ADS)

Geža, V.; Jakovičs, A.; Gendelis, S.; Usiļonoks, I.; Timofejevs, J.

2017-10-01

Development of new, more efficient thermal insulation materials is a key to reduction of heat losses and contribution to greenhouse gas emissions. Two innovative materials developed at Thermeko LLC are Izoprok and Izopearl. This research is devoted to experimental study of thermal insulation properties of both materials as well as their mixture. Results show that mixture of 40% Izoprok and 60% of Izopearl has lower thermal conductivity than pure materials. In this work, material thermal conductivity dependence temperature is also measured. Novel modelling approach is used to model spatial distribution of disperse insulation material. Computational fluid dynamics approach is also used to estimate role of different heat transfer phenomena in such porous mixture. Modelling results show that thermal convection plays small role in heat transfer despite large fraction of air within material pores.

Association in ethylammonium nitrate-dimethyl sulfoxide mixtures: First structural and dynamical evidences

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

Russina, Olga; Macchiagodena, Marina; Kirchner, Barbara

2015-01-01

Here we report the first structural and dynamic investigation on ethylammonium nitrate, a representative protic Ionic liquid, and dimethylsulfoxide. By using joined x/ray and neutron diffraction, we exploit the EPSR approach to extract structural information at atomistic level. EAN/DMSO turns out to be homogeneous at microscopic scales and indications for the existence of a structural leit motiv with stoichiometric composition 2DMSO:1EAN are found. Dielectric spectroscopy is used to access the relaxation map of the DMSO:EAN = 60:40 mixture. No crystallisation is detected and three relaxation processes could be characterised. Overall this study provides new indications of strict analogies between watermore » and ethylammonium nitrate. (c) 2014 Elsevier B.V. All rights reserved.« less

Applicability of effective fragment potential version 2 - Molecular dynamics (EFP2-MD) simulations for predicting excess properties of mixed solvents

NASA Astrophysics Data System (ADS)

Kuroki, Nahoko; Mori, Hirotoshi

2018-02-01

Effective fragment potential version 2 - molecular dynamics (EFP2-MD) simulations, where the EFP2 is a polarizable force field based on ab initio electronic structure calculations were applied to water-methanol binary mixture. Comparing EFP2s defined with (aug-)cc-pVXZ (X = D,T) basis sets, it was found that large sets are necessary to generate sufficiently accurate EFP2 for predicting mixture properties. It was shown that EFP2-MD could predict the excess molar volume. Since the computational cost of EFP2-MD are far less than ab initio MD, the results presented herein demonstrate that EFP2-MD is promising for predicting physicochemical properties of novel mixed solvents.

Analysis of high injection pressure and ambient temperature on biodiesel spray characteristics using computational fluid dynamics

NASA Astrophysics Data System (ADS)

Hashim, Akasha; Khalid, Amir; Jaat, Norrizam; Sapit, Azwan; Razali, Azahari; Nizam, Akmal

2017-09-01

Efficiency of combustion engines are highly affected by the formation of air-fuel mixture prior to ignition and combustion process. This research investigate the mixture formation and spray characteristics of biodiesel blends under variant in high ambient and injection conditions using Computational Fluid Dynamics (CFD). The spray characteristics such as spray penetration length, spray angle and fluid flow were observe under various operating conditions. Results show that increase in injection pressure increases the spray penetration length for both biodiesel and diesel. Results also indicate that higher spray angle of biodiesel can be seen as the injection pressure increases. This study concludes that spray characteristics of biodiesel blend is greatly affected by the injection and ambient conditions.

Kepler AutoRegressive Planet Search

NASA Astrophysics Data System (ADS)

Caceres, Gabriel Antonio; Feigelson, Eric

2016-01-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real-data tests of the KARPS methodology will be discussed including confirmation of some Kepler Team `candidate' planets. We also present cases of new possible planetary signals.

