Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

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

Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2014-04-14

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation.more » We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.« less

deltaGseg: macrostate estimation via molecular dynamics simulations and multiscale time series analysis.

PubMed

Low, Diana H P; Motakis, Efthymios

2013-10-01

Binding free energy calculations obtained through molecular dynamics simulations reflect intermolecular interaction states through a series of independent snapshots. Typically, the free energies of multiple simulated series (each with slightly different starting conditions) need to be estimated. Previous approaches carry out this task by moving averages at certain decorrelation times, assuming that the system comes from a single conformation description of binding events. Here, we discuss a more general approach that uses statistical modeling, wavelets denoising and hierarchical clustering to estimate the significance of multiple statistically distinct subpopulations, reflecting potential macrostates of the system. We present the deltaGseg R package that performs macrostate estimation from multiple replicated series and allows molecular biologists/chemists to gain physical insight into the molecular details that are not easily accessible by experimental techniques. deltaGseg is a Bioconductor R package available at http://bioconductor.org/packages/release/bioc/html/deltaGseg.html.

Degree-Pruning Dynamic Programming Approaches to Central Time Series Minimizing Dynamic Time Warping Distance.

PubMed

Sun, Tao; Liu, Hongbo; Yu, Hong; Chen, C L Philip

2016-06-28

The central time series crystallizes the common patterns of the set it represents. In this paper, we propose a global constrained degree-pruning dynamic programming (g(dp)²) approach to obtain the central time series through minimizing dynamic time warping (DTW) distance between two time series. The DTW matching path theory with global constraints is proved theoretically for our degree-pruning strategy, which is helpful to reduce the time complexity and computational cost. Our approach can achieve the optimal solution between two time series. An approximate method to the central time series of multiple time series [called as m_g(dp)²] is presented based on DTW barycenter averaging and our g(dp)² approach by considering hierarchically merging strategy. As illustrated by the experimental results, our approaches provide better within-group sum of squares and robustness than other relevant algorithms.

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

Cross-Sectional Time Series Designs: A General Transformation Approach.

ERIC Educational Resources Information Center

Velicer, Wayne F.; McDonald, Roderick P.

1991-01-01

The general transformation approach to time series analysis is extended to the analysis of multiple unit data by the development of a patterned transformation matrix. The procedure includes alternatives for special cases and requires only minor revisions in existing computer software. (SLD)

Documentation of a spreadsheet for time-series analysis and drawdown estimation

USGS Publications Warehouse

Halford, Keith J.

2006-01-01

Drawdowns during aquifer tests can be obscured by barometric pressure changes, earth tides, regional pumping, and recharge events in the water-level record. These stresses can create water-level fluctuations that should be removed from observed water levels prior to estimating drawdowns. Simple models have been developed for estimating unpumped water levels during aquifer tests that are referred to as synthetic water levels. These models sum multiple time series such as barometric pressure, tidal potential, and background water levels to simulate non-pumping water levels. The amplitude and phase of each time series are adjusted so that synthetic water levels match measured water levels during periods unaffected by an aquifer test. Differences between synthetic and measured water levels are minimized with a sum-of-squares objective function. Root-mean-square errors during fitting and prediction periods were compared multiple times at four geographically diverse sites. Prediction error equaled fitting error when fitting periods were greater than or equal to four times prediction periods. The proposed drawdown estimation approach has been implemented in a spreadsheet application. Measured time series are independent so that collection frequencies can differ and sampling times can be asynchronous. Time series can be viewed selectively and magnified easily. Fitting and prediction periods can be defined graphically or entered directly. Synthetic water levels for each observation well are created with earth tides, measured time series, moving averages of time series, and differences between measured and moving averages of time series. Selected series and fitting parameters for synthetic water levels are stored and drawdowns are estimated for prediction periods. Drawdowns can be viewed independently and adjusted visually if an anomaly skews initial drawdowns away from 0. The number of observations in a drawdown time series can be reduced by averaging across user-defined periods. Raw or reduced drawdown estimates can be copied from the spreadsheet application or written to tab-delimited ASCII files.

Guidelines and Procedures for Computing Time-Series Suspended-Sediment Concentrations and Loads from In-Stream Turbidity-Sensor and Streamflow Data

USGS Publications Warehouse

Rasmussen, Patrick P.; Gray, John R.; Glysson, G. Douglas; Ziegler, Andrew C.

2009-01-01

In-stream continuous turbidity and streamflow data, calibrated with measured suspended-sediment concentration data, can be used to compute a time series of suspended-sediment concentration and load at a stream site. Development of a simple linear (ordinary least squares) regression model for computing suspended-sediment concentrations from instantaneous turbidity data is the first step in the computation process. If the model standard percentage error (MSPE) of the simple linear regression model meets a minimum criterion, this model should be used to compute a time series of suspended-sediment concentrations. Otherwise, a multiple linear regression model using paired instantaneous turbidity and streamflow data is developed and compared to the simple regression model. If the inclusion of the streamflow variable proves to be statistically significant and the uncertainty associated with the multiple regression model results in an improvement over that for the simple linear model, the turbidity-streamflow multiple linear regression model should be used to compute a suspended-sediment concentration time series. The computed concentration time series is subsequently used with its paired streamflow time series to compute suspended-sediment loads by standard U.S. Geological Survey techniques. Once an acceptable regression model is developed, it can be used to compute suspended-sediment concentration beyond the period of record used in model development with proper ongoing collection and analysis of calibration samples. Regression models to compute suspended-sediment concentrations are generally site specific and should never be considered static, but they represent a set period in a continually dynamic system in which additional data will help verify any change in sediment load, type, and source.

Geochemistry of lavas from Taal volcano, southwestern Luzon, Philippines: evidence for multiple magma supply systems and mantle source heterogeneity

USGS Publications Warehouse

Miklius, Asta; Flower, M.F.J.; Huijsmans, J.P.P.; Mukasa, S.B.; Castillo, P.

1991-01-01

Taal lava series can be distinguished from each other by differences in major and trace element trends and trace element ratios, indicating multiple magmatic systems associated with discrete centers in time and space. On Volcano Island, contemporaneous lava series range from typically calc-alkaline to iron-enriched. Major and trace element variation in these series can be modelled by fractionation of similar assemblages, with early fractionation of titano-magnetite in less iron-enriched series. However, phase compositional and petrographic evidence of mineral-liquid disequilibrium suggests that magma mixing played an important role in the evolution of these series. -from Authors

Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting

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

Sun, Yannan; Hou, Zhangshuan; Meng, Da

2016-07-17

In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.

Time Series Model Identification by Estimating Information, Memory, and Quantiles.

DTIC Science & Technology

1983-07-01

Standards, Sect. D, 68D, 937-951. Parzen, Emanuel (1969) "Multiple time series modeling" Multivariate Analysis - II, edited by P. Krishnaiah , Academic... Krishnaiah , North Holland: Amsterdam, 283-295. Parzen, Emanuel (1979) "Forecasting and Whitening Filter Estimation" TIMS Studies in the Management...principle. Applications of Statistics, P. R. Krishnaiah , ed. North Holland: Amsterdam, 27-41. Box, G. E. P. and Jenkins, G. M. (1970) Time Series Analysis

PSO-MISMO modeling strategy for multistep-ahead time series prediction.

PubMed

Bao, Yukun; Xiong, Tao; Hu, Zhongyi

2014-05-01

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

Refined composite multiscale weighted-permutation entropy of financial time series

NASA Astrophysics Data System (ADS)

Zhang, Yongping; Shang, Pengjian

2018-04-01

For quantifying the complexity of nonlinear systems, multiscale weighted-permutation entropy (MWPE) has recently been proposed. MWPE has incorporated amplitude information and been applied to account for the multiple inherent dynamics of time series. However, MWPE may be unreliable, because its estimated values show large fluctuation for slight variation of the data locations, and a significant distinction only for the different length of time series. Therefore, we propose the refined composite multiscale weighted-permutation entropy (RCMWPE). By comparing the RCMWPE results with other methods' results on both synthetic data and financial time series, RCMWPE method shows not only the advantages inherited from MWPE but also lower sensitivity to the data locations, more stable and much less dependent on the length of time series. Moreover, we present and discuss the results of RCMWPE method on the daily price return series from Asian and European stock markets. There are significant differences between Asian markets and European markets, and the entropy values of Hang Seng Index (HSI) are close to but higher than those of European markets. The reliability of the proposed RCMWPE method has been supported by simulations on generated and real data. It could be applied to a variety of fields to quantify the complexity of the systems over multiple scales more accurately.

A simple and fast representation space for classifying complex time series

NASA Astrophysics Data System (ADS)

Zunino, Luciano; Olivares, Felipe; Bariviera, Aurelio F.; Rosso, Osvaldo A.

2017-03-01

In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

77 FR 69508 - Inservice Inspection of Prestressed Concrete Containment Structures With Grouted Tendons

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-19

... real-time, multiple-strategy approach (i.e., appropriate grout design and installation, installed... is available in ADAMS) is provided the first time that a document is referenced. Revision 2 of... ``Regulatory Guide'' series. This series was developed to describe and make available to the public information...

MULTIVARIATE STATISTICAL MODELS FOR EFFECTS OF PM AND COPOLLUTANTS IN A DAILY TIME SERIES EPIDEMIOLOGY STUDY

EPA Science Inventory

Most analyses of daily time series epidemiology data relate mortality or morbidity counts to PM and other air pollutants by means of single-outcome regression models using multiple predictors, without taking into account the complex statistical structure of the predictor variable...

Short Term Rain Prediction For Sustainability of Tanks in the Tropic Influenced by Shadow Rains

NASA Astrophysics Data System (ADS)

Suresh, S.

2007-07-01

Rainfall and flow prediction, adapting the Venkataraman single time series approach and Wiener multiple time series approach were conducted for Aralikottai tank system, and Kothamangalam tank system, Tamilnadu, India. The results indicated that the raw prediction of daily values is closer to actual values than trend identified predictions. The sister seasonal time series were more amenable for prediction than whole parent time series. Venkataraman single time approach was more suited for rainfall prediction. Wiener approach proved better for daily prediction of flow based on rainfall. The major conclusion is that the sister seasonal time series of rain and flow have their own identities even though they form part of the whole parent time series. Further studies with other tropical small watersheds are necessary to establish this unique characteristic of independent but not exclusive behavior of seasonal stationary stochastic processes as compared to parent non stationary stochastic processes.

Semi-autonomous remote sensing time series generation tool

NASA Astrophysics Data System (ADS)

Babu, Dinesh Kumar; Kaufmann, Christof; Schmidt, Marco; Dhams, Thorsten; Conrad, Christopher

2017-10-01

High spatial and temporal resolution data is vital for crop monitoring and phenology change detection. Due to the lack of satellite architecture and frequent cloud cover issues, availability of daily high spatial data is still far from reality. Remote sensing time series generation of high spatial and temporal data by data fusion seems to be a practical alternative. However, it is not an easy process, since it involves multiple steps and also requires multiple tools. In this paper, a framework of Geo Information System (GIS) based tool is presented for semi-autonomous time series generation. This tool will eliminate the difficulties by automating all the steps and enable the users to generate synthetic time series data with ease. Firstly, all the steps required for the time series generation process are identified and grouped into blocks based on their functionalities. Later two main frameworks are created, one to perform all the pre-processing steps on various satellite data and the other one to perform data fusion to generate time series. The two frameworks can be used individually to perform specific tasks or they could be combined to perform both the processes in one go. This tool can handle most of the known geo data formats currently available which makes it a generic tool for time series generation of various remote sensing satellite data. This tool is developed as a common platform with good interface which provides lot of functionalities to enable further development of more remote sensing applications. A detailed description on the capabilities and the advantages of the frameworks are given in this paper.

Advanced interferometric synthetic aperture radar (InSAR) time series analysis using interferograms of multiple-orbit tracks: A case study on Miyake-jima

NASA Astrophysics Data System (ADS)

Ozawa, Taku; Ueda, Hideki

2011-12-01

InSAR time series analysis is an effective tool for detecting spatially and temporally complicated volcanic deformation. To obtain details of such deformation, we developed an advanced InSAR time series analysis using interferograms of multiple-orbit tracks. Considering only right- (or only left-) looking SAR observations, incidence directions for different orbit tracks are mostly included in a common plane. Therefore, slant-range changes in their interferograms can be expressed by two components in the plane. This approach estimates the time series of their components from interferograms of multiple-orbit tracks by the least squares analysis, and higher accuracy is obtained if many interferograms of different orbit tracks are available. Additionally, this analysis can combine interferograms for different incidence angles. In a case study on Miyake-jima, we obtained a deformation time series corresponding to GPS observations from PALSAR interferograms of six orbit tracks. The obtained accuracy was better than that with the SBAS approach, demonstrating its effectiveness. Furthermore, it is expected that higher accuracy would be obtained if SAR observations were carried out more frequently in all orbit tracks. The deformation obtained in the case study indicates uplift along the west coast and subsidence with contraction around the caldera. The speed of the uplift was almost constant, but the subsidence around the caldera decelerated from 2009. A flat deformation source was estimated near sea level under the caldera, implying that deceleration of subsidence was related to interaction between volcanic thermal activity and the aquifer.

Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

PubMed

Henriques, Teresa S; Mariani, Sara; Burykin, Anton; Rodrigues, Filipa; Silva, Tiago F; Goldberger, Ary L

2016-02-09

Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

Parametric, nonparametric and parametric modelling of a chaotic circuit time series

NASA Astrophysics Data System (ADS)

Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.

2000-09-01

The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.

Time-Critical Cooperative Path Following of Multiple UAVs over Time-Varying Networks

DTIC Science & Technology

2011-01-01

Notes in Control and Information Systems Series (K. Y. Pettersen, T. Gravdahl, and H. Nijmeijer, Eds.). Springer-Verlag, 2006. 29M. Breivik , V...Information Systems Series (K. Y. Pettersen, T. Gravdahl, and H. Nijmeijer, Eds.). Springer-Verlag, 2006. 31M. Breivik , E. Hovstein, and T. I. Fossen. Ship

Discovering time-lagged rules from microarray data using gene profile classifiers

PubMed Central

2011-01-01

Background Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes. Results This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations. Conclusions A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation. PMID:21524308

Detection of bifurcations in noisy coupled systems from multiple time series

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

Williamson, Mark S., E-mail: m.s.williamson@exeter.ac.uk; Lenton, Timothy M.

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, themore » possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.« less

Detection of bifurcations in noisy coupled systems from multiple time series

NASA Astrophysics Data System (ADS)

Williamson, Mark S.; Lenton, Timothy M.

2015-03-01

We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, the possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.

Application of a time-series methodology to Federal program allocations. [Modified Box and Jenkins method

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

Bronfman, B. H.

Time-series analysis provides a useful tool in the evaluation of public policy outputs. It is shown that the general Box and Jenkins method, when extended to allow for multiple interrupts, enables researchers simultaneously to examine changes in drift and level of a series, and to select the best fit model for the series. As applied to urban renewal allocations, results show significant changes in the level of the series, corresponding to changes in party control of the Executive. No support is given to the ''incrementalism'' hypotheses as no significant changes in drift are found.

A harmonic linear dynamical system for prominent ECG feature extraction.

PubMed

Thi, Ngoc Anh Nguyen; Yang, Hyung-Jeong; Kim, SunHee; Do, Luu Ngoc

2014-01-01

Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.

DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data.

PubMed

Wang, Zhuo; Jin, Shuilin; Liu, Guiyou; Zhang, Xiurui; Wang, Nan; Wu, Deliang; Hu, Yang; Zhang, Chiping; Jiang, Qinghua; Xu, Li; Wang, Yadong

2017-05-23

The development of single-cell RNA sequencing has enabled profound discoveries in biology, ranging from the dissection of the composition of complex tissues to the identification of novel cell types and dynamics in some specialized cellular environments. However, the large-scale generation of single-cell RNA-seq (scRNA-seq) data collected at multiple time points remains a challenge to effective measurement gene expression patterns in transcriptome analysis. We present an algorithm based on the Dynamic Time Warping score (DTWscore) combined with time-series data, that enables the detection of gene expression changes across scRNA-seq samples and recovery of potential cell types from complex mixtures of multiple cell types. The DTWscore successfully classify cells of different types with the most highly variable genes from time-series scRNA-seq data. The study was confined to methods that are implemented and available within the R framework. Sample datasets and R packages are available at https://github.com/xiaoxiaoxier/DTWscore .

17 CFR 20.5 - Series S filings.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 17 Commodity and Securities Exchanges 1 2012-04-01 2012-04-01 false Series S filings. 20.5 Section... FOR PHYSICAL COMMODITY SWAPS § 20.5 Series S filings. (a) 102S filing. (1) When a counterparty... 102S filing only once for each counterparty, even if such persons at various times have multiple...

Visual analytics techniques for large multi-attribute time series data

NASA Astrophysics Data System (ADS)

Hao, Ming C.; Dayal, Umeshwar; Keim, Daniel A.

2008-01-01

Time series data commonly occur when variables are monitored over time. Many real-world applications involve the comparison of long time series across multiple variables (multi-attributes). Often business people want to compare this year's monthly sales with last year's sales to make decisions. Data warehouse administrators (DBAs) want to know their daily data loading job performance. DBAs need to detect the outliers early enough to act upon them. In this paper, two new visual analytic techniques are introduced: The color cell-based Visual Time Series Line Charts and Maps highlight significant changes over time in a long time series data and the new Visual Content Query facilitates finding the contents and histories of interesting patterns and anomalies, which leads to root cause identification. We have applied both methods to two real-world applications to mine enterprise data warehouse and customer credit card fraud data to illustrate the wide applicability and usefulness of these techniques.

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

DOE PAGES

Steed, Chad A.; Halsey, William; Dehoff, Ryan; ...

2017-02-16

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

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

Steed, Chad A.; Halsey, William; Dehoff, Ryan

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

2011-04-01

Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

A note on an attempt at more efficient Poisson series evaluation. [for lunar libration

NASA Technical Reports Server (NTRS)

Shelus, P. J.; Jefferys, W. H., III

1975-01-01

A substantial reduction has been achieved in the time necessary to compute lunar libration series. The method involves eliminating many of the trigonometric function calls by a suitable transformation and applying a short SNOBOL processor to the FORTRAN coding of the transformed series, which obviates many of the multiplication operations during the course of series evaluation. It is possible to accomplish similar results quite easily with other Poisson series.

Application of time series analysis for assessing reservoir trophic status

Treesearch

Paris Honglay Chen; Ka-Chu Leung

2000-01-01

This study is to develop and apply a practical procedure for the time series analysis of reservoir eutrophication conditions. A multiplicative decomposition method is used to determine the trophic variations including seasonal, circular, long-term and irregular changes. The results indicate that (1) there is a long high peak for seven months from April to October...

Using time series structural characteristics to analyze grain prices in food insecure countries

USGS Publications Warehouse

Davenport, Frank; Funk, Chris

2015-01-01

Two components of food security monitoring are accurate forecasts of local grain prices and the ability to identify unusual price behavior. We evaluated a method that can both facilitate forecasts of cross-country grain price data and identify dissimilarities in price behavior across multiple markets. This method, characteristic based clustering (CBC), identifies similarities in multiple time series based on structural characteristics in the data. Here, we conducted a simulation experiment to determine if CBC can be used to improve the accuracy of maize price forecasts. We then compared forecast accuracies among clustered and non-clustered price series over a rolling time horizon. We found that the accuracy of forecasts on clusters of time series were equal to or worse than forecasts based on individual time series. However, in the following experiment we found that CBC was still useful for price analysis. We used the clusters to explore the similarity of price behavior among Kenyan maize markets. We found that price behavior in the isolated markets of Mandera and Marsabit has become increasingly dissimilar from markets in other Kenyan cities, and that these dissimilarities could not be explained solely by geographic distance. The structural isolation of Mandera and Marsabit that we find in this paper is supported by field studies on food security and market integration in Kenya. Our results suggest that a market with a unique price series (as measured by structural characteristics that differ from neighboring markets) may lack market integration and food security.

Forecasting Non-Stationary Diarrhea, Acute Respiratory Infection, and Malaria Time-Series in Niono, Mali

PubMed Central

Medina, Daniel C.; Findley, Sally E.; Guindo, Boubacar; Doumbia, Seydou

2007-01-01

Background Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods. Methodology/Principal Findings In this longitudinal retrospective (01/1996–06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%. Conclusions/Significance The multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel. PMID:18030322

Forecasting non-stationary diarrhea, acute respiratory infection, and malaria time-series in Niono, Mali.

PubMed

Medina, Daniel C; Findley, Sally E; Guindo, Boubacar; Doumbia, Seydou

2007-11-21

Much of the developing world, particularly sub-Saharan Africa, exhibits high levels of morbidity and mortality associated with diarrhea, acute respiratory infection, and malaria. With the increasing awareness that the aforementioned infectious diseases impose an enormous burden on developing countries, public health programs therein could benefit from parsimonious general-purpose forecasting methods to enhance infectious disease intervention. Unfortunately, these disease time-series often i) suffer from non-stationarity; ii) exhibit large inter-annual plus seasonal fluctuations; and, iii) require disease-specific tailoring of forecasting methods. In this longitudinal retrospective (01/1996-06/2004) investigation, diarrhea, acute respiratory infection of the lower tract, and malaria consultation time-series are fitted with a general-purpose econometric method, namely the multiplicative Holt-Winters, to produce contemporaneous on-line forecasts for the district of Niono, Mali. This method accommodates seasonal, as well as inter-annual, fluctuations and produces reasonably accurate median 2- and 3-month horizon forecasts for these non-stationary time-series, i.e., 92% of the 24 time-series forecasts generated (2 forecast horizons, 3 diseases, and 4 age categories = 24 time-series forecasts) have mean absolute percentage errors circa 25%. The multiplicative Holt-Winters forecasting method: i) performs well across diseases with dramatically distinct transmission modes and hence it is a strong general-purpose forecasting method candidate for non-stationary epidemiological time-series; ii) obliquely captures prior non-linear interactions between climate and the aforementioned disease dynamics thus, obviating the need for more complex disease-specific climate-based parametric forecasting methods in the district of Niono; furthermore, iii) readily decomposes time-series into seasonal components thereby potentially assisting with programming of public health interventions, as well as monitoring of disease dynamics modification. Therefore, these forecasts could improve infectious diseases management in the district of Niono, Mali, and elsewhere in the Sahel.

Filter-based multiscale entropy analysis of complex physiological time series.

PubMed

Xu, Yuesheng; Zhao, Liang

2013-08-01

Multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiological time series. We reinterpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce filter-based multiscale entropy (FME), which filters a time series to generate multiple frequency components, and then we compute the blockwise entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide more flexibility for studying its complexity. Motivated by the heart rate turbulence theory, which suggests that the human heartbeat interval time series can be described in piecewise linear patterns, we propose piecewise linear filter multiscale entropy (PLFME) for the complexity analysis of the time series. Numerical results from PLFME are more robust to data of various lengths than those from MSE. The numerical performance of the adaptive piecewise constant filter multiscale entropy without prior information is comparable to that of PLFME, whose design takes prior information into account.

Homogenising time series: beliefs, dogmas and facts

NASA Astrophysics Data System (ADS)

Domonkos, P.

2011-06-01

In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

A multiple-fan active control wind tunnel for outdoor wind speed and direction simulation

NASA Astrophysics Data System (ADS)

Wang, Jia-Ying; Meng, Qing-Hao; Luo, Bing; Zeng, Ming

2018-03-01

This article presents a new type of active controlled multiple-fan wind tunnel. The wind tunnel consists of swivel plates and arrays of direct current fans, and the rotation speed of each fan and the shaft angle of each swivel plate can be controlled independently for simulating different kinds of outdoor wind fields. To measure the similarity between the simulated wind field and the outdoor wind field, wind speed and direction time series of two kinds of wind fields are recorded by nine two-dimensional ultrasonic anemometers, and then statistical properties of the wind signals in different time scales are analyzed based on the empirical mode decomposition. In addition, the complexity of wind speed and direction time series is also investigated using multiscale entropy and multivariate multiscale entropy. Results suggest that the simulated wind field in the multiple-fan wind tunnel has a high degree of similarity with the outdoor wind field.

Monitoring the Performance of Groups of Formal and Concrete Cognitive Tendency Students Using an Intensive Time-Series Design.

ERIC Educational Resources Information Center

Monk, John S.; And Others

A multiple-group, single-intervention intensive time-series design was used to examine the achievement of an abstract concept, plate tectonics, of students grouped on the basis of cognitive tendency. Two questions were addressed: (1) How do daily achievement patterns differ between formal and concrete cognitive tendency groups when learning an…

A radarsat-2 quad-polarized time series for monitoring crop and soil conditions in Barrax, Spain

USDA-ARS?s Scientific Manuscript database

The European Space Agency (ESA) along with multiple university and agency investigators joined to conduct the AgriSAR Campaign in 2009. The main objective was to analyze a dense time series of RADARSAT-2 quad-pol data to define and quantify the performance of Sentinel-1 and other future ESA C-Band ...

waterData--An R package for retrieval, analysis, and anomaly calculation of daily hydrologic time series data, version 1.0

USGS Publications Warehouse

Ryberg, Karen R.; Vecchia, Aldo V.

2012-01-01

Hydrologic time series data and associated anomalies (multiple components of the original time series representing variability at longer-term and shorter-term time scales) are useful for modeling trends in hydrologic variables, such as streamflow, and for modeling water-quality constituents. An R package, called waterData, has been developed for importing daily hydrologic time series data from U.S. Geological Survey streamgages into the R programming environment. In addition to streamflow, data retrieval may include gage height and continuous physical property data, such as specific conductance, pH, water temperature, turbidity, and dissolved oxygen. The package allows for importing daily hydrologic data into R, plotting the data, fixing common data problems, summarizing the data, and the calculation and graphical presentation of anomalies.

Multiple-time scales analysis of physiological time series under neural control

NASA Technical Reports Server (NTRS)

Peng, C. K.; Hausdorff, J. M.; Havlin, S.; Mietus, J. E.; Stanley, H. E.; Goldberger, A. L.

1998-01-01

We discuss multiple-time scale properties of neurophysiological control mechanisms, using heart rate and gait regulation as model systems. We find that scaling exponents can be used as prognostic indicators. Furthermore, detection of more subtle degradation of scaling properties may provide a novel early warning system in subjects with a variety of pathologies including those at high risk of sudden death.

A KST framework for correlation network construction from time series signals

NASA Astrophysics Data System (ADS)

Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

2018-04-01

A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.

Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

ERIC Educational Resources Information Center

Choi, Kilchan

2011-01-01

This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

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

PubMed

Liu, Siwei; Molenaar, Peter C M

2014-12-01

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

A method for analyzing temporal patterns of variability of a time series from Poincare plots.

PubMed

Fishman, Mikkel; Jacono, Frank J; Park, Soojin; Jamasebi, Reza; Thungtong, Anurak; Loparo, Kenneth A; Dick, Thomas E

2012-07-01

The Poincaré plot is a popular two-dimensional, time series analysis tool because of its intuitive display of dynamic system behavior. Poincaré plots have been used to visualize heart rate and respiratory pattern variabilities. However, conventional quantitative analysis relies primarily on statistical measurements of the cumulative distribution of points, making it difficult to interpret irregular or complex plots. Moreover, the plots are constructed to reflect highly correlated regions of the time series, reducing the amount of nonlinear information that is presented and thereby hiding potentially relevant features. We propose temporal Poincaré variability (TPV), a novel analysis methodology that uses standard techniques to quantify the temporal distribution of points and to detect nonlinear sources responsible for physiological variability. In addition, the analysis is applied across multiple time delays, yielding a richer insight into system dynamics than the traditional circle return plot. The method is applied to data sets of R-R intervals and to synthetic point process data extracted from the Lorenz time series. The results demonstrate that TPV complements the traditional analysis and can be applied more generally, including Poincaré plots with multiple clusters, and more consistently than the conventional measures and can address questions regarding potential structure underlying the variability of a data set.

An improvement of the measurement of time series irreversibility with visibility graph approach

NASA Astrophysics Data System (ADS)

Wu, Zhenyu; Shang, Pengjian; Xiong, Hui

2018-07-01

We propose a method to improve the measure of real-valued time series irreversibility which contains two tools: the directed horizontal visibility graph and the Kullback-Leibler divergence. The degree of time irreversibility is estimated by the Kullback-Leibler divergence between the in and out degree distributions presented in the associated visibility graph. In our work, we reframe the in and out degree distributions by encoding them with different embedded dimensions used in calculating permutation entropy(PE). With this improved method, we can not only estimate time series irreversibility efficiently, but also detect time series irreversibility from multiple dimensions. We verify the validity of our method and then estimate the amount of time irreversibility of series generated by chaotic maps as well as global stock markets over the period 2005-2015. The result shows that the amount of time irreversibility reaches the peak with embedded dimension d = 3 under circumstances of experiment and financial markets.

Writing and applications of fiber Bragg grating arrays

NASA Astrophysics Data System (ADS)

LaRochelle, Sophie; Cortes, Pierre-Yves; Fathallah, H.; Rusch, Leslie A.; Jaafar, H. B.

2000-12-01

Multiple Bragg gratings are written in a single fibre strand with accurate positioning to achieve predetermined time delays between optical channels. Applications of fibre Bragg grating arrays include encoders/decoders with series of identical gratings for optical code-division multiple access.

