Autoregressive model in the Lp norm space for EEG analysis.

PubMed

Li, Peiyang; Wang, Xurui; Li, Fali; Zhang, Rui; Ma, Teng; Peng, Yueheng; Lei, Xu; Tian, Yin; Guo, Daqing; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2015-01-30

The autoregressive (AR) model is widely used in electroencephalogram (EEG) analyses such as waveform fitting, spectrum estimation, and system identification. In real applications, EEGs are inevitably contaminated with unexpected outlier artifacts, and this must be overcome. However, most of the current AR models are based on the L2 norm structure, which exaggerates the outlier effect due to the square property of the L2 norm. In this paper, a novel AR object function is constructed in the Lp (p≤1) norm space with the aim to compress the outlier effects on EEG analysis, and a fast iteration procedure is developed to solve this new AR model. The quantitative evaluation using simulated EEGs with outliers proves that the proposed Lp (p≤1) AR can estimate the AR parameters more robustly than the Yule-Walker, Burg and LS methods, under various simulated outlier conditions. The actual application to the resting EEG recording with ocular artifacts also demonstrates that Lp (p≤1) AR can effectively address the outliers and recover a resting EEG power spectrum that is more consistent with its physiological basis. Copyright © 2014 Elsevier B.V. All rights reserved.

Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability.

PubMed

Beda, Alessandro; Simpson, David M; Faes, Luca

2017-01-01

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits' width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications in biomedical signal processing and beyond, providing a powerful new tool for assessing the confidence limits of indexes estimated from individual recordings.

Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability

PubMed Central

2017-01-01

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits' width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications in biomedical signal processing and beyond, providing a powerful new tool for assessing the confidence limits of indexes estimated from individual recordings. PMID:28968394

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.

Studies in astronomical time series analysis: Modeling random processes in the time domain

NASA Technical Reports Server (NTRS)

Scargle, J. D.

1979-01-01

Random process models phased in the time domain are used to analyze astrophysical time series data produced by random processes. A moving average (MA) model represents the data as a sequence of pulses occurring randomly in time, with random amplitudes. An autoregressive (AR) model represents the correlations in the process in terms of a linear function of past values. The best AR model is determined from sampled data and transformed to an MA for interpretation. The randomness of the pulse amplitudes is maximized by a FORTRAN algorithm which is relatively stable numerically. Results of test cases are given to study the effects of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the optical light curve of the quasar 3C 273 is given.

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.

Gas Chromatography Data Classification Based on Complex Coefficients of an Autoregressive Model

DOE PAGES

Zhao, Weixiang; Morgan, Joshua T.; Davis, Cristina E.

2008-01-01

This paper introduces autoregressive (AR) modeling as a novel method to classify outputs from gas chromatography (GC). The inverse Fourier transformation was applied to the original sensor data, and then an AR model was applied to transform data to generate AR model complex coefficients. This series of coefficients effectively contains a compressed version of all of the information in the original GC signal output. We applied this method to chromatograms resulting from proliferating bacteria species grown in culture. Three types of neural networks were used to classify the AR coefficients: backward propagating neural network (BPNN), radial basis function-principal component analysismore » (RBF-PCA) approach, and radial basis function-partial least squares regression (RBF-PLSR) approach. This exploratory study demonstrates the feasibility of using complex root coefficient patterns to distinguish various classes of experimental data, such as those from the different bacteria species. This cognition approach also proved to be robust and potentially useful for freeing us from time alignment of GC signals.« less

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.

Three-dimensional dominant frequency mapping using autoregressive spectral analysis of atrial electrograms of patients in persistent atrial fibrillation.

PubMed

Salinet, João L; Masca, Nicholas; Stafford, Peter J; Ng, G André; Schlindwein, Fernando S

2016-03-08

Areas with high frequency activity within the atrium are thought to be 'drivers' of the rhythm in patients with atrial fibrillation (AF) and ablation of these areas seems to be an effective therapy in eliminating DF gradient and restoring sinus rhythm. Clinical groups have applied the traditional FFT-based approach to generate the three-dimensional dominant frequency (3D DF) maps during electrophysiology (EP) procedures but literature is restricted on using alternative spectral estimation techniques that can have a better frequency resolution that FFT-based spectral estimation. Autoregressive (AR) model-based spectral estimation techniques, with emphasis on selection of appropriate sampling rate and AR model order, were implemented to generate high-density 3D DF maps of atrial electrograms (AEGs) in persistent atrial fibrillation (persAF). For each patient, 2048 simultaneous AEGs were recorded for 20.478 s-long segments in the left atrium (LA) and exported for analysis, together with their anatomical locations. After the DFs were identified using AR-based spectral estimation, they were colour coded to produce sequential 3D DF maps. These maps were systematically compared with maps found using the Fourier-based approach. 3D DF maps can be obtained using AR-based spectral estimation after AEGs downsampling (DS) and the resulting maps are very similar to those obtained using FFT-based spectral estimation (mean 90.23 %). There were no significant differences between AR techniques (p = 0.62). The processing time for AR-based approach was considerably shorter (from 5.44 to 5.05 s) when lower sampling frequencies and model order values were used. Higher levels of DS presented higher rates of DF agreement (sampling frequency of 37.5 Hz). We have demonstrated the feasibility of using AR spectral estimation methods for producing 3D DF maps and characterised their differences to the maps produced using the FFT technique, offering an alternative approach for 3D DF computation in human persAF studies.

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

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.

Autoregressive modeling for the spectral analysis of oceanographic data

NASA Technical Reports Server (NTRS)

Gangopadhyay, Avijit; Cornillon, Peter; Jackson, Leland B.

1989-01-01

Over the last decade there has been a dramatic increase in the number and volume of data sets useful for oceanographic studies. Many of these data sets consist of long temporal or spatial series derived from satellites and large-scale oceanographic experiments. These data sets are, however, often 'gappy' in space, irregular in time, and always of finite length. The conventional Fourier transform (FT) approach to the spectral analysis is thus often inapplicable, or where applicable, it provides questionable results. Here, through comparative analysis with the FT for different oceanographic data sets, the possibilities offered by autoregressive (AR) modeling to perform spectral analysis of gappy, finite-length series, are discussed. The applications demonstrate that as the length of the time series becomes shorter, the resolving power of the AR approach as compared with that of the FT improves. For the longest data sets examined here, 98 points, the AR method performed only slightly better than the FT, but for the very short ones, 17 points, the AR method showed a dramatic improvement over the FT. The application of the AR method to a gappy time series, although a secondary concern of this manuscript, further underlines the value of this approach.

Granger causality for state-space models

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Seth, Anil K.

2015-04-01

Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations—commonplace in application domains as diverse as climate science, econometrics, and the neurosciences—induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.

An algebraic method for constructing stable and consistent autoregressive filters

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

Harlim, John, E-mail: jharlim@psu.edu; Department of Meteorology, the Pennsylvania State University, University Park, PA 16802; Hong, Hoon, E-mail: hong@ncsu.edu

2015-02-15

In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides amore » discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.« less

Conjugate Gradient Parametric Detection of Multichannel Signals (Preprint)

DTIC Science & Technology

2012-05-01

aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information...if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YY) 2...processing (STAP) detection is re- examined in this paper. Originally, the PAMF detector was introduced by using a multichannel autoregressive (AR

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.

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.

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.

Vibration-based damage detection in a concrete beam under temperature variations using AR models and state-space approaches

NASA Astrophysics Data System (ADS)

Clément, A.; Laurens, S.

2011-07-01

The Structural Health Monitoring of civil structures subjected to ambient vibrations is very challenging. Indeed, the variations of environmental conditions and the difficulty to characterize the excitation make the damage detection a hard task. Auto-regressive (AR) models coefficients are often used as damage sensitive feature. The presented work proposes a comparison of the AR approach with a state-space feature formed by the Jacobian matrix of the dynamical process. Since the detection of damage can be formulated as a novelty detection problem, Mahalanobis distance is applied to track new points from an undamaged reference collection of feature vectors. Data from a concrete beam subjected to temperature variations and damaged by several static loading are analyzed. It is observed that the damage sensitive features are effectively sensitive to temperature variations. However, the use of the Mahalanobis distance makes possible the detection of cracking with both of them. Early damage (before cracking) is only revealed by the AR coefficients with a good sensibility.

Detection of shallow buried objects using an autoregressive model on the ground penetrating radar signal

NASA Astrophysics Data System (ADS)

Nabelek, Daniel P.; Ho, K. C.

2013-06-01

The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.

Detection of tripping gait patterns in the elderly using autoregressive features and support vector machines.

PubMed

Lai, Daniel T H; Begg, Rezaul K; Taylor, Simon; Palaniswami, Marimuthu

2008-01-01

Elderly tripping falls cost billions annually in medical funds and result in high mortality rates often perpetrated by pulmonary embolism (internal bleeding) and infected fractures that do not heal well. In this paper, we propose an intelligent gait detection system (AR-SVM) for screening elderly individuals at risk of suffering tripping falls. The motivation of this system is to provide early detection of elderly gait reminiscent of tripping characteristics so that preventive measures could be administered. Our system is composed of two stages, a predictor model estimated by an autoregressive (AR) process and a support vector machine (SVM) classifier. The system input is a digital signal constructed from consecutive measurements of minimum toe clearance (MTC) representative of steady-state walking. The AR-SVM system was tested on 23 individuals (13 healthy and 10 having suffered at least one tripping fall in the past year) who each completed a minimum of 10 min of walking on a treadmill at a self-selected pace. In the first stage, a fourth order AR model required at least 64 MTC values to correctly detect all fallers and non-fallers. Detection was further improved to less than 1 min of walking when the model coefficients were used as input features to the SVM classifier. The system achieved a detection accuracy of 95.65% with the leave one out method using only 16 MTC samples, but was reduced to 69.57% when eight MTC samples were used. These results demonstrate a fast and efficient system requiring a small number of strides and only MTC measurements for accurate detection of tripping gait characteristics.

A Modified LS+AR Model to Improve the Accuracy of the Short-term Polar Motion Prediction

NASA Astrophysics Data System (ADS)

Wang, Z. W.; Wang, Q. X.; Ding, Y. Q.; Zhang, J. J.; Liu, S. S.

2017-03-01

There are two problems of the LS (Least Squares)+AR (AutoRegressive) model in polar motion forecast: the inner residual value of LS fitting is reasonable, but the residual value of LS extrapolation is poor; and the LS fitting residual sequence is non-linear. It is unsuitable to establish an AR model for the residual sequence to be forecasted, based on the residual sequence before forecast epoch. In this paper, we make solution to those two problems with two steps. First, restrictions are added to the two endpoints of LS fitting data to fix them on the LS fitting curve. Therefore, the fitting values next to the two endpoints are very close to the observation values. Secondly, we select the interpolation residual sequence of an inward LS fitting curve, which has a similar variation trend as the LS extrapolation residual sequence, as the modeling object of AR for the residual forecast. Calculation examples show that this solution can effectively improve the short-term polar motion prediction accuracy by the LS+AR model. In addition, the comparison results of the forecast models of RLS (Robustified Least Squares)+AR, RLS+ARIMA (AutoRegressive Integrated Moving Average), and LS+ANN (Artificial Neural Network) confirm the feasibility and effectiveness of the solution for the polar motion forecast. The results, especially for the polar motion forecast in the 1-10 days, show that the forecast accuracy of the proposed model can reach the world level.

Multiscale Granger causality

NASA Astrophysics Data System (ADS)

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

2017-10-01

In the study of complex physical and biological systems represented by multivariate stochastic processes, an issue of great relevance is the description of the system dynamics spanning multiple temporal scales. While methods to assess the dynamic complexity of individual processes at different time scales are well established, multiscale analysis of directed interactions has never been formalized theoretically, and empirical evaluations are complicated by practical issues such as filtering and downsampling. Here we extend the very popular measure of Granger causality (GC), a prominent tool for assessing directed lagged interactions between joint processes, to quantify information transfer across multiple time scales. We show that the multiscale processing of a vector autoregressive (AR) process introduces a moving average (MA) component, and describe how to represent the resulting ARMA process using state space (SS) models and to combine the SS model parameters for computing exact GC values at arbitrarily large time scales. We exploit the theoretical formulation to identify peculiar features of multiscale GC in basic AR processes, and demonstrate with numerical simulations the much larger estimation accuracy of the SS approach compared to pure AR modeling of filtered and downsampled data. The improved computational reliability is exploited to disclose meaningful multiscale patterns of information transfer between global temperature and carbon dioxide concentration time series, both in paleoclimate and in recent years.

Researches on High Accuracy Prediction Methods of Earth Orientation Parameters

NASA Astrophysics Data System (ADS)

Xu, X. Q.

2015-09-01

The Earth rotation reflects the coupling process among the solid Earth, atmosphere, oceans, mantle, and core of the Earth on multiple spatial and temporal scales. The Earth rotation can be described by the Earth's orientation parameters, which are abbreviated as EOP (mainly including two polar motion components PM_X and PM_Y, and variation in the length of day ΔLOD). The EOP is crucial in the transformation between the terrestrial and celestial reference systems, and has important applications in many areas such as the deep space exploration, satellite precise orbit determination, and astrogeodynamics. However, the EOP products obtained by the space geodetic technologies generally delay by several days to two weeks. The growing demands for modern space navigation make high-accuracy EOP prediction be a worthy topic. This thesis is composed of the following three aspects, for the purpose of improving the EOP forecast accuracy. (1) We analyze the relation between the length of the basic data series and the EOP forecast accuracy, and compare the EOP prediction accuracy for the linear autoregressive (AR) model and the nonlinear artificial neural network (ANN) method by performing the least squares (LS) extrapolations. The results show that the high precision forecast of EOP can be realized by appropriate selection of the basic data series length according to the required time span of EOP prediction: for short-term prediction, the basic data series should be shorter, while for the long-term prediction, the series should be longer. The analysis also showed that the LS+AR model is more suitable for the short-term forecasts, while the LS+ANN model shows the advantages in the medium- and long-term forecasts. (2) We develop for the first time a new method which combines the autoregressive model and Kalman filter (AR+Kalman) in short-term EOP prediction. The equations of observation and state are established using the EOP series and the autoregressive coefficients respectively, which are used to improve/re-evaluate the AR model. Comparing to the single AR model, the AR+Kalman method performs better in the prediction of UT1-UTC and ΔLOD, and the improvement in the prediction of the polar motion is significant. (3) Following the successful Earth Orientation Parameter Prediction Comparison Campaign (EOP PCC), the Earth Orientation Parameter Combination of Prediction Pilot Project (EOPC PPP) was sponsored in 2010. As one of the participants from China, we update and submit the short- and medium-term (1 to 90 days) EOP predictions every day. From the current comparative statistics, our prediction accuracy is on the medium international level. We will carry out more innovative researches to improve the EOP forecast accuracy and enhance our level in EOP forecast.

Autocorrelated residuals in inverse modelling of soil hydrological processes: a reason for concern or something that can safely be ignored?

NASA Astrophysics Data System (ADS)

Scharnagl, Benedikt; Durner, Wolfgang

2013-04-01

Models are inherently imperfect because they simplify processes that are themselves imperfectly known and understood. Moreover, the input variables and parameters needed to run a model are typically subject to various sources of error. As a consequence of these imperfections, model predictions will always deviate from corresponding observations. In most applications in soil hydrology, these deviations are clearly not random but rather show a systematic structure. From a statistical point of view, this systematic mismatch may be a reason for concern because it violates one of the basic assumptions made in inverse parameter estimation: the assumption of independence of the residuals. But what are the consequences of simply ignoring the autocorrelation in the residuals, as it is current practice in soil hydrology? Are the parameter estimates still valid even though the statistical foundation they are based on is partially collapsed? Theory and practical experience from other fields of science have shown that violation of the independence assumption will result in overconfident uncertainty bounds and that in some cases it may lead to significantly different optimal parameter values. In our contribution, we present three soil hydrological case studies, in which the effect of autocorrelated residuals on the estimated parameters was investigated in detail. We explicitly accounted for autocorrelated residuals using a formal likelihood function that incorporates an autoregressive model. The inverse problem was posed in a Bayesian framework, and the posterior probability density function of the parameters was estimated using Markov chain Monte Carlo simulation. In contrast to many other studies in related fields of science, and quite surprisingly, we found that the first-order autoregressive model, often abbreviated as AR(1), did not work well in the soil hydrological setting. We showed that a second-order autoregressive, or AR(2), model performs much better in these applications, leading to parameter and uncertainty estimates that satisfy all the underlying statistical assumptions. For theoretical reasons, these estimates are deemed more reliable than those estimates based on the neglect of autocorrelation in the residuals. In compliance with theory and results reported in the literature, our results showed that parameter uncertainty bounds were substantially wider if autocorrelation in the residuals was explicitly accounted for, and also the optimal parameter vales were slightly different in this case. We argue that the autoregressive model presented here should be used as a matter of routine in inverse modeling of soil hydrological processes.

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

PubMed Central

Atyabi, Adham; Shic, Frederick; Naples, Adam

2016-01-01

Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR’s modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely & correctly responsiveness of such systems to operator’s thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets. PMID:28740331

Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.

PubMed

Khan, Adil Mehmood; Siddiqi, Muhammad Hameed; Lee, Seok-Won

2013-09-27

Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.

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.

Development of a Robust Identifier for NPPs Transients Combining ARIMA Model and EBP Algorithm

NASA Astrophysics Data System (ADS)

Moshkbar-Bakhshayesh, Khalil; Ghofrani, Mohammad B.

2014-08-01

This study introduces a novel identification method for recognition of nuclear power plants (NPPs) transients by combining the autoregressive integrated moving-average (ARIMA) model and the neural network with error backpropagation (EBP) learning algorithm. The proposed method consists of three steps. First, an EBP based identifier is adopted to distinguish the plant normal states from the faulty ones. In the second step, ARIMA models use integrated (I) process to convert non-stationary data of the selected variables into stationary ones. Subsequently, ARIMA processes, including autoregressive (AR), moving-average (MA), or autoregressive moving-average (ARMA) are used to forecast time series of the selected plant variables. In the third step, for identification the type of transients, the forecasted time series are fed to the modular identifier which has been developed using the latest advances of EBP learning algorithm. Bushehr nuclear power plant (BNPP) transients are probed to analyze the ability of the proposed identifier. Recognition of transient is based on similarity of its statistical properties to the reference one, rather than the values of input patterns. More robustness against noisy data and improvement balance between memorization and generalization are salient advantages of the proposed identifier. Reduction of false identification, sole dependency of identification on the sign of each output signal, selection of the plant variables for transients training independent of each other, and extendibility for identification of more transients without unfavorable effects are other merits of the proposed identifier.

Analysis Monthly Import of Palm Oil Products Using Box-Jenkins Model

NASA Astrophysics Data System (ADS)

Ahmad, Nurul F. Y.; Khalid, Kamil; Saifullah Rusiman, Mohd; Ghazali Kamardan, M.; Roslan, Rozaini; Che-Him, Norziha

2018-04-01

The palm oil industry has been an important component of the national economy especially the agriculture sector. The aim of this study is to identify the pattern of import of palm oil products, to model the time series using Box-Jenkins model and to forecast the monthly import of palm oil products. The method approach is included in the statistical test for verifying the equivalence model and statistical measurement of three models, namely Autoregressive (AR) model, Moving Average (MA) model and Autoregressive Moving Average (ARMA) model. The model identification of all product import palm oil is different in which the AR(1) was found to be the best model for product import palm oil while MA(3) was found to be the best model for products import palm kernel oil. For the palm kernel, MA(4) was found to be the best model. The results forecast for the next four months for products import palm oil, palm kernel oil and palm kernel showed the most significant decrease compared to the actual data.

An improved portmanteau test for autocorrelated errors in interrupted time-series regression models.

PubMed

Huitema, Bradley E; McKean, Joseph W

2007-08-01

A new portmanteau test for autocorrelation among the errors of interrupted time-series regression models is proposed. Simulation results demonstrate that the inferential properties of the proposed Q(H-M) test statistic are considerably more satisfactory than those of the well known Ljung-Box test and moderately better than those of the Box-Pierce test. These conclusions generally hold for a wide variety of autoregressive (AR), moving averages (MA), and ARMA error processes that are associated with time-series regression models of the form described in Huitema and McKean (2000a, 2000b).

A time series model: First-order integer-valued autoregressive (INAR(1))

NASA Astrophysics Data System (ADS)

Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.

2017-07-01

Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.

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.

Prediction of UT1-UTC, LOD and AAM χ3 by combination of least-squares and multivariate stochastic methods

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Kosek, Wiesław

2008-02-01

This article presents the application of a multivariate prediction technique for predicting universal time (UT1-UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ 3). The multivariate predictions of LOD and UT1-UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ 3 time-series as the explanatory variable for the computation of LOD or UT1-UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ 3 data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ 3 predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1-UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1-UTC based on LS extrapolation or on LS + AR. In particular, the UT1-UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ 3 time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ 3 reveal the best accuracy for LS + AR.

Verification of ARMA identification for modelling temporal correlation of GPS observations using the toolbox ARMASA

NASA Astrophysics Data System (ADS)

Luo, Xiaoguang; Mayer, Michael; Heck, Bernhard

2010-05-01

One essential deficiency of the stochastic model used in many GNSS (Global Navigation Satellite Systems) software products consists in neglecting temporal correlation of GNSS observations. Analysing appropriately detrended time series of observation residuals resulting from GPS (Global Positioning System) data processing, the temporal correlation behaviour of GPS observations can be sufficiently described by means of so-called autoregressive moving average (ARMA) processes. Using the toolbox ARMASA which is available free of charge in MATLAB® Central (open exchange platform for the MATLAB® and SIMULINK® user community), a well-fitting time series model can be identified automatically in three steps. Firstly, AR, MA, and ARMA models are computed up to some user-specified maximum order. Subsequently, for each model type, the best-fitting model is selected using the combined (for AR processes) resp. generalised (for MA and ARMA processes) information criterion. The final model identification among the best-fitting AR, MA, and ARMA models is performed based on the minimum prediction error characterising the discrepancies between the given data and the fitted model. The ARMA coefficients are computed using Burg's maximum entropy algorithm (for AR processes), Durbin's first (for MA processes) and second (for ARMA processes) methods, respectively. This paper verifies the performance of the automated ARMA identification using the toolbox ARMASA. For this purpose, a representative data base is generated by means of ARMA simulation with respect to sample size, correlation level, and model complexity. The model error defined as a transform of the prediction error is used as measure for the deviation between the true and the estimated model. The results of the study show that the recognition rates of underlying true processes increase with increasing sample sizes and decrease with rising model complexity. Considering large sample sizes, the true underlying processes can be correctly recognised for nearly 80% of the analysed data sets. Additionally, the model errors of first-order AR resp. MA processes converge clearly more rapidly to the corresponding asymptotical values than those of high-order ARMA processes.

EMG circuit design and AR analysis of EMG signs.

PubMed

Hardalaç, Firat; Canal, Rahmi

2004-12-01

In this study, electromyogram (EMG) circuit was designed and tested on 27 people. Autoregressive (AR) analysis of EMG signals recorded on the ulnar nerve region of the right hand in resting position was performed. AR method, especially in the calculation of the spectrums of stable signs, is used for frequency analysis of signs, which give frequency response as sharp peaks and valleys. In this study, as the result of AR method analysis of EMG signals frequency-time domain, frequency spectrum curves (histogram curves) were obtained. As the images belonging to these histograms were evaluated, fibrillation potential widths of the muscle fibers of the ulnar nerve region of the people (material of the study) were examined. According to the degeneration degrees of the motor nerves, nine people had myopathy, nine had neuropathy, and nine were normal.

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

The comparison study among several data transformations in autoregressive modeling

NASA Astrophysics Data System (ADS)

Setiyowati, Susi; Waluyo, Ramdhani Try

2015-12-01

In finance, the adjusted close of stocks are used to observe the performance of a company. The extreme prices, which may increase or decrease drastically, are often become particular concerned since it can impact to bankruptcy. As preventing action, the investors have to observe the future (forecasting) stock prices comprehensively. For that purpose, time series analysis could be one of statistical methods that can be implemented, for both stationary and non-stationary processes. Since the variability process of stocks prices tend to large and also most of time the extreme values are always exist, then it is necessary to do data transformation so that the time series models, i.e. autoregressive model, could be applied appropriately. One of popular data transformation in finance is return model, in addition to ratio of logarithm and some others Tukey ladder transformation. In this paper these transformations are applied to AR stationary models and non-stationary ARCH and GARCH models through some simulations with varying parameters. As results, this work present the suggestion table that shows transformations behavior for some condition of parameters and models. It is confirmed that the better transformation is obtained, depends on type of data distributions. In other hands, the parameter conditions term give significant influence either.

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.

An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth

NASA Astrophysics Data System (ADS)

Barraza-Barraza, Diana; Tercero-Gómez, Víctor G.; Beruvides, Mario G.; Limón-Robles, Jorge

2017-01-01

A wide variety of Condition-Based Maintenance (CBM) techniques deal with the problem of predicting the time for an asset fault. Most statistical approaches rely on historical failure data that might not be available in several practical situations. To address this issue, practitioners might require the use of self-starting approaches that consider only the available knowledge about the current degradation process and the asset operating context to update the prognostic model. Some authors use Autoregressive (AR) models for this purpose that are adequate when the asset operating context is constant, however, if it is variable, the accuracy of the models can be affected. In this paper, three autoregressive models with exogenous variables (ARX) were constructed, and their capability to estimate the remaining useful life (RUL) of a process was evaluated following the case of the aluminum crack growth problem. An existing stochastic model of aluminum crack growth was implemented and used to assess RUL estimation performance of the proposed ARX models through extensive Monte Carlo simulations. Point and interval estimations were made based only on individual history, behavior, operating conditions and failure thresholds. Both analytic and bootstrapping techniques were used in the estimation process. Finally, by including recursive parameter estimation and a forgetting factor, the ARX methodology adapts to changing operating conditions and maintain the focus on the current degradation level of an asset.

Potential predictability and forecast skill in ensemble climate forecast: the skill-persistence rule

NASA Astrophysics Data System (ADS)

Jin, Y.; Rong, X.; Liu, Z.

2017-12-01

This study investigates the factors that impact the forecast skill for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill of sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further examined using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but can be distorted by the sampling error and non-AR1 processes.

A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

PubMed

Khan, Adil Mehmood; Lee, Young-Koo; Lee, Sungyoung Y; Kim, Tae-Seong

2010-09-01

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.

Scaling analysis and model estimation of solar corona index

NASA Astrophysics Data System (ADS)

Ray, Samujjwal; Ray, Rajdeep; Khondekar, Mofazzal Hossain; Ghosh, Koushik

2018-04-01

A monthly average solar green coronal index time series for the period from January 1939 to December 2008 collected from NOAA (The National Oceanic and Atmospheric Administration) has been analysed in this paper in perspective of scaling analysis and modelling. Smoothed and de-noising have been done using suitable mother wavelet as a pre-requisite. The Finite Variance Scaling Method (FVSM), Higuchi method, rescaled range (R/S) and a generalized method have been applied to calculate the scaling exponents and fractal dimensions of the time series. Autocorrelation function (ACF) is used to find autoregressive (AR) process and Partial autocorrelation function (PACF) has been used to get the order of AR model. Finally a best fit model has been proposed using Yule-Walker Method with supporting results of goodness of fit and wavelet spectrum. The results reveal an anti-persistent, Short Range Dependent (SRD), self-similar property with signatures of non-causality, non-stationarity and nonlinearity in the data series. The model shows the best fit to the data under observation.

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

An accurate nonlinear stochastic model for MEMS-based inertial sensor error with wavelet networks

NASA Astrophysics Data System (ADS)

El-Diasty, Mohammed; El-Rabbany, Ahmed; Pagiatakis, Spiros

2007-12-01

The integration of Global Positioning System (GPS) with Inertial Navigation System (INS) has been widely used in many applications for positioning and orientation purposes. Traditionally, random walk (RW), Gauss-Markov (GM), and autoregressive (AR) processes have been used to develop the stochastic model in classical Kalman filters. The main disadvantage of classical Kalman filter is the potentially unstable linearization of the nonlinear dynamic system. Consequently, a nonlinear stochastic model is not optimal in derivative-based filters due to the expected linearization error. With a derivativeless-based filter such as the unscented Kalman filter or the divided difference filter, the filtering process of a complicated highly nonlinear dynamic system is possible without linearization error. This paper develops a novel nonlinear stochastic model for inertial sensor error using a wavelet network (WN). A wavelet network is a highly nonlinear model, which has recently been introduced as a powerful tool for modelling and prediction. Static and kinematic data sets are collected using a MEMS-based IMU (DQI-100) to develop the stochastic model in the static mode and then implement it in the kinematic mode. The derivativeless-based filtering method using GM, AR, and the proposed WN-based processes are used to validate the new model. It is shown that the first-order WN-based nonlinear stochastic model gives superior positioning results to the first-order GM and AR models with an overall improvement of 30% when 30 and 60 seconds GPS outages are introduced.

Are Public Master's Institutions Cost Efficient? A Stochastic Frontier and Spatial Analysis

ERIC Educational Resources Information Center

Titus, Marvin A.; Vamosiu, Adriana; McClure, Kevin R.

2017-01-01

The current study examines costs, measured by educational and general (E&G) spending, and cost efficiency at 252 public master's institutions in the United States over a nine-year (2004-2012) period. We use a multi-product quadratic cost function and results from a random-effects model with a first-order autoregressive (AR1) disturbance term…

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.

The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

PubMed Central

Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

2014-01-01

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

A univariate model of river water nitrate time series

NASA Astrophysics Data System (ADS)

Worrall, F.; Burt, T. P.

1999-01-01

Four time series were taken from three catchments in the North and South of England. The sites chosen included two in predominantly agricultural catchments, one at the tidal limit and one downstream of a sewage treatment works. A time series model was constructed for each of these series as a means of decomposing the elements controlling river water nitrate concentrations and to assess whether this approach could provide a simple management tool for protecting water abstractions. Autoregressive (AR) modelling of the detrended and deseasoned time series showed a "memory effect". This memory effect expressed itself as an increase in the winter-summer difference in nitrate levels that was dependent upon the nitrate concentration 12 or 6 months previously. Autoregressive moving average (ARMA) modelling showed that one of the series contained seasonal, non-stationary elements that appeared as an increasing trend in the winter-summer difference. The ARMA model was used to predict nitrate levels and predictions were tested against data held back from the model construction process - predictions gave average percentage errors of less than 10%. Empirical modelling can therefore provide a simple, efficient method for constructing management models for downstream water abstraction.

New Approach To Hour-By-Hour Weather Forecast

NASA Astrophysics Data System (ADS)

Liao, Q. Q.; Wang, B.

2017-12-01

Fine hourly forecast in single station weather forecast is required in many human production and life application situations. Most previous MOS (Model Output Statistics) which used a linear regression model are hard to solve nonlinear natures of the weather prediction and forecast accuracy has not been sufficient at high temporal resolution. This study is to predict the future meteorological elements including temperature, precipitation, relative humidity and wind speed in a local region over a relatively short period of time at hourly level. By means of hour-to-hour NWP (Numeral Weather Prediction)meteorological field from Forcastio (https://darksky.net/dev/docs/forecast) and real-time instrumental observation including 29 stations in Yunnan and 3 stations in Tianjin of China from June to October 2016, predictions are made of the 24-hour hour-by-hour ahead. This study presents an ensemble approach to combine the information of instrumental observation itself and NWP. Use autoregressive-moving-average (ARMA) model to predict future values of the observation time series. Put newest NWP products into the equations derived from the multiple linear regression MOS technique. Handle residual series of MOS outputs with autoregressive (AR) model for the linear property presented in time series. Due to the complexity of non-linear property of atmospheric flow, support vector machine (SVM) is also introduced . Therefore basic data quality control and cross validation makes it able to optimize the model function parameters , and do 24 hours ahead residual reduction with AR/SVM model. Results show that AR model technique is better than corresponding multi-variant MOS regression method especially at the early 4 hours when the predictor is temperature. MOS-AR combined model which is comparable to MOS-SVM model outperform than MOS. Both of their root mean square error and correlation coefficients for 2 m temperature are reduced to 1.6 degree Celsius and 0.91 respectively. The forecast accuracy of 24- hour forecast deviation no more than 2 degree Celsius is 78.75 % for MOS-AR model and 81.23 % for AR model.

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.

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.

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.

Damage classification and estimation in experimental structures using time series analysis and pattern recognition

NASA Astrophysics Data System (ADS)

de Lautour, Oliver R.; Omenzetter, Piotr

2010-07-01

Developed for studying long sequences of regularly sampled data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring (SHM). In this research, Autoregressive (AR) models were used to fit the acceleration time histories obtained from two experimental structures: a 3-storey bookshelf structure and the ASCE Phase II Experimental SHM Benchmark Structure, in undamaged and limited number of damaged states. The coefficients of the AR models were considered to be damage-sensitive features and used as input into an Artificial Neural Network (ANN). The ANN was trained to classify damage cases or estimate remaining structural stiffness. The results showed that the combination of AR models and ANNs are efficient tools for damage classification and estimation, and perform well using small number of damage-sensitive features and limited sensors.

Simulation-based power calculation for designing interrupted time series analyses of health policy interventions.

PubMed

Zhang, Fang; Wagner, Anita K; Ross-Degnan, Dennis

2011-11-01

Interrupted time series is a strong quasi-experimental research design to evaluate the impacts of health policy interventions. Using simulation methods, we estimated the power requirements for interrupted time series studies under various scenarios. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9 and effect size was 0.5, 1.0, and 2.0, investigating balanced and unbalanced numbers of time periods before and after an intervention. Simple scenarios of autoregressive conditional heteroskedasticity (ARCH) models were also explored. For AR models, power increased when sample size or effect size increased, and tended to decrease when autocorrelation increased. Compared with a balanced number of study periods before and after an intervention, designs with unbalanced numbers of periods had less power, although that was not the case for ARCH models. The power to detect effect size 1.0 appeared to be reasonable for many practical applications with a moderate or large number of time points in the study equally divided around the intervention. Investigators should be cautious when the expected effect size is small or the number of time points is small. We recommend conducting various simulations before investigation. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

Singular Spectrum Analysis for Astronomical Time Series: Constructing a Parsimonious Hypothesis Test

NASA Astrophysics Data System (ADS)

Greco, G.; Kondrashov, D.; Kobayashi, S.; Ghil, M.; Branchesi, M.; Guidorzi, C.; Stratta, G.; Ciszak, M.; Marino, F.; Ortolan, A.

We present a data-adaptive spectral method - Monte Carlo Singular Spectrum Analysis (MC-SSA) - and its modification to tackle astrophysical problems. Through numerical simulations we show the ability of the MC-SSA in dealing with 1/f β power-law noise affected by photon counting statistics. Such noise process is simulated by a first-order autoregressive, AR(1) process corrupted by intrinsic Poisson noise. In doing so, we statistically estimate a basic stochastic variation of the source and the corresponding fluctuations due to the quantum nature of light. In addition, MC-SSA test retains its effectiveness even when a significant percentage of the signal falls below a certain level of detection, e.g., caused by the instrument sensitivity. The parsimonious approach presented here may be broadly applied, from the search for extrasolar planets to the extraction of low-intensity coherent phenomena probably hidden in high energy transients.

Representation of high frequency Space Shuttle data by ARMA algorithms and random response spectra

NASA Technical Reports Server (NTRS)

Spanos, P. D.; Mushung, L. J.

1990-01-01

High frequency Space Shuttle lift-off data are treated by autoregressive (AR) and autoregressive-moving-average (ARMA) digital algorithms. These algorithms provide useful information on the spectral densities of the data. Further, they yield spectral models which lend themselves to incorporation to the concept of the random response spectrum. This concept yields a reasonably smooth power spectrum for the design of structural and mechanical systems when the available data bank is limited. Due to the non-stationarity of the lift-off event, the pertinent data are split into three slices. Each of the slices is associated with a rather distinguishable phase of the lift-off event, where stationarity can be expected. The presented results are rather preliminary in nature; it is aimed to call attention to the availability of the discussed digital algorithms and to the need to augment the Space Shuttle data bank as more flights are completed.

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

NASA Astrophysics Data System (ADS)

Omenzetter, Piotr; de Lautour, Oliver R.

2010-04-01

Developed for studying long, periodic records of various measured quantities, time series analysis methods are inherently suited and offer interesting possibilities for Structural Health Monitoring (SHM) applications. However, their use in SHM can still be regarded as an emerging application and deserves more studies. In this research, Autoregressive (AR) models were used to fit experimental acceleration time histories from two experimental structural systems, a 3- storey bookshelf-type laboratory structure and the ASCE Phase II SHM Benchmark Structure, in healthy and several damaged states. The coefficients of the AR models were chosen as damage sensitive features. Preliminary visual inspection of the large, multidimensional sets of AR coefficients to check the presence of clusters corresponding to different damage severities was achieved using Sammon mapping - an efficient nonlinear data compression technique. Systematic classification of damage into states based on the analysis of the AR coefficients was achieved using two supervised classification techniques: Nearest Neighbor Classification (NNC) and Learning Vector Quantization (LVQ), and one unsupervised technique: Self-organizing Maps (SOM). This paper discusses the performance of AR coefficients as damage sensitive features and compares the efficiency of the three classification techniques using experimental data.

An autoregressive model-based particle filtering algorithms for extraction of respiratory rates as high as 90 breaths per minute from pulse oximeter.

PubMed

Lee, Jinseok; Chon, Ki H

2010-09-01

We present particle filtering (PF) algorithms for an accurate respiratory rate extraction from pulse oximeter recordings over a broad range: 12-90 breaths/min. These methods are based on an autoregressive (AR) model, where the aim is to find the pole angle with the highest magnitude as it corresponds to the respiratory rate. However, when SNR is low, the pole angle with the highest magnitude may not always lead to accurate estimation of the respiratory rate. To circumvent this limitation, we propose a probabilistic approach, using a sequential Monte Carlo method, named PF, which is combined with the optimal parameter search (OPS) criterion for an accurate AR model-based respiratory rate extraction. The PF technique has been widely adopted in many tracking applications, especially for nonlinear and/or non-Gaussian problems. We examine the performances of five different likelihood functions of the PF algorithm: the strongest neighbor, nearest neighbor (NN), weighted nearest neighbor (WNN), probability data association (PDA), and weighted probability data association (WPDA). The performance of these five combined OPS-PF algorithms was measured against a solely OPS-based AR algorithm for respiratory rate extraction from pulse oximeter recordings. The pulse oximeter data were collected from 33 healthy subjects with breathing rates ranging from 12 to 90 breaths/ min. It was found that significant improvement in accuracy can be achieved by employing particle filters, and that the combined OPS-PF employing either the NN or WNN likelihood function achieved the best results for all respiratory rates considered in this paper. The main advantage of the combined OPS-PF with either the NN or WNN likelihood function is that for the first time, respiratory rates as high as 90 breaths/min can be accurately extracted from pulse oximeter recordings.

Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule

NASA Astrophysics Data System (ADS)

Jin, Yishuai; Rong, Xinyao; Liu, Zhengyu

2017-12-01

This study investigates the factors relationship between the forecast skills for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill for sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further proved using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but could be distorted by sampling errors and non-AR1 processes. This study suggests that the so called "perfect skill" is model dependent and cannot serve as an accurate estimate of the true upper limit of real world prediction skill, unless the model can capture at least the persistence property of the observation.

Applying the LANL Statistical Pattern Recognition Paradigm for Structural Health Monitoring to Data from a Surface-Effect Fast Patrol Boat

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

Hoon Sohn; Charles Farrar; Norman Hunter

2001-01-01

This report summarizes the analysis of fiber-optic strain gauge data obtained from a surface-effect fast patrol boat being studied by the staff at the Norwegian Defense Research Establishment (NDRE) in Norway and the Naval Research Laboratory (NRL) in Washington D.C. Data from two different structural conditions were provided to the staff at Los Alamos National Laboratory. The problem was then approached from a statistical pattern recognition paradigm. This paradigm can be described as a four-part process: (1) operational evaluation, (2) data acquisition & cleansing, (3) feature extraction and data reduction, and (4) statistical model development for feature discrimination. Given thatmore » the first two portions of this paradigm were mostly completed by the NDRE and NRL staff, this study focused on data normalization, feature extraction, and statistical modeling for feature discrimination. The feature extraction process began by looking at relatively simple statistics of the signals and progressed to using the residual errors from auto-regressive (AR) models fit to the measured data as the damage-sensitive features. Data normalization proved to be the most challenging portion of this investigation. A novel approach to data normalization, where the residual errors in the AR model are considered to be an unmeasured input and an auto-regressive model with exogenous inputs (ARX) is then fit to portions of the data exhibiting similar waveforms, was successfully applied to this problem. With this normalization procedure, a clear distinction between the two different structural conditions was obtained. A false-positive study was also run, and the procedure developed herein did not yield any false-positive indications of damage. Finally, the results must be qualified by the fact that this procedure has only been applied to very limited data samples. A more complete analysis of additional data taken under various operational and environmental conditions as well as other structural conditions is necessary before one can definitively state that the procedure is robust enough to be used in practice.« less

The sequentially discounting autoregressive (SDAR) method for on-line automatic seismic event detecting on long term observation

NASA Astrophysics Data System (ADS)

Wang, L.; Toshioka, T.; Nakajima, T.; Narita, A.; Xue, Z.

2017-12-01

In recent years, more and more Carbon Capture and Storage (CCS) studies focus on seismicity monitoring. For the safety management of geological CO2 storage at Tomakomai, Hokkaido, Japan, an Advanced Traffic Light System (ATLS) combined different seismic messages (magnitudes, phases, distributions et al.) is proposed for injection controlling. The primary task for ATLS is the seismic events detection in a long-term sustained time series record. Considering the time-varying characteristics of Signal to Noise Ratio (SNR) of a long-term record and the uneven energy distributions of seismic event waveforms will increase the difficulty in automatic seismic detecting, in this work, an improved probability autoregressive (AR) method for automatic seismic event detecting is applied. This algorithm, called sequentially discounting AR learning (SDAR), can identify the effective seismic event in the time series through the Change Point detection (CPD) of the seismic record. In this method, an anomaly signal (seismic event) can be designed as a change point on the time series (seismic record). The statistical model of the signal in the neighborhood of event point will change, because of the seismic event occurrence. This means the SDAR aims to find the statistical irregularities of the record thought CPD. There are 3 advantages of SDAR. 1. Anti-noise ability. The SDAR does not use waveform messages (such as amplitude, energy, polarization) for signal detecting. Therefore, it is an appropriate technique for low SNR data. 2. Real-time estimation. When new data appears in the record, the probability distribution models can be automatic updated by SDAR for on-line processing. 3. Discounting property. the SDAR introduces a discounting parameter to decrease the influence of present statistic value on future data. It makes SDAR as a robust algorithm for non-stationary signal processing. Within these 3 advantages, the SDAR method can handle the non-stationary time-varying long-term series and achieve real-time monitoring. Finally, we employ the SDAR on a synthetic model and Tomakomai Ocean Bottom Cable (OBC) baseline data to prove the feasibility and advantage of our method.

Heart rate measurement based on a time-lapse image.

PubMed

Takano, Chihiro; Ohta, Yuji

2007-10-01

Using a time-lapse image acquired from a CCD camera, we developed a non-contact and non-invasive device, which could measure both the respiratory and pulse rate simultaneously. The time-lapse image of a part of the subject's skin was consecutively captured, and the changes in the average image brightness of the region of interest (ROI) were measured for 30s. The brightness data were processed by a series of operations of interpolation as follows a first-order derivative, a low pass filter of 2 Hz, and a sixth-order auto-regressive (AR) spectral analysis. Fourteen sound and healthy female subjects (22-27 years of age) participated in the experiments. Each subject was told to keep a relaxed seating posture with no physical restriction. At the same time, heart rate was measured by a pulse oximeter and respiratory rate was measured by a thermistor placed at the external naris. Using AR spectral analysis, two clear peaks could be detected at approximately 0.3 and 1.2 Hz. The peaks were thought to correspond to the respiratory rate and the heart rate. Correlation coefficients of 0.90 and 0.93 were obtained for the measurement of heart rate and respiratory rate, respectively.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Subordinated continuous-time AR processes and their application to modeling behavior of mechanical system

NASA Astrophysics Data System (ADS)

Gajda, Janusz; Wyłomańska, Agnieszka; Zimroz, Radosław

2016-12-01

Many real data exhibit behavior adequate to subdiffusion processes. Very often it is manifested by so-called ;trapping events;. The visible evidence of subdiffusion we observe not only in financial time series but also in technical data. In this paper we propose a model which can be used for description of such kind of data. The model is based on the continuous time autoregressive time series with stable noise delayed by the infinitely divisible inverse subordinator. The proposed system can be applied to real datasets with short-time dependence, visible jumps and mentioned periods of stagnation. In this paper we extend the theoretical considerations in analysis of subordinated processes and propose a new model that exhibits mentioned properties. We concentrate on the main characteristics of the examined subordinated process expressed mainly in the language of the measures of dependence which are main tools used in statistical investigation of real data. We present also the simulation procedure of the considered system and indicate how to estimate its parameters. The theoretical results we illustrate by the analysis of real technical data.

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.

Efficient hierarchical trans-dimensional Bayesian inversion of magnetotelluric data

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Predicting long-term catchment nutrient export: the use of nonlinear time series models

NASA Astrophysics Data System (ADS)

Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda

2010-05-01

After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the ARMA class. In most cases the relative improvement of SETAR models against AR models of first order was low ranging between 1% and 4% with the exception of the three-regime model for the River Stour time-series where the improvement was 48.9%. In comparison, the relative improvement of MSW models was between 44.6% and 52.5 for two-regime and from 60.4% to 75% for three-regime models. However, the visual assessment of models plotted against original datasets showed that despite a high value of RSS, some ARMA models could describe the analyzed time-series better than AR, MA and SETAR models with lower values of RSS. In both datasets MSW models provided a very good visual fit describing most of the extreme values.

Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface.

PubMed

Besio, Walter G; Cao, Hongbao; Zhou, Peng

2008-04-01

For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.

Toward blind removal of unwanted sound from orchestrated music

NASA Astrophysics Data System (ADS)

Chang, Soo-Young; Chun, Joohwan

2000-11-01

The problem addressed in this paper is to removing unwanted sounds from music sound. The sound to be removed could be disturbance such as cough. We shall present some preliminary results on this problem using statistical properties of signals. Our approach consists of three steps. We first estimate the fundamental frequencies and partials given noise-corrupted music sound. This gives us the autoregressive (AR) model of the music sound. Then we filter the noise-corrupted sound using the AR parameters. The filtered signal is then subtracted from the original noise-corrupted signal to get the disturbance. Finally, the obtained disturbance is used a reference signal to eliminate the disturbance from the noise- corrupted music signal. Above three steps are carried out in a recursive manner using a sliding window or an infinitely growing window with an appropriate forgetting factor.

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.

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

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

ARMA Cholesky Factor Models for the Covariance Matrix of Linear Models.