Study on homogenization of synthetic GNSS-retrieved IWV time series and its impact on trend estimates with autoregressive noise

NASA Astrophysics Data System (ADS)

Klos, Anna; Pottiaux, Eric; Van Malderen, Roeland; Bock, Olivier; Bogusz, Janusz

2017-04-01

A synthetic benchmark dataset of Integrated Water Vapour (IWV) was created within the activity of "Data homogenisation" of sub-working group WG3 of COST ES1206 Action. The benchmark dataset was created basing on the analysis of IWV differences retrieved by Global Positioning System (GPS) International GNSS Service (IGS) stations using European Centre for Medium-Range Weather Forecats (ECMWF) reanalysis data (ERA-Interim). Having analysed a set of 120 series of IWV differences (ERAI-GPS) derived for IGS stations, we delivered parameters of a number of gaps and breaks for every certain station. Moreover, we estimated values of trends, significant seasonalities and character of residuals when deterministic model was removed. We tested five different noise models and found that a combination of white and autoregressive processes of first order describes the stochastic part with a good accuracy. Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different types of noise: white as well as combination of white and autoregressive processes. We also added few strictly defined offsets, creating three variants of synthetic dataset: easy, less-complicated and fully-complicated. The 'Easy' dataset included seasonal signals (annual, semi-annual, 3 and 4 months if present for a particular station), offsets and white noise. The 'Less-complicated' dataset included above-mentioned, as well as the combination of white and first order autoregressive processes (AR(1)+WH). The 'Fully-complicated' dataset included, beyond above, a trend and gaps. In this research, we show the impact of manual homogenisation on the estimates of trend and its error. We also cross-compare the results for three above-mentioned datasets, as the synthetized noise type might have a significant influence on manual homogenisation. Therefore, it might mostly affect the values of trend and their uncertainties when inappropriately handled. In a future, the synthetic dataset we present is going to be used as a benchmark to test various statistical tools in terms of homogenisation task.

Numerical study of shock-induced combustion in methane-air mixtures

NASA Technical Reports Server (NTRS)

Yungster, Shaye; Rabinowitz, Martin J.

1993-01-01

The shock-induced combustion of methane-air mixtures in hypersonic flows is investigated using a new reaction mechanism consisting of 19 reacting species and 52 elementary reactions. This reduced model is derived from a full kinetic mechanism via the Detailed Reduction technique. Zero-dimensional computations of several shock-tube experiments are presented first. The reaction mechanism is then combined with a fully implicit Navier-Stokes computational fluid dynamics (CFD) code to conduct numerical simulations of two-dimensional and axisymmetric shock-induced combustion experiments of stoichiometric methane-air mixtures at a Mach number of M = 6.61. Applications to the ram accelerator concept are also presented.

Solute Dynamics In Liquid Systems: Experiments and Molecular Dynamics Simulations

NASA Astrophysics Data System (ADS)

Rumble, Christopher A.

This work reports on explorations into the effect of the liquid environment on the dynamics and kinetics of a range solute processes. The first study (Chapter 3) explores the photoisomerization of the rotor probe 9-(2-carboxy-2-cyanovinyl)julolidine, or CCVJ. Rotor probes are a class of fluorophores that undergo photo-induced isomerization reactions resulting in non-radiative relaxation out of the excited state. Literature reports had suggested that CCVJ exhibited a 'flow effect,' in which the emission intensity of CCVJ increases when the fluorophore solution is flowed at modest rates. Using steady-state and time-resolved fluorescence and 1H-NMR spectroscopy we show that the flow effect can be attributed to creation of a mixture of fluorescent and non-fluorescent CCVJ isomers by the excitation. The next study, Chapter 4, examines the the fluorescence of DNA G-quadruplex structures (GQSs), non-helical single-stranded DNA structures that exhibit quantum yields significantly higher than helical DNA or its constituent bases. By using a constant GQS core sequence we show that the addition of 'dangling' nucleotides can modulate emission from the GQS whereas conventional quenchers do not. The emission can also be altered by changes in temperature and addition of crowding reagents such as poly(ethylene glycol). Using time-resolved emission spectroscopy we show that GQS emission can be approximately dissected into two emitting populations with distinct kinetics. Chapters 5 and 6 report on the effects of solvation on charge transfer reactions in conventional molecular solvents and ionic liquid/conventional solvent mixtures. In Chapter 5 the excited state intramolecular proton transfer reaction of 40-N,N-diethylamino-3-hydroxyflavone (DEAHF) is studied using sub-picosecond Kerr-gated emission spectroscopy in mixtures of acetonitrile and propylene carbonate. Previous studies of DEAHF tautomerization had shown that the proton transfer rate and equilibrium constant are highly dependent on both solvation dynamics and solvent polarity. Using acetonitrile/propylene carbonate mixtures, which have nearly identical polarity but have solvation times that vary over an order of magnitude, we were able to demonstrate that fast solvation dynamics introduces a barrier to the reaction and slows down the proton transfer rate. In Chapter 6 the intramolecular electron transfer reaction of 9-(4-biphenyl)-10-methylacridinium (BPAc+) is studied in mixtures of an ionic liquid and acetonitrile. Using KGE and picosecond time-correlated single photon counting measurements we show that the BPAc+ electron transfer rate is highly correlated with the mixture solvation time, consistent with rates observed in conventional solvents. Finally, Chapters 7 and 8 are an exploration of solute rotational dynamics in ionic liquids (ILs). Solute rotations in these unique solvents have been shown to be non-diffusive and poorly predicted by hydrodynamic theories of friction. We set out to explore the mechanisms of solute rotation in ILs using a combination of experimental methods and molecular dynamics (MD) simulations. In Chapter 7 the rotational dynamics of benzene and the IL cation 1- ethyl-3-methylimidizolium are studied using a combination of 2H longitudinal spin relaxation (T1) measurements and MD simulations. Using the simulations for guidance, we were able to interpret T1 measurements outside of the extreme narrowing limit. After the realism of the simulations was validated, they were then used to show that benzene exhibits markedly different dynamics for 'spinning' about the C6 symmetry axis and 'tumbling' (rotation of the C6 axis), and that large amplitude jump motions and orientational caging are prominent features of benzene's rotations in ILs. Chapter 8 extends the benzene work to examine the effect of molecular size and charge distribution on solute rotational dynamics in ILs. Combining fluorescence anisotropy and T1 relaxation measurements with MD simulations of a carefully chosen set of probe molecules we show that molecular charge has only a modest effect of friction experienced by a rotating solute, whereas an increase in molecular size results in a substantial increase in rotation times. After validation of the simulations, we showed that large amplitude jumps and orientational caging dynamics, similar to what was observed with benzene, are also present in these solutes.