Coupling Poisson rectangular pulse and multiplicative microcanonical random cascade models to generate sub-daily precipitation timeseries

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Müller, Hannes; Schümberg, Sabine; Zha, Tingting; Maurer, Thomas; Hinz, Christoph

2018-07-01

To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long precipitation time series with high temporal resolution are required. Due to limited availability of observed data such time series are typically obtained from stochastic models. However, most existing rainfall models are limited in their ability to conserve rainfall event statistics which are relevant for hydrological processes. Poisson rectangular pulse models are widely applied to generate long time series of alternating precipitation events durations and mean intensities as well as interstorm period durations. Multiplicative microcanonical random cascade (MRC) models are used to disaggregate precipitation time series from coarse to fine temporal resolution. To overcome the inconsistencies between the temporal structure of the Poisson rectangular pulse model and the MRC model, we developed a new coupling approach by introducing two modifications to the MRC model. These modifications comprise (a) a modified cascade model ("constrained cascade") which preserves the event durations generated by the Poisson rectangular model by constraining the first and last interval of a precipitation event to contain precipitation and (b) continuous sigmoid functions of the multiplicative weights to consider the scale-dependency in the disaggregation of precipitation events of different durations. The constrained cascade model was evaluated in its ability to disaggregate observed precipitation events in comparison to existing MRC models. For that, we used a 20-year record of hourly precipitation at six stations across Germany. The constrained cascade model showed a pronounced better agreement with the observed data in terms of both the temporal pattern of the precipitation time series (e.g. the dry and wet spell durations and autocorrelations) and event characteristics (e.g. intra-event intermittency and intensity fluctuation within events). The constrained cascade model also slightly outperformed the other MRC models with respect to the intensity-frequency relationship. To assess the performance of the coupled Poisson rectangular pulse and constrained cascade model, precipitation events were stochastically generated by the Poisson rectangular pulse model and then disaggregated by the constrained cascade model. We found that the coupled model performs satisfactorily in terms of the temporal pattern of the precipitation time series, event characteristics and the intensity-frequency relationship.

Time irreversibility and intrinsics revealing of series with complex network approach

NASA Astrophysics Data System (ADS)

Xiong, Hui; Shang, Pengjian; Xia, Jianan; Wang, Jing

2018-06-01

In this work, we analyze time series on the basis of the visibility graph algorithm that maps the original series into a graph. By taking into account the all-round information carried by the signals, the time irreversibility and fractal behavior of series are evaluated from a complex network perspective, and considered signals are further classified from different aspects. The reliability of the proposed analysis is supported by numerical simulations on synthesized uncorrelated random noise, short-term correlated chaotic systems and long-term correlated fractal processes, and by the empirical analysis on daily closing prices of eleven worldwide stock indices. Obtained results suggest that finite size has a significant effect on the evaluation, and that there might be no direct relation between the time irreversibility and long-range correlation of series. Similarity and dissimilarity between stock indices are also indicated from respective regional and global perspectives, showing the existence of multiple features of underlying systems.

Seasonality of childhood infectious diseases in Niono, Mali.

PubMed

Findley, S E; Medina, D C; Sogoba, N; Guindo, B; Doumbia, S

2010-01-01

Common childhood diseases vary seasonally in Mali, much of the Sahel, and other parts of the world, yet patterns for multiple diseases have rarely been simultaneously described for extended periods at single locations. In this retrospective longitudinal (1996-2004) investigation, we studied the seasonality of malaria, acute respiratory infection and diarrhoea time-series in the district of Niono, Sahelian Mali. We extracted and analysed seasonal patterns from each time-series with the Multiplicative Holt-Winters and Wavelet Transform methods. Subsequently, we considered hypothetical scenarios where successful prevention and intervention measures reduced disease seasonality by 25 or 50% to assess the impact of health programmes on annual childhood morbidity. The results showed that all three disease time-series displayed remarkable seasonal stability. Malaria, acute respiratory infection and diarrhoea peaked in December, March (and September) and August, respectively. Finally, the annual childhood morbidity stemming from each disease diminished 7-26% in the considered hypothetical scenarios. We concluded that seasonality may assist with guiding the development of integrated seasonal disease calendars for programmatic child health promotion activities.

Spectral decompositions of multiple time series: a Bayesian non-parametric approach.

PubMed

Macaro, Christian; Prado, Raquel

2014-01-01

We consider spectral decompositions of multiple time series that arise in studies where the interest lies in assessing the influence of two or more factors. We write the spectral density of each time series as a sum of the spectral densities associated to the different levels of the factors. We then use Whittle's approximation to the likelihood function and follow a Bayesian non-parametric approach to obtain posterior inference on the spectral densities based on Bernstein-Dirichlet prior distributions. The prior is strategically important as it carries identifiability conditions for the models and allows us to quantify our degree of confidence in such conditions. A Markov chain Monte Carlo (MCMC) algorithm for posterior inference within this class of frequency-domain models is presented.We illustrate the approach by analyzing simulated and real data via spectral one-way and two-way models. In particular, we present an analysis of functional magnetic resonance imaging (fMRI) brain responses measured in individuals who participated in a designed experiment to study pain perception in humans.

Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data.

PubMed

Kim, Jongrae; Bates, Declan G; Postlethwaite, Ian; Heslop-Harrison, Pat; Cho, Kwang-Hyun

2008-05-15

Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles. A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in Dictyostelium discoideum, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized Dictyostelium cells. The software used in this article is available from http://sbie.kaist.ac.kr/software

Delay differential analysis of time series.

PubMed

Lainscsek, Claudia; Sejnowski, Terrence J

2015-03-01

Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time compared with frequency-based methods such as the DFT and cross-spectral analysis.

Multiple Questions Require Multiple Designs: An Evaluation of the 1981 Changes to the AFDC Program.

ERIC Educational Resources Information Center

Hedrick, Terry E.; Shipman, Stephanie L.

1988-01-01

Changes made in 1981 to the Aid to Families with Dependent Children (AFDC) program under the Omnibus Budget Reconciliation Act were evaluated. Multiple quasi-experimental designs (interrupted time series, non-equivalent comparison groups, and simple pre-post designs) used to address evaluation questions illustrate the issues faced by evaluators in…

Development of a Prototype System for Accessing Linked NCES Data. Working Paper Series.

ERIC Educational Resources Information Center

Salvucci, Sameena; Wenck, Stephen; Tyson, James

A project has been developed to advance the capabilities of the National Center for Education Statistics (NCES) to support the dissemination of linked data from multiple surveys, multiple components within a survey, and multiple time points. An essential element of this study is the development of a software prototype system to facilitate NCES…

q-Gaussian distributions and multiplicative stochastic processes for analysis of multiple financial time series

NASA Astrophysics Data System (ADS)

Sato, Aki-Hiro

2010-12-01

This study considers q-Gaussian distributions and stochastic differential equations with both multiplicative and additive noises. In the M-dimensional case a q-Gaussian distribution can be theoretically derived as a stationary probability distribution of the multiplicative stochastic differential equation with both mutually independent multiplicative and additive noises. By using the proposed stochastic differential equation a method to evaluate a default probability under a given risk buffer is proposed.

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Indicator saturation: a novel approach to detect multiple breaks in geodetic time series.

NASA Astrophysics Data System (ADS)

Jackson, L. P.; Pretis, F.; Williams, S. D. P.

2016-12-01

Geodetic time series can record long term trends, quasi-periodic signals at a variety of time scales from days to decades, and sudden breaks due to natural or anthropogenic causes. The causes of breaks range from instrument replacement to earthquakes to unknown (i.e. no attributable cause). Furthermore, breaks can be permanent or short-lived and range at least two orders of magnitude in size (mm to 100's mm). To account for this range of possible signal-characteristics requires a flexible time series method that can distinguish between true and false breaks, outliers and time-varying trends. One such method, Indicator Saturation (IS) comes from the field of econometrics where analysing stochastic signals in these terms is a common problem. The IS approach differs from alternative break detection methods by considering every point in the time series as a break until it is demonstrated statistically that it is not. A linear model is constructed with a break function at every point in time, and all but statistically significant breaks are removed through a general-to-specific model selection algorithm for more variables than observations. The IS method is flexible because it allows multiple breaks of different forms (e.g. impulses, shifts in the mean, and changing trends) to be detected, while simultaneously modelling any underlying variation driven by additional covariates. We apply the IS method to identify breaks in a suite of synthetic GPS time series used for the Detection of Offsets in GPS Experiments (DOGEX). We optimise the method to maximise the ratio of true-positive to false-positive detections, which improves estimates of errors in the long term rates of land motion currently required by the GPS community.

Units Matter

ERIC Educational Resources Information Center

I, Ji Yeong; Dougherty, Barbara J.; Berkaliev, Zaur

2015-01-01

Young children spend a much greater amount of time on practicing multiplication facts compared to understanding the concept of multiplication. When students have long-term, foundational concepts rather than a series of fragmented algorithms or facts, they are more likely to understand and generalize the mathematics. Using generalized models that…

Functional linear models to test for differences in prairie wetland hydraulic gradients

USGS Publications Warehouse

Greenwood, Mark C.; Sojda, Richard S.; Preston, Todd M.; Swayne, David A.; Yang, Wanhong; Voinov, A.A.; Rizzoli, A.; Filatova, T.

2010-01-01

Functional data analysis provides a framework for analyzing multiple time series measured frequently in time, treating each series as a continuous function of time. Functional linear models are used to test for effects on hydraulic gradient functional responses collected from three types of land use in Northeastern Montana at fourteen locations. Penalized regression-splines are used to estimate the underlying continuous functions based on the discretely recorded (over time) gradient measurements. Permutation methods are used to assess the statistical significance of effects. A method for accommodating missing observations in each time series is described. Hydraulic gradients may be an initial and fundamental ecosystem process that responds to climate change. We suggest other potential uses of these methods for detecting evidence of climate change.

Biogeochemistry from Gliders at the Hawaii Ocean Times-Series

NASA Astrophysics Data System (ADS)

Nicholson, D. P.; Barone, B.; Karl, D. M.

2016-02-01

At the Hawaii Ocean Time-series (HOT) autonomous, underwater gliders equipped with biogeochemical sensors observe the oceans for months at a time, sampling spatiotemporal scales missed by the ship-based programs. Over the last decade, glider data augmented by a foundation of time-series observations have shed light on biogeochemical dynamics occuring spatially at meso- and submesoscales and temporally on scales from diel to annual. We present insights gained from the synergy between glider observations, time-series measurements and remote sensing in the subtropical North Pacific. We focus on diel variability observed in dissolved oxygen and bio-optics and approaches to autonomously quantify net community production and gross primary production (GPP) as developed during the 2012 Hawaii Ocean Experiment - DYnamics of Light And Nutrients (HOE-DYLAN). Glider-based GPP measurements were extended to explore the relationship between GPP and mesoscale context over multiple years of Seaglider deployments.

Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data.

PubMed

Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S

2016-06-01

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

FINDING A COMMON DATA REPRESENTATION AND INTERCHANGE APPROACH FOR MULTIMEDIA MODELS

EPA Science Inventory

Within many disciplines, multiple approaches are used to represent and access very similar data (e.g., a time series of values), often due to the lack of commonly accepted standards. When projects must use data from multiple disciplines, the problems quickly compound. Often sig...

Multiple Time Series Node Synchronization Utilizing Ambient Reference

DTIC Science & Technology

2014-12-31

assessment, is the need for fine scale synchronization among communicating nodes and across multiple domains. The severe requirements that Special...processing targeted to performance assessment, is the need for fine scale synchronization among communicating nodes and across multiple domains. The...research community and it is well documented and characterized. The datasets considered from this project (listed below) were used to derive the

A Comparison of Taped Problems with and without a Brief Post-Treatment Assessment on Multiplication Fluency

ERIC Educational Resources Information Center

Bliss, Stacy L.; Skinner, Christopher H.; McCallum, Elizabeth; Saecker, Lee B.; Rowland-Bryant, Emily; Brown, Katie S.

2010-01-01

An adapted alternating treatments design was used to compare the effectiveness of a taped-problems (TP) intervention with TP and an additional immediate assessment (TP + AIA) on the multiplication fluency of six fifth-grade students. During TP, the students listened to a tape playing a series of multiplication problems and answers three times.…

Simulating the complex output of rainfall and hydrological processes using the information contained in large data sets: the Direct Sampling approach.

NASA Astrophysics Data System (ADS)

Oriani, Fabio

2017-04-01

The unpredictable nature of rainfall makes its estimation as much difficult as it is essential to hydrological applications. Stochastic simulation is often considered a convenient approach to asses the uncertainty of rainfall processes, but preserving their irregular behavior and variability at multiple scales is a challenge even for the most advanced techniques. In this presentation, an overview on the Direct Sampling technique [1] and its recent application to rainfall and hydrological data simulation [2, 3] is given. The algorithm, having its roots in multiple-point statistics, makes use of a training data set to simulate the outcome of a process without inferring any explicit probability measure: the data are simulated in time or space by sampling the training data set where a sufficiently similar group of neighbor data exists. This approach allows preserving complex statistical dependencies at different scales with a good approximation, while reducing the parameterization to the minimum. The straights and weaknesses of the Direct Sampling approach are shown through a series of applications to rainfall and hydrological data: from time-series simulation to spatial rainfall fields conditioned by elevation or a climate scenario. In the era of vast databases, is this data-driven approach a valid alternative to parametric simulation techniques? [1] Mariethoz G., Renard P., and Straubhaar J. (2010), The Direct Sampling method to perform multiple-point geostatistical simulations, Water. Rerous. Res., 46(11), http://dx.doi.org/10.1029/2008WR007621 [2] Oriani F., Straubhaar J., Renard P., and Mariethoz G. (2014), Simulation of rainfall time series from different climatic regions using the direct sampling technique, Hydrol. Earth Syst. Sci., 18, 3015-3031, http://dx.doi.org/10.5194/hess-18-3015-2014 [3] Oriani F., Borghi A., Straubhaar J., Mariethoz G., Renard P. (2016), Missing data simulation inside flow rate time-series using multiple-point statistics, Environ. Model. Softw., vol. 86, pp. 264 - 276, http://dx.doi.org/10.1016/j.envsoft.2016.10.002

Analyzing Landsat time-series data across adjacent path/rows and across multiple cycles of FIA: Lessons learned in southern Missouri

Treesearch

Mark Nelson; Sean Healey; W. Keith Moser; Mark Hansen; Warren Cohen; Mark Hatfield; Nancy Thomas; Jeff Masek

2009-01-01

The North American Forest Dynamics (NAFD) Program is assessing disturbance and regrowth in the forests of the continent. These forest dynamics are interpreted from per-pixel estimates of forest biomass, which are produced for a time series of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced TM Plus images. Image data are combined with sample plot data from the...

Non-Markovian properties and multiscale hidden Markovian network buried in single molecule time series

NASA Astrophysics Data System (ADS)

Sultana, Tahmina; Takagi, Hiroaki; Morimatsu, Miki; Teramoto, Hiroshi; Li, Chun-Biu; Sako, Yasushi; Komatsuzaki, Tamiki

2013-12-01

We present a novel scheme to extract a multiscale state space network (SSN) from single-molecule time series. The multiscale SSN is a type of hidden Markov model that takes into account both multiple states buried in the measurement and memory effects in the process of the observable whenever they exist. Most biological systems function in a nonstationary manner across multiple timescales. Combined with a recently established nonlinear time series analysis based on information theory, a simple scheme is proposed to deal with the properties of multiscale and nonstationarity for a discrete time series. We derived an explicit analytical expression of the autocorrelation function in terms of the SSN. To demonstrate the potential of our scheme, we investigated single-molecule time series of dissociation and association kinetics between epidermal growth factor receptor (EGFR) on the plasma membrane and its adaptor protein Ash/Grb2 (Grb2) in an in vitro reconstituted system. We found that our formula successfully reproduces their autocorrelation function for a wide range of timescales (up to 3 s), and the underlying SSNs change their topographical structure as a function of the timescale; while the corresponding SSN is simple at the short timescale (0.033-0.1 s), the SSN at the longer timescales (0.1 s to ˜3 s) becomes rather complex in order to capture multiscale nonstationary kinetics emerging at longer timescales. It is also found that visiting the unbound form of the EGFR-Grb2 system approximately resets all information of history or memory of the process.

Multiparametric evaluation of hindlimb ischemia using time-series indocyanine green fluorescence imaging.

PubMed

Guang, Huizhi; Cai, Chuangjian; Zuo, Simin; Cai, Wenjuan; Zhang, Jiulou; Luo, Jianwen

2017-03-01

Peripheral arterial disease (PAD) can further cause lower limb ischemia. Quantitative evaluation of the vascular perfusion in the ischemic limb contributes to diagnosis of PAD and preclinical development of new drug. In vivo time-series indocyanine green (ICG) fluorescence imaging can noninvasively monitor blood flow and has a deep tissue penetration. The perfusion rate estimated from the time-series ICG images is not enough for the evaluation of hindlimb ischemia. The information relevant to the vascular density is also important, because angiogenesis is an essential mechanism for post-ischemic recovery. In this paper, a multiparametric evaluation method is proposed for simultaneous estimation of multiple vascular perfusion parameters, including not only the perfusion rate but also the vascular perfusion density and the time-varying ICG concentration in veins. The target method is based on a mathematical model of ICG pharmacokinetics in the mouse hindlimb. The regression analysis performed on the time-series ICG images obtained from a dynamic reflectance fluorescence imaging system. The results demonstrate that the estimated multiple parameters are effective to quantitatively evaluate the vascular perfusion and distinguish hypo-perfused tissues from well-perfused tissues in the mouse hindlimb. The proposed multiparametric evaluation method could be useful for PAD diagnosis. The estimated perfusion rate and vascular perfusion density maps (left) and the time-varying ICG concentration in veins of the ankle region (right) of the normal and ischemic hindlimbs. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations.

PubMed

Almog, Assaf; Besamusca, Ferry; MacMahon, Mel; Garlaschelli, Diego

2015-01-01

The mesoscopic organization of complex systems, from financial markets to the brain, is an intermediate between the microscopic dynamics of individual units (stocks or neurons, in the mentioned cases), and the macroscopic dynamics of the system as a whole. The organization is determined by "communities" of units whose dynamics, represented by time series of activity, is more strongly correlated internally than with the rest of the system. Recent studies have shown that the binary projections of various financial and neural time series exhibit nontrivial dynamical features that resemble those of the original data. This implies that a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. Here, we explore whether the binary signatures of multiple time series can replicate the same complex community organization of the financial market, as the original weighted time series. We adopt a method that has been specifically designed to detect communities from cross-correlation matrices of time series data. Our analysis shows that the simpler binary representation leads to a community structure that is almost identical with that obtained using the full weighted representation. These results confirm that binary projections of financial time series contain significant structural information.

Statistical analysis of long-term monitoring data for persistent organic pollutants in the atmosphere at 20 monitoring stations broadly indicates declining concentrations.

PubMed

Kong, Deguo; MacLeod, Matthew; Hung, Hayley; Cousins, Ian T

2014-11-04

During recent decades concentrations of persistent organic pollutants (POPs) in the atmosphere have been monitored at multiple stations worldwide. We used three statistical methods to analyze a total of 748 time series of selected POPs in the atmosphere to determine if there are statistically significant reductions in levels of POPs that have had control actions enacted to restrict or eliminate manufacture, use and emissions. Significant decreasing trends were identified in 560 (75%) of the 748 time series collected from the Arctic, North America, and Europe, indicating that the atmospheric concentrations of these POPs are generally decreasing, consistent with the overall effectiveness of emission control actions. Statistically significant trends in synthetic time series could be reliably identified with the improved Mann-Kendall (iMK) test and the digital filtration (DF) technique in time series longer than 5 years. The temporal trends of new (or emerging) POPs in the atmosphere are often unclear because time series are too short. A statistical detrending method based on the iMK test was not able to identify abrupt changes in the rates of decline of atmospheric POP concentrations encoded into synthetic time series.

Characterizing rainfall of hot arid region by using time-series modeling and sustainability approaches: a case study from Gujarat, India

NASA Astrophysics Data System (ADS)

Machiwal, Deepesh; Kumar, Sanjay; Dayal, Devi

2016-05-01

This study aimed at characterization of rainfall dynamics in a hot arid region of Gujarat, India by employing time-series modeling techniques and sustainability approach. Five characteristics, i.e., normality, stationarity, homogeneity, presence/absence of trend, and persistence of 34-year (1980-2013) period annual rainfall time series of ten stations were identified/detected by applying multiple parametric and non-parametric statistical tests. Furthermore, the study involves novelty of proposing sustainability concept for evaluating rainfall time series and demonstrated the concept, for the first time, by identifying the most sustainable rainfall series following reliability ( R y), resilience ( R e), and vulnerability ( V y) approach. Box-whisker plots, normal probability plots, and histograms indicated that the annual rainfall of Mandvi and Dayapar stations is relatively more positively skewed and non-normal compared with that of other stations, which is due to the presence of severe outlier and extreme. Results of Shapiro-Wilk test and Lilliefors test revealed that annual rainfall series of all stations significantly deviated from normal distribution. Two parametric t tests and the non-parametric Mann-Whitney test indicated significant non-stationarity in annual rainfall of Rapar station, where the rainfall was also found to be non-homogeneous based on the results of four parametric homogeneity tests. Four trend tests indicated significantly increasing rainfall trends at Rapar and Gandhidham stations. The autocorrelation analysis suggested the presence of persistence of statistically significant nature in rainfall series of Bhachau (3-year time lag), Mundra (1- and 9-year time lag), Nakhatrana (9-year time lag), and Rapar (3- and 4-year time lag). Results of sustainability approach indicated that annual rainfall of Mundra and Naliya stations ( R y = 0.50 and 0.44; R e = 0.47 and 0.47; V y = 0.49 and 0.46, respectively) are the most sustainable and dependable compared with that of other stations. The highest values of sustainability index at Mundra (0.120) and Naliya (0.112) stations confirmed the earlier findings of R y- R e- V y approach. In general, annual rainfall of the study area is less reliable, less resilient, and moderately vulnerable, which emphasizes the need of developing suitable strategies for managing water resources of the area on sustainable basis. Finally, it is recommended that multiple statistical tests (at least two) should be used in time-series modeling for making reliable decisions. Moreover, methodology and findings of the sustainability concept in rainfall time series can easily be adopted in other arid regions of the world.

Multiscale Analysis of Time Irreversibility Based on Phase-Space Reconstruction and Horizontal Visibility Graph Approach

NASA Astrophysics Data System (ADS)

Zhang, Yongping; Shang, Pengjian; Xiong, Hui; Xia, Jianan

Time irreversibility is an important property of nonequilibrium dynamic systems. A visibility graph approach was recently proposed, and this approach is generally effective to measure time irreversibility of time series. However, its result may be unreliable when dealing with high-dimensional systems. In this work, we consider the joint concept of time irreversibility and adopt the phase-space reconstruction technique to improve this visibility graph approach. Compared with the previous approach, the improved approach gives a more accurate estimate for the irreversibility of time series, and is more effective to distinguish irreversible and reversible stochastic processes. We also use this approach to extract the multiscale irreversibility to account for the multiple inherent dynamics of time series. Finally, we apply the approach to detect the multiscale irreversibility of financial time series, and succeed to distinguish the time of financial crisis and the plateau. In addition, Asian stock indexes away from other indexes are clearly visible in higher time scales. Simulations and real data support the effectiveness of the improved approach when detecting time irreversibility.

Functional mixed effects spectral analysis

PubMed Central

KRAFTY, ROBERT T.; HALL, MARTICA; GUO, WENSHENG

2011-01-01

SUMMARY In many experiments, time series data can be collected from multiple units and multiple time series segments can be collected from the same unit. This article introduces a mixed effects Cramér spectral representation which can be used to model the effects of design covariates on the second-order power spectrum while accounting for potential correlations among the time series segments collected from the same unit. The transfer function is composed of a deterministic component to account for the population-average effects and a random component to account for the unit-specific deviations. The resulting log-spectrum has a functional mixed effects representation where both the fixed effects and random effects are functions in the frequency domain. It is shown that, when the replicate-specific spectra are smooth, the log-periodograms converge to a functional mixed effects model. A data-driven iterative estimation procedure is offered for the periodic smoothing spline estimation of the fixed effects, penalized estimation of the functional covariance of the random effects, and unit-specific random effects prediction via the best linear unbiased predictor. PMID:26855437

A Proposed Data Fusion Architecture for Micro-Zone Analysis and Data Mining

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

Kevin McCarthy; Milos Manic

Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presentsmore » an architecture designed and used to facilitate the collection of disparate data sets well suited to Time Series analysis as well as other predictive data mining techniques. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.« less

Rainfall disaggregation for urban hydrology: Effects of spatial consistence

NASA Astrophysics Data System (ADS)

Müller, Hannes; Haberlandt, Uwe

2015-04-01

For urban hydrology rainfall time series with a high temporal resolution are crucial. Observed time series of this kind are very short in most cases, so they cannot be used. On the contrary, time series with lower temporal resolution (daily measurements) exist for much longer periods. The objective is to derive time series with a long duration and a high resolution by disaggregating time series of the non-recording stations with information of time series of the recording stations. The multiplicative random cascade model is a well-known disaggregation model for daily time series. For urban hydrology it is often assumed, that a day consists of only 1280 minutes in total as starting point for the disaggregation process. We introduce a new variant for the cascade model, which is functional without this assumption and also outperforms the existing approach regarding time series characteristics like wet and dry spell duration, average intensity, fraction of dry intervals and extreme value representation. However, in both approaches rainfall time series of different stations are disaggregated without consideration of surrounding stations. This yields in unrealistic spatial patterns of rainfall. We apply a simulated annealing algorithm that has been used successfully for hourly values before. Relative diurnal cycles of the disaggregated time series are resampled to reproduce the spatial dependence of rainfall. To describe spatial dependence we use bivariate characteristics like probability of occurrence, continuity ratio and coefficient of correlation. Investigation area is a sewage system in Northern Germany. We show that the algorithm has the capability to improve spatial dependence. The influence of the chosen disaggregation routine and the spatial dependence on overflow occurrences and volumes of the sewage system will be analyzed.

Multifractal analysis of time series generated by discrete Ito equations

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

Telesca, Luciano; Czechowski, Zbigniew; Lovallo, Michele

2015-06-15

In this study, we show that discrete Ito equations with short-tail Gaussian marginal distribution function generate multifractal time series. The multifractality is due to the nonlinear correlations, which are hidden in Markov processes and are generated by the interrelation between the drift and the multiplicative stochastic forces in the Ito equation. A link between the range of the generalized Hurst exponents and the mean of the squares of all averaged net forces is suggested.

Detection of Undocumented Changepoints Using Multiple Test Statistics and Composite Reference Series.

NASA Astrophysics Data System (ADS)

Menne, Matthew J.; Williams, Claude N., Jr.

2005-10-01

An evaluation of three hypothesis test statistics that are commonly used in the detection of undocumented changepoints is described. The goal of the evaluation was to determine whether the use of multiple tests could improve undocumented, artificial changepoint detection skill in climate series. The use of successive hypothesis testing is compared to optimal approaches, both of which are designed for situations in which multiple undocumented changepoints may be present. In addition, the importance of the form of the composite climate reference series is evaluated, particularly with regard to the impact of undocumented changepoints in the various component series that are used to calculate the composite.In a comparison of single test changepoint detection skill, the composite reference series formulation is shown to be less important than the choice of the hypothesis test statistic, provided that the composite is calculated from the serially complete and homogeneous component series. However, each of the evaluated composite series is not equally susceptible to the presence of changepoints in its components, which may be erroneously attributed to the target series. Moreover, a reference formulation that is based on the averaging of the first-difference component series is susceptible to random walks when the composition of the component series changes through time (e.g., values are missing), and its use is, therefore, not recommended. When more than one test is required to reject the null hypothesis of no changepoint, the number of detected changepoints is reduced proportionately less than the number of false alarms in a wide variety of Monte Carlo simulations. Consequently, a consensus of hypothesis tests appears to improve undocumented changepoint detection skill, especially when reference series homogeneity is violated. A consensus of successive hypothesis tests using a semihierarchic splitting algorithm also compares favorably to optimal solutions, even when changepoints are not hierarchic.

Metabolic Imaging in Multiple Time Scales

PubMed Central

Ramanujan, V Krishnan

2013-01-01

We report here a novel combination of time-resolved imaging methods for probing mitochondrial metabolism multiple time scales at the level of single cells. By exploiting a mitochondrial membrane potential reporter fluorescence we demonstrate the single cell metabolic dynamics in time scales ranging from milliseconds to seconds to minutes in response to glucose metabolism and mitochondrial perturbations in real time. Our results show that in comparison with normal human mammary epithelial cells, the breast cancer cells display significant alterations in metabolic responses at all measured time scales by single cell kinetics, fluorescence recovery after photobleaching and by scaling analysis of time-series data obtained from mitochondrial fluorescence fluctuations. Furthermore scaling analysis of time-series data in living cells with distinct mitochondrial dysfunction also revealed significant metabolic differences thereby suggesting the broader applicability (e.g. in mitochondrial myopathies and other metabolic disorders) of the proposed strategies beyond the scope of cancer metabolism. We discuss the scope of these findings in the context of developing portable, real-time metabolic measurement systems that can find applications in preclinical and clinical diagnostics. PMID:24013043

Three-dimensional time reversal communications in elastic media

DOE PAGES

Anderson, Brian E.; Ulrich, Timothy J.; Le Bas, Pierre-Yves; ...