PubMed

Lee, Keunbaik; Baek, Changryong; Daniels, Michael J

2017-11-01

In longitudinal studies, serial dependence of repeated outcomes must be taken into account to make correct inferences on covariate effects. As such, care must be taken in modeling the covariance matrix. However, estimation of the covariance matrix is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcomes these limitations, two Cholesky decomposition approaches have been proposed: modified Cholesky decomposition for autoregressive (AR) structure and moving average Cholesky decomposition for moving average (MA) structure, respectively. However, the correlations of repeated outcomes are often not captured parsimoniously using either approach separately. In this paper, we propose a class of flexible, nonstationary, heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the covariance matrix that we denote as ARMACD. We analyze a recent lung cancer study to illustrate the power of our proposed methods.

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

[Primary branch size of Pinus koraiensis plantation: a prediction based on linear mixed effect model].

PubMed

Dong, Ling-Bo; Liu, Zhao-Gang; Li, Feng-Ri; Jiang, Li-Chun

2013-09-01

By using the branch analysis data of 955 standard branches from 60 sampled trees in 12 sampling plots of Pinus koraiensis plantation in Mengjiagang Forest Farm in Heilongjiang Province of Northeast China, and based on the linear mixed-effect model theory and methods, the models for predicting branch variables, including primary branch diameter, length, and angle, were developed. Considering tree effect, the MIXED module of SAS software was used to fit the prediction models. The results indicated that the fitting precision of the models could be improved by choosing appropriate random-effect parameters and variance-covariance structure. Then, the correlation structures including complex symmetry structure (CS), first-order autoregressive structure [AR(1)], and first-order autoregressive and moving average structure [ARMA(1,1)] were added to the optimal branch size mixed-effect model. The AR(1) improved the fitting precision of branch diameter and length mixed-effect model significantly, but all the three structures didn't improve the precision of branch angle mixed-effect model. In order to describe the heteroscedasticity during building mixed-effect model, the CF1 and CF2 functions were added to the branch mixed-effect model. CF1 function improved the fitting effect of branch angle mixed model significantly, whereas CF2 function improved the fitting effect of branch diameter and length mixed model significantly. Model validation confirmed that the mixed-effect model could improve the precision of prediction, as compare to the traditional regression model for the branch size prediction of Pinus koraiensis plantation.

A statistical approach for generating synthetic tip stress data from limited CPT soundings

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

Basalams, M.K.

CPT tip stress data obtained from a Uranium mill tailings impoundment are treated as time series. A statistical class of models that was developed to model time series is explored to investigate its applicability in modeling the tip stress series. These models were developed by Box and Jenkins (1970) and are known as Autoregressive Moving Average (ARMA) models. This research demonstrates how to apply the ARMA models to tip stress series. Generation of synthetic tip stress series that preserve the main statistical characteristics of the measured series is also investigated. Multiple regression analysis is used to model the regional variationmore » of the ARMA model parameters as well as the regional variation of the mean and the standard deviation of the measured tip stress series. The reliability of the generated series is investigated from a geotechnical point of view as well as from a statistical point of view. Estimation of the total settlement using the measured and the generated series subjected to the same loading condition are performed. The variation of friction angle with depth of the impoundment materials is also investigated. This research shows that these series can be modeled by the Box and Jenkins ARMA models. A third degree Autoregressive model AR(3) is selected to represent these series. A theoretical double exponential density function is fitted to the AR(3) model residuals. Synthetic tip stress series are generated at nearby locations. The generated series are shown to be reliable in estimating the total settlement and the friction angle variation with depth for this particular site.« less

Nowcasting influenza outbreaks using open-source media report.

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

Ray, Jaideep; Brownstein, John S.

We construct and verify a statistical method to nowcast influenza activity from a time-series of the frequency of reports concerning influenza related topics. Such reports are published electronically by both public health organizations as well as newspapers/media sources, and thus can be harvested easily via web crawlers. Since media reports are timely, whereas reports from public health organization are delayed by at least two weeks, using timely, open-source data to compensate for the lag in %E2%80%9Cofficial%E2%80%9D reports can be useful. We use morbidity data from networks of sentinel physicians (both the Center of Disease Control's ILINet and France's Sentinelles network)more » as the gold standard of influenza-like illness (ILI) activity. The time-series of media reports is obtained from HealthMap (http://healthmap.org). We find that the time-series of media reports shows some correlation ( 0.5) with ILI activity; further, this can be leveraged into an autoregressive moving average model with exogenous inputs (ARMAX model) to nowcast ILI activity. We find that the ARMAX models have more predictive skill compared to autoregressive (AR) models fitted to ILI data i.e., it is possible to exploit the information content in the open-source data. We also find that when the open-source data are non-informative, the ARMAX models reproduce the performance of AR models. The statistical models are tested on data from the 2009 swine-flu outbreak as well as the mild 2011-2012 influenza season in the U.S.A.« less

Model-Based Referenceless Quality Metric of 3D Synthesized Images Using Local Image Description.

PubMed

Gu, Ke; Jakhetiya, Vinit; Qiao, Jun-Fei; Li, Xiaoli; Lin, Weisi; Thalmann, Daniel

2017-07-28

New challenges have been brought out along with the emerging of 3D-related technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR). Free viewpoint video (FVV), due to its applications in remote surveillance, remote education, etc, based on the flexible selection of direction and viewpoint, has been perceived as the development direction of next-generation video technologies and has drawn a wide range of researchers' attention. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a reliable real-time blind quality evaluation and monitoring system is urgently required. But existing assessment metrics do not render human judgments faithfully mainly because geometric distortions are generated by DIBR. To this end, this paper proposes a novel referenceless quality metric of DIBR-synthesized images using the autoregression (AR)-based local image description. It was found that, after the AR prediction, the reconstructed error between a DIBR-synthesized image and its AR-predicted image can accurately capture the geometry distortion. The visual saliency is then leveraged to modify the proposed blind quality metric to a sizable margin. Experiments validate the superiority of our no-reference quality method as compared with prevailing full-, reduced- and no-reference models.

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…

Two dynamic regimes in the human gut microbiome

PubMed Central

Smillie, Chris S.; Alm, Eric J.

2017-01-01

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)—a multivariate method developed for econometrics—to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes. PMID:28222117

Two dynamic regimes in the human gut microbiome.

PubMed

Gibbons, Sean M; Kearney, Sean M; Smillie, Chris S; Alm, Eric J

2017-02-01

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)-a multivariate method developed for econometrics-to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain.

PubMed

Barba, Lida; Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain

PubMed Central

Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT. PMID:28261267

A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services.

PubMed

Fujiyama, Toshifumi; Matsui, Chihiro; Takemura, Akimichi

2016-01-01

We propose a power-law growth and decay model for posting data to social networking services before and after social events. We model the time series structure of deviations from the power-law growth and decay with a conditional Poisson autoregressive (AR) model. Online postings related to social events are described by five parameters in the power-law growth and decay model, each of which characterizes different aspects of interest in the event. We assess the validity of parameter estimates in terms of confidence intervals, and compare various submodels based on likelihoods and information criteria.

Stochastic-Constraints Method in Nonstationary Hot-Clutter Cancellation Part I: Fundamentals and Supervised Training Applications

DTIC Science & Technology

2003-04-01

any of the P interfering sources, and Hkt i (1) (P)] T is defined below. The P-variate vector = t kt , • t J consists of complex waveforms radiated by...line. More precisely, the (i, j ) t element of the matrix Hke is a complex 4-4 coefficient which is practically constant over the kth PRI, and is a...multivariate auto-regressive (AR) model of order n: Ykt + Z Bj Yk- j , t = tkt (25) j =l In the above equation, Bj are the M-variate matrices which are the

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.

A Volterra series-based method for extracting target echoes in the seafloor mining environment.

PubMed

Zhao, Haiming; Ji, Yaqian; Hong, Yujiu; Hao, Qi; Ma, Liyong

2016-09-01

The purpose of this research was to evaluate the applicability of the Volterra adaptive method to predict the target echo of an ultrasonic signal in an underwater seafloor mining environment. There is growing interest in mining of seafloor minerals because they offer an alternative source of rare metals. Mining the minerals cause the seafloor sediments to be stirred up and suspended in sea water. In such an environment, the target signals used for seafloor mapping are unable to be detected because of the unavoidable presence of volume reverberation induced by the suspended sediments. The detection of target signals in reverberation is currently performed using a stochastic model (for example, the autoregressive (AR) model) based on the statistical characterisation of reverberation. However, we examined a new method of signal detection in volume reverberation based on the Volterra series by confirming that the reverberation is a chaotic signal and generated by a deterministic process. The advantage of this method over the stochastic model is that attributions of the specific physical process are considered in the signal detection problem. To test the Volterra series based method and its applicability to target signal detection in the volume reverberation environment derived from the seafloor mining process, we simulated the real-life conditions of seafloor mining in a water filled tank of dimensions of 5×3×1.8m. The bottom of the tank was covered with 10cm of an irregular sand layer under which 5cm of an irregular cobalt-rich crusts layer was placed. The bottom was interrogated by an acoustic wave generated as 16μs pulses of 500kHz frequency. This frequency is demonstrated to ensure a resolution on the order of one centimetre, which is adequate in exploration practice. Echo signals were collected with a data acquisition card (PCI 1714 UL, 12-bit). Detection of the target echo in these signals was performed by both the Volterra series based model and the AR model. The results obtained confirm that the Volterra series based method is more efficient in the detection of the signal in reverberation than the conventional AR model (the accuracy is 80% for the PIM-Volterra prediction model versus 40% for the AR model). Copyright © 2016 Elsevier B.V. All rights reserved.

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

Confounding environmental colour and distribution shape leads to underestimation of population extinction risk.

PubMed

Fowler, Mike S; Ruokolainen, Lasse

2013-01-01

The colour of environmental variability influences the size of population fluctuations when filtered through density dependent dynamics, driving extinction risk through dynamical resonance. Slow fluctuations (low frequencies) dominate in red environments, rapid fluctuations (high frequencies) in blue environments and white environments are purely random (no frequencies dominate). Two methods are commonly employed to generate the coloured spatial and/or temporal stochastic (environmental) series used in combination with population (dynamical feedback) models: autoregressive [AR(1)] and sinusoidal (1/f) models. We show that changing environmental colour from white to red with 1/f models, and from white to red or blue with AR(1) models, generates coloured environmental series that are not normally distributed at finite time-scales, potentially confounding comparison with normally distributed white noise models. Increasing variability of sample Skewness and Kurtosis and decreasing mean Kurtosis of these series alter the frequency distribution shape of the realised values of the coloured stochastic processes. These changes in distribution shape alter patterns in the probability of single and series of extreme conditions. We show that the reduced extinction risk for undercompensating (slow growing) populations in red environments previously predicted with traditional 1/f methods is an artefact of changes in the distribution shapes of the environmental series. This is demonstrated by comparison with coloured series controlled to be normally distributed using spectral mimicry. Changes in the distribution shape that arise using traditional methods lead to underestimation of extinction risk in normally distributed, red 1/f environments. AR(1) methods also underestimate extinction risks in traditionally generated red environments. This work synthesises previous results and provides further insight into the processes driving extinction risk in model populations. We must let the characteristics of known natural environmental covariates (e.g., colour and distribution shape) guide us in our choice of how to best model the impact of coloured environmental variation on population dynamics.

Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor.

PubMed

Zhou, Tony; Dickson, Jennifer L; Geoffrey Chase, J

2018-01-01

Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK). Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass. The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°). The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.

The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients.

PubMed

Lu, Yinghui; Gribok, Andrei V; Ward, W Kenneth; Reifman, Jaques

2010-08-01

We investigated the relative importance and predictive power of different frequency bands of subcutaneous glucose signals for the short-term (0-50 min) forecasting of glucose concentrations in type 1 diabetic patients with data-driven autoregressive (AR) models. The study data consisted of minute-by-minute glucose signals collected from nine deidentified patients over a five-day period using continuous glucose monitoring devices. AR models were developed using single and pairwise combinations of frequency bands of the glucose signal and compared with a reference model including all bands. The results suggest that: for open-loop applications, there is no need to explicitly represent exogenous inputs, such as meals and insulin intake, in AR models; models based on a single-frequency band, with periods between 60-120 min and 150-500 min, yield good predictive power (error <3 mg/dL) for prediction horizons of up to 25 min; models based on pairs of bands produce predictions that are indistinguishable from those of the reference model as long as the 60-120 min period band is included; and AR models can be developed on signals of short length (approximately 300 min), i.e., ignoring long circadian rhythms, without any detriment in prediction accuracy. Together, these findings provide insights into efficient development of more effective and parsimonious data-driven models for short-term prediction of glucose concentrations in diabetic patients.

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

Stochastic Models in the DORIS Position Time Series: Estimates from the IDS Contribution to the ITRF2014

NASA Astrophysics Data System (ADS)

Klos, A.; Bogusz, J.; Moreaux, G.

2017-12-01

This research focuses on the investigation of the deterministic and stochastic parts of the DORIS (Doppler Orbitography and Radiopositioning Integrated by Satellite) weekly coordinate time series from the IDS contribution to the ITRF2014A set of 90 stations was divided into three groups depending on when the data was collected at an individual station. To reliably describe the DORIS time series, we employed a mathematical model that included the long-term nonlinear signal, linear trend, seasonal oscillations (these three sum up to produce the Polynomial Trend Model) and a stochastic part, all being resolved with Maximum Likelihood Estimation (MLE). We proved that the values of the parameters delivered for DORIS data are strictly correlated with the time span of the observations, meaning that the most recent data are the most reliable ones. Not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. We examined five different noise models to be applied to the stochastic part of the DORIS time series: a pure white noise (WN), a pure power-law noise (PL), a combination of white and power-law noise (WNPL), an autoregressive process of first order (AR(1)) and a Generalized Gauss Markov model (GGM). From our study it arises that the PL process may be chosen as the preferred one for most of the DORIS data. Moreover, the preferred noise model has changed through the years from AR(1) to pure PL with few stations characterized by a positive spectral index.

Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System.

PubMed

Chai, Rifai; Naik, Ganesh R; Nguyen, Tuan Nghia; Ling, Sai Ho; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-05-01

This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.

A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services

PubMed Central

Fujiyama, Toshifumi; Matsui, Chihiro; Takemura, Akimichi

2016-01-01

We propose a power-law growth and decay model for posting data to social networking services before and after social events. We model the time series structure of deviations from the power-law growth and decay with a conditional Poisson autoregressive (AR) model. Online postings related to social events are described by five parameters in the power-law growth and decay model, each of which characterizes different aspects of interest in the event. We assess the validity of parameter estimates in terms of confidence intervals, and compare various submodels based on likelihoods and information criteria. PMID:27505155

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.

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.

Modulation of electroencephalograph activity by manual acupuncture stimulation in healthy subjects: An autoregressive spectral analysis

NASA Astrophysics Data System (ADS)

Yi, Guo-Sheng; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Han, Chun-Xiao

2013-02-01

To investigate whether and how manual acupuncture (MA) modulates brain activities, we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph (EEG) signals in healthy subjects. We adopt the autoregressive (AR) Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta (0 Hz-4 Hz), theta (4 Hz-8 Hz), alpha (8 Hz-13 Hz), and beta (13 Hz-30 Hz) bands. Our results show that MA at ST36 can significantly increase the EEG slow wave relative power (delta band) and reduce the fast wave relative powers (alpha and beta bands), while there are no statistical differences in theta band relative power between different acupuncture states. In order to quantify the ratio of slow to fast wave EEG activity, we compute the power ratio index. It is found that the MA can significantly increase the power ratio index, especially in frontal and central lobes. All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture. The proposed quantitative method of acupuncture signals may be further used to make MA more standardized.

Optimizing Estimates of Instantaneous Heart Rate from Pulse Wave Signals with the Synchrosqueezing Transform.

PubMed

Wu, Hau-Tieng; Lewis, Gregory F; Davila, Maria I; Daubechies, Ingrid; Porges, Stephen W

2016-10-17

With recent advances in sensor and computer technologies, the ability to monitor peripheral pulse activity is no longer limited to the laboratory and clinic. Now inexpensive sensors, which interface with smartphones or other computer-based devices, are expanding into the consumer market. When appropriate algorithms are applied, these new technologies enable ambulatory monitoring of dynamic physiological responses outside the clinic in a variety of applications including monitoring fatigue, health, workload, fitness, and rehabilitation. Several of these applications rely upon measures derived from peripheral pulse waves measured via contact or non-contact photoplethysmography (PPG). As technologies move from contact to non-contact PPG, there are new challenges. The technology necessary to estimate average heart rate over a few seconds from a noncontact PPG is available. However, a technology to precisely measure instantaneous heat rate (IHR) from non-contact sensors, on a beat-to-beat basis, is more challenging. The objective of this paper is to develop an algorithm with the ability to accurately monitor IHR from peripheral pulse waves, which provides an opportunity to measure the neural regulation of the heart from the beat-to-beat heart rate pattern (i.e., heart rate variability). The adaptive harmonic model is applied to model the contact or non-contact PPG signals, and a new methodology, the Synchrosqueezing Transform (SST), is applied to extract IHR. The body sway rhythm inherited in the non-contact PPG signal is modeled and handled by the notion of wave-shape function. The SST optimizes the extraction of IHR from the PPG signals and the technique functions well even during periods of poor signal to noise. We contrast the contact and non-contact indices of PPG derived heart rate with a criterion electrocardiogram (ECG). ECG and PPG signals were monitored in 21 healthy subjects performing tasks with different physical demands. The root mean square error of IHR estimated by SST is significantly better than commonly applied methods such as autoregressive (AR) method. In the walking situation, while AR method fails, SST still provides a reasonably good result. The SST processed PPG data provided an accurate estimate of the ECG derived IHR and consistently performed better than commonly applied methods such as autoregressive method.

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 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.

Sea-Level Trend Uncertainty With Pacific Climatic Variability and Temporally-Correlated Noise

NASA Astrophysics Data System (ADS)

Royston, Sam; Watson, Christopher S.; Legrésy, Benoît; King, Matt A.; Church, John A.; Bos, Machiel S.

2018-03-01

Recent studies have identified climatic drivers of the east-west see-saw of Pacific Ocean satellite altimetry era sea level trends and a number of sea-level trend and acceleration assessments attempt to account for this. We investigate the effect of Pacific climate variability, together with temporally-correlated noise, on linear trend error estimates and determine new time-of-emergence (ToE) estimates across the Indian and Pacific Oceans. Sea-level trend studies often advocate the use of auto-regressive (AR) noise models to adequately assess formal uncertainties, yet sea level often exhibits colored but non-AR(1) noise. Standard error estimates are over- or under-estimated by an AR(1) model for much of the Indo-Pacific sea level. Allowing for PDO and ENSO variability in the trend estimate only reduces standard errors across the tropics and we find noise characteristics are largely unaffected. Of importance for trend and acceleration detection studies, formal error estimates remain on average up to 1.6 times those from an AR(1) model for long-duration tide gauge data. There is an even chance that the observed trend from the satellite altimetry era exceeds the noise in patches of the tropical Pacific and Indian Oceans and the south-west and north-east Pacific gyres. By including climate indices in the trend analysis, the time it takes for the observed linear sea-level trend to emerge from the noise reduces by up to 2 decades.

A back-fitting algorithm to improve real-time flood forecasting

NASA Astrophysics Data System (ADS)

Zhang, Xiaojing; Liu, Pan; Cheng, Lei; Liu, Zhangjun; Zhao, Yan

2018-07-01

Real-time flood forecasting is important for decision-making with regards to flood control and disaster reduction. The conventional approach involves a postprocessor calibration strategy that first calibrates the hydrological model and then estimates errors. This procedure can simulate streamflow consistent with observations, but obtained parameters are not optimal. Joint calibration strategies address this issue by refining hydrological model parameters jointly with the autoregressive (AR) model. In this study, five alternative schemes are used to forecast floods. Scheme I uses only the hydrological model, while scheme II includes an AR model for error correction. In scheme III, differencing is used to remove non-stationarity in the error series. A joint inference strategy employed in scheme IV calibrates the hydrological and AR models simultaneously. The back-fitting algorithm, a basic approach for training an additive model, is adopted in scheme V to alternately recalibrate hydrological and AR model parameters. The performance of the five schemes is compared with a case study of 15 recorded flood events from China's Baiyunshan reservoir basin. Our results show that (1) schemes IV and V outperform scheme III during the calibration and validation periods and (2) scheme V is inferior to scheme IV in the calibration period, but provides better results in the validation period. Joint calibration strategies can therefore improve the accuracy of flood forecasting. Additionally, the back-fitting recalibration strategy produces weaker overcorrection and a more robust performance compared with the joint inference strategy.

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).

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.

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.

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.

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.

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.

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.

Explanation of power law behavior of autoregressive conditional duration processes based on the random multiplicative process.

PubMed

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.

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

Predictive Monitoring for Improved Management of Glucose Levels

PubMed Central

Reifman, Jaques; Rajaraman, Srinivasan; Gribok, Andrei; Ward, W. Kenneth

2007-01-01

Background Recent developments and expected near-future improvements in continuous glucose monitoring (CGM) devices provide opportunities to couple them with mathematical forecasting models to produce predictive monitoring systems for early, proactive glycemia management of diabetes mellitus patients before glucose levels drift to undesirable levels. This article assesses the feasibility of data-driven models to serve as the forecasting engine of predictive monitoring systems. Methods We investigated the capabilities of data-driven autoregressive (AR) models to (1) capture the correlations in glucose time-series data, (2) make accurate predictions as a function of prediction horizon, and (3) be made portable from individual to individual without any need for model tuning. The investigation is performed by employing CGM data from nine type 1 diabetic subjects collected over a continuous 5-day period. Results With CGM data serving as the gold standard, AR model-based predictions of glucose levels assessed over nine subjects with Clarke error grid analysis indicated that, for a 30-minute prediction horizon, individually tuned models yield 97.6 to 100.0% of data in the clinically acceptable zones A and B, whereas cross-subject, portable models yield 95.8 to 99.7% of data in zones A and B. Conclusions This study shows that, for a 30-minute prediction horizon, data-driven AR models provide sufficiently-accurate and clinically-acceptable estimates of glucose levels for timely, proactive therapy and should be considered as the modeling engine for predictive monitoring of patients with type 1 diabetes mellitus. It also suggests that AR models can be made portable from individual to individual with minor performance penalties, while greatly reducing the burden associated with model tuning and data collection for model development. PMID:19885110

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

PubMed

Lie, Octavian V; van Mierlo, Pieter

2017-01-01

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

A statistical model for predicting muscle performance

NASA Astrophysics Data System (ADS)

Byerly, Diane Leslie De Caix

The objective of these studies was to develop a capability for predicting muscle performance and fatigue to be utilized for both space- and ground-based applications. To develop this predictive model, healthy test subjects performed a defined, repetitive dynamic exercise to failure using a Lordex spinal machine. Throughout the exercise, surface electromyography (SEMG) data were collected from the erector spinae using a Mega Electronics ME3000 muscle tester and surface electrodes placed on both sides of the back muscle. These data were analyzed using a 5th order Autoregressive (AR) model and statistical regression analysis. It was determined that an AR derived parameter, the mean average magnitude of AR poles, significantly correlated with the maximum number of repetitions (designated Rmax) that a test subject was able to perform. Using the mean average magnitude of AR poles, a test subject's performance to failure could be predicted as early as the sixth repetition of the exercise. This predictive model has the potential to provide a basis for improving post-space flight recovery, monitoring muscle atrophy in astronauts and assessing the effectiveness of countermeasures, monitoring astronaut performance and fatigue during Extravehicular Activity (EVA) operations, providing pre-flight assessment of the ability of an EVA crewmember to perform a given task, improving the design of training protocols and simulations for strenuous International Space Station assembly EVA, and enabling EVA work task sequences to be planned enhancing astronaut performance and safety. Potential ground-based, medical applications of the predictive model include monitoring muscle deterioration and performance resulting from illness, establishing safety guidelines in the industry for repetitive tasks, monitoring the stages of rehabilitation for muscle-related injuries sustained in sports and accidents, and enhancing athletic performance through improved training protocols while reducing injury.

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

PubMed Central

Lutaif, N.A.; Palazzo, R.; Gontijo, J.A.R.

2014-01-01

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile. PMID:24519093

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis.

PubMed

Lutaif, N A; Palazzo, R; Gontijo, J A R

2014-01-01

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.

Using a generalized additive model with autoregressive terms to study the effects of daily temperature on mortality.

PubMed

Yang, Lei; Qin, Guoyou; Zhao, Naiqing; Wang, Chunfang; Song, Guixiang

2012-10-30

Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points. Here, a GAM with Autoregressive terms (GAMAR) is introduced to fill this gap. Parameters in GAMAR are estimated by maximum partial likelihood using modified Newton's method, and the difference between GAM and GAMAR is demonstrated using two simulation studies and a real data example. GAMM is also compared to GAMAR in simulation study 1. In the simulation studies, the bias of the mean estimates from GAM and GAMAR are similar but GAMAR has better coverage and smaller relative error. While the results from GAMM are similar to GAMAR, the estimation procedure of GAMM is much slower than GAMAR. In the case study, the Pearson residuals from the GAM are correlated, while those from GAMAR are quite close to white noise. In addition, the estimates of the temperature effects are different between GAM and GAMAR. GAMAR incorporates both explanatory variables and AR terms so it can quantify the nonlinear impact of environmental factors on health outcome as well as the serial correlation between the observations. It can be a useful tool in environmental epidemiological studies.

Using a generalized additive model with autoregressive terms to study the effects of daily temperature on mortality

PubMed Central

2012-01-01

Background Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points. Here, a GAM with Autoregressive terms (GAMAR) is introduced to fill this gap. Methods Parameters in GAMAR are estimated by maximum partial likelihood using modified Newton’s method, and the difference between GAM and GAMAR is demonstrated using two simulation studies and a real data example. GAMM is also compared to GAMAR in simulation study 1. Results In the simulation studies, the bias of the mean estimates from GAM and GAMAR are similar but GAMAR has better coverage and smaller relative error. While the results from GAMM are similar to GAMAR, the estimation procedure of GAMM is much slower than GAMAR. In the case study, the Pearson residuals from the GAM are correlated, while those from GAMAR are quite close to white noise. In addition, the estimates of the temperature effects are different between GAM and GAMAR. Conclusions GAMAR incorporates both explanatory variables and AR terms so it can quantify the nonlinear impact of environmental factors on health outcome as well as the serial correlation between the observations. It can be a useful tool in environmental epidemiological studies. PMID:23110601

Middle and long-term prediction of UT1-UTC based on combination of Gray Model and Autoregressive Integrated Moving Average

NASA Astrophysics Data System (ADS)

Jia, Song; Xu, Tian-he; Sun, Zhang-zhen; Li, Jia-jing

2017-02-01

UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth's zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth's zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR.

Multispectral code excited linear prediction coding and its application in magnetic resonance images.

PubMed

Hu, J H; Wang, Y; Cahill, P T

1997-01-01

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

(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.

Motion Control of Drives for Prosthetic Hand Using Continuous Myoelectric Signals

NASA Astrophysics Data System (ADS)

Purushothaman, Geethanjali; Ray, Kalyan Kumar

2016-03-01

In this paper the authors present motion control of a prosthetic hand, through continuous myoelectric signal acquisition, classification and actuation of the prosthetic drive. A four channel continuous electromyogram (EMG) signal also known as myoelectric signals (MES) are acquired from the abled-body to classify the six unique movements of hand and wrist, viz, hand open (HO), hand close (HC), wrist flexion (WF), wrist extension (WE), ulnar deviation (UD) and radial deviation (RD). The classification technique involves in extracting the features/pattern through statistical time domain (TD) parameter/autoregressive coefficients (AR), which are reduced using principal component analysis (PCA). The reduced statistical TD features and or AR coefficients are used to classify the signal patterns through k nearest neighbour (kNN) as well as neural network (NN) classifier and the performance of the classifiers are compared. Performance comparison of the above two classifiers clearly shows that kNN classifier in identifying the hidden intended motion in the myoelectric signals is better than that of NN classifier. Once the classifier identifies the intended motion, the signal is amplified to actuate the three low power DC motor to perform the above mentioned movements.

A general Bayesian framework for calibrating and evaluating stochastic models of annual multi-site hydrological data

NASA Astrophysics Data System (ADS)

Frost, Andrew J.; Thyer, Mark A.; Srikanthan, R.; Kuczera, George

2007-07-01

SummaryMulti-site simulation of hydrological data are required for drought risk assessment of large multi-reservoir water supply systems. In this paper, a general Bayesian framework is presented for the calibration and evaluation of multi-site hydrological data at annual timescales. Models included within this framework are the hidden Markov model (HMM) and the widely used lag-1 autoregressive (AR(1)) model. These models are extended by the inclusion of a Box-Cox transformation and a spatial correlation function in a multi-site setting. Parameter uncertainty is evaluated using Markov chain Monte Carlo techniques. Models are evaluated by their ability to reproduce a range of important extreme statistics and compared using Bayesian model selection techniques which evaluate model probabilities. The case study, using multi-site annual rainfall data situated within catchments which contribute to Sydney's main water supply, provided the following results: Firstly, in terms of model probabilities and diagnostics, the inclusion of the Box-Cox transformation was preferred. Secondly the AR(1) and HMM performed similarly, while some other proposed AR(1)/HMM models with regionally pooled parameters had greater posterior probability than these two models. The practical significance of parameter and model uncertainty was illustrated using a case study involving drought security analysis for urban water supply. It was shown that ignoring parameter uncertainty resulted in a significant overestimate of reservoir yield and an underestimation of system vulnerability to severe drought.

Non-parametric directionality analysis - Extension for removal of a single common predictor and application to time series.

PubMed

Halliday, David M; Senik, Mohd Harizal; Stevenson, Carl W; Mason, Rob

2016-08-01

The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity. We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats. The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data. The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data. Copyright © 2016 Elsevier B.V. All rights reserved.

Study on Wind-induced Vibration and Fatigue Life of Cable-stayed Flexible Antenna

NASA Astrophysics Data System (ADS)

He, Kongde; He, Xuehui; Fang, Zifan; Zheng, Xiaowei; Yu, Hongchang

2018-03-01

The cable-stayed flexible antenna is a large-span space structure composed of flexible multibody, with low frequency of vibration, vortex-induced resonance can occur under the action of Stochastic wind, and a larger amplitude is generated when resonance occurs. To solve this problem, based on the theory of vortex-induced vibration, this paper analyzes the vortex-induced vibration of a cable-stayed flexible antenna under the action of Wind. Based on the sinusoidal force model and Autoregressive Model (AR) method, the vortex-induced force is simulated, then the fatigue analysis of the structure is based on the linear fatigue cumulative damage principle and the rain-flow method. The minimum fatigue life of the structure is calculated to verify the vibration fatigue performance of the structure.

The asymmetry of U.S. monetary policy: Evidence from a threshold Taylor rule with time-varying threshold values

NASA Astrophysics Data System (ADS)

Zhu, Yanli; Chen, Haiqiang

2017-05-01

In this paper, we revisit the issue whether U.S. monetary policy is asymmetric by estimating a forward-looking threshold Taylor rule with quarterly data from 1955 to 2015. In order to capture the potential heterogeneity for regime shift mechanism under different economic conditions, we modify the threshold model by assuming the threshold value as a latent variable following an autoregressive (AR) dynamic process. We use the unemployment rate as the threshold variable and separate the sample into two periods: expansion periods and recession periods. Our findings support that the U.S. monetary policy operations are asymmetric in these two regimes. More precisely, the monetary authority tends to implement an active Taylor rule with a weaker response to the inflation gap (the deviation of inflation from its target) and a stronger response to the output gap (the deviation of output from its potential level) in recession periods. The threshold value, interpreted as the targeted unemployment rate of monetary authorities, exhibits significant time-varying properties, confirming the conjecture that policy makers may adjust their reference point for the unemployment rate accordingly to reflect their attitude on the health of general economy.

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.

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…

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.

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.

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.

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Linear mixed-effects models to describe individual tree crown width for China-fir in Fujian Province, southeast China.

PubMed

Hao, Xu; Yujun, Sun; Xinjie, Wang; Jin, Wang; Yao, Fu

2015-01-01

A multiple linear model was developed for individual tree crown width of Cunninghamia lanceolata (Lamb.) Hook in Fujian province, southeast China. Data were obtained from 55 sample plots of pure China-fir plantation stands. An Ordinary Linear Least Squares (OLS) regression was used to establish the crown width model. To adjust for correlations between observations from the same sample plots, we developed one level linear mixed-effects (LME) models based on the multiple linear model, which take into account the random effects of plots. The best random effects combinations for the LME models were determined by the Akaike's information criterion, the Bayesian information criterion and the -2logarithm likelihood. Heteroscedasticity was reduced by three residual variance functions: the power function, the exponential function and the constant plus power function. The spatial correlation was modeled by three correlation structures: the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)], and the compound symmetry structure (CS). Then, the LME model was compared to the multiple linear model using the absolute mean residual (AMR), the root mean square error (RMSE), and the adjusted coefficient of determination (adj-R2). For individual tree crown width models, the one level LME model showed the best performance. An independent dataset was used to test the performance of the models and to demonstrate the advantage of calibrating LME models.

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

Comparison of ANN and SVM for classification of eye movements in EOG signals

NASA Astrophysics Data System (ADS)

Qi, Lim Jia; Alias, Norma

2018-03-01

Nowadays, electrooculogram is regarded as one of the most important biomedical signal in measuring and analyzing eye movement patterns. Thus, it is helpful in designing EOG-based Human Computer Interface (HCI). In this research, electrooculography (EOG) data was obtained from five volunteers. The (EOG) data was then preprocessed before feature extraction methods were employed to further reduce the dimensionality of data. Three feature extraction approaches were put forward, namely statistical parameters, autoregressive (AR) coefficients using Burg method, and power spectral density (PSD) using Yule-Walker method. These features would then become input to both artificial neural network (ANN) and support vector machine (SVM). The performance of the combination of different feature extraction methods and classifiers was presented and analyzed. It was found that statistical parameters + SVM achieved the highest classification accuracy of 69.75%.

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

NASA Astrophysics Data System (ADS)

Singh, Navneet K.; Singh, Asheesh K.; Tripathy, Manoj

2012-05-01

For power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planning etc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and forecasted data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.

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.

Real-time spectral analysis of HRV signals: an interactive and user-friendly PC system.

PubMed

Basano, L; Canepa, F; Ottonello, P

1998-01-01

We present a real-time system, built around a PC and a low-cost data acquisition board, for the spectral analysis of the heart rate variability signal. The Windows-like operating environment on which it is based makes the computer program very user-friendly even for non-specialized personnel. The Power Spectral Density is computed through the use of a hybrid method, in which a classical FFT analysis follows an autoregressive finite-extension of data; the stationarity of the sequence is continuously checked. The use of this algorithm gives a high degree of robustness of the spectral estimation. Moreover, always in real time, the FFT of every data block is computed and displayed in order to corroborate the results as well as to allow the user to interactively choose a proper AR model order.

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.

Real-time processing of radar return on a parallel computer

NASA Technical Reports Server (NTRS)

Aalfs, David D.

1992-01-01

NASA is working with the FAA to demonstrate the feasibility of pulse Doppler radar as a candidate airborne sensor to detect low altitude windshears. The need to provide the pilot with timely information about possible hazards has motivated a demand for real-time processing of a radar return. Investigated here is parallel processing as a means of accommodating the high data rates required. A PC based parallel computer, called the transputer, is used to investigate issues in real time concurrent processing of radar signals. A transputer network is made up of an array of single instruction stream processors that can be networked in a variety of ways. They are easily reconfigured and software development is largely independent of the particular network topology. The performance of the transputer is evaluated in light of the computational requirements. A number of algorithms have been implemented on the transputers in OCCAM, a language specially designed for parallel processing. These include signal processing algorithms such as the Fast Fourier Transform (FFT), pulse-pair, and autoregressive modelling, as well as routing software to support concurrency. The most computationally intensive task is estimating the spectrum. Two approaches have been taken on this problem, the first and most conventional of which is to use the FFT. By using table look-ups for the basis function and other optimizing techniques, an algorithm has been developed that is sufficient for real time. The other approach is to model the signal as an autoregressive process and estimate the spectrum based on the model coefficients. This technique is attractive because it does not suffer from the spectral leakage problem inherent in the FFT. Benchmark tests indicate that autoregressive modeling is feasible in real time.

Nonlinear Directed Interactions Between HRV and EEG Activity in Children With TLE.

PubMed

Schiecke, Karin; Pester, Britta; Piper, Diana; Benninger, Franz; Feucht, Martha; Leistritz, Lutz; Witte, Herbert

2016-12-01

Epileptic seizure activity influences the autonomic nervous system (ANS) in different ways. Heart rate variability (HRV) is used as indicator for alterations of the ANS. It was shown that linear, nondirected interactions between HRV and EEG activity before, during, and after epileptic seizure occur. Accordingly, investigations of directed nonlinear interactions are logical steps to provide, e.g., deeper insight into the development of seizure onsets. Convergent cross mapping (CCM) investigates nonlinear, directed interactions between time series by using nonlinear state space reconstruction. CCM is applied to simulated and clinically relevant data, i.e., interactions between HRV and specific EEG components of children with temporal lobe epilepsy (TLE). In addition, time-variant multivariate Autoregressive model (AR)-based estimation of partial directed coherence (PDC) was performed for the same data. Influence of estimation parameters and time-varying behavior of CCM estimation could be demonstrated by means of simulated data. AR-based estimation of PDC failed for the investigation of our clinical data. Time-varying interval-based application of CCM on these data revealed directed interactions between HRV and delta-related EEG activity. Interactions between HRV and alpha-related EEG activity were visible but less pronounced. EEG components mainly drive HRV. The interaction pattern and directionality clearly changed with onset of seizure. Statistical relevant interactions were quantified by bootstrapping and surrogate data approach. In contrast to AR-based estimation of PDC CCM was able to reveal time-courses and frequency-selective views of nonlinear interactions for the further understanding of complex interactions between the epileptic network and the ANS in children with TLE.

A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors.

PubMed

El-Diasty, Mohammed; Pagiatakis, Spiros

2009-01-01

In this paper, we examine the effect of changing the temperature points on MEMS-based inertial sensor random error. We collect static data under different temperature points using a MEMS-based inertial sensor mounted inside a thermal chamber. Rigorous stochastic models, namely Autoregressive-based Gauss-Markov (AR-based GM) models are developed to describe the random error behaviour. The proposed AR-based GM model is initially applied to short stationary inertial data to develop the stochastic model parameters (correlation times). It is shown that the stochastic model parameters of a MEMS-based inertial unit, namely the ADIS16364, are temperature dependent. In addition, field kinematic test data collected at about 17 °C are used to test the performance of the stochastic models at different temperature points in the filtering stage using Unscented Kalman Filter (UKF). It is shown that the stochastic model developed at 20 °C provides a more accurate inertial navigation solution than the ones obtained from the stochastic models developed at -40 °C, -20 °C, 0 °C, +40 °C, and +60 °C. The temperature dependence of the stochastic model is significant and should be considered at all times to obtain optimal navigation solution for MEMS-based INS/GPS integration.

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.

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.

Assessing the performance of eight real-time updating models and procedures for the Brosna River

NASA Astrophysics Data System (ADS)

Goswami, M.; O'Connor, K. M.; Bhattarai, K. P.; Shamseldin, A. Y.

2005-10-01

The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km2), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naive form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing lead-time discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.

Model Identification of Integrated ARMA Processes

ERIC Educational Resources Information Center

Stadnytska, Tetiana; Braun, Simone; Werner, Joachim

2008-01-01

This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…

[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.

[Establishing and applying of autoregressive integrated moving average model to predict the incidence rate of dysentery in Shanghai].

PubMed

Li, Jian; Wu, Huan-Yu; Li, Yan-Ting; Jin, Hui-Ming; Gu, Bao-Ke; Yuan, Zheng-An

2010-01-01

To explore the feasibility of establishing and applying of autoregressive integrated moving average (ARIMA) model to predict the incidence rate of dysentery in Shanghai, so as to provide the theoretical basis for prevention and control of dysentery. ARIMA model was established based on the monthly incidence rate of dysentery of Shanghai from 1990 to 2007. The parameters of model were estimated through unconditional least squares method, the structure was determined according to criteria of residual un-correlation and conclusion, and the model goodness-of-fit was determined through Akaike information criterion (AIC) and Schwarz Bayesian criterion (SBC). The constructed optimal model was applied to predict the incidence rate of dysentery of Shanghai in 2008 and evaluate the validity of model through comparing the difference of predicted incidence rate and actual one. The incidence rate of dysentery in 2010 was predicted by ARIMA model based on the incidence rate from January 1990 to June 2009. The model ARIMA (1, 1, 1) (0, 1, 2)(12) had a good fitness to the incidence rate with both autoregressive coefficient (AR1 = 0.443) during the past time series, moving average coefficient (MA1 = 0.806) and seasonal moving average coefficient (SMA1 = 0.543, SMA2 = 0.321) being statistically significant (P < 0.01). AIC and SBC were 2.878 and 16.131 respectively and predicting error was white noise. The mathematic function was (1-0.443B) (1-B) (1-B(12))Z(t) = (1-0.806B) (1-0.543B(12)) (1-0.321B(2) x 12) micro(t). The predicted incidence rate in 2008 was consistent with the actual one, with the relative error of 6.78%. The predicted incidence rate of dysentery in 2010 based on the incidence rate from January 1990 to June 2009 would be 9.390 per 100 thousand. ARIMA model can be used to fit the changes of incidence rate of dysentery and to forecast the future incidence rate in Shanghai. It is a predicted model of high precision for short-time forecast.

A multimodel approach to interannual and seasonal prediction of Danube discharge anomalies

NASA Astrophysics Data System (ADS)

Rimbu, Norel; Ionita, Monica; Patrut, Simona; Dima, Mihai

2010-05-01

Interannual and seasonal predictability of Danube river discharge is investigated using three model types: 1) time series models 2) linear regression models of discharge with large-scale climate mode indices and 3) models based on stable teleconnections. All models are calibrated using discharge and climatic data for the period 1901-1977 and validated for the period 1978-2008 . Various time series models, like autoregressive (AR), moving average (MA), autoregressive and moving average (ARMA) or singular spectrum analysis and autoregressive moving average (SSA+ARMA) models have been calibrated and their skills evaluated. The best results were obtained using SSA+ARMA models. SSA+ARMA models proved to have the highest forecast skill also for other European rivers (Gamiz-Fortis et al. 2008). Multiple linear regression models using large-scale climatic mode indices as predictors have a higher forecast skill than the time series models. The best predictors for Danube discharge are the North Atlantic Oscillation (NAO) and the East Atlantic/Western Russia patterns during winter and spring. Other patterns, like Polar/Eurasian or Tropical Northern Hemisphere (TNH) are good predictors for summer and autumn discharge. Based on stable teleconnection approach (Ionita et al. 2008) we construct prediction models through a combination of sea surface temperature (SST), temperature (T) and precipitation (PP) from the regions where discharge and SST, T and PP variations are stable correlated. Forecast skills of these models are higher than forecast skills of the time series and multiple regression models. The models calibrated and validated in our study can be used for operational prediction of interannual and seasonal Danube discharge anomalies. References Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part I: intearannual predictability. J. Climate, 2484-2501, 2008. Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part II: seasonal predictability. J. Climate, 2503-2518, 2008. Ionita, M., G. Lohmann, and N. Rimbu, Prediction of spring Elbe river discharge based on stable teleconnections with global temperature and precipitation. J. Climate. 6215-6226, 2008.

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.

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.

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.

Global estimation of long-term persistence in annual river runoff

NASA Astrophysics Data System (ADS)

Markonis, Y.; Moustakis, Y.; Nasika, C.; Sychova, P.; Dimitriadis, P.; Hanel, M.; Máca, P.; Papalexiou, S. M.

2018-03-01

Long-term persistence (LTP) of annual river runoff is a topic of ongoing hydrological research, due to its implications to water resources management. Here, we estimate its strength, measured by the Hurst coefficient H, in 696 annual, globally distributed, streamflow records with at least 80 years of data. We use three estimation methods (maximum likelihood estimator, Whittle estimator and least squares variance) resulting in similar mean values of H close to 0.65. Subsequently, we explore potential factors influencing H by two linear (Spearman's rank correlation, multiple linear regression) and two non-linear (self-organizing maps, random forests) techniques. Catchment area is found to be crucial for medium to larger watersheds, while climatic controls, such as aridity index, have higher impact to smaller ones. Our findings indicate that long-term persistence is weaker than found in other studies, suggesting that enhanced LTP is encountered in large-catchment rivers, were the effect of spatial aggregation is more intense. However, we also show that the estimated values of H can be reproduced by a short-term persistence stochastic model such as an auto-regressive AR(1) process. A direct consequence is that some of the most common methods for the estimation of H coefficient, might not be suitable for discriminating short- and long-term persistence even in long observational records.

Structural Equation Modeling of Multivariate Time Series

ERIC Educational Resources Information Center

du Toit, Stephen H. C.; Browne, Michael W.

2007-01-01

The covariance structure of a vector autoregressive process with moving average residuals (VARMA) is derived. It differs from other available expressions for the covariance function of a stationary VARMA process and is compatible with current structural equation methodology. Structural equation modeling programs, such as LISREL, may therefore be…

A novel method for pediatric heart sound segmentation without using the ECG.

PubMed

Sepehri, Amir A; Gharehbaghi, Arash; Dutoit, Thierry; Kocharian, Armen; Kiani, A

2010-07-01

In this paper, we propose a novel method for pediatric heart sounds segmentation by paying special attention to the physiological effects of respiration on pediatric heart sounds. The segmentation is accomplished in three steps. First, the envelope of a heart sounds signal is obtained with emphasis on the first heart sound (S(1)) and the second heart sound (S(2)) by using short time spectral energy and autoregressive (AR) parameters of the signal. Then, the basic heart sounds are extracted taking into account the repetitive and spectral characteristics of S(1) and S(2) sounds by using a Multi-Layer Perceptron (MLP) neural network classifier. In the final step, by considering the diastolic and systolic intervals variations due to the effect of a child's respiration, a complete and precise heart sounds end-pointing and segmentation is achieved. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

Classification of EEG signals using a genetic-based machine learning classifier.

PubMed

Skinner, B T; Nguyen, H T; Liu, D K

2007-01-01

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

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

Cavanaugh, J.E.; McQuarrie, A.D.; Shumway, R.H.