Molecular dynamics and a spectroscopic study of sulfur dioxide absorption by an ionic liquid and its mixtures with PEO.

PubMed

Hoher, Karina; Cardoso, Piercarlo F; Lepre, Luiz F; Ando, Rômulo A; Siqueira, Leonardo J A

2016-10-19

An investigation comprising experimental techniques (absorption capacity of SO 2 and vibrational spectroscopy) and molecular simulations (thermodynamics, structure, and dynamics) has been performed for the polymer poly(ethylene oxide) (PEO), the ionic liquid butyltrimethylammonium bis(trifluoromethylsulfonyl)imide ([N 4111 ][Tf 2 N]) and their mixtures as sulfur dioxide (SO 2 ) absorbing materials. The polymer PEO has higher capacity to absorb SO 2 than the neat ionic liquid, whereas the mixtures presented intermediary absorption capacities. The band assigned to the symmetric stretching band of SO 2 at ca. 1140 cm -1 , which is considered a spectroscopic probe for the strength of SO 2 interactions with its neighborhood, shifts to lower wavenumbers as more negative total interaction energy values of SO 2 were evaluated from the simulations. The solvation free energy of SO 2 , ΔG sol , correlates linearly with the absorption capacity of SO 2 . The negative values of ΔG sol are due to negative and positive values of enthalpy and entropy, respectively. In the ionic liquid, SO 2 weakens the cation-anion interactions, whereas in the mixture with a high content of PEO these interactions are slightly increased. Such effects were correlated with the relative population of cisoid and transoid conformers of Tf 2 N anions as revealed by Raman spectroscopy. Moreover, the presence of SO 2 in the systems provokes the increase of diffusion coefficients of the absorbing species in comparison with the systems without the gas. Proper to the slow dynamics of the polymer, the diffusion coefficient of ions and SO 2 diminishes with the increase of the PEO content.

Excess protons in water-acetone mixtures. II. A conductivity study.

PubMed

Semino, Rocío; Longinotti, M Paula

2013-10-28

In the present work we complement a previous simulation study [R. Semino and D. Laria, J. Chem. Phys. 136, 194503 (2012)] on the disruption of the proton transfer mechanism in water by the addition of an aprotic solvent, such as acetone. We provide experimental measurements of the mobility of protons in aqueous-acetone mixtures in a wide composition range, for water molar fractions, xw, between 0.05 and 1.00. Furthermore, new molecular dynamics simulation results are presented for rich acetone mixtures, which provide further insight into the proton transport mechanism in water-non-protic solvent mixtures. The proton mobility was analyzed between xw 0.05 and 1.00 and compared to molecular dynamics simulation data. Results show two qualitative changes in the proton transport composition dependence at xw ∼ 0.25 and 0.8. At xw < 0.25 the ratio of the infinite dilution molar conductivities of HCl and LiCl, Λ(0)(HCl).Λ(0)(LiCl)(-1), is approximately constant and equal to one, since the proton diffusion is vehicular and equal to that of Li(+). At xw ∼ 0.25, proton mobility starts to differ from that of Li(+) indicating that above this concentration the Grotthuss transport mechanism starts to be possible. Molecular dynamics simulation results showed that at this threshold concentration the probability of interconversion between two Eigen structures starts to be non-negligible. At xw ∼ 0.8, the infinite molar conductivity of HCl concentration dependence qualitatively changes. This result is in excellent agreement with the analysis presented in the previous simulation work and it has been ascribed to the interchange of water and acetone molecules in the second solvation shell of the hydronium ion.