2016-02-23

Our letter presents a series of vibrational communication experiments, using time reversal, conducted on a set of cast iron pipes. Time reversal has been used to provide robust, private, and clean communications in many underwater acoustic applications. Also, the use of time reversal to communicate along sections of pipes and through a wall is demonstrated here in order to overcome the complications of dispersion and multiple scattering. These demonstrations utilize a single source transducer and a single sensor, a triaxial accelerometer, enabling multiple channels of simultaneous communication streams to a single location.

Homogenising time series: Beliefs, dogmas and facts

NASA Astrophysics Data System (ADS)

Domonkos, P.

2010-09-01

For obtaining reliable information about climate change and climate variability the use of high quality data series is essentially important, and one basic tool of quality improvements is the statistical homogenisation of observed time series. In the recent decades large number of homogenisation methods has been developed, but the real effects of their application on time series are still not known entirely. The ongoing COST HOME project (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. The author believes that several old theoretical rules have to be re-evaluated. Some examples of the hot questions, a) Statistically detected change-points can be accepted only with the confirmation of metadata information? b) Do semi-hierarchic algorithms for detecting multiple change-points in time series function effectively in practise? c) Is it good to limit the spatial comparison of candidate series with up to five other series in the neighbourhood? Empirical results - those from the COST benchmark, and other experiments too - show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities seem like part of the climatic variability, thus the pure application of classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality, than in raw time series. The developers and users of homogenisation methods have to bear in mind that the eventual purpose of homogenisation is not to find change-points, but to have the observed time series with statistical properties those characterise well the climate change and climate variability.

Multivariate multiscale entropy of financial markets

NASA Astrophysics Data System (ADS)

Lu, Yunfan; Wang, Jun

2017-11-01

In current process of quantifying the dynamical properties of the complex phenomena in financial market system, the multivariate financial time series are widely concerned. In this work, considering the shortcomings and limitations of univariate multiscale entropy in analyzing the multivariate time series, the multivariate multiscale sample entropy (MMSE), which can evaluate the complexity in multiple data channels over different timescales, is applied to quantify the complexity of financial markets. Its effectiveness and advantages have been detected with numerical simulations with two well-known synthetic noise signals. For the first time, the complexity of four generated trivariate return series for each stock trading hour in China stock markets is quantified thanks to the interdisciplinary application of this method. We find that the complexity of trivariate return series in each hour show a significant decreasing trend with the stock trading time progressing. Further, the shuffled multivariate return series and the absolute multivariate return series are also analyzed. As another new attempt, quantifying the complexity of global stock markets (Asia, Europe and America) is carried out by analyzing the multivariate returns from them. Finally we utilize the multivariate multiscale entropy to assess the relative complexity of normalized multivariate return volatility series with different degrees.

Time series smoother for effect detection.

PubMed

You, Cheng; Lin, Dennis K J; Young, S Stanley

2018-01-01

In environmental epidemiology, it is often encountered that multiple time series data with a long-term trend, including seasonality, cannot be fully adjusted by the observed covariates. The long-term trend is difficult to separate from abnormal short-term signals of interest. This paper addresses how to estimate the long-term trend in order to recover short-term signals. Our case study demonstrates that the current spline smoothing methods can result in significant positive and negative cross-correlations from the same dataset, depending on how the smoothing parameters are chosen. To circumvent this dilemma, three classes of time series smoothers are proposed to detrend time series data. These smoothers do not require fine tuning of parameters and can be applied to recover short-term signals. The properties of these smoothers are shown with both a case study using a factorial design and a simulation study using datasets generated from the original dataset. General guidelines are provided on how to discover short-term signals from time series with a long-term trend. The benefit of this research is that a problem is identified and characteristics of possible solutions are determined.

A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

NASA Astrophysics Data System (ADS)

Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

2016-12-01

Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

Riemannian multi-manifold modeling and clustering in brain networks

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Time series smoother for effect detection

PubMed Central

Lin, Dennis K. J.; Young, S. Stanley

2018-01-01

In environmental epidemiology, it is often encountered that multiple time series data with a long-term trend, including seasonality, cannot be fully adjusted by the observed covariates. The long-term trend is difficult to separate from abnormal short-term signals of interest. This paper addresses how to estimate the long-term trend in order to recover short-term signals. Our case study demonstrates that the current spline smoothing methods can result in significant positive and negative cross-correlations from the same dataset, depending on how the smoothing parameters are chosen. To circumvent this dilemma, three classes of time series smoothers are proposed to detrend time series data. These smoothers do not require fine tuning of parameters and can be applied to recover short-term signals. The properties of these smoothers are shown with both a case study using a factorial design and a simulation study using datasets generated from the original dataset. General guidelines are provided on how to discover short-term signals from time series with a long-term trend. The benefit of this research is that a problem is identified and characteristics of possible solutions are determined. PMID:29684033

Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.

PubMed

Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi

2015-02-01

We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.

Exploring total cardiac variability in healthy and pathophysiological subjects using improved refined multiscale entropy.

PubMed

Marwaha, Puneeta; Sunkaria, Ramesh Kumar

2017-02-01

Multiscale entropy (MSE) and refined multiscale entropy (RMSE) techniques are being widely used to evaluate the complexity of a time series across multiple time scales 't'. Both these techniques, at certain time scales (sometimes for the entire time scales, in the case of RMSE), assign higher entropy to the HRV time series of certain pathologies than that of healthy subjects, and to their corresponding randomized surrogate time series. This incorrect assessment of signal complexity may be due to the fact that these techniques suffer from the following limitations: (1) threshold value 'r' is updated as a function of long-term standard deviation and hence unable to explore the short-term variability as well as substantial variability inherited in beat-to-beat fluctuations of long-term HRV time series. (2) In RMSE, entropy values assigned to different filtered scaled time series are the result of changes in variance, but do not completely reflect the real structural organization inherited in original time series. In the present work, we propose an improved RMSE (I-RMSE) technique by introducing a new procedure to set the threshold value by taking into account the period-to-period variability inherited in a signal and evaluated it on simulated and real HRV database. The proposed I-RMSE assigns higher entropy to the age-matched healthy subjects than that of patients suffering from atrial fibrillation, congestive heart failure, sudden cardiac death and diabetes mellitus, for the entire time scales. The results strongly support the reduction in complexity of HRV time series in female group, old-aged, patients suffering from severe cardiovascular and non-cardiovascular diseases, and in their corresponding surrogate time series.

Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations

PubMed Central

Almog, Assaf; Besamusca, Ferry; MacMahon, Mel; Garlaschelli, Diego

2015-01-01

The mesoscopic organization of complex systems, from financial markets to the brain, is an intermediate between the microscopic dynamics of individual units (stocks or neurons, in the mentioned cases), and the macroscopic dynamics of the system as a whole. The organization is determined by “communities” of units whose dynamics, represented by time series of activity, is more strongly correlated internally than with the rest of the system. Recent studies have shown that the binary projections of various financial and neural time series exhibit nontrivial dynamical features that resemble those of the original data. This implies that a significant piece of information is encoded into the binary projection (i.e. the sign) of such increments. Here, we explore whether the binary signatures of multiple time series can replicate the same complex community organization of the financial market, as the original weighted time series. We adopt a method that has been specifically designed to detect communities from cross-correlation matrices of time series data. Our analysis shows that the simpler binary representation leads to a community structure that is almost identical with that obtained using the full weighted representation. These results confirm that binary projections of financial time series contain significant structural information. PMID:26226226

Multimedia Language Learning Courseware: A Design Solution to the Production of a Series of CD-ROMs.

ERIC Educational Resources Information Center

Brett, P. A.; Nash, M.

1999-01-01

Discusses multimedia software and describes the production and the learning rationale of a series of six multimedia CD-ROMs that develop the listening skills of learners of Business English. Describes problems of cost, time, and quality in producing multiple courseware and explains the programming solution which gives control to subject experts.…

Normalization methods in time series of platelet function assays

PubMed Central

Van Poucke, Sven; Zhang, Zhongheng; Roest, Mark; Vukicevic, Milan; Beran, Maud; Lauwereins, Bart; Zheng, Ming-Hua; Henskens, Yvonne; Lancé, Marcus; Marcus, Abraham

2016-01-01

Abstract Platelet function can be quantitatively assessed by specific assays such as light-transmission aggregometry, multiple-electrode aggregometry measuring the response to adenosine diphosphate (ADP), arachidonic acid, collagen, and thrombin-receptor activating peptide and viscoelastic tests such as rotational thromboelastometry (ROTEM). The task of extracting meaningful statistical and clinical information from high-dimensional data spaces in temporal multivariate clinical data represented in multivariate time series is complex. Building insightful visualizations for multivariate time series demands adequate usage of normalization techniques. In this article, various methods for data normalization (z-transformation, range transformation, proportion transformation, and interquartile range) are presented and visualized discussing the most suited approach for platelet function data series. Normalization was calculated per assay (test) for all time points and per time point for all tests. Interquartile range, range transformation, and z-transformation demonstrated the correlation as calculated by the Spearman correlation test, when normalized per assay (test) for all time points. When normalizing per time point for all tests, no correlation could be abstracted from the charts as was the case when using all data as 1 dataset for normalization. PMID:27428217

Multiple imputation for multivariate data with missing and below-threshold measurements: time-series concentrations of pollutants in the Arctic.

PubMed

Hopke, P K; Liu, C; Rubin, D B

2001-03-01

Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week-long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete-data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple-imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets.

A stochastical event-based continuous time step rainfall generator based on Poisson rectangular pulse and microcanonical random cascade models

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Zha, Tingting; Schümberg, Sabine; Müller, Hannes; Maurer, Thomas; Hinz, Christoph

2017-04-01

Rainfall variability within a storm is of major importance for fast hydrological processes, e.g. surface runoff, erosion and solute dissipation from surface soils. To investigate and simulate the impacts of within-storm variabilities on these processes, long time series of rainfall with high resolution are required. Yet, observed precipitation records of hourly or higher resolution are in most cases available only for a small number of stations and only for a few years. To obtain long time series of alternating rainfall events and interstorm periods while conserving the statistics of observed rainfall events, the Poisson model can be used. Multiplicative microcanonical random cascades have been widely applied to disaggregate rainfall time series from coarse to fine temporal resolution. We present a new coupling approach of the Poisson rectangular pulse model and the multiplicative microcanonical random cascade model that preserves the characteristics of rainfall events as well as inter-storm periods. In the first step, a Poisson rectangular pulse model is applied to generate discrete rainfall events (duration and mean intensity) and inter-storm periods (duration). The rainfall events are subsequently disaggregated to high-resolution time series (user-specified, e.g. 10 min resolution) by a multiplicative microcanonical random cascade model. One of the challenges of coupling these models is to parameterize the cascade model for the event durations generated by the Poisson model. In fact, the cascade model is best suited to downscale rainfall data with constant time step such as daily precipitation data. Without starting from a fixed time step duration (e.g. daily), the disaggregation of events requires some modifications of the multiplicative microcanonical random cascade model proposed by Olsson (1998): Firstly, the parameterization of the cascade model for events of different durations requires continuous functions for the probabilities of the multiplicative weights, which we implemented through sigmoid functions. Secondly, the branching of the first and last box is constrained to preserve the rainfall event durations generated by the Poisson rectangular pulse model. The event-based continuous time step rainfall generator has been developed and tested using 10 min and hourly rainfall data of four stations in North-Eastern Germany. The model performs well in comparison to observed rainfall in terms of event durations and mean event intensities as well as wet spell and dry spell durations. It is currently being tested using data from other stations across Germany and in different climate zones. Furthermore, the rainfall event generator is being applied in modelling approaches aimed at understanding the impact of rainfall variability on hydrological processes. Reference Olsson, J.: Evaluation of a scaling cascade model for temporal rainfall disaggregation, Hydrology and Earth System Sciences, 2, 19.30

12 CFR 1402.27 - Aggregating requests.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Information § 1402.27 Aggregating requests. A requester may not file multiple requests at the same time, each... in concert, is attempting to break a request down into a series of requests for the purpose of... reasonable is the time period over which the requests have occurred. ...

12 CFR 1402.27 - Aggregating requests.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Information § 1402.27 Aggregating requests. A requester may not file multiple requests at the same time, each... in concert, is attempting to break a request down into a series of requests for the purpose of... reasonable is the time period over which the requests have occurred. ...

12 CFR 1402.27 - Aggregating requests.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Information § 1402.27 Aggregating requests. A requester may not file multiple requests at the same time, each... in concert, is attempting to break a request down into a series of requests for the purpose of... reasonable is the time period over which the requests have occurred. ...

Ocean Color and Earth Science Data Records

NASA Astrophysics Data System (ADS)

Maritorena, S.

2014-12-01

The development of consistent, high quality time series of biogeochemical products from a single ocean color sensor is a difficult task that involves many aspects related to pre- and post-launch instrument calibration and characterization, stability monitoring and the removal of the contribution of the atmosphere which represents most of the signal measured at the sensor. It is even more challenging to build Climate Data Records (CDRs) or Earth Science Data Records (ESDRs) from multiple sensors as design, technology and methodologies (bands, spectral/spatial resolution, Cal/Val, algorithms) differ from sensor to sensor. NASA MEaSUREs, ESA Climate Change Initiative (CCI) and IOCCG Virtual Constellation are some of the underway efforts that investigate or produce ocean color CDRs or ESDRs from the recent and current global missions (SeaWiFS, MODIS, MERIS). These studies look at key aspects of the development of unified data records from multiple sensors, e.g. the concatenation of the "best" individual records vs. the merging of multiple records or band homogenization vs. spectral diversity. The pros and cons of the different approaches are closely dependent upon the overall science purpose of the data record and its temporal resolution. While monthly data are generally adequate for biogeochemical modeling or to assess decadal trends, higher temporal resolution data records are required to look into changes in phenology or the dynamics of phytoplankton blooms. Similarly, short temporal resolution (daily to weekly) time series may benefit more from being built through the merging of data from multiple sensors while a simple concatenation of data from individual sensors might be better suited for longer temporal resolution (e.g. monthly time series). Several Ocean Color ESDRs were developed as part of the NASA MEaSUREs project. Some of these time series are built by merging the reflectance data from SeaWiFS, MODIS-Aqua and Envisat-MERIS in a semi-analytical ocean color model that generates both merged reflectance and merged biogeochemical products. The benefits and limitations of this merging approach to develop ESDRs will be presented and discussed along with those of alternative approaches.

Quantifying and modeling long-range cross correlations in multiple time series with applications to world stock indices

NASA Astrophysics Data System (ADS)

Wang, Duan; Podobnik, Boris; Horvatić, Davor; Stanley, H. Eugene

2011-04-01

We propose a modified time lag random matrix theory in order to study time-lag cross correlations in multiple time series. We apply the method to 48 world indices, one for each of 48 different countries. We find long-range power-law cross correlations in the absolute values of returns that quantify risk, and find that they decay much more slowly than cross correlations between the returns. The magnitude of the cross correlations constitutes “bad news” for international investment managers who may believe that risk is reduced by diversifying across countries. We find that when a market shock is transmitted around the world, the risk decays very slowly. We explain these time-lag cross correlations by introducing a global factor model (GFM) in which all index returns fluctuate in response to a single global factor. For each pair of individual time series of returns, the cross correlations between returns (or magnitudes) can be modeled with the autocorrelations of the global factor returns (or magnitudes). We estimate the global factor using principal component analysis, which minimizes the variance of the residuals after removing the global trend. Using random matrix theory, a significant fraction of the world index cross correlations can be explained by the global factor, which supports the utility of the GFM. We demonstrate applications of the GFM in forecasting risks at the world level, and in finding uncorrelated individual indices. We find ten indices that are practically uncorrelated with the global factor and with the remainder of the world indices, which is relevant information for world managers in reducing their portfolio risk. Finally, we argue that this general method can be applied to a wide range of phenomena in which time series are measured, ranging from seismology and physiology to atmospheric geophysics.

Quantifying and modeling long-range cross correlations in multiple time series with applications to world stock indices.

PubMed

Wang, Duan; Podobnik, Boris; Horvatić, Davor; Stanley, H Eugene

2011-04-01

We propose a modified time lag random matrix theory in order to study time-lag cross correlations in multiple time series. We apply the method to 48 world indices, one for each of 48 different countries. We find long-range power-law cross correlations in the absolute values of returns that quantify risk, and find that they decay much more slowly than cross correlations between the returns. The magnitude of the cross correlations constitutes "bad news" for international investment managers who may believe that risk is reduced by diversifying across countries. We find that when a market shock is transmitted around the world, the risk decays very slowly. We explain these time-lag cross correlations by introducing a global factor model (GFM) in which all index returns fluctuate in response to a single global factor. For each pair of individual time series of returns, the cross correlations between returns (or magnitudes) can be modeled with the autocorrelations of the global factor returns (or magnitudes). We estimate the global factor using principal component analysis, which minimizes the variance of the residuals after removing the global trend. Using random matrix theory, a significant fraction of the world index cross correlations can be explained by the global factor, which supports the utility of the GFM. We demonstrate applications of the GFM in forecasting risks at the world level, and in finding uncorrelated individual indices. We find ten indices that are practically uncorrelated with the global factor and with the remainder of the world indices, which is relevant information for world managers in reducing their portfolio risk. Finally, we argue that this general method can be applied to a wide range of phenomena in which time series are measured, ranging from seismology and physiology to atmospheric geophysics.

A cluster merging method for time series microarray with production values.

PubMed

Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio

2014-09-01

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

A Method for Oscillation Errors Restriction of SINS Based on Forecasted Time Series.

PubMed

Zhao, Lin; Li, Jiushun; Cheng, Jianhua; Jia, Chun; Wang, Qiufan

2015-07-17

Continuity, real-time, and accuracy are the key technical indexes of evaluating comprehensive performance of a strapdown inertial navigation system (SINS). However, Schuler, Foucault, and Earth periodic oscillation errors significantly cut down the real-time accuracy of SINS. A method for oscillation error restriction of SINS based on forecasted time series is proposed by analyzing the characteristics of periodic oscillation errors. The innovative method gains multiple sets of navigation solutions with different phase delays in virtue of the forecasted time series acquired through the measurement data of the inertial measurement unit (IMU). With the help of curve-fitting based on least square method, the forecasted time series is obtained while distinguishing and removing small angular motion interference in the process of initial alignment. Finally, the periodic oscillation errors are restricted on account of the principle of eliminating the periodic oscillation signal with a half-wave delay by mean value. Simulation and test results show that the method has good performance in restricting the Schuler, Foucault, and Earth oscillation errors of SINS.

A Method for Oscillation Errors Restriction of SINS Based on Forecasted Time Series

PubMed Central

Zhao, Lin; Li, Jiushun; Cheng, Jianhua; Jia, Chun; Wang, Qiufan

2015-01-01

Continuity, real-time, and accuracy are the key technical indexes of evaluating comprehensive performance of a strapdown inertial navigation system (SINS). However, Schuler, Foucault, and Earth periodic oscillation errors significantly cut down the real-time accuracy of SINS. A method for oscillation error restriction of SINS based on forecasted time series is proposed by analyzing the characteristics of periodic oscillation errors. The innovative method gains multiple sets of navigation solutions with different phase delays in virtue of the forecasted time series acquired through the measurement data of the inertial measurement unit (IMU). With the help of curve-fitting based on least square method, the forecasted time series is obtained while distinguishing and removing small angular motion interference in the process of initial alignment. Finally, the periodic oscillation errors are restricted on account of the principle of eliminating the periodic oscillation signal with a half-wave delay by mean value. Simulation and test results show that the method has good performance in restricting the Schuler, Foucault, and Earth oscillation errors of SINS. PMID:26193283

Trends and Patterns in a New Time Series of Natural and Anthropogenic Methane Emissions, 1980-2000

NASA Astrophysics Data System (ADS)

Matthews, E.; Bruhwiler, L.; Themelis, N. J.

2007-12-01

We report on a new time series of methane (CH4) emissions from anthropogenic and natural sources developed for a multi-decadal methane modeling study (see following presentation by Bruhwiler et al.). The emission series extends from 1980 through the early 2000s with annual emissions for all countries has several features distinct from the source histories based on IPCC methods typically employed in modeling the global methane cycle. Fossil fuel emissions rely on 7 fuel-process emission combinations and minimize reliance on highly-uncertain emission factors. Emissions from ruminant animals employ regional profiles of bovine populations that account for the influence of variable age- and size-demographics on emissions and are ~15% lower than other estimates. Waste-related emissions are developed using an approach that avoids using of data-poor emission factors and accounts for impacts of recycling and thermal treatment of waste on diverting material from landfills and CH4 capture at landfill facilities. Emissions from irrigated rice use rice-harvest areas under 3 water-management systems and a new historical data set that analyzes multiple sources for trends in water management since 1980. A time series of emissions from natural wetlands was developed by applying a multiple-regression model derived from full process-based model of Walter with analyzed meteorology from the ERA-40 reanalysis.

Identification of varying time scales in sediment transport using the Hilbert-Huang Transform method

NASA Astrophysics Data System (ADS)

Kuai, Ken Z.; Tsai, Christina W.

2012-02-01

SummarySediment transport processes vary at a variety of time scales - from seconds, hours, days to months and years. Multiple time scales exist in the system of flow, sediment transport and bed elevation change processes. As such, identification and selection of appropriate time scales for flow and sediment processes can assist in formulating a system of flow and sediment governing equations representative of the dynamic interaction of flow and particles at the desired details. Recognizing the importance of different varying time scales in the fluvial processes of sediment transport, we introduce the Hilbert-Huang Transform method (HHT) to the field of sediment transport for the time scale analysis. The HHT uses the Empirical Mode Decomposition (EMD) method to decompose a time series into a collection of the Intrinsic Mode Functions (IMFs), and uses the Hilbert Spectral Analysis (HSA) to obtain instantaneous frequency data. The EMD extracts the variability of data with different time scales, and improves the analysis of data series. The HSA can display the succession of time varying time scales, which cannot be captured by the often-used Fast Fourier Transform (FFT) method. This study is one of the earlier attempts to introduce the state-of-the-art technique for the multiple time sales analysis of sediment transport processes. Three practical applications of the HHT method for data analysis of both suspended sediment and bedload transport time series are presented. The analysis results show the strong impact of flood waves on the variations of flow and sediment time scales at a large sampling time scale, as well as the impact of flow turbulence on those time scales at a smaller sampling time scale. Our analysis reveals that the existence of multiple time scales in sediment transport processes may be attributed to the fractal nature in sediment transport. It can be demonstrated by the HHT analysis that the bedload motion time scale is better represented by the ratio of the water depth to the settling velocity, h/ w. In the final part, HHT results are compared with an available time scale formula in literature.

Reduced rank models for travel time estimation of low order mode pulses.

PubMed

Chandrayadula, Tarun K; Wage, Kathleen E; Worcester, Peter F; Dzieciuch, Matthew A; Mercer, James A; Andrew, Rex K; Howe, Bruce M

2013-10-01

Mode travel time estimation in the presence of internal waves (IWs) is a challenging problem. IWs perturb the sound speed, which results in travel time wander and mode scattering. A standard approach to travel time estimation is to pulse compress the broadband signal, pick the peak of the compressed time series, and average the peak time over multiple receptions to reduce variance. The peak-picking approach implicitly assumes there is a single strong arrival and does not perform well when there are multiple arrivals due to scattering. This article presents a statistical model for the scattered mode arrivals and uses the model to design improved travel time estimators. The model is based on an Empirical Orthogonal Function (EOF) analysis of the mode time series. Range-dependent simulations and data from the Long-range Ocean Acoustic Propagation Experiment (LOAPEX) indicate that the modes are represented by a small number of EOFs. The reduced-rank EOF model is used to construct a travel time estimator based on the Matched Subspace Detector (MSD). Analysis of simulation and experimental data show that the MSDs are more robust to IW scattering than peak picking. The simulation analysis also highlights how IWs affect the mode excitation by the source.

Bayesian wavelet PCA methodology for turbomachinery damage diagnosis under uncertainty

NASA Astrophysics Data System (ADS)

Xu, Shengli; Jiang, Xiaomo; Huang, Jinzhi; Yang, Shuhua; Wang, Xiaofang

2016-12-01

Centrifugal compressor often suffers various defects such as impeller cracking, resulting in forced outage of the total plant. Damage diagnostics and condition monitoring of such a turbomachinery system has become an increasingly important and powerful tool to prevent potential failure in components and reduce unplanned forced outage and further maintenance costs, while improving reliability, availability and maintainability of a turbomachinery system. This paper presents a probabilistic signal processing methodology for damage diagnostics using multiple time history data collected from different locations of a turbomachine, considering data uncertainty and multivariate correlation. The proposed methodology is based on the integration of three advanced state-of-the-art data mining techniques: discrete wavelet packet transform, Bayesian hypothesis testing, and probabilistic principal component analysis. The multiresolution wavelet analysis approach is employed to decompose a time series signal into different levels of wavelet coefficients. These coefficients represent multiple time-frequency resolutions of a signal. Bayesian hypothesis testing is then applied to each level of wavelet coefficient to remove possible imperfections. The ratio of posterior odds Bayesian approach provides a direct means to assess whether there is imperfection in the decomposed coefficients, thus avoiding over-denoising. Power spectral density estimated by the Welch method is utilized to evaluate the effectiveness of Bayesian wavelet cleansing method. Furthermore, the probabilistic principal component analysis approach is developed to reduce dimensionality of multiple time series and to address multivariate correlation and data uncertainty for damage diagnostics. The proposed methodology and generalized framework is demonstrated with a set of sensor data collected from a real-world centrifugal compressor with impeller cracks, through both time series and contour analyses of vibration signal and principal components.

Spatial and Temporal Distribution of Multiple Cropping Indices in the North China Plain Using a Long Remote Sensing Data Time Series.

PubMed

Zhao, Yan; Bai, Linyan; Feng, Jianzhong; Lin, Xiaosong; Wang, Li; Xu, Lijun; Ran, Qiyun; Wang, Kui

2016-04-19

Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province.

Spatial and Temporal Distribution of Multiple Cropping Indices in the North China Plain Using a Long Remote Sensing Data Time Series

PubMed Central

Zhao, Yan; Bai, Linyan; Feng, Jianzhong; Lin, Xiaosong; Wang, Li; Xu, Lijun; Ran, Qiyun; Wang, Kui

2016-01-01

Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province. PMID:27104536

12 CFR 602.16 - Combining requests.

Code of Federal Regulations, 2013 CFR

2013-01-01

....16 Combining requests. You may not avoid paying fees by filing multiple requests at the same time. When FCA reasonably believes that you, alone or with others, are breaking down a request into a series...

12 CFR 602.16 - Combining requests.

Code of Federal Regulations, 2011 CFR

2011-01-01

....16 Combining requests. You may not avoid paying fees by filing multiple requests at the same time. When FCA reasonably believes that you, alone or with others, are breaking down a request into a series...

12 CFR 602.16 - Combining requests.

Code of Federal Regulations, 2012 CFR

2012-01-01

....16 Combining requests. You may not avoid paying fees by filing multiple requests at the same time. When FCA reasonably believes that you, alone or with others, are breaking down a request into a series...

Data imputation analysis for Cosmic Rays time series

NASA Astrophysics Data System (ADS)

Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.

2017-05-01

The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China.

PubMed

Zhang, Hong; Zhang, Sheng; Wang, Ping; Qin, Yuzhe; Wang, Huifeng

2017-07-01

Particulate matter with aerodynamic diameter below 10 μm (PM 10 ) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM 10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM 10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM 10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM 10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM 10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM 10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM 10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM 10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM 10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM 10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM 10 forecasting field.

Comparison of Effects of Teaching English to Thai Undergraduate Teacher-Students through Cross-Curricular Thematic Instruction Program Based on Multiple Intelligence Theory and Conventional Instruction

ERIC Educational Resources Information Center

Rattanavich, Saowalak

2013-01-01

This study is aimed at comparing the effects of teaching English to Thai undergraduate teacher-students through cross-curricular thematic instruction program based on multiple intelligence theory and through conventional instruction. Two experimental groups, which utilized Randomized True Control Group-Pretest-posttest Time Series Design and…

40 CFR 86.132-96 - Vehicle preconditioning.

Code of Federal Regulations, 2013 CFR

2013-07-01

... outdoors awaiting testing, to prevent unusual loading of the canisters. During this time care must be taken... idle again for 1 minute. (H) After the vehicle is turned off the last time, it may be tested for... preconditioned according to the following procedure. For vehicles with multiple canisters in a series...

Using Derivative Estimates to Describe Intraindividual Variability at Multiple Time Scales

ERIC Educational Resources Information Center

Deboeck, Pascal R.; Montpetit, Mignon A.; Bergeman, C. S.; Boker, Steven M.

2009-01-01

The study of intraindividual variability is central to the study of individuals in psychology. Previous research has related the variance observed in repeated measurements (time series) of individuals to traitlike measures that are logically related. Intraindividual measures, such as intraindividual standard deviation or the coefficient of…

Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

PubMed

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

PubMed Central

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

75 FR 3647 - Federal Agricultural Mortgage Corporation Funding and Fiscal Affairs; Risk-Based Capital...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-22

... secured borrowers within each year), the coefficients of variation of the time series of annual default... the method you use, please do not submit your comment multiple times via different methods. You may... component to directly recognize the credit risk on such loans.\\4\\ At the time of the Farm Bill's enactment...

Characterizing and estimating noise in InSAR and InSAR time series with MODIS

USGS Publications Warehouse

Barnhart, William D.; Lohman, Rowena B.