Conventional methods for discriminating between earthquakes and explosions at regional distances have concentrated on extracting specific features such as amplitude and spectral ratios from the waveforms of the P and S phases. We consider here an optimum nonparametric classification procedure derived from the classical approach to discriminating between two Gaussian processes with unequal spectra. Two robust variations based on the minimum discrimination information statistic and Renyi's entropy are also considered. We compare the optimum classification procedure with various amplitude and spectral ratio discriminants and show that its performance is superior when applied to a small population of 8 land-based earthquakesmore » and 8 mining explosions recorded in Scandinavia. Several parametric characterizations of the notion of complexity based on modeling earthquakes and explosions as autoregressive or modulated autoregressive processes are also proposed and their performance compared with the nonparametric and feature extraction approaches.« less

Variation in the terrestrial isotopic composition and atomic weight of argon

USGS Publications Warehouse

Böhlke, John Karl

2014-01-01

The isotopic composition and atomic weight of argon (Ar) are variable in terrestrial materials. Those variations are a source of uncertainty in the assignment of standard properties for Ar, but they provide useful information in many areas of science. Variations in the stable isotopic composition and atomic weight of Ar are caused by several different processes, including (1) isotope production from other elements by radioactive decay (radiogenic isotopes) or other nuclear transformations (e.g., nucleogenic isotopes), and (2) isotopic fractionation by physical-chemical processes such as diffusion or phase equilibria. Physical-chemical processes cause correlated mass-dependent variations in the Ar isotope-amount ratios (40Ar/36Ar, 38Ar/36Ar), whereas nuclear transformation processes cause non-mass-dependent variations. While atmospheric Ar can serve as an abundant and homogeneous isotopic reference, deviations from the atmospheric isotopic ratios in other Ar occurrences limit the precision with which a standard atomic weight can be given for Ar. Published data indicate variation of Ar atomic weights in normal terrestrial materials between about 39.7931 and 39.9624. The upper bound of this interval is given by the atomic mass of 40Ar, as some samples contain almost pure radiogenic 40Ar. The lower bound is derived from analyses of pitchblende (uranium mineral) containing large amounts of nucleogenic 36Ar and 38Ar. Within this interval, measurements of different isotope ratios (40Ar/36Ar or 38Ar/36Ar) at various levels of precision are widely used for studies in geochronology, water–rock interaction, atmospheric evolution, and other fields.

KARMA4

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

Khalil, Mohammad; Salloum, Maher; Lee, Jina

2017-07-10

KARMA4 is a C++ library for autoregressive moving average (ARMA) modeling and forecasting of time-series data while incorporating both process and observation error. KARMA4 is designed for fitting and forecasting of time-series data for predictive purposes.

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.

The scaling of wild events in stochastic models: The Fisher limit, the Mandelbrot limit, and FARIMA as a model of the intermediate cases

NASA Astrophysics Data System (ADS)

Watkins, Nicholas

2013-04-01

Stochastic modelling is of increasing importance, both specifically in climate science and more broadly across the whole of nonlinear geophysics. Traditionally, the noise components of such models would be spectrally white (delta-correlated) and Gaussian in amplitude, and their variance (first named by Fisher in 1918) would well characterise the likely size of fluctuations. Integration, for example in autoregressive models like AR(1), would redden a noise spectrum, while multiplication in turbulent cascades could greatly increase the range of fluctuation amplitudes, but such processes would still inherit aspects of their finite variance building blocks. In the 60s and 70s, however, Mandelbrot and others [see e.g. Watkins, GRL Frontiers, 2013] began to present evidence in nature for much stronger departures from Gaussianity (via very heavy tailed, infinite variance, distributions) and from white noise (through long range dependence (LRD) in time). He also observed intermittency, defined here as correlations between absolute magnitudes in some time series, in, for example, finance and turbulence. He proposed various models, including self-similar ones for heavy tails and LRD, and multifractal cascades for intermittency. In this presentation we compare contrasting types of model by looking at the "wild" events that they produce. The notion of a "wild" event can be made more precise in many ways, including by its duration in time, peak amplitude, and spatial extent. Our chosen measure will be the "burst", defined as the area of a time series above a fixed threshold. We will compare burst scaling in a self-similar, LRD, heavy tailed model (LFSM, e.g. Watkins et al, PRE, 2009] with our newer results for multifractal random walks [with M. Rypdal and O. Lovsletten], and for the heavy tailed extended version of the FARIMA (1,d,0) process, which combines long range dependence with the high frequency structure familiar from AR(1). We will also discuss the physical meaning of FARIMA and its potential as a modelling tool.

Comparison of two non-convex mixed-integer nonlinear programming algorithms applied to autoregressive moving average model structure and parameter estimation

NASA Astrophysics Data System (ADS)

Uilhoorn, F. E.

2016-10-01

In this article, the stochastic modelling approach proposed by Box and Jenkins is treated as a mixed-integer nonlinear programming (MINLP) problem solved with a mesh adaptive direct search and a real-coded genetic class of algorithms. The aim is to estimate the real-valued parameters and non-negative integer, correlated structure of stationary autoregressive moving average (ARMA) processes. The maximum likelihood function of the stationary ARMA process is embedded in Akaike's information criterion and the Bayesian information criterion, whereas the estimation procedure is based on Kalman filter recursions. The constraints imposed on the objective function enforce stability and invertibility. The best ARMA model is regarded as the global minimum of the non-convex MINLP problem. The robustness and computational performance of the MINLP solvers are compared with brute-force enumeration. Numerical experiments are done for existing time series and one new data set.

A comparative simulation study of AR(1) estimators in short time series.

PubMed

Krone, Tanja; Albers, Casper J; Timmerman, Marieke E

2017-01-01

Various estimators of the autoregressive model exist. We compare their performance in estimating the autocorrelation in short time series. In Study 1, under correct model specification, we compare the frequentist r 1 estimator, C-statistic, ordinary least squares estimator (OLS) and maximum likelihood estimator (MLE), and a Bayesian method, considering flat (B f ) and symmetrized reference (B sr ) priors. In a completely crossed experimental design we vary lengths of time series (i.e., T = 10, 25, 40, 50 and 100) and autocorrelation (from -0.90 to 0.90 with steps of 0.10). The results show a lowest bias for the B sr , and a lowest variability for r 1 . The power in different conditions is highest for B sr and OLS. For T = 10, the absolute performance of all measurements is poor, as expected. In Study 2, we study robustness of the methods through misspecification by generating the data according to an ARMA(1,1) model, but still analysing the data with an AR(1) model. We use the two methods with the lowest bias for this study, i.e., B sr and MLE. The bias gets larger when the non-modelled moving average parameter becomes larger. Both the variability and power show dependency on the non-modelled parameter. The differences between the two estimation methods are negligible for all measurements.

A robust damage-detection technique with environmental variability combining time-series models with principal components

NASA Astrophysics Data System (ADS)

Lakshmi, K.; Rama Mohan Rao, A.

2014-10-01

In this paper, a novel output-only damage-detection technique based on time-series models for structural health monitoring in the presence of environmental variability and measurement noise is presented. The large amount of data obtained in the form of time-history response is transformed using principal component analysis, in order to reduce the data size and thereby improve the computational efficiency of the proposed algorithm. The time instant of damage is obtained by fitting the acceleration time-history data from the structure using autoregressive (AR) and AR with exogenous inputs time-series prediction models. The probability density functions (PDFs) of damage features obtained from the variances of prediction errors corresponding to references and healthy current data are found to be shifting from each other due to the presence of various uncertainties such as environmental variability and measurement noise. Control limits using novelty index are obtained using the distances of the peaks of the PDF curves in healthy condition and used later for determining the current condition of the structure. Numerical simulation studies have been carried out using a simply supported beam and also validated using an experimental benchmark data corresponding to a three-storey-framed bookshelf structure proposed by Los Alamos National Laboratory. Studies carried out in this paper clearly indicate the efficiency of the proposed algorithm for damage detection in the presence of measurement noise and environmental variability.

Large deviation probabilities for correlated Gaussian stochastic processes and daily temperature anomalies

NASA Astrophysics Data System (ADS)

Massah, Mozhdeh; Kantz, Holger

2016-04-01

As we have one and only one earth and no replicas, climate characteristics are usually computed as time averages from a single time series. For understanding climate variability, it is essential to understand how close a single time average will typically be to an ensemble average. To answer this question, we study large deviation probabilities (LDP) of stochastic processes and characterize them by their dependence on the time window. In contrast to iid variables for which there exists an analytical expression for the rate function, the correlated variables such as auto-regressive (short memory) and auto-regressive fractionally integrated moving average (long memory) processes, have not an analytical LDP. We study LDP for these processes, in order to see how correlation affects this probability in comparison to iid data. Although short range correlations lead to a simple correction of sample size, long range correlations lead to a sub-exponential decay of LDP and hence to a very slow convergence of time averages. This effect is demonstrated for a 120 year long time series of daily temperature anomalies measured in Potsdam (Germany).

[Study on the ARIMA model application to predict echinococcosis cases in China].

PubMed

En-Li, Tan; Zheng-Feng, Wang; Wen-Ce, Zhou; Shi-Zhu, Li; Yan, Lu; Lin, Ai; Yu-Chun, Cai; Xue-Jiao, Teng; Shun-Xian, Zhang; Zhi-Sheng, Dang; Chun-Li, Yang; Jia-Xu, Chen; Wei, Hu; Xiao-Nong, Zhou; Li-Guang, Tian

2018-02-26

To predict the monthly reported echinococcosis cases in China with the autoregressive integrated moving average (ARIMA) model, so as to provide a reference for prevention and control of echinococcosis. SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported echinococcosis cases of time series from 2007 to 2015 and 2007 to 2014, respectively, and the accuracies of the two ARIMA models were compared. The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2015 was ARIMA (1, 0, 0) (1, 1, 0) 12 , the relative error among reported cases and predicted cases was -13.97%, AR (1) = 0.367 ( t = 3.816, P < 0.001), SAR (1) = -0.328 ( t = -3.361, P = 0.001), and Ljung-Box Q = 14.119 ( df = 16, P = 0.590) . The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2014 was ARIMA (1, 0, 0) (1, 0, 1) 12 , the relative error among reported cases and predicted cases was 0.56%, AR (1) = 0.413 ( t = 4.244, P < 0.001), SAR (1) = 0.809 ( t = 9.584, P < 0.001), SMA (1) = 0.356 ( t = 2.278, P = 0.025), and Ljung-Box Q = 18.924 ( df = 15, P = 0.217). The different time series may have different ARIMA models as for the same infectious diseases. It is needed to be further verified that the more data are accumulated, the shorter time of predication is, and the smaller the average of the relative error is. The establishment and prediction of an ARIMA model is a dynamic process that needs to be adjusted and optimized continuously according to the accumulated data, meantime, we should give full consideration to the intensity of the work related to infectious diseases reported (such as disease census and special investigation).

7 CFR 510.4 - Multitrack processing.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AGRICULTURE PUBLIC INFORMATION § 510.4 Multitrack processing. (a) When ARS has a significant number of... involved in processing the request, and whether the request qualifies for expedited processing. (b) ARS may... possible. (d) ARS shall process requests in each track on a “first-in, first-out” basis, unless there are...

7 CFR 510.4 - Multitrack processing.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AGRICULTURE PUBLIC INFORMATION § 510.4 Multitrack processing. (a) When ARS has a significant number of... involved in processing the request, and whether the request qualifies for expedited processing. (b) ARS may... possible. (d) ARS shall process requests in each track on a “first-in, first-out” basis, unless there are...

7 CFR 510.4 - Multitrack processing.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AGRICULTURE PUBLIC INFORMATION § 510.4 Multitrack processing. (a) When ARS has a significant number of... involved in processing the request, and whether the request qualifies for expedited processing. (b) ARS may... possible. (d) ARS shall process requests in each track on a “first-in, first-out” basis, unless there are...

7 CFR 510.4 - Multitrack processing.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AGRICULTURE PUBLIC INFORMATION § 510.4 Multitrack processing. (a) When ARS has a significant number of... involved in processing the request, and whether the request qualifies for expedited processing. (b) ARS may... possible. (d) ARS shall process requests in each track on a “first-in, first-out” basis, unless there are...

7 CFR 510.4 - Multitrack processing.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AGRICULTURE PUBLIC INFORMATION § 510.4 Multitrack processing. (a) When ARS has a significant number of... involved in processing the request, and whether the request qualifies for expedited processing. (b) ARS may... possible. (d) ARS shall process requests in each track on a “first-in, first-out” basis, unless there are...

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.

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

Hexavalent chromium content in stainless steel welding fumes is dependent on the welding process and shield gas type.

PubMed

Keane, Michael; Stone, Samuel; Chen, Bean; Slaven, James; Schwegler-Berry, Diane; Antonini, James

2009-02-01

Occupational exposure to welding fumes is a known health hazard. To isolate elements in stainless steel welding fumes with high potential for adverse health outcomes, fumes were generated using a robotic gas metal arc system, using four shield gases of varying oxygen content. The objective was to measure Cr(VI) concentrations in a broad spectrum of gas metal arc welding processes, and identify processes of exceptionally high or low Cr(VI) content. The gases used were 95% Ar/5% O(2), 98% Ar/2% O(2), 95% Ar/5%CO(2), and 75% He/25% Ar. The welder was operated in axial spray mode (Ar/O(2), Ar/CO(2)), short-circuit (SC) mode (Ar/CO(2) low voltage and He/Ar), and pulsed axial-spray mode (98% Ar/2% O(2)). Results indicate large differences in Cr(VI) in the fumes, with Ar/O(2) (Pulsed)>Ar/O(2)>Ar/CO(2)>Ar/CO(2) (SC)>He/Ar; values were 3000+/-300, 2800+/-85, 2600+/-120, 1400+/-190, and 320+/-290 ppm respectively (means +/- standard errors for 2 runs and 3 replicates per run). Respective rates of Cr(VI) generation were 1.5, 3.2, 4.4, 1.3, and 0.46 microg/min; generation rates were also calculated in terms of microg Cr(VI) per metre of wire used. The generation rates of Cr(VI) increased with increasing O(3) concentrations. Particle size measurements indicated similar distributions, but somewhat higher >0.6 microm fractions for the short-circuit mode samples. Fumes were also sampled into 2 selected size ranges, a microspatter fraction (>or=0.6 microm) and a fine (<0.6 microm) fraction; analysis indicated that Cr(VI) is primarily associated with particles <0.6 microm. The conclusion of the study is that Cr(VI) concentrations vary significantly with welding type and shield gas type, and this presents an opportunity to tailor welding practices to lessen Cr(VI) exposures in workplaces by selecting low Cr(VI)-generating processes. Short-circuit processes generated less Cr(VI) than axial-spray methods, and inert gas shielding gave lower Cr(VI) content than shielding with active gases. A short circuit He/Ar shielded process and a pulsed axial spray Ar/O(2) process were both identified as having substantially lower Cr(VI) generation rates per unit of wire used relative to the other processes studied.

Celastrol Induces Autophagy by Targeting AR/miR-101 in Prostate Cancer Cells

PubMed Central

Guo, Jianquan; Huang, Xuemei; Wang, Hui; Yang, Huanjie

2015-01-01

Autophagy is an evolutionarily conserved process responsible for the degradation and recycling of cytoplasmic components through autolysosomes. Targeting AR axis is a standard strategy for prostate cancer treatment; however, the role of AR in autophagic processes is still not fully understood. In the present study, we found that AR played a negative role in AR degrader celastrol-induced autophagy. Knockdown of AR in AR-positive prostate cancer cells resulted in enhanced autophagy. Ectopic expression of AR in AR-negative prostate cancer cells, or gain of function of the AR signaling in AR-positive cells, led to suppression of autophagy. Since miR-101 is an inhibitor of autophagy and its expression was decreased along with AR in the process of celastrol-induced autophagy, we hypothesize that AR inhibits autophagy through transactivation of miR-101. AR binding site was defined in the upstream of miR-101 gene by luciferase reporter and ChIP assays. MiR-101 expression correlated with AR status in prostate cancer cell lines. The inhibition of celastrol-induced autophagy by AR was compromised by blocking miR-101; while transfection of miR-101 led to inhibition of celastrol-induced autophagy in spite of AR depletion. Furthermore, mutagenesis of the AR binding site in miR-101 gene led to decreased suppression of autophagy by AR. Finally, autophagy inhibition by miR-101 mimic was found to enhance the cytotoxic effect of celastrol in prostate cancer cells. Our results demonstrate that AR inhibits autophagy via transactivation of miR-101, thus combination of miR-101 mimics with celastrol may represent a promising therapeutic approach for treating prostate cancer. PMID:26473737

Optimization of the time series NDVI-rainfall relationship using linear mixed-effects modeling for the anti-desertification area in the Beijing and Tianjin sandstorm source region

NASA Astrophysics Data System (ADS)

Wang, Jin; Sun, Tao; Fu, Anmin; Xu, Hao; Wang, Xinjie

2018-05-01

Degradation in drylands is a critically important global issue that threatens ecosystem and environmental in many ways. Researchers have tried to use remote sensing data and meteorological data to perform residual trend analysis and identify human-induced vegetation changes. However, complex interactions between vegetation and climate, soil units and topography have not yet been considered. Data used in the study included annual accumulated Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m normalized difference vegetation index (NDVI) from 2002 to 2013, accumulated rainfall from September to August, digital elevation model (DEM) and soil units. This paper presents linear mixed-effect (LME) modeling methods for the NDVI-rainfall relationship. We developed linear mixed-effects models that considered the random effects of sample points nested in soil units for nested two-level modeling and single-level modeling of soil units and sample points, respectively. Additionally, three functions, including the exponential function (exp), the power function (power), and the constant plus power function (CPP), were tested to remove heterogeneity, and an additional three correlation structures, including the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)] and the compound symmetry structure (CS), were used to address the spatiotemporal correlations. It was concluded that the nested two-level model considering both heteroscedasticity with (CPP) and spatiotemporal correlation with [ARMA(1,1)] showed the best performance (AMR = 0.1881, RMSE = 0.2576, adj- R 2 = 0.9593). Variations between soil units and sample points that may have an effect on the NDVI-rainfall relationship should be included in model structures, and linear mixed-effects modeling achieves this in an effective and accurate way.

Classifying multiple types of hand motions using electrocorticography during intraoperative awake craniotomy and seizure monitoring processes—case studies

PubMed Central

Xie, Tao; Zhang, Dingguo; Wu, Zehan; Chen, Liang; Zhu, Xiangyang

2015-01-01

In this work, some case studies were conducted to classify several kinds of hand motions from electrocorticography (ECoG) signals during intraoperative awake craniotomy & extraoperative seizure monitoring processes. Four subjects (P1, P2 with intractable epilepsy during seizure monitoring and P3, P4 with brain tumor during awake craniotomy) participated in the experiments. Subjects performed three types of hand motions (Grasp, Thumb-finger motion and Index-finger motion) contralateral to the motor cortex covered with ECoG electrodes. Two methods were used for signal processing. Method I: autoregressive (AR) model with burg method was applied to extract features, and additional waveform length (WL) feature has been considered, finally the linear discriminative analysis (LDA) was used as the classifier. Method II: stationary subspace analysis (SSA) was applied for data preprocessing, and the common spatial pattern (CSP) was used for feature extraction before LDA decoding process. Applying method I, the three-class accuracy of P1~P4 were 90.17, 96.00, 91.77, and 92.95% respectively. For method II, the three-class accuracy of P1~P4 were 72.00, 93.17, 95.22, and 90.36% respectively. This study verified the possibility of decoding multiple hand motion types during an awake craniotomy, which is the first step toward dexterous neuroprosthetic control during surgical implantation, in order to verify the optimal placement of electrodes. The accuracy during awake craniotomy was comparable to results during seizure monitoring. This study also indicated that ECoG was a promising approach for precise identification of eloquent cortex during awake craniotomy, and might form a promising BCI system that could benefit both patients and neurosurgeons. PMID:26483627

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).

Influence of weather on the synchrony of gypsy moth (Lepidoptera: Lymantriidae) outbreaks in New England

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

Williams, D.W.; Liebhold, A.M.

1995-10-01

Outbreaks of the gypsy moth, Lymantria dispar (L.), were partially synchronous across New England states (Massachusetts, Maine, New Hampshire, and Vermont) from 1938 to 1992. To explain this synchrony, we investigated the Moran effect, a hypothesis that local population oscillations, which result form similar density-dependent mechanisms operating at time lags, may be synchronized over wide areas by exposure to common weather patterns. We also investigated the theory of climatic release, which ostulates that outbreaks are triggered by climatic factors favorable for population growth. Time series analysis revealed defoliation series in 2 states as 1st-order autoregressive processes and the other 2more » as periodic 2nd-order autoregressive processes. Defoliation residuals series computed using the autoregressive models for each state were cross correlated with series of weather variables recorded in the respective states. The weather variables significantly correlated with defoliation residuals in all 4 states were minimum temperature and precipitation in mid-December in the same gypsy moth generation and minimum temperature in mid- to late July of the previous generation. These weather variables also were correlated strongly among the 4 states. The analyses supported the predictions of the Moran effect and suggest the common weather may synchronize local populations so as to produce pest outbreaks over wide areas. We did not find convincing evidence to support the theory of climatic release. 41 refs., 7 figs., 4 tabs.« less

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

Estimating error statistics for Chambon-la-Forêt observatory definitive data

NASA Astrophysics Data System (ADS)

Lesur, Vincent; Heumez, Benoît; Telali, Abdelkader; Lalanne, Xavier; Soloviev, Anatoly

2017-08-01

We propose a new algorithm for calibrating definitive observatory data with the goal of providing users with estimates of the data error standard deviations (SDs). The algorithm has been implemented and tested using Chambon-la-Forêt observatory (CLF) data. The calibration process uses all available data. It is set as a large, weakly non-linear, inverse problem that ultimately provides estimates of baseline values in three orthogonal directions, together with their expected standard deviations. For this inverse problem, absolute data error statistics are estimated from two series of absolute measurements made within a day. Similarly, variometer data error statistics are derived by comparing variometer data time series between different pairs of instruments over few years. The comparisons of these time series led us to use an autoregressive process of order 1 (AR1 process) as a prior for the baselines. Therefore the obtained baselines do not vary smoothly in time. They have relatively small SDs, well below 300 pT when absolute data are recorded twice a week - i.e. within the daily to weekly measures recommended by INTERMAGNET. The algorithm was tested against the process traditionally used to derive baselines at CLF observatory, suggesting that statistics are less favourable when this latter process is used. Finally, two sets of definitive data were calibrated using the new algorithm. Their comparison shows that the definitive data SDs are less than 400 pT and may be slightly overestimated by our process: an indication that more work is required to have proper estimates of absolute data error statistics. For magnetic field modelling, the results show that even on isolated sites like CLF observatory, there are very localised signals over a large span of temporal frequencies that can be as large as 1 nT. The SDs reported here encompass signals of a few hundred metres and less than a day wavelengths.

Functional mapping of reaction norms to multiple environmental signals through nonparametric covariance estimation

PubMed Central

2011-01-01

Background The identification of genes or quantitative trait loci that are expressed in response to different environmental factors such as temperature and light, through functional mapping, critically relies on precise modeling of the covariance structure. Previous work used separable parametric covariance structures, such as a Kronecker product of autoregressive one [AR(1)] matrices, that do not account for interaction effects of different environmental factors. Results We implement a more robust nonparametric covariance estimator to model these interactions within the framework of functional mapping of reaction norms to two signals. Our results from Monte Carlo simulations show that this estimator can be useful in modeling interactions that exist between two environmental signals. The interactions are simulated using nonseparable covariance models with spatio-temporal structural forms that mimic interaction effects. Conclusions The nonparametric covariance estimator has an advantage over separable parametric covariance estimators in the detection of QTL location, thus extending the breadth of use of functional mapping in practical settings. PMID:21269481

State space model approach for forecasting the use of electrical energy (a case study on: PT. PLN (Persero) district of Kroya)

NASA Astrophysics Data System (ADS)

Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

2018-05-01

Time series data is a series of data taken or measured based on observations at the same time interval. Time series data analysis is used to perform data analysis considering the effect of time. The purpose of time series analysis is to know the characteristics and patterns of a data and predict a data value in some future period based on data in the past. One of the forecasting methods used for time series data is the state space model. This study discusses the modeling and forecasting of electric energy consumption using the state space model for univariate data. The modeling stage is began with optimal Autoregressive (AR) order selection, determination of state vector through canonical correlation analysis, estimation of parameter, and forecasting. The result of this research shows that modeling of electric energy consumption using state space model of order 4 with Mean Absolute Percentage Error (MAPE) value 3.655%, so the model is very good forecasting category.

Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces

PubMed Central

Kamrunnahar, M.; Dias, N. S.; Schiff, S. J.

2013-01-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain–computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8–23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications. PMID:21267657

Toward a model-based predictive controller design in brain-computer interfaces.

PubMed

Kamrunnahar, M; Dias, N S; Schiff, S J

2011-05-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

Defense Applications of Signal Processing

DTIC Science & Technology

1999-08-27

class of multiscale autoregressive moving average (MARMA) processes. These are generalisations of ARMA models in time series analysis , and they contain...including the two theoretical sinusoidal components. Analysis of the amplitude and frequency time series provided some novel insight into the real...communication channels, underwater acoustic signals, radar systems , economic time series and biomedical signals [7]. The alpha stable (aS) distribution has

Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.

PubMed

Komasi, Mehdi; Sharghi, Soroush

2016-01-01

Because of the importance of water resources management, the need for accurate modeling of the rainfall-runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall-runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall-runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.

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.

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 %.

Glucose Prediction Algorithms from Continuous Monitoring Data: Assessment of Accuracy via Continuous Glucose Error-Grid Analysis.

PubMed

Zanderigo, Francesca; Sparacino, Giovanni; Kovatchev, Boris; Cobelli, Claudio

2007-09-01

The aim of this article was to use continuous glucose error-grid analysis (CG-EGA) to assess the accuracy of two time-series modeling methodologies recently developed to predict glucose levels ahead of time using continuous glucose monitoring (CGM) data. We considered subcutaneous time series of glucose concentration monitored every 3 minutes for 48 hours by the minimally invasive CGM sensor Glucoday® (Menarini Diagnostics, Florence, Italy) in 28 type 1 diabetic volunteers. Two prediction algorithms, based on first-order polynomial and autoregressive (AR) models, respectively, were considered with prediction horizons of 30 and 45 minutes and forgetting factors (ff) of 0.2, 0.5, and 0.8. CG-EGA was used on the predicted profiles to assess their point and dynamic accuracies using original CGM profiles as reference. Continuous glucose error-grid analysis showed that the accuracy of both prediction algorithms is overall very good and that their performance is similar from a clinical point of view. However, the AR model seems preferable for hypoglycemia prevention. CG-EGA also suggests that, irrespective of the time-series model, the use of ff = 0.8 yields the highest accurate readings in all glucose ranges. For the first time, CG-EGA is proposed as a tool to assess clinically relevant performance of a prediction method separately at hypoglycemia, euglycemia, and hyperglycemia. In particular, we have shown that CG-EGA can be helpful in comparing different prediction algorithms, as well as in optimizing their parameters.

Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins.

PubMed

Ranasinghe, Anuradha; Sornkarn, Nantachai; Dasgupta, Prokar; Althoefer, Kaspar; Penders, Jacques; Nanayakkara, Thrishantha

2016-02-01

This paper presents salient features of human-human interaction where one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several salient features of the interaction between the guider and follower such as: 1) the order of an autoregressive (AR) control policy that maps states of the follower to actions of the guider; 2) how the guider may modulate the pulling force in response to the trust level of the follower; and 3) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a third-order AR predictive control policy and followers tend to adopt second-order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the trust level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the trust level of the follower monitored using a time varying virtual damped inertial model.

Models for Serially Correlated Over or Underdispersed Unequally Spaced Longitudinal Count Data with Applications to Asthma Inhaler Use

DTIC Science & Technology

2007-08-01

the gamma prior and Poisson counts are conditioned on an unobserved AR( 1 ) process that accounts for the time since the last observation . This model did...to the observation equation. For unequally spaced observations the AR( 1 ) errors are replaced by a continuous time AR( 1 ) process , and the distance...unequal spaced observations are handled in the XJG model by assuming an underlying continuous time AR( 1 ) (CAR(l)) process . It is implemented by

Study of inelastic processes in Li+-Ar, K+-Ar, and Na+-He collisions in the energy range 0.5-10 keV

NASA Astrophysics Data System (ADS)

Lomsadze, Ramaz A.; Gochitashvili, Malkhaz R.; Kezerashvili, Roman Ya; Schulz, Michael

2017-11-01

Absolute cross sections are measured for charge-exchange, ionization, and excitation processes within the same experimental setup for the Li{}+-Ar, K{}+-Ar, and Na{}+-He collisions in the ion energy range of 0.5-10 keV. The results of the measurements and schematic correlation diagrams are used to analyze and determine the mechanisms for these processes. The experimental results show that the charge-exchange processes occur with high probabilities and electrons are predominantly captured in ground states. The contributions of various partial inelastic channels to the total ionization cross section are estimated, and a primary mechanism for the process is identified. In addition, the energy-loss spectrum is applied in order to estimate the relative contribution of different inelastic channels, and to determine the mechanisms for the ionization and for some excitation processes of Ar resonance lines for the {{{K}}}+-Ar collision system. The excitation cross sections for the helium and for the sodium doublet lines for the Na{}+-He collision system both reveal some unexpected features. A mechanism to explain this observation is suggested.

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.

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.

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.

ArF halftone PSM cleaning process optimization for next-generation lithography

NASA Astrophysics Data System (ADS)

Son, Yong-Seok; Jeong, Seong-Ho; Kim, Jeong-Bae; Kim, Hong-Seok

2000-07-01

ArF lithography which is expected for the next generation optical lithography is adapted for 0.13 micrometers design-rule and beyond. ArF half-tone phase shift mask (HT PSM) will be applied as 1st generation of ArF lithography. Also ArF PSM cleaning demands by means of tighter controls related to phase angle, transmittance and contamination on the masks. Phase angle on ArF HT PSM should be controlled within at least +/- 3 degree and transmittance controlled within at least +/- 3 percent after cleaning process and pelliclization. In the cleaning process of HT PSM, requires not only the remove the particle on mask, but also control to half-tone material for metamorphosis. Contamination defects on the Qz of half tone type PSM is not easy to remove on the photomask surface. New technology and methods of cleaning will be developed in near future, but we try to get out for limit contamination on the mask, without variation of phase angle and transmittance after cleaning process.

Comparison of fMRI data from passive listening and active-response story processing tasks in children.

PubMed

Vannest, Jennifer J; Karunanayaka, Prasanna R; Altaye, Mekibib; Schmithorst, Vincent J; Plante, Elena M; Eaton, Kenneth J; Rasmussen, Jerod M; Holland, Scott K

2009-04-01

To use functional MRI (fMRI) methods to visualize a network of auditory and language-processing brain regions associated with processing an aurally-presented story. We compare a passive listening (PL) story paradigm to an active-response (AR) version including online performance monitoring and a sparse acquisition technique. Twenty children (ages 11-13 years) completed PL and AR story processing tasks. The PL version presented alternating 30-second blocks of stories and tones; the AR version presented story segments, comprehension questions, and 5-second tone sequences, with fMRI acquisitions between stimuli. fMRI data was analyzed using a general linear model approach and paired t-test identifying significant group activation. Both tasks showed activation in the primary auditory cortex, superior temporal gyrus bilaterally, and left inferior frontal gyrus (IFG). The AR task demonstrated more extensive activation, including the dorsolateral prefrontal cortex and anterior/posterior cingulate cortex. Comparison of effect size in each paradigm showed a larger effect for the AR paradigm in a left inferior frontal region-of-interest (ROI). Activation patterns for story processing in children are similar in PL and AR tasks. Increases in extent and magnitude of activation in the AR task are likely associated with memory and attention resources engaged across acquisition intervals.

Autoregressive processes with exponentially decaying probability distribution functions: applications to daily variations of a stock market index.

PubMed

Porto, Markus; Roman, H Eduardo

2002-04-01

We consider autoregressive conditional heteroskedasticity (ARCH) processes in which the variance sigma(2)(y) depends linearly on the absolute value of the random variable y as sigma(2)(y) = a+b absolute value of y. While for the standard model, where sigma(2)(y) = a + b y(2), the corresponding probability distribution function (PDF) P(y) decays as a power law for absolute value of y-->infinity, in the linear case it decays exponentially as P(y) approximately exp(-alpha absolute value of y), with alpha = 2/b. We extend these results to the more general case sigma(2)(y) = a+b absolute value of y(q), with 0 < q < 2. We find stretched exponential decay for 1 < q < 2 and stretched Gaussian behavior for 0 < q < 1. As an application, we consider the case q=1 as our starting scheme for modeling the PDF of daily (logarithmic) variations in the Dow Jones stock market index. When the history of the ARCH process is taken into account, the resulting PDF becomes a stretched exponential even for q = 1, with a stretched exponent beta = 2/3, in a much better agreement with the empirical data.

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

Industrial energy systems and assessment opportunities

NASA Astrophysics Data System (ADS)

Barringer, Frank Leonard, III

Industrial energy assessments are performed primarily to increase energy system efficiency and reduce energy costs in industrial facilities. The most common energy systems are lighting, compressed air, steam, process heating, HVAC, pumping, and fan systems, and these systems are described in this document. ASME has produced energy assessment standards for four energy systems, and these systems include compressed air, steam, process heating, and pumping systems. ASHRAE has produced an energy assessment standard for HVAC systems. Software tools for energy systems were developed for the DOE, and there are software tools for almost all of the most common energy systems. The software tools are AIRMaster+ and LogTool for compressed air systems, SSAT and 3E Plus for steam systems, PHAST and 3E Plus for process heating systems, eQUEST for HVAC systems, PSAT for pumping systems, and FSAT for fan systems. The recommended assessment procedures described in this thesis are used to set up an energy assessment for an industrial facility, collect energy system data, and analyze the energy system data. The assessment recommendations (ARs) are opportunities to increase efficiency and reduce energy consumption for energy systems. A set of recommended assessment procedures and recommended assessment opportunities are presented for each of the most common energy systems. There are many assessment opportunities for industrial facilities, and this thesis describes forty-three ARs for the seven different energy systems. There are seven ARs for lighting systems, ten ARs for compressed air systems, eight ARs for boiler and steam systems, four ARs for process heating systems, six ARs for HVAC systems, and four ARs for both pumping and fan systems. Based on a history of past assessments, average potential energy savings and typical implementation costs are shared in this thesis for most ARs. Implementing these ARs will increase efficiency and reduce energy consumption for energy systems in industrial facilities. This thesis does not explain all energy saving ARs that are available, but does describe the most common ARs.

Accurate derivation of heart rate variability signal for detection of sleep disordered breathing in children.

PubMed

Chatlapalli, S; Nazeran, H; Melarkod, V; Krishnam, R; Estrada, E; Pamula, Y; Cabrera, S

2004-01-01

The electrocardiogram (ECG) signal is used extensively as a low cost diagnostic tool to provide information concerning the heart's state of health. Accurate determination of the QRS complex, in particular, reliable detection of the R wave peak, is essential in computer based ECG analysis. ECG data from Physionet's Sleep-Apnea database were used to develop, test, and validate a robust heart rate variability (HRV) signal derivation algorithm. The HRV signal was derived from pre-processed ECG signals by developing an enhanced Hilbert transform (EHT) algorithm with built-in missing beat detection capability for reliable QRS detection. The performance of the EHT algorithm was then compared against that of a popular Hilbert transform-based (HT) QRS detection algorithm. Autoregressive (AR) modeling of the HRV power spectrum for both EHT- and HT-derived HRV signals was achieved and different parameters from their power spectra as well as approximate entropy were derived for comparison. Poincare plots were then used as a visualization tool to highlight the detection of the missing beats in the EHT method After validation of the EHT algorithm on ECG data from the Physionet, the algorithm was further tested and validated on a dataset obtained from children undergoing polysomnography for detection of sleep disordered breathing (SDB). Sensitive measures of accurate HRV signals were then derived to be used in detecting and diagnosing sleep disordered breathing in children. All signal processing algorithms were implemented in MATLAB. We present a description of the EHT algorithm and analyze pilot data for eight children undergoing nocturnal polysomnography. The pilot data demonstrated that the EHT method provides an accurate way of deriving the HRV signal and plays an important role in extraction of reliable measures to distinguish between periods of normal and sleep disordered breathing (SDB) in children.

Automated Analysis of CT Images for the Inspection of Hardwood Logs

Treesearch

Harbin Li; A. Lynn Abbott; Daniel L. Schmoldt

1996-01-01

This paper investigates several classifiers for labeling internal features of hardwood logs using computed tomography (CT) images. A primary motivation is to locate and classify internal defects so that an optimal cutting strategy can be chosen. Previous work has relied on combinations of low-level processing, image segmentation, autoregressive texture modeling, and...

An INAR(1) Negative Multinomial Regression Model for Longitudinal Count Data.

ERIC Educational Resources Information Center

Bockenholt, Ulf

1999-01-01

Discusses a regression model for the analysis of longitudinal count data in a panel study by adapting an integer-valued first-order autoregressive (INAR(1)) Poisson process to represent time-dependent correlation between counts. Derives a new negative multinomial distribution by combining INAR(1) representation with a random effects approach.…

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.

Stock price forecasting based on time series analysis

NASA Astrophysics Data System (ADS)

Chi, Wan Le

2018-05-01

Using the historical stock price data to set up a sequence model to explain the intrinsic relationship of data, the future stock price can forecasted. The used models are auto-regressive model, moving-average model and autoregressive-movingaverage model. The original data sequence of unit root test was used to judge whether the original data sequence was stationary. The non-stationary original sequence as a first order difference needed further processing. Then the stability of the sequence difference was re-inspected. If it is still non-stationary, the second order differential processing of the sequence is carried out. Autocorrelation diagram and partial correlation diagram were used to evaluate the parameters of the identified ARMA model, including coefficients of the model and model order. Finally, the model was used to forecast the fitting of the shanghai composite index daily closing price with precision. Results showed that the non-stationary original data series was stationary after the second order difference. The forecast value of shanghai composite index daily closing price was closer to actual value, indicating that the ARMA model in the paper was a certain accuracy.

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.

Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models

USGS Publications Warehouse

Tipton, John; Hooten, Mevin B.; Pederson, Neil; Tingley, Martin; Bishop, Daniel

2016-01-01

Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one-dimensional summary of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function to describe the response of a tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts reasonable climate scenarios given tree ring width data.

Action Research and Response to Intervention: Bridging the Discourse Divide

ERIC Educational Resources Information Center

Little, Mary E.

2012-01-01

The purpose of this article is to define and clarify the process of instructional problem-solving using assessment data within action research (AR) and Response to Intervention (RtI). Similarities between AR and RtI are defined and compared. Lastly, specific resources and examples of the instructional problem-solving process of AR within…

Mitochondria-associated Endoplasmic Reticulum Membrane (MAM) Regulates Steroidogenic Activity via Steroidogenic Acute Regulatory Protein (StAR)-Voltage-dependent Anion Channel 2 (VDAC2) Interaction*

PubMed Central

Prasad, Manoj; Kaur, Jasmeet; Pawlak, Kevin J.; Bose, Mahuya; Whittal, Randy M.; Bose, Himangshu S.

2015-01-01

Steroid hormones are essential for carbohydrate metabolism, stress management, and reproduction and are synthesized from cholesterol in mitochondria of adrenal glands and gonads/ovaries. In acute stress or hormonal stimulation, steroidogenic acute regulatory protein (StAR) transports substrate cholesterol into the mitochondria for steroidogenesis by an unknown mechanism. Here, we report for the first time that StAR interacts with voltage-dependent anion channel 2 (VDAC2) at the mitochondria-associated endoplasmic reticulum membrane (MAM) prior to its translocation to the mitochondrial matrix. In the MAM, StAR interacts with mitochondrial proteins Tom22 and VDAC2. However, Tom22 knockdown by siRNA had no effect on pregnenolone synthesis. In the absence of VDAC2, StAR was expressed but not processed into the mitochondria as a mature 30-kDa protein. VDAC2 interacted with StAR via its C-terminal 20 amino acids and N-terminal amino acids 221–229, regulating the mitochondrial processing of StAR into the mature protein. In the absence of VDAC2, StAR could not enter the mitochondria or interact with MAM-associated proteins, and therefore steroidogenesis was inhibited. Furthermore, the N terminus was not essential for StAR activity, and the N-terminal deletion mutant continued to interact with VDAC2. The endoplasmic reticulum-targeting prolactin signal sequence did not affect StAR association with the MAM and thus its mitochondrial targeting. Therefore, VDAC2 controls StAR processing and activity, and MAM is thus a central location for initiating mitochondrial steroidogenesis. PMID:25505173

Action research methodology in clinical pharmacy: how to involve and change.

PubMed

Nørgaard, Lotte Stig; Sørensen, Ellen Westh

2016-06-01

Introduction The focus in clinical pharmacy practice is and has for the last 30-35 years been on changing the role of pharmacy staff into service orientation and patient counselling. One way of doing this is by involving staff in change process and as a researcher to take part in the change process by establishing partnerships with staff. On the background of the authors' widespread action research (AR)-based experiences, recommendations and comments for how to conduct an AR-study is described, and one of their AR-based studies illustrate the methodology and the research methods used. Methodology AR is defined as an approach to research which is based on a problem-solving relationship between researchers and clients, which aims at both solving a problem and at collaboratively generating new knowledge. Research questions relevant in AR-studies are: what was the working process in this change oriented study? What learning and/or changes took place? What challenges/pitfalls had to be overcome? What were the influence/consequences for the involved parts? When to use If you want to implement new services and want to involve staff and others in the process, an AR methodology is very suitable. The basic advantages of doing AR-based studies are grounded in their participatory and democratic basis and their starting point in problems experienced in practice. Limitations Some of the limitations in AR-studies are that neither of the participants in a project steering group are the only ones to decide. Furthermore, the collective process makes the decision-making procedures relatively complex.

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data.

PubMed

de Haan-Rietdijk, Silvia; Voelkle, Manuel C; Keijsers, Loes; Hamaker, Ellen L

2017-01-01

The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available.

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.

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

PubMed Central

de Haan-Rietdijk, Silvia; Voelkle, Manuel C.; Keijsers, Loes; Hamaker, Ellen L.

2017-01-01

The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available. PMID:29104554

Mean-variance portfolio optimization by using time series approaches based on logarithmic utility function

NASA Astrophysics Data System (ADS)

Soeryana, E.; Fadhlina, N.; Sukono; Rusyaman, E.; Supian, S.

2017-01-01

Investments in stocks investors are also faced with the issue of risk, due to daily price of stock also fluctuate. For minimize the level of risk, investors usually forming an investment portfolio. Establishment of a portfolio consisting of several stocks are intended to get the optimal composition of the investment portfolio. This paper discussed about optimizing investment portfolio of Mean-Variance to stocks by using mean and volatility is not constant based on logarithmic utility function. Non constant mean analysed using models Autoregressive Moving Average (ARMA), while non constant volatility models are analysed using the Generalized Autoregressive Conditional heteroscedastic (GARCH). Optimization process is performed by using the Lagrangian multiplier technique. As a numerical illustration, the method is used to analyse some Islamic stocks in Indonesia. The expected result is to get the proportion of investment in each Islamic stock analysed.

Mean-Variance portfolio optimization by using non constant mean and volatility based on the negative exponential utility function

NASA Astrophysics Data System (ADS)

Soeryana, Endang; Halim, Nurfadhlina Bt Abdul; Sukono, Rusyaman, Endang; Supian, Sudradjat

2017-03-01

Investments in stocks investors are also faced with the issue of risk, due to daily price of stock also fluctuate. For minimize the level of risk, investors usually forming an investment portfolio. Establishment of a portfolio consisting of several stocks are intended to get the optimal composition of the investment portfolio. This paper discussed about optimizing investment portfolio of Mean-Variance to stocks by using mean and volatility is not constant based on the Negative Exponential Utility Function. Non constant mean analyzed using models Autoregressive Moving Average (ARMA), while non constant volatility models are analyzed using the Generalized Autoregressive Conditional heteroscedastic (GARCH). Optimization process is performed by using the Lagrangian multiplier technique. As a numerical illustration, the method is used to analyze some stocks in Indonesia. The expected result is to get the proportion of investment in each stock analyzed

Directionality volatility in electroencephalogram time series

NASA Astrophysics Data System (ADS)

Mansor, Mahayaudin M.; Green, David A.; Metcalfe, Andrew V.

2016-06-01

We compare time series of electroencephalograms (EEGs) from healthy volunteers with EEGs from subjects diagnosed with epilepsy. The EEG time series from the healthy group are recorded during awake state with their eyes open and eyes closed, and the records from subjects with epilepsy are taken from three different recording regions of pre-surgical diagnosis: hippocampal, epileptogenic and seizure zone. The comparisons for these 5 categories are in terms of deviations from linear time series models with constant variance Gaussian white noise error inputs. One feature investigated is directionality, and how this can be modelled by either non-linear threshold autoregressive models or non-Gaussian errors. A second feature is volatility, which is modelled by Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes. Other features include the proportion of variability accounted for by time series models, and the skewness and the kurtosis of the residuals. The results suggest these comparisons may have diagnostic potential for epilepsy and provide early warning of seizures.

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

Androgen and AR contribute to breast cancer development and metastasis: an insight of mechanisms.

PubMed

Feng, J; Li, L; Zhang, N; Liu, J; Zhang, L; Gao, H; Wang, G; Li, Y; Zhang, Y; Li, X; Liu, D; Lu, J; Huang, B

2017-05-18

The role of androgen and androgen receptor (AR) in breast carcinogenesis has long been a disputed issue. This report provides a mechanistic insight into how androgen and AR contributes to invasion and metastasis of breast cancer. We find that dihydrotestosterone (DHT) is able to induce the epithelial-to-mesenchymal transition in breast cancer cells in an AR-dependent/estrogen receptor-independent manner. This process is dependent on the demethylation activity of lysine-specific demethylase 1A (LSD1) by epigenetically regulating the target genes E-cadherin and vimentin. In vivo, DHT promotes metastasis in a nude mouse model, and AR and LSD1 are indispensable in this process. We establish that higher expression of nucleus AR to cytoplasm AR associated with worse prognostic outcomes in breast cancer patient samples. This study maps an 'androgen-AR/LSD1-target genes' pathway in breast carcinogenesis, implicating the importance of hormonal balance in women, and the potential clinical significance of serum androgen and AR in prediction of breast cancer and selection of breast cancer therapy.

40Ar/39Ar geochronology of terrestrial pyroxene

NASA Astrophysics Data System (ADS)

Ware, Bryant; Jourdan, Fred

2018-06-01

Geochronological techniques such as U/Pb in zircon and baddeleyite and 40Ar/39Ar on a vast range of minerals, including sanidine, plagioclase, and biotite, provide means to date an array of different geologic processes. Many of these minerals, however, are not always present in a given rock, or can be altered by secondary processes (e.g. plagioclase in mafic rocks) limiting our ability to derive an isotopic age. Pyroxene is a primary rock forming mineral for both mafic and ultramafic rocks and is resistant to alteration process but attempts to date this phase with 40Ar/39Ar has been met with little success so far. In this study, we analyzed pyroxene crystals from two different Large Igneous Provinces using a multi-collector noble gas mass spectrometer (ARGUS VI) since those machines have been shown to significantly improve analytical precision compared to the previous single-collector instruments. We obtain geologically meaningful and relatively precise 40Ar/39Ar plateau ages ranging from 184.6 ± 3.9 to 182.4 ± 0.8 Ma (2σ uncertainties of ±1.8-0.4%) and 506.3 ± 3.4 Ma for Tasmanian and Kalkarindji dolerites, respectively. Those data are indistinguishable from new and/or published U-Pb and 40Ar/39Ar plagioclase ages showing that 40Ar/39Ar dating of pyroxene is a suitable geochronological tool. Scrutinizing the analytical results of the pyroxene analyses as well as comparing them to the analytical result from plagioclase of the same samples indicate pure pyroxene was dated. Numerical models of argon diffusion in plagioclase and pyroxene support these observations. However, we found that the viability of 40Ar/39Ar dating approach of pyroxene can be affected by irradiation-induced recoil redistribution between thin pyroxene exsolution lamellae and the main pyroxene crystal, hence requiring careful petrographic observations before analysis. Finally, diffusion modeling show that 40Ar/39Ar of pyroxene can be used as a powerful tool to date the formation age of mafic rocks affected by greenschist metamorphism and will likely play an important role in high temperature thermochronology.