Rapid estimation of characteristics of gas dynamic lasers

NASA Technical Reports Server (NTRS)

Murty, S. S. R.

1974-01-01

Sudden-freeze approximation is applied to the flow of a CO2-N2-He mixture in wedge-type nozzles. This approximation permits rapid estimation of the freezing temperature of the upper laser level as a function of the stagnation pressure and the nozzle geometry. The stagnation temperature and the composition of the mixture appear as parameters. Gain and power output may then be estimated and calculations are presented for two cases.

Function investigation of stone mastic asphalt (SMA) mixture partly containing basic oxygen furnace (BOF) slag.

PubMed

Chen, Zongwu; Wu, Shaopeng; Pang, Ling; Xie, Jun

2016-07-04

In this paper, the effect of the size gradations of basic oxygen furnace (BOF) slag on the functional performances of stone mastic asphalt (SMA) mixture including skid and deformation resistances was investigated. The industrially produced BOF slag coarse aggregates (BSCA) with size gradations of 4.75-9.5 mm and 9.5-16 mm were used. SMA mixtures were designed according to Marshall procedure. British pendulum number (BPN), indicating the skid resistance of asphalt mixture, was measured by a British pendulum skid resistance device. Flow number (FN) and Marshall quotient (MQ), reflecting the deformation resistance of asphalt mixture, were determined, respectively, based on the results of dynamic creep test and Marshall test (stability and flow value). Showed that BSCA with a size gradation of 9.5-16 mm performed better in improving the skid and deformation resistance of SMA mixture than BSCA with a size gradation of 4.75-9.5 mm. Furthermore, BSCA with combined size gradations, namely, 4.75-16 mm, worked the best. These conclusions would benefit the future extensive utilization of BSCA in asphalt pavement.

Recognition of the Component Odors in Mixtures

PubMed Central

Fletcher, Dane B; Hettinger, Thomas P

2017-01-01

Abstract Natural olfactory stimuli are volatile-chemical mixtures in which relative perceptual saliencies determine which odor-components are identified. Odor identification also depends on rapid selective adaptation, as shown for 4 odor stimuli in an earlier experimental simulation of natural conditions. Adapt-test pairs of mixtures of water-soluble, distinct odor stimuli with chemical features in common were studied. Identification decreased for adapted components but increased for unadapted mixture-suppressed components, showing compound identities were retained, not degraded to individual molecular features. Four additional odor stimuli, 1 with 2 perceptible odor notes, and an added “water-adapted” control tested whether this finding would generalize to other 4-compound sets. Selective adaptation of mixtures of the compounds (odors): 3 mM benzaldehyde (cherry), 5 mM maltol (caramel), 1 mM guaiacol (smoke), and 4 mM methyl anthranilate (grape-smoke) again reciprocally unmasked odors of mixture-suppressed components in 2-, 3-, and 4-component mixtures with 2 exceptions. The cherry note of “benzaldehyde” (itself) and the shared note of “methyl anthranilate and guaiacol” (together) were more readily identified. The pervasive mixture-component dominance and dynamic perceptual salience may be mediated through peripheral adaptation and central mutual inhibition of neural responses. Originating in individual olfactory receptor variants, it limits odor identification and provides analytic properties for momentary recognition of a few remaining mixture-components. PMID:28641388

Experimental evidence for an effect of early-diagenetic interaction between labile and refractory marine sedimentary organic matter on nitrogen dynamics

NASA Astrophysics Data System (ADS)