2013-01-01

InSAR time series analysis is increasingly used to image subcentimeter displacement rates of the ground surface. The precision of InSAR observations is often affected by several noise sources, including spatially correlated noise from the turbulent atmosphere. Under ideal scenarios, InSAR time series techniques can substantially mitigate these effects; however, in practice the temporal distribution of InSAR acquisitions over much of the world exhibit seasonal biases, long temporal gaps, and insufficient acquisitions to confidently obtain the precisions desired for tectonic research. Here, we introduce a technique for constraining the magnitude of errors expected from atmospheric phase delays on the ground displacement rates inferred from an InSAR time series using independent observations of precipitable water vapor from MODIS. We implement a Monte Carlo error estimation technique based on multiple (100+) MODIS-based time series that sample date ranges close to the acquisitions times of the available SAR imagery. This stochastic approach allows evaluation of the significance of signals present in the final time series product, in particular their correlation with topography and seasonality. We find that topographically correlated noise in individual interferograms is not spatially stationary, even over short-spatial scales (<10 km). Overall, MODIS-inferred displacements and velocities exhibit errors of similar magnitude to the variability within an InSAR time series. We examine the MODIS-based confidence bounds in regions with a range of inferred displacement rates, and find we are capable of resolving velocities as low as 1.5 mm/yr with uncertainties increasing to ∼6 mm/yr in regions with higher topographic relief.

A tool for NDVI time series extraction from wide-swath remotely sensed images

NASA Astrophysics Data System (ADS)

Li, Zhishan; Shi, Runhe; Zhou, Cong

2015-09-01

Normalized Difference Vegetation Index (NDVI) is one of the most widely used indicators for monitoring the vegetation coverage in land surface. The time series features of NDVI are capable of reflecting dynamic changes of various ecosystems. Calculating NDVI via Moderate Resolution Imaging Spectrometer (MODIS) and other wide-swath remotely sensed images provides an important way to monitor the spatial and temporal characteristics of large-scale NDVI. However, difficulties are still existed for ecologists to extract such information correctly and efficiently because of the problems in several professional processes on the original remote sensing images including radiometric calibration, geometric correction, multiple data composition and curve smoothing. In this study, we developed an efficient and convenient online toolbox for non-remote sensing professionals who want to extract NDVI time series with a friendly graphic user interface. It is based on Java Web and Web GIS technically. Moreover, Struts, Spring and Hibernate frameworks (SSH) are integrated in the system for the purpose of easy maintenance and expansion. Latitude, longitude and time period are the key inputs that users need to provide, and the NDVI time series are calculated automatically.

Time series pCO2 at a coastal mooring: Internal consistency, seasonal cycles, and interannual variability

NASA Astrophysics Data System (ADS)

Reimer, Janet J.; Cai, Wei-Jun; Xue, Liang; Vargas, Rodrigo; Noakes, Scott; Hu, Xinping; Signorini, Sergio R.; Mathis, Jeremy T.; Feely, Richard A.; Sutton, Adrienne J.; Sabine, Christopher; Musielewicz, Sylvia; Chen, Baoshan; Wanninkhof, Rik

2017-08-01

Marine carbonate system monitoring programs often consist of multiple observational methods that include underway cruise data, moored autonomous time series, and discrete water bottle samples. Monitored parameters include all, or some of the following: partial pressure of CO2 of the water (pCO2w) and air, dissolved inorganic carbon (DIC), total alkalinity (TA), and pH. Any combination of at least two of the aforementioned parameters can be used to calculate the others. In this study at the Gray's Reef (GR) mooring in the South Atlantic Bight (SAB) we: examine the internal consistency of pCO2w from underway cruise, moored autonomous time series, and calculated from bottle samples (DIC-TA pairing); describe the seasonal to interannual pCO2w time series variability and air-sea flux (FCO2), as well as describe the potential sources of pCO2w variability; and determine the source/sink for atmospheric pCO2. Over the 8.5 years of GR mooring time series, mooring-underway and mooring-bottle calculated-pCO2w strongly correlate with r-values > 0.90. pCO2w and FCO2 time series follow seasonal thermal patterns; however, seasonal non-thermal processes, such as terrestrial export, net biological production, and air-sea exchange also influence variability. The linear slope of time series pCO2w increases by 5.2 ± 1.4 μatm y-1 with FCO2 increasing 51-70 mmol m-2 y-1. The net FCO2 sign can switch interannually with the magnitude varying greatly. Non-thermal pCO2w is also increasing over the time series, likely indicating that terrestrial export and net biological processes drive the long term pCO2w increase.

Modeling multiple time series annotations as noisy distortions of the ground truth: An Expectation-Maximization approach.

PubMed

Gupta, Rahul; Audhkhasi, Kartik; Jacokes, Zach; Rozga, Agata; Narayanan, Shrikanth

2018-01-01

Studies of time-continuous human behavioral phenomena often rely on ratings from multiple annotators. Since the ground truth of the target construct is often latent, the standard practice is to use ad-hoc metrics (such as averaging annotator ratings). Despite being easy to compute, such metrics may not provide accurate representations of the underlying construct. In this paper, we present a novel method for modeling multiple time series annotations over a continuous variable that computes the ground truth by modeling annotator specific distortions. We condition the ground truth on a set of features extracted from the data and further assume that the annotators provide their ratings as modification of the ground truth, with each annotator having specific distortion tendencies. We train the model using an Expectation-Maximization based algorithm and evaluate it on a study involving natural interaction between a child and a psychologist, to predict confidence ratings of the children's smiles. We compare and analyze the model against two baselines where: (i) the ground truth in considered to be framewise mean of ratings from various annotators and, (ii) each annotator is assumed to bear a distinct time delay in annotation and their annotations are aligned before computing the framewise mean.

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.

Operational Overview for UAS Integration in the NAS Project Flight Test Series 3

NASA Technical Reports Server (NTRS)

Valkov, Steffi B.; Sternberg, Daniel; Marston, Michael

2018-01-01

The National Aeronautics and Space Administration Unmanned Aircraft Systems Integration in the National Airspace System Project has conducted a series of flight tests intended to support the reduction of barriers that prevent unmanned aircraft from flying without the required waivers from the Federal Aviation Administration. The 2015 Flight Test Series 3, supported two separate test configurations. The first configuration investigated the timing of Detect and Avoid alerting thresholds using a radar equipped unmanned vehicle and multiple live intruders flown at varying encounter geometries.

The international performance of healthcare systems in population health: capabilities of pooled cross-sectional time series methods.

PubMed

Reibling, Nadine

2013-09-01

This paper outlines the capabilities of pooled cross-sectional time series methodology for the international comparison of health system performance in population health. It shows how common model specifications can be improved so that they not only better address the specific nature of time series data on population health but are also more closely aligned with our theoretical expectations of the effect of healthcare systems. Three methodological innovations for this field of applied research are discussed: (1) how dynamic models help us understand the timing of effects, (2) how parameter heterogeneity can be used to compare performance across countries, and (3) how multiple imputation can be used to deal with incomplete data. We illustrate these methodological strategies with an analysis of infant mortality rates in 21 OECD countries between 1960 and 2008 using OECD Health Data. Copyright © 2013 The Author. Published by Elsevier Ireland Ltd.. All rights reserved.

Correlation and Stacking of Relative Paleointensity and Oxygen Isotope Data

NASA Astrophysics Data System (ADS)

Lurcock, P. C.; Channell, J. E.; Lee, D.

2012-12-01

The transformation of a depth-series into a time-series is routinely implemented in the geological sciences. This transformation often involves correlation of a depth-series to an astronomically calibrated time-series. Eyeball tie-points with linear interpolation are still regularly used, although these have the disadvantages of being non-repeatable and not based on firm correlation criteria. Two automated correlation methods are compared: the simulated annealing algorithm (Huybers and Wunsch, 2004) and the Match protocol (Lisiecki and Lisiecki, 2002). Simulated annealing seeks to minimize energy (cross-correlation) as "temperature" is slowly decreased. The Match protocol divides records into intervals, applies penalty functions that constrain accumulation rates, and minimizes the sum of the squares of the differences between two series while maintaining the data sequence in each series. Paired relative paleointensity (RPI) and oxygen isotope records, such as those from IODP Site U1308 and/or reference stacks such as LR04 and PISO, are warped using known warping functions, and then the un-warped and warped time-series are correlated to evaluate the efficiency of the correlation methods. Correlations are performed in tandem to simultaneously optimize RPI and oxygen isotope data. Noise spectra are introduced at differing levels to determine correlation efficiency as noise levels change. A third potential method, known as dynamic time warping, involves minimizing the sum of distances between correlated point pairs across the whole series. A "cost matrix" between the two series is analyzed to find a least-cost path through the matrix. This least-cost path is used to nonlinearly map the time/depth of one record onto the depth/time of another. Dynamic time warping can be expanded to more than two dimensions and used to stack multiple time-series. This procedure can improve on arithmetic stacks, which often lose coherent high-frequency content during the stacking process.

Analyzing the effect of selected control policy measures and sociodemographic factors on alcoholic beverage consumption in Europe within the AMPHORA project: statistical methods.

PubMed

Baccini, Michela; Carreras, Giulia

2014-10-01

This paper describes the methods used to investigate variations in total alcoholic beverage consumption as related to selected control intervention policies and other socioeconomic factors (unplanned factors) within 12 European countries involved in the AMPHORA project. The analysis presented several critical points: presence of missing values, strong correlation among the unplanned factors, long-term waves or trends in both the time series of alcohol consumption and the time series of the main explanatory variables. These difficulties were addressed by implementing a multiple imputation procedure for filling in missing values, then specifying for each country a multiple regression model which accounted for time trend, policy measures and a limited set of unplanned factors, selected in advance on the basis of sociological and statistical considerations are addressed. This approach allowed estimating the "net" effect of the selected control policies on alcohol consumption, but not the association between each unplanned factor and the outcome.

Analysis of crude oil markets with improved multiscale weighted permutation entropy

NASA Astrophysics Data System (ADS)

Niu, Hongli; Wang, Jun; Liu, Cheng

2018-03-01

Entropy measures are recently extensively used to study the complexity property in nonlinear systems. Weighted permutation entropy (WPE) can overcome the ignorance of the amplitude information of time series compared with PE and shows a distinctive ability to extract complexity information from data having abrupt changes in magnitude. Improved (or sometimes called composite) multi-scale (MS) method possesses the advantage of reducing errors and improving the accuracy when applied to evaluate multiscale entropy values of not enough long time series. In this paper, we combine the merits of WPE and improved MS to propose the improved multiscale weighted permutation entropy (IMWPE) method for complexity investigation of a time series. Then it is validated effective through artificial data: white noise and 1 / f noise, and real market data of Brent and Daqing crude oil. Meanwhile, the complexity properties of crude oil markets are explored respectively of return series, volatility series with multiple exponents and EEMD-produced intrinsic mode functions (IMFs) which represent different frequency components of return series. Moreover, the instantaneous amplitude and frequency of Brent and Daqing crude oil are analyzed by the Hilbert transform utilized to each IMF.

VizieR Online Data Catalog: Photometry of multiple stars at NAOR&ASV in 2015 (Cvetkovic+, 2017)

NASA Astrophysics Data System (ADS)

Cvetkovic, Z.; Pavlovic, R.; Boeva, S.

2018-05-01

This is the ninth series of CCD observations of double and multiple stars, obtained at the Bulgarian National Astronomical Observatory at Rozhen (NAOR) over five nights. As previously, the CCD camera VersArray 1300B was used, which was attached to the 2 m telescope. For each double or multiple star, five CCD frames in the Johnson B filter and five frames in the Johnson V filter were taken, which enabled us to determine the magnitude difference for these filters. In 2015 at the Astronomical Station at Vidojevica (ASV), over a total of 23 nights, observations were carried out by using the 60 cm telescope with a Cassegrain optical system. This is the fourth observational series at ASV since the work started there in 2011. In the observations we used the Apogee Alta U42 CCD camera whose characteristics can be found in the paper by Cvetkovic et al. (2016, J/AJ/151/58). Every pair was observed five times in the Cousins/Bessel B filter and five times in the Cousins/Bessel V one. (3 data files).

TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

PubMed

Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

2017-12-01

Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. sunkim.bioinfo@snu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Increasing the quality, comparability and accessibility of phytoplankton species composition time-series data

NASA Astrophysics Data System (ADS)

Zingone, Adriana; Harrison, Paul J.; Kraberg, Alexandra; Lehtinen, Sirpa; McQuatters-Gollop, Abigail; O'Brien, Todd; Sun, Jun; Jakobsen, Hans H.

2015-09-01

Phytoplankton diversity and its variation over an extended time scale can provide answers to a wide range of questions relevant to societal needs. These include human health, the safe and sustained use of marine resources and the ecological status of the marine environment, including long-term changes under the impact of multiple stressors. The analysis of phytoplankton data collected at the same place over time, as well as the comparison among different sampling sites, provide key information for assessing environmental change, and evaluating new actions that must be made to reduce human induced pressures on the environment. To achieve these aims, phytoplankton data may be used several decades later by users that have not participated in their production, including automatic data retrieval and analysis. The methods used in phytoplankton species analysis vary widely among research and monitoring groups, while quality control procedures have not been implemented in most cases. Here we highlight some of the main differences in the sampling and analytical procedures applied to phytoplankton analysis and identify critical steps that are required to improve the quality and inter-comparability of data obtained at different sites and/or times. Harmonization of methods may not be a realistic goal, considering the wide range of purposes of phytoplankton time-series data collection. However, we propose that more consistent and detailed metadata and complementary information be recorded and made available along with phytoplankton time-series datasets, including description of the procedures and elements allowing for a quality control of the data. To keep up with the progress in taxonomic research, there is a need for continued training of taxonomists, and for supporting and complementing existing web resources, in order to allow a constant upgrade of knowledge in phytoplankton classification and identification. Efforts towards the improvement of metadata recording, data annotation and quality control procedures will ensure the internal consistency of phytoplankton time series and facilitate their comparability and accessibility, thus strongly increasing the value of the precious information they provide. Ultimately, the sharing of quality controlled data will allow one to recoup the high cost of obtaining the data through the multiple use of the time-series data in various projects over many decades.

Multiple Deltas Built Out Over Time

NASA Image and Video Library

2014-12-08

This diagram depicts a vertical cross section through geological layers deposited by rivers, deltas and lakes. Deposits from a series of successive deltas build out increasingly high in elevation as they migrate toward the center of the basin.

Drug Safety: Managing Multiple Drugs

MedlinePlus

... you take the right pill at the right time. Resources To learn more about your drugs, visit: • www.ConsumerReportsHealth.org/BestBuyDrugs • www.medlineplus.gov This series is produced by Consumers Union and Consumer Reports ...

Comparison of time series models for predicting campylobacteriosis risk in New Zealand.

PubMed

Al-Sakkaf, A; Jones, G

2014-05-01

Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. © 2013 Blackwell Verlag GmbH.

The Many Hazards of Trend Evaluation

NASA Astrophysics Data System (ADS)

Henebry, G. M.; de Beurs, K.; Zhang, X.; Kimball, J. S.; Small, C.

2014-12-01

Given the awareness in the scientific community of global scale drivers such as population growth, globalization, and climatic variation and change, many studies seek to identify temporal patterns in data that may be plausibly related to one or more aspect of global change. Here we explore two questions: "What constitutes a trend in a time series?" and "How can a trend be misinterpreted?" There are manifold hazards—both methodological and psychological—in detecting a trend, quantifying its magnitude, assessing its significance, identifying probable causes, and evaluating the implications of the trend. These hazards can combine to elevate the risk of misinterpreting the trend. In contrast, evaluation of multiple trends within a biogeophysical framework can attenuate the risk of misinterpretation. We review and illustrate these hazards and demonstrate the efficacy of an approach using multiple indicators detecting significant trends (MIDST) applied to time series of remote sensing data products.

Time Series Observations of the 2015 Eclipse of b Persei (not beta Persei) (Abstract)

NASA Astrophysics Data System (ADS)

Collins, D. F.

2016-06-01

(Abstract only) The bright (V = 4.6) ellipsoidal variable b Persei consists of a close non-eclipsing binary pair that shows a nearly sinusoidal light curve with a ~1.5 day period. This system also contains a third star that orbits the binary pair every 702 days. AAVSO observers recently detected the first ever optical eclipse of A-B binary pair by the third star as a series of snapshots (D. Collins, R. Zavala, J. Sanborn - AAVSO Spring Meeting, 2013); abstract published in Collins, JAAVSO, 41, 2, 391 (2013); b Per mis-printed as b Per therein. A follow-up eclipse campaign in mid-January 2015 recorded time-series observations. These new time-series observations clearly show multiple ingress and egress of each component of the binary system by the third star over the eclipse duration of 2 to 3 days. A simulation of the eclipse was created. Orbital and some astrophysical parameters were adjusted within constraints to give a reasonable fit to the observed light curve.

Time-series analysis of multiple foreign exchange rates using time-dependent pattern entropy

NASA Astrophysics Data System (ADS)

Ishizaki, Ryuji; Inoue, Masayoshi

2018-01-01

Time-dependent pattern entropy is a method that reduces variations to binary symbolic dynamics and considers the pattern of symbols in a sliding temporal window. We use this method to analyze the instability of daily variations in multiple foreign exchange rates. The time-dependent pattern entropy of 7 foreign exchange rates (AUD/USD, CAD/USD, CHF/USD, EUR/USD, GBP/USD, JPY/USD, and NZD/USD) was found to be high in the long period after the Lehman shock, and be low in the long period after Mar 2012. We compared the correlation matrix between exchange rates in periods of high and low of the time-dependent pattern entropy.

75 FR 51075 - National Registry of Evidence-Based Programs and Practices (NREPP): Open Submission Period for...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-18

... and pre- and post-intervention assessments; this category includes longitudinal/multiple time series... Registry of Evidence-based Programs and Practices (NREPP) is a voluntary rating system designed to provide... acceptance status at that time. The number of reviews conducted will depend on the availability of funds...

What's on Your Radar Screen? Distance-Rate-Time Problems from NASA

ERIC Educational Resources Information Center

Condon, Gregory W.; Landesman, Miriam F.; Calasanz-Kaiser, Agnes

2006-01-01

This article features NASA's FlyBy Math, a series of six standards-based distance-rate-time investigations in air traffic control. Sixth-grade students--acting as pilots, air traffic controllers, and NASA scientists--conduct an experiment and then use multiple mathematical representations to analyze and solve a problem involving two planes flying…

A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network.

PubMed

Gharehbaghi, Arash; Linden, Maria

2017-10-12

This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk.

A Numerical Method for Calculating the Wave Drag of a Configuration from the Second Derivative of the Area Distribution of a Series of Equivalent Bodies of Revolution

NASA Technical Reports Server (NTRS)

Levy, Lionel L., Jr.; Yoshikawa, Kenneth K.

1959-01-01

A method based on linearized and slender-body theories, which is easily adapted to electronic-machine computing equipment, is developed for calculating the zero-lift wave drag of single- and multiple-component configurations from a knowledge of the second derivative of the area distribution of a series of equivalent bodies of revolution. The accuracy and computational time required of the method to calculate zero-lift wave drag is evaluated relative to another numerical method which employs the Tchebichef form of harmonic analysis of the area distribution of a series of equivalent bodies of revolution. The results of the evaluation indicate that the total zero-lift wave drag of a multiple-component configuration can generally be calculated most accurately as the sum of the zero-lift wave drag of each component alone plus the zero-lift interference wave drag between all pairs of components. The accuracy and computational time required of both methods to calculate total zero-lift wave drag at supersonic Mach numbers is comparable for airplane-type configurations. For systems of bodies of revolution both methods yield similar results with comparable accuracy; however, the present method only requires up to 60 percent of the computing time required of the harmonic-analysis method for two bodies of revolution and less time for a larger number of bodies.

Intrinsic vs. spurious long-range memory in high-frequency records of environmental radioactivity - Critical re-assessment and application to indoor 222Rn concentrations from Coimbra, Portugal

NASA Astrophysics Data System (ADS)

Donner, Reik V.; Potirakis, Stelios M.; Barbosa, Susana M.; Matos, Jose A. O.

2015-04-01

The presence or absence of long-range correlations in environmental radioactivity fluctuations has recently attracted considerable interest. Among a multiplicity of practically relevant applications, identifying and disentangling the environmental factors controlling the variable concentrations of the radioactive noble gas Radon is important for estimating its effect on human health and the efficiency of possible measures for reducing the corresponding exposition. In this work, we present a critical re-assessment of a multiplicity of complementary methods that have been previously applied for evaluating the presence of long-range correlations and fractal scaling in environmental Radon variations with a particular focus on the specific properties of the underlying time series. As an illustrative case study, we subsequently re-analyze two high-frequency records of indoor Radon concentrations from Coimbra, Portugal, each of which spans several months of continuous measurements at a high temporal resolution of five minutes. Our results reveal that at the study site, Radon concentrations exhibit complex multi-scale dynamics with qualitatively different properties at different time-scales: (i) essentially white noise in the high-frequency part (up to time-scales of about one hour), (ii) spurious indications of a non-stationary, apparently long-range correlated process (at time scales between hours and one day) arising from marked periodic components probably related to tidal frequencies, and (iii) low-frequency variability indicating a true long-range dependent process, which might be dominated by a response to meteorological drivers. In the presence of such multi-scale variability, common estimators of long-range memory in time series are necessarily prone to fail if applied to the raw data without previous separation of time-scales with qualitatively different dynamics. We emphasize that similar properties can be found in other types of geophysical time series (for example, tide gauge records), calling for a careful application of time series analysis tools when studying such data.

Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

NASA Astrophysics Data System (ADS)

Zhou, Ya-Tong; Fan, Yu; Chen, Zi-Yi; Sun, Jian-Cheng

2017-05-01

The contribution of this work is twofold: (1) a multimodality prediction method of chaotic time series with the Gaussian process mixture (GPM) model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. (2) An effective sparse hard-cut expectation maximization (SHC-EM) learning algorithm for the GPM model is proposed to improve the prediction performance. SHC-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval. SHC-EM outperforms the traditional variational learning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning. Supported by the National Natural Science Foundation of China under Grant No 60972106, the China Postdoctoral Science Foundation under Grant No 2014M561053, the Humanity and Social Science Foundation of Ministry of Education of China under Grant No 15YJA630108, and the Hebei Province Natural Science Foundation under Grant No E2016202341.

Method and Excel VBA Algorithm for Modeling Master Recession Curve Using Trigonometry Approach.

PubMed

Posavec, Kristijan; Giacopetti, Marco; Materazzi, Marco; Birk, Steffen

2017-11-01

A new method was developed and implemented into an Excel Visual Basic for Applications (VBAs) algorithm utilizing trigonometry laws in an innovative way to overlap recession segments of time series and create master recession curves (MRCs). Based on a trigonometry approach, the algorithm horizontally translates succeeding recession segments of time series, placing their vertex, that is, the highest recorded value of each recession segment, directly onto the appropriate connection line defined by measurement points of a preceding recession segment. The new method and algorithm continues the development of methods and algorithms for the generation of MRC, where the first published method was based on a multiple linear/nonlinear regression model approach (Posavec et al. 2006). The newly developed trigonometry-based method was tested on real case study examples and compared with the previously published multiple linear/nonlinear regression model-based method. The results show that in some cases, that is, for some time series, the trigonometry-based method creates narrower overlaps of the recession segments, resulting in higher coefficients of determination R 2 , while in other cases the multiple linear/nonlinear regression model-based method remains superior. The Excel VBA algorithm for modeling MRC using the trigonometry approach is implemented into a spreadsheet tool (MRCTools v3.0 written by and available from Kristijan Posavec, Zagreb, Croatia) containing the previously published VBA algorithms for MRC generation and separation. All algorithms within the MRCTools v3.0 are open access and available free of charge, supporting the idea of running science on available, open, and free of charge software. © 2017, National Ground Water Association.

A PC-based telemetry system for acquiring and reducing data from multiple PCM streams

NASA Astrophysics Data System (ADS)

Simms, D. A.; Butterfield, C. P.

1991-07-01

The Solar Energy Research Institute's (SERI) Wind Research Program is using Pulse Code Modulation (PCM) Telemetry Data-Acquisition Systems to study horizontal-axis wind turbines. Many PCM systems are combined for use in test installations that require accurate measurements from a variety of different locations. SERI has found them ideal for data-acquisition from multiple wind turbines and meteorological towers in wind parks. A major problem has been in providing the capability to quickly combine and examine incoming data from multiple PCM sources in the field. To solve this problem, SERI has developed a low-cost PC-based PCM Telemetry Data-Reduction System (PC-PCM System) to facilitate quick, in-the-field multiple-channel data analysis. The PC-PCM System consists of two basic components. First, PC-compatible hardware boards are used to decode and combine multiple PCM data streams. Up to four hardware boards can be installed in a single PC, which provides the capability to combine data from four PCM streams directly to PC disk or memory. Each stream can have up to 62 data channels. Second, a software package written for use under DOS was developed to simplify data-acquisition control and management. The software, called the Quick-Look Data Management Program, provides a quick, easy-to-use interface between the PC and multiple PCM data streams. The Quick-Look Data Management Program is a comprehensive menu-driven package used to organize, acquire, process, and display information from incoming PCM data streams. The paper describes both hardware and software aspects of the SERI PC-PCM system, concentrating on features that make it useful in an experiment test environment to quickly examine and verify incoming data from multiple PCM streams. Also discussed are problems and techniques associated with PC-based telemetry data-acquisition, processing, and real-time display.

Structure of public transit costs in the presence of multiple serial correlation

DOT National Transportation Integrated Search

1999-12-01

Most studies indicate that public transit systems operate under increasing returns to capital stock utilization and are significantly overcapitalized. Existing flexible form time series analyses, however, fail to correct for serial correlation. In th...

Statistical Evaluation of Time Series Analysis Techniques

NASA Technical Reports Server (NTRS)

Benignus, V. A.

1973-01-01

The performance of a modified version of NASA's multivariate spectrum analysis program is discussed. A multiple regression model was used to make the revisions. Performance improvements were documented and compared to the standard fast Fourier transform by Monte Carlo techniques.

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.

An Evaluation of Subsurface Plumbing of a Hydrothermal Seep Field and Possible Influence from Local Seismicity from New Time-Series Data Collected at the Davis-Schrimpf Seep Field, Salton Trough, California

NASA Astrophysics Data System (ADS)

Rao, A.; Onderdonk, N.

2016-12-01

The Davis­-Schrimpf Seep Field (DSSF) is a group of approximately 50 geothermal mud seeps (gryphons) in the Salton Trough of southeastern California. Its location puts it in line with the mapped San Andreas Fault, if extended further south, as well as within the poorly-understood Brawley Seismic Zone. Much of the geomorphology, geochemistry, and other characteristics of the DSSF have been analyzed, but its subsurface structure remains unknown. Here we present data and interpretations from five new temperature time­series from four separate gryphons at the DSSF, and compare them both amongst themselves, and within the context of all previously collected data to identify possible patterns constraining the subsurface dynamics. Simultaneously collected time-series from different seeps were cross-correlated to quantify similarity. All years' time-series were checked against the record of local seismicity to identify any seismic influence on temperature excursions. Time-series captured from the same feature in different years were statistically summarized and the results plotted to examine their evolution over time. We found that adjacent vents often alternate in temperature, suggesting a switching of flow path of the erupted mud at the scale of a few meters or less. Noticeable warming over time was observed in most of the features with time-series covering multiple years. No synchronicity was observed between DSSF features' temperature excursions, and seismic events within a 24 kilometer radius covering most of the width of the surrounding Salton Trough.

Multiple Acquisition InSAR Analysis: Persistent Scatterer and Small Baseline Approaches

NASA Astrophysics Data System (ADS)

Hooper, A.

2006-12-01

InSAR techniques that process data from multiple acquisitions enable us to form time series of deformation and also allow us to reduce error terms present in single interferograms. There are currently two broad categories of methods that deal with multiple images: persistent scatterer methods and small baseline methods. The persistent scatterer approach relies on identifying pixels whose scattering properties vary little with time and look angle. Pixels that are dominated by a singular scatterer best meet these criteria; therefore, images are processed at full resolution to both increase the chance of there being only one dominant scatterer present, and to reduce the contribution from other scatterers within each pixel. In images where most pixels contain multiple scatterers of similar strength, even at the highest possible resolution, the persistent scatterer approach is less optimal, as the scattering characteristics of these pixels vary substantially with look angle. In this case, an approach that interferes only pairs of images for which the difference in look angle is small makes better sense, and resolution can be sacrificed to reduce the effects of the look angle difference by band-pass filtering. This is the small baseline approach. Existing small baseline methods depend on forming a series of multilooked interferograms and unwrapping each one individually. This approach fails to take advantage of two of the benefits of processing multiple acquisitions, however, which are usually embodied in persistent scatterer methods: the ability to find and extract the phase for single-look pixels with good signal-to-noise ratio that are surrounded by noisy pixels, and the ability to unwrap more robustly in three dimensions, the third dimension being that of time. We have developed, therefore, a new small baseline method to select individual single-look pixels that behave coherently in time, so that isolated stable pixels may be found. After correction for various error terms, the phase values of the selected pixels are unwrapped using a new three-dimensional algorithm. We apply our small baseline method to an area in southern Iceland that includes Katla and Eyjafjallajökull volcanoes, and retrieve a time series of deformation that shows transient deformation due to intrusion of magma beneath Eyjafjallajökull. We also process the data using the Stanford method for persistent scatterers (StaMPS) for comparison.