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.

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

76 FR 62330 - Radio Broadcasting Services; Alamo, GA; Alton, MO; Boscobel, WI; Buffalo, OK; Cove, AR; Clayton...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-07

... Broadcasting Services; Alamo, GA; Alton, MO; Boscobel, WI; Buffalo, OK; Cove, AR; Clayton, LA; Daisy, AR; Ennis... competitive bidding process, and are considered unsold permits that were included in Auction 91. Interested... competitive bidding process. DATES: Comments must be filed on or before October 31, 2011, and reply comments...

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system.

PubMed

Min, Jianliang; Wang, Ping; Hu, Jianfeng

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1-2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.

Army Family Policies and Practices: A Summary of Regulations, Letters, Pamphlets, and Circulars That Impact on Army Families

DTIC Science & Technology

1990-05-01

AR 60-10 Army and Air Force Exchange Service (AAFES) General Policies.. ................. . . . 12 AR 60-2 0 Army and Air Force Exchange Service (AAFES...Initial Active Duty, Initial Active Duty for Training, and Reserve Forces Duty . . . . . . ........... 29 AR 601-27 Military Entrance Processing...AR 608-20 Voting by Personnel of the Armed Forces . . . . .... 35 AR 608-25 Retirement Services Program . ...... 36 AR 608-61 Application for

Nitrate affects sensu-stricto germination of after-ripened Sisymbrium officinale seeds by modifying expression of SoNCED5, SoCYP707A2 and SoGA3ox2 genes.

PubMed

Carrillo-Barral, Néstor; Matilla, Angel J; Rodríguez-Gacio, María del Carmen; Iglesias-Fernández, Raquel

2014-03-01

The influence of nitrate upon the germination of Sisymbrium officinale seeds is not entirely controlled by after-ripening (AR), a process clearly influenced by nitrate. Recently, we have reported that nitrate affects sensu-stricto germination of non-AR (AR0) seeds by modifying the expression of crucial genes involved in the metabolism of GA and ABA. In this study, we demonstrate that nitrate affects also the germination of AR seeds because: (i) the AR negatively alters the ABA sensitivity being the seed more ABA-sensible as the AR is farthest from optimal (AR0 and AR20 versus AR7); in the presence of diniconazole (DZ), a competitive inhibitor of ABA 8'-hydroxylase, testa rupture is affected while the endosperm rupture is not. (ii) AR7 seed-coat rupture is not inhibited by paclobutrazol (PBZ) suggesting that nitrate can act by a mechanism GA-independent. (iii) The germination process is accelerated by nitrate, most probably by the increase in the expression of SoNCED5, SoCYP707A2 and SoGA3ox2 genes. Taken together, these and previous results demonstrate that nitrate promotes germination of AR and non-AR seeds through transcriptional changes of different genes involved in ABA and GA metabolism. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Time Series in Education: The Analysis of Daily Attendance in Two High Schools

ERIC Educational Resources Information Center

Koopmans, Matthijs

2011-01-01

This presentation discusses the use of a time series approach to the analysis of daily attendance in two urban high schools over the course of one school year (2009-10). After establishing that the series for both schools were stationary, they were examined for moving average processes, autoregression, seasonal dependencies (weekly cycles),…

The Effects of Autocorrelation on the Curve-of-Factors Growth Model

ERIC Educational Resources Information Center

Murphy, Daniel L.; Beretvas, S. Natasha; Pituch, Keenan A.

2011-01-01

This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a…

Examining the Predictive Relations between Two Aspects of Self-Regulation and Growth in Preschool Children's Early Literacy Skills

ERIC Educational Resources Information Center

Lonigan, Christopher J.; Allan, Darcey M.; Phillips, Beth M.

2017-01-01

There is strong evidence that self-regulatory processes are linked to early academic skills, both concurrently and longitudinally. The majority of extant longitudinal studies, however, have been conducted using autoregressive techniques that may not accurately model change across time. The purpose of this study was to examine the unique…

7 CFR 520.3 - Policy.

Code of Federal Regulations, 2011 CFR

2011-01-01

... PROCEDURES FOR IMPLEMENTING NATIONAL ENVIRONMENTAL POLICY ACT § 520.3 Policy. (a) It is ARS policy to comply... are unanticipated and extraordinary may be made in the Office of the Administrator of ARS. (e) ARS... activities involving the ARS to assure that NEPA considerations are addressed early in the planning process...

7 CFR 520.3 - Policy.

Code of Federal Regulations, 2012 CFR

2012-01-01

... PROCEDURES FOR IMPLEMENTING NATIONAL ENVIRONMENTAL POLICY ACT § 520.3 Policy. (a) It is ARS policy to comply... are unanticipated and extraordinary may be made in the Office of the Administrator of ARS. (e) ARS... activities involving the ARS to assure that NEPA considerations are addressed early in the planning process...

7 CFR 520.3 - Policy.

Code of Federal Regulations, 2010 CFR

2010-01-01

... PROCEDURES FOR IMPLEMENTING NATIONAL ENVIRONMENTAL POLICY ACT § 520.3 Policy. (a) It is ARS policy to comply... are unanticipated and extraordinary may be made in the Office of the Administrator of ARS. (e) ARS... activities involving the ARS to assure that NEPA considerations are addressed early in the planning process...

7 CFR 520.3 - Policy.

Code of Federal Regulations, 2014 CFR

2014-01-01

... PROCEDURES FOR IMPLEMENTING NATIONAL ENVIRONMENTAL POLICY ACT § 520.3 Policy. (a) It is ARS policy to comply... are unanticipated and extraordinary may be made in the Office of the Administrator of ARS. (e) ARS... activities involving the ARS to assure that NEPA considerations are addressed early in the planning process...

7 CFR 520.3 - Policy.

Code of Federal Regulations, 2013 CFR

2013-01-01

... PROCEDURES FOR IMPLEMENTING NATIONAL ENVIRONMENTAL POLICY ACT § 520.3 Policy. (a) It is ARS policy to comply... are unanticipated and extraordinary may be made in the Office of the Administrator of ARS. (e) ARS... activities involving the ARS to assure that NEPA considerations are addressed early in the planning process...

Comparison of fMRI data from passive listening and active-response story processing tasks in children

PubMed Central

Vannest, Jennifer J.; Karunanayaka, Prasanna R.; Altaye, Mekibib; Schmithorst, Vincent J.; Plante, Elena M.; Eaton, Kenneth J.; Rasmussen, Jerod M.; Holland, Scott K.

2009-01-01

Purpose To use functional MRI methods to visualize a network of auditory and language-processing brain regions associated with processing an aurally-presented story. We compare a passive listening (PL) story paradigm to an active-response (AR) version including on-line performance monitoring and a sparse acquisition technique. Materials/Methods Twenty children (ages 11−13) completed PL and AR story processing tasks. The PL version presented alternating 30-second blocks of stories and tones; the AR version presented story segments, comprehension questions, and 5s tone sequences, with fMRI acquisitions between stimuli. fMRI data was analyzed using a general linear model approach and paired t-test identifying significant group activation. Results Both tasks activated in primary auditory cortex, superior temporal gyrus bilaterally, left inferior frontal gyrus. The AR task demonstrated more extensive activation, including dorsolateral prefrontal cortex and anterior/posterior cingulate cortex. Comparison of effect size in each paradigm showed a larger effect for the AR paradigm in a left inferior frontal ROI. Conclusion Activation patterns for story processing in children are similar in passive listening and active-response tasks. Increases in extent and magnitude of activation in the AR task are likely associated with memory and attention resources engaged across acquisition intervals. PMID:19306445

Mitochondria-associated endoplasmic reticulum membrane (MAM) regulates steroidogenic activity via steroidogenic acute regulatory protein (StAR)-voltage-dependent anion channel 2 (VDAC2) interaction.

PubMed

Prasad, Manoj; Kaur, Jasmeet; Pawlak, Kevin J; Bose, Mahuya; Whittal, Randy M; Bose, Himangshu S

2015-01-30

Steroid hormones are essential for carbohydrate metabolism, stress management, and reproduction and are synthesized from cholesterol in mitochondria of adrenal glands and gonads/ovaries. In acute stress or hormonal stimulation, steroidogenic acute regulatory protein (StAR) transports substrate cholesterol into the mitochondria for steroidogenesis by an unknown mechanism. Here, we report for the first time that StAR interacts with voltage-dependent anion channel 2 (VDAC2) at the mitochondria-associated endoplasmic reticulum membrane (MAM) prior to its translocation to the mitochondrial matrix. In the MAM, StAR interacts with mitochondrial proteins Tom22 and VDAC2. However, Tom22 knockdown by siRNA had no effect on pregnenolone synthesis. In the absence of VDAC2, StAR was expressed but not processed into the mitochondria as a mature 30-kDa protein. VDAC2 interacted with StAR via its C-terminal 20 amino acids and N-terminal amino acids 221-229, regulating the mitochondrial processing of StAR into the mature protein. In the absence of VDAC2, StAR could not enter the mitochondria or interact with MAM-associated proteins, and therefore steroidogenesis was inhibited. Furthermore, the N terminus was not essential for StAR activity, and the N-terminal deletion mutant continued to interact with VDAC2. The endoplasmic reticulum-targeting prolactin signal sequence did not affect StAR association with the MAM and thus its mitochondrial targeting. Therefore, VDAC2 controls StAR processing and activity, and MAM is thus a central location for initiating mitochondrial steroidogenesis. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Comparative Performance Evaluation of Rainfall-runoff Models, Six of Black-box Type and One of Conceptual Type, From The Galway Flow Forecasting System (gffs) Package, Applied On Two Irish Catchments

NASA Astrophysics Data System (ADS)

Goswami, M.; O'Connor, K. M.; Shamseldin, A. Y.

The "Galway Real-Time River Flow Forecasting System" (GFFS) is a software pack- age developed at the Department of Engineering Hydrology, of the National University of Ireland, Galway, Ireland. It is based on a selection of lumped black-box and con- ceptual rainfall-runoff models, all developed in Galway, consisting primarily of both the non-parametric (NP) and parametric (P) forms of two black-box-type rainfall- runoff models, namely, the Simple Linear Model (SLM-NP and SLM-P) and the seasonally-based Linear Perturbation Model (LPM-NP and LPM-P), together with the non-parametric wetness-index-based Linearly Varying Gain Factor Model (LVGFM), the black-box Artificial Neural Network (ANN) Model, and the conceptual Soil Mois- ture Accounting and Routing (SMAR) Model. Comprised of the above suite of mod- els, the system enables the user to calibrate each model individually, initially without updating, and it is capable also of producing combined (i.e. consensus) forecasts us- ing the Simple Average Method (SAM), the Weighted Average Method (WAM), or the Artificial Neural Network Method (NNM). The updating of each model output is achieved using one of four different techniques, namely, simple Auto-Regressive (AR) updating, Linear Transfer Function (LTF) updating, Artificial Neural Network updating (NNU), and updating by the Non-linear Auto-Regressive Exogenous-input method (NARXM). The models exhibit a considerable range of variation in degree of complexity of structure, with corresponding degrees of complication in objective func- tion evaluation. Operating in continuous river-flow simulation and updating modes, these models and techniques have been applied to two Irish catchments, namely, the Fergus and the Brosna. A number of performance evaluation criteria have been used to comparatively assess the model discharge forecast efficiency.

Energy and charge transfer in ionized argon coated water clusters.

PubMed

Kočišek, J; Lengyel, J; Fárník, M; Slavíček, P

2013-12-07

We investigate the electron ionization of clusters generated in mixed Ar-water expansions. The electron energy dependent ion yields reveal the neutral cluster composition and structure: water clusters fully covered with the Ar solvation shell are formed under certain expansion conditions. The argon atoms shield the embedded (H2O)n clusters resulting in the ionization threshold above ≈15 eV for all fragments. The argon atoms also mediate more complex reactions in the clusters: e.g., the charge transfer between Ar(+) and water occurs above the threshold; at higher electron energies above ~28 eV, an excitonic transfer process between Ar(+)* and water opens leading to new products Ar(n)H(+) and (H2O)(n)H(+). On the other hand, the excitonic transfer from the neutral Ar* state at lower energies is not observed although this resonant process was demonstrated previously in a photoionization experiment. Doubly charged fragments (H2O)(n)H2(2+) and (H2O)(n)(2+) ions are observed and Intermolecular Coulomb decay (ICD) processes are invoked to explain their thresholds. The Coulomb explosion of the doubly charged cluster formed within the ICD process is prevented by the stabilization effect of the argon solvent.

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.

An ergonomics action research demonstration: integrating human factors into assembly design processes.

PubMed

Village, J; Greig, M; Salustri, F; Zolfaghari, S; Neumann, W P

2014-01-01

In action research (AR), the researcher participates 'in' the actions in an organisation, while simultaneously reflecting 'on' the actions to promote learning for both the organisation and the researchers. This paper demonstrates a longitudinal AR collaboration with an electronics manufacturing firm where the goal was to improve the organisation's ability to integrate human factors (HF) proactively into their design processes. During the three-year collaboration, all meetings, workshops, interviews and reflections were digitally recorded and qualitatively analysed to inform new 'actions'. By the end of the collaboration, HF tools with targets and sign-off by the HF specialist were integrated into several stages of the design process, and engineers were held accountable for meeting the HF targets. We conclude that the AR approach combined with targeting multiple initiatives at different stages of the design process helped the organisation find ways to integrate HF into their processes in a sustainable way. Researchers acted as a catalyst to help integrate HF into the engineering design process in a sustainable way. This paper demonstrates how an AR approach can help achieve HF integration, the benefits of using a reflective stance and one method for reporting an AR study.

Advancements in cosmogenic 38Ar exposure dating of terrestrial rocks

NASA Astrophysics Data System (ADS)

Oostingh, K. F.; Jourdan, F.; Danišík, M.; Evans, N. J.

2017-11-01

Cosmogenic exposure dating of Ca-rich minerals using 38Ar on terrestrial rocks could be a valuable new dating tool to determine timescales of geological surface processes on Earth. Here, we show that advancement in analytical precision, using the new generation multi-collector ARGUSVI mass spectrometer on irradiated pyroxene and apatite samples, allows determination of cosmogenic 38Ar abundances above background values, as well as discrimination of 38Ar/36Ar ratios (1σ absolute precision of ±0.3%) from the non-cosmogenic background value. Four statistically significant cosmochron (38Ar/36Ar vs37Ar/36Ar) diagrams could be constructed for southeast Australian pyroxene samples from the Mt Elephant scoria cone for which a combined apparent exposure age of 313 ± 179 ka (2σ) was obtained when using a 38Ar production rate (Ca) of 250 atoms /g Ca/ yr. This exposure age overlaps within error with the known 40Ar/39Ar eruption age of 184 ± 15 ka (2σ). Although apatite shows much larger 38Ar abundances than pyroxene, our modelling and analyses of unirradiated apatite suggest that apatite suffers from both natural and reactor-derived chlorogenic as well as natural nucleogenic contributions of 38Ar. Hence, we suggest that cosmogenic 38Ar exposure dating on irradiated Ca-rich (and eventually K-rich), but Cl-free, terrestrial minerals is a potential valuable and accessible tool to determine geological surface processes on timescales of a few Ma. Calculations show that with the new generation multi-collector mass spectrometers an analytical uncertainty better than 5% (2σ) can be achieved on samples with expected exposure ages of >4 Ma.

Ultrastructural and biochemical evidence for the presence of mature steroidogenic acute regulatory protein (StAR) in the cytoplasm of human luteal cells.

PubMed

Sierralta, Walter D; Kohen, Paulina; Castro, Olga; Muñoz, Alex; Strauss, Jerome F; Devoto, Luigi

2005-10-20

The distribution of the steroidogenic acute regulatory protein (StAR) inside thecal and granulosa-lutein cells of human corpus luteum (CL) was assessed by immunoelectron microscopy. We found greater levels of StAR immunolabeling in steroidogenic cells from early- and mid-than in late luteal phase CL and lower levels in cells from women treated with a GnRH antagonist in the mid-luteal phase. Immunoelectron microscopy revealed significant levels of StAR antigen in the mitochondria and in the cytoplasm of luteal cells. The 30 kDa mature StAR protein was present in both mitochondria and cytosol (post-mitochondrial) fractions from homogenates of CL at different ages, whereas cytochrome c and mitochondrial HSP70 were detected only in the mitochondrial fraction. Therefore, we hypothesized that either appreciable processing of StAR 37 kDa pre-protein occurs outside the mitochondria, or mature StAR protein is selectively released into the cytoplasm after mitochondrial processing. The presence of mature StAR in the cytoplasm is consonant with the notion that StAR acts on the outer mitochondrial membrane to effect sterol import, and that StAR may interact with other cytoplasmic proteins involved in cholesterol metabolism, including hormone sensitive lipase.

Lessons Learnt from Applying Action Research to Support Strategy Formation Processes in Long-Term Care Networks

ERIC Educational Resources Information Center

Cramer, Hendrik; Dewulf, Geert; Voordijk, Hans

2015-01-01

This study demonstrates how action research (AR) that is aimed at scaling-up experiments can be applied to support a strategy formation process (SFP) in a subsidized long-term care network. Previous research has developed numerous AR frameworks to support experiments in various domains, but has failed to explain how to apply AR and action learning…

Spatial Dynamics and Determinants of County-Level Education Expenditure in China

ERIC Educational Resources Information Center

Gu, Jiafeng

2012-01-01

In this paper, a multivariate spatial autoregressive model of local public education expenditure determination with autoregressive disturbance is developed and estimated. The existence of spatial interdependence is tested using Moran's I statistic and Lagrange multiplier test statistics for both the spatial error and spatial lag models. The full…

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...

Cassette structures associated with antibiotic resistance genes in Salmonella enterica isolated from processing plants, food animals, and retail meats

USDA-ARS?s Scientific Manuscript database

Slowing the spread of antibiotic resistance (AR) is one of the most urgent tasks currently facing the field of microbiology. Mobile genetic elements, like plasmids and integrons, allow AR genes to transfer horizontally, thus increasing the spread of AR genes. Determining which AR genes are found on ...

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

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.

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.

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process.

PubMed

Moran, John L; Solomon, Patricia J

2013-05-24

Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. Monthly mean raw mortality (at hospital discharge) time series, 1995-2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) "in-control" status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag40 and 35% had autocorrelation through to lag40; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.

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.

Automatic Information Processing and High Performance Skills: Individual Differences and Mechanisms of Performance Improvement in Search-Detection and Complex Task

DTIC Science & Technology

1992-09-01

abilities is fit along with the autoregressive process. Initially, the influences on search performance of within-group age and sex were included as control...Results: PerformanceLAbility Structure Measurement Model: Ability Structure The correlations between all the ability measures, age, and sex are...subsequent analyses for young adults. Age and sex were included as control variables. There was an age range of 15 years; this range is sufficiently large that

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

Comparative simulation analysis on the ignition threshold of atmospheric He and Ar dielectric barrier discharge

NASA Astrophysics Data System (ADS)

Yao, Congwei; Chang, Zhengshi; Chen, Sile; Ma, Hengchi; Mu, Haibao; Zhang, Guan-Jun

2017-09-01

Dielectric barrier discharge (DBD) is widely applied in many fields, and the discharge characteristics of insert gas have been the research focus for years. In this paper, fluid models of atmospheric Ar and He DBDs driven by 22 kHz sinusoidal voltage are built to analyze their ignition processes. The contributions of different electron sources in ignition process are analyzed, including the direct ionization of ground state atom, stepwise ionization of metastable particles, and secondary electron emission from dielectric wall, and they play different roles in different discharge stages. The Townsend direct ionization coefficient of He is higher than Ar with the same electrical field intensity, which is the direct reason for the different ignition thresholds between He and Ar. Further, the electron energy loss per free electron produced in Ar and He DBDs is discussed. It is found that the total electron energy loss rate of Ar is higher than He when the same electrical field is applied. The excitation reaction of Ar consumes the major electron energy but cannot produce free electrons effectively, which is the essential reason for the higher ignition threshold of Ar. The computation results of He and Ar extinction voltages can be explained in the view of electron energy loss, as well as the experimental results of different extinction voltages between Ar/NH3 and He DBDs.

The effect of case management and vector-control interventions on space-time patterns of malaria incidence in Uganda.

PubMed

Ssempiira, Julius; Kissa, John; Nambuusi, Betty; Kyozira, Carol; Rutazaana, Damian; Mukooyo, Eddie; Opigo, Jimmy; Makumbi, Fredrick; Kasasa, Simon; Vounatsou, Penelope

2018-04-12

Electronic reporting of routine health facility data in Uganda began with the adoption of the District Health Information Software System version 2 (DHIS2) in 2011. This has improved health facility reporting and overall data quality. In this study, the effects of case management with artemisinin-based combination therapy (ACT) and vector control interventions on space-time patterns of disease incidence were determined using DHIS2 data reported during 2013-2016. Bayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. The effects of interventions were adjusted for socio-economic and climatic factors. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive AR(1) process on district and monthly specific random effects, respectively. Fourier trigonometric functions were incorporated in the models to take into account seasonal fluctuations in malaria transmission. The temporal variation in incidence was similar in both age groups and depicted a steady decline up to February 2014, followed by an increase from March 2015 onwards. The trends were characterized by a strong bi-annual seasonal pattern with two peaks during May-July and September-December. Average monthly incidence in children < 5 years declined from 74.7 cases (95% CI 72.4-77.1) in 2013 to 49.4 (95% CI 42.9-55.8) per 1000 in 2015 and followed by an increase in 2016 of up to 51.3 (95% CI 42.9-55.8). In individuals ≥ 5 years, a decline in incidence from 2013 to 2015 was followed by an increase in 2016. A 100% increase in insecticide-treated nets (ITN) coverage was associated with a decline in incidence by 44% (95% BCI 28-59%). Similarly, a 100% increase in ACT coverage reduces incidence by 28% (95% BCI 11-45%) and 25% (95% BCI 20-28%) in children < 5 years and individuals ≥ 5 years, respectively. The ITN effect was not statistically important in older individuals. The space-time patterns of malaria incidence in children < 5 are similar to those of parasitaemia risk predicted from the malaria indicator survey of 2014-15. The decline in malaria incidence highlights the effectiveness of vector-control interventions and case management with ACT in Uganda. This calls for optimizing and sustaining interventions to achieve universal coverage and curb reverses in malaria decline.

Conduction velocity of the uterine contraction in serial magnetomyogram (MMG) data: event based simulation and validation.

PubMed

Furdea, Adrian; Preissl, Hubert; Lowery, Curtis L; Eswaran, Hari; Govindan, Rathinaswamy B

2011-01-01

We propose a novel approach to calculate the conduction velocity (CV) of the uterine contraction bursts in magnetomyogram (MMG) signals measured using a multichannel SQUID array. For this purpose, we partition the sensor coordinates into four different quadrants and identify the contractile bursts using a previously proposed Hilbert-wavelet transform approach. If contractile burst is identified in more than one quadrant, we calculate the center of gravity (CoG) in each quadrant for each time point as the sum of the product of the sensor coordinates with the Hilbert amplitude of the MMG signals normalized by the sum of the Hilbert amplitude of the signals over all sensors. Following this we compute the delay between the CoGs of all (six) possible quadrant pairs combinations. As a first step, we validate this approach by simulating a stochastic model based on independent second-order autoregressive processes (AR2) and we divide them into 30 second disjoint windows and insert burst activity at specific time instances in preselected sensors. Also we introduce a lag of 5 ± 1 seconds between different quadrants. Using our approach we calculate the CoG of the signals in a quadrant. To this end, we compute the delay between CoGs obtained from different quadrants and show that our approach is able to reliably capture the delay incorporated in the model. We apply the proposed approach to 19 serial MMG data obtained from two subjects and show an increase in the CV as the subjects approached labor.

Allergic rhinitis and inflammatory airway disease: interactions within the unified airspace.

PubMed

Marple, Bradley F

2010-01-01

Allergic rhinitis (AR), the most common chronic allergic condition in outpatient medicine, is associated with immense health care costs and socioeconomic consequences. AR's impact may be partly from interacting of respiratory conditions via allergic inflammation. This study was designed to review potential interactive mechanisms of AR and associated conditions and consider the relevance of a bidirectional "unified airway" respiratory inflammation model on diagnosis and treatment of inflammatory airway disease. MEDLINE was searched for pathophysiology and pathophysiological and epidemiologic links between AR and diseases of the sinuses, lungs, middle ear, and nasopharynx. Allergic-related inflammatory responses or neural and systemic processes fostering inflammatory changes distant from initial allergen provocation may link AR and comorbidities. Treating AR may benefit associated respiratory tract comorbidities. Besides improving AR outcomes, treatment inhibiting eosinophil recruitment and migration, normalizing cytokine profiles, and reducing asthma-associated health care use in atopic subjects would likely ameliorate other upper airway diseases such as acute rhinosinusitis, chronic rhinosinusitis (CRS) with nasal polyposis (NP), adenoidal hypertrophy, and otitis media with effusion. Epidemiological concordance of AR with several airway diseases conforms to a bidirectional "unified airway" respiratory inflammation model based on anatomic and histological upper and lower airway connections. Epidemiology and current understanding of inflammatory, humoral, and neural processes make links between AR and disorders including asthma, otitis media, NP, and CRS plausible. Combining AR with associated conditions increases disease burden; worsened associated illness may accompany worsened AR. AR pharmacotherapies include antihistamines, leukotriene antagonists, intranasal corticosteroids, and immunotherapy; treatments attenuating proinflammatory responses may also benefit associated conditions.

Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleoclimate data

NASA Astrophysics Data System (ADS)

Henley, B. J.; Thyer, M. A.; Kuczera, G. A.

2012-12-01

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. To characterize long-term variability for the first level of the hierarchy, paleoclimate and instrumental data describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yrs is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run-lengths, with 90% between 3 and 33 yr and a mean of 15 yr. Model selection techniques were used to determine a suitable stochastic model to simulate these run-lengths. The Markov chain model, previously used to simulate oscillating wet/dry climate states, was found to underestimate the probability of wet/dry periods >5 yr, and was rejected in favor of a gamma distribution. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. Application to two high-quality rainfall sites close to water supply reservoirs found that mean seasonal rainfall in the IPO-PDO dry state was 15%-28% lower than the wet state. The model was able to replicate observed statistics such as seasonal and multi-year accumulated rainfall distributions and interannual autocorrelations for the case study sites. In comparison, an annual lag-one autoregressive AR(1) model was unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Furthermore, analysis of the impact of the CIMSS framework on drought risk analysis found that short-term drought risks conditional on IPO/PDO state were considerably higher than the traditional AR(1) model.hort-term conditional water supply drought risks for the CIMSS and AR(1) models for the dry IPO-PDO scenario with a range of initial storage levels expressed as a proportion of the annual demand (yield).

Stimulation of StAR expression by cAMP is controlled by inhibition of highly inducible SIK1 via CRTC2, a co-activator of CREB.

PubMed

Lee, Jinwoo; Tong, Tiegang; Takemori, Hiroshi; Jefcoate, Colin

2015-06-15

In mouse steroidogenic cells the activation of cholesterol metabolism is mediated by steroidogenic acute regulatory protein (StAR). Here, we visualized a coordinated regulation of StAR transcription, splicing and post-transcriptional processing, which are synchronized by salt inducible kinase (SIK1) and CREB-regulated transcription coactivator (CRTC2). To detect primary RNA (pRNA), spliced primary RNA (Sp-RNA) and mRNA in single cells, we generated probe sets by using fluorescence in situ hybridization (FISH). These methods allowed us to address the nature of StAR gene expression and to visualize protein-nucleic acid interactions through direct detection. We show that SIK1 represses StAR expression in Y1 adrenal and MA10 testis cells through inhibition of processing mediated by CRTC2. Digital image analysis matches qPCR analyses of the total cell culture. Evidence is presented for spatially separate accumulation of StAR pRNA and Sp-RNA at the gene loci in the nucleus. These findings establish that cAMP, SIK and CRTC mediate StAR expression through activation of individual StAR gene loci. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

An investigation of Ar metastable state density in low pressure dual-frequency capacitively coupled argon and argon-diluted plasmas

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

Liu, Wen-Yao; Xu, Yong, E-mail: yongxu@dlut.edu.cn; Peng, Fei

2015-01-14

An tunable diode laser absorption spectroscopy has been used to determine the Ar*({sup 3}P{sub 2}) and Ar*({sup 3}P{sub 0}) metastable atoms densities in dual-frequency capacitively coupled plasmas. The effects of different control parameters, such as high-frequency power, gas pressure and content of Ar, on the densities of two metastable atoms and electron density were discussed in single-frequency and dual-frequency Ar discharges, respectively. Particularly, the effects of the pressure on the axial profile of the electron and Ar metastable state densities were also discussed. Furthermore, a simple rate model was employed and its results were compared with experiments to analyze themore » main production and loss processes of Ar metastable states. It is found that Ar metastable state is mainly produced by electron impact excitation from the ground state, and decayed by diffusion and collision quenching with electrons and neutral molecules. Besides, the addition of CF{sub 4} was found to significantly increase the metastable destruction rate by the CF{sub 4} quenching, especially for large CF{sub 4} content and high pressure, it becomes the dominant depopulation process.« less

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

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks.

PubMed

Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R; Nguyen, Tuan N; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-01-01

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks

PubMed Central

Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R.; Nguyen, Tuan N.; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T.

2017-01-01

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively. PMID:28326009

Analysis of volatile compounds in exhaled breath condensate in patients with severe pulmonary arterial hypertension.

PubMed

Mansoor, J K; Schelegle, Edward S; Davis, Cristina E; Walby, William F; Zhao, Weixiang; Aksenov, Alexander A; Pasamontes, Alberto; Figueroa, Jennifer; Allen, Roblee

2014-01-01

An important challenge to pulmonary arterial hypertension (PAH) diagnosis and treatment is early detection of occult pulmonary vascular pathology. Symptoms are frequently confused with other disease entities that lead to inappropriate interventions and allow for progression to advanced states of disease. There is a significant need to develop new markers for early disease detection and management of PAH. Exhaled breath condensate (EBC) samples were compared from 30 age-matched normal healthy individuals and 27 New York Heart Association functional class III and IV idiopathic pulmonary arterial hypertenion (IPAH) patients, a subgroup of PAH. Volatile organic compounds (VOC) in EBC samples were analyzed using gas chromatography/mass spectrometry (GC/MS). Individual peaks in GC profiles were identified in both groups and correlated with pulmonary hemodynamic and clinical endpoints in the IPAH group. Additionally, GC/MS data were analyzed using autoregression followed by partial least squares regression (AR/PLSR) analysis to discriminate between the IPAH and control groups. After correcting for medicaitons, there were 62 unique compounds in the control group, 32 unique compounds in the IPAH group, and 14 in-common compounds between groups. Peak-by-peak analysis of GC profiles of IPAH group EBC samples identified 6 compounds significantly correlated with pulmonary hemodynamic variables important in IPAH diagnosis. AR/PLSR analysis of GC/MS data resulted in a distinct and identifiable metabolic signature for IPAH patients. These findings indicate the utility of EBC VOC analysis to discriminate between severe IPAH and a healthy population; additionally, we identified potential novel biomarkers that correlated with IPAH pulmonary hemodynamic variables that may be important in screening for less severe forms IPAH.

Analysis of Volatile Compounds in Exhaled Breath Condensate in Patients with Severe Pulmonary Arterial Hypertension

PubMed Central

Mansoor, J. K.; Schelegle, Edward S.; Davis, Cristina E.; Walby, William F.; Zhao, Weixiang; Aksenov, Alexander A.; Pasamontes, Alberto; Figueroa, Jennifer; Allen, Roblee

2014-01-01

Background An important challenge to pulmonary arterial hypertension (PAH) diagnosis and treatment is early detection of occult pulmonary vascular pathology. Symptoms are frequently confused with other disease entities that lead to inappropriate interventions and allow for progression to advanced states of disease. There is a significant need to develop new markers for early disease detection and management of PAH. Methodolgy and Findings Exhaled breath condensate (EBC) samples were compared from 30 age-matched normal healthy individuals and 27 New York Heart Association functional class III and IV idiopathic pulmonary arterial hypertenion (IPAH) patients, a subgroup of PAH. Volatile organic compounds (VOC) in EBC samples were analyzed using gas chromatography/mass spectrometry (GC/MS). Individual peaks in GC profiles were identified in both groups and correlated with pulmonary hemodynamic and clinical endpoints in the IPAH group. Additionally, GC/MS data were analyzed using autoregression followed by partial least squares regression (AR/PLSR) analysis to discriminate between the IPAH and control groups. After correcting for medicaitons, there were 62 unique compounds in the control group, 32 unique compounds in the IPAH group, and 14 in-common compounds between groups. Peak-by-peak analysis of GC profiles of IPAH group EBC samples identified 6 compounds significantly correlated with pulmonary hemodynamic variables important in IPAH diagnosis. AR/PLSR analysis of GC/MS data resulted in a distinct and identifiable metabolic signature for IPAH patients. Conclusions These findings indicate the utility of EBC VOC analysis to discriminate between severe IPAH and a healthy population; additionally, we identified potential novel biomarkers that correlated with IPAH pulmonary hemodynamic variables that may be important in screening for less severe forms IPAH. PMID:24748102

Assessing Precipitation Isotope Variations during Atmospheric River Events to Reveal Dominant Atmospheric/Hydrologic Processes

NASA Astrophysics Data System (ADS)

McCabe-Glynn, S. E.; Johnson, K. R.; Yoshimura, K.; Buenning, N. H.; Welker, J. M.

2015-12-01

Extreme precipitation events across the Western US commonly associated with atmospheric rivers (ARs), whereby extensive fluxes of moisture are transported from the subtropics, can result in major damage and are projected by most climate models to increase in frequency and severity. However, they are difficult to project beyond ~ten days and the location of landfall and topographically induced precipitation is even more uncertain. Water isotopes, often used to reconstruct past rainfall variability, are useful natural tracers of atmospheric hydrologic processes. Because of the typical tropical and sub-tropical origins, ARs can carry unique water isotope (δ18O and δ2H, d-excess) signatures that can be utilized to provide source and process information that can lead to improving AR predictions. Recent analysis of the top 10 weekly precipitation total samples from Sequoia National Park, CA, of which 9 contained AR events, shows a high variability in the isotopic values. NOAA Hysplit back trajectory analyses reveals a variety of trajectories and varying latitudinal source regions contributed to moisture delivered to this site, which may explain part of the high variability (δ2H = -150.03 to -49.52 ‰, δ18O = -19.27 to -7.20 ‰, d-excess = 4.1 to 25.8). Here we examine the top precipitation totals occurring during AR events and the associated isotopic composition of precipitation samples from several sites across the Western US. We utilize IsoGSM, an isotope-enabled atmospheric general circulation model, to characterize the hydrologic processes and physical dynamics contributing to the observed isotopic variations. We investigate isotopic influences from moisture source location, AR speed, condensation height, and associated temperature. We explore the dominant controls on spatial and temporal variations of the isotopic composition of AR precipitation which highlights different physical processes for different AR events.

Biological and physical controls on O2/Ar, Ar and pCO2 variability at the Western Antarctic Peninsula and in the Drake Passage

NASA Astrophysics Data System (ADS)

Eveleth, R.; Cassar, N.; Doney, S. C.; Munro, D. R.; Sweeney, C.

2017-05-01

Using simultaneous sub-kilometer resolution underway measurements of surface O2/Ar, total O2 and pCO2 from annual austral summer surveys in 2012, 2013 and 2014, we explore the impacts of biological and physical processes on the O2 and pCO2 system spatial and interannual variability at the Western Antarctic Peninsula (WAP). In the WAP, mean O2/Ar supersaturation was (7.6±9.1)% and mean pCO2 supersaturation was (-28±22)%. We see substantial spatial variability in O2 and pCO2 including sub-mesoscale/mesoscale variability with decorrelation length scales of 4.5 km, consistent with the regional Rossby radius. This variability is embedded within onshore-offshore gradients. O2 in the LTER grid region is driven primarily by biological processes as seen by the median ratio of the magnitude of biological oxygen (O2/Ar) to physical oxygen (Ar) supersaturation anomalies (%) relative to atmospheric equilibrium (2.6), however physical processes have a more pronounced influence in the southern onshore region of the grid where we see active sea-ice melting. Total O2 measurements should be interpreted with caution in regions of significant sea-ice formation and melt and glacial meltwater input. pCO2 undersaturation predominantly reflects biological processes in the LTER grid. In contrast we compare these results to the Drake Passage where gas supersaturations vary by smaller magnitudes and decorrelate at length scales of 12 km, in line with latitudinal changes in the regional Rossby radius. Here biological processes induce smaller O2/Ar supersaturations (mean (0.14±1.3)%) and pCO2 undersaturations (mean (-2.8±3.9)%) than in the WAP, and pressure changes, bubble and gas exchange fluxes drive stable Ar supersaturations.

Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation.

PubMed

Zhang, Xiangjun; Wu, Xiaolin

2008-06-01

The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

Automatic load forecasting. Final report

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

Nelson, D.J.; Vemuri, S.

A method which 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 consists 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. A procedure is also defined for incorporating temperature as a variable to improve forecasts where loads are temperature dependent. The method presented has several advantages in comparison to the Box-Jenkins method including much less human intervention and improvedmore » model identification. The method has been tested using three-hourly data from the Lincoln Electric System, Lincoln, Nebraska. In the exhaustive analyses performed on this data base this method produced significantly better results than the Box-Jenkins method. The method also proved to be more robust in that greater confidence could be placed in the accuracy of models based upon the various measures available at the identification stage.« less

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Time-series analysis of delta13C from tree rings. I. Time trends and autocorrelation.

PubMed

Monserud, R A; Marshall, J D

2001-09-01

Univariate time-series analyses were conducted on stable carbon isotope ratios obtained from tree-ring cellulose. We looked for the presence and structure of autocorrelation. Significant autocorrelation violates the statistical independence assumption and biases hypothesis tests. Its presence would indicate the existence of lagged physiological effects that persist for longer than the current year. We analyzed data from 28 trees (60-85 years old; mean = 73 years) of western white pine (Pinus monticola Dougl.), ponderosa pine (Pinus ponderosa Laws.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca) growing in northern Idaho. Material was obtained by the stem analysis method from rings laid down in the upper portion of the crown throughout each tree's life. The sampling protocol minimized variation caused by changing light regimes within each tree. Autoregressive moving average (ARMA) models were used to describe the autocorrelation structure over time. Three time series were analyzed for each tree: the stable carbon isotope ratio (delta(13)C); discrimination (delta); and the difference between ambient and internal CO(2) concentrations (c(a) - c(i)). The effect of converting from ring cellulose to whole-leaf tissue did not affect the analysis because it was almost completely removed by the detrending that precedes time-series analysis. A simple linear or quadratic model adequately described the time trend. The residuals from the trend had a constant mean and variance, thus ensuring stationarity, a requirement for autocorrelation analysis. The trend over time for c(a) - c(i) was particularly strong (R(2) = 0.29-0.84). Autoregressive moving average analyses of the residuals from these trends indicated that two-thirds of the individual tree series contained significant autocorrelation, whereas the remaining third were random (white noise) over time. We were unable to distinguish between individuals with and without significant autocorrelation beforehand. Significant ARMA models were all of low order, with either first- or second-order (i.e., lagged 1 or 2 years, respectively) models performing well. A simple autoregressive (AR(1)), model was the most common. The most useful generalization was that the same ARMA model holds for each of the three series (delta(13)C, delta, c(a) - c(i)) for an individual tree, if the time trend has been properly removed for each series. The mean series for the two pine species were described by first-order ARMA models (1-year lags), whereas the Douglas-fir mean series were described by second-order models (2-year lags) with negligible first-order effects. Apparently, the process of constructing a mean time series for a species preserves an underlying signal related to delta(13)C while canceling some of the random individual tree variation. Furthermore, the best model for the overall mean series (e.g., for a species) cannot be inferred from a consensus of the individual tree model forms, nor can its parameters be estimated reliably from the mean of the individual tree parameters. Because two-thirds of the individual tree time series contained significant autocorrelation, the normal assumption of a random structure over time is unwarranted, even after accounting for the time trend. The residuals of an appropriate ARMA model satisfy the independence assumption, and can be used to make hypothesis tests.

Adaptive clutter rejection filters for airborne Doppler weather radar applied to the detection of low altitude windshear

NASA Technical Reports Server (NTRS)

Keel, Byron M.

1989-01-01

An optimum adaptive clutter rejection filter for use with airborne Doppler weather radar is presented. The radar system is being designed to operate at low-altitudes for the detection of windshear in an airport terminal area where ground clutter returns may mask the weather return. The coefficients of the adaptive clutter rejection filter are obtained using a complex form of a square root normalized recursive least squares lattice estimation algorithm which models the clutter return data as an autoregressive process. The normalized lattice structure implementation of the adaptive modeling process for determining the filter coefficients assures that the resulting coefficients will yield a stable filter and offers possible fixed point implementation. A 10th order FIR clutter rejection filter indexed by geographical location is designed through autoregressive modeling of simulated clutter data. Filtered data, containing simulated dry microburst and clutter return, are analyzed using pulse-pair estimation techniques. To measure the ability of the clutter rejection filters to remove the clutter, results are compared to pulse-pair estimates of windspeed within a simulated dry microburst without clutter. In the filter evaluation process, post-filtered pulse-pair width estimates and power levels are also used to measure the effectiveness of the filters. The results support the use of an adaptive clutter rejection filter for reducing the clutter induced bias in pulse-pair estimates of windspeed.

Incremental heating of Bishop Tuff sanidine reveals preeruptive radiogenic Ar and rapid remobilization from cold storage

NASA Astrophysics Data System (ADS)

Andersen, Nathan L.; Jicha, Brian R.; Singer, Brad S.; Hildreth, Wes

2017-11-01

Accurate and precise ages of large silicic eruptions are critical to calibrating the geologic timescale and gauging the tempo of changes in climate, biologic evolution, and magmatic processes throughout Earth history. The conventional approach to dating these eruptive products using the 40Ar/39Ar method is to fuse dozens of individual feldspar crystals. However, dispersion of fusion dates is common and interpretation is complicated by increasingly precise data obtained via multicollector mass spectrometry. Incremental heating of 49 individual Bishop Tuff (BT) sanidine crystals produces 40Ar/39Ar dates with reduced dispersion, yet we find a 16-ky range of plateau dates that is not attributable to excess Ar. We interpret this dispersion to reflect cooling of the magma reservoir margins below ˜475 °C, accumulation of radiogenic Ar, and rapid preeruption remobilization. Accordingly, these data elucidate the recycling of subsolidus material into voluminous rhyolite magma reservoirs and the effect of preeruptive magmatic processes on the 40Ar/39Ar system. The youngest sanidine dates, likely the most representative of the BT eruption age, yield a weighted mean of 764.8 ± 0.3/0.6 ka (2σ analytical/full uncertainty) indicating eruption only ˜7 ky following the Matuyama‑Brunhes magnetic polarity reversal. Single-crystal incremental heating provides leverage with which to interpret complex populations of 40Ar/39Ar sanidine and U-Pb zircon dates and a substantially improved capability to resolve the timing and causal relationship of events in the geologic record.

Incremental heating of Bishop Tuff sanidine reveals preeruptive radiogenic Ar and rapid remobilization from cold storage.

PubMed

Andersen, Nathan L; Jicha, Brian R; Singer, Brad S; Hildreth, Wes

2017-11-21

Accurate and precise ages of large silicic eruptions are critical to calibrating the geologic timescale and gauging the tempo of changes in climate, biologic evolution, and magmatic processes throughout Earth history. The conventional approach to dating these eruptive products using the 40 Ar/ 39 Ar method is to fuse dozens of individual feldspar crystals. However, dispersion of fusion dates is common and interpretation is complicated by increasingly precise data obtained via multicollector mass spectrometry. Incremental heating of 49 individual Bishop Tuff (BT) sanidine crystals produces 40 Ar/ 39 Ar dates with reduced dispersion, yet we find a 16-ky range of plateau dates that is not attributable to excess Ar. We interpret this dispersion to reflect cooling of the magma reservoir margins below ∼475 °C, accumulation of radiogenic Ar, and rapid preeruption remobilization. Accordingly, these data elucidate the recycling of subsolidus material into voluminous rhyolite magma reservoirs and the effect of preeruptive magmatic processes on the 40 Ar/ 39 Ar system. The youngest sanidine dates, likely the most representative of the BT eruption age, yield a weighted mean of 764.8 ± 0.3/0.6 ka (2σ analytical/full uncertainty) indicating eruption only ∼7 ky following the Matuyama-Brunhes magnetic polarity reversal. Single-crystal incremental heating provides leverage with which to interpret complex populations of 40 Ar/ 39 Ar sanidine and U-Pb zircon dates and a substantially improved capability to resolve the timing and causal relationship of events in the geologic record.

Equivalent Dynamic Models.

PubMed

Molenaar, Peter C M

2017-01-01

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

The Effect of Augmented Reality Applications in the Learning Process: A Meta-Analysis Study

ERIC Educational Resources Information Center

Ozdemir, Muzaffer; Sahin, Cavus; Arcagok, Serdar; Demir, M. Kaan

2018-01-01

Purpose: The aim of this research is to investigate the effect of Augmented Reality (AR) applications in the learning process. Problem: Research that determines the effectiveness of Augmented Reality (AR) applications in the learning process with different variables has not been encountered in national or international literature. Research…

Establishment of a novel immortalized human prostatic epithelial cell line stably expressing androgen receptor and its application for the functional screening of androgen receptor modulators

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

Yu, Shan; Wang, Ming-Wei; Yao, Xiaoqiang

2009-05-15

In this study, we developed a human prostatic epithelial cell line BPH-1-AR stably expressing AR by lentiviral transduction. Characterization by immunoblot and RT-PCR showed that AR was stably expressed in all representative BPH-1-AR clones. Androgen treatment induced a secretory differentiation phenotype in BPH-1-AR cells but suppressed their cell proliferation. Treatments with AR agonists induced transactivation of a transfected PSA-gene promoter reporter in BPH-1-AR cells, whereas this transactivation was suppressed by an AR antagonist flutamide, indicating that the transduced AR in BPH-1-AR cells was functional. Finally, we utilized BPH-1-AR cells to evaluate the androgenic activities and growth effects of five newlymore » developed non-steroidal compounds. Results showed that these compounds showed androgenic activities and growth-inhibitory effects on BPH-1-AR cells. Our results showed that BPH-1-AR cell line would be a valuable in vitro model for the study of androgen-regulated processes in prostatic epithelial cells and identification of compounds with AR-modulating activities.« less

The potential of AR-V7 as a therapeutic target.