Turnewitsch, Robert; Domeyer, Bettina; Graf, Gerhard

2007-05-01

In most natural sedimentary systems labile and refractory organic material (OM) occur concomitantly. Little, however, is known on how different kinds of OM interact and how such interactions affect early diagenesis in sediments. In a simple sediment experiment, we investigated how interactions of OM substrates of different degradability affect benthic nitrogen (N) dynamics. Temporal evolution of a set of selected biogeochemical parameters was monitored in sandy sediment over 116 days in three experimental set-ups spiked with labile OM (tissue of Mytilus edulis), refractory OM (mostly aged Zostera marina and macroalgae), and a 1:1 mixture of labile and refractory OM. The initial amounts of particulate organic carbon (POC) were identical in the three set-ups. To check for non-linear interactions between labile and refractory OM, the evolution of the mixture system was compared with the evolution of the simple sum of the labile and refractory systems, divided by two. The sum system is the experimental control where labile and refractory OM are virtually combined but not allowed to interact. During the first 30 days there was evidence for net dissolved-inorganic-nitrogen (DIN) production followed by net DIN consumption. (Here 'DIN' is the sum of ammonium, nitrite and nitrate.) After ˜ 30 days a quasi steady state was reached. Non-linear interactions between the two types of OM were reflected by three main differences between the early-diagenetic evolutions of nitrogen dynamics of the mixture and sum (control) systems: (1) In the mixture system the phases of net DIN production and consumption commenced more rapidly and were more intense. (2) The mixture system was shifted towards a more oxidised state of DIN products [as indicated by increased (nitrite + nitrate)/(ammonium) ratios]. (3) There was some evidence that more OM, POC and particulate nitrogen were preserved in the mixture system. That is, in the mixture system more particulate OM was preserved while a higher proportion of the decomposed particulate N was converted into inorganic N. It can be concluded that during the first days and weeks of early diagenesis the magnitude and composition of the flux of decompositional dissolved N-compounds from sediments into the overlying water was influenced by non-linear interactions of OM substrates of different degradability. Given these experimental results it is likely that the relative spatial distributions of OM of differing degradability in sediments control the magnitude and composition of the return flux of dissolved N-bearing compounds from sediments into the overlying water column.

First-Principles Molecular Dynamics Study of a Deep Eutectic Solvent: Choline Chloride/Urea and Its Mixture with Water

DOE PAGES

Fetisov, Evgenii O.; Harwood, David B.; Kuo, I-Feng William; ...

2017-12-07

First-principles molecular dynamics simulations in the canonical ensemble at temperatures of 333 and 363 K and at the corresponding experimental densities are carried out to investigate the behavior of the 1:2 choline chloride/urea (reline) deep eutectic solvent and its equimolar mixture with water. Analysis of atom–atom radial and spatial distribution functions and of the H-bond network reveals the microheterogeneous structure of these complex liquid mixtures. In neat reline, the structure is governed by strong H-bonds of the trans- and cis-H atoms of urea to the chloride ion. In hydrous reline, water competes for the anions, and the H atoms ofmore » urea have similar propensities to bond to the chloride ions and the O atoms of urea and water. Finally, the vibrational spectra exhibit relatively broad peaks reflecting the heterogeneity of the environment. Although the 100 ps trajectories allow only for a qualitative assessment of transport properties, the simulations indicate that water is more mobile than the other species and its addition also fosters faster motion of urea.« less

Explanation of power law behavior of autoregressive conditional duration processes based on the random multiplicative process

NASA Astrophysics Data System (ADS)

Sato, Aki-Hiro

2004-04-01

Autoregressive conditional duration (ACD) processes, which have the potential to be applied to power law distributions of complex systems found in natural science, life science, and social science, are analyzed both numerically and theoretically. An ACD(1) process exhibits the singular second order moment, which suggests that its probability density function (PDF) has a power law tail. It is verified that the PDF of the ACD(1) has a power law tail with an arbitrary exponent depending on a model parameter. On the basis of theory of the random multiplicative process a relation between the model parameter and the power law exponent is theoretically derived. It is confirmed that the relation is valid from numerical simulations. An application of the ACD(1) to intervals between two successive transactions in a foreign currency market is shown.

[Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].

PubMed

Ke-Wei, Wang; Yu, Wu; Jin-Ping, Li; Yu-Yu, Jiang

2016-07-12

To explore the effect of the autoregressive integrated moving average model-nonlinear auto-regressive neural network (ARIMA-NARNN) model on predicting schistosomiasis infection rates of population. The ARIMA model, NARNN model and ARIMA-NARNN model were established based on monthly schistosomiasis infection rates from January 2005 to February 2015 in Jiangsu Province, China. The fitting and prediction performances of the three models were compared. Compared to the ARIMA model and NARNN model, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4, respectively. The ARIMA-NARNN model could effectively fit and predict schistosomiasis infection rates of population, which might have a great application value for the prevention and control of schistosomiasis.

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.