Dealing with Multiple Solutions in Structural Vector Autoregressive Models.

PubMed

Beltz, Adriene M; Molenaar, Peter C M

2016-01-01

Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.

Multiscale characterization and prediction of monsoon rainfall in India using Hilbert-Huang transform and time-dependent intrinsic correlation analysis

NASA Astrophysics Data System (ADS)

Adarsh, S.; Reddy, M. Janga

2017-07-01

In this paper, the Hilbert-Huang transform (HHT) approach is used for the multiscale characterization of All India Summer Monsoon Rainfall (AISMR) time series and monsoon rainfall time series from five homogeneous regions in India. The study employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for multiscale decomposition of monsoon rainfall in India and uses the Normalized Hilbert Transform and Direct Quadrature (NHT-DQ) scheme for the time-frequency characterization. The cross-correlation analysis between orthogonal modes of All India monthly monsoon rainfall time series and that of five climate indices such as Quasi Biennial Oscillation (QBO), El Niño Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multi Decadal Oscillation (AMO), and Equatorial Indian Ocean Oscillation (EQUINOO) in the time domain showed that the links of different climate indices with monsoon rainfall are expressed well only for few low-frequency modes and for the trend component. Furthermore, this paper investigated the hydro-climatic teleconnection of ISMR in multiple time scales using the HHT-based running correlation analysis technique called time-dependent intrinsic correlation (TDIC). The results showed that both the strength and nature of association between different climate indices and ISMR vary with time scale. Stemming from this finding, a methodology employing Multivariate extension of EMD and Stepwise Linear Regression (MEMD-SLR) is proposed for prediction of monsoon rainfall in India. The proposed MEMD-SLR method clearly exhibited superior performance over the IMD operational forecast, M5 Model Tree (MT), and multiple linear regression methods in ISMR predictions and displayed excellent predictive skill during 1989-2012 including the four extreme events that have occurred during this period.

Generalized multiplicative error models: Asymptotic inference and empirical analysis

NASA Astrophysics Data System (ADS)

Li, Qian

This dissertation consists of two parts. The first part focuses on extended Multiplicative Error Models (MEM) that include two extreme cases for nonnegative series. These extreme cases are common phenomena in high-frequency financial time series. The Location MEM(p,q) model incorporates a location parameter so that the series are required to have positive lower bounds. The estimator for the location parameter turns out to be the minimum of all the observations and is shown to be consistent. The second case captures the nontrivial fraction of zero outcomes feature in a series and combines a so-called Zero-Augmented general F distribution with linear MEM(p,q). Under certain strict stationary and moment conditions, we establish a consistency and asymptotic normality of the semiparametric estimation for these two new models. The second part of this dissertation examines the differences and similarities between trades in the home market and trades in the foreign market of cross-listed stocks. We exploit the multiplicative framework to model trading duration, volume per trade and price volatility for Canadian shares that are cross-listed in the New York Stock Exchange (NYSE) and the Toronto Stock Exchange (TSX). We explore the clustering effect, interaction between trading variables, and the time needed for price equilibrium after a perturbation for each market. The clustering effect is studied through the use of univariate MEM(1,1) on each variable, while the interactions among duration, volume and price volatility are captured by a multivariate system of MEM(p,q). After estimating these models by a standard QMLE procedure, we exploit the Impulse Response function to compute the calendar time for a perturbation in these variables to be absorbed into price variance, and use common statistical tests to identify the difference between the two markets in each aspect. These differences are of considerable interest to traders, stock exchanges and policy makers.

Trends and Solar Cycle Effects in Temperature Versus Altitude From the Halogen Occultation Experiment for the Mesosphere and Upper Stratosphere

NASA Technical Reports Server (NTRS)

Remsberg, Ellis E.

2009-01-01

Fourteen-year time series of mesospheric and upper stratospheric temperatures from the Halogen Occultation Experiment (HALOE) are analyzed and reported. The data have been binned according to ten-degree wide latitude zones from 40S to 40N and at 10 altitudes from 43 to 80 km-a total of 90 separate time series. Multiple linear regression (MLR) analysis techniques have been applied to those time series. This study focuses on resolving their 11-yr solar cycle (or SC-like) responses and their linear trend terms. Findings for T(z) from HALOE are compared directly with published results from ground-based Rayleigh lidar and rocketsonde measurements. SC-like responses from HALOE compare well with those from lidar station data at low latitudes. The cooling trends from HALOE also agree reasonably well with those from the lidar data for the concurrent decade. Cooling trends of the lower mesosphere from HALOE are not as large as those from rocketsondes and from lidar station time series of the previous two decades, presumably because the changes in the upper stratospheric ozone were near zero during the HALOE time period and did not affect those trends.

Production and Uses of Multi-Decade Geodetic Earth Science Data Records

NASA Astrophysics Data System (ADS)

Bock, Y.; Kedar, S.; Moore, A. W.; Fang, P.; Liu, Z.; Sullivan, A.; Argus, D. F.; Jiang, S.; Marshall, S. T.

2017-12-01

The Solid Earth Science ESDR System (SESES) project funded under the NASA MEaSUREs program produces and disseminates mature, long-term, calibrated and validated, GNSS based Earth Science Data Records (ESDRs) that encompass multiple diverse areas of interest in Earth Science, such as tectonic motion, transient slip and earthquake dynamics, as well as meteorology, climate, and hydrology. The ESDRs now span twenty-five years for the earliest stations and today are available for thousands of global and regional stations. Using a unified metadata database and a combination of GNSS solutions generated by two independent analysis centers, the project currently produces four long-term ESDR's: Geodetic Displacement Time Series: Daily, combined, cleaned and filtered, GIPSY and GAMIT long-term time series of continuous GPS station positions (global and regional) in the latest version of ITRF, automatically updated weekly. Geodetic Velocities: Weekly updated velocity field + velocity field histories in various reference frames; compendium of all model parameters including earthquake catalog, coseismic offsets, and postseismic model parameters (exponential or logarithmic). Troposphere Delay Time Series: Long-term time series of troposphere delay (30-min resolution) at geodetic stations, necessarily estimated during position time series production and automatically updated weekly. Seismogeodetic records for historic earthquakes: High-rate broadband displacement and seismic velocity time series combining 1 Hz GPS displacements and 100 Hz accelerometer data for select large earthquakes and collocated cGPS and seismic instruments from regional networks. We present several recent notable examples of the ESDR's usage: A transient slip study that uses the combined position time series to unravel "tremor-less" slow tectonic transient events. Fault geometry determination from geodetic slip rates. Changes in water resources across California's physiographic provinces at a spatial resolution of 75 km. Retrospective study of a southern California summer monsoon event.

A Python-based interface to examine motions in time series of solar images

NASA Astrophysics Data System (ADS)

Campos-Rozo, J. I.; Vargas Domínguez, S.

2017-10-01

Python is considered to be a mature programming language, besides of being widely accepted as an engaging option for scientific analysis in multiple areas, as will be presented in this work for the particular case of solar physics research. SunPy is an open-source library based on Python that has been recently developed to furnish software tools to solar data analysis and visualization. In this work we present a graphical user interface (GUI) based on Python and Qt to effectively compute proper motions for the analysis of time series of solar data. This user-friendly computing interface, that is intended to be incorporated to the Sunpy library, uses a local correlation tracking technique and some extra tools that allows the selection of different parameters to calculate, vizualize and analyze vector velocity fields of solar data, i.e. time series of solar filtergrams and magnetograms.

Error-based Extraction of States and Energy Landscapes from Experimental Single-Molecule Time-Series

NASA Astrophysics Data System (ADS)

Taylor, J. Nicholas; Li, Chun-Biu; Cooper, David R.; Landes, Christy F.; Komatsuzaki, Tamiki

2015-03-01

Characterization of states, the essential components of the underlying energy landscapes, is one of the most intriguing subjects in single-molecule (SM) experiments due to the existence of noise inherent to the measurements. Here we present a method to extract the underlying state sequences from experimental SM time-series. Taking into account empirical error and the finite sampling of the time-series, the method extracts a steady-state network which provides an approximation of the underlying effective free energy landscape. The core of the method is the application of rate-distortion theory from information theory, allowing the individual data points to be assigned to multiple states simultaneously. We demonstrate the method's proficiency in its application to simulated trajectories as well as to experimental SM fluorescence resonance energy transfer (FRET) trajectories obtained from isolated agonist binding domains of the AMPA receptor, an ionotropic glutamate receptor that is prevalent in the central nervous system.

A scalable database model for multiparametric time series: a volcano observatory case study

NASA Astrophysics Data System (ADS)

Montalto, Placido; Aliotta, Marco; Cassisi, Carmelo; Prestifilippo, Michele; Cannata, Andrea

2014-05-01

The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.

A multidisciplinary database for geophysical time series management

NASA Astrophysics Data System (ADS)

Montalto, P.; Aliotta, M.; Cassisi, C.; Prestifilippo, M.; Cannata, A.

2013-12-01

The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.

Novel Flood Detection and Analysis Method Using Recurrence Property

NASA Astrophysics Data System (ADS)

Wendi, Dadiyorto; Merz, Bruno; Marwan, Norbert

2016-04-01

Temporal changes in flood hazard are known to be difficult to detect and attribute due to multiple drivers that include processes that are non-stationary and highly variable. These drivers, such as human-induced climate change, natural climate variability, implementation of flood defence, river training, or land use change, could impact variably on space-time scales and influence or mask each other. Flood time series may show complex behavior that vary at a range of time scales and may cluster in time. This study focuses on the application of recurrence based data analysis techniques (recurrence plot) for understanding and quantifying spatio-temporal changes in flood hazard in Germany. The recurrence plot is known as an effective tool to visualize the dynamics of phase space trajectories i.e. constructed from a time series by using an embedding dimension and a time delay, and it is known to be effective in analyzing non-stationary and non-linear time series. The emphasis will be on the identification of characteristic recurrence properties that could associate typical dynamic behavior to certain flood situations.

76 FR 67343 - Airworthiness Directives; The Boeing Company Model 737-300, -400, and -500 Series Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-01

... times for those actions. This AD was prompted by reports of cracking in the frame, or in the frame and frame reinforcement, common to the 1.04- inch nominal diameter wire penetration hole intended for wire routing; and recent reports of multiple adjacent frame cracking found before the compliance time required...

Entropic multiple-relaxation-time multirange pseudopotential lattice Boltzmann model for two-phase flow

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

Qin, Feifei; Mazloomi Moqaddam, Ali; Kang, Qinjun

Here, an entropic multiple-relaxation-time lattice Boltzmann approach is coupled to a multirange Shan-Chen pseudopotential model to study the two-phase flow. Compared with previous multiple-relaxation-time multiphase models, this model is stable and accurate for the simulation of a two-phase flow in a much wider range of viscosity and surface tension at a high liquid-vapor density ratio. A stationary droplet surrounded by equilibrium vapor is first simulated to validate this model using the coexistence curve and Laplace’s law. Then, two series of droplet impact behavior, on a liquid film and a flat surface, are simulated in comparison with theoretical or experimental results.more » Droplet impact on a liquid film is simulated for different Reynolds numbers at high Weber numbers. With the increase of the Sommerfeld parameter, onset of splashing is observed and multiple secondary droplets occur. The droplet spreading ratio agrees well with the square root of time law and is found to be independent of Reynolds number. Moreover, shapes of simulated droplets impacting hydrophilic and superhydrophobic flat surfaces show good agreement with experimental observations through the entire dynamic process. The maximum spreading ratio of a droplet impacting the superhydrophobic flat surface is studied for a large range of Weber numbers. Results show that the rescaled maximum spreading ratios are in good agreement with a universal scaling law. This series of simulations demonstrates that the proposed model accurately captures the complex fluid-fluid and fluid-solid interfacial physical processes for a wide range of Reynolds and Weber numbers at high density ratios.« less

Entropic multiple-relaxation-time multirange pseudopotential lattice Boltzmann model for two-phase flow

NASA Astrophysics Data System (ADS)

Qin, Feifei; Mazloomi Moqaddam, Ali; Kang, Qinjun; Derome, Dominique; Carmeliet, Jan

2018-03-01

An entropic multiple-relaxation-time lattice Boltzmann approach is coupled to a multirange Shan-Chen pseudopotential model to study the two-phase flow. Compared with previous multiple-relaxation-time multiphase models, this model is stable and accurate for the simulation of a two-phase flow in a much wider range of viscosity and surface tension at a high liquid-vapor density ratio. A stationary droplet surrounded by equilibrium vapor is first simulated to validate this model using the coexistence curve and Laplace's law. Then, two series of droplet impact behavior, on a liquid film and a flat surface, are simulated in comparison with theoretical or experimental results. Droplet impact on a liquid film is simulated for different Reynolds numbers at high Weber numbers. With the increase of the Sommerfeld parameter, onset of splashing is observed and multiple secondary droplets occur. The droplet spreading ratio agrees well with the square root of time law and is found to be independent of Reynolds number. Moreover, shapes of simulated droplets impacting hydrophilic and superhydrophobic flat surfaces show good agreement with experimental observations through the entire dynamic process. The maximum spreading ratio of a droplet impacting the superhydrophobic flat surface is studied for a large range of Weber numbers. Results show that the rescaled maximum spreading ratios are in good agreement with a universal scaling law. This series of simulations demonstrates that the proposed model accurately captures the complex fluid-fluid and fluid-solid interfacial physical processes for a wide range of Reynolds and Weber numbers at high density ratios.

Entropic multiple-relaxation-time multirange pseudopotential lattice Boltzmann model for two-phase flow

DOE PAGES

Qin, Feifei; Mazloomi Moqaddam, Ali; Kang, Qinjun; ...

2018-03-22

Here, an entropic multiple-relaxation-time lattice Boltzmann approach is coupled to a multirange Shan-Chen pseudopotential model to study the two-phase flow. Compared with previous multiple-relaxation-time multiphase models, this model is stable and accurate for the simulation of a two-phase flow in a much wider range of viscosity and surface tension at a high liquid-vapor density ratio. A stationary droplet surrounded by equilibrium vapor is first simulated to validate this model using the coexistence curve and Laplace’s law. Then, two series of droplet impact behavior, on a liquid film and a flat surface, are simulated in comparison with theoretical or experimental results.more » Droplet impact on a liquid film is simulated for different Reynolds numbers at high Weber numbers. With the increase of the Sommerfeld parameter, onset of splashing is observed and multiple secondary droplets occur. The droplet spreading ratio agrees well with the square root of time law and is found to be independent of Reynolds number. Moreover, shapes of simulated droplets impacting hydrophilic and superhydrophobic flat surfaces show good agreement with experimental observations through the entire dynamic process. The maximum spreading ratio of a droplet impacting the superhydrophobic flat surface is studied for a large range of Weber numbers. Results show that the rescaled maximum spreading ratios are in good agreement with a universal scaling law. This series of simulations demonstrates that the proposed model accurately captures the complex fluid-fluid and fluid-solid interfacial physical processes for a wide range of Reynolds and Weber numbers at high density ratios.« less

An evaluation of Dynamic TOPMODEL for low flow simulation

NASA Astrophysics Data System (ADS)

Coxon, G.; Freer, J. E.; Quinn, N.; Woods, R. A.; Wagener, T.; Howden, N. J. K.

2015-12-01

Hydrological models are essential tools for drought risk management, often providing input to water resource system models, aiding our understanding of low flow processes within catchments and providing low flow predictions. However, simulating low flows and droughts is challenging as hydrological systems often demonstrate threshold effects in connectivity, non-linear groundwater contributions and a greater influence of water resource system elements during low flow periods. These dynamic processes are typically not well represented in commonly used hydrological models due to data and model limitations. Furthermore, calibrated or behavioural models may not be effectively evaluated during more extreme drought periods. A better understanding of the processes that occur during low flows and how these are represented within models is thus required if we want to be able to provide robust and reliable predictions of future drought events. In this study, we assess the performance of dynamic TOPMODEL for low flow simulation. Dynamic TOPMODEL was applied to a number of UK catchments in the Thames region using time series of observed rainfall and potential evapotranspiration data that captured multiple historic droughts over a period of several years. The model performance was assessed against the observed discharge time series using a limits of acceptability framework, which included uncertainty in the discharge time series. We evaluate the models against multiple signatures of catchment low-flow behaviour and investigate differences in model performance between catchments, model diagnostics and for different low flow periods. We also considered the impact of surface water and groundwater abstractions and discharges on the observed discharge time series and how this affected the model evaluation. From analysing the model performance, we suggest future improvements to Dynamic TOPMODEL to improve the representation of low flow processes within the model structure.

Semi-Cooperative Learning in Smart Grid Agents

DTIC Science & Technology

2013-12-01

2009 HOEP and its ACF/PACF diagnostic functions. . . 37 2.21 Residuals from multiplicative seasonal ARIMA model fit on 2009 HOEP. . . . . . . . . . 38...2.22 Typical forecast for the next 72 hours based on the ARIMA model of Eq. 2.14. . . . . . . 40 3.1 Consumption capacity of two small villages over...1 (the training series). . 49 3.4 ARIMA model (Eq. 3.1) forecasts based on the full village 1 time series (top subfigure) and the first 24 hours of

MAKING THE WEASELS WILD AGAIN: ENSURING FUTURE AIR DOMINANCE THROUGH EFFECTIVE SEAD TRAINING

DTIC Science & Technology

2016-06-01

both multi-mission design series (MMDS) and joint SEAD training as well as improve the capabilities of its electronic warfare (EW) ranges in order...USAF units to train for multi-mission design series (MMDS) SEAD operations.14 MMDS training includes the use of multiple USAF airborne platforms...not provided SEAD aircrews with either the quantity or quality of training required to conduct effective operations.2 At that time , Major Jon Norman

The spectrum of psychosis in multiple sclerosis: a clinical case series

PubMed Central

Gilberthorpe, Thomas G; O’Connell, Kara E; Carolan, Alison; Silber, Eli; Brex, Peter A; Sibtain, Naomi A; David, Anthony S

2017-01-01

Psychosis in the context of multiple sclerosis (MS) has previously been reported as a rare occurrence. However, recent epidemiological studies have found prevalence rates of psychosis in MS that are two to three times higher than those in the general population. Untreated psychosis in patients with MS can adversely impact on adherence to MS medication, levels of disability, and quality of life. This retrospective case series describes the spectrum of psychotic disorders occurring in association with MS using demographic, clinical, and neuroimaging data. In the discussion, we highlight the particular diagnostic and treatment challenges that such disorders can pose for clinicians and through our case vignettes provide examples of potential interventions for this complex patient population. PMID:28203081

Estimating vehicle height using homographic projections

DOEpatents

Cunningham, Mark F; Fabris, Lorenzo; Gee, Timothy F; Ghebretati, Jr., Frezghi H; Goddard, James S; Karnowski, Thomas P; Ziock, Klaus-peter

2013-07-16

Multiple homography transformations corresponding to different heights are generated in the field of view. A group of salient points within a common estimated height range is identified in a time series of video images of a moving object. Inter-salient point distances are measured for the group of salient points under the multiple homography transformations corresponding to the different heights. Variations in the inter-salient point distances under the multiple homography transformations are compared. The height of the group of salient points is estimated to be the height corresponding to the homography transformation that minimizes the variations.

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

NASA Technical Reports Server (NTRS)

Rudasill-Neigh, Christopher S.; Bolton, Douglas K.; Diabate, Mouhamad; Williams, Jennifer J.; Carvalhais, Nuno

2014-01-01

Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer's and user's accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km2). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.

Time series analyses of breathing patterns of lung cancer patients using nonlinear dynamical system theory.

PubMed

Tewatia, D K; Tolakanahalli, R P; Paliwal, B R; Tomé, W A

2011-04-07

The underlying requirements for successful implementation of any efficient tumour motion management strategy are regularity and reproducibility of a patient's breathing pattern. The physiological act of breathing is controlled by multiple nonlinear feedback and feed-forward couplings. It would therefore be appropriate to analyse the breathing pattern of lung cancer patients in the light of nonlinear dynamical system theory. The purpose of this paper is to analyse the one-dimensional respiratory time series of lung cancer patients based on nonlinear dynamics and delay coordinate state space embedding. It is very important to select a suitable pair of embedding dimension 'm' and time delay 'τ' when performing a state space reconstruction. Appropriate time delay and embedding dimension were obtained using well-established methods, namely mutual information and the false nearest neighbour method, respectively. Establishing stationarity and determinism in a given scalar time series is a prerequisite to demonstrating that the nonlinear dynamical system that gave rise to the scalar time series exhibits a sensitive dependence on initial conditions, i.e. is chaotic. Hence, once an appropriate state space embedding of the dynamical system has been reconstructed, we show that the time series of the nonlinear dynamical systems under study are both stationary and deterministic in nature. Once both criteria are established, we proceed to calculate the largest Lyapunov exponent (LLE), which is an invariant quantity under time delay embedding. The LLE for all 16 patients is positive, which along with stationarity and determinism establishes the fact that the time series of a lung cancer patient's breathing pattern is not random or irregular, but rather it is deterministic in nature albeit chaotic. These results indicate that chaotic characteristics exist in the respiratory waveform and techniques based on state space dynamics should be employed for tumour motion management.

Multiple GISS AGCM Hindcasts and MSU Versions of 1979-1998

NASA Technical Reports Server (NTRS)

Shah, Kathryn Pierce; Rind, David; Druyan, Leonard; Lonergan, Patrick; Chandler, Mark

1998-01-01

Multiple realizations of the 1979-1998 time period have been simulated by the Goddard Institute for Space Studies Atmospheric General Circulation Model (GISS AGCM) to explore its responsiveness to accumulated forcings, particularly over sensitive agricultural regions. A microwave radiative transfer postprocessor has produced the AGCM's lower tropospheric, tropospheric and lower stratospheric brightness temperature (Tb) time series for correlations with the various Microwave Sounding Unit (MSU) time series available. MSU maps of monthly means and anomalies were also used to assess the AGCM's mean annual cycle and regional variability. Seven realizations by the AGCM were forced by observed sea surface temperatures (sst) through 1992 to gather rough standard deviations associated with internal model variability. Subsequent runs hindcast January 1979 through April 1998 with an accumulation of forcings: observed ssts, greenhouse gases, stratospheric volcanic aerosols. stratospheric and tropospheric ozone and tropospheric sulfate and black carbon aerosols. The goal of narrowing gaps between AGCM and MSU time series was complicated by MSU time series, by Tb simulation concerns and by unforced climatic variability in the AGCM and in the real world. Lower stratospheric Tb correlations between the AGCM and MSU for 1979-1998 reached as high as 0.91 +/-0.16 globally with sst, greenhouse gases, volcanic aerosol, stratospheric ozone forcings and tropospheric aerosols. Mid-tropospheric Tb correlations reached as high as 0.66 +/-.04 globally and 0.84 +/-.02 in the tropics. Oceanic lower tropospheric Tb correlations similarly reached 0.61 +/-.06 globally and 0.79 +/-.02 in the tropics. Of the sensitive agricultural areas considered, Nordeste in northeastern Brazil was simulated best with mid-tropospheric Tb correlations up to 0.75 +/- .03. The two other agricultural regions, in Africa and in the northern mid-latitudes, suffered from higher levels of non-sst variability. Zimbabwe had a maximum mid-tropospheric correlation of 0.54 +/- 0.11 while the U.S. Cornbelt had only 0.25 +/- .10. Precipitation and surface temperature performance are also examined over these regions. Correlations of MSU and AGCM time series mostly improved with addition of explicit atmospheric forcings in zonal bands but not in agricultural regional bins each encompassing only six AGCM gridcells.

Comparison of missing value imputation methods in time series: the case of Turkish meteorological data

NASA Astrophysics Data System (ADS)

Yozgatligil, Ceylan; Aslan, Sipan; Iyigun, Cem; Batmaz, Inci

2013-04-01

This study aims to compare several imputation methods to complete the missing values of spatio-temporal meteorological time series. To this end, six imputation methods are assessed with respect to various criteria including accuracy, robustness, precision, and efficiency for artificially created missing data in monthly total precipitation and mean temperature series obtained from the Turkish State Meteorological Service. Of these methods, simple arithmetic average, normal ratio (NR), and NR weighted with correlations comprise the simple ones, whereas multilayer perceptron type neural network and multiple imputation strategy adopted by Monte Carlo Markov Chain based on expectation-maximization (EM-MCMC) are computationally intensive ones. In addition, we propose a modification on the EM-MCMC method. Besides using a conventional accuracy measure based on squared errors, we also suggest the correlation dimension (CD) technique of nonlinear dynamic time series analysis which takes spatio-temporal dependencies into account for evaluating imputation performances. Depending on the detailed graphical and quantitative analysis, it can be said that although computational methods, particularly EM-MCMC method, are computationally inefficient, they seem favorable for imputation of meteorological time series with respect to different missingness periods considering both measures and both series studied. To conclude, using the EM-MCMC algorithm for imputing missing values before conducting any statistical analyses of meteorological data will definitely decrease the amount of uncertainty and give more robust results. Moreover, the CD measure can be suggested for the performance evaluation of missing data imputation particularly with computational methods since it gives more precise results in meteorological time series.

Advanced methods for modeling water-levels and estimating drawdowns with SeriesSEE, an Excel add-in

USGS Publications Warehouse

Halford, Keith; Garcia, C. Amanda; Fenelon, Joe; Mirus, Benjamin B.

2012-12-21

Water-level modeling is used for multiple-well aquifer tests to reliably differentiate pumping responses from natural water-level changes in wells, or “environmental fluctuations.” Synthetic water levels are created during water-level modeling and represent the summation of multiple component fluctuations, including those caused by environmental forcing and pumping. Pumping signals are modeled by transforming step-wise pumping records into water-level changes by using superimposed Theis functions. Water-levels can be modeled robustly with this Theis-transform approach because environmental fluctuations and pumping signals are simulated simultaneously. Water-level modeling with Theis transforms has been implemented in the program SeriesSEE, which is a Microsoft® Excel add-in. Moving average, Theis, pneumatic-lag, and gamma functions transform time series of measured values into water-level model components in SeriesSEE. Earth tides and step transforms are additional computed water-level model components. Water-level models are calibrated by minimizing a sum-of-squares objective function where singular value decomposition and Tikhonov regularization stabilize results. Drawdown estimates from a water-level model are the summation of all Theis transforms minus residual differences between synthetic and measured water levels. The accuracy of drawdown estimates is limited primarily by noise in the data sets, not the Theis-transform approach. Drawdowns much smaller than environmental fluctuations have been detected across major fault structures, at distances of more than 1 mile from the pumping well, and with limited pre-pumping and recovery data at sites across the United States. In addition to water-level modeling, utilities exist in SeriesSEE for viewing, cleaning, manipulating, and analyzing time-series data.

Merging climate and multi-sensor time-series data in real-time drought monitoring across the U.S.A.

USGS Publications Warehouse

Brown, Jesslyn F.; Miura, T.; Wardlow, B.; Gu, Yingxin

2011-01-01

Droughts occur repeatedly in the United States resulting in billions of dollars of damage. Monitoring and reporting on drought conditions is a necessary function of government agencies at multiple levels. A team of Federal and university partners developed a drought decision- support tool with higher spatial resolution relative to traditional climate-based drought maps. The Vegetation Drought Response Index (VegDRI) indicates general canopy vegetation condition assimilation of climate, satellite, and biophysical data via geospatial modeling. In VegDRI, complementary drought-related data are merged to provide a comprehensive, detailed representation of drought stress on vegetation. Time-series data from daily polar-orbiting earth observing systems [Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS)] providing global measurements of land surface conditions are ingested into VegDRI. Inter-sensor compatibility is required to extend multi-sensor data records; thus, translations were developed using overlapping observations to create consistent, long-term data time series. 

Time delay spectrum conditioner

DOEpatents

Greiner, Norman R.

1980-01-01

A device for delaying specified frequencies of a multiple frequency laser beam. The device separates the multiple frequency beam into a series of spatially separated single frequency beams. The propagation distance of the single frequency beam is subsequently altered to provide the desired delay for each specific frequency. Focusing reflectors can be utilized to provide a simple but nonadjustable system or, flat reflectors with collimating and focusing optics can be utilized to provide an adjustable system.

Dynamic Dependence Analysis : Modeling and Inference of Changing Dependence Among Multiple Time-Series

DTIC Science & Technology

2009-06-01

isolation. In addition to being inherently multi-modal, human perception takes advantages of multiple sources of information within a single modality...restric- tion was reasonable for the applications we looked at. However, consider using a TIM to model a teacher student relationship among moving objects...That is, imagine one teacher object demonstrating a behavior for a student object. The student can observe the teacher and then recreate the behavior

MetaRNA-Seq: An Interactive Tool to Browse and Annotate Metadata from RNA-Seq Studies.

PubMed

Kumar, Pankaj; Halama, Anna; Hayat, Shahina; Billing, Anja M; Gupta, Manish; Yousri, Noha A; Smith, Gregory M; Suhre, Karsten

2015-01-01

The number of RNA-Seq studies has grown in recent years. The design of RNA-Seq studies varies from very simple (e.g., two-condition case-control) to very complicated (e.g., time series involving multiple samples at each time point with separate drug treatments). Most of these publically available RNA-Seq studies are deposited in NCBI databases, but their metadata are scattered throughout four different databases: Sequence Read Archive (SRA), Biosample, Bioprojects, and Gene Expression Omnibus (GEO). Although the NCBI web interface is able to provide all of the metadata information, it often requires significant effort to retrieve study- or project-level information by traversing through multiple hyperlinks and going to another page. Moreover, project- and study-level metadata lack manual or automatic curation by categories, such as disease type, time series, case-control, or replicate type, which are vital to comprehending any RNA-Seq study. Here we describe "MetaRNA-Seq," a new tool for interactively browsing, searching, and annotating RNA-Seq metadata with the capability of semiautomatic curation at the study level.