PubMed

Uo, Takuma; Plymate, Stephen R; Sprenger, Cynthia C

2018-03-01

The androgen receptor variant AR-V7 is gaining attention as a potential predictive marker for as well as one of the resistance mechanisms to the most current anti-androgen receptor (AR) therapies in castration-resistant prostate cancer (CRPC). Accordingly, development of next-generation drugs that directly or indirectly target AR-V7 signaling is urgently needed. Areas covered: We review proposed mechanisms of drug resistance in relation to AR-V7 status, the mechanisms of generation of AR-V7, and its transcriptome, cistrome, and interactome. Pharmacological agents that interfere with these processes are being developed to counteract pan AR and AR-V7-specific signaling. Also, we address the current status of the preclinical and clinical studies targeting AR-V7 signaling. Expert opinion: AR-V7 is considered a true therapeutic target, however, it remains to be determined if AR-V7 is a principal driver or merely a bystander requiring heterodimerization with co-expressed full-length AR or other variants to drive CRPC progression. While untangling AR-V7 biology, multiple strategies are being developed to counteract drug resistance, including selective blockade of AR-V7 signaling as well as inhibition of pan-AR signaling. Ideally anti-AR therapies will be combined with agents preventing activation and enrichment of AR negative tumor cells that are otherwise depressed by AR activity axis.

An approach estimating the short-term effect of NO2 on daily mortality in Spanish cities.

PubMed

Linares, Cristina; Falcón, Isabel; Ortiz, Cristina; Díaz, Julio

2018-07-01

Road traffic is the most significant source of urban air pollution. PM 2.5 is the air pollutant whose health effects have been most closely studied, and is the variable most commonly used as a proxy indicator of exposure to air pollution, whereas evidence on NO 2 concentrations per se is still under study. In the case of Spain, there are no specific updated studies which calculate short-term NO 2 -related mortality. To quantify the relative risks (RRs) and attributable risks (ARs) of daily mortality associated with NO 2 concentrations recorded in Spain across the study period, 2000-2009; and to calculate the number of NO 2 -related deaths. We calculated daily mortality due to natural causes (ICD-10: A00 R99), circulatory causes (ICD-10: I00 I99) and respiratory causes (ICD-10: J00 J99) for each province across the period 2000-2009, using data supplied by the National Statistics Institute. Mean daily NO 2 concentrations in μg/m 3 for each provincial capital were furnished by the Ministry of Agriculture & Environment, along with the equivalent figures for the control pollutants (PM 10 ). To estimate RRs and ARs, we used generalised linear models with a Poisson link, controlling for maximum and minimum daily temperature, trend of the series, seasonalities, and the autoregressive nature of the series. A meta-analysis with random effects was used to estimate RRs and ARs nationwide. The overall RRs obtained for Spain, corresponding to increases of 10 μg/m 3 in NO 2 concentrations were 1.012 (95% CI: 1.010 1.014) for natural-cause mortality, 1.028 (95% CI: 1.019 1.037) for respiratory-cause mortality, and 1.016 (95% CI: 1.012 1.021) for circulatory-cause mortality. This amounted to an annual overall 6085 deaths (95% CI: 3288 9427) due to natural causes, 1031 (95% CI: 466 1585) due to respiratory causes, and 1978 (95% CI: 828 3197) due to circulatory causes. By virtue of the number of cities involved and the nature of the analysis performed, with quantification of the RRs and ARs of the short-term impact of NO 2 on daily mortality in Spain, this study provides an updated estimate of the effect had by this type of pollutant on causes of mortality, and constitutes an important basis for reinforcing public health measures at a national level. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evaluation of humic substances during co-composting of sewage sludge and corn stalk under different aeration rates.

PubMed

Li, Shuyan; Li, Danyang; Li, Jijin; Li, Guoxue; Zhang, Bangxi

2017-12-01

Sewage sludge and corn stalk were co-composted under different aeration rates 0.12 (AR0.12), 0.24 (AR0.24), 0.36 (AR0.36)L·kg -1 DMmin -1 , respectively. Transformation of humic substance was evaluated by a series of chemical and spectroscopic methods to reveal compost humification. Results showed that aeration rate could significantly affect compost stability and humification process. Humic acid contents in AR0.24 were significantly higher than those in the other two treatments. The final humic acid/fulvic acid ratios in AR0.12, AR0.24 and AR0.36 treatment were 1.0, 1.9 and 0.8, respectively, corresponding to the final E 4 /E 6 of 4.7, 3.2 and 5.5. Moreover, compost in AR0.24 treatment had a high stability degree due to the low C/N atom ratio and high C/H atom ratio. However, it is noteworthy that composting could not significantly affect the structure of HA in a 35-day period. These results indicate that composting with the aeration rate of 0.24L·kg -1 DMmin -1 could accelerated the humification process. Copyright © 2017 Elsevier Ltd. All rights reserved.

Measurements of population densities of metastable and resonant levels of argon using laser induced fluorescence

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

Nikolić, M.; Newton, J.; Sukenik, C. I.

2015-01-14

We present a new approach to measure population densities of Ar I metastable and resonant excited states in low temperature Ar plasmas at pressures higher than 1 Torr. This approach combines the time resolved laser induced fluorescence technique with the kinetic model of Ar. The kinetic model of Ar is based on calculating the population rates of metastable and resonant levels by including contributions from the processes that affect population densities of Ar I excited states. In particular, we included collisional quenching processes between atoms in the ground state and excited states, since we are investigating plasma at higher pressures. Wemore » also determined time resolved population densities of Ar I 2 p excited states by employing optical emission spectroscopy technique. Time resolved Ar I excited state populations are presented for the case of the post-discharge of the supersonic flowing microwave discharge at pressures of 1.7 and 2.3 Torr. The experimental set-up consists of a pulsed tunable dye laser operating in the near infrared region and a cylindrical resonance cavity operating in TE{sub 111} mode at 2.45 GHz. Results show that time resolved population densities of Ar I metastable and resonant states oscillate with twice the frequency of the discharge.« less

Time-series panel analysis (TSPA): multivariate modeling of temporal associations in psychotherapy process.

PubMed

Ramseyer, Fabian; Kupper, Zeno; Caspar, Franz; Znoj, Hansjörg; Tschacher, Wolfgang

2014-10-01

Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. PsycINFO Database Record (c) 2014 APA, all rights reserved.

ARS labs update to California Cotton Ginners and Growers

USDA-ARS?s Scientific Manuscript database

There are four USDA-ARS labs involved in cotton harvesting, processing & fiber quality research; The Southwestern Cotton Ginning Research Laboratory (Mesilla Park, NM); The Cotton Production and Processing Unit (Lubbock, TX); The Cotton Ginning Research Unit (Stoneville, MS); and The Cotton Structur...

Nature Inspired Strategies for New Organic Materials

DTIC Science & Technology

2007-01-14

8217/. Compound Side-products: lir- NOH Ar AH ÷ R IS Ar • R Ph Ph 65 3a Ar benzoin product Ph NHDF 04 Ar-1A- bnzinpodcNaH/DMF Ph Ph H 61 3b 1 2 up to 65% yield...NHC-Catalyzed Acylatlon0 process is depicted in Scheme 2. The major N 0 side products of the reaction were identified o as benzoin -type products...derived from the Ph 3C\\NJM Ar H addition of intermediate IV to the starting carbene aldehyde) and more importantly, the acylated benzoin adduct 4

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

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...

Processes Driving Natural Acidification of Western Pacific Coral Reef Waters

NASA Astrophysics Data System (ADS)

Shamberger, K. E.; Cohen, A. L.; Golbuu, Y.; McCorkle, D. C.; Lentz, S. J.; Barkley, H. C.

2013-12-01

Rising levels of atmospheric carbon dioxide (CO2) are acidifying the oceans, reducing seawater pH, aragonite saturation state (Ωar) and the availability of carbonate ions (CO32-) that calcifying organisms use to build coral reefs. Today's most extensive reef ecosystems are located where open ocean CO32- concentration ([CO32-]) and Ωar exceed 200 μmol kg-1 and 3.3, respectively. However, high rates of biogeochemical cycling and long residence times of water can result in carbonate chemistry conditions within coral reef systems that differ greatly from those of nearby open ocean waters. In the Palauan archipelago, water moving across the reef platform is altered by both biological and hydrographic processes that combine to produce seawater pH, Ωar, [CO32-] significantly lower than that of open ocean source water. Just inshore of the barrier reefs, average Ωar values are 0.2 to 0.3 and pH values are 0.02 to 0.03 lower than they are offshore, declining further as water moves across the back reef, lagoon and into the meandering bays and inlets that characterize the Rock Islands. In the Rock Island bays, coral communities inhabit seawater with average Ωar values of 2.7 or less, and as low as 1.9. Levels of Ωar as low as these are not predicted to occur in the western tropical Pacific open ocean until near the end of the century. Calcification by coral reef organisms is the principal biological process responsible for lowering Ωar and pH, accounting for 68 - 99 % of the difference in Ωar between offshore source water and reef water at our sites. However, in the Rock Island bays where Ωar is lowest, CO2 production by net respiration contributes between 17 - 30 % of the difference in Ωar between offshore source water and reef water. Furthermore, the residence time of seawater in the Rock Island bays is much longer than at the well flushed exposed sites, enabling calcification and respiration to drive Ωar to very low levels despite lower net ecosystem calcification rates in the Rock Island bays than on the barrier reef.

Real-time mental arithmetic task recognition from EEG signals.

PubMed

Wang, Qiang; Sourina, Olga

2013-03-01

Electroencephalography (EEG)-based monitoring the state of the user's brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as attention deficit hyperactivity disorder (ADHD), where concentration function deficit exists, autism spectrum disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals. Other features such as power spectrum density (PSD), autoregressive model (AR), and statistical features were analyzed as well. The usage of the proposed fractal dimension spectrum of EEG signal in combination with other features improved the mental arithmetic task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87% and 84.15% correspondingly. Based on the channel ranking, four channels were chosen which gave the accuracy up to 97.11%. Reliable real-time neurofeedback system could be implemented based on the algorithms proposed in this paper.

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system

PubMed Central

Min, Jianliang; Wang, Ping

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1–2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver. PMID:29220351

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.

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.

Paleotemperatures at the lunar surfaces from open system behavior of cosmogenic 38Ar and radiogenic 40Ar

DOE PAGES

Shuster, David L.; Cassata, William S.

2015-02-10

The simultaneous diffusion of both cosmogenic 38Ar and radiogenic 40Ar from solid phases is controlled by the thermal conditions of rocks while residing near planetary surfaces. Combined observations of 38Ar/ 37Ar and 40Ar/ 39Ar ratios during stepwise degassing analyses of neutron-irradiated Apollo samples can distinguish between diffusive loss of Ar due to solar heating of the rocks and that associated with elevated temperatures during or following impact events; the data provide quantitative constraints on the durations and temperatures of each process. From sequentially degassed 38Ar/ 37Ar ratios can be calculated a spectrum of apparent 38Ar exposure ages versus the cumulativemore » release fraction of 37Ar, which is particularly sensitive to conditions at the lunar surface typically over ~106–108 year timescales. Due to variable proportions of K- and Ca-bearing glass, plagioclase and pyroxene, with variability in the grain sizes of these phases, each sample will have distinct sensitivity to, and therefore different resolving power on, past near-surface thermal conditions. Furthermore, we present the underlying assumptions, and the analytical and numerical methods used to quantify the Ar diffusion kinetics in multi-phase whole-rock analyses that provide these constraints.« less

Detection of gear cracks in a complex gearbox of wind turbines using supervised bounded component analysis of vibration signals collected from multi-channel sensors

NASA Astrophysics Data System (ADS)

Li, Zhixiong; Yan, Xinping; Wang, Xuping; Peng, Zhongxiao

2016-06-01

In the complex gear transmission systems, in wind turbines a crack is one of the most common failure modes and can be fatal to the wind turbine power systems. A single sensor may suffer with issues relating to its installation position and direction, resulting in the collection of weak dynamic responses of the cracked gear. A multi-channel sensor system is hence applied in the signal acquisition and the blind source separation (BSS) technologies are employed to optimally process the information collected from multiple sensors. However, literature review finds that most of the BSS based fault detectors did not address the dependence/correlation between different moving components in the gear systems; particularly, the popular used independent component analysis (ICA) assumes mutual independence of different vibration sources. The fault detection performance may be significantly influenced by the dependence/correlation between vibration sources. In order to address this issue, this paper presents a new method based on the supervised order tracking bounded component analysis (SOTBCA) for gear crack detection in wind turbines. The bounded component analysis (BCA) is a state of art technology for dependent source separation and is applied limitedly to communication signals. To make it applicable for vibration analysis, in this work, the order tracking has been appropriately incorporated into the BCA framework to eliminate the noise and disturbance signal components. Then an autoregressive (AR) model built with prior knowledge about the crack fault is employed to supervise the reconstruction of the crack vibration source signature. The SOTBCA only outputs one source signal that has the closest distance with the AR model. Owing to the dependence tolerance ability of the BCA framework, interfering vibration sources that are dependent/correlated with the crack vibration source could be recognized by the SOTBCA, and hence, only useful fault information could be preserved in the reconstructed signal. The crack failure thus could be precisely identified by the cyclic spectral correlation analysis. A series of numerical simulations and experimental tests have been conducted to illustrate the advantages of the proposed SOTBCA method for fatigue crack detection. Comparisons to three representative techniques, i.e. Erdogan's BCA (E-BCA), joint approximate diagonalization of eigen-matrices (JADE), and FastICA, have demonstrated the effectiveness of the SOTBCA. Hence the proposed approach is suitable for accurate gear crack detection in practical applications.

Monitoring sodium levels in commercially processed and restaurant foods - dataset and webpages.

USDA-ARS?s Scientific Manuscript database

Nutrient Data Laboratory (NDL), Agriculture Research Service (ARS) in collaboration with Food Surveys Research Group, ARS, and the Centers for Disease Control and Prevention has been monitoring commercially processed and restaurant foods in the United States since 2010. About 125 highly consumed, s...

Expanding the utility of the Agricultural Research Service (ARS) process bleaching

USDA-ARS?s Scientific Manuscript database

The ARS Process for bleaching, biopolishing, and shrinkproofing wool is a novel alternative to chlorination and conventional bleaching. Consumer acceptance of domestic machine-washable, comfortable wool which can be worn next to the skin will lead to niche-market- potential and competitive, increas...

Modifying exchange bias effects of Mn/NiFe bilayers by in-situ Ar+ bombardment

NASA Astrophysics Data System (ADS)

Causer, G. L.; Manna, P. K.; Chiu, C.-C.; van Lierop, J.; Ionescu, M.; Lin, K.-W.; Klose, F.

2017-10-01

In this work, we present a procedure to modify the exchange bias (EB) properties of antiferromagnetic Mn/ferromagnetic NiFe bilayers by in-situ low energy Ar+ bombardment of the Mn layer during sample deposition. We present structural and magnetic results for unassisted and Ar+ assisted Mn/NiFe bilayers. X-ray diffraction, transmission electron microscopy and electron diffraction results establish different preferred Mn orientation directions between the two samples as a result of the Ar+ bombardment process. Hysteresis loops taken over several temperatures reveal that samples assisted with Ar+ ions during the Mn layer deposition had suppressed EB properties at low temperature as compared to samples grown without Ar+ assistance.

Augmented Reality 2.0

NASA Astrophysics Data System (ADS)

Schmalstieg, Dieter; Langlotz, Tobias; Billinghurst, Mark

Augmented Reality (AR) was first demonstrated in the 1960s, but only recently have technologies emerged that can be used to easily deploy AR applications to many users. Camera-equipped cell phones with significant processing power and graphics abilities provide an inexpensive and versatile platform for AR applications, while the social networking technology of Web 2.0 provides a large-scale infrastructure for collaboratively producing and distributing geo-referenced AR content. This combination of widely used mobile hardware and Web 2.0 software allows the development of a new type of AR platform that can be used on a global scale. In this paper we describe the Augmented Reality 2.0 concept and present existing work on mobile AR and web technologies that could be used to create AR 2.0 applications.

Mentoring Graduate Students through the Action Research Journey Using Guiding Principles

ERIC Educational Resources Information Center

Spencer, Joi A.; Molina, Sarina Chugani

2018-01-01

Our department has adopted action research (AR) projects as the culminating task for our master's degree candidates. This article presents our work on mentoring graduate students towards the completion of their final AR research projects and details the deliberate structures put in place to guide them through the AR process. These structures…

Characterization of karyopherins in androgen receptor intracellular trafficking in the yeast model

PubMed Central

Nguyen, Minh M; Harmon, Robert M; Wang, Zhou

2014-01-01

Background: Mechanisms regulating androgen receptor (AR) subcellular localization represent an essential component of AR signaling. Karyopherins are a family of nucleocytoplasmic trafficking factors. In this paper, we used the yeast model to study the effects of karyopherins on the subcellular localization of the AR. Methods: Yeast mutants deficient in different nuclear transport factors were transformed with various AR based, GFP tagged constructs and their localization was monitored using microscopy. Results: We showed that yeast can mediate androgen-induced AR nuclear localization and that in addition to the import factor, Importinα/β, this process required the import karyopherin Sxm1. We also showed that a previously identified nuclear export sequence (NESAR) in the ligand binding domain of AR does not appear to rely on karyopherins for cytoplasmic localization. Conclusions: These results suggest that while AR nuclear import relies on karyopherin activity, AR nuclear export and/or cytoplasmic localization may require other undefined mechanisms. PMID:25031696

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.

Development of optical coatings for 157-nm lithography. II. Reflectance, absorption, and scatter measurement

NASA Astrophysics Data System (ADS)

Otani, Minoru; Biro, Ryuji; Ouchi, Chidane; Hasegawa, Masanobu; Suzuki, Yasuyuki; Sone, Kazuho; Niisaka, Shunsuke; Saito, Tadahiko; Saito, Jun; Tanaka, Akira

2002-06-01

The total loss that can be suffered by an antireflection (AR) coating consists of reflectance loss, absorption loss, and scatter loss. To separate these losses we developed a calorimetric absorption measurement apparatus and an ellipsoidal Coblentz hemisphere based scatterometer for 157-nm optics. Reflectance, absorption, and scatter of AR coatings were measured with these apparatuses. The AR coating samples were supplied by Japanese vendors. Each AR coating as supplied was coated with the vendor's coating design by that vendor's coating process. Our measurement apparatuses, methods, and results for these AR coatings are presented here.

Effects of high-pressure argon and nitrogen treatments on respiration, browning and antioxidant potential of minimally processed pineapples during shelf life.

PubMed

Wu, Zhi-shuang; Zhang, Min; Wang, Shao-jin

2012-08-30

High-pressure (HP) inert gas processing causes inert gas and water molecules to form clathrate hydrates that restrict intracellular water activity and enzymatic reactions. This technique can be used to preserve fruits and vegetables. In this study, minimally processed (MP) pineapples were treated with HP (∼10 MPa) argon (Ar) and nitrogen (N) for 20 min. The effects of these treatments on respiration, browning and antioxidant potential of MP pineapples were investigated after cutting and during 20 days of storage at 4 °C. Lower respiration rate and ethylene production were found in HP Ar- and HP N-treated samples compared with control samples. HP Ar and HP N treatments effectively reduced browning and loss of total phenols and ascorbic acid and maintained antioxidant capacity of MP pineapples. They did not cause a significant decline in tissue firmness or increase in juice leakage. HP Ar treatments had greater effects than HP N treatments on reduction of respiration rate and ethylene production and maintenance of phenolic compounds and DPPH(•) and ABTS(•+) radical-scavenging activities. Both HP Ar and HP N processing had beneficial effects on MP pineapples throughout 20 days of storage at 4 °C. Copyright © 2012 Society of Chemical Industry.

Computational Study on the Different Ligands Induced Conformation Change of β2 Adrenergic Receptor-Gs Protein Complex

PubMed Central

Bai, Qifeng; Zhang, Yang; Ban, Yihe; Liu, Huanxiang; Yao, Xiaojun

2013-01-01

β2 adrenergic receptor (β2AR) regulated many key physiological processes by activation of a heterotrimeric GTP binding protein (Gs protein). This process could be modulated by different types of ligands. But the details about this modulation process were still not depicted. Here, we performed molecular dynamics (MD) simulations on the structures of β2AR-Gs protein in complex with different types of ligands. The simulation results demonstrated that the agonist BI-167107 could form hydrogen bonds with Ser2035.42, Ser2075.46 and Asn2936.55 more than the inverse agonist ICI 118,551. The different binding modes of ligands further affected the conformation of β2AR. The energy landscape profiled the energy contour map of the stable and dissociated conformation of Gαs and Gβγ when different types of ligands bound to β2AR. It also showed the minimum energy pathway about the conformational change of Gαs and Gβγ along the reaction coordinates. By using interactive essential dynamics analysis, we found that Gαs and Gβγ domain of Gs protein had the tendency to separate when the inverse agonist ICI 118,551 bound to β2AR. The α5-helix had a relatively quick movement with respect to transmembrane segments of β2AR when the inverse agonist ICI 118,551 bound to β2AR. Besides, the analysis of the centroid distance of Gαs and Gβγ showed that the Gαs was separated from Gβγ during the MD simulations. Our results not only could provide details about the different types of ligands that induced conformational change of β2AR and Gs protein, but also supplied more information for different efficacies of drug design of β2AR. PMID:23922653

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process

PubMed Central

2013-01-01

Background Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. Methods Monthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. Results The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag40 and 35% had autocorrelation through to lag40; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. Conclusions The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues. PMID:23705957

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.

The use of Meteonorm weather generator for climate change studies

NASA Astrophysics Data System (ADS)

Remund, J.; Müller, S. C.; Schilter, C.; Rihm, B.

2010-09-01

The global climatological database Meteonorm (www.meteonorm.com) is widely used as meteorological input for simulation of solar applications and buildings. It's a combination of a climate database, a spatial interpolation tool and a stochastic weather generator. Like this typical years with hourly or minute time resolution can be calculated for any site. The input of Meteonorm for global radiation is the Global Energy Balance Archive (GEBA, http://proto-geba.ethz.ch). All other meteorological parameters are taken from databases of WMO and NCDC (periods 1961-90 and 1996-2005). The stochastic generation of global radiation is based on a Markov chain model for daily values and an autoregressive model for hourly and minute values (Aguiar and Collares-Pereira, 1988 and 1992). The generation of temperature is based on global radiation and measured distribution of daily temperature values of approx. 5000 sites. Meteonorm generates also additional parameters like precipitation, wind speed or radiation parameters like diffuse and direct normal irradiance. Meteonorm can also be used for climate change studies. Instead of climate values, the results of IPCC AR4 results are used as input. From all 18 public models an average has been made at a resolution of 1°. The anomalies of the parameters temperature, precipitation and global radiation and the three scenarios B1, A1B and A2 have been included. With the combination of Meteonorm's current database 1961-90, the interpolation algorithms and the stochastic generation typical years can be calculated for any site, for different scenarios and for any period between 2010 and 2200. From the analysis of variations of year to year and month to month variations of temperature, precipitation and global radiation of the past ten years as well of climate model forecasts (from project prudence, http://prudence.dmi.dk) a simple autoregressive model has been formed which is used to generate realistic monthly time series of future periods. Meteonorm can therefore be used as a relatively simple method to enhance the spatial and temporal resolution instead of using complicated and time consuming downscaling methods based on regional climate models. The combination of Meteonorm, gridded historical (based on work of Luterbach et al.) and IPCC results has been used for studies of vegetation simulation between 1660 and 2600 (publication of first version based on IS92a scenario and limited time period 1950 - 2100: http://www.pbl.nl/images/H5_Part2_van%20CCE_opmaak%28def%29_tcm61-46625.pdf). It's also applicable for other adaptation studies for e.g. road surfaces or building simulation. In Meteonorm 6.1 one scenario (IS92a) and one climate model has been included (Hadley CM3). In the new Meteonorm 7 (coming spring 2011) the model averages of the three above mentioned scenarios of the IPCC AR4 will be included.

Kumaraswamy autoregressive moving average models for double bounded environmental data

NASA Astrophysics Data System (ADS)

Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme

2017-12-01

In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.

Fractal and chaotic laws on seismic dissipated energy in an energy system of engineering structures

NASA Astrophysics Data System (ADS)

Cui, Yu-Hong; Nie, Yong-An; Yan, Zong-Da; Wu, Guo-You

1998-09-01

Fractal and chaotic laws of engineering structures are discussed in this paper, it means that the intrinsic essences and laws on dynamic systems which are made from seismic dissipated energy intensity E d and intensity of seismic dissipated energy moment I e are analyzed. Based on the intrinsic characters of chaotic and fractal dynamic system of E d and I e, three kinds of approximate dynamic models are rebuilt one by one: index autoregressive model, threshold autoregressive model and local-approximate autoregressive model. The innate laws, essences and systematic error of evolutional behavior I e are explained over all, the short-term behavior predictability and long-term behavior probability of which are analyzed in the end. That may be valuable for earthquake-resistant theory and analysis method in practical engineering structures.

Fluid simulation of species concentrations in capacitively coupled N2/Ar plasmas: Effect of gas proportion

NASA Astrophysics Data System (ADS)

Liang, Ying-Shuang; Liu, Gang-Hu; Xue, Chan; Liu, Yong-Xin; Wang, You-Nian

2017-05-01

A two-dimensional self-consistent fluid model and the experimental diagnostic are employed to investigate the dependencies of species concentrations on the gas proportion in the capacitive N2/Ar discharges operated at 60 MHz, 50 Pa, and 140 W. The results indicate that the N2/Ar proportion has a considerable impact on the species densities. As the N2 fraction increases, the electron density, as well as the Ar+ and Arm densities, decreases remarkably. On the contrary, the N2 + density is demonstrated to increase monotonically with the N2 fraction. Moreover, the N density is observed to increase significantly with the N2 fraction at the N2 fractions below 40%, beyond which it decreases slightly. The electrons are primarily generated via the electron impact ionization of the feed gases. The electron impact ionization of Ar essentially determines the Ar+ density. For the N2 + production, the charge transition process between the Ar+ ions and the feed gas N2 dominates at low N2 fraction, while the electron impact ionization of N2 plays the more important role at high N2 fraction. At any gas mixtures, more than 60% Arm atoms are generated through the radiative decay process from Ar(4p). The dissociation of the feed gas N2 by the excited Ar atoms and by the electrons is responsible for the N formation at low N2 fraction and high N2 fraction, respectively. To validate the simulation results, the floating double probe and the optical emission spectroscopy are employed to measure the total positive ion density and the emission intensity originating from Ar(4p) transitions, respectively. The results from the simulation show a qualitative agreement with that from the experiment, which indicates the reliable model.

Using an action research process in pharmacy practice research--a cooperative project between university and internship pharmacies.

PubMed

Sørensen, Ellen Westh; Haugbølle, Lotte Stig

2008-12-01

Action research (AR) is a common research-based methodology useful for development and organizational changes in health care when participant involvement is key. However, AR is not widely used for research in the development of pharmaceutical care services in pharmacy practice. To disseminate the experience from using AR methodology to develop cognitive services in pharmacies by describing how the AR process was conducted in a specific study, and to describe the outcome for participants. The study was conducted over a 3-year period and run by a steering group of researchers, pharmacy students, and preceptors. The study design was based on AR methodology. The following data production methods were used to describe and evaluate the AR model: documentary analysis, qualitative interviews, and questionnaires. Experiences from using AR methodology and the outcome for participants are described. A set of principles was followed while the study, called the Pharmacy-University study, was being conducted. These principles are considered useful for designing future AR studies. Outcome for participating pharmacies was registered for staff-oriented and patient-oriented activities. Outcome for students was practice as project leaders and enhancement of clinical pharmacy-based skills. Outcome for researchers and the steering group conducting the study was in-depth knowledge of the status of pharmacies in giving advice to patient groups, and effective learning methods for students. Developing and implementing cognitive pharmaceutical services (CPS) involves wide-reaching changes that require the willingness of pharmacy and staff as well as external partners. The use of AR methodology creates a platform that supports raising the awareness and the possible inclusion of these partners. During this study, a set of tools was developed for use in implementing CPS as part of AR.

Assessment of variability in the hydrological cycle of the Loess Plateau, China: examining dependence structures of hydrological processes

NASA Astrophysics Data System (ADS)

Guo, A.; Wang, Y.

2017-12-01

Investigating variability in dependence structures of hydrological processes is of critical importance for developing an understanding of mechanisms of hydrological cycles in changing environments. In focusing on this topic, present work involves the following: (1) identifying and eliminating serial correlation and conditional heteroscedasticity in monthly streamflow (Q), precipitation (P) and potential evapotranspiration (PE) series using the ARMA-GARCH model (ARMA: autoregressive moving average; GARCH: generalized autoregressive conditional heteroscedasticity); (2) describing dependence structures of hydrological processes using partial copula coupled with the ARMA-GARCH model and identifying their variability via copula-based likelihood-ratio test method; and (3) determining conditional probability of annual Q under different climate scenarios on account of above results. This framework enables us to depict hydrological variables in the presence of conditional heteroscedasticity and to examine dependence structures of hydrological processes while excluding the influence of covariates by using partial copula-based ARMA-GARCH model. Eight major catchments across the Loess Plateau (LP) are used as study regions. Results indicate that (1) The occurrence of change points in dependence structures of Q and P (PE) varies across the LP. Change points of P-PE dependence structures in all regions almost fully correspond to the initiation of global warming, i.e., the early 1980s. (3) Conditional probabilities of annual Q under various P and PE scenarios are estimated from the 3-dimensional joint distribution of (Q, P and PE) based on the above change points. These findings shed light on mechanisms of the hydrological cycle and can guide water supply planning and management, particularly in changing environments.

AR, HEA and AAS in Rural Development Projects--Benchmarking towards the Best Processes.

ERIC Educational Resources Information Center

Westermarck, Harri

In most countries, agricultural research (AR), institutions of higher education in agriculture (HEA), and agricultural advisory services (AAS) function as separate agencies. So far, in most countries, AR, HEA, and AAS have not had a common vision for rural development. In Finland, domination of agricultural production in Finland has led to a lack…

Investigation on the effect of nonlinear processes on similarity law in high-pressure argon discharges

NASA Astrophysics Data System (ADS)

Fu, Yangyang; Parsey, Guy M.; Verboncoeur, John P.; Christlieb, Andrew J.

2017-11-01

In this paper, the effect of nonlinear processes (such as three-body collisions and stepwise ionizations) on the similarity law in high-pressure argon discharges has been studied by the use of the Kinetic Global Model framework. In the discharge model, the ground state argon atoms (Ar), electrons (e), atom ions (Ar+), molecular ions (Ar2+), and fourteen argon excited levels Ar*(4s and 4p) are considered. The steady-state electron and ion densities are obtained with nonlinear processes included and excluded in the designed models, respectively. It is found that in similar gas gaps, keeping the product of gas pressure and linear dimension unchanged, with the nonlinear processes included, the normalized density relations deviate from the similarity relations gradually as the scale-up factor decreases. Without the nonlinear processes, the parameter relations are in good agreement with the similarity law predictions. Furthermore, the pressure and the dimension effects are also investigated separately with and without the nonlinear processes. It is shown that the gas pressure effect on the results is less obvious than the dimension effect. Without the nonlinear processes, the pressure and the dimension effects could be estimated from one to the other based on the similarity relations.

On the Feed-back Mechanism of Chinese Stock Markets

NASA Astrophysics Data System (ADS)

Lu, Shu Quan; Ito, Takao; Zhang, Jianbo

Feed-back models in the stock markets research imply an adjustment process toward investors' expectation for current information and past experiences. Error-correction and cointegration are often used to evaluate the long-run relation. The Efficient Capital Market Hypothesis, which had ignored the effect of the accumulation of information, cannot explain some anomalies such as bubbles and partial predictability in the stock markets. In order to investigate the feed-back mechanism and to determine an effective model, we use daily data of the stock index of two Chinese stock markets with the expectational model, which is one kind of geometric lag models. Tests and estimations of error-correction show that long-run equilibrium seems to be seldom achieved in Chinese stock markets. Our result clearly shows the common coefficient of expectations and fourth-order autoregressive disturbance exist in the two Chinese stock markets. Furthermore, we find the same coefficient of expectations has an autoregressive effect on disturbances in the two Chinese stock markets. Therefore the presence of such feed-back is also supported in Chinese stock markets.

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

Processes in the development of mathematics in kindergarten children from Title 1 schools.

PubMed

Foster, Matthew E; Anthony, Jason L; Clements, Doug H; Sarama, Julie H

2015-12-01

This study examined how well nonverbal IQ (or fluid intelligence), vocabulary, phonological awareness (PA), rapid autonomized naming (RAN), and phonological short-term memory (STM) predicted mathematics outcomes. The 208 participating kindergartners were administered tests of fluid intelligence, vocabulary, PA, RAN, STM, and numeracy in the fall of kindergarten, whereas tests of numeracy and applied problems were administered in the spring of kindergarten. Fall numeracy scores accounted for substantial variation in spring outcomes (R(2) values = .49 and .32 for numeracy and applied problems, respectively), which underscores the importance of preschool math instruction and screening for mathematics learning difficulties on entry into kindergarten. Fluid intelligence and PA significantly predicted unique variation in spring numeracy scores (ΔR(2) = .05) after controlling for autoregressive effects and classroom nesting. Fluid intelligence, PA, and STM significantly predicted unique variation in spring applied problems scores (ΔR(2) = .14) after controlling for autoregressive effects and classroom nesting. Although the contributions of fluid intelligence, PA, and STM toward math outcomes were reliable and arguably important, they were small. Copyright © 2015 Elsevier Inc. All rights reserved.

Central vasopressin V1a receptor activation is independently necessary for both partner preference formation and expression in socially monogamous male prairie voles.

PubMed

Donaldson, Zoe R; Spiegel, Lauren; Young, Larry J

2010-02-01

The neuropeptide arginine vasopressin (AVP) modulates a variety of species-specific social behaviors. In socially monogamous male prairie voles, AVP acts centrally via vasopressin V1a receptor (V1aR) to facilitate mating induced partner preferences. The display of a partner preference requires at least 2 temporally distinct processes: social bond formation as well as its recall, or expression. Studies to date have not determined in which of these processes V1aR acts to promote partner preferences. Here, male prairie voles were administered intracerebroventricularly a V1aR antagonist (AVPA) at different time points to investigate the role of V1aR in social bond formation and expression. Animals receiving AVPA prior to cohabitation with mating or immediately prior to partner preference testing failed to display a partner preference, while animals receiving AVPA immediately after cohabitation with mating and control animals receiving vehicle at all 3 time points displayed partner preferences. These results suggest that V1aR signaling is necessary for both the formation and expression of partner preferences and that these processes are dissociable. (c) 2009 APA, all rights reserved.

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.

Palaeomagnetism and K-Ar and 40Ar/39Ar ages in the Ali Sabieh area (Republic of Djibouti and Ethiopia): constraints on the mechanism of Aden ridge propagation into southeastern Afar during the last 10 Myr

NASA Astrophysics Data System (ADS)

Audin, L.; Quidelleur, X.; Coulié, E.; Courtillot, V.; Gilder, S.; Manighetti, I.; Gillot, P.-Y.; Tapponnier, P.; Kidane, T.

2004-07-01

A new detailed palaeomagnetic study of Tertiary volcanics, including extensive K-Ar and 40Ar/39Ar dating, helps constrain the deformation mechanisms related to the opening processes of the Afar depression (Ethiopia and Djibouti). Much of the Afar depression is bounded by 30 Myr old flood basalts and floored by the ca 2 Myr old Stratoid basalts, and evidence for pre-2 Ma deformation processes is accessible only on its borders. K-Ar and 40Ar/39Ar dating of several mineral phases from rhyolitic samples from the Ali Sabieh block shows indistinguishable ages around 20 Myr. These ages can be linked to separation of this block in relation to continental breakup. Different amounts of rotation are found to the north and south of the Holhol fault zone, which cuts across the northern part of the Ali Sabieh block. The southern domain did not record any rotation for the last 8 Myr, whereas the northern domain experienced approximately 12 +/- 9° of clockwise rotation. We propose to link this rotation to the counter-clockwise rotation observed in the Danakil block since 7 Ma. This provides new constraints on the early phases of rifting and opening of the southern Afar depression in connection with the propagation of the Aden ridge. A kinematic model of propagation and transfer of extension within southern Afar is proposed, with particular emphasis on the previously poorly-known period from 10 to 4 Ma.

Printing of structures less than 0,3 μm by i-line exposure using resists TDMR-AR80 and TDMR-AR95

NASA Astrophysics Data System (ADS)

Behrendt, A.; Dow, T.; Stoeflin, K.

2007-03-01

There is increasing interest in high resolution i-line resists which allow the printing of structures smaller than 0.3μm. We have evaluated the resists TDMR-AR80 und TDMR-AR95 from TOK Company in order to check their potential concerning minimum line sizes with sufficient process window in regard to focus/exposure process latitude, with our main focus on trench structures. The Bossung Plots of dense lines and semi-dense lines were determined. The resist and etch profiles were characterised both by inline-SEM measurements and cross-sections. The influence of several stepper illumination modes and Off Axis Illumination (OAI) on the focus/exposure process window was investigated. The resists TDMR-AR80 and TDMR-AR95 enable printing of trench structures less than 0.3μm. For 0.3μm lines, our specification limit of 0.3μm +/- 10% was reached within a focus range from - 0.1 to 1.0 microns. OAI illumination mode enlarged the focus window by 20% in comparison to the standard illumination mode. Structures of 0.28μm and 0.26μm were printed with a focus window of 0.7μm which shows the high potential of this resist generation. The implementation of the resist in production provides large amounts of data which enable the calculation of parameters related to process stability (wafer to wafer and lot to lot CD-standard deviation, Cp-, Cpk-values etc.).

The RNA helicase DDX39B and its paralog DDX39A regulate androgen receptor splice variant AR-V7 generation.

PubMed

Nakata, Daisuke; Nakao, Shoichi; Nakayama, Kazuhide; Araki, Shinsuke; Nakayama, Yusuke; Aparicio, Samuel; Hara, Takahito; Nakanishi, Atsushi

2017-01-29

Mounting evidence suggests that constitutively active androgen receptor (AR) splice variants, typified by AR-V7, are associated with poor prognosis and resistance to androgen deprivation therapy in prostate cancer patients. However, mechanisms governing the generation of AR splice variants are not fully understood. In this study, we aimed to investigate the dynamics of AR splice variant generation using the JDCaP prostate cancer model that expresses AR splice variants under androgen depletion. Microarray analysis of JDCaP xenografts before and after expression of AR splice variants suggested that dysregulation of RNA processing pathways is likely involved in AR splice variant generation. To explore factors contributing to generation of AR-V7 mRNA, we conducted a focused RNA interference screen in AR-V7-positive JDCaP-hr cells using an shRNA library targeting spliceosome-related genes. This screen identified DDX39B as a regulator of AR-V7 mRNA expression. Simultaneous knockdown of DDX39B and its paralog DDX39A drastically and selectively downregulated AR-V7 mRNA expression in multiple AR-V7-positive prostate cancer cell lines. DDX39B was upregulated in relapsed JDCaP xenografts expressing AR splice variants, suggesting its role in expression of AR splice variants. Taken together, our findings offer insight into the mechanisms of AR splice variant generation and identify DDX39 as a potential drug target for the treatment of AR splice variant-positive prostate cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Evaluation of an Atmosphere Revitalization Subsystem for Deep Space Exploration Missions

NASA Technical Reports Server (NTRS)

Perry, Jay L.; Abney, Morgan B.; Conrad, Ruth E.; Frederick, Kenneth R.; Greenwood, Zachary W.; Kayatin, Matthew J.; Knox, James C.; Newton, Robert L.; Parrish, Keith J.; Takada, Kevin C.;

Androgen receptor (AR) pathophysiological roles in androgen-related diseases in skin, bone/muscle, metabolic syndrome and neuron/immune systems: lessons learned from mice lacking AR in specific cells

PubMed Central

Chang, Chawnshang; Yeh, Shuyuan; Lee, Soo Ok; Chang, Ta-min

2013-01-01

The androgen receptor (AR) is expressed ubiquitously and plays a variety of roles in a vast number of physiological and pathophysiological processes. Recent studies of AR knockout (ARKO) mouse models, particularly the cell type- or tissue-specific ARKO models, have uncovered many AR cell type- or tissue-specific pathophysiological roles in mice, which otherwise would not be delineated from conventional castration and androgen insensitivity syndrome studies. Thus, the AR in various specific cell types plays pivotal roles in production and maturation of immune cells, bone mineralization, and muscle growth. In metabolism, the ARs in brain, particularly in the hypothalamus, and the liver appear to participate in regulation of insulin sensitivity and glucose homeostasis. The AR also plays key roles in cutaneous wound healing and cardiovascular diseases, including atherosclerosis and abdominal aortic aneurysm. This article will discuss the results obtained from the total, cell type-, or tissue-specific ARKO models. The understanding of AR cell type- or tissue-specific physiological and pathophysiological roles using these in vivo mouse models will provide useful information in uncovering AR roles in humans and eventually help us to develop better therapies via targeting the AR or its downstream signaling molecules to combat androgen/AR-related diseases. PMID:24653668

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.

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.

Determinants of Ligand Subtype-Selectivity at α1A-Adrenoceptor Revealed Using Saturation Transfer Difference (STD) NMR.

PubMed

Yong, Kelvin J; Vaid, Tasneem M; Shilling, Patrick J; Wu, Feng-Jie; Williams, Lisa M; Deluigi, Mattia; Plückthun, Andreas; Bathgate, Ross A D; Gooley, Paul R; Scott, Daniel J

2018-04-20

α 1A - and α 1B -adrenoceptors (α 1A -AR and α 1B -AR) are closely related G protein-coupled receptors (GPCRs) that modulate the cardiovascular and nervous systems in response to binding epinephrine and norepinephrine. The GPCR gene superfamily is made up of numerous subfamilies that, like α 1A -AR and α 1B -AR, are activated by the same endogenous agonists but may modulate different physiological processes. A major challenge in GPCR research and drug discovery is determining how compounds interact with receptors at the molecular level, especially to assist in the optimization of drug leads. Nuclear magnetic resonance spectroscopy (NMR) can provide great insight into ligand-binding epitopes, modes, and kinetics. Ideally, ligand-based NMR methods require purified, well-behaved protein samples. The instability of GPCRs upon purification in detergents, however, makes the application of NMR to study ligand binding challenging. Here, stabilized α 1A -AR and α 1B -AR variants were engineered using Cellular High-throughput Encapsulation, Solubilization, and Screening (CHESS), allowing the analysis of ligand binding with Saturation Transfer Difference NMR (STD NMR). STD NMR was used to map the binding epitopes of epinephrine and A-61603 to both receptors, revealing the molecular determinants for the selectivity of A-61603 for α 1A -AR over α 1B -AR. The use of stabilized GPCRs for ligand-observed NMR experiments will lead to a deeper understanding of binding processes and assist structure-based drug design.

High-precision 14C and 40Ar/39Ar dating of the Campanian Ignimbrite (Y-5) reconciles the time-scales of climatic-cultural processes at 40 ka.

PubMed

Giaccio, Biagio; Hajdas, Irka; Isaia, Roberto; Deino, Alan; Nomade, Sebastien

2017-04-06

The Late Pleistocene Campanian Ignimbrite (CI) super-eruption (Southern Italy) is the largest known volcanic event in the Mediterranean area. The CI tephra is widely dispersed through western Eurasia and occurs in close stratigraphic association with significant palaeoclimatic and Palaeolithic cultural events. Here we present new high-precision 14 C (34.29 ± 0.09 14 C kyr BP, 1σ) and 40 Ar/ 39 Ar (39.85 ± 0.14 ka, 95% confidence level) dating results for the age of the CI eruption, which substantially improve upon or augment previous age determinations and permit fuller exploitation of the chronological potential of the CI tephra marker. These results provide a robust pair of 14 C and 40 Ar/ 39 Ar ages for refining both the radiocarbon calibration curve and the Late Pleistocene time-scale at ca. 40 ka. In addition, these new age constraints provide compelling chronological evidence for the significance of the combined influence of the CI eruption and Heinrich Event 4 on European climate and potentially evolutionary processes of the Early Upper Palaeolithic.

High-precision 14C and 40Ar/39Ar dating of the Campanian Ignimbrite (Y-5) reconciles the time-scales of climatic-cultural processes at 40 ka

PubMed Central

Giaccio, Biagio; Hajdas, Irka; Isaia, Roberto; Deino, Alan; Nomade, Sebastien

2017-01-01

The Late Pleistocene Campanian Ignimbrite (CI) super-eruption (Southern Italy) is the largest known volcanic event in the Mediterranean area. The CI tephra is widely dispersed through western Eurasia and occurs in close stratigraphic association with significant palaeoclimatic and Palaeolithic cultural events. Here we present new high-precision 14C (34.29 ± 0.09 14C kyr BP, 1σ) and 40Ar/39Ar (39.85 ± 0.14 ka, 95% confidence level) dating results for the age of the CI eruption, which substantially improve upon or augment previous age determinations and permit fuller exploitation of the chronological potential of the CI tephra marker. These results provide a robust pair of 14C and 40Ar/39Ar ages for refining both the radiocarbon calibration curve and the Late Pleistocene time-scale at ca. 40 ka. In addition, these new age constraints provide compelling chronological evidence for the significance of the combined influence of the CI eruption and Heinrich Event 4 on European climate and potentially evolutionary processes of the Early Upper Palaeolithic. PMID:28383570

Application of excitation and emission matrix fluorescence (EEM) and UV-vis absorption to monitor the characteristics of Alizarin Red S (ARS) during electro-Fenton degradation process.

PubMed

Lai, Bo; Zhou, Yuexi; Wang, Juling; Yang, Zhishan; Chen, Zhiqiang

2013-11-01

Oxidative degradation of Alizarin Red S (ARS) in aqueous solutions by using electro-Fenton was studied. At first, effect of operating parameters such as current density, aeration rate and initial pH on the degradation of ARS were studied by using UV-vis spectrum, respectively. Then, under the optimal operating conditions (current density: 10.0mAcm(-2), aeration rate: 1000mLmin(-1), initial pH: 2.8), the identification of degradation products of ARS was carried out by using GC-MS and HPLC, meanwhile its degradation pathway was proposed according to the intermediates. Considering the location, intensity and intensity ratio of fluorescence center peak of the ARS in aqueous solution, a convenient and quick monitoring method by using excitation-emission matrix fluorescence spectrum technology was developed to monitor the degradation degree of ARS through electro-Fenton process. Furthermore, it is suggested that the developed method would be promising for the quick analysis and evaluation of the degradation degree of the pollutants with π-conjugated system. Copyright © 2013 Elsevier Ltd. All rights reserved.

Examining the role of attention and instruction in at-risk kindergarteners: electrophysiological measures of selective auditory attention before and after an early literacy intervention.

PubMed

Stevens, Courtney; Harn, Beth; Chard, David J; Currin, Jeff; Parisi, Danielle; Neville, Helen

2013-01-01

Several studies report that adults and adolescents with reading disabilities also experience difficulties with selective attention. In the present study, event-related brain potentials (ERPs) were used to examine the neural mechanisms of selective attention in kindergarten children at risk for reading disabilities (AR group, n = 8) or on track in early literacy skills (OT group, n = 6) across the first semester of kindergarten. The AR group also received supplemental instruction with the Early Reading Intervention (ERI). Following ERI, the AR group demonstrated improved skills on standardized early literacy measures such that there were no significant differences between the AR and OT groups at posttest or winter follow-up. Analysis of the ERP data revealed that at the start of kindergarten, the AR group displayed reduced effects of attention on sensorineural processing compared to the OT group. Following intervention, this difference between groups disappeared, with the AR group only showing improvements in the effect of attention on sensorineural processing. These data indicate that the neural mechanisms of selective attention are atypical in kindergarten children at risk for reading failure but can be improved by effective reading interventions.

A Specific Transitory Increase in Intracellular Calcium Induced by Progesterone Promotes Acrosomal Exocytosis in Mouse Sperm1

PubMed Central

Romarowski, Ana; Sánchez-Cárdenas, Claudia; Ramírez-Gómez, Héctor V.; Puga Molina, Lis del C.; Treviño, Claudia L.; Hernández-Cruz, Arturo; Darszon, Alberto; Buffone, Mariano G

2016-01-01

During capacitation, sperm acquire the ability to undergo the acrosome reaction (AR), an essential step in fertilization. Progesterone produced by cumulus cells has been associated with various physiological processes in sperm, including stimulation of AR. An increase in intracellular Ca2+ ([Ca2+]i) is necessary for AR to occur. In this study, we investigated the spatiotemporal correlation between the changes in [Ca2+]i and AR in single mouse spermatozoa in response to progesterone. We found that progesterone stimulates an [Ca2+]i increase in five different patterns: gradual increase, oscillatory, late transitory, immediate transitory, and sustained. We also observed that the [Ca2+]i increase promoted by progesterone starts at either the flagellum or the head. We validated the use of FM4-64 as an indicator for the occurrence of the AR by simultaneously detecting its fluorescence increase and the loss of EGFP in transgenic EGFPAcr sperm. For the first time, we have simultaneously visualized the rise in [Ca2+]i and the process of exocytosis in response to progesterone and found that only a specific transitory increase in [Ca2+]i originating in the sperm head promotes the initiation of AR. PMID:26819478

Ar-Ar_Redux: rigorous error propagation of 40Ar/39Ar data, including covariances

NASA Astrophysics Data System (ADS)

Vermeesch, P.

2015-12-01

Rigorous data reduction and error propagation algorithms are needed to realise Earthtime's objective to improve the interlaboratory accuracy of 40Ar/39Ar dating to better than 1% and thereby facilitate the comparison and combination of the K-Ar and U-Pb chronometers. Ar-Ar_Redux is a new data reduction protocol and software program for 40Ar/39Ar geochronology which takes into account two previously underappreciated aspects of the method: 1. 40Ar/39Ar measurements are compositional dataIn its simplest form, the 40Ar/39Ar age equation can be written as: t = log(1+J [40Ar/39Ar-298.5636Ar/39Ar])/λ = log(1 + JR)/λ Where λ is the 40K decay constant and J is the irradiation parameter. The age t does not depend on the absolute abundances of the three argon isotopes but only on their relative ratios. Thus, the 36Ar, 39Ar and 40Ar abundances can be normalised to unity and plotted on a ternary diagram or 'simplex'. Argon isotopic data are therefore subject to the peculiar mathematics of 'compositional data', sensu Aitchison (1986, The Statistical Analysis of Compositional Data, Chapman & Hall). 2. Correlated errors are pervasive throughout the 40Ar/39Ar methodCurrent data reduction protocols for 40Ar/39Ar geochronology propagate the age uncertainty as follows: σ2(t) = [J2 σ2(R) + R2 σ2(J)] / [λ2 (1 + R J)], which implies zero covariance between R and J. In reality, however, significant error correlations are found in every step of the 40Ar/39Ar data acquisition and processing, in both single and multi collector instruments, during blank, interference and decay corrections, age calculation etc. Ar-Ar_Redux revisits every aspect of the 40Ar/39Ar method by casting the raw mass spectrometer data into a contingency table of logratios, which automatically keeps track of all covariances in a compositional context. Application of the method to real data reveals strong correlations (r2 of up to 0.9) between age measurements within a single irradiation batch. Propertly taking into account these correlations significantly improves the precision and accuracy of 40Ar/39Ar data, at no financial cost. A prototype version of Ar-Ar_Redux was written in R and is available from http://redux.london-geochron.com. A standalone GUI is under development.

The single grain fusion dating approach: Determining the factors that control white mica 40Ar/39Ar age formation during HP metamorphism of the Cycladic Blueschist Unit, Greece

NASA Astrophysics Data System (ADS)

Uunk, Bertram; Wijbrans, Jan; Brouwer, Fraukje

2017-04-01

White mica 40Ar/39Ar dating is a proven powerful tool for constraining the timing and rate of metamorphism, deformation and exhumation. However, for high-pressure metamorphic rocks dating often results in wide age ranges, which are not in agreement with constraints from other isotopic systems, indicating that geological and chemical processes complicate straightforward 40Ar/39Ar dating. Despite hosting one of the largest geochronological datasets in the world, the Cycladic Blueschist Unit in Greece is presently one of the focal areas in the discussion on the interpretation of metamorphic 40Ar/39Ar ages. Previous phengite multi grain step heating experiments commonly yielded undulating age spectra ranging between 20 - 60 Ma. While some studies attempt to assign geological significance to these ages, others argue the ages are geologically meaningless and the result of the interplay between partial diffusive resetting and continued crystallization. By taking an alternative approach of multiple single grain fusion experiments, this study investigates age heterogeneity between samples of contrasting metamorphic facies, rheology and strain from the Cycladic islands of Syros and Sifnos. Comparing the size and shape of single grain fusion age distributions at the grain, rock, outcrop and island scale allows determination of the scale at which different age-forming processes operate. Resulting ages show a previously unreported consistent variation between different outcrops, moving from the eclogite-blueschist facies (55-45 Ma) to greenschist overprinting (40-30 Ma). This indicates that outcrop scale homogeneous resetting is the dominant processes for age formation in the CBU. Single grain age variation at the sample and outcrop scale is only limited to 10 Ma, indicating a smaller but observable role for local age perturbing processes of incomplete resetting, continued (re)crystallization or infiltration of excess argon. Some of the partially overprinted samples show homogeneous single grain age populations, indicating at least a partial role for efficient resetting by thermally activated diffusion at the outcrop scale. Traditional multi grain step heating experiments on the same samples yield flat plateaus for various single grain age distributions, indicating that age heterogeneities resolved by single grain fusion dating are mixed to a meaningless average in step heating experiments. In contrast, our approach leads to a better understanding of the processes responsible for age formation during high pressure metamorphism.

Granger-causality maps of diffusion processes.

PubMed

Wahl, Benjamin; Feudel, Ulrike; Hlinka, Jaroslav; Wächter, Matthias; Peinke, Joachim; Freund, Jan A

2016-02-01

Granger causality is a statistical concept devised to reconstruct and quantify predictive information flow between stochastic processes. Although the general concept can be formulated model-free it is often considered in the framework of linear stochastic processes. Here we show how local linear model descriptions can be employed to extend Granger causality into the realm of nonlinear systems. This novel treatment results in maps that resolve Granger causality in regions of state space. Through examples we provide a proof of concept and illustrate the utility of these maps. Moreover, by integration we convert the local Granger causality into a global measure that yields a consistent picture for a global Ornstein-Uhlenbeck process. Finally, we recover invariance transformations known from the theory of autoregressive processes.

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

Stromal androgen receptor roles in the development of normal prostate, benign prostate hyperplasia, and prostate cancer.

PubMed

Wen, Simeng; Chang, Hong-Chiang; Tian, Jing; Shang, Zhiqun; Niu, Yuanjie; Chang, Chawnshang

2015-02-01

The prostate is an androgen-sensitive organ that needs proper androgen/androgen receptor (AR) signals for normal development. The progression of prostate diseases, including benign prostate hyperplasia (BPH) and prostate cancer (PCa), also needs proper androgen/AR signals. Tissue recombination studies report that stromal, but not epithelial, AR plays more critical roles via the mesenchymal-epithelial interactions to influence the early process of prostate development. However, in BPH and PCa, much more attention has been focused on epithelial AR roles. However, accumulating evidence indicates that stromal AR is also irreplaceable and plays critical roles in prostate disease progression. Herein, we summarize the roles of stromal AR in the development of normal prostate, BPH, and PCa, with evidence from the recent results of in vitro cell line studies, tissue recombination experiments, and AR knockout animal models. Current evidence suggests that stromal AR may play positive roles to promote BPH and PCa progression, and targeting stromal AR selectively with AR degradation enhancer, ASC-J9, may allow development of better therapies with fewer adverse effects to battle BPH and PCa. Copyright © 2015 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

Splicing Factor Prp8 Interacts With NES(AR) and Regulates Androgen Receptor in Prostate Cancer Cells.

PubMed

Wang, Dan; Nguyen, Minh M; Masoodi, Khalid Z; Singh, Prabhpreet; Jing, Yifeng; O'Malley, Katherine; Dar, Javid A; Dhir, Rajiv; Wang, Zhou

2015-12-01

Androgen receptor (AR) plays a pivotal role in the development of primary as well as advanced castration-resistant prostate cancer. Previous work in our lab identified a novel nuclear export signal (NES) (NES(AR)) in AR ligand-binding domain essential for AR nucleocytoplasmic trafficking. By characterizing the localization of green fluorescence protein (GFP)-tagged NES(AR), we designed and executed a yeast mutagenesis screen and isolated 7 yeast mutants that failed to display the NES(AR) export function. One of those mutants was identified as the splicing factor pre-mRNA processing factor 8 (Prp8). We further showed that Prp8 could regulate NES(AR) function using short hairpin RNA knockdown of Prp8 coupled with a rapamycin export assay in mammalian cells and knockdown of Prp8 could induce nuclear accumulation of GFP-tagged AR in PC3 cells. Prp8 expression was decreased in castration-resistant LuCaP35 xenograft tumors as compared with androgen-sensitive xenografts. Laser capture microdissection and quantitative PCR showed Prp8 mRNA levels were decreased in human prostate cancer specimens with high Gleason scores. In prostate cancer cells, coimmunoprecipitation and deletion mutagenesis revealed a physical interaction between Prp8 and AR mainly mediated by NES(AR). Luciferase assay with prostate specific antigen promoter-driven reporter demonstrated that Prp8 regulated AR transcription activity in prostate cancer cells. Interestingly, Prp8 knockdown also increased polyubiquitination of endogenous AR. This may be 1 possible mechanism by which it modulates AR activity. These results show that Prp8 is a novel AR cofactor that interacts with NES(AR) and regulates AR function in prostate cancer cells.

Regulation of StAR by the N-terminal Domain and Coinduction of SIK1 and TIS11b/Znf36l1 in Single Cells

PubMed Central

Lee, Jinwoo; Tong, Tiegang; Duan, Haichuan; Foong, Yee Hoon; Musaitif, Ibrahim; Yamazaki, Takeshi; Jefcoate, Colin

2016-01-01

The cholesterol transfer function of steroidogenic acute regulatory protein (StAR) is uniquely integrated into adrenal cells, with mRNA translation and protein kinase A (PKA) phosphorylation occurring at the mitochondrial outer membrane (OMM). The StAR C-terminal cholesterol-binding domain (CBD) initiates mitochondrial intermembrane contacts to rapidly direct cholesterol to Cyp11a1 in the inner membrane (IMM). The conserved StAR N-terminal regulatory domain (NTD) includes a leader sequence targeting the CBD to OMM complexes that initiate cholesterol transfer. Here, we show how the NTD functions to enhance CBD activity delivers more efficiently from StAR mRNA in adrenal cells, and then how two factors hormonally restrain this process. NTD processing at two conserved sequence sites is selectively affected by StAR PKA phosphorylation. The CBD functions as a receptor to stimulate the OMM/IMM contacts that mediate transfer. The NTD controls the transit time that integrates extramitochondrial StAR effects on cholesterol homeostasis with other mitochondrial functions, including ATP generation, inter-organelle fusion, and the major permeability transition pore in partnership with other OMM proteins. PKA also rapidly induces two additional StAR modulators: salt-inducible kinase 1 (SIK1) and Znf36l1/Tis11b. Induced SIK1 attenuates the activity of CRTC2, a key mediator of StAR transcription and splicing, but only as cAMP levels decline. TIS11b inhibits translation and directs the endonuclease-mediated removal of the 3.5-kb StAR mRNA. Removal of either of these functions individually enhances cAMP-mediated induction of StAR. High-resolution fluorescence in situ hybridization (HR-FISH) of StAR RNA reveals asymmetric transcription at the gene locus and slow RNA splicing that delays mRNA formation, potentially to synchronize with cholesterol import. Adrenal cells may retain slow transcription to integrate with intermembrane NTD activation. HR-FISH resolves individual 3.5-kb StAR mRNA molecules via dual hybridization at the 3′- and 5′-ends and reveals an unexpectedly high frequency of 1:1 pairing with mitochondria marked by the matrix StAR protein. This pairing may be central to translation-coupled cholesterol transfer. Altogether, our results show that adrenal cells exhibit high-efficiency StAR activity that needs to integrate rapid cholesterol transfer with homeostasis and pulsatile hormonal stimulation. StAR NBD, the extended 3.5-kb mRNA, SIK1, and Tis11b play important roles. PMID:27531991

Regulation of StAR by the N-terminal Domain and Coinduction of SIK1 and TIS11b/Znf36l1 in Single Cells.

PubMed

Lee, Jinwoo; Tong, Tiegang; Duan, Haichuan; Foong, Yee Hoon; Musaitif, Ibrahim; Yamazaki, Takeshi; Jefcoate, Colin

2016-01-01

The cholesterol transfer function of steroidogenic acute regulatory protein (StAR) is uniquely integrated into adrenal cells, with mRNA translation and protein kinase A (PKA) phosphorylation occurring at the mitochondrial outer membrane (OMM). The StAR C-terminal cholesterol-binding domain (CBD) initiates mitochondrial intermembrane contacts to rapidly direct cholesterol to Cyp11a1 in the inner membrane (IMM). The conserved StAR N-terminal regulatory domain (NTD) includes a leader sequence targeting the CBD to OMM complexes that initiate cholesterol transfer. Here, we show how the NTD functions to enhance CBD activity delivers more efficiently from StAR mRNA in adrenal cells, and then how two factors hormonally restrain this process. NTD processing at two conserved sequence sites is selectively affected by StAR PKA phosphorylation. The CBD functions as a receptor to stimulate the OMM/IMM contacts that mediate transfer. The NTD controls the transit time that integrates extramitochondrial StAR effects on cholesterol homeostasis with other mitochondrial functions, including ATP generation, inter-organelle fusion, and the major permeability transition pore in partnership with other OMM proteins. PKA also rapidly induces two additional StAR modulators: salt-inducible kinase 1 (SIK1) and Znf36l1/Tis11b. Induced SIK1 attenuates the activity of CRTC2, a key mediator of StAR transcription and splicing, but only as cAMP levels decline. TIS11b inhibits translation and directs the endonuclease-mediated removal of the 3.5-kb StAR mRNA. Removal of either of these functions individually enhances cAMP-mediated induction of StAR. High-resolution fluorescence in situ hybridization (HR-FISH) of StAR RNA reveals asymmetric transcription at the gene locus and slow RNA splicing that delays mRNA formation, potentially to synchronize with cholesterol import. Adrenal cells may retain slow transcription to integrate with intermembrane NTD activation. HR-FISH resolves individual 3.5-kb StAR mRNA molecules via dual hybridization at the 3'- and 5'-ends and reveals an unexpectedly high frequency of 1:1 pairing with mitochondria marked by the matrix StAR protein. This pairing may be central to translation-coupled cholesterol transfer. Altogether, our results show that adrenal cells exhibit high-efficiency StAR activity that needs to integrate rapid cholesterol transfer with homeostasis and pulsatile hormonal stimulation. StAR NBD, the extended 3.5-kb mRNA, SIK1, and Tis11b play important roles.

A New First Break Picking for Three-Component VSP Data Using Gesture Sensor and Polarization Analysis

PubMed Central

Li, Huailiang; Tuo, Xianguo; Shen, Tong; Wang, Ruili; Courtois, Jérémie; Yan, Minhao

2017-01-01

A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data. PMID:28925981

SIRT6 Is a Positive Regulator of Aldose Reductase Expression in U937 and HeLa cells under Osmotic Stress: In Vitro and In Silico Insights

PubMed Central

Timucin, Ahmet Can; Basaga, Huveyda

2016-01-01

SIRT6 is a protein deacetylase, involved in various intracellular processes including suppression of glycolysis and DNA repair. Aldose Reductase (AR), first enzyme of polyol pathway, was proposed to be indirectly associated to these SIRT6 linked processes. Despite these associations, presence of SIRT6 based regulation of AR still remains ambiguous. Thus, regulation of AR expression by SIRT6 was investigated under hyperosmotic stress. A unique model of osmotic stress in U937 cells was used to demonstrate the presence of a potential link between SIRT6 and AR expression. By overexpressing SIRT6 in HeLa cells under hyperosmotic stress, its role on upregulation of AR was revealed. In parallel, increased SIRT6 activity was shown to upregulate AR in U937 cells under hyperosmotic milieu by using pharmacological modulators. Since these modulators also target SIRT1, binding of the inhibitor, Ex-527, specifically to SIRT6 was analyzed in silico. Computational observations indicated that Ex-527 may also target SIRT6 active site residues under high salt concentration, thus, validating in vitro findings. Based on these evidences, a novel regulatory step by SIRT6, modifying AR expression under hyperosmotic stress was presented and its possible interactions with intracellular machinery was discussed. PMID:27536992

Evidence for Environmental Dissemination of Antibiotic Resistance Mediated by Wild Birds.

PubMed

Wu, Jiao; Huang, Ye; Rao, Dawei; Zhang, Yongkui; Yang, Kun

2018-01-01

The aquatic bird, egret, could carry antibiotic resistance (AR) from a contaminated waterway (Jin River, Chengdu, China) into the surrounding environment (Wangjianglou Park). A systematic study was carried out on the unique environmental dissemination mode of AR mediated by birds. The minimum inhibitory concentrations of various antibiotics against the environmental Escherichia coli isolates were used to evaluate the bacterial AR at the environmental locations where these isolates were recovered, i.e., the Jin River water, the egret feces, the park soil, and the campus soil. The level of AR in the park soil was significantly higher than that in the campus soil that was seldom affected by the egrets, which suggested that the egrets mediated the transportation of AR from the polluted waterway to the park. Genotyping of the resistant E. coli isolates via repetitive-element PCR gave no strong correlation between the genotypes and the AR patterns of the bacteria. So, the transfer of resistant strains should not be the main mode of AR transportation in this process. The results of real-time PCR revealed that the abundance of antibiotic resistance genes (ARGs) and mobile genetic element (MGE) sequences (transposase and integrase genes) declined along the putative transportation route. The transportation of ARGs could be due to their linkage with MGE sequences, and horizontal gene transfer should have contributed to the process. The movable colistin-resistance gene mcr-1 was detected among the colistin-resistant E. coli strains isolated from the river water and the egret feces, which indicated the possibility of the environmental dissemination of this gene. Birds, especially the migratory birds, for the role they played on the dissemination of environmental AR, should be considered when studying the ecology of AR.

Parametric modulation of neural activity during face emotion processing in unaffected youth at familial risk for bipolar disorder.

PubMed

Brotman, Melissa A; Deveney, Christen M; Thomas, Laura A; Hinton, Kendra E; Yi, Jennifer Y; Pine, Daniel S; Leibenluft, Ellen

2014-11-01

Both patients with pediatric bipolar disorder (BD) and unaffected youth at familial risk (AR) for the illness show impairments in face emotion labeling. Few studies, however, have examined brain regions engaged in AR youth when processing emotional faces. Moreover, studies have yet to explore neural responsiveness to subtle changes in face emotion in AR youth. Sixty-four unrelated youth, including 20 patients with BD, 15 unaffected AR youth, and 29 healthy comparisons (HC), completed functional magnetic resonance imaging. Neutral faces were morphed with angry or happy faces in 25% intervals. In specific phases of the task, youth alternatively made explicit (hostility) or implicit (nose width) ratings of the faces. The slope of blood oxygenated level-dependent activity was calculated across neutral to angry and neutral to happy face stimuli. Behaviorally, both subjects with BD (p ≤ 0.001) and AR youth (p ≤ 0.05) rated faces as less hostile relative to HC. Consistent with this, in response to increasing anger on the face, patients with BD and AR youth showed decreased modulation in the amygdala and inferior frontal gyrus (IFG; BA 46) compared to HC (all p ≤ 0.05). Amygdala dysfunction was present across both implicit and explicit rating conditions, but IFG modulation deficits were specific to the explicit condition. With increasing happiness, AR youth showed aberrant modulation in the IFG, which was also sensitive to task demands (all p ≤ 0.05). Decreased amygdala and IFG modulation in patients with BD and AR youth may be pathophysiological risk markers for BD, and may underlie the social cognition and face emotion labeling deficits observed in BD and AR youth. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Simulating extreme low-discharge events for the Rhine using a stochastic model

NASA Astrophysics Data System (ADS)

Macian-Sorribes, Hector; Mens, Marjolein; Schasfoort, Femke; Diermanse, Ferdinand; Pulido-Velazquez, Manuel

2017-04-01

The specific features of hydrological droughts make them more difficult to be analysed than other water-related phenomena: longer time scales (months to several years) so less historical events are available, and the drought severity and associate damage depends on a combination of variables with no clear prevalence (e.g., total water deficit, maximum deficit and duration). As part of drought risk analysis, which aims to provide insight into the variability of hydrological conditions and associated socio-economic impacts, long synthetic time series should therefore be developed. In this contribution, we increase the length of the available inflow time series using stochastic autoregressive modelling. This enhancement could improve the characterization of the extreme range and can define extreme droughts with similar periods of return but different patterns that can lead to distinctly different damages. The methodology consists of: 1) fitting an autoregressive model (AR, ARMA…) to the available records; 2) generating extended time series (thousands of years); 3) performing a frequency analysis with different characteristic variables (total, deficit, maximum deficit and so on); and 4) selecting extreme drought events associated with different characteristic variables and return periods. The methodology was applied to the Rhine river discharge at location Lobith, where the Rhine enters The Netherlands. A monthly ARMA(1,1) autoregressive model with seasonally varying parameters was fitted and successfully validated to the historical records available since year 1901. The maximum monthly deficit with respect to a threshold value of 1800 m3/s and the average discharge for a given time span in m3/s were chosen as indicators to identify drought periods. A synthetic series of 10,000 years of discharges was generated using the validated ARMA model. Two time spans were considered in the analysis: the whole calendar year and the half-year period between April and September (the summer half year, where water demands are highest). Frequency analysis was performed for both indicators and time spans for the generated time series and the historical records. The comparison between observed and generated series showed that the ARMA model provides a good reproduction of the maximum deficits and total discharges, especially for the summer half-year period. The resulting synthetic series are therefore considered credible. These synthetic series, with its wealth of information, can then be used as inputs for the damage assessment models, together with information on precipitation deficits, in order to estimate the risk that lower inflows can have on the urban, the agricultural, the shipping sector and so on. This will help in associating economic losses and periods of return, as well as for estimating how droughts with similar periods of return but different patterns can lead to different damages. ACKNOWLEDGEMENT This study has been supported by the European Union's Horizon 2020 research and innovation programme under the IMPREX project (grant agreement no: 641.811), and by the Climate-KIC Pioneers into Practice Program supported by the European Union's EIT.

ON POLAR MAGNETIC FIELD REVERSAL AND SURFACE FLUX TRANSPORT DURING SOLAR CYCLE 24

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

Sun, Xudong; Todd Hoeksema, J.; Liu, Yang

As each solar cycle progresses, remnant magnetic flux from active regions (ARs) migrates poleward to cancel the old-cycle polar field. We describe this polarity reversal process during Cycle 24 using four years (2010.33-2014.33) of line-of-sight magnetic field measurements from the Helioseismic and Magnetic Imager. The total flux associated with ARs reached maximum in the north in 2011, more than two years earlier than the south; the maximum is significantly weaker than Cycle 23. The process of polar field reversal is relatively slow, north-south asymmetric, and episodic. We estimate that the global axial dipole changed sign in 2013 October; the northernmore » and southern polar fields (mean above 60° latitude) reversed in 2012 November and 2014 March, respectively, about 16 months apart. Notably, the poleward surges of flux in each hemisphere alternated in polarity, giving rise to multiple reversals in the north. We show that the surges of the trailing sunspot polarity tend to correspond to normal mean AR tilt, higher total AR flux, or slower mid-latitude near-surface meridional flow, while exceptions occur during low magnetic activity. In particular, the AR flux and the mid-latitude poleward flow speed exhibit a clear anti-correlation. We discuss how these features can be explained in a surface flux transport process that includes a field-dependent converging flow toward the ARs, a characteristic that may contribute to solar cycle variability.« less

Low-temperature volume radiation annealing of cold-worked bands of Al-Li-Cu-Mg alloy by 20-40 keV Ar+ ion

NASA Astrophysics Data System (ADS)

Ovchinnikov, V. V.; Gushchina, N. V.; Mozharovsky, S. M.; Kaigorodova, L. I.

2017-01-01

The processes of radiation-dynamic nature (in contrast to the thermally-activated processes) in the course of short-term irradiation of 1 mm thick bands of cold-worked aluminum alloy 1441 (of system Al-Li-Cu-Mg) with Ar+ 20-40 keV were studied. An effect of in-the-bulk (throughout the whole of metal bands thickness) low-temperature radiation annealing of the named alloy, multiply accelerated as compared with common thermal annealing processes was registered (with projected ranges of ions of considered energies definitely not exceeding 0.1 μm). The processes of recrystallization and intermetallic structure changes (occurring within a few seconds of Ar+ irradiation) have the common features as well as the differences in comparison with the results of two hour standard thermal annealing.

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).

Affordances of Augmented Reality in Science Learning: Suggestions for Future Research

NASA Astrophysics Data System (ADS)

Cheng, Kun-Hung; Tsai, Chin-Chung

2013-08-01

Augmented reality (AR) is currently considered as having potential for pedagogical applications. However, in science education, research regarding AR-aided learning is in its infancy. To understand how AR could help science learning, this review paper firstly has identified two major approaches of utilizing AR technology in science education, which are named as image- based AR and location- based AR. These approaches may result in different affordances for science learning. It is then found that students' spatial ability, practical skills, and conceptual understanding are often afforded by image-based AR and location-based AR usually supports inquiry-based scientific activities. After examining what has been done in science learning with AR supports, several suggestions for future research are proposed. For example, more research is required to explore learning experience (e.g., motivation or cognitive load) and learner characteristics (e.g., spatial ability or perceived presence) involved in AR. Mixed methods of investigating learning process (e.g., a content analysis and a sequential analysis) and in-depth examination of user experience beyond usability (e.g., affective variables of esthetic pleasure or emotional fulfillment) should be considered. Combining image-based and location-based AR technology may bring new possibility for supporting science learning. Theories including mental models, spatial cognition, situated cognition, and social constructivist learning are suggested for the profitable uses of future AR research in science education.

Meat quality and rigor mortis development in broiler chickens with gas-induced anoxia and postmortem electrical stimulation.

PubMed

Sams, A R; Dzuik, C S

1999-10-01

This study was conducted to evaluate the combined rigor-accelerating effects of postmortem electrical stimulation (ES) and argon-induced anoxia (Ar) of broiler chickens. One hundred broilers were processed in the following treatments: untreated controls, ES, Ar, or Ar with ES (Ar + ES). Breast fillets were harvested at 1 h postmortem for all treatments or at 1 and 6 h postmortem for the control carcasses. Fillets were sampled for pH and ratio of inosine to adenosine (R-value) and were then individually quick frozen (IQF) or aged on ice (AOI) until 24 h postmortem. Color was measured in the AOI fillets at 24 h postmortem. All fillets were then cooked and evaluated for Allo-Kramer shear value. The Ar treatment accelerated the normal pH decline, whereas the ES and AR + ES treatments yielded even lower pH values at 1 h postmortem. The Ar + ES treatment had a greater R-value than the ES treatment, which was greater than either the Ar or 1-h controls, which, in turn, were not different from each other. The ES treatment had the lowest L* value, and ES, Ar, and Ar + ES produced significantly higher a* values than the 1-h controls. For the IQF fillets, the ES and Ar + ES treatments were not different in shear value but were lower than Ar, which was lower than the 1-h controls. The same was true for the AOI fillets except that the ES and the Ar treatments were not different. These results indicated that although ES and Ar had rigor-accelerating and tenderizing effects, ES seemed to be more effective than Ar; there was little enhancement when Ar was added to the ES treatment and fillets were deboned at 1 h postmortem.

Mechanisms of Alizarin Red S and Methylene blue biosorption onto olive stone by-product: Isotherm study in single and binary systems.

PubMed

Albadarin, Ahmad B; Mangwandi, Chirangano

2015-12-01

The biosorption process of anionic dye Alizarin Red S (ARS) and cationic dye methylene blue (MB) as a function of contact time, initial concentration and solution pH onto olive stone (OS) biomass has been investigated. Equilibrium biosorption isotherms in single and binary systems and kinetics in batch mode were also examined. The kinetic data of the two dyes were better described by the pseudo second-order model. At low concentration, ARS dye appeared to follow a two-step diffusion process, while MB dye followed a three-step diffusion process. The biosorption experimental data for ARS and MB dyes were well suited to the Redlich-Peterson isotherm. The maximum biosorption of ARS dye, qmax = 16.10 mg/g, was obtained at pH 3.28 and the maximum biosorption of MB dye, qmax = 13.20 mg/g, was observed at basic pH values. In the binary system, it was indicated that the MB dye diffuses firstly inside the biosorbent particle and occupies the biosorption sites forming a monodentate complex and then the ARS dye enters and can only bind to untaken sites; forms a tridentate complex with OS active sites. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

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.

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.

The effect of thermal resetting and recrystallisation on white mica 40Ar/39Ar ages during retrograde metamorphism on Syros, Greece

NASA Astrophysics Data System (ADS)

Uunk, Bertram; Wijbrans, Jan; Brouwer, Fraukje

2015-04-01

White mica 40Ar/39Ar dating is a proven powerful tool for constraining timing of metamorphism, deformation and exhumation. However, in high-pressure metamorphic rocks, dating often results in wide age ranges which are not in agreement with constraints from other isotopic systems, indicating that geological and chemical processes complicate straightforward 40Ar/39Ar dating. In this research project, white mica ages from rocks of the Cycladic Blueschist Unit on Syros, Greece with contrasting rheology and strain mechanisms are compared, in order to better understand the role of deformation, recrystallization and fluid flow on 40Ar/39Ar ages of white mica during retrograde metamorphism. Resulting ages vary along different sections on the island, inconsistent with other isotopic constraints on eclogite-blueschist metamorphism (55-50 Ma) and greenschist overprinting (41-30 Ma). Two end-member models are possible: 1) Results represent continuous crystallization of white mica while moving from blueschist to greenschist conditions in the metamorphic P-T loop, or 2) white mica equilibrated in eclogite-blueschist conditions and their diffusion systematics were progressively perturbed during greenschist overprinting. The single grain fusion analyses yielded contrasting age distributions, which indicate contrasts in degree of re-equilibration during retrograde metamorphism. Step wise heating of larger grain populations resulted in flat plateau shapes, providing no evidence for partial resetting. Electron microprobe measurements of Si per formula unit, as a proxy for pressure during crystallisation, do not explain age variation within sections or on the island scale. The previously unreported north-south age trend and age ranges per sample, as shown only in the 40Ar/39Ar system of the metapelitic and marble lithologies, contains key information that will allow us to test between different scenarios for age formation. Excess argon infiltration at this stage seems to have been of minor importance. Our new approach should lead to a better understanding of the interplay of these processes during and after HP metamorphism.

Functional β-Adrenoceptors Are Important for Early Muscle Regeneration in Mice through Effects on Myoblast Proliferation and Differentiation

PubMed Central

Church, Jarrod E.; Trieu, Jennifer; Sheorey, Radhika; Chee, Annabel Y. -M.; Naim, Timur; Baum, Dale M.; Ryall, James G.; Gregorevic, Paul; Lynch, Gordon S.

2014-01-01

Muscles can be injured in different ways and the trauma and subsequent loss of function and physical capacity can impact significantly on the lives of patients through physical impairments and compromised quality of life. The relative success of muscle repair after injury will largely determine the extent of functional recovery. Unfortunately, regenerative processes are often slow and incomplete, and so developing novel strategies to enhance muscle regeneration is important. While the capacity to enhance muscle repair by stimulating β2-adrenoceptors (β-ARs) using β2-AR agonists (β2-agonists) has been demonstrated previously, the exact role β-ARs play in regulating the regenerative process remains unclear. To investigate β-AR-mediated signaling in muscle regeneration after myotoxic damage, we examined the regenerative capacity of tibialis anterior and extensor digitorum longus muscles from mice lacking either β1-AR (β1-KO) and/or β2-ARs (β2-KO), testing the hypothesis that muscles from mice lacking the β2-AR would exhibit impaired functional regeneration after damage compared with muscles from β1-KO or β1/β2-AR null (β1/β2-KO) KO mice. At 7 days post-injury, regenerating muscles from β1/β2-KO mice produced less force than those of controls but muscles from β1-KO or β2-KO mice did not exhibit any delay in functional restoration. Compared with controls, β1/β2-KO mice exhibited an enhanced inflammatory response to injury, which delayed early muscle regeneration, but an enhanced myoblast proliferation later during regeneration ensured a similar functional recovery (to controls) by 14 days post-injury. This apparent redundancy in the β-AR signaling pathway was unexpected and may have important implications for manipulating β-AR signaling to improve the rate, extent and efficacy of muscle regeneration to enhance functional recovery after injury. PMID:25000590

The Viability and Value of Student- and Teacher-Created Augmented Reality Experiences

ERIC Educational Resources Information Center

O'Shea, Patrick; Curry-Corcoran, Daniel

2013-01-01

This paper describes the process and results of a project to incorporate Augmented Reality (AR) technologies and pedagogical approaches into a Virginian elementary school. The process involved training 5th grade teachers on the design and production of narrative-based AR games in order to give them the skills that they could then pass on to their…

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

Effect of the heat shock protein HSP27 on androgen receptor expression and function in prostate cancer cells.

PubMed

Stope, Matthias B; Schubert, Tina; Staar, Doreen; Rönnau, Cindy; Streitbörger, Andreas; Kroeger, Nils; Kubisch, Constanze; Zimmermann, Uwe; Walther, Reinhard; Burchardt, Martin

2012-06-01

Heat shock proteins (HSP) are involved in processes of folding, activation, trafficking and transcriptional activity of most steroid receptors including the androgen receptor (AR). Accumulating evidence links rising heat shock protein 27 (HSP27) levels with the development of castration-resistant prostate cancer. In order to study the functional relationship between HSP27 and the AR, we modulated the expression of the small heat shock protein HSP27 in human prostate cancer (PC) cell lines. HSP27 protein concentrations in LNCaP and PC-3 cells were modulated by over-expression or silencing of HSP27. The effects of HSP27 on AR protein and mRNA levels were monitored by Western blotting and quantitative RT-PCR. Treatment for the AR-positive LNCaP with HSP27-specific siRNA resulted in a down-regulation of AR levels. This down-regulation of protein was paralleled by a decrease in AR mRNA. Most interestingly, over-expression of HSP27 in PC-3 cells led to a significant increase in AR mRNA although the cells were unable to produce functional AR protein. The observation that HSP27 is involved in the regulation of AR mRNA by a yet unknown mechanism highlights the complexity of HSP27-AR signaling network.

Ion-plating of solar cell arrays encapsulation task: LSA project 32

NASA Technical Reports Server (NTRS)

Volkers, J. C.

1983-01-01

An ion plating process by which solar cells can be metallized and AR coated, yielding efficiencies equal to or better than state-of-the-art cells, was developed. It was demonstrated that ion plated AR films may be used as an effective encapsulant, offering primary protection for the metallization. It was also shown that ion plated metallization and AR coatings can be consistent with the project cost goals.

Particle-in-cell/Monte Carlo collisions treatment of an Ar/O2 magnetron discharge used for the reactive sputter deposition of TiOx films

NASA Astrophysics Data System (ADS)

Bultinck, E.; Bogaerts, A.

2009-10-01

The physical processes in an Ar/O2 magnetron discharge used for the reactive sputter deposition of TiOx thin films were simulated with a 2d3v particle-in-cell/Monte Carlo collisions (PIC/MCC) model. The plasma species taken into account are electrons, Ar+ ions, fast Arf atoms, metastable Arm* atoms, Ti+ ions, Ti atoms, O+ ions, O2+ ions, O- ions and O atoms. This model accounts for plasma-target interactions, such as secondary electron emission and target sputtering, and the effects of target poisoning. Furthermore, the deposition process is described by an analytical surface model. The influence of the O2/Ar gas ratio on the plasma potential and on the species densities and fluxes is investigated. Among others, it is shown that a higher O2 pressure causes the region of positive plasma potential and the O- density to be more spread, and the latter to decrease. On the other hand, the deposition rates of Ti and O are not much affected by the O2/Ar proportion. Indeed, the predicted stoichiometry of the deposited TiOx film approaches x=2 for nearly all the investigated O2/Ar proportions.

Fluxless eutectic bonding of GaAs-on-Si by using Ag/Sn solder

NASA Astrophysics Data System (ADS)

Eo, Sung-Hwa; Kim, Dae-Seon; Jeong, Ho-Jung; Jang, Jae-Hyung

2013-11-01

Fluxless GaAs-on-Si wafer bonding using Ag/Sn solder was investigated to realize uniform and void-free heterogeneous material integration. The effects of the diffusion barrier, Ag/Sn thickness, and Ar plasma treatment were studied to achieve the optimal fluxless bonding process. Pt on a GaAs wafer and Mo on a Si wafer act as diffusion barriers by preventing the flow of Ag/Sn solder into both the wafers. The bonding strength is closely related to the Ag/Sn thickness and Ar plasma treatment. A shear strength test was carried out to investigate the bonding strength. Under identical bonding conditions, the Ag/Sn thickness was optimized to achieve higher bonding strength and to avoid the formation of voids due to thermal stress. An Ar plasma pretreatment process improved the bonding strength because the Ar plasma removed carbon contaminants and metal-oxide bonds from the metal surface.

Enhancing a Multi-body Mechanism with Learning-Aided Cues in an Augmented Reality Environment

NASA Astrophysics Data System (ADS)

Singh Sidhu, Manjit

2013-06-01

Augmented Reality (AR) is a potential area of research for education, covering issues such as tracking and calibration, and realistic rendering of virtual objects. The ability to augment real world with virtual information has opened the possibility of using AR technology in areas such as education and training as well. In the domain of Computer Aided Learning (CAL), researchers have long been looking into enhancing the effectiveness of the teaching and learning process by providing cues that could assist learners to better comprehend the materials presented. Although a number of works were done looking into the effectiveness of learning-aided cues, but none has really addressed this issue for AR-based learning solutions. This paper discusses the design and model of an AR based software that uses visual cues to enhance the learning process and the outcome perception results of the cues.

Astrocytic β2-adrenergic receptors mediate hippocampal long-term memory consolidation.

PubMed

Gao, Virginia; Suzuki, Akinobu; Magistretti, Pierre J; Lengacher, Sylvain; Pollonini, Gabriella; Steinman, Michael Q; Alberini, Cristina M

2016-07-26

Emotionally relevant experiences form strong and long-lasting memories by critically engaging the stress hormone/neurotransmitter noradrenaline, which mediates and modulates the consolidation of these memories. Noradrenaline acts through adrenergic receptors (ARs), of which β2-adrenergic receptors (βARs) are of particular importance. The differential anatomical and cellular distribution of βAR subtypes in the brain suggests that they play distinct roles in memory processing, although much about their specific contributions and mechanisms of action remains to be understood. Here we show that astrocytic rather than neuronal β2ARs in the hippocampus play a key role in the consolidation of a fear-based contextual memory. These hippocampal β2ARs, but not β1ARs, are coupled to the training-dependent release of lactate from astrocytes, which is necessary for long-term memory formation and for underlying molecular changes. This key metabolic role of astrocytic β2ARs may represent a novel target mechanism for stress-related psychopathologies and neurodegeneration.

Stochastic time series analysis of fetal heart-rate variability

NASA Astrophysics Data System (ADS)

Shariati, M. A.; Dripps, J. H.

1990-06-01

Fetal Heart Rate(FHR) is one of the important features of fetal biophysical activity and its long term monitoring is used for the antepartum(period of pregnancy before labour) assessment of fetal well being. But as yet no successful method has been proposed to quantitatively represent variety of random non-white patterns seen in FHR. Objective of this paper is to address this issue. In this study the Box-Jenkins method of model identification and diagnostic checking was used on phonocardiographic derived FHR(averaged) time series. Models remained exclusively autoregressive(AR). Kalman filtering in conjunction with maximum likelihood estimation technique forms the parametric estimator. Diagnosrics perfonned on the residuals indicated that a second order model may be adequate in capturing type of variability observed in 1 up to 2 mm data windows of FHR. The scheme may be viewed as a means of data reduction of a highly redundant information source. This allows a much more efficient transmission of FHR information from remote locations to places with facilities and expertise for doser analysis. The extracted parameters is aimed to reflect numerically the important FHR features. These are normally picked up visually by experts for their assessments. As a result long term FHR recorded during antepartum period could then be screened quantitatively for detection of patterns considered normal or abnonnal. 1.

Acoustic Emission Detected by Matched Filter Technique in Laboratory Earthquake Experiment

NASA Astrophysics Data System (ADS)

Wang, B.; Hou, J.; Xie, F.; Ren, Y.

2017-12-01

Acoustic Emission in laboratory earthquake experiment is a fundamental measures to study the mechanics of the earthquake for instance to characterize the aseismic, nucleation, as well as post seismic phase or in stick slip experiment. Compared to field earthquake, AEs are generally recorded when they are beyond threshold, so some weak signals may be missing. Here we conducted an experiment on a 1.1m×1.1m granite with a 1.5m fault and 13 receivers with the same sample rate of 3MHz are placed on the surface. We adopt continues record and a matched filter technique to detect low-SNR signals. We found there are too many signals around the stick-slip and the P- arrival picked by manual may be time-consuming. So, we combined the short-term average to long-tem-average ratio (STA/LTA) technique with Autoregressive-Akaike information criterion (AR-AIC) technique to pick the arrival automatically and found mostly of the P- arrival accuracy can satisfy our demand to locate signals. Furthermore, we will locate the signals and apply a matched filter technique to detect low-SNR signals. Then, we can see if there is something interesting in laboratory earthquake experiment. Detailed and updated results will be present in the meeting.

Modification of the Sandwich Estimator in Generalized Estimating Equations with Correlated Binary Outcomes in Rare Event and Small Sample Settings

PubMed Central

Rogers, Paul; Stoner, Julie

2016-01-01

Regression models for correlated binary outcomes are commonly fit using a Generalized Estimating Equations (GEE) methodology. GEE uses the Liang and Zeger sandwich estimator to produce unbiased standard error estimators for regression coefficients in large sample settings even when the covariance structure is misspecified. The sandwich estimator performs optimally in balanced designs when the number of participants is large, and there are few repeated measurements. The sandwich estimator is not without drawbacks; its asymptotic properties do not hold in small sample settings. In these situations, the sandwich estimator is biased downwards, underestimating the variances. In this project, a modified form for the sandwich estimator is proposed to correct this deficiency. The performance of this new sandwich estimator is compared to the traditional Liang and Zeger estimator as well as alternative forms proposed by Morel, Pan and Mancl and DeRouen. The performance of each estimator was assessed with 95% coverage probabilities for the regression coefficient estimators using simulated data under various combinations of sample sizes and outcome prevalence values with an Independence (IND), Autoregressive (AR) and Compound Symmetry (CS) correlation structure. This research is motivated by investigations involving rare-event outcomes in aviation data. PMID:26998504

Development of the Complex General Linear Model in the Fourier Domain: Application to fMRI Multiple Input-Output Evoked Responses for Single Subjects

PubMed Central

Rio, Daniel E.; Rawlings, Robert R.; Woltz, Lawrence A.; Gilman, Jodi; Hommer, Daniel W.

2013-01-01

A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI) blood-oxygen level-dependent (BOLD) multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF) where prewhitening of the data is attempted using autoregressive (AR) models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain). This is especially important for experimental designs involving multiple states (either stimulus or drug induced) that may alter the form of the response function. PMID:23840281

Development of the complex general linear model in the Fourier domain: application to fMRI multiple input-output evoked responses for single subjects.

PubMed

Rio, Daniel E; Rawlings, Robert R; Woltz, Lawrence A; Gilman, Jodi; Hommer, Daniel W

2013-01-01

A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI) blood-oxygen level-dependent (BOLD) multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF) where prewhitening of the data is attempted using autoregressive (AR) models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain). This is especially important for experimental designs involving multiple states (either stimulus or drug induced) that may alter the form of the response function.

Improved sulfide mitigation in sewers through on-line control of ferrous salt dosing.

PubMed

Ganigué, Ramon; Jiang, Guangming; Liu, Yiqi; Sharma, Keshab; Wang, Yue-Cong; Gonzalez, José; Nguyen, Tung; Yuan, Zhiguo

2018-05-15

Water utilities worldwide spend annually billions of dollars to control sulfide-induced corrosion in sewers. Iron salts chemically oxidize and/or precipitate dissolved sulfide in sewage and are especially used in medium- and large-size sewers. Iron salt dosing rates are defined ad hoc, ignoring variation in sewage flows and sulfide levels. This often results in iron overdosing or poor sulfide control. Online dosing control can adjust the chemical dosing rates to current (and future) state of the sewer system, allowing high-precision, stable and cost-effective sulfide control. In this paper, we report a novel and robust online control strategy for the dosing of ferrous salt in sewers. The control considers the fluctuation of sewage flow, pH, sulfide levels and also the perturbation from rainfall. Sulfide production in the pipe is predicted using auto-regressive models (AR) based on current flow measurements, which in turn can be used to determine the dose of ferrous salt required for cost-effective sulfide control. Following comprehensive model-based assesment, the control was successfully validated and its effectiveness demonstrated in a 3-week field trial. The online control algorithm controlled sulfide below the target level (0.5 mg S/L) while reducing chemical dosing up to 30%. Copyright © 2018 Elsevier Ltd. All rights reserved.

Measurement techniques for analysis of fission fragment excited gases

NASA Technical Reports Server (NTRS)

Schneider, R. T.; Carroll, E. E.; Davis, J. F.; Davie, R. N.; Maguire, T. C.; Shipman, R. G.

1976-01-01

Spectroscopic analysis of fission fragment excited He, Ar, Xe, N2, Ne, Ar-N2, and Ne-N2 have been conducted. Boltzmann plot analysis of He, Ar and Xe have indicated a nonequilibrium, recombining plasma, and population inversions have been found in these gases. The observed radiating species in helium have been adequately described by a simple kinetic model. A more extensive model for argon, nitrogen and Ar-N2 mixtures was developed which adequately describes the energy flow in the system and compares favorably with experimental measurements. The kinetic processes involved in these systems are discussed.

Prediction of mean monthly river discharges in Colombia through Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Carmona, A. M.; Poveda, G.