40 CFR Appendix A to Subpart Dddd... - Alternative Procedure To Determine Capture Efficiency From Enclosures Around Hot Presses in the...

Code of Federal Regulations, 2014 CFR

2014-07-01

....2Response Time Test. Conduct this test once prior to each test series. Introduce zero gas into the... analysis. 3.0Definitions 3.1Capture efficiency (CE). The weight per unit time of SF6 entering the control device divided by the weight per unit time of SF6 released through manifolds at multiple locations within...

40 CFR Appendix A to Subpart Dddd... - Alternative Procedure To Determine Capture Efficiency From Enclosures Around Hot Presses in the...

Code of Federal Regulations, 2010 CFR

2010-07-01

....2Response Time Test. Conduct this test once prior to each test series. Introduce zero gas into the... analysis. 3.0Definitions 3.1Capture efficiency (CE). The weight per unit time of SF6 entering the control device divided by the weight per unit time of SF6 released through manifolds at multiple locations within...

40 CFR Appendix A to Subpart Dddd... - Alternative Procedure To Determine Capture Efficiency From Enclosures Around Hot Presses in the...

Code of Federal Regulations, 2013 CFR

2013-07-01

....2Response Time Test. Conduct this test once prior to each test series. Introduce zero gas into the... analysis. 3.0Definitions 3.1Capture efficiency (CE). The weight per unit time of SF6 entering the control device divided by the weight per unit time of SF6 released through manifolds at multiple locations within...

40 CFR Appendix A to Subpart Dddd... - Alternative Procedure To Determine Capture Efficiency From Enclosures Around Hot Presses in the...

Code of Federal Regulations, 2011 CFR

2011-07-01

....2Response Time Test. Conduct this test once prior to each test series. Introduce zero gas into the... analysis. 3.0Definitions 3.1Capture efficiency (CE). The weight per unit time of SF6 entering the control device divided by the weight per unit time of SF6 released through manifolds at multiple locations within...

Simulation of Ground Winds Time Series for the NASA Crew Launch Vehicle (CLV)

NASA Technical Reports Server (NTRS)

Adelfang, Stanley I.

2008-01-01

Simulation of wind time series based on power spectrum density (PSD) and spectral coherence models for ground wind turbulence is described. The wind models, originally developed for the Shuttle program, are based on wind measurements at the NASA 150-m meteorological tower at Cape Canaveral, FL. The current application is for the design and/or protection of the CLV from wind effects during on-pad exposure during periods from as long as days prior to launch, to seconds or minutes just prior to launch and seconds after launch. The evaluation of vehicle response to wind will influence the design and operation of constraint systems for support of the on-pad vehicle. Longitudinal and lateral wind component time series are simulated at critical vehicle locations. The PSD model for wind turbulence is a function of mean wind speed, elevation and temporal frequency. Integration of the PSD equation over a selected frequency range yields the variance of the time series to be simulated. The square root of the PSD defines a low-pass filter that is applied to adjust the components of the Fast Fourier Transform (FFT) of Gaussian white noise. The first simulated time series near the top of the launch vehicle is the inverse transform of the adjusted FFT. Simulation of the wind component time series at the nearest adjacent location (and all other succeeding next nearest locations) is based on a model for the coherence between winds at two locations as a function of frequency and separation distance, where the adjacent locations are separated vertically and/or horizontally. The coherence function is used to calculate a coherence weighted FFT of the wind at the next nearest location, given the FFT of the simulated time series at the previous location and the essentially incoherent FFT of the wind at the selected location derived a priori from the PSD model. The simulated time series at each adjacent location is the inverse Fourier transform of the coherence weighted FFT. For a selected design case, the equations, the process and the simulated time series at multiple vehicle stations are presented.

Reconfigurable optical interconnections via dynamic computer-generated holograms

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Zhou, Shaomin (Inventor)

1994-01-01

A system is proposed for optically providing one-to-many irregular interconnections, and strength-adjustable many-to-many irregular interconnections which may be provided with strengths (weights) w(sub ij) using multiple laser beams which address multiple holograms and means for combining the beams modified by the holograms to form multiple interconnections, such as a cross-bar switching network. The optical means for interconnection is based on entering a series of complex computer-generated holograms on an electrically addressed spatial light modulator for real-time reconfigurations, thus providing flexibility for interconnection networks for largescale practical use. By employing multiple sources and holograms, the number of interconnection patterns achieved is increased greatly.

Contraction Options and Optimal Multiple-Stopping in Spectrally Negative Lévy Models

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

Yamazaki, Kazutoshi, E-mail: kyamazak@kansai-u.ac.jp

This paper studies the optimal multiple-stopping problem arising in the context of the timing option to withdraw from a project in stages. The profits are driven by a general spectrally negative Lévy process. This allows the model to incorporate sudden declines of the project values, generalizing greatly the classical geometric Brownian motion model. We solve the one-stage case as well as the extension to the multiple-stage case. The optimal stopping times are of threshold-type and the value function admits an expression in terms of the scale function. A series of numerical experiments are conducted to verify the optimality and tomore » evaluate the efficiency of the algorithm.« less

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

PubMed

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

2018-04-16

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

Time-Series Analysis of Remotely-Sensed SeaWiFS Chlorophyll in River-Influenced Coastal Regions

NASA Technical Reports Server (NTRS)

Acker, James G.; McMahon, Erin; Shen, Suhung; Hearty, Thomas; Casey, Nancy

2009-01-01

The availability of a nearly-continuous record of remotely-sensed chlorophyll a data (chl a) from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission, now longer than ten years, enables examination of time-series trends for multiple global locations. Innovative data analysis technology available on the World Wide Web facilitates such analyses. In coastal regions influenced by river outflows, chl a is not always indicative of actual trends in phytoplankton chlorophyll due to the interference of colored dissolved organic matter and suspended sediments; significant chl a timeseries trends for coastal regions influenced by river outflows may nonetheless be indicative of important alterations of the hydrologic and coastal environment. Chl a time-series analysis of nine marine regions influenced by river outflows demonstrates the simplicity and usefulness of this technique. The analyses indicate that coastal time-series are significantly influenced by unusual flood events. Major river systems in regions with relatively low human impact did not exhibit significant trends. Most river systems with demonstrated human impact exhibited significant negative trends, with the noteworthy exception of the Pearl River in China, which has a positive trend.

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.

Comparison analysis in growth process in Asian cities by using tandem time series remote sensing of different satellite

NASA Astrophysics Data System (ADS)

Hashiba, Hideki; Nakayama, Yasunori; Sugimura, Toshiro

The growth of major cities in Asia, as a consequence of economic development, is feared to have adverse influences on the natural environment of the surrounding areas. Comparison of land cover changes in major cities from the viewpoints of both spatial and time series is necessary to fully understand the characteristics of urban development in Asia. To accomplish this, multiple satellite remote sensing data were analyzed across a wide range and over a long term in this study. The process of transition of a major Asian city in Tokyo, Osaka, Beijing, Shanghai, and Hong Kong was analyzed from the characteristic changes of the vegetation index value and the land cover over about 40 years, from 1972 to 2010. Image data for LANDSAT/MSS, LAND-SAT/TM, ALOS/AVNIR-2, and ALOS/PRISM were obtained using a tandem time series. The ratio and state of detailed distribution of land cover were clarified by the classification processing. The time series clearly showed different change characteristics for each city and its surrounding natural environment of vegetation and forest etc. as a result of development processes.

Apparatus for gas sorption measurement with integrated gas composition measurement device and gas mixing

DOEpatents

Micklash. II, Kenneth James; Dutton, Justin James; Kaye, Steven

2014-06-03

An apparatus for testing of multiple material samples includes a gas delivery control system operatively connectable to the multiple material samples and configured to provide gas to the multiple material samples. Both a gas composition measurement device and pressure measurement devices are included in the apparatus. The apparatus includes multiple selectively openable and closable valves and a series of conduits configured to selectively connect the multiple material samples individually to the gas composition device and the pressure measurement devices by operation of the valves. A mixing system is selectively connectable to the series of conduits and is operable to cause forced mixing of the gas within the series of conduits to achieve a predetermined uniformity of gas composition within the series of conduits and passages.

Identifying Changes of Complex Flood Dynamics with Recurrence Analysis

NASA Astrophysics Data System (ADS)

Wendi, D.; Merz, B.; Marwan, N.

2016-12-01

Temporal changes in flood hazard system are known to be difficult to detect and attribute due to multiple drivers that include complex processes that are non-stationary and highly variable. These drivers, such as human-induced climate change, natural climate variability, implementation of flood defense, river training, or land use change, could impact variably on space-time scales and influence or mask each other. Flood time series may show complex behavior that vary at a range of time scales and may cluster in time. Moreover hydrological time series (i.e. discharge) are often subject to measurement errors, such as rating curve error especially in the case of extremes where observation are actually derived through extrapolation. This study focuses on the application of recurrence based data analysis techniques (recurrence plot) for understanding and quantifying spatio-temporal changes in flood hazard in Germany. The recurrence plot is known as an effective tool to visualize the dynamics of phase space trajectories i.e. constructed from a time series by using an embedding dimension and a time delay, and it is known to be effective in analyzing non-stationary and non-linear time series. Sensitivity of the common measurement errors and noise on recurrence analysis will also be analyzed and evaluated against conventional methods. The emphasis will be on the identification of characteristic recurrence properties that could associate typical dynamic to certain flood events.

Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology

NASA Astrophysics Data System (ADS)

Müller, H.; Haberlandt, U.

2018-01-01

Rainfall time series of high temporal resolution and spatial density are crucial for urban hydrology. The multiplicative random cascade model can be used for temporal rainfall disaggregation of daily data to generate such time series. Here, the uniform splitting approach with a branching number of 3 in the first disaggregation step is applied. To achieve a final resolution of 5 min, subsequent steps after disaggregation are necessary. Three modifications at different disaggregation levels are tested in this investigation (uniform splitting at Δt = 15 min, linear interpolation at Δt = 7.5 min and Δt = 3.75 min). Results are compared both with observations and an often used approach, based on the assumption that a time steps with Δt = 5.625 min, as resulting if a branching number of 2 is applied throughout, can be replaced with Δt = 5 min (called the 1280 min approach). Spatial consistence is implemented in the disaggregated time series using a resampling algorithm. In total, 24 recording stations in Lower Saxony, Northern Germany with a 5 min resolution have been used for the validation of the disaggregation procedure. The urban-hydrological suitability is tested with an artificial combined sewer system of about 170 hectares. The results show that all three variations outperform the 1280 min approach regarding reproduction of wet spell duration, average intensity, fraction of dry intervals and lag-1 autocorrelation. Extreme values with durations of 5 min are also better represented. For durations of 1 h, all approaches show only slight deviations from the observed extremes. The applied resampling algorithm is capable to achieve sufficient spatial consistence. The effects on the urban hydrological simulations are significant. Without spatial consistence, flood volumes of manholes and combined sewer overflow are strongly underestimated. After resampling, results using disaggregated time series as input are in the range of those using observed time series. Best overall performance regarding rainfall statistics are obtained by the method in which the disaggregation process ends at time steps with 7.5 min duration, deriving the 5 min time steps by linear interpolation. With subsequent resampling this method leads to a good representation of manhole flooding and combined sewer overflow volume in terms of hydrological simulations and outperforms the 1280 min approach.

Some Recent Developments on Complex Multivariate Distributions

ERIC Educational Resources Information Center

Krishnaiah, P. R.

1976-01-01

In this paper, the author gives a review of the literature on complex multivariate distributions. Some new results on these distributions are also given. Finally, the author discusses the applications of the complex multivariate distributions in the area of the inference on multiple time series. (Author)

Methods for forecasting freight in uncertainty : time series analysis of multiple factors.

DOT National Transportation Integrated Search

2011-01-31

The main goal of this research was to analyze and more accurately model freight movement in : Alabama. Ultimately, the goal of this project was to provide an overall approach to the : integration of accurate freight models into transportation plans a...

The cross-correlation analysis of multi property of stock markets based on MM-DFA

NASA Astrophysics Data System (ADS)

Yang, Yujun; Li, Jianping; Yang, Yimei

2017-09-01

In this paper, we propose a new method called DH-MXA based on distribution histograms of Hurst surface and multiscale multifractal detrended fluctuation analysis. The method allows us to investigate the cross-correlation characteristics among multiple properties of different stock time series. It may provide a new way of measuring the nonlinearity of several signals. It also can provide a more stable and faithful description of cross-correlation of multiple properties of stocks. The DH-MXA helps us to present much richer information than multifractal detrented cross-correlation analysis and allows us to assess many universal and subtle cross-correlation characteristics of stock markets. We show DH-MXA by selecting four artificial data sets and five properties of four stock time series from different countries. The results show that our proposed method can be adapted to investigate the cross-correlation of stock markets. In general, the American stock markets are more mature and less volatile than the Chinese stock markets.

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems

PubMed Central

Farrington, C. Paddy; Noufaily, Angela; Andrews, Nick J.; Charlett, Andre

2016-01-01

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace. PMID:27513749

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

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

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-07-25

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load valuesmore » to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Environmental flow allocation and statistics calculator

USGS Publications Warehouse

Konrad, Christopher P.

2011-01-01

The Environmental Flow Allocation and Statistics Calculator (EFASC) is a computer program that calculates hydrologic statistics based on a time series of daily streamflow values. EFASC will calculate statistics for daily streamflow in an input file or will generate synthetic daily flow series from an input file based on rules for allocating and protecting streamflow and then calculate statistics for the synthetic time series. The program reads dates and daily streamflow values from input files. The program writes statistics out to a series of worksheets and text files. Multiple sites can be processed in series as one run. EFASC is written in MicrosoftRegistered Visual BasicCopyright for Applications and implemented as a macro in MicrosoftOffice Excel 2007Registered. EFASC is intended as a research tool for users familiar with computer programming. The code for EFASC is provided so that it can be modified for specific applications. All users should review how output statistics are calculated and recognize that the algorithms may not comply with conventions used to calculate streamflow statistics published by the U.S. Geological Survey.

DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

PubMed

Lawhern, Vernon; Hairston, W David; Robbins, Kay

2013-01-01

Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.

DETECT: A MATLAB Toolbox for Event Detection and Identification in Time Series, with Applications to Artifact Detection in EEG Signals

PubMed Central

Lawhern, Vernon; Hairston, W. David; Robbins, Kay

2013-01-01

Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration. PMID:23638169

Comparative Perspectives on Recent Trends in Land Surface Dynamics in the Grasslands of North and South America

NASA Astrophysics Data System (ADS)

Henebry, G. M.; Valle De Carvalho E Oliveira, P.; Zheng, B.; de Beurs, K.; Owsley, B.

2015-12-01

In our current era of intensive earth observation the time is ripe to shift away from studies relying on single sensors or single products to the synergistic use of multiple sensors and products at complementary spatial, temporal, and spectral scales. The use of multiple time series can not only reveal hotspots of change in land surface dynamics, but can indicate plausible proximate causes of the changes and suggest their possible consequences. Here we explore recent trends in the land surface dynamics of exemplary semi-arid grasslands in the western hemisphere, including the shortgrass prairie of eastern Colorado and New Mexico, the sandhills prairie of Nebraska, the "savana gramineo-lenhosa" variety of cerrado in central Brazil, and the pampas of Argentina. Observational datasets include (1) NBAR-based vegetation indices, land surface temperature, and evapotranspiration from MODIS, (2) air temperature, water vapor, and vegetation optical depth from AMSR-E and AMSR2, (3) surface air temperature, water vapor, and relative humidity from AIRS, and (4) surface shortwave, longwave, and total net flux from CERES. The spatial resolutions of these nested data include 500 m, 1000 m, 0.05 degree, 25 km, and 1 degree. We apply the nonparametric Seasonal Kendall trend test to each time series independently to identify areas of significant change. We then examine polygons of co-occurrence of significant change in two or more types of products using the surface radiation and energy budgets as guides to interpret the multiple changes. Changes occurring across broad areas are more likely to be of climatic origin; whereas, changes that are abrupt in space and time and of limited area are more likely anthropogenic. Results illustrate the utility of considering multiple remote sensing products as complementary views of land surface dynamics.

MEM spectral analysis for predicting influenza epidemics in Japan.

PubMed

Sumi, Ayako; Kamo, Ken-ichi

2012-03-01

The prediction of influenza epidemics has long been the focus of attention in epidemiology and mathematical biology. In this study, we tested whether time series analysis was useful for predicting the incidence of influenza in Japan. The method of time series analysis we used consists of spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. Using this time series analysis, we analyzed the incidence data of influenza in Japan from January 1948 to December 1998; these data are unique in that they covered the periods of pandemics in Japan in 1957, 1968, and 1977. On the basis of the MEM spectral analysis, we identified the periodic modes explaining the underlying variations of the incidence data. The optimum least squares fitting (LSF) curve calculated with the periodic modes reproduced the underlying variation of the incidence data. An extension of the LSF curve could be used to predict the incidence of influenza quantitatively. Our study suggested that MEM spectral analysis would allow us to model temporal variations of influenza epidemics with multiple periodic modes much more effectively than by using the method of conventional time series analysis, which has been used previously to investigate the behavior of temporal variations in influenza data.

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

NASA Technical Reports Server (NTRS)

McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall

2008-01-01

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify a variety of plant phenomena and improve monitoring capabilities.

Genome-wide Selective Sweeps in Natural Bacterial Populations Revealed by Time-series Metagenomics

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

Chan, Leong-Keat; Bendall, Matthew L.; Malfatti, Stephanie

2014-06-18

Multiple evolutionary models have been proposed to explain the formation of genetically and ecologically distinct bacterial groups. Time-series metagenomics enables direct observation of evolutionary processes in natural populations, and if applied over a sufficiently long time frame, this approach could capture events such as gene-specific or genome-wide selective sweeps. Direct observations of either process could help resolve how distinct groups form in natural microbial assemblages. Here, from a three-year metagenomic study of a freshwater lake, we explore changes in single nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in populations of Chlorobiaceae and Methylophilaceae. SNP analyses revealedmore » substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied considerably among closely related, co-occurring Methylophilaceae populations. SNP allele frequencies, as well as the relative abundance of certain genes, changed dramatically over time in each population. Interestingly, SNP diversity was purged at nearly every genome position in one of the Chlorobiaceae populations over the course of three years, while at the same time multiple genes either swept through or were swept from this population. These patterns were consistent with a genome-wide selective sweep, a process predicted by the ‘ecotype model’ of diversification, but not previously observed in natural populations.« less

Genome-wide Selective Sweeps in Natural Bacterial Populations Revealed by Time-series Metagenomics

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

Chan, Leong-Keat; Bendall, Matthew L.; Malfatti, Stephanie

2014-05-12

Multiple evolutionary models have been proposed to explain the formation of genetically and ecologically distinct bacterial groups. Time-series metagenomics enables direct observation of evolutionary processes in natural populations, and if applied over a sufficiently long time frame, this approach could capture events such as gene-specific or genome-wide selective sweeps. Direct observations of either process could help resolve how distinct groups form in natural microbial assemblages. Here, from a three-year metagenomic study of a freshwater lake, we explore changes in single nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in populations of Chlorobiaceae and Methylophilaceae. SNP analyses revealedmore » substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied considerably among closely related, co-occurring Methylophilaceae populations. SNP allele frequencies, as well as the relative abundance of certain genes, changed dramatically over time in each population. Interestingly, SNP diversity was purged at nearly every genome position in one of the Chlorobiaceae populations over the course of three years, while at the same time multiple genes either swept through or were swept from this population. These patterns were consistent with a genome-wide selective sweep, a process predicted by the ecotype model? of diversification, but not previously observed in natural populations.« less

InSAR time series analysis of ALOS-2 ScanSAR data and its implications for NISAR

NASA Astrophysics Data System (ADS)

Liang, C.; Liu, Z.; Fielding, E. J.; Huang, M. H.; Burgmann, R.

2017-12-01

The JAXA's ALOS-2 mission was launched on May 24, 2014. It operates at L-band and can acquire data in multiple modes. ScanSAR is the main operational mode and has a 350 km swath, somewhat larger than the 250 km swath of the SweepSAR mode planned for the NASA-ISRO SAR (NISAR) mission. ALOS-2 has been acquiring a wealth of L-band InSAR data. These data are of particular value in areas of dense vegetation and high relief. The InSAR technical development for ALOS-2 also enables the preparation for the upcoming NISAR mission. We have been developing advanced InSAR processing techniques for ALOS-2 over the past two years. Here, we report the important issues for doing InSAR time series analysis using ALOS-2 ScanSAR data. First, we present ionospheric correction techniques for both regular ScanSAR InSAR and MAI (multiple aperture InSAR) ScanSAR InSAR. We demonstrate the large-scale ionospheric signals in the ScanSAR interferograms. They can be well mitigated by the correction techniques. Second, based on our technical development of burst-by-burst InSAR processing for ALOS-2 ScanSAR data, we find that the azimuth Frequency Modulation (FM) rate error is an important issue not only for MAI, but also for regular InSAR time series analysis. We identify phase errors caused by azimuth FM rate errors during the focusing process of ALOS-2 product. The consequence is mostly a range ramp in the InSAR time series result. This error exists in all of the time series results we have processed. We present the correction techniques for this error following a theoretical analysis. After corrections, we present high quality ALOS-2 ScanSAR InSAR time series results in a number of areas. The development for ALOS-2 can provide important implications for NISAR mission. For example, we find that in most cases the relative azimuth shift caused by ionosphere can be as large as 4 m in a large area imaged by ScanSAR. This azimuth shift is half of the 8 m azimuth resolution of the SweepSAR mode planned for NISAR, which implies that a good coregistration strategy for NISAR's SweepSAR mode is geometrical coregistration followed by MAI or spectral diversity analysis. Besides, our development also provides implications for the processing and system parameter requirements of NISAR, such as the accuracy requirement of azimuth FM rate and range timing.

Two graphical user interfaces for managing and analyzing MODFLOW groundwater-model scenarios

USGS Publications Warehouse

Banta, Edward R.

2014-01-01

Scenario Manager and Scenario Analyzer are graphical user interfaces that facilitate the use of calibrated, MODFLOW-based groundwater models for investigating possible responses to proposed stresses on a groundwater system. Scenario Manager allows a user, starting with a calibrated model, to design and run model scenarios by adding or modifying stresses simulated by the model. Scenario Analyzer facilitates the process of extracting data from model output and preparing such display elements as maps, charts, and tables. Both programs are designed for users who are familiar with the science on which groundwater modeling is based but who may not have a groundwater modeler’s expertise in building and calibrating a groundwater model from start to finish. With Scenario Manager, the user can manipulate model input to simulate withdrawal or injection wells, time-variant specified hydraulic heads, recharge, and such surface-water features as rivers and canals. Input for stresses to be simulated comes from user-provided geographic information system files and time-series data files. A Scenario Manager project can contain multiple scenarios and is self-documenting. Scenario Analyzer can be used to analyze output from any MODFLOW-based model; it is not limited to use with scenarios generated by Scenario Manager. Model-simulated values of hydraulic head, drawdown, solute concentration, and cell-by-cell flow rates can be presented in display elements. Map data can be represented as lines of equal value (contours) or as a gradated color fill. Charts and tables display time-series data obtained from output generated by a transient-state model run or from user-provided text files of time-series data. A display element can be based entirely on output of a single model run, or, to facilitate comparison of results of multiple scenarios, an element can be based on output from multiple model runs. Scenario Analyzer can export display elements and supporting metadata as a Portable Document Format file.

Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm

NASA Astrophysics Data System (ADS)

Ahmed, Oumer S.; Franklin, Steven E.; Wulder, Michael A.; White, Joanne C.

2015-03-01

Many forest management activities, including the development of forest inventories, require spatially detailed forest canopy cover and height data. Among the various remote sensing technologies, LiDAR (Light Detection and Ranging) offers the most accurate and consistent means for obtaining reliable canopy structure measurements. A potential solution to reduce the cost of LiDAR data, is to integrate transects (samples) of LiDAR data with frequently acquired and spatially comprehensive optical remotely sensed data. Although multiple regression is commonly used for such modeling, often it does not fully capture the complex relationships between forest structure variables. This study investigates the potential of Random Forest (RF), a machine learning technique, to estimate LiDAR measured canopy structure using a time series of Landsat imagery. The study is implemented over a 2600 ha area of industrially managed coastal temperate forests on Vancouver Island, British Columbia, Canada. We implemented a trajectory-based approach to time series analysis that generates time since disturbance (TSD) and disturbance intensity information for each pixel and we used this information to stratify the forest land base into two strata: mature forests and young forests. Canopy cover and height for three forest classes (i.e. mature, young and mature and young (combined)) were modeled separately using multiple regression and Random Forest (RF) techniques. For all forest classes, the RF models provided improved estimates relative to the multiple regression models. The lowest validation error was obtained for the mature forest strata in a RF model (R2 = 0.88, RMSE = 2.39 m and bias = -0.16 for canopy height; R2 = 0.72, RMSE = 0.068% and bias = -0.0049 for canopy cover). This study demonstrates the value of using disturbance and successional history to inform estimates of canopy structure and obtain improved estimates of forest canopy cover and height using the RF algorithm.

Monitoring Forest Regrowth Using a Multi-Platform Time Series

NASA Technical Reports Server (NTRS)

Sabol, Donald E., Jr.; Smith, Milton O.; Adams, John B.; Gillespie, Alan R.; Tucker, Compton J.

1996-01-01

Over the past 50 years, the forests of western Washington and Oregon have been extensively harvested for timber. This has resulted in a heterogeneous mosaic of remaining mature forests, clear-cuts, new plantations, and second-growth stands that now occur in areas that formerly were dominated by extensive old-growth forests and younger forests resulting from fire disturbance. Traditionally, determination of seral stage and stand condition have been made using aerial photography and spot field observations, a methodology that is not only time- and resource-intensive, but falls short of providing current information on a regional scale. These limitations may be solved, in part, through the use of multispectral images which can cover large areas at spatial resolutions in the order of tens of meters. The use of multiple images comprising a time series potentially can be used to monitor land use (e.g. cutting and replanting), and to observe natural processes such as regeneration, maturation and phenologic change. These processes are more likely to be spectrally observed in a time series composed of images taken during different seasons over a long period of time. Therefore, for many areas, it may be necessary to use a variety of images taken with different imaging systems. A common framework for interpretation is needed that reduces topographic, atmospheric, instrumental, effects as well as differences in lighting geometry between images. The present state of remote-sensing technology in general use does not realize the full potential of the multispectral data in areas of high topographic relief. For example, the primary method for analyzing images of forested landscapes in the Northwest has been with statistical classifiers (e.g. parallelepiped, nearest-neighbor, maximum likelihood, etc.), often applied to uncalibrated multispectral data. Although this approach has produced useful information from individual images in some areas, landcover classes defined by these techniques typically are not consistent for the same scene imaged under different illumination conditions, especially in the mountainous regions. In addition, it is difficult to correct for atmospheric and instrumental differences between multiple scenes in a time series. In this paper, we present an approach for monitoring forest cutting/regrowth in a semi-mountainous portion of the southern Gifford Pinchot National Forest using a multisensor-time series composed of MSS, TM, and AVIRIS images.

CCD Measurements of Double and Multiple Stars at NAO Rozhen and ASV in 2015

NASA Astrophysics Data System (ADS)

Cvetković, Z.; Pavlović, R.; Boeva, S.

2017-04-01

Results of CCD observations of 154 double or multiple stars, made with the 2 m telescope of the Bulgarian National Astronomical Observatory at Rozhen over five nights in 2015, are presented. This is the ninth series of measurements of CCD frames obtained at Rozhen. We also present results of CCD observations of 323 double or multiple stars made with the 0.6 m telescope of the Serbian Astronomical Station on the mountain of Vidojevica over 23 nights in 2015. This is the fourth series of measurements of CCD frames obtained at this station. This paper contains the results for the position angle and angular separation for 801 pairs and residuals for 127 pairs with published orbital elements or linear solutions. The angular separations are in the range from 1.″52 to 201.″56, with a median angular separation of 8.″26. We also present eight pairs that are measured for the first time and linear elements for five pairs.

Velocity landscape correlation resolves multiple flowing protein populations from fluorescence image time series.

PubMed

Pandžić, Elvis; Abu-Arish, Asmahan; Whan, Renee M; Hanrahan, John W; Wiseman, Paul W

2018-02-16

Molecular, vesicular and organellar flows are of fundamental importance for the delivery of nutrients and essential components used in cellular functions such as motility and division. With recent advances in fluorescence/super-resolution microscopy modalities we can resolve the movements of these objects at higher spatio-temporal resolutions and with better sensitivity. Previously, spatio-temporal image correlation spectroscopy has been applied to map molecular flows by correlation analysis of fluorescence fluctuations in image series. However, an underlying assumption of this approach is that the sampled time windows contain one dominant flowing component. Although this was true for most of the cases analyzed earlier, in some situations two or more different flowing populations can be present in the same spatio-temporal window. We introduce an approach, termed velocity landscape correlation (VLC), which detects and extracts multiple flow components present in a sampled image region via an extension of the correlation analysis of fluorescence intensity fluctuations. First we demonstrate theoretically how this approach works, test the performance of the method with a range of computer simulated image series with varying flow dynamics. Finally we apply VLC to study variable fluxing of STIM1 proteins on microtubules connected to the plasma membrane of Cystic Fibrosis Bronchial Epithelial (CFBE) cells. Copyright © 2018 Elsevier Inc. All rights reserved.

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

The Relationship of Item-Level Response Times with Test-Taker and Item Variables in an Operational CAT Environment. LSAC Research Report Series.

ERIC Educational Resources Information Center

Swygert, Kimberly A.

In this study, data from an operational computerized adaptive test (CAT) were examined in order to gather information concerning item response times in a CAT environment. The CAT under study included multiple-choice items measuring verbal, quantitative, and analytical reasoning. The analyses included the fitting of regression models describing the…

Active Learning and Just-in-Time Teaching in a Material and Energy Balances Course

ERIC Educational Resources Information Center

Liberatore, Matthew W.

2013-01-01

The delivery of a material and energy balances course is enhanced through a series of in-class and out-of-class exercises. An active learning classroom is achieved, even at class sizes over 150 students, using multiple instructors in a single classroom, problem solving in teams, problems based on YouTube videos, and just-in-time teaching. To avoid…

Critical Issues in Social Studies Research for the 21st Century. Research in Social Education Series.

ERIC Educational Resources Information Center

Stanley, William B., Ed.

Social studies is a field struggling to reconcile multiple and, at times, conflicting rationales. The beginning of a century is an appropriate time to reflect on the condition of social studies and to question where the world has been and where it is going. The essays in this collection explore possible answers to these questions as they apply to…

Nonlinear Time Series Analysis of Nodulation Factor Induced Calcium Oscillations: Evidence for Deterministic Chaos?

PubMed Central

Hazledine, Saul; Sun, Jongho; Wysham, Derin; Downie, J. Allan; Oldroyd, Giles E. D.; Morris, Richard J.

2009-01-01

Legume plants form beneficial symbiotic interactions with nitrogen fixing bacteria (called rhizobia), with the rhizobia being accommodated in unique structures on the roots of the host plant. The legume/rhizobial symbiosis is responsible for a significant proportion of the global biologically available nitrogen. The initiation of this symbiosis is governed by a characteristic calcium oscillation within the plant root hair cells and this signal is activated by the rhizobia. Recent analyses on calcium time series data have suggested that stochastic effects have a large role to play in defining the nature of the oscillations. The use of multiple nonlinear time series techniques, however, suggests an alternative interpretation, namely deterministic chaos. We provide an extensive, nonlinear time series analysis on the nature of this calcium oscillation response. We build up evidence through a series of techniques that test for determinism, quantify linear and nonlinear components, and measure the local divergence of the system. Chaos is common in nature and it seems plausible that properties of chaotic dynamics might be exploited by biological systems to control processes within the cell. Systems possessing chaotic control mechanisms are more robust in the sense that the enhanced flexibility allows more rapid response to environmental changes with less energetic costs. The desired behaviour could be most efficiently targeted in this manner, supporting some intriguing speculations about nonlinear mechanisms in biological signaling. PMID:19675679

Therapeutic assessment for preadolescent boys with oppositional defiant disorder: a replicated single-case time-series design.

PubMed

Smith, Justin D; Handler, Leonard; Nash, Michael R

2010-09-01

The Therapeutic Assessment (TA) model is a relatively new treatment approach that fuses assessment and psychotherapy. The study examines the efficacy of this model with preadolescent boys with oppositional defiant disorder and their families. A replicated single-case time-series design with daily measures is used to assess the effects of TA and to track the process of change as it unfolds. All 3 families benefitted from participation in TA across multiple domains of functioning, but the way in which change unfolded was unique for each family. These findings are substantiated by the Behavior Assessment System for Children (Reynolds & Kamphaus, 2004). The TA model is shown to be an effective treatment for preadolescent boys with oppositional defiant disorder and their families. Further, the time-series design of this study illustrated how this empirically grounded case-based methodology reveals when and how change unfolds during treatment in a way that is usually not possible with other research designs.

A multi-tiered time-series modelling approach to forecasting respiratory syncytial virus incidence at the local level.

PubMed

Spaeder, M C; Fackler, J C

2012-04-01

Respiratory syncytial virus (RSV) is the most common cause of documented viral respiratory infections, and the leading cause of hospitalization, in young children. We performed a retrospective time-series analysis of all patients aged <18 years with laboratory-confirmed RSV within a network of multiple affiliated academic medical institutions. Forecasting models of weekly RSV incidence for the local community, inpatient paediatric hospital and paediatric intensive-care unit (PICU) were created. Ninety-five percent confidence intervals calculated around our models' 2-week forecasts were accurate to ±9·3, ±7·5 and ±1·5 cases/week for the local community, inpatient hospital and PICU, respectively. Our results suggest that time-series models may be useful tools in forecasting the burden of RSV infection at the local and institutional levels, helping communities and institutions to optimize distribution of resources based on the changing burden and severity of illness in their respective communities.

Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems

USGS Publications Warehouse

Vogelmann, James E.; Xian, George; Homer, Collin G.; Tolk, Brian

2012-01-01

The focus of the study was to assess gradual changes occurring throughout a range of natural ecosystems using decadal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM +) time series data. Time series data stacks were generated for four study areas: (1) a four scene area dominated by forest and rangeland ecosystems in the southwestern United States, (2) a sagebrush-dominated rangeland in Wyoming, (3) woodland adjacent to prairie in northwestern Nebraska, and (4) a forested area in the White Mountains of New Hampshire. Through analyses of time series data, we found evidence of gradual systematic change in many of the natural vegetation communities in all four areas. Many of the conifer forests in the southwestern US are showing declines related to insects and drought, but very few are showing evidence of improving conditions or increased greenness. Sagebrush communities are showing decreases in greenness related to fire, mining, and probably drought, but very few of these communities are showing evidence of increased greenness or improving conditions. In Nebraska, forest communities are showing local expansion and increased canopy densification in the prairie–woodland interface, and in the White Mountains high elevation understory conifers are showing range increases towards lower elevations. The trends detected are not obvious through casual inspection of the Landsat images. Analyses of time series data using many scenes and covering multiple years are required in order to develop better impressions and representations of the changing ecosystem patterns and trends that are occurring. The approach described in this paper demonstrates that Landsat time series data can be used operationally for assessing gradual ecosystem change across large areas. Local knowledge and available ancillary data are required in order to fully understand the nature of these trends.

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

Reconfigurable Optical Interconnections Via Dynamic Computer-Generated Holograms

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Zhou, Shao-Min (Inventor)

1996-01-01

A system is presented for optically providing one-to-many irregular interconnections, and strength-adjustable many-to-many irregular interconnections which may be provided with strengths (weights) w(sub ij) using multiple laser beams which address multiple holograms and means for combining the beams modified by the holograms to form multiple interconnections, such as a cross-bar switching network. The optical means for interconnection is based on entering a series of complex computer-generated holograms on an electrically addressed spatial light modulator for real-time reconfigurations, thus providing flexibility for interconnection networks for large-scale practical use. By employing multiple sources and holograms, the number of interconnection patterns achieved is increased greatly.

Wavelet entropy of BOLD time series: An application to Rolandic epilepsy.

PubMed

Gupta, Lalit; Jansen, Jacobus F A; Hofman, Paul A M; Besseling, René M H; de Louw, Anton J A; Aldenkamp, Albert P; Backes, Walter H

2017-12-01

To assess the wavelet entropy for the characterization of intrinsic aberrant temporal irregularities in the time series of resting-state blood-oxygen-level-dependent (BOLD) signal fluctuations. Further, to evaluate the temporal irregularities (disorder/order) on a voxel-by-voxel basis in the brains of children with Rolandic epilepsy. The BOLD time series was decomposed using the discrete wavelet transform and the wavelet entropy was calculated. Using a model time series consisting of multiple harmonics and nonstationary components, the wavelet entropy was compared with Shannon and spectral (Fourier-based) entropy. As an application, the wavelet entropy in 22 children with Rolandic epilepsy was compared to 22 age-matched healthy controls. The images were obtained by performing resting-state functional magnetic resonance imaging (fMRI) using a 3T system, an 8-element receive-only head coil, and an echo planar imaging pulse sequence ( T2*-weighted). The wavelet entropy was also compared to spectral entropy, regional homogeneity, and Shannon entropy. Wavelet entropy was found to identify the nonstationary components of the model time series. In Rolandic epilepsy patients, a significantly elevated wavelet entropy was observed relative to controls for the whole cerebrum (P = 0.03). Spectral entropy (P = 0.41), regional homogeneity (P = 0.52), and Shannon entropy (P = 0.32) did not reveal significant differences. The wavelet entropy measure appeared more sensitive to detect abnormalities in cerebral fluctuations represented by nonstationary effects in the BOLD time series than more conventional measures. This effect was observed in the model time series as well as in Rolandic epilepsy. These observations suggest that the brains of children with Rolandic epilepsy exhibit stronger nonstationary temporal signal fluctuations than controls. 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1728-1737. © 2017 International Society for Magnetic Resonance in Medicine.

Statistical Analysis of Time-Series from Monitoring of Active Volcanic Vents

NASA Astrophysics Data System (ADS)

Lachowycz, S.; Cosma, I.; Pyle, D. M.; Mather, T. A.; Rodgers, M.; Varley, N. R.

2016-12-01

Despite recent advances in the collection and analysis of time-series from volcano monitoring, and the resulting insights into volcanic processes, challenges remain in forecasting and interpreting activity from near real-time analysis of monitoring data. Statistical methods have potential to characterise the underlying structure and facilitate intercomparison of these time-series, and so inform interpretation of volcanic activity. We explore the utility of multiple statistical techniques that could be widely applicable to monitoring data, including Shannon entropy and detrended fluctuation analysis, by their application to various data streams from volcanic vents during periods of temporally variable activity. Each technique reveals changes through time in the structure of some of the data that were not apparent from conventional analysis. For example, we calculate the Shannon entropy (a measure of the randomness of a signal) of time-series from the recent dome-forming eruptions of Volcán de Colima (Mexico) and Soufrière Hills (Montserrat). The entropy of real-time seismic measurements and the count rate of certain volcano-seismic event types from both volcanoes is found to be temporally variable, with these data generally having higher entropy during periods of lava effusion and/or larger explosions. In some instances, the entropy shifts prior to or coincident with changes in seismic or eruptive activity, some of which were not clearly recognised by real-time monitoring. Comparison with other statistics demonstrates the sensitivity of the entropy to the data distribution, but that it is distinct from conventional statistical measures such as coefficient of variation. We conclude that each analysis technique examined could provide valuable insights for interpretation of diverse monitoring time-series.

New Tools for Comparing Beliefs about the Timing of Recurrent Events with Climate Time Series Datasets

NASA Astrophysics Data System (ADS)

Stiller-Reeve, Mathew; Stephenson, David; Spengler, Thomas

2017-04-01

For climate services to be relevant and informative for users, scientific data definitions need to match users' perceptions or beliefs. This study proposes and tests novel yet simple methods to compare beliefs of timing of recurrent climatic events with empirical evidence from multiple historical time series. The methods are tested by applying them to the onset date of the monsoon in Bangladesh, where several scientific monsoon definitions can be applied, yielding different results for monsoon onset dates. It is a challenge to know which monsoon definition compares best with people's beliefs. Time series from eight different scientific monsoon definitions in six regions are compared with respondent beliefs from a previously completed survey concerning the monsoon onset. Beliefs about the timing of the monsoon onset are represented probabilistically for each respondent by constructing a probability mass function (PMF) from elicited responses about the earliest, normal, and latest dates for the event. A three-parameter circular modified triangular distribution (CMTD) is used to allow for the possibility (albeit small) of the onset at any time of the year. These distributions are then compared to the historical time series using two approaches: likelihood scores, and the mean and standard deviation of time series of dates simulated from each belief distribution. The methods proposed give the basis for further iterative discussion with decision-makers in the development of eventual climate services. This study uses Jessore, Bangladesh, as an example and finds that a rainfall definition, applying a 10 mm day-1 threshold to NCEP-NCAR reanalysis (Reanalysis-1) data, best matches the survey respondents' beliefs about monsoon onset.

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling

PubMed Central

Zhou, Fuqun; Zhang, Aining

2016-01-01

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2–3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests’ features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data. PMID:27792152

Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling.

PubMed

Zhou, Fuqun; Zhang, Aining

2016-10-25

Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.

40 CFR 86.1232-96 - Vehicle preconditioning.

Code of Federal Regulations, 2013 CFR

2013-07-01

... awaiting testing, to prevent unusual loading of the canisters. During this time care must be taken to... vehicles with multiple canisters in a series configuration, the set of canisters must be preconditioned as... designed for vapor load or purge steps, the service port shall be used during testing to precondition the...

Crew factors in flight operations IX : effects of planned cockpit rest on crew performance and alertness in long-haul operations

DOT National Transportation Integrated Search

1994-07-01

This report is the ninth in a series on physiological and psychological effects of flight operations on flight crews, and on the operational significance of these effects. Long-haul flight operations often involve rapid multiple time-zone changes, sl...

Feature Assignment in Perception of Auditory Figure

ERIC Educational Resources Information Center

Gregg, Melissa K.; Samuel, Arthur G.

2012-01-01

Because the environment often includes multiple sounds that overlap in time, listeners must segregate a sound of interest (the auditory figure) from other co-occurring sounds (the unattended auditory ground). We conducted a series of experiments to clarify the principles governing the extraction of auditory figures. We distinguish between auditory…

Pediatric emotional dysregulation and behavioral disruptiveness treated with hypnosis: a time-series design.

PubMed

Iglesias, Alex; Iglesias, Adam

2014-01-01

A case of pediatric oppositional defiant disorder (ODD) with concomitant emotional dysregulation and secondary behavioral disruptiveness was treated with hypnosis by means of the hypnotic hold, a method adapted by the authors. An A-B-A-B time-series design with multiple replications was employed to measure the relationship of the hypnotic treatment to the dependent measure: episodes of emotional dysregulation with accompanying behavioral disruptiveness. The findings indicated a statistically significant relationship between the degree of change from phase to phase and the treatment. Follow-up at 6 months indicated a significant reduction of the frequency of targeted episodes of emotional dysregulation and behavioral disruptiveness at home.

Findings of multiple HPV genotypes in cervical carcinoma are associated with poor cancer-specific survival in a Swedish cohort of cervical cancer primarily treated with radiotherapy.

PubMed

Kaliff, Malin; Sorbe, Bengt; Mordhorst, Louise Bohr; Helenius, Gisela; Karlsson, Mats G; Lillsunde-Larsson, Gabriella

2018-04-10

Cervical cancer (CC) is one of the most common cancers in women and virtually all cases of CC are a result of a persistent infection of human papillomavirus (HPV). For disease detected in early stages there is curing treatment but when diagnosed late with recurring disease and metastasis there are limited possibilities. Here we evaluate HPV impact on treatment resistance and metastatic disease progression. Prevalence and distribution of HPV genotypes and HPV16 variants in a Swedish CC patient cohort (n=209) was evaluated, as well as HPV influence on patient prognosis. Tumor samples suitable for analysis (n=204) were genotyped using two different real-time PCR methods. HPV16 variant analysis was made using pyrosequencing. Results showed that HPV prevalence in the total series was 93%. Of the HPV-positive samples, 13% contained multiple infections, typically with two high-risk HPV together. Primary cure rate for the complete series was 95%. Recurrence rate of the complete series was 28% and distant recurrences were most frequent (20%). Patients with tumors containing multiple HPV-strains and particularly HPV genotypes belonging to the alpha 7 and 9 species together had a significantly higher rate of distant tumor recurrences and worse cancer-specific survival rate.

The genomic response of skeletal muscle to methylprednisolone using microarrays: tailoring data mining to the structure of the pharmacogenomic time series

PubMed Central

DuBois, Debra C; Piel, William H; Jusko, William J

2008-01-01

High-throughput data collection using gene microarrays has great potential as a method for addressing the pharmacogenomics of complex biological systems. Similarly, mechanism-based pharmacokinetic/pharmacodynamic modeling provides a tool for formulating quantitative testable hypotheses concerning the responses of complex biological systems. As the response of such systems to drugs generally entails cascades of molecular events in time, a time series design provides the best approach to capturing the full scope of drug effects. A major problem in using microarrays for high-throughput data collection is sorting through the massive amount of data in order to identify probe sets and genes of interest. Due to its inherent redundancy, a rich time series containing many time points and multiple samples per time point allows for the use of less stringent criteria of expression, expression change and data quality for initial filtering of unwanted probe sets. The remaining probe sets can then become the focus of more intense scrutiny by other methods, including temporal clustering, functional clustering and pharmacokinetic/pharmacodynamic modeling, which provide additional ways of identifying the probes and genes of pharmacological interest. PMID:15212590

Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2018-02-01

Conventional bias correction is usually applied on a grid-by-grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two-step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back-transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA-based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.

Quasi-measures on the group G{sup m}, Dirichlet sets, and uniqueness problems for multiple Walsh series

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

Plotnikov, Mikhail G

2011-02-11

Multiple Walsh series (S) on the group G{sup m} are studied. It is proved that every at most countable set is a uniqueness set for series (S) under convergence over cubes. The recovery problem is solved for the coefficients of series (S) that converge outside countable sets or outside sets of Dirichlet type. A number of analogues of the de la Vallee Poussin theorem are established for series (S). Bibliography: 28 titles.

Constructing Weyl group multiple Dirichlet series

NASA Astrophysics Data System (ADS)

Chinta, Gautam; Gunnells, Paul E.

2010-01-01

Let Phi be a reduced root system of rank r . A Weyl group multiple Dirichlet series for Phi is a Dirichlet series in r complex variables s_1,dots,s_r , initially converging for {Re}(s_i) sufficiently large, that has meromorphic continuation to {{C}}^r and satisfies functional equations under the transformations of {{C}}^r corresponding to the Weyl group of Phi . A heuristic definition of such a series was given by Brubaker, Bump, Chinta, Friedberg, and Hoffstein, and they have been investigated in certain special cases by others. In this paper we generalize results by Chinta and Gunnells to construct Weyl group multiple Dirichlet series by a uniform method and show in all cases that they have the expected properties.

Combining Fourier and lagged k-nearest neighbor imputation for biomedical time series data.

PubMed

Rahman, Shah Atiqur; Huang, Yuxiao; Claassen, Jan; Heintzman, Nathaniel; Kleinberg, Samantha

2015-12-01

Most clinical and biomedical data contain missing values. A patient's record may be split across multiple institutions, devices may fail, and sensors may not be worn at all times. While these missing values are often ignored, this can lead to bias and error when the data are mined. Further, the data are not simply missing at random. Instead the measurement of a variable such as blood glucose may depend on its prior values as well as that of other variables. These dependencies exist across time as well, but current methods have yet to incorporate these temporal relationships as well as multiple types of missingness. To address this, we propose an imputation method (FLk-NN) that incorporates time lagged correlations both within and across variables by combining two imputation methods, based on an extension to k-NN and the Fourier transform. This enables imputation of missing values even when all data at a time point is missing and when there are different types of missingness both within and across variables. In comparison to other approaches on three biological datasets (simulated and actual Type 1 diabetes datasets, and multi-modality neurological ICU monitoring) the proposed method has the highest imputation accuracy. This was true for up to half the data being missing and when consecutive missing values are a significant fraction of the overall time series length. Copyright © 2015 Elsevier Inc. All rights reserved.

Key design elements of a data utility for national biosurveillance: event-driven architecture, caching, and Web service model.

PubMed

Tsui, Fu-Chiang; Espino, Jeremy U; Weng, Yan; Choudary, Arvinder; Su, Hoah-Der; Wagner, Michael M

2005-01-01

The National Retail Data Monitor (NRDM) has monitored over-the-counter (OTC) medication sales in the United States since December 2002. The NRDM collects data from over 18,600 retail stores and processes over 0.6 million sales records per day. This paper describes key architectural features that we have found necessary for a data utility component in a national biosurveillance system. These elements include event-driven architecture to provide analyses of data in near real time, multiple levels of caching to improve query response time, high availability through the use of clustered servers, scalable data storage through the use of storage area networks and a web-service function for interoperation with affiliated systems. The methods and architectural principles are relevant to the design of any production data utility for public health surveillance-systems that collect data from multiple sources in near real time for use by analytic programs and user interfaces that have substantial requirements for time-series data aggregated in multiple dimensions.

A covariant multiple scattering series for elastic projectile-target scattering

NASA Technical Reports Server (NTRS)

Gross, Franz; Maung-Maung, Khin

1989-01-01

A covariant formulation of the multiple scattering series for the optical potential is presented. The case of a scalar nucleon interacting with a spin zero isospin zero A-body target through meson exchange, is considered. It is shown that a covariant equation for the projectile-target t-matrix can be obtained which sums the ladder and crossed ladder diagrams efficiently. From this equation, a multiple scattering series for the optical potential is derived, and it is shown that in the impulse approximation, the two-body t-matrix associated with the first order optical potential is the one in which one particle is kept on mass-shell. The meaning of various terms in the multiple scattering series is given. The construction of the first-order optical potential for elastic scattering calculations is described.

A multi-centennial time series of well-constrained ΔR values for the Irish Sea derived using absolutely-dated shell samples from the mollusc Arctica islandica

NASA Astrophysics Data System (ADS)

Butler, P. G.; Scourse, J. D.; Richardson, C. A.; Wanamaker, A. D., Jr.

2009-04-01

Determinations of the local correction (ΔR) to the globally averaged marine radiocarbon reservoir age are often isolated in space and time, derived from heterogeneous sources and constrained by significant uncertainties. Although time series of ΔR at single sites can be obtained from sediment cores, these are subject to multiple uncertainties related to sedimentation rates, bioturbation and interspecific variations in the source of radiocarbon in the analysed samples. Coral records provide better resolution, but these are available only for tropical locations. It is shown here that it is possible to use the shell of the long-lived bivalve mollusc Arctica islandica as a source of high resolution time series of absolutely-dated marine radiocarbon determinations for the shelf seas surrounding the North Atlantic ocean. Annual growth increments in the shell can be crossdated and chronologies can be constructed in a precise analogue with the use of tree-rings. Because the calendar dates of the samples are known, ΔR can be determined with high precision and accuracy and because all the samples are from the same species, the time series of ΔR values possesses a high degree of internal consistency. Presented here is a multi-centennial (AD 1593 - AD 1933) time series of 31 ΔR values for a site in the Irish Sea close to the Isle of Man. The mean value of ΔR (-62 14C yrs) does not change significantly during this period but increased variability is apparent before AD 1750.

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

DOE PAGES

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong; ...

2017-12-18

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

Toward automatic time-series forecasting using neural networks.

PubMed

Yan, Weizhong

2012-07-01

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

A Method to Assess Seasonality of Urinary Tract Infections Based on Medication Sales and Google Trends

PubMed Central

Lambert, Bruno; Flahault, Antoine; Chartier-Kastler, Emmanuel; Hanslik, Thomas

2013-01-01

Background Despite the fact that urinary tract infection (UTI) is a very frequent disease, little is known about its seasonality in the community. Methods and Findings To estimate seasonality of UTI using multiple time series constructed with available proxies of UTI. Eight time series based on two databases were used: sales of urinary antibacterial medications reported by a panel of pharmacy stores in France between 2000 and 2012, and search trends on the Google search engine for UTI-related terms between 2004 and 2012 in France, Germany, Italy, the USA, China, Australia and Brazil. Differences between summers and winters were statistically assessed with the Mann-Whitney test. We evaluated seasonality by applying the Harmonics Product Spectrum on Fast Fourier Transform. Seven time series out of eight displayed a significant increase in medication sales or web searches in the summer compared to the winter, ranging from 8% to 20%. The eight time series displayed a periodicity of one year. Annual increases were seen in the summer for UTI drug sales in France and Google searches in France, the USA, Germany, Italy, and China. Increases occurred in the austral summer for Google searches in Brazil and Australia. Conclusions An annual seasonality of UTIs was evidenced in seven different countries, with peaks during the summer. PMID:24204587

A combined solar and geomagnetic index for thermospheric climate

PubMed Central

Mlynczak, Martin G; Hunt, Linda A; Marshall, B Thomas; Russell, James M; Mertens, Christopher J; Thompson, R Earl; Gordley, Larry L

2015-01-01

Infrared radiation from nitric oxide (NO) at 5.3 µm is a primary mechanism by which the thermosphere cools to space. The Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the NASA Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics satellite has been measuring thermospheric cooling by NO for over 13 years. In this letter we show that the SABER time series of globally integrated infrared power (watts) radiated by NO can be replicated accurately by a multiple linear regression fit using the F10.7, Ap, and Dst indices. This allows reconstruction of the NO power time series back nearly 70 years with extant databases of these indices. The relative roles of solar ultraviolet and geomagnetic processes in determining the NO cooling are derived and shown to vary significantly over the solar cycle. The NO power is a fundamental integral constraint on the thermospheric climate, and the time series presented here can be used to test upper atmosphere models over seven different solar cycles. Key Points F10.7, Ap, and Dst replicate time series of radiative cooling by nitric oxide Quantified relative roles of solar irradiance, geomagnetism in radiative cooling Establish a new index and extend record of thermospheric cooling back 70 years PMID:26709319

A combined solar and geomagnetic index for thermospheric climate.

PubMed

Mlynczak, Martin G; Hunt, Linda A; Marshall, B Thomas; Russell, James M; Mertens, Christopher J; Thompson, R Earl; Gordley, Larry L

2015-05-28

Infrared radiation from nitric oxide (NO) at 5.3 µm is a primary mechanism by which the thermosphere cools to space. The Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the NASA Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics satellite has been measuring thermospheric cooling by NO for over 13 years. In this letter we show that the SABER time series of globally integrated infrared power (watts) radiated by NO can be replicated accurately by a multiple linear regression fit using the F 10.7 , Ap , and Dst indices. This allows reconstruction of the NO power time series back nearly 70 years with extant databases of these indices. The relative roles of solar ultraviolet and geomagnetic processes in determining the NO cooling are derived and shown to vary significantly over the solar cycle. The NO power is a fundamental integral constraint on the thermospheric climate, and the time series presented here can be used to test upper atmosphere models over seven different solar cycles. F 10.7 , Ap , and Dst replicate time series of radiative cooling by nitric oxide Quantified relative roles of solar irradiance, geomagnetism in radiative cooling Establish a new index and extend record of thermospheric cooling back 70 years.

Quantifying Surface Water Dynamics at 30 Meter Spatial Resolution in the North American High Northern Latitudes 1991-2011

NASA Technical Reports Server (NTRS)

Carroll, Mark; Wooten, Margaret; DiMiceli, Charlene; Sohlberg, Robert; Kelly, Maureen

2016-01-01

The availability of a dense time series of satellite observations at moderate (30 m) spatial resolution is enabling unprecedented opportunities for understanding ecosystems around the world. A time series of data from Landsat was used to generate a series of three maps at decadal time step to show how surface water has changed from 1991 to 2011 in the high northern latitudes of North America. Previous attempts to characterize the change in surface water in this region have been limited in either spatial or temporal resolution, or both. This series of maps was generated for the NASA Arctic and Boreal Vulnerability Experiment (ABoVE), which began in fall 2015. These maps show a nominal extent of surface water by using multiple observations to make a single map for each time step. This increases the confidence that any detected changes are related to climate or ecosystem changes not simply caused by short duration weather events such as flood or drought. The methods and comparison to other contemporary maps of the region are presented here. Initial verification results indicate 96% producer accuracy and 54% user accuracy when compared to 2-m resolution World View-2 data. All water bodies that were omitted were one Landsat pixel or smaller, hence below detection limits of the instrument.

Aggregate Measures of Watershed Health from Reconstructed ...

EPA Pesticide Factsheets

Risk-based indices such as reliability, resilience, and vulnerability (R-R-V), have the potential to serve as watershed health assessment tools. Recent research has demonstrated the applicability of such indices for water quality (WQ) constituents such as total suspended solids and nutrients on an individual basis. However, the calculations can become tedious when time-series data for several WQ constituents have to be evaluated individually. Also, comparisons between locations with different sets of constituent data can prove difficult. In this study, data reconstruction using relevance vector machine algorithm was combined with dimensionality reduction via variational Bayesian noisy principal component analysis to reconstruct and condense sparse multidimensional WQ data sets into a single time series. The methodology allows incorporation of uncertainty in both the reconstruction and dimensionality-reduction steps. The R-R-V values were calculated using the aggregate time series at multiple locations within two Indiana watersheds. Results showed that uncertainty present in the reconstructed WQ data set propagates to the aggregate time series and subsequently to the aggregate R-R-V values as well. serving as motivating examples. Locations with different WQ constituents and different standards for impairment were successfully combined to provide aggregate measures of R-R-V values. Comparisons with individual constituent R-R-V values showed that v

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

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

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

Water Column Variability in Coastal Regions

DTIC Science & Technology

1997-09-30

to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data... 1 . REPORT DATE 30 SEP 1997 2. REPORT TYPE 3. DATES COVERED 00-00-1997 to 00-00-1997 4. TITLE AND SUBTITLE Water Column Variability in...Andrews, Woods, and Kester deployed a spar buoy at a central location in Narragansett Bay to obtain time-series variations at multiple depths ( 1 , 4

Automatic Multi-sensor Data Quality Checking and Event Detection for Environmental Sensing

NASA Astrophysics Data System (ADS)

LIU, Q.; Zhang, Y.; Zhao, Y.; Gao, D.; Gallaher, D. W.; Lv, Q.; Shang, L.