2015-04-01

The hydro-climatology of Colombia exhibits strong natural variability at a broad range of time scales including: inter-decadal, decadal, inter-annual, annual, intra-annual, intra-seasonal, and diurnal. Diverse applied sectors rely on quantitative predictions of river discharges for operational purposes including hydropower generation, agriculture, human health, fluvial navigation, territorial planning and management, risk preparedness and mitigation, among others. Various methodologies have been used to predict monthly mean river discharges that are based on "Predictive Analytics", an area of statistical analysis that studies the extraction of information from historical data to infer future trends and patterns. Our study couples the Empirical Mode Decomposition (EMD) with traditional methods, e.g. Autoregressive Model of Order 1 (AR1) and Neural Networks (NN), to predict mean monthly river discharges in Colombia, South America. The EMD allows us to decompose the historical time series of river discharges into a finite number of intrinsic mode functions (IMF) that capture the different oscillatory modes of different frequencies associated with the inherent time scales coexisting simultaneously in the signal (Huang et al. 1998, Huang and Wu 2008, Rao and Hsu, 2008). Our predictive method states that it is easier and simpler to predict each IMF at a time and then add them up together to obtain the predicted river discharge for a certain month, than predicting the full signal. This method is applied to 10 series of monthly mean river discharges in Colombia, using calibration periods of more than 25 years, and validation periods of about 12 years. Predictions are performed for time horizons spanning from 1 to 12 months. Our results show that predictions obtained through the traditional methods improve when the EMD is used as a previous step, since errors decrease by up to 13% when the AR1 model is used, and by up to 18% when using Neural Networks is combined with the EMD.

Business cycles and fertility dynamics in the United States: a vector autoregressive model.

PubMed

Mocan, N H

1990-01-01

"Using vector-autoregressions...this paper shows that fertility moves countercyclically over the business cycle....[It] shows that the United States fertility is not governed by a deterministic trend as was assumed by previous studies. Rather, fertility evolves around a stochastic trend. It is shown that a bivariate analysis between fertility and unemployment yields a procyclical picture of fertility. However, when one considers the effects on fertility of early marriages and the divorce behavior as well as economic activity, fertility moves countercyclically." excerpt

Targeting congestion in allergic rhinitis: the importance of intranasal corticosteroids.

PubMed

Marple, Bradley F

2008-01-01

The cardinal nasal symptoms of allergic rhinitis (AR) are sustained by an underlying inflammatory process. Congestion is one of the most prominent and distressing symptoms for patients and is strongly associated with a broadly deteriorated quality of life and significant losses in productivity. The purpose of this study was to explore the role of intranasal corticosteroids (INSs) in down-regulating the inflammatory response to allergen and their clinical efficacy on AR symptoms, particularly congestion. AR is characterized by an influx of inflammatory cells and mediators into the nasal mucosa after antigen exposure. The response is biphasic, encompassing an early and a late phase. Antigen exposure has a priming effect, decreasing the threshold for subsequent allergic reaction on rechallenge and increasing the responsiveness of the nasal mucosa. INSs are a mainstay of therapy for AR and the most effective intervention for nasal congestion and other nasal symptoms, with established superiority to antihistamines, decongestants, and leukotriene antagonists. In addition to symptom relief, INSs suppress numerous stages of the inflammatory cascade, inhibiting the influx of inflammatory cells and mediators. Topical nasal corticosteroids have a low incidence of local adverse effects, negligible systemic absorption, and excellent safety. Congestion is one of the most bothersome symptoms of AR. INS therapy improves AR symptoms, with particular efficacy in relieving congestion, by attenuating nasal hyperresponsiveness. Pretreatment with INSs has been shown to relieve early and late-phase clinical symptoms of AR. Modification of the disease process results in significant relief of symptoms and leads to fewer disease exacerbations.

Automatic Adviser on stationary devices status identification and anticipated change

NASA Astrophysics Data System (ADS)

Shabelnikov, A. N.; Liabakh, N. N.; Gibner, Ya M.; Pushkarev, E. A.

2018-05-01

A task is defined to synthesize an Automatic Adviser to identify the automation systems stationary devices status using an autoregressive model of changing their key parameters. An applied model type was rationalized and the research objects monitoring process algorithm was developed. A complex of mobile objects status operation simulation and prediction results analysis was proposed. Research results are commented using a specific example of a hump yard compressor station. The work was supported by the Russian Fundamental Research Fund, project No. 17-20-01040.

Proceedings of the Conference on the Design of Experiments in Army Research Development and Testing (32nd)

DTIC Science & Technology

1987-06-01

number of series among the 63 which were identified as a particular ARIMA form and were "best" modeled by a particular technique. Figure 1 illustrates a...th time from xe’s. The integrbted autoregressive - moving average model , denoted by ARIMA (p,d,q) is a result of combining d-th differencing process...Experiments, (4) Data Analysis and Modeling , (5) Theory and Probablistic Inference, (6) Fuzzy Statistics, (7) Forecasting and Prediction, (8) Small Sample

Revised error propagation of 40Ar/39Ar data, including covariances

NASA Astrophysics Data System (ADS)

Vermeesch, Pieter

2015-12-01

The main advantage of the 40Ar/39Ar method over conventional K-Ar dating is that it does not depend on any absolute abundance or concentration measurements, but only uses the relative ratios between five isotopes of the same element -argon- which can be measured with great precision on a noble gas mass spectrometer. The relative abundances of the argon isotopes are subject to a constant sum constraint, which imposes a covariant structure on the data: the relative amount of any of the five isotopes can always be obtained from that of the other four. Thus, the 40Ar/39Ar method is a classic example of a 'compositional data problem'. In addition to the constant sum constraint, covariances are introduced by a host of other processes, including data acquisition, blank correction, detector calibration, mass fractionation, decay correction, interference correction, atmospheric argon correction, interpolation of the irradiation parameter, and age calculation. The myriad of correlated errors arising during the data reduction are best handled by casting the 40Ar/39Ar data reduction protocol in a matrix form. The completely revised workflow presented in this paper is implemented in a new software platform, Ar-Ar_Redux, which takes raw mass spectrometer data as input and generates accurate 40Ar/39Ar ages and their (co-)variances as output. Ar-Ar_Redux accounts for all sources of analytical uncertainty, including those associated with decay constants and the air ratio. Knowing the covariance matrix of the ages removes the need to consider 'internal' and 'external' uncertainties separately when calculating (weighted) mean ages. Ar-Ar_Redux is built on the same principles as its sibling program in the U-Pb community (U-Pb_Redux), thus improving the intercomparability of the two methods with tangible benefits to the accuracy of the geologic time scale. The program can be downloaded free of charge from http://redux.london-geochron.com.

Targeting the androgen receptor in prostate and breast cancer – several new agents in development

PubMed Central

Proverbs-Singh, Tracy; Feldman, Jarett L.; Morris, Michael J.; Autio, Karen A.; Traina, Tiffany A.

2016-01-01

Prostate cancer and breast cancer share similarities as hormone-sensitive cancers with a wide heterogeneity of both phenotype and biology. The androgen receptor (AR) is a hormone receptor involved in both benign and malignant processes. Targeting androgen synthesis and the AR pathway has been and remains central to prostate cancer therapy. Recently, there is increased interest in the role of the AR in breast cancer development and growth, with data suggesting AR co-expression with estrogen, progesterone and human epidermal growth factor receptors, across all intrinsic subtypes of breast cancer. Targeting the AR axis is an evolving field with novel therapies in development which may ultimately be applicable for both tumor types. In this review, we offer an overview of available agents which target the AR axis in both prostate and breast cancer and provide insight into the novel drugs in development for targeting this signaling pathway. PMID:25722318

Production of nitrate-rich compost from the solid fraction of dairy manure by a lab-scale composting system.

PubMed

Sun, Zhao-Yong; Zhang, Jing; Zhong, Xiao-Zhong; Tan, Li; Tang, Yue-Qin; Kida, Kenji

2016-05-01

In the present study, we developed an efficient composting process for the solid fraction of dairy manure (SFDM) using lab-scale systems. We first evaluated the factors affecting the SFDM composting process using different thermophilic phase durations (TPD, 6 or 3days) and aeration rates (AR, 0.4 or 0.2 lmin(-1)kg(-1)-total solid (TS)). Results indicated that a similar volatile total solid (VTS) degradation efficiency (approximately 60%) was achieved with a TPD of 6 or 3days and an AR of 0.4 l min(-1) kg(-1)-TS (hereafter called higher AR), and a TPD of 3days resulted in less N loss caused by ammonia stripping. N loss was least when AR was decreased to 0.2 l min(-1) kg(-1)-TS (hereafter called lower AR) during the SFDM composting process. However, moisture content (MC) in the composting pile increased at the lower AR because of water production by VTS degradation and less water volatilization. Reduced oxygen availability caused by excess water led to lower VTS degradation efficiency and inhibition of nitrification. Adding sawdust to adjust the C/N ratio and decrease the MC improved nitrification during the composing processes; however, the addition of increasing amounts of sawdust decreased NO3(-) concentration in matured compost. When an improved composting reactor with a condensate removal and collection system was used for the SFDM composting process, the MC of the composting pile was significantly reduced, and nitrification was detected 10-14days earlier. This was attributed to the activity of ammonia-oxidizing bacteria (AOB). Highly matured compost could be generated within 40-50days. The VTS degradation efficiency reached 62.0% and the final N content, NO3(-) concentration, and germination index (GI) at the end of the composting process were 3.3%, 15.5×10(3)mg kg(-1)-TS, and 112.1%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Landfalling Atmospheric Rivers in California—Historical and Future Impacts

NASA Astrophysics Data System (ADS)

Dettinger, M. D.; Ralph, F. M.

2014-12-01

During the past decade, a wide range of insights about the character and causes of extreme orographic precipitation in California has emerged, based on our growing understanding of the presence, mechanisms and impacts of "atmospheric rivers" (ARs) in the extratropical atmosphere. When an AR reaches and encounters the Coastal Ranges and Sierra Nevada of California, the resulting orographically driven storms are key players in many important weather, hydrologic and ecological processes in the State, including floods and floodplain inundations, droughts, groundwater recharge, and surface-water resources (see table). The intensities, storm totals, geographical distributions and impacts of AR storms in California are determined by many factors, including among the most straightforward: The numbers of ARs making landfall each year The amounts of vapor being transported by the ARs The direction of vapor transport by the AR relative to perpendiculars to the mountain ranges (for maximum uplift) The duration of AR passage overhead of a given location The temperature of an AR as a determinant of snowline altitudes The stability of the atmosphere within which the AR is embedded The closeness of the air in the AR to saturation (how much uplift is needed to drive intense precipitation) ARs are present in weather forecast models as well as in the long-range climate models used to project future climate changes in response to increasing greenhouse-gas concentrations in the atmosphere. Research into the future of ARs over California was first reported in the literature in 2011 (based on IPCC AR4 climate models) and is being extended now (to IPCC AR5 models) to assess projected changes in the full range of factors listed above with the aim of predicting how climate change will affect these important storms and their impacts in coming decades.

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.

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.

Advances in nowcasting influenza-like illness rates using search query logs

NASA Astrophysics Data System (ADS)

Lampos, Vasileios; Miller, Andrew C.; Crossan, Steve; Stefansen, Christian

2015-08-01

User-generated content can assist epidemiological surveillance in the early detection and prevalence estimation of infectious diseases, such as influenza. Google Flu Trends embodies the first public platform for transforming search queries to indications about the current state of flu in various places all over the world. However, the original model significantly mispredicted influenza-like illness rates in the US during the 2012-13 flu season. In this work, we build on the previous modeling attempt, proposing substantial improvements. Firstly, we investigate the performance of a widely used linear regularized regression solver, known as the Elastic Net. Then, we expand on this model by incorporating the queries selected by the Elastic Net into a nonlinear regression framework, based on a composite Gaussian Process. Finally, we augment the query-only predictions with an autoregressive model, injecting prior knowledge about the disease. We assess predictive performance using five consecutive flu seasons spanning from 2008 to 2013 and qualitatively explain certain shortcomings of the previous approach. Our results indicate that a nonlinear query modeling approach delivers the lowest cumulative nowcasting error, and also suggest that query information significantly improves autoregressive inferences, obtaining state-of-the-art performance.

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.

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.

Advances in nowcasting influenza-like illness rates using search query logs.

PubMed

Lampos, Vasileios; Miller, Andrew C; Crossan, Steve; Stefansen, Christian

2015-08-03

User-generated content can assist epidemiological surveillance in the early detection and prevalence estimation of infectious diseases, such as influenza. Google Flu Trends embodies the first public platform for transforming search queries to indications about the current state of flu in various places all over the world. However, the original model significantly mispredicted influenza-like illness rates in the US during the 2012-13 flu season. In this work, we build on the previous modeling attempt, proposing substantial improvements. Firstly, we investigate the performance of a widely used linear regularized regression solver, known as the Elastic Net. Then, we expand on this model by incorporating the queries selected by the Elastic Net into a nonlinear regression framework, based on a composite Gaussian Process. Finally, we augment the query-only predictions with an autoregressive model, injecting prior knowledge about the disease. We assess predictive performance using five consecutive flu seasons spanning from 2008 to 2013 and qualitatively explain certain shortcomings of the previous approach. Our results indicate that a nonlinear query modeling approach delivers the lowest cumulative nowcasting error, and also suggest that query information significantly improves autoregressive inferences, obtaining state-of-the-art performance.

HMI Data Driven Magnetohydrodynamic Model Predicted Active Region Photospheric Heating Rates: Their Scale Invariant, Flare Like Power Law Distributions, and Their Possible Association With Flares

NASA Technical Reports Server (NTRS)

Goodman, Michael L.; Kwan, Chiman; Ayhan, Bulent; Shang, Eric L.

2017-01-01

A data driven, near photospheric, 3 D, non-force free magnetohydrodynamic model pre- dicts time series of the complete current density, and the resistive heating rate Q at the photosphere in neutral line regions (NLRs) of 14 active regions (ARs). The model is driven by time series of the magnetic field B observed by the Helioseismic & Magnetic Imager on the Solar Dynamics Observatory (SDO) satellite. Spurious Doppler periods due to SDO orbital motion are filtered out of the time series for B in every AR pixel. Errors in B due to these periods can be significant. The number of occurrences N(q) of values of Q > or = q for each AR time series is found to be a scale invariant power law distribution, N(Q) / Q-s, above an AR dependent threshold value of Q, where 0.3952 < or = s < or = 0.5298 with mean and standard deviation of 0.4678 and 0.0454, indicating little variation between ARs. Observations show that the number of occurrences N(E) of coronal flares with a total energy released > or = E obeys the same type of distribution, N(E) / E-S, above an AR dependent threshold value of E, with 0.38 < or approx. S < or approx. 0.60, also with little variation among ARs. Within error margins the ranges of s and S are nearly identical. This strong similarity between N(Q) and N(E) suggests a fundamental connection between the process that drives coronal flares and the process that drives photospheric NLR heating rates in ARs. In addition, results suggest it is plausible that spikes in Q, several orders of magnitude above background values, are correlated with times of the subsequent occurrence of M or X flares.

HMI Data Driven Magnetohydrodynamic Model Predicted Active Region Photospheric Heating Rates: Their Scale Invariant, Flare Like Power Law Distributions, and Their Possible Association With Flares

NASA Technical Reports Server (NTRS)

Goodman, Michael L.; Kwan, Chiman; Ayhan, Bulent; Shang, Eric L.

2017-01-01

A data driven, near photospheric, 3 D, non-force free magnetohydrodynamic model predicts time series of the complete current density, and the resistive heating rate Q at the photosphere in neutral line regions (NLRs) of 14 active regions (ARs). The model is driven by time series of the magnetic field B observed by the Helioseismic and Magnetic Imager on the Solar Dynamics Observatory (SDO) satellite. Spurious Doppler periods due to SDO orbital motion are filtered out of the time series for B in every AR pixel. Errors in B due to these periods can be significant. The number of occurrences N(q) of values of Q > or = q for each AR time series is found to be a scale invariant power law distribution, N(Q) / Q-s, above an AR dependent threshold value of Q, where 0.3952 < or = s < or = 0.5298 with mean and standard deviation of 0.4678 and 0.0454, indicating little variation between ARs. Observations show that the number of occurrences N(E) of coronal flares with a total energy released > or = E obeys the same type of distribution, N(E) / E-S, above an AR dependent threshold value of E, with 0.38 < or approx. S < or approx. 0.60, also with little variation among ARs. Within error margins the ranges of s and S are nearly identical. This strong similarity between N(Q) and N(E) suggests a fundamental connection between the process that drives coronal flares and the process that drives photospheric NLR heating rates in ARs. In addition, results suggest it is plausible that spikes in Q, several orders of magnitude above background values, are correlated with times of the subsequent occurrence of M or X flares.

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.

Studies in astronomical time series analysis. I - Modeling random processes in the time domain

NASA Technical Reports Server (NTRS)

Scargle, J. D.

1981-01-01

Several random process models in the time domain are defined and discussed. Attention is given to the moving average model, the autoregressive model, and relationships between and combinations of these models. Consideration is then given to methods for investigating pulse structure, procedures of model construction, computational methods, and numerical experiments. A FORTRAN algorithm of time series analysis has been developed which is relatively stable numerically. Results of test cases are given to study the effect of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the light curve of the quasar 3C 272 is considered as an example.

Characterization of autoregressive processes using entropic quantifiers

NASA Astrophysics Data System (ADS)

Traversaro, Francisco; Redelico, Francisco O.

2018-01-01

The aim of the contribution is to introduce a novel information plane, the causal-amplitude informational plane. As previous works seems to indicate, Bandt and Pompe methodology for estimating entropy does not allow to distinguish between probability distributions which could be fundamental for simulation or for probability analysis purposes. Once a time series is identified as stochastic by the causal complexity-entropy informational plane, the novel causal-amplitude gives a deeper understanding of the time series, quantifying both, the autocorrelation strength and the probability distribution of the data extracted from the generating processes. Two examples are presented, one from climate change model and the other from financial markets.

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.

Effects of risk disclosure prominence in direct-to-consumer advertising (DTCA) of prescription drugs: An integrative cognitive process model.

PubMed

Ju, Ilwoo; Park, Jin Seong

2018-01-01

The literature shows that the prominence of risk disclosure influences consumer responses to direct-to-consumer advertising of prescription drugs. However, little is known about the psychological process whereby disclosure prominence exerts its influences on health beliefs and behavior. Based on a review of the literature on health cognition and behavior, the current study proposed and tested a model to show that risk disclosure prominence affects consumers' drug choice intention through the mediating roles of awareness of drug adverse reactions (ARs), perceived control over ARs, and perceived risk of ARs. The findings were discussed in terms of their theoretical and managerial implications.

78 FR 39821 - Arkansas Disaster #AR-00064

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-02

... SMALL BUSINESS ADMINISTRATION [Disaster Declaration 13637 and 13638] Arkansas Disaster AR-00064 AGENCY: U.S. Small Business Administration. ACTION: Notice. SUMMARY: This is a Notice of the Presidential.... Small Business Administration, Processing And Disbursement Center, 14925 Kingsport Road, Fort Worth, TX...

76 FR 35937 - Arkansas Disaster Number AR-00048

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-20

... U.S. SMALL BUSINESS ADMINISTRATION [Disaster Declaration 12560 and 12561] Arkansas Disaster Number AR-00048 AGENCY: U.S. Small Business Administration. ACTION: Amendment 6. SUMMARY: This is an...: U.S. Small Business Administration, Processing and Disbursement Center, 14925 Kingsport Road, Fort...

76 FR 38717 - Arkansas Disaster Number AR-00048

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-01

... U.S. SMALL BUSINESS ADMINISTRATION [Disaster Declaration 12560 and 12561] Arkansas Disaster Number AR-00048 AGENCY: U.S. Small Business Administration. ACTION: Amendment 8. SUMMARY: This is an...: U.S. Small Business Administration, Processing and Disbursement Center, 14925 Kingsport Road, Fort...

On-The-Fly Data Processing with Scanamorphos: Application To ArTéMiS

NASA Astrophysics Data System (ADS)

Roussel, Hélène

2018-03-01

Scanamorphos is a suite of IDL based routines to optimally subtract low-frequency noise making maximal use of the redundancy in the data. The procedures were adapted to be applicable to ArTéMiS data.

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.

Abundance and Isotopic Composition of Gases in the Martian Atmosphere: First Results from the Mars Curiosity Rover

NASA Technical Reports Server (NTRS)

Mahaffy, Paul; Webster, Chris R.; Atreya, Sushil K.; Franz, Heather; Wong, Michael; Conrad, Pamela G.; Harpold, Dan; Jones, John J.; Leshin, Laurie, A.; Manning, Heidi;

Structural Diversity of Ligand-Binding Androgen Receptors Revealed by Microsecond Long Molecular Dynamics Simulations and Enhanced Sampling.

PubMed

Duan, Mojie; Liu, Na; Zhou, Wenfang; Li, Dan; Yang, Minghui; Hou, Tingjun

2016-09-13

Androgen receptor (AR) plays important roles in the development of prostate cancer (PCa). The antagonistic drugs, which suppress the activity of AR, are widely used in the treatment of PCa. However, the molecular mechanism of antagonism about how ligands affect the structures of AR remains elusive. To better understand the conformational variability of ARs bound with agonists or antagonists, we performed long time unbiased molecular dynamics (MD) simulations and enhanced sampling simulations for the ligand binding domain of AR (AR-LBD) in complex with various ligands. Based on the simulation results, we proposed an allosteric pathway linking ligands and helix 12 (H12) of AR-LBD, which involves the interactions among the ligands and the residues W741, H874, and I899. The interaction pathway provides an atomistic explanation of how ligands affect the structure of AR-LBD. A repositioning of H12 was observed, but it is facilitated by the C-terminal of H12, instead of by the loop between helix 11 (H11) and H12. The bias-exchange metadynamics simulations further demonstrated the above observations. More importantly, the free energy profiles constructed by the enhanced sampling simulations revealed the transition process between the antagonistic form and agonistic form of AR-LBD. Our results would be helpful for the design of more efficient antagonists of AR to combat PCa.

Jet evolution in a dense medium: event-by-event fluctuations and multi-particle correlations

NASA Astrophysics Data System (ADS)

Escobedo, Miguel A.; Iancu, Edmond

2017-11-01

We study the gluon distribution produced via successive medium-induced branchings by an energetic jet propagating through a weakly-coupled quark-gluon plasma. We show that under suitable approximations, the jet evolution is a Markovian stochastic process, which is exactly solvable. For this process, we construct exact analytic solutions for all the n-point correlation functions describing the gluon distribution in the space of energy [M. A. Escobedo, E. Iancu, Event-by-event fluctuations in the medium-induced jet evolution, JHEP 05 (2016) 008. arXiv:arxiv:arXiv:1601.03629, doi:http://dx.doi.org/10.1007/JHEP05(2016)008, M. A. Escobedo, E. Iancu, Multi-particle correlations and KNO scaling in the medium-induced jet evolution, JHEP 12 (2016) 104. arXiv:arxiv:arXiv:1609.06104, doi:http://dx.doi.org/10.1007/JHEP12(2016)104]. Using these results, we study the event-by-event distribution of the energy lost by the jet at large angles and of the multiplicities of the soft particles which carry this energy. We find that the event-by-event fluctuations are huge: the standard deviation in the energy loss is parametrically as large as its mean value [M. A. Escobedo, E. Iancu, Event-by-event fluctuations in the medium-induced jet evolution, JHEP 05 (2016) 008. arXiv:arxiv:arXiv:1601.03629, doi:http://dx.doi.org/10.1007/JHEP05(2016)008]. This has important consequences for the phenomenology of di-jet asymmetry in Pb+Pb collisions at the LHC: it implies that the fluctuations in the branching process can contribute to the measured asymmetry on an equal footing with the geometry of the di-jet event (i.e. as the difference between the in-medium path lengths of the two jets). We compute the higher moments of the multiplicity distribution and identify a remarkable regularity known as Koba-Nielsen-Olesen (KNO) scaling [M. A. Escobedo, E. Iancu, Multi-particle correlations and KNO scaling in the medium-induced jet evolution, JHEP 12 (2016) 104. arXiv:arxiv:arXiv:1609.06104, doi:http://dx.doi.org/10.1007/JHEP12(2016)104

Occurrence and potential causes of androgenic activities in source and drinking water in China.

PubMed

Hu, Xinxin; Shi, Wei; Wei, Si; Zhang, Xiaowei; Feng, Jianfang; Hu, Guanjiu; Chen, Sulan; Giesy, John P; Yu, Hongxia

2013-09-17

The increased incidences of disorders of male reproductive tract as well as testicular and prostate cancers have been attributed to androgenic pollutants in the environment. Drinking water is one pathway of exposure through which humans can be exposed. In this study, both potencies of androgen receptor (AR) agonists and antagonists were determined in organic extracts of raw source water as well as finished water from waterworks, tap water, boiled water, and poured boiled water in eastern China. Ten of 13 samples of source water exhibited detectable AR antagonistic potencies with AR antagonist equivalents (Ant-AR-EQs) ranging from <15.3 (detection limit) to 140 μg flutamide/L. However, no AR agonistic activity was detected in any source water. All finished water from waterworks, tap water, boiled water, and poured boiled water exhibited neither AR agonistic nor antagonistic activity. Although potential risks are posed by source water, water treatment processes effectively removed AR antagonists. Boiling and pouring of water further removed these pollutants. Phthalate esters (PAEs) including diisobutyl phthalate (DIBP) and dibutyl phthalate (DBP) were identified as major contributors to AR antagonistic potencies in source waters. Metabolites of PAEs exhibited no AR antagonistic activity and did not increase potencies of PAEs when they coexist.

CaMKII prevents spontaneous acrosomal exocytosis in sperm through induction of actin polymerization.

PubMed

Shabtay, Ortal; Breitbart, Haim

2016-07-01

In order to interact with the egg and undergo acrosomal exocytosis or the acrosome reaction (AR), mammalian spermatozoa must undergo a series of biochemical changes in the female reproductive tract, collectively called capacitation. We showed that F-actin is formed during sperm capacitation and fast depolymerization occurs prior to the AR. We hypothesized that F-actin protects the sperm from undergoing spontaneous-AR (sAR) which decreases fertilization rate. We show that activation of the actin-severing protein gelsolin induces a significant increase in sAR. Moreover, inhibition of CaMKII or PLD during sperm capacitation, caused an increase in sAR and inhibition of F-actin formation. Spermine, which leads to PLD activation, was able to reverse the effects of CaMKII inhibition on sAR-increase and F-actin-decrease. Furthermore, the increase in sAR and the decrease in F-actin caused by the inactivation of the PLD-pathway, were reversed by activation of CaMKII using H2O2 or by inhibiting protein phosphatase 1 which enhance the phosphorylation and oxidation states of CaMKII. These results indicate that two distinct pathways lead to F-actin formation in the sperm capacitation process which prevents the occurrence of sAR. Copyright © 2016 Elsevier Inc. All rights reserved.

WHY IS A FLARE-RICH ACTIVE REGION CME-POOR?

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

Liu, Lijuan; Wang, Yuming; Shen, Chenglong

Solar active regions (ARs) are the major sources of two of the most violent solar eruptions, namely flares and coronal mass ejections (CMEs). The largest AR in the past 24 years, NOAA AR 12192, which crossed the visible disk from 2014 October 17 to 30, unusually produced more than one hundred flares, including 32 M-class and 6 X-class ones, but only one small CME. Flares and CMEs are believed to be two phenomena in the same eruptive process. Why is such a flare-rich AR so CME-poor? We compared this AR with other four ARs; two were productive in both andmore » two were inert. The investigation of the photospheric parameters based on the SDO /HMI vector magnetogram reveals that the flare-rich AR 12192, as with the other two productive ARs, has larger magnetic flux, current, and free magnetic energy than the two inert ARs but, in contrast to the two productive ARs, it has no strong, concentrated current helicity along both sides of the flaring neutral line, indicating the absence of a mature magnetic structure consisting of highly sheared or twisted field lines. Furthermore, the decay index above the AR 12192 is relatively low, showing strong constraint. These results suggest that productive ARs are always large and have enough current and free energy to power flares, but whether or not a flare is accompanied by a CME is seemingly related to (1) the presence of a mature sheared or twisted core field serving as the seed of the CME, or (2) a weak enough constraint of the overlying arcades.« less

Preparation of water and ice samples for 39Ar dating by atom trap trace analysis (ATTA)

NASA Astrophysics Data System (ADS)

Schwefel, R.; Reichel, T.; Aeschbach-Hertig, W.; Wagenbach, D.

2012-04-01

Atom trap trace analysis (ATTA) is a new and promising method to measure very rare noble gas radioisotopes in the environment. The applicability of this method for the dating of very old groundwater with 81Kr has already been demonstrated [1]. Recent developments now show its feasibility also for the analysis of 39Ar [2,3], which is an ideal dating tracer for the age range between 50 and 1000 years. This range is of interest in the fields of hydro(geo)logy, oceanography, and glaciology. We present preparation (gas extraction and Ar separation) methods for groundwater and ice samples for later analysis by the ATTA technique. For groundwater, the sample size is less of a limitation than for applications in oceanography or glaciology. Large samples are furthermore needed to enable a comparison with the classical method of 39Ar detection by low-level counting. Therefore, a system was built that enables gas extraction from several thousand liters of water using membrane contactors. This system provides degassing efficiencies greater than 80 % and has successfully been tested in the field. Gas samples are further processed to separate a pure Ar fraction by a gas-chromatographic method based on Li-LSX zeolite as selective adsorber material at very low temperatures. The gas separation achieved by this system is controlled by a quadrupole mass spectrometer. It has successfully been tested and used on real samples. The separation efficiency was found to be strongly temperature dependent in the range of -118 to -130 °C. Since ATTA should enable the analysis of 39Ar on samples of less than 1 ccSTP of Ar (corresponding to about 100 ml of air, 2.5 l of water or 1 kg of ice), a method to separate Ar from small amounts of gas was developed. Titanium sponge was found to absorb 60 ccSTP of reactive gases per g of the getter material with reasonably high absorption rates at high operating temperatures (~ 800 ° C). Good separation (higher than 92 % Ar content in residual gas) was achieved by this gettering process. The other main remaining component is H2, which can be further reduced by operating the Ti getter at lower temperature. Furthermore, a system was designed to degas ice samples, followed by Ar separation by gettering. Ice from an alpine glacier was successfully processed on this system.

Binding properties of food colorant allura red with human serum albumin in vitro.

PubMed

Wang, Langhong; Zhang, Guowen; Wang, Yaping

2014-05-01

Allura red (AR) is a widely used colorant in food industry, but may have a potential security risk. In this study, the properties of interaction between AR and human serum albumin (HSA) in vitro were determined by fluorescence, UV-Vis absorption and circular dichroism (CD) spectroscopy combining with multivariate curve resolution-alternating least squares (MCR-ALS) chemometrics and molecular modeling approaches. An expanded UV-Vis data matrix was resolved by MCR-ALS method, and the concentration profiles and pure spectra for the three reaction components (AR, HSA, and AR-HSA complex) of the system were then successfully obtained to evaluate the progress interaction of AR with HSA. The calculated thermodynamic parameters indicated that hydrogen binding and hydrophobic interactions played major roles in the binding process, and the interaction induced a decrease in the protein surface hydrophobicity. The competitive experiments revealed that AR mainly located in Sudlow's site I of HSA, and this result was further supported by molecular modeling studies. Analysis of CD spectra found that the addition of AR induced the conformational changes of HSA. This study have provided new insight into the mechanism of interaction between AR and HSA.

MicroRNAs Are Mediators of Androgen Action in Prostate and Muscle

PubMed Central

Narayanan, Ramesh; Jiang, Jinmai; Gusev, Yuriy; Jones, Amanda; Kearbey, Jeffrey D.; Miller, Duane D.; Schmittgen, Thomas D.; Dalton, James T.

2010-01-01

Androgen receptor (AR) function is critical for the development of male reproductive organs, muscle, bone and other tissues. Functionally impaired AR results in androgen insensitivity syndrome (AIS). The interaction between AR and microRNA (miR) signaling pathways was examined to understand the role of miRs in AR function. Reduction of androgen levels in Sprague-Dawley rats by castration inhibited the expression of a large set of miRs in prostate and muscle, which was reversed by treatment of castrated rats with 3 mg/day dihydrotestosterone (DHT) or selective androgen receptor modulators. Knockout of the miR processing enzyme, DICER, in LNCaP prostate cancer cells or tissue specifically in mice inhibited AR function leading to AIS. Since the only function of miRs is to bind to 3′ UTR and inhibit translation of target genes, androgens might induce miRs to inhibit repressors of AR function. In concordance, knock-down of DICER in LNCaP cells and in tissues in mice induced the expression of corepressors, NCoR and SMRT. These studies demonstrate a feedback loop between miRs, corepressors and AR and the imperative role of miRs in AR function in non-cancerous androgen-responsive tissues. PMID:21048966

Coral calcifying fluid aragonite saturation states derived from Raman spectroscopy

NASA Astrophysics Data System (ADS)

DeCarlo, Thomas M.; D'Olivo, Juan P.; Foster, Taryn; Holcomb, Michael; Becker, Thomas; McCulloch, Malcolm T.

2017-11-01

Quantifying the saturation state of aragonite (ΩAr) within the calcifying fluid of corals is critical for understanding their biomineralization process and sensitivity to environmental changes including ocean acidification. Recent advances in microscopy, microprobes, and isotope geochemistry enable the determination of calcifying fluid pH and [CO32-], but direct quantification of ΩAr (where ΩAr = [CO32-][Ca2+]/Ksp) has proved elusive. Here we test a new technique for deriving ΩAr based on Raman spectroscopy. First, we analysed abiogenic aragonite crystals precipitated under a range of ΩAr from 10 to 34, and we found a strong dependence of Raman peak width on ΩAr with no significant effects of other factors including pH, Mg/Ca partitioning, and temperature. Validation of our Raman technique for corals is difficult because there are presently no direct measurements of calcifying fluid ΩAr available for comparison. However, Raman analysis of the international coral standard JCp-1 produced ΩAr of 12.3 ± 0.3, which we demonstrate is consistent with published skeletal Mg/Ca, Sr/Ca, B/Ca, δ11B, and δ44Ca data. Raman measurements are rapid ( ≤ 1 s), high-resolution ( ≤ 1 µm), precise (derived ΩAr ± 1 to 2 per spectrum depending on instrument configuration), accurate ( ±2 if ΩAr < 20), and require minimal sample preparation, making the technique well suited for testing the sensitivity of coral calcifying fluid ΩAr to ocean acidification and warming using samples from natural and laboratory settings. To demonstrate this, we also show a high-resolution time series of ΩAr over multiple years of growth in a Porites skeleton from the Great Barrier Reef, and we evaluate the response of ΩAr in juvenile Acropora cultured under elevated CO2 and temperature.

The Development of Adultoid Reproductives and Brachypterous Neotenic Reproductives From the Last Instar Nymphs in Reticulitermes labralis (Isoptera: Rhinotermitidae): A Comparative Study

PubMed Central

Su, Xiao Hong; Xue, Wei; Liu, He; Chen, Jiao Ling; Zhang, Xiao Jing; Xing, Lian Xi; Liu, Ming Hua

2015-01-01

Secondary reproductives develop primarily from nymphs. However, they have been rarely studied; in particular, the development of adultoid reproductives (AR) with floppy wings is still unclear. In this study, the change in juvenile hormone (JH) levels, vitellogenin gene expression, and oogenesis during the development of AR and brachypterous neotenic reproductives (BN) from the last instar nymphs of Reticulitermes labralis are investigated and compared. The results showed that the AR derived from the last instar nymphs by molting, and they were more similar to neotenic reproductives in morphology. In addition, the paired AR were not able to survive in the absence of workers. In R. labralis, the process of the last instar nymphs developing into AR and BN took an increase in JH level as a starting point. The JH level of the last instar nymphs molting into BN was approximately 1.5-fold higher than that of the AR. Additionally, The JHIII level of BN peaked on day 5, and that of AR peaked on day 10, which induced the onset of vitellogenesis in BN and AR, respectively. After molting, the vitellogenin gene expression levels of both BN and AR initially increased and then declined, and the expression levels in the BN were significantly higher than those in the AR. In addition, the oocytes of BN matured earlier than those of the AR, and the number of eggs laid by the BN was higher than the number laid by the AR. Our results demonstrate that, in R. labralis, the last instar nymphs can develop into AR, which are significantly different from BN in their development. PMID:26494776

Functional Characterization of Paralogous Gonadotropin-Releasing Hormone-Type and Corazonin-Type Neuropeptides in an Echinoderm

PubMed Central

Tian, Shi; Egertová, Michaela; Elphick, Maurice R.

2017-01-01

Homologs of the vertebrate neuropeptide gonadotropin-releasing hormone (GnRH) have been identified in invertebrates, including the insect neuropeptide corazonin (CRZ). Recently, we reported the discovery of GnRH-type and CRZ-type signaling systems in an echinoderm, the starfish Asterias rubens, demonstrating that the evolutionary origin of paralogous GnRH-type and CRZ-type neuropeptides can be traced back to the common ancestor of protostomes and deuterostomes. Here, we have investigated the physiological roles of the GnRH-type (ArGnRH) and the CRZ-type (ArCRZ) neuropeptides in A. rubens, using mRNA in situ hybridization, immunohistochemistry and in vitro pharmacology. ArGnRH precursor (ArGnRHP)-expressing cells and ArGnRH-immunoreactive cells and/or processes are present in the radial nerve cords, circumoral nerve ring, digestive system (e.g., cardiac stomach and pyloric stomach), body wall-associated muscle (apical muscle), and appendages (tube feet, terminal tentacle). The general distribution of ArCRZ precursor (ArCRZP)-expressing cells is similar to that of ArGnRHP, but with specific local differences. For example, cells expressing ArGnRHP are present in both the ectoneural and hyponeural regions of the radial nerve cords and circumoral nerve ring, whereas cells expressing ArCRZP were only observed in the ectoneural region. In vitro pharmacological experiments revealed that both ArGnRH and ArCRZ cause contraction of cardiac stomach, apical muscle, and tube foot preparations. However, ArGnRH was more potent/effective than ArCRZ as a contractant of the cardiac stomach, whereas ArCRZ was more potent/effective than ArGnRH as a contractant of the apical muscle. These findings demonstrate that both ArGnRH and ArCRZ are myoexcitatory neuropeptides in starfish, but differences in their expression patterns and pharmacological activities are indicative of distinct physiological roles. This is the first study to investigate the physiological roles of both GnRH-type and CRZ-type neuropeptides in a deuterostome, providing new insights into the evolution and comparative physiology of these paralogous neuropeptide signaling systems in the Bilateria. PMID:29033898

Antigen Masking During Fixation and Embedding, Dissected

PubMed Central

Scalia, Carla Rossana; Boi, Giovanna; Bolognesi, Maddalena Maria; Riva, Lorella; Manzoni, Marco; DeSmedt, Linde; Bosisio, Francesca Maria; Ronchi, Susanna; Leone, Biagio Eugenio; Cattoretti, Giorgio

2016-01-01

Antigen masking in routinely processed tissue is a poorly understood process caused by multiple factors. We sought to dissect the effect on antigenicity of each step of processing by using frozen sections as proxies of the whole tissue. An equivalent extent of antigen masking occurs across variable fixation times at room temperature. Most antigens benefit from longer fixation times (>24 hr) for optimal detection after antigen retrieval (AR; for example, Ki-67, bcl-2, ER). The transfer to a graded alcohol series results in an enhanced staining effect, reproduced by treating the sections with detergents, possibly because of a better access of the polymeric immunohistochemical detection system to tissue structures. A second round of masking occurs upon entering the clearing agent, mostly at the paraffin embedding step. This may depend on the non-freezable water removal. AR fully reverses the masking due both to the fixation time and the paraffin embedding. AR itself destroys some epitopes which do not survive routine processing. Processed frozen sections are a tool to investigate fixation and processing requirements for antigens in routine specimens. PMID:27798289

K-Ar constraints on fluid-rock interaction and dissolution-precipitation events within the actively creeping shear zones from SAFOD cores

NASA Astrophysics Data System (ADS)

Ali, S.; Hemming, S. R.; Torgersen, T.; Fleisher, M. Q.; Cox, S. E.; Stute, M.

2009-12-01

The San Andreas Fault Observatory at Depth (SAFOD) was drilled to study the physical and chemical processes responsible for faulting and earthquake generation along an active, plate-bounding fault at depth. SAFOD drill cores show multiple zones of alteration and deformation due to fluid-rock interaction in the fault rocks(Schleicher et al. 2008). In context of fluid studies in the SAFZ, noble gas and potassium measurements were performed on solid samples of sedimentary rocks obtained from drill cores across the fault (3050-4000m-MD). We used a combination of 40Ar/39Ar and K-Ar methods on crushed samples of mudrock with variable amounts of visible slickensides to constrain the degree of resetting of the K-Ar system across the San Andreas Fault zone. 40Ar/39Ar was analyzed from small fragments (sand sized grains) while K-Ar was measured in crushed bulk rock samples (100-250 mg for Ar, and 5-10 mg for K analyses). The apparent 40Ar/39Ar ages based on single step laser fusion of small fragments corresponding to the detrital component in the coarse fraction, show varying ages ranging from the provenance age to <13Ma. Although more data are needed to make detailed comparisons, the apparent K-Ar ages of bulk samples in the fault zone are biased toward authigenic materials contained in the fine fraction, similar to the 40Ar/39Ar ages reported for mineralogical separates from very fine size fractions of samples obtained from 3065.98m-MD and 3294.89m-MD (Schleicher et al., submitted to Geology). The small samples measured for 40Ar/39Ar show scatter in the apparent ages, generally bracketing the bulk ages. However they are picked from sieved portions of the samples, and it is likely that there may be a loss of the younger (finer) material. Detrital provenance ages appear to be 50-60Ma in the Pacific Plate, and 100Ma in the North American Plate. 40Ar/39Ar ages within the SAFZ, as defined by geophysical logs (3200-3400m MD), are dominated by apparent detrital ages of ˜100Ma. More work is needed to test whether this is a real provenance age, or if there could be some systematic process that could lead to age bias towards older values. We observe nearly complete resetting of K-Ar ages, indicating that the K content is dominated by newly formed authigenic minerals as a result of fluid rock interaction in the SAFZ. Because the authigenic minerals are subject to successive dissolution-precipitation events over a range of time (3 to 0 Ma) and because the detrital component may not be fully reset, the K-Ar apparent ages (<300,000 years) in the SAFZ provide a maximum age on the resetting event. Similar trends of relatively young ages across the SAFZ compared to the surrounding country rock in the Pacific and North American Plates are also observed in the apparent fluid ‘ages’, corresponding to the fluid event responsible for the fluid-rock interaction in the fault (Ali et al. this session).

AR-NE3A, a New Macromolecular Crystallography Beamline for Pharmaceutical Applications at the Photon Factory

NASA Astrophysics Data System (ADS)

Yamada, Yusuke; Hiraki, Masahiko; Sasajima, Kumiko; Matsugaki, Naohiro; Igarashi, Noriyuki; Amano, Yasushi; Warizaya, Masaichi; Sakashita, Hitoshi; Kikuchi, Takashi; Mori, Takeharu; Toyoshima, Akio; Kishimoto, Shunji; Wakatsuki, Soichi

2010-06-01

Recent advances in high-throughput techniques for macromolecular crystallography have highlighted the importance of structure-based drug design (SBDD), and the demand for synchrotron use by pharmaceutical researchers has increased. Thus, in collaboration with Astellas Pharma Inc., we have constructed a new high-throughput macromolecular crystallography beamline, AR-NE3A, which is dedicated to SBDD. At AR-NE3A, a photon flux up to three times higher than those at existing high-throughput beams at the Photon Factory, AR-NW12A and BL-5A, can be realized at the same sample positions. Installed in the experimental hutch are a high-precision diffractometer, fast-readout, high-gain CCD detector, and sample exchange robot capable of handling more than two hundred cryo-cooled samples stored in a Dewar. To facilitate high-throughput data collection required for pharmaceutical research, fully automated data collection and processing systems have been developed. Thus, sample exchange, centering, data collection, and data processing are automatically carried out based on the user's pre-defined schedule. Although Astellas Pharma Inc. has a priority access to AR-NE3A, the remaining beam time is allocated to general academic and other industrial users.

Use of the cumulative sum method (CUSUM) to assess the learning curves of ultrasound-guided continuous femoral nerve block.

PubMed

Kollmann-Camaiora, A; Brogly, N; Alsina, E; Gilsanz, F

2017-10-01

Although ultrasound is a basic competence for anaesthesia residents (AR) there is few data available on the learning process. This prospective observational study aims to assess the learning process of ultrasound-guided continuous femoral nerve block and to determine the number of procedures that a resident would need to perform in order to reach proficiency using the cumulative sum (CUSUM) method. We recruited 19 AR without previous experience. Learning curves were constructed using the CUSUM method for ultrasound-guided continuous femoral nerve block considering 2 success criteria: a decrease of pain score>2 in a [0-10] scale after 15minutes, and time required to perform it. We analyse data from 17 AR for a total of 237 ultrasound-guided continuous femoral nerve blocks. 8/17 AR became proficient for pain relief, however all the AR who did more than 12 blocks (8/8) became proficient. As for time of performance 5/17 of AR achieved the objective of 12minutes, however all the AR who did more than 20 blocks (4/4) achieved it. The number of procedures needed to achieve proficiency seems to be 12, however it takes more procedures to reduce performance time. The CUSUM methodology could be useful in training programs to allow early interventions in case of repeated failures, and develop competence-based curriculum. Copyright © 2017 Sociedad Española de Anestesiología, Reanimación y Terapéutica del Dolor. Publicado por Elsevier España, S.L.U. All rights reserved.

β2‑adrenergic receptor functionality and genotype in two different models of chronic inflammatory disease: Liver cirrhosis and osteoarthritis.

PubMed

Roca, Reyes; Esteban, Pablo; Zapater, Pedro; Inda, María-Del-Mar; Conte, Anna Lucia; Gómez-Escolar, Laura; Martínez, Helena; Horga, José F; Palazon, José M; Peiró, Ana M

2018-06-01

The present study was designed to investigate the functional status of β2 adrenoceptors (β2AR) in two models of chronic inflammatory disease: liver cirrhosis (LC) and osteoarthritis (OA). The β2AR gene contains three single nucleotide polymorphisms at amino acid positions 16, 27 and 164. The aim of the present study was to investigate the potential influence of lymphocyte β2AR receptor functionality and genotype in LC and OA patients. Blood samples from cirrhotic patients (n=52, hepatic venous pressure gradient 13±4 mmHg, CHILD 7±2 and MELD 11±4 scores), OA patients (n=30, 84% Kellgren‑Lawrence severity 4 grade, 14% knee replacement joint) and healthy volunteers as control group (n=26) were analyzed. Peripheral blood mononuclear cells (PBMC) were isolated from whole blood and basal and isoproterenol induced adenylate cyclase activity (isoproterenol stimulus from 10‑9 to 10‑4 mM), and β2AR allelic variants (rs1042713, rs1042714, rs1800888) were determined. β2AR functionality was decreased in the two different models of chronic inflammatory disease studied, OA (50% vs. control) and LC (85% vs. control). In these patients, the strength of the β2AR response to adrenergic stimulation was very limited. Adrenergic modulation of PBMC function through the β2AR stimulus is decreased in chronic inflammatory processes including LC and OA, suggesting that the adrenergic system may be important in the development of these processes.

Atmospheric River impacts in British Columbia and the Pacific Northwest on 22-24 January 2015 during the CalWater 2015 field campaign

NASA Astrophysics Data System (ADS)

Gaggini, N. G.; Spackman, J. R.; Neiman, P. J.; White, A. B.; Fairall, C. W.; Barnet, C.; Gambacorta, A.; Hughes, M.

2015-12-01

Over 30 dropsonde transects were performed across atmospheric rivers (ARs) over the eastern Pacific during CalWater 2015. An event in late January allowed first-of-its-kind coordinated dropsonde transects of an AR using the NOAA G-IV aircraft in tandem with the NOAA Ronald H. Brown (RHB), which observed the marine boundary layer during the passage of this major AR. Dropsonde data collected on 22 January 2015 sampled the early stages of the AR, when the AR began making landfall near Vancouver Island, British Columbia. At the same time the RHB collected precipitation and oceanic moisture flux measurements on the warm side of the AR. A second flight on 24 January 2015 sampled the later stages of the AR, again passing over the RHB stationed beneath the AR. During this later period, the AR axis of moisture shifted north-northeast and fanned out along the coast, affecting regions from Northern Washington to Southern Alaska. Multi-day landfalling AR conditions led to flooding in British Columbia and northern Washington. The influence of the coastal orography combined with the shift in AR orientation is examined to understand the orographic control of precipitation that triggered the flooding. In addition, cross section analysis of the AR using dropsonde and reanalysis data are used to better understand the synoptic influences, water vapor transport, and moisture evolution during the lifecycle of the AR. To gain greater insight into AR development and prolonged AR conditions that led to enhanced flooding, a comparison of aircraft and ship data from CalWater 2015 and NOAA Unique CrIS/ATMS Processing System (NUCAPS) retrievals (integrated water vapor, vertical temperature and moisture profiles, and an experimental ATMS-only rain rate product) will be compared for the 22-24 January period.

Fixed point theorems of GPS carrier phase ambiguity resolution and their application to massive network processing: Ambizap

NASA Astrophysics Data System (ADS)

Blewitt, Geoffrey

2008-12-01

Precise point positioning (PPP) has become popular for Global Positioning System (GPS) geodetic network analysis because for n stations, PPP has O(n) processing time, yet solutions closely approximate those of O(n3) full network analysis. Subsequent carrier phase ambiguity resolution (AR) further improves PPP precision and accuracy; however, full-network bootstrapping AR algorithms are O(n4), limiting single network solutions to n < 100. In this contribution, fixed point theorems of AR are derived and then used to develop "Ambizap," an O(n) algorithm designed to give results that closely approximate full network AR. Ambizap has been tested to n ≈ 2800 and proves to be O(n) in this range, adding only ˜50% to PPP processing time. Tests show that a 98-station network is resolved on a 3-GHz CPU in 7 min, versus 22 h using O(n4) AR methods. Ambizap features a novel network adjustment filter, producing solutions that precisely match O(n4) full network analysis. The resulting coordinates agree to ≪1 mm with current AR methods, much smaller than the ˜3-mm RMS precision of PPP alone. A 2000-station global network can be ambiguity resolved in ˜2.5 h. Together with PPP, Ambizap enables rapid, multiple reanalysis of large networks (e.g., ˜1000-station EarthScope Plate Boundary Observatory) and facilitates the addition of extra stations to an existing network solution without need to reprocess all data. To meet future needs, PPP plus Ambizap is designed to handle ˜10,000 stations per day on a 3-GHz dual-CPU desktop PC.

Timing of strain localization in high-pressure low-temperature shear zones: The argon isotopic record

NASA Astrophysics Data System (ADS)

Laurent, Valentin; Scaillet, Stéphane; Jolivet, Laurent; Augier, Romain

2017-04-01

The complex interplay between rheology, temperature and deformation profoundly influences how crustal-scale shear zones form and then evolve across a deforming lithosphere. Understanding early exhumation processes in subduction zones requires quantitative age constraints on the timing of strain localization within high-pressure shear zones. Using both the in situ laser ablation and conventional step-heating 40Ar/39Ar dating (on phengite single grains and populations) methods, this study aims at quantifying the duration of ductile deformation and the timing of strain localization within HP-LT shear zones of the Cycladic Blueschist Unit (CBU, Greece). The rate of this progressive strain localization is unknown, and in general, poorly known in similar geological contexts. Critical to retrieve realistic estimates of rates of strain localization during exhumation, dense 40Ar/39Ar age transects were sampled along shear zones recently identified on Syros and Sifnos islands. There, field observations suggest that deformation progressively localized downward in the CBU during exhumation. In parallel, these shear zones are characterized by different degrees of retrogression from blueschist-facies to greenschist-facies P-T conditions overprinting eclogite-facies record throughout the CBU. Results show straightforward correlations between the degree of retrogression, the finite strain intensity and 40Ar/39Ar ages; the most ductilely deformed and retrograded rocks yielded the youngest 40Ar/39Ar ages. The possible effects of strain localization during exhumation on the record of the argon isotopic system in HP-LT shear zones are addressed. Our results show that strain has localized in shear zones over a 30 Ma long period and that individual shear zones evolve during 7-15 Ma. We also discuss these results at small-scale to see whether deformation and fluid circulations, channelled within shear bands, can homogenize chemical compositions and reset the 40Ar/39Ar isotopic record. This study brings new perspective on the process of strain localization through the dating of structures along strain gradients, especially on possible variation of rates of localisation through the entire exhumation history.

The cloud-radiative forcing of the U.S. landfalling atmospheric rivers

NASA Astrophysics Data System (ADS)

Luo, Qianwen

Atmospheric rivers (ARs) are narrow channels in the atmosphere that transport an enormous amount of moisture from the tropics to the higher latitudes. Streaks of highly reflective clouds are observed along with the ARs in satellite imagery. These clouds both influence the moisture transport of ARs, as well as modify the Earth-Atmospheric energy budget through pathways such as cloud-radiative forcing (CRF). This dissertation studies the CRF of the U.S. Landfalling ARs in weather and climate scales. Three crucial questions are addressed. First, how do clouds produced by the ARs modulate the moisture and heat balance of the Earth-Atmospheric system? Even though studies of ARs date back to the 90s, past research has been primarily focused on their hydrological impacts. We addressed this research gap by comparing the dominant types of precipitating clouds and convection of two ARs. Through quantifying their effects on the energy balance in the midlatitudes, we found that when deep convection was the dominant cloud types of an AR, impressive CRF cooling was produced. Second, what are the sufficient climate conditions for the extensive CRF in the continental U.S.? We studied 60 ARs that reached the California coast (the Southwest ARs) and 60 ARs that reached Pacific Northwest during Nov-Mar, 2000-2008. It was found that when these West-Coast ARs were followed by the moisture surge from the Gulf of Mexico (the Gulf-Coast AR), it resulted in apparent statewide CRF. Such condition happened more frequently in the Southwest-AR scenario. Third, how does the subgrid-scale-convection-induced CRF influence the moisture transport of ARs?We ran two WRF ARW simulations for a Southwest-AR that was followed by a Gulf-Coast AR. The only difference between the two simulations was one considered the CRF of subgrid-scale clouds while the other did not. By comparing the two simulations, we found that the subgrid-scale-convection-induced CRF helped prolong the lifespan of clouds in an AR, thus enabling moisture to be transported further downstream. In short, this work helps improve our understanding of CRF of the U.S. landfalling ARs from both weather and climate perspectives. Our results are useful for validating the representation of clouds and radiation processes in weather and climate models, thereby help to improve AR predictions.

Influence of inert gases on the reactive high power pulsed magnetron sputtering process of carbon-nitride thin films

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

Schmidt, Susann; Czigany, Zsolt; Greczynski, Grzegorz

2013-01-15

The influence of inert gases (Ne, Ar, Kr) on the sputter process of carbon and carbon-nitride (CN{sub x}) thin films was studied using reactive high power pulsed magnetron sputtering (HiPIMS). Thin solid films were synthesized in an industrial deposition chamber from a graphite target. The peak target current during HiPIMS processing was found to decrease with increasing inert gas mass. Time averaged and time resolved ion mass spectroscopy showed that the addition of nitrogen, as reactive gas, resulted in less energetic ion species for processes employing Ne, whereas the opposite was noticed when Ar or Kr were employed as inertmore » gas. Processes in nonreactive ambient showed generally lower total ion fluxes for the three different inert gases. As soon as N{sub 2} was introduced into the process, the deposition rates for Ne and Ar-containing processes increased significantly. The reactive Kr-process, in contrast, showed slightly lower deposition rates than the nonreactive. The resulting thin films were characterized regarding their bonding and microstructure by x-ray photoelectron spectroscopy and transmission electron microscopy. Reactively deposited CN{sub x} thin films in Ar and Kr ambient exhibited an ordering toward a fullerene-like structure, whereas carbon and CN{sub x} films deposited in Ne atmosphere were found to be amorphous. This is attributed to an elevated amount of highly energetic particles observed during ion mass spectrometry and indicated by high peak target currents in Ne-containing processes. These results are discussed with respect to the current understanding of the structural evolution of a-C and CN{sub x} thin films.« less

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

32 CFR 651.13 - Classified actions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... ENVIRONMENTAL ANALYSIS OF ARMY ACTIONS (AR 200-2) National Environmental Policy Act and the Decision Process § 651.13 Classified actions. (a) For proposed actions and NEPA analyses involving classified information, AR 380-5 (Department of the Army Information Security Program) will be followed. (b) Classification...

Characterization of pure Ni ultrafine/nanoparticles synthesized by electromagnetic levitational gas condensation method

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

Khodaei, Azin, E-mail: Azin.Khodaei@gmail.com; Hasannasab, Malihe; Amousoltani, Narges

2016-02-15

Highlights: • Ni ultrafine/nanoparticles were produced using the single-step ELGC method. • Ar and He–20%Ar gas mixtures were used as the condensing gas under 1 atm. • Effects of gas type and flow rate on particle size distribution were investigated. • The nanoparticles showed both high saturation magnetization and low coercivity. - Abstract: In this work, Ni ultrafine/nanoparticles were directly produced using the one-step, relatively large-scale electromagnetic levitational gas condensation method. In this process, Ni vapors ascending from the levitated droplet were condensed by Ar and He–20%Ar gas mixtures under atmospheric pressure. Effects of type and flow rate of themore » condensing gas on the size, size distribution and crystallinity of Ni particles were investigated. The particles were characterized by scanning electron microscopy, X-ray diffraction and vibrating sample magnetometer (VSM). The process parameters for the synthesis of the crystalline Ni ultrafine/nanoparticles were determined.« less

New resonances from the coherence of Auger and intercoulombic (ICD) processes in the photoionization of endohedral fullerenes

NASA Astrophysics Data System (ADS)

Chakraborty, Himadri; Wise, Jacob; de, Ruma; Javani, Mohammad; Manson, Steve; Madjet, Mohamed

2014-05-01

Considering the photoionization of Ar@C60 , we predict resonant femtosecond decays of both Ar and C60 vacancies through the continua of atom-fullerene hybrid final states. The resulting resonances emerge from the interference between simultaneous autoionizing and intercoulombic decay (ICD) processes. For Ar 3s --> np excitations, these resonances are far stronger than the Ar-to-C60 resonant ICDs, while for C60 excitations they are strikingly larger than the corresponding Auger features. The results indicate the power of hybridization to enhance decay rates, and modify lifetimes and line profiles. These decays are also likely to exist generally in the ionization of molecules, nano-dimers and -polymers, and fullerene onions that support hybridized electrons as well. A jellium based time-dependent local density approximation (TDLDA), with the Leeuwen and Baerends exchange-correlation functional to produce accurate asymptotic behavior, is employed to calculate the dynamical response of the system to the photon field.

Molecular Basis of Ligand Dissociation in β-Adrenergic Receptors

PubMed Central

González, Angel; Perez-Acle, Tomas; Pardo, Leonardo; Deupi, Xavier

2011-01-01

The important and diverse biological functions of β-adrenergic receptors (βARs) have promoted the search for compounds to stimulate or inhibit their activity. In this regard, unraveling the molecular basis of ligand binding/unbinding events is essential to understand the pharmacological properties of these G protein-coupled receptors. In this study, we use the steered molecular dynamics simulation method to describe, in atomic detail, the unbinding process of two inverse agonists, which have been recently co-crystallized with β1 and β2ARs subtypes, along four different channels. Our results indicate that this type of compounds likely accesses the orthosteric binding site of βARs from the extracellular water environment. Importantly, reconstruction of forces and energies from the simulations of the dissociation process suggests, for the first time, the presence of secondary binding sites located in the extracellular loops 2 and 3 and transmembrane helix 7, where ligands are transiently retained by electrostatic and Van der Waals interactions. Comparison of the residues that form these new transient allosteric binding sites in both βARs subtypes reveals the importance of non-conserved electrostatic interactions as well as conserved aromatic contacts in the early steps of the binding process. PMID:21915263

Measurements and kinetic modeling of atomic species in fuel-oxidizer mixtures excited by a repetitive nanosecond pulse discharge

NASA Astrophysics Data System (ADS)

Winters, C.; Eckert, Z.; Yin, Z.; Frederickson, K.; Adamovich, I. V.

2018-01-01

This work presents the results of number density measurements of metastable Ar atoms and ground state H atoms in diluted mixtures of H2 and O2 with Ar, as well as ground state O atoms in diluted H2-O2-Ar, CH4-O2-Ar, C3H8-O2-Ar, and C2H4-O2-Ar mixtures excited by a repetitive nanosecond pulse discharge. The measurements have been made in a nanosecond pulse, double dielectric barrier discharge plasma sustained in a flow reactor between two plane electrodes encapsulated within dielectric material, at an initial temperature of 500 K and pressures ranging from 300 Torr to 700 Torr. Metastable Ar atom number density distribution in the afterglow is measured by tunable diode laser absorption spectroscopy, and used to characterize plasma uniformity. Temperature rise in the reacting flow is measured by Rayleigh scattering. H atom and O atom number densities are measured by two-photon absorption laser induced fluorescence. The results are compared with kinetic model predictions, showing good agreement, with the exception of extremely lean mixtures. O atoms and H atoms in the plasma are produced mainly during quenching of electronically excited Ar atoms generated by electron impact. In H2-Ar and O2-Ar mixtures, the atoms decay by three-body recombination. In H2-O2-Ar, CH4-O2-Ar, and C3H8-O2-Ar mixtures, O atoms decay in a reaction with OH, generated during H atom reaction with HO2, with the latter produced by three-body H atom recombination with O2. The net process of O atom decay is O  +  H  →  OH, such that the decay rate is controlled by the amount of H atoms produced in the discharge. In extra lean mixtures of propane and ethylene with O2-Ar the model underpredicts the O atom decay rate. At these conditions, when fuel is completely oxidized by the end of the discharge burst, the net process of O atom decay, O  +  O  →  O2, becomes nearly independent of H atom number density. Lack of agreement with the data at these conditions is likely due to diffusion of H atoms from the partially oxidized regions near the side walls of the reactor into the plasma. Although significant fractions of hydrogen and hydrocarbon fuels are oxidized by O atoms produced in the plasma, chain branching remains a minor effect at these relatively low temperature conditions.

Constructing Stories of Self-Growth: How Individual Differences in Patterns of Autobiographical Reasoning Relate to Well-being in Midlife

PubMed Central

Lilgendahl, Jennifer Pals; McAdams, Dan P.

2010-01-01

Although growth has been a central focus in narrative research, few studies have examined growth comprehensively, as a story that emerges across the interpretation of many events. In this study, we examined how individual differences in autobiographical reasoning (AR) about self-growth relate to traits and well-being in midlife adults. Two patterns of growth-related AR were identified: 1) positive processing, defined as the average tendency to interpret events positively (vs. negatively), and 2) differentiated processing, defined as the extent to which past events are interpreted as causing a variety of forms of self-growth. Results showed that positive processing was negatively related to neuroticism and predicted well-being even after controlling for the average valence of past events. Additionally, differentiated processing of negative events but not positive events was positively related to openness and predictive of well-being. Finally, growth-related AR patterns independently predicted well-being beyond the effects of traits and demographic factors. PMID:21395593

Electron emission from transfer ionization reaction in 30 keV amu‑1 He 2+ on Ar collision

NASA Astrophysics Data System (ADS)

Amaya-Tapia, A.; Antillón, A.; Estrada, C. D.

2018-06-01

A model is presented that describes the transfer ionization process in H{e}2++Ar collision at a projectile energy of 30 keV amu‑1. It is based on a semiclassical independent-particle close-coupling method that yields a reasonable agreement between calculated and experimental values of the total single-ionization and single-capture cross sections. It is found that the transfer ionization reaction is predominantly carried out through simultaneous capture and ionization, rather than by sequential processes. The transfer-ionization differential cross section in energy that is obtained satisfactorily reproduces the global behavior of the experimental data. Additionally, the probabilities of capture and ionization as function of the impact parameter for H{e}2++A{r}+ and H{e}++A{r}+ collisions are calculated, as far as we know, for the first time. The results suggest that the model captures essential elements that describe the two-electron transfer ionization process and could be applied to systems and processes of two electrons.

REPHLEX II: An information management system for the ARS Water Data Base

NASA Astrophysics Data System (ADS)

Thurman, Jane L.

1993-08-01

The REPHLEX II computer system is an on-line information management system which allows scientists, engineers, and other researchers to retrieve data from the ARS Water Data Base using asynchronous communications. The system features two phone lines handling baud rates from 300 to 2400, customized menus to facilitate browsing, help screens, direct access to information and data files, electronic mail processing, file transfers using the XMODEM protocol, and log-in procedures which capture information on new users, process passwords, and log activity for a permanent audit trail. The primary data base on the REPHLEX II system is the ARS Water Data Base which consists of rainfall and runoff data from experimental agricultural watersheds located in the United States.

Stromal Androgen Receptor in Prostate Cancer Development and Progression

PubMed Central

Leach, Damien A.; Buchanan, Grant

2017-01-01

Prostate cancer development and progression is the result of complex interactions between epithelia cells and fibroblasts/myofibroblasts, in a series of dynamic process amenable to regulation by hormones. Whilst androgen action through the androgen receptor (AR) is a well-established component of prostate cancer biology, it has been becoming increasingly apparent that changes in AR signalling in the surrounding stroma can dramatically influence tumour cell behavior. This is reflected in the consistent finding of a strong association between stromal AR expression and patient outcomes. In this review, we explore the relationship between AR signalling in fibroblasts/myofibroblasts and prostate cancer cells in the primary site, and detail the known functions, actions, and mechanisms of fibroblast AR signaling. We conclude with an evidence-based summary of how androgen action in stroma dramatically influences disease progression. PMID:28117763

Scalable Production of Mechanically Robust Antireflection Film for Omnidirectional Enhanced Flexible Thin Film Solar Cells.

PubMed

Wang, Min; Ma, Pengsha; Yin, Min; Lu, Linfeng; Lin, Yinyue; Chen, Xiaoyuan; Jia, Wei; Cao, Xinmin; Chang, Paichun; Li, Dongdong

2017-09-01

Antireflection (AR) at the interface between the air and incident window material is paramount to boost the performance of photovoltaic devices. 3D nanostructures have attracted tremendous interest to reduce reflection, while the structure is vulnerable to the harsh outdoor environment. Thus the AR film with improved mechanical property is desirable in an industrial application. Herein, a scalable production of flexible AR films is proposed with microsized structures by roll-to-roll imprinting process, which possesses hydrophobic property and much improved robustness. The AR films can be potentially used for a wide range of photovoltaic devices whether based on rigid or flexible substrates. As a demonstration, the AR films are integrated with commercial Si-based triple-junction thin film solar cells. The AR film works as an effective tool to control the light travel path and utilize the light inward more efficiently by exciting hybrid optical modes, which results in a broadband and omnidirectional enhanced performance.

Chemistry and chronology of magmatic processes, Central Kenya Peralkaline province, East African Rift

NASA Astrophysics Data System (ADS)

Anthony, E.; Deino, A. L.; White, J. C.; Omenda, P. A.

2014-12-01

We report here a synthesis of the geochemistry of magma evolution correlated with 40Ar/39Ar, 14 C, and U-series chronology for volcanoes in the Central Kenya Peralkaline Province (CKPP). The volcanic centers - Menengai, Eburru, Olkaria, Longonot, and Suswa - are at the apex of the Kenya Dome, and consist of trachyte, phonolite, comendite, and pantellerite. These volcanic centers are within the graben of the EARS and are characterized by a shield-building phase followed by caldera collapse and subsequent post-caldera eruptions. Geochemical modeling demonstrates that the magmas are the result of fractional crystallization of alkali basaltic magmas and magma mixing. Longonot and Suswa have the most chronologic data -14 C, Ar/Ar and U-series - and they show that the youngest eruptions have 230 Th/232Th of 0.8, which was inherited from the magma system prior to eruption. Subsequent changes in 230 Th/232 Th are due to post-eruptive decay of 230 Th and correlate well with 14 C and Ar/Ar.

Scalable Production of Mechanically Robust Antireflection Film for Omnidirectional Enhanced Flexible Thin Film Solar Cells

PubMed Central

Wang, Min; Ma, Pengsha; Lu, Linfeng; Lin, Yinyue; Chen, Xiaoyuan; Jia, Wei; Cao, Xinmin; Chang, Paichun

2017-01-01

Antireflection (AR) at the interface between the air and incident window material is paramount to boost the performance of photovoltaic devices. 3D nanostructures have attracted tremendous interest to reduce reflection, while the structure is vulnerable to the harsh outdoor environment. Thus the AR film with improved mechanical property is desirable in an industrial application. Herein, a scalable production of flexible AR films is proposed with microsized structures by roll‐to‐roll imprinting process, which possesses hydrophobic property and much improved robustness. The AR films can be potentially used for a wide range of photovoltaic devices whether based on rigid or flexible substrates. As a demonstration, the AR films are integrated with commercial Si‐based triple‐junction thin film solar cells. The AR film works as an effective tool to control the light travel path and utilize the light inward more efficiently by exciting hybrid optical modes, which results in a broadband and omnidirectional enhanced performance. PMID:28932667

A two-model hydrologic ensemble prediction of hydrograph: case study from the upper Nysa Klodzka river basin (SW Poland)

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej

2016-04-01

The HydroProg system has been elaborated in frame of the research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland and is steadily producing multimodel ensemble predictions of hydrograph in real time. Although there are six ensemble members available at present, the longest record of predictions and their statistics is available for two data-based models (uni- and multivariate autoregressive models). Thus, we consider 3-hour predictions of water levels, with lead times ranging from 15 to 180 minutes, computed every 15 minutes since August 2013 for the Nysa Klodzka basin (SW Poland) using the two approaches and their two-model ensemble. Since the launch of the HydroProg system there have been 12 high flow episodes, and the objective of this work is to present the performance of the two-model ensemble in the process of forecasting these events. For a sake of brevity, we limit our investigation to a single gauge located at the Nysa Klodzka river in the town of Klodzko, which is centrally located in the studied basin. We identified certain regular scenarios of how the models perform in predicting the high flows in Klodzko. At the initial phase of the high flow, well before the rising limb of hydrograph, the two-model ensemble is found to provide the most skilful prognoses of water levels. However, while forecasting the rising limb of hydrograph, either the two-model solution or the vector autoregressive model offers the best predictive performance. In addition, it is hypothesized that along with the development of the rising limb phase, the vector autoregression becomes the most skilful approach amongst the scrutinized ones. Our simple two-model exercise confirms that multimodel hydrologic ensemble predictions cannot be treated as universal solutions suitable for forecasting the entire high flow event, but their superior performance may hold only for certain phases of a high flow.

The RNA binding protein Ars2 supports hematopoiesis at multiple levels.

PubMed

Elahi, Seerat; Egan, Shawn M; Holling, G Aaron; Kandefer, Rachel L; Nemeth, Michael J; Olejniczak, Scott H

2018-05-15

Recent biochemical characterization of Arsenic resistance protein 2 (Ars2) has established it as central to determining the fate of nascent RNA polymerase II (RNAPII) transcripts. Through interactions with the nuclear 5'-7-methylguanosine (7mG) cap binding complex (CBC), Ars2 promotes co-transcriptional processing coupled with nuclear export or degradation of several classes of RNAPII transcripts, allowing for gene expression programs that facilitate rapid and sustained proliferation of immortalized cells in culture. However, rapidly dividing cells in culture do not represent the physiological condition of the vast majority of cells in an adult mammal. To examine functions of Ars2 in a physiological setting we generated inducible Ars2 knockout mice and found that deletion of Ars2 from adult mice resulted in defective hematopoiesis in bone marrow and thymus. Importantly, only some of this defect could be explained by the requirement of Ars2 for rapid proliferation, which we found to be cell-type specific in vivo. Rather Ars2 was required for survival of developing thymocytes and for limiting differentiation of bone marrow resident long-term hematopoietic stem cells (LT-HSCs). As a result, Ars2 knockout led to rapid thymic involution and loss of the ability of mice to regenerate peripheral blood following myeloablation. These in vivo data demonstrate that Ars2 expression is important at several steps of hematopoiesis, likely because Ars2 acts on gene expression programs underlying essential cell fate decisions such as the decision to die, to proliferate, or to differentiate. Copyright © 2018. Published by Elsevier Inc.

Selective inactivation of adenosine A2A receptors in striatal neurons enhances working memory and reversal learning

PubMed Central

Wei, Catherine J.; Singer, Philipp; Coelho, Joana; Boison, Detlev; Feldon, Joram; Yee, Benjamin K.; Chen, Jiang-Fan

2011-01-01

The adenosine A2A receptor (A2AR) is highly enriched in the striatum where it is uniquely positioned to integrate dopaminergic, glutamatergic, and other signals to modulate cognition. Although previous studies support the hypothesis that A2AR inactivation can be pro-cognitive, analyses of A2AR's effects on cognitive functions have been restricted to a small subset of cognitive domains. Furthermore, the relative contribution of A2ARs in distinct brain regions remains largely unknown. Here, we studied the regulation of multiple memory processes by brain region-specific populations of A2ARs. Specifically, we evaluated the cognitive impacts of conditional A2AR deletion restricted to either the entire forebrain (i.e., cerebral cortex, hippocampus, and striatum, fb-A2AR KO) or to striatum alone (st-A2AR KO) in recognition memory, working memory, reference memory, and reversal learning. This comprehensive, comparative analysis showed for the first time that depletion of A2AR-dependent signaling in either the entire forebrain or striatum alone is associated with two specific phenotypes indicative of cognitive flexibility—enhanced working memory and enhanced reversal learning. These selective pro-cognitive phenotypes seemed largely attributed to inactivation of striatal A2ARs as they were captured by A2AR deletion restricted to striatal neurons. Neither spatial reference memory acquisition nor spatial recognition memory were grossly affected, and no evidence for compensatory changes in striatal or cortical D1, D2, or A1 receptor expression was found. This study provides the first direct demonstration that targeting striatal A2ARs may be an effective, novel strategy to facilitate cognitive flexibility under normal and pathologic conditions. PMID:21693634

Investigation of metastable production in a closed-cell dielectric capillary variable pressure He plasma jet with Ar admixture

NASA Astrophysics Data System (ADS)

Sands, Brian; Ganguly, Biswa

2011-10-01

For plasma processing applications of streamer-like atmospheric pressure plasma jets generated in a dielectric capillary, we have demonstrated that an admixture of Ar to the He gas flow greatly increases the lifetime of energetic species in the core flow through enhanced afterglow production of Ar 1s5 metastable species. To study this effect in more detail, we have used a closed-cell plasma jet that allows control over the background gas pressure and composition. We used a 20 ns risetime positive unipolar voltage pulse for excitation. A He flow with a 0-30% Ar admixture was studied using time-resolved emission and tunable diode laser absorption spectroscopy of the Ar 1s5 and He 23S metastable states. Nitrogen was used as the background gas. In pure He and pure Ar gases the He and Ar metastables respectively are produced in the first ~100 ns only in the active discharge. With Ar added to the He gas flow, He metastables produced in the active discharge are quickly quenched via Penning ionization of Ar while Ar 1s5 is enhanced over 1-2 μs in the afterglow, increasing the number density as high as 1013/cc and extending the effective lifetime up to 10 μs. This implies that He heavy particle kinetics are a key driver of enhanced afterglow plasma chemistry in plasma jets with rare gas mixtures.

Fluorocarbon assisted atomic layer etching of SiO 2 and Si using cyclic Ar/C 4F 8 and Ar/CHF 3 plasma

DOE PAGES

Metzler, Dominik; Li, Chen; Engelmann, Sebastian; ...

2015-11-11

The need for atomic layer etching (ALE) is steadily increasing as smaller critical dimensions and pitches are required in device patterning. A flux-control based cyclic Ar/C 4F 8 ALE based on steady-state Ar plasma in conjunction with periodic, precise C 4F 8 injection and synchronized plasma-based low energy Ar + ion bombardment has been established for SiO 2. 1 In this work, the cyclic process is further characterized and extended to ALE of silicon under similar process conditions. The use of CHF 3 as a precursor is examined and compared to C 4F 8. CHF 3 is shown to enablemore » selective SiO 2/Si etching using a fluorocarbon (FC) film build up. Other critical process parameters investigated are the FC film thickness deposited per cycle, the ion energy, and the etch step length. Etching behavior and mechanisms are studied using in situ real time ellipsometry and X-ray photoelectron spectroscopy. Silicon ALE shows less self-limitation than silicon oxide due to higher physical sputtering rates for the maximum ion energies used in this work, ranged from 20 to 30 eV. The surface chemistry is found to contain fluorinated silicon oxide during the etching of silicon. As a result, plasma parameters during ALE are studied using a Langmuir probe and establish the impact of precursor addition on plasma properties.« less

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

PubMed Central

Li, Xiaoqing; Wang, Yu

2018-01-01

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology. PMID:29351254

How Is the Effect of Adolescent E-cigarette Use on Smoking Onset Mediated: A Longitudinal Analysis

PubMed Central

Wills, Thomas A.; Gibbons, Frederick X.; Sargent, James D.; Schweitzer, Rebecca J.

2016-01-01

E-cigarette use by adolescents has been related to onset of cigarette smoking but there is little knowledge about the process(es) through which this occurs. Accordingly, we tested the role of cognitive and social factors for mediating the relation between e-cigarette use and smoking onset. A school-based survey was conducted with a baseline sample of 2,338 students in Hawaii (9th and 10th graders, mean age 14.7 years) who were surveyed in 2013 (Time 1, T1) and followed up 1 year later (Time 2, T2). We assessed e-cigarette use, cigarette smoking, demographic covariates, and four hypothesized mediators: smoking-related expectancies, prototypes, and peer affiliations as well as marijuana use. The primary structural modeling analysis, based on initial never-smokers, used an autoregressive model (entering T2 mediator values adjusted for T1 values) to test for mediational pathways in the relation between e-cigarette use at T1 and cigarette smoking status at T2. Results showed that e-cigarette use was related to all of the mediators and tests of indirect effects indicated that changes in expectancies, affiliations, and marijuana use were significant pathways in the relation between e-cigarette use and smoking onset. A direct effect from e-cigarette use to smoking onset was nonsignificant. Findings were replicated across autoregressive and prospective models. We conclude that the relation between adolescent e-cigarette use and smoking onset is in part attributable to cognitive and social processes that follow from e-cigarette use. Further research is needed to understand the relative role of nicotine and psychosocial factors in smoking onset. PMID:27669093

How is the effect of adolescent e-cigarette use on smoking onset mediated: A longitudinal analysis.

PubMed

Wills, Thomas A; Gibbons, Frederick X; Sargent, James D; Schweitzer, Rebecca J

2016-12-01

E-cigarette use by adolescents has been related to onset of cigarette smoking but there is little knowledge about the process(es) through which this occurs. Accordingly, we tested the role of cognitive and social factors for mediating the relation between e-cigarette use and smoking onset. A school-based survey was conducted with a baseline sample of 2,338 students in Hawaii (9th and 10th graders, mean age 14.7 years) who were surveyed in 2013 (Time 1, T1) and followed up 1 year later (Time 2, T2). We assessed e-cigarette use, cigarette smoking, demographic covariates, and 4 hypothesized mediators: smoking-related expectancies, prototypes, and peer affiliations as well as marijuana use. The primary structural modeling analysis, based on initial never-smokers, used an autoregressive model (entering T2 mediator values adjusted for T1 values) to test for mediational pathways in the relation between e-cigarette use at T1 and cigarette smoking status at T2. Results showed that e-cigarette use was related to all of the mediators. Tests of indirect effects indicated that changes in expectancies, affiliations, and marijuana use were significant pathways in the relation between e-cigarette use and smoking onset. A direct effect from e-cigarette use to smoking onset was nonsignificant. Findings were replicated across autoregressive and prospective models. We conclude that the relation between adolescent e-cigarette use and smoking onset is in part attributable to cognitive and social processes that follow from e-cigarette use. Further research is needed to understand the relative role of nicotine and psychosocial factors in smoking onset. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model.

PubMed

Xin, Jingzhou; Zhou, Jianting; Yang, Simon X; Li, Xiaoqing; Wang, Yu

2018-01-19

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology.

32 CFR 518.5 - Authority.

Code of Federal Regulations, 2014 CFR

2014-07-01

... records); (18) AR 600-8-104 (military personnel information management records); (19) AR 600-85 (alcohol... FREEDOM OF INFORMATION ACT PROGRAM General Provisions § 518.5 Authority. (a) This part governs written... information under the FOIA. (c) Requests for DA records processed under the FOIA may be denied only in...

32 CFR 518.5 - Authority.

Code of Federal Regulations, 2012 CFR

2012-07-01

... records); (18) AR 600-8-104 (military personnel information management records); (19) AR 600-85 (alcohol... FREEDOM OF INFORMATION ACT PROGRAM General Provisions § 518.5 Authority. (a) This part governs written... information under the FOIA. (c) Requests for DA records processed under the FOIA may be denied only in...

32 CFR 518.5 - Authority.

Code of Federal Regulations, 2013 CFR

2013-07-01

... records); (18) AR 600-8-104 (military personnel information management records); (19) AR 600-85 (alcohol... FREEDOM OF INFORMATION ACT PROGRAM General Provisions § 518.5 Authority. (a) This part governs written... information under the FOIA. (c) Requests for DA records processed under the FOIA may be denied only in...

Measurements of Argon-39 at the U20az underground nuclear explosion site.

PubMed

McIntyre, J I; Aalseth, C E; Alexander, T R; Back, H O; Bellgraph, B J; Bowyer, T W; Chipman, V; Cooper, M W; Day, A R; Drellack, S; Foxe, M P; Fritz, B G; Hayes, J C; Humble, P; Keillor, M E; Kirkham, R R; Krogstad, E J; Lowrey, J D; Mace, E K; Mayer, M F; Milbrath, B D; Misner, A; Morley, S M; Panisko, M E; Olsen, K B; Ripplinger, M D; Seifert, A; Suarez, R

2017-11-01

Pacific Northwest National Laboratory reports on the detection of 39 Ar at the location of an underground nuclear explosion on the Nevada Nuclear Security Site. The presence of 39 Ar was not anticipated at the outset of the experimental campaign but results from this work demonstrated that it is present, along with 37 Ar and 85 Kr in the subsurface at the site of an underground nuclear explosion. Our analysis showed that by using state-of-the-art technology optimized for radioargon measurements, it was difficult to distinguish 39 Ar from the fission product 85 Kr. Proportional counters are currently used for high-sensitivity measurement of 37 Ar and 39 Ar. Physical and chemical separation processes are used to separate argon from air or soil gas, yielding pure argon with contaminant gases reduced to the parts-per-million level or below. However, even with purification at these levels, the beta decay signature of 85 Kr can be mistaken for that of 39 Ar, and the presence of either isotope increases the measurement background level for the measurement of 37 Ar. Measured values for the 39 Ar measured at the site ranged from 36,000 milli- Becquerel/standard-cubic-meter-of-air (mBq/SCM) for shallow bore holes to 997,000 mBq/SCM from the rubble chimney from the underground nuclear explosion. Published by Elsevier Ltd.

Augmented reality enabling intelligence exploitation at the edge

NASA Astrophysics Data System (ADS)

Kase, Sue E.; Roy, Heather; Bowman, Elizabeth K.; Patton, Debra

2015-05-01

Today's Warfighters need to make quick decisions while interacting in densely populated environments comprised of friendly, hostile, and neutral host nation locals. However, there is a gap in the real-time processing of big data streams for edge intelligence. We introduce a big data processing pipeline called ARTEA that ingests, monitors, and performs a variety of analytics including noise reduction, pattern identification, and trend and event detection in the context of an area of operations (AOR). Results of the analytics are presented to the Soldier via an augmented reality (AR) device Google Glass (Glass). Non-intrusive AR devices such as Glass can visually communicate contextually relevant alerts to the Soldier based on the current mission objectives, time, location, and observed or sensed activities. This real-time processing and AR presentation approach to knowledge discovery flattens the intelligence hierarchy enabling the edge Soldier to act as a vital and active participant in the analysis process. We report preliminary observations testing ARTEA and Glass in a document exploitation and person of interest scenario simulating edge Soldier participation in the intelligence process in disconnected deployment conditions.

Parametric and Non-Parametric Vibration-Based Structural Identification Under Earthquake Excitation

NASA Astrophysics Data System (ADS)

Pentaris, Fragkiskos P.; Fouskitakis, George N.

2014-05-01

The problem of modal identification in civil structures is of crucial importance, and thus has been receiving increasing attention in recent years. Vibration-based methods are quite promising as they are capable of identifying the structure's global characteristics, they are relatively easy to implement and they tend to be time effective and less expensive than most alternatives [1]. This paper focuses on the off-line structural/modal identification of civil (concrete) structures subjected to low-level earthquake excitations, under which, they remain within their linear operating regime. Earthquakes and their details are recorded and provided by the seismological network of Crete [2], which 'monitors' the broad region of south Hellenic arc, an active seismic region which functions as a natural laboratory for earthquake engineering of this kind. A sufficient number of seismic events are analyzed in order to reveal the modal characteristics of the structures under study, that consist of the two concrete buildings of the School of Applied Sciences, Technological Education Institute of Crete, located in Chania, Crete, Hellas. Both buildings are equipped with high-sensitivity and accuracy seismographs - providing acceleration measurements - established at the basement (structure's foundation) presently considered as the ground's acceleration (excitation) and at all levels (ground floor, 1st floor, 2nd floor and terrace). Further details regarding the instrumentation setup and data acquisition may be found in [3]. The present study invokes stochastic, both non-parametric (frequency-based) and parametric methods for structural/modal identification (natural frequencies and/or damping ratios). Non-parametric methods include Welch-based spectrum and Frequency response Function (FrF) estimation, while parametric methods, include AutoRegressive (AR), AutoRegressive with eXogeneous input (ARX) and Autoregressive Moving-Average with eXogeneous input (ARMAX) models[4, 5]. Preliminary results indicate that parametric methods are capable of sufficiently providing the structural/modal characteristics such as natural frequencies and damping ratios. The study also aims - at a further level of investigation - to provide a reliable statistically-based methodology for structural health monitoring after major seismic events which potentially cause harming consequences in structures. Acknowledgments This work was supported by the State Scholarships Foundation of Hellas. References [1] J. S. Sakellariou and S. D. Fassois, "Stochastic output error vibration-based damage detection and assessment in structures under earthquake excitation," Journal of Sound and Vibration, vol. 297, pp. 1048-1067, 2006. [2] G. Hloupis, I. Papadopoulos, J. P. Makris, and F. Vallianatos, "The South Aegean seismological network - HSNC," Adv. Geosci., vol. 34, pp. 15-21, 2013. [3] F. P. Pentaris, J. Stonham, and J. P. Makris, "A review of the state-of-the-art of wireless SHM systems and an experimental set-up towards an improved design," presented at the EUROCON, 2013 IEEE, Zagreb, 2013. [4] S. D. Fassois, "Parametric Identification of Vibrating Structures," in Encyclopedia of Vibration, S. G. Braun, D. J. Ewins, and S. S. Rao, Eds., ed London: Academic Press, London, 2001. [5] S. D. Fassois and J. S. Sakellariou, "Time-series methods for fault detection and identification in vibrating structures," Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 365, pp. 411-448, February 15 2007.

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.

Indole-3-butyric acid promotes adventitious rooting in Arabidopsis thaliana thin cell layers by conversion into indole-3-acetic acid and stimulation of anthranilate synthase activity.

PubMed

Fattorini, L; Veloccia, A; Della Rovere, F; D'Angeli, S; Falasca, G; Altamura, M M

2017-07-11

Indole-3-acetic acid (IAA), and its precursor indole-3-butyric acid (IBA), control adventitious root (AR) formation in planta. Adventitious roots are also crucial for propagation via cuttings. However, IBA role(s) is/are still far to be elucidated. In Arabidopsis thaliana stem cuttings, 10 μM IBA is more AR-inductive than 10 μM IAA, and, in thin cell layers (TCLs), IBA induces ARs when combined with 0.1 μM kinetin (Kin). It is unknown whether arabidopsis TCLs produce ARs under IBA alone (10 μM) or IAA alone (10 μM), and whether they contain endogenous IAA/IBA at culture onset, possibly interfering with the exogenous IBA/IAA input. Moreover, it is unknown whether an IBA-to-IAA conversion is active in TCLs, and positively affects AR formation, possibly through the activity of the nitric oxide (NO) deriving from the conversion process. Revealed undetectable levels of both auxins at culture onset, showing that arabidopsis TCLs were optimal for investigating AR-formation under the total control of exogenous auxins. The AR-response of TCLs from various ecotypes, transgenic lines and knockout mutants was analyzed under different treatments. It was shown that ARs are better induced by IBA than IAA and IBA + Kin. IBA induced IAA-efflux (PIN1) and IAA-influx (AUX1/LAX3) genes, IAA-influx carriers activities, and expression of ANTHRANILATE SYNTHASE -alpha1 (ASA1), a gene involved in IAA-biosynthesis. ASA1 and ANTHRANILATE SYNTHASE -beta1 (ASB1), the other subunit of the same enzyme, positively affected AR-formation in the presence of exogenous IBA, because the AR-response in the TCLs of their mutant wei2wei7 was highly reduced. The AR-response of IBA-treated TCLs from ech2ibr10 mutant, blocked into IBA-to-IAA-conversion, was also strongly reduced. Nitric oxide, an IAA downstream signal and a by-product of IBA-to-IAA conversion, was early detected in IAA- and IBA-treated TCLs, but at higher levels in the latter explants. Altogether, results showed that IBA induced AR-formation by conversion into IAA involving NO activity, and by a positive action on IAA-transport and ASA1/ASB1-mediated IAA-biosynthesis. Results are important for applications aimed to overcome rooting recalcitrance in species of economic value, but mainly for helping to understand IBA involvement in the natural process of adventitious rooting.

Collisional radiative model for Ar-O2 mixture plasma with fully relativistic fine structure cross sections

NASA Astrophysics Data System (ADS)

Priti, Gangwar, Reetesh Kumar; Srivastava, Rajesh

2018-04-01

A collisional radiative (C-R) model has been developed to diagnose the rf generated Ar-O2 (0%-5%) mixture plasma at low temperatures. Since in such plasmas the most dominant process is an electron impact excitation process, we considered several electron impact fine structure transitions in an argon atom from its ground as well as excited states. The cross-sections for these transitions have been obtained using the reliable fully relativistic distorted wave theory. Processes which account for the coupling of argon with the oxygen molecules have been further added to the model. We couple our model to the optical spectroscopic measurements reported by Jogi et al. [J. Phys. D: Appl. Phys. 47, 335206 (2014)]. The plasma parameters, viz. the electron density (ne) and the electron temperature (Te) as a function of O2 concentration have been obtained using thirteen intense emission lines out of 3p54p → 3p54s transitions observed in their spectroscopic measurements. It is found that as the content of O2 in Ar increases from 0%-5%, Te increases in the range 0.85-1.7 eV, while the electron density decreases from 2.76 × 1012-2.34 × 1011 cm-3. The Ar-3p54s (1si) fine-structure level populations at our extracted plasma parameters are found to be in very good agreement with those obtained from the measurements. Furthermore, we have estimated the individual contributions coming from the ground state, 1si manifolds and cascade contributions to the population of the radiating Ar-3p54p (2pi) states as a function of a trace amount of O2. Such information is very useful to understand the importance of various processes occurring in the plasma.

Annual Review Clinic improves care in children with cystic fibrosis.

PubMed

Chuang, Sandra; Doumit, Michael; McDonald, Rebecca; Hennessy, Erika; Katz, Tamarah; Jaffe, Adam

2014-03-01

It is unclear whether annual multidisciplinary reviews in cystic fibrosis (CF) patients should be conducted in dedicated annual review (AR) clinics or during continuous assessments throughout the year. Our aim was to assess the effect of introducing an AR clinic. A retrospective written and electronic record review of CF patients was carried out for 2007 (no AR Clinic) and 2010 (established AR Clinic) calendar years. An internet-based satisfaction survey was distributed to families attending the AR clinic. In total, 123 children (mean age 9.5 years, range 1.32-18.8 years) and 141 children (8.3 years, 1.1-18.3 years) were included in 2007 and 2010 respectively. There was a significant increase in multidisciplinary reviews (documented annual review 28% vs 85%, P < 0.001; dietary assessment 46% vs 92%, P < 0.001) and investigations (OGTT 2% vs 74%, P < 0.001; abdominal ultrasound 35% vs 85%, P < 0.001) conducted after the introduction of AR clinic. The majority of the families surveyed (85%) were satisfied or very satisfied with the AR clinic. CF AR clinic significantly improves the number of annual investigations and multidisciplinary reviews performed. Families were satisfied with this new process. © 2013. Published by Elsevier B.V. on behalf of European Cystic Fibrosis Society. All rights reserved.

The development of AR book for computer learning

NASA Astrophysics Data System (ADS)

Phadung, Muneeroh; Wani, Najela; Tongmnee, Nur-aiynee

2017-08-01

Educators need to provide the alternative educational tools to foster learning outcomes of students. By using AR technology to create exciting edutainment experiences, this paper presents how augmented reality (AR) can be applied in the education. This study aims to develop the AR book for tenth grade students (age 15-16) and evaluate its quality. The AR book was developed based on ADDIE framework processes to provide computer learning on software computer knowledge. The content was accorded with the current Thai education curriculum. The AR book had 10 pages in three topics (the first was "Introduction," the second was "System Software" and the third was "Application Software"). Each page contained markers that placed virtual objects (2D animation and video clip). The obtained data were analyzed in terms of average and standard deviation. The validity of multimedia design of the AR book was assessed by three experts in multimedia design. A five-point Likert scale was used and the values were X¯ =4 .84 , S.D. = 1.27 which referred to very high. Moreover, three content experts, who specialize in computer teaching, evaluated the AR book's validity. The values determined by the experts were X¯ =4 .69 , S.D. = 0.29 which referred to very high. Implications for future study and education are discussed.

Acceleration and Velocity Sensing from Measured Strain

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi; Truax, Roger

2016-01-01

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. shape sensing, fiber optic strain sensor, system equivalent reduction and expansion process.

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.

On The Value at Risk Using Bayesian Mixture Laplace Autoregressive Approach for Modelling the Islamic Stock Risk Investment

NASA Astrophysics Data System (ADS)

Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika

2017-06-01

Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.

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.