2017-12-01

With the advances in sensing technologies, large-scale environmental sensing infrastructures are pervasively deployed to continuously collect data for various research and application fields, such as air quality study and weather condition monitoring. In such infrastructures, many sensor nodes are distributed in a specific area and each individual sensor node is capable of measuring several parameters (e.g., humidity, temperature, and pressure), providing massive data for natural event detection and analysis. However, due to the dynamics of the ambient environment, sensor data can be contaminated by errors or noise. Thus, data quality is still a primary concern for scientists before drawing any reliable scientific conclusions. To help researchers identify potential data quality issues and detect meaningful natural events, this work proposes a novel algorithm to automatically identify and rank anomalous time windows from multiple sensor data streams. More specifically, (1) the algorithm adaptively learns the characteristics of normal evolving time series and (2) models the spatial-temporal relationship among multiple sensor nodes to infer the anomaly likelihood of a time series window for a particular parameter in a sensor node. Case studies using different data sets are presented and the experimental results demonstrate that the proposed algorithm can effectively identify anomalous time windows, which may resulted from data quality issues and natural events.

Products of multiple Fourier series with application to the multiblade transformation

NASA Technical Reports Server (NTRS)

Kunz, D. L.

1981-01-01

A relatively simple and systematic method for forming the products of multiple Fourier series using tensor like operations is demonstrated. This symbolic multiplication can be performed for any arbitrary number of series, and the coefficients of a set of linear differential equations with periodic coefficients from a rotating coordinate system to a nonrotating system is also demonstrated. It is shown that using Fourier operations to perform this transformation make it easily understood, simple to apply, and generally applicable.

Forecasting daily patient volumes in the emergency department.

PubMed

Jones, Spencer S; Thomas, Alun; Evans, R Scott; Welch, Shari J; Haug, Peter J; Snow, Gregory L

2008-02-01

Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

Validation of a national hydrological model

NASA Astrophysics Data System (ADS)

McMillan, H. K.; Booker, D. J.; Cattoën, C.

2016-10-01

Nationwide predictions of flow time-series are valuable for development of policies relating to environmental flows, calculating reliability of supply to water users, or assessing risk of floods or droughts. This breadth of model utility is possible because various hydrological signatures can be derived from simulated flow time-series. However, producing national hydrological simulations can be challenging due to strong environmental diversity across catchments and a lack of data available to aid model parameterisation. A comprehensive and consistent suite of test procedures to quantify spatial and temporal patterns in performance across various parts of the hydrograph is described and applied to quantify the performance of an uncalibrated national rainfall-runoff model of New Zealand. Flow time-series observed at 485 gauging stations were used to calculate Nash-Sutcliffe efficiency and percent bias when simulating between-site differences in daily series, between-year differences in annual series, and between-site differences in hydrological signatures. The procedures were used to assess the benefit of applying a correction to the modelled flow duration curve based on an independent statistical analysis. They were used to aid understanding of climatological, hydrological and model-based causes of differences in predictive performance by assessing multiple hypotheses that describe where and when the model was expected to perform best. As the procedures produce quantitative measures of performance, they provide an objective basis for model assessment that could be applied when comparing observed daily flow series with competing simulated flow series from any region-wide or nationwide hydrological model. Model performance varied in space and time with better scores in larger and medium-wet catchments, and in catchments with smaller seasonal variations. Surprisingly, model performance was not sensitive to aquifer fraction or rain gauge density.

The long-term changes in total ozone, as derived from Dobson measurements at Arosa (1948-2001)

NASA Astrophysics Data System (ADS)

Krzyscin, J. W.

2003-04-01

The longest possible total ozone time series (Arosa, Switzerland) is examined for a detection of trends. Two-step procedure is proposed to estimate the long-term (decadal) variations in the ozone time series. The first step consists of a standard least-squares multiple regression applied to the total ozone monthly means to parameterize "natural" (related to the oscillations in the atmospheric dynamics) variations in the analyzed time series. The standard proxies for the dynamical ozone variations are used including; the 11-year solar activity cycle, and indices of QBO, ENSO and NAO. We use the detrended time series of temperature at 100 hPa and 500 hPa over Arosa to parameterize short-term variations (with time periods<1 year) in total ozone related to local changes in the meteorological conditions over the station. The second step consists of a smooth-curve fitting to the total ozone residuals (original minus modeled "natural" time series), the time derivation applied to this curve to obtain local trends, and bootstrapping of the residual time series to estimate the standard error of local trends. Locally weighted regression and the wavelet analysis methodology are used to extract the smooth component out of the residual time series. The time integral over the local trend values provides the cumulative long-term change since the data beginning. Examining the pattern of the cumulative change we see the periods with total ozone loss (the end of 50s up to early 60s - probably the effect of the nuclear bomb tests), recovery (mid 60s up to beginning of 70s), apparent decrease (beginning of 70s lasting to mid 90s - probably the effect of the atmosphere contamination by anthropogenic substances containing chlorine), and with a kind of stabilization or recovery (starting in the mid of 90s - probably the effect of the Montreal protocol to eliminate substances reducing the ozone layer). We can also estimate that a full ozone recovery (return to the undisturbed total ozone level from the beginning of 70s) is expected around 2050. We propose to calculate both time series of local trends and the cumulative long-term change instead single trend value derived as a slope of straight line fit to the data.

Benchmarking homogenization algorithms for monthly data

NASA Astrophysics Data System (ADS)

Venema, V. K. C.; Mestre, O.; Aguilar, E.; Auer, I.; Guijarro, J. A.; Domonkos, P.; Vertacnik, G.; Szentimrey, T.; Stepanek, P.; Zahradnicek, P.; Viarre, J.; Müller-Westermeier, G.; Lakatos, M.; Williams, C. N.; Menne, M. J.; Lindau, R.; Rasol, D.; Rustemeier, E.; Kolokythas, K.; Marinova, T.; Andresen, L.; Acquaotta, F.; Fratianni, S.; Cheval, S.; Klancar, M.; Brunetti, M.; Gruber, C.; Prohom Duran, M.; Likso, T.; Esteban, P.; Brandsma, T.

2012-01-01

The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

Benchmarking monthly homogenization algorithms

NASA Astrophysics Data System (ADS)

Venema, V. K. C.; Mestre, O.; Aguilar, E.; Auer, I.; Guijarro, J. A.; Domonkos, P.; Vertacnik, G.; Szentimrey, T.; Stepanek, P.; Zahradnicek, P.; Viarre, J.; Müller-Westermeier, G.; Lakatos, M.; Williams, C. N.; Menne, M.; Lindau, R.; Rasol, D.; Rustemeier, E.; Kolokythas, K.; Marinova, T.; Andresen, L.; Acquaotta, F.; Fratianni, S.; Cheval, S.; Klancar, M.; Brunetti, M.; Gruber, C.; Prohom Duran, M.; Likso, T.; Esteban, P.; Brandsma, T.

2011-08-01

The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random break-type inhomogeneities were added to the simulated datasets modeled as a Poisson process with normally distributed breakpoint sizes. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones.

Model-Based Design of Long-Distance Tracer Transport Experiments in Plants.

PubMed

Bühler, Jonas; von Lieres, Eric; Huber, Gregor J

2018-01-01

Studies of long-distance transport of tracer isotopes in plants offer a high potential for functional phenotyping, but so far measurement time is a bottleneck because continuous time series of at least 1 h are required to obtain reliable estimates of transport properties. Hence, usual throughput values are between 0.5 and 1 samples h -1 . Here, we propose to increase sample throughput by introducing temporal gaps in the data acquisition of each plant sample and measuring multiple plants one after each other in a rotating scheme. In contrast to common time series analysis methods, mechanistic tracer transport models allow the analysis of interrupted time series. The uncertainties of the model parameter estimates are used as a measure of how much information was lost compared to complete time series. A case study was set up to systematically investigate different experimental schedules for different throughput scenarios ranging from 1 to 12 samples h -1 . Selected designs with only a small amount of data points were found to be sufficient for an adequate parameter estimation, implying that the presented approach enables a substantial increase of sample throughput. The presented general framework for automated generation and evaluation of experimental schedules allows the determination of a maximal sample throughput and the respective optimal measurement schedule depending on the required statistical reliability of data acquired by future experiments.

Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems

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

Chen, Jun; Rabiti, Cristian

Hybrid energy systems consisting of multiple energy inputs and multiple energy outputs have been proposed to be an effective element to enable ever increasing penetration of clean energy. In order to better understand the dynamic and probabilistic behavior of hybrid energy systems, this paper proposes a model combining Fourier series and autoregressive moving average (ARMA) to characterize historical weather measurements and to generate synthetic weather (e.g., wind speed) data. In particular, Fourier series is used to characterize the seasonal trend in historical data, while ARMA is applied to capture the autocorrelation in residue time series (e.g., measurements minus seasonal trends).more » The generated synthetic wind speed data is then utilized to perform probabilistic analysis of a particular hybrid energy system con guration, which consists of nuclear power plant, wind farm, battery storage, natural gas boiler, and chemical plant. As a result, requirements on component ramping rate, economic and environmental impacts of hybrid energy systems, and the effects of deploying different sizes of batteries in smoothing renewable variability, are all investigated.« less

Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems

DOE PAGES

Chen, Jun; Rabiti, Cristian

2016-11-25

Hybrid energy systems consisting of multiple energy inputs and multiple energy outputs have been proposed to be an effective element to enable ever increasing penetration of clean energy. In order to better understand the dynamic and probabilistic behavior of hybrid energy systems, this paper proposes a model combining Fourier series and autoregressive moving average (ARMA) to characterize historical weather measurements and to generate synthetic weather (e.g., wind speed) data. In particular, Fourier series is used to characterize the seasonal trend in historical data, while ARMA is applied to capture the autocorrelation in residue time series (e.g., measurements minus seasonal trends).more » The generated synthetic wind speed data is then utilized to perform probabilistic analysis of a particular hybrid energy system con guration, which consists of nuclear power plant, wind farm, battery storage, natural gas boiler, and chemical plant. As a result, requirements on component ramping rate, economic and environmental impacts of hybrid energy systems, and the effects of deploying different sizes of batteries in smoothing renewable variability, are all investigated.« less

A Combined Solar and Geomagnetic Index for Thermospheric Climate

NASA Technical Reports Server (NTRS)

Hunt, Linda; Mlynczak, Marty

2015-01-01

Infrared radiation from nitric oxide (NO) at 5.3 Â is a primary mechanism by which the thermosphere cools to space. The SABER instrument on the NASA TIMED satellite has been measuring thermospheric cooling by NO for over 13 years. Physically, changes in NO emission are due to changes in temperature, atomic oxygen, and the NO density. These physical changes however are driven by changes in solar irradiance and changes in geomagnetic conditions. We show that the SABER time series of globally integrated infrared power (Watts) radiated by NO can be replicated accurately by a multiple linear regression fit using the F10.7, Ap, and Dst indices. This fit enables several fundamental properties of NO cooling to be determined as well as their variability with time, permitting reconstruction of the NO power time series back nearly 70 years with extant databases of these indices. The relative roles of solar ultraviolet and geomagnetic processes in determining the NO cooling are derived and shown to be solar cycle dependent. This reconstruction provides a long-term time series of an integral radiative constraint on thermospheric climate that can be used to test climate models.

Characterizing a decade of behavior at Volcán de Colima, Mexico using long term InSAR and thermal remote sensing data

NASA Astrophysics Data System (ADS)

Sorge, J.; Williams-Jones, G.; Wright, R.; Varley, N. R.

2010-12-01

Satellite imagery is playing an increasingly prominent role in volcanology as it allows for consistent monitoring of remote, dangerous, and/or under-monitored volcanoes. One such system is Volcán de Colima (Mexico), a persistently active andesitic stratovolcano. Its characteristic and hazardous activity includes lava dome growth, pyroclastic flows, explosions, and Plinian to Subplinian eruptions, which have historically occurred at the end of Volcán de Colima’s eruptive cycle. Despite the availability of large amounts of historical satellite imagery, methods to process and interpret these images over long time periods are limited. Furthermore, while time-series InSAR data from a previous study (December 2002 to August 2006) detected an overall subsidence between 1 and 3 km from the summit, there is insufficient temporal resolution to unambiguously constrain the source processes. To address this issue, a semi-automated process for time-based characterization of persistent volcanic activity at Volcán de Colima has been developed using a combination of MODIS and GOES satellite imagery to identify thermal anomalies on the volcano edifice. This satellite time-series data is then combined with available geodetic data, a detailed eruption history, and other geophysical time-series data (e.g., seismicity, explosions/day, effusion rate, environmental data, etc.) and examined for possible correlations and recurring patterns in the multiple data sets to investigate potential trigger mechanisms responsible for the changes in volcanic activity. GOES and MODIS images are available from 2000 to present at a temporal resolution of one image every 30 minutes and up to four images per day, respectively, creating a data set of approximately 180,000 images. Thermal anomalies over Volcán de Colima are identified in both night- and day-time images by applying a time-series approach to the analysis of MODIS data. Detection of false anomalies, caused by non-volcanic heat sources such as fires or solar heating (in the daytime images), is mitigated by adjusting the MODIS detection thresholds, through comparison of daytime versus nighttime results, and by observing the spatial distribution of the anomalies on the edifice. Conversely, anomalies may not be detected due to cloud cover; clouds absorb thermal radiation limiting or preventing the ability of the satellite to measure thermal events; therefore, the anomaly data is supplemented with a cloud cover time-series data set. Fast Fourier and Wavelet transforms are then applied to the continuous, uninterrupted intervals of satellite observation to compare and correlate with the multiple time-series data sets. The result is the characterization of the behavior of an individual volcano, based on an extended time period. This volcano specific, comprehensive characterization can then be used as a predictive tool in the real-time monitoring of volcanic activity.

Two-pass imputation algorithm for missing value estimation in gene expression time series.

PubMed

Tsiporkova, Elena; Boeva, Veselka

2007-10-01

Gene expression microarray experiments frequently generate datasets with multiple values missing. However, most of the analysis, mining, and classification methods for gene expression data require a complete matrix of gene array values. Therefore, the accurate estimation of missing values in such datasets has been recognized as an important issue, and several imputation algorithms have already been proposed to the biological community. Most of these approaches, however, are not particularly suitable for time series expression profiles. In view of this, we propose a novel imputation algorithm, which is specially suited for the estimation of missing values in gene expression time series data. The algorithm utilizes Dynamic Time Warping (DTW) distance in order to measure the similarity between time expression profiles, and subsequently selects for each gene expression profile with missing values a dedicated set of candidate profiles for estimation. Three different DTW-based imputation (DTWimpute) algorithms have been considered: position-wise, neighborhood-wise, and two-pass imputation. These have initially been prototyped in Perl, and their accuracy has been evaluated on yeast expression time series data using several different parameter settings. The experiments have shown that the two-pass algorithm consistently outperforms, in particular for datasets with a higher level of missing entries, the neighborhood-wise and the position-wise algorithms. The performance of the two-pass DTWimpute algorithm has further been benchmarked against the weighted K-Nearest Neighbors algorithm, which is widely used in the biological community; the former algorithm has appeared superior to the latter one. Motivated by these findings, indicating clearly the added value of the DTW techniques for missing value estimation in time series data, we have built an optimized C++ implementation of the two-pass DTWimpute algorithm. The software also provides for a choice between three different initial rough imputation methods.

Time-series analysis to study the impact of an intersection on dispersion along a street canyon.

PubMed

Richmond-Bryant, Jennifer; Eisner, Alfred D; Hahn, Intaek; Fortune, Christopher R; Drake-Richman, Zora E; Brixey, Laurie A; Talih, M; Wiener, Russell W; Ellenson, William D

2009-12-01

This paper presents data analysis from the Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) study to assess the transport of ultrafine particulate matter (PM) across urban intersections. Experiments were performed in a street canyon perpendicular to a highway in Brooklyn, NY, USA. Real-time ultrafine PM samplers were positioned on either side of an intersection at multiple locations along a street to collect time-series number concentration data. Meteorology equipment was positioned within the street canyon and at an upstream background site to measure wind speed and direction. Time-series analysis was performed on the PM data to compute a transport velocity along the direction of the street for the cases where background winds were parallel and perpendicular to the street. The data were analyzed for sampler pairs located (1) on opposite sides of the intersection and (2) on the same block. The time-series analysis demonstrated along-street transport, including across the intersection when background winds were parallel to the street canyon and there was minimal transport and no communication across the intersection when background winds were perpendicular to the street canyon. Low but significant values of the cross-correlation function (CCF) underscore the turbulent nature of plume transport along the street canyon. The low correlations suggest that flow switching around corners or traffic-induced turbulence at the intersection may have aided dilution of the PM plume from the highway. This observation supports similar findings in the literature. Furthermore, the time-series analysis methodology applied in this study is introduced as a technique for studying spatiotemporal variation in the urban microscale environment.

Undertaking Experiments in Social Sciences: Sequential, Multiple Time Series Designs for Consideration

ERIC Educational Resources Information Center

Phan, Huy P.; Ngu, Bing H.

2017-01-01

In social sciences, the use of stringent methodological approaches is gaining increasing emphasis. Researchers have recognized the limitations of cross-sectional, non-manipulative data in the study of causality. True experimental designs, in contrast, are preferred as they represent rigorous standards for achieving causal flows between variables.…

Precision of Curriculum-Based Measurement Reading Data: Considerations for Multiple-Baseline Designs

ERIC Educational Resources Information Center

Klingbeil, David A.; Van Norman, Ethan R.; Nelson, Peter M.

2017-01-01

Single-case designs provide an established technology for evaluating the effects of academic interventions. Researchers interested in studying the long-term effects of reading interventions often use curriculum-based measures of reading (CBM-R) as they possess many of the desirable characteristics for use in a time-series design. The reliability…

40 CFR 63.11511 - What definitions apply to this subpart?

Code of Federal Regulations, 2012 CFR

2012-07-01

... eliminator except that the device is designed with multiple pads in series that are woven with layers of... unit (i.e., as a batch) for a predetermined period of time, during which none of the parts are removed... given capture system design: duct intake devices, hoods, enclosures, ductwork, dampers, manifolds...

Bayesian Estimation of Random Coefficient Dynamic Factor Models

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2012-01-01

Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across…

A Comparison of Joint Model and Fully Conditional Specification Imputation for Multilevel Missing Data

ERIC Educational Resources Information Center

Mistler, Stephen A.; Enders, Craig K.

2017-01-01

Multiple imputation methods can generally be divided into two broad frameworks: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution, whereas FCS imputes variables one at a time from a series of univariate conditional…

Military Enlistments: What Can We Learn from Geographic Variation? Technical Report 620.

ERIC Educational Resources Information Center

Brown, Charles

Some economic variables were examined that affect enlistment decisions and therefore affect the continued success of the All-Volunteer Force. The study used a multiple regression, pooled cross-section/time-series model over the 1975-1982 period, including pay, unemployment, educational benefits, and recruiting resources as independent variables.…

Special Education in General Education Classrooms: Cooperative Teaching Using Supportive Learning Activities.

ERIC Educational Resources Information Center

Johnson, Robin R.; And Others

1995-01-01

Supportive learning activities were implemented in a multiple-baseline time series design across four fifth-grade classrooms to evaluate the effects of a cooperative teaching alternative (supportive learning) on teaching behavior, the behavior and grades of general and special education students, and the opinions of general education teachers.…

Fiscal Impacts and Redistributive Effects of the New Federalism on Michigan School Districts.

ERIC Educational Resources Information Center

Kearney, C. Philip; Kim, Taewan

1990-01-01

The fiscal impacts and redistribution effects of the recently enacted (1981) federal education block grant on 525 elementary and secondary school districts in Michigan were examined using a quasi-experimental time-series design and multiple regression and analysis of covariance techniques. Implications of changes in federal policy are discussed.…

40 CFR 63.11511 - What definitions apply to this subpart?

Code of Federal Regulations, 2014 CFR

2014-07-01

... eliminator except that the device is designed with multiple pads in series that are woven with layers of... unit (i.e., as a batch) for a predetermined period of time, during which none of the parts are removed... given capture system design: duct intake devices, hoods, enclosures, ductwork, dampers, manifolds...

40 CFR 63.11511 - What definitions apply to this subpart?

Code of Federal Regulations, 2013 CFR

2013-07-01

... eliminator except that the device is designed with multiple pads in series that are woven with layers of... unit (i.e., as a batch) for a predetermined period of time, during which none of the parts are removed... given capture system design: duct intake devices, hoods, enclosures, ductwork, dampers, manifolds...

40 CFR 63.11511 - What definitions apply to this subpart?

Code of Federal Regulations, 2010 CFR

2010-07-01

... eliminator except that the device is designed with multiple pads in series that are woven with layers of... unit (i.e., as a batch) for a predetermined period of time, during which none of the parts are removed... given capture system design: duct intake devices, hoods, enclosures, ductwork, dampers, manifolds...

Past Asian Monsoon circulation from multiple tree-ring proxies and models (Invited)

NASA Astrophysics Data System (ADS)

Anchukaitis, K. J.; Herzog, M.; Hernandez, M.; Martin-Benito, D.; Gagen, M.; LeGrande, A. N.; Ummenhofer, C.; Buckley, B.; Cook, E. R.

2013-12-01

The Asian monsoon can be characterized in terms of precipitation variability as well as features of regional atmospheric circulation across a range of spatial and temporal scales. While multicentury time series of tree-ring widths at hundreds of sites across Asia provide estimates of past rainfall, the oxygen isotope ratios of annual rings at some of these sites can reveal broader regional atmosphere-ocean dynamics. Here we present a replicated, multicentury stable isotope series from Vietnam that integrates the influence of monsoon circulation on water isotopes. Stronger (weaker) monsoon flow over Indochina is associated with lower (higher) oxygen isotope values in our long-lived tropical conifers. Ring width and isotopes show particular coherence at multidecadal time scales, and together allow past precipitation amount and circulation strength to be disentangled. Combining multiple tree-ring proxies with simulations from isotope-enabled and paleoclimate general circulation models allows us to independently assess the mechanisms responsible for proxy formation and to evaluate how monsoon rainfall is influenced by ocean-atmosphere interactions at timescales from interannual to multidecadal.

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

ℓ p-Norm Multikernel Learning Approach for Stock Market Price Forecasting

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model. PMID:23365561

Time series geophysical monitoring of permanganate injections and in situ chemical oxidation of PCE, OU1 area, Savage Superfund Site, Milford, NH, USA

NASA Astrophysics Data System (ADS)

Harte, Philip T.; Smith, Thor E.; Williams, John H.; Degnan, James R.

2012-05-01

In situ chemical oxidation (ISCO) treatment with sodium permanganate, an electrically conductive oxidant, provides a strong electrical signal for tracking of injectate transport using time series geophysical surveys including direct current (DC) resistivity and electromagnetic (EM) methods. Effective remediation is dependent upon placing the oxidant in close contact with the contaminated aquifer. Therefore, monitoring tools that provide enhanced tracking capability of the injectate offer considerable benefit to guide subsequent ISCO injections. Time-series geophysical surveys were performed at a superfund site in New Hampshire, USA over a one-year period to identify temporal changes in the bulk electrical conductivity of a tetrachloroethylene (PCE; also called tetrachloroethene) contaminated, glacially deposited aquifer due to the injection of sodium permanganate. The ISCO treatment involved a series of pulse injections of sodium permanganate from multiple injection wells within a contained area of the aquifer. After the initial injection, the permanganate was allowed to disperse under ambient groundwater velocities. Time series geophysical surveys identified the downward sinking and pooling of the sodium permanganate atop of the underlying till or bedrock surface caused by density-driven flow, and the limited horizontal spread of the sodium permanganate in the shallow parts of the aquifer during this injection period. When coupled with conventional monitoring, the surveys allowed for an assessment of ISCO treatment effectiveness in targeting the PCE plume and helped target areas for subsequent treatment.

Time series geophysical monitoring of permanganate injections and in situ chemical oxidation of PCE, OU1 area, Savage Superfund Site, Milford, NH, USA

USGS Publications Warehouse

Harte, Philip T.; Smith, Thor E.; Williams, John H.; Degnan, James R.

2012-01-01

In situ chemical oxidation (ISCO) treatment with sodium permanganate, an electrically conductive oxidant, provides a strong electrical signal for tracking of injectate transport using time series geophysical surveys including direct current (DC) resistivity and electromagnetic (EM) methods. Effective remediation is dependent upon placing the oxidant in close contact with the contaminated aquifer. Therefore, monitoring tools that provide enhanced tracking capability of the injectate offer considerable benefit to guide subsequent ISCO injections. Time-series geophysical surveys were performed at a superfund site in New Hampshire, USA over a one-year period to identify temporal changes in the bulk electrical conductivity of a tetrachloroethylene (PCE; also called tetrachloroethene) contaminated, glacially deposited aquifer due to the injection of sodium permanganate. The ISCO treatment involved a series of pulse injections of sodium permanganate from multiple injection wells within a contained area of the aquifer. After the initial injection, the permanganate was allowed to disperse under ambient groundwater velocities. Time series geophysical surveys identified the downward sinking and pooling of the sodium permanganate atop of the underlying till or bedrock surface caused by density-driven flow, and the limited horizontal spread of the sodium permanganate in the shallow parts of the aquifer during this injection period. When coupled with conventional monitoring, the surveys allowed for an assessment of ISCO treatment effectiveness in targeting the PCE plume and helped target areas for subsequent treatment.

Time series geophysical monitoring of permanganate injections and in situ chemical oxidation of PCE, OU1 area, Savage Superfund Site, Milford, NH, USA.

PubMed

Harte, Philip T; Smith, Thor E; Williams, John H; Degnan, James R

2012-05-01

In situ chemical oxidation (ISCO) treatment with sodium permanganate, an electrically conductive oxidant, provides a strong electrical signal for tracking of injectate transport using time series geophysical surveys including direct current (DC) resistivity and electromagnetic (EM) methods. Effective remediation is dependent upon placing the oxidant in close contact with the contaminated aquifer. Therefore, monitoring tools that provide enhanced tracking capability of the injectate offer considerable benefit to guide subsequent ISCO injections. Time-series geophysical surveys were performed at a superfund site in New Hampshire, USA over a one-year period to identify temporal changes in the bulk electrical conductivity of a tetrachloroethylene (PCE; also called tetrachloroethene) contaminated, glacially deposited aquifer due to the injection of sodium permanganate. The ISCO treatment involved a series of pulse injections of sodium permanganate from multiple injection wells within a contained area of the aquifer. After the initial injection, the permanganate was allowed to disperse under ambient groundwater velocities. Time series geophysical surveys identified the downward sinking and pooling of the sodium permanganate atop of the underlying till or bedrock surface caused by density-driven flow, and the limited horizontal spread of the sodium permanganate in the shallow parts of the aquifer during this injection period. When coupled with conventional monitoring, the surveys allowed for an assessment of ISCO treatment effectiveness in targeting the PCE plume and helped target areas for subsequent treatment. Published by Elsevier B.V.

'TIME': A Web Application for Obtaining Insights into Microbial Ecology Using Longitudinal Microbiome Data.

PubMed

Baksi, Krishanu D; Kuntal, Bhusan K; Mande, Sharmila S

2018-01-01

Realization of the importance of microbiome studies, coupled with the decreasing sequencing cost, has led to the exponential growth of microbiome data. A number of these microbiome studies have focused on understanding changes in the microbial community over time. Such longitudinal microbiome studies have the potential to offer unique insights pertaining to the microbial social networks as well as their responses to perturbations. In this communication, we introduce a web based framework called 'TIME' (Temporal Insights into Microbial Ecology'), developed specifically to obtain meaningful insights from microbiome time series data. The TIME web-server is designed to accept a wide range of popular formats as input with options to preprocess and filter the data. Multiple samples, defined by a series of longitudinal time points along with their metadata information, can be compared in order to interactively visualize the temporal variations. In addition to standard microbiome data analytics, the web server implements popular time series analysis methods like Dynamic time warping, Granger causality and Dickey Fuller test to generate interactive layouts for facilitating easy biological inferences. Apart from this, a new metric for comparing metagenomic time series data has been introduced to effectively visualize the similarities/differences in the trends of the resident microbial groups. Augmenting the visualizations with the stationarity information pertaining to the microbial groups is utilized to predict the microbial competition as well as community structure. Additionally, the 'causality graph analysis' module incorporated in TIME allows predicting taxa that might have a higher influence on community structure in different conditions. TIME also allows users to easily identify potential taxonomic markers from a longitudinal microbiome analysis. We illustrate the utility of the web-server features on a few published time series microbiome data and demonstrate the ease with which it can be used to perform complex analysis.

Time Series Analysis of Technology Trends based on the Internet Resources

NASA Astrophysics Data System (ADS)

Kobayashi, Shin-Ichi; Shirai, Yasuyuki; Hiyane, Kazuo; Kumeno, Fumihiro; Inujima, Hiroshi; Yamauchi, Noriyoshi

Information technology is increasingly important in recent years for the development of our society. IT has brought many changes to everything in our society with incredible speed. Hence, when we investigate R & D themes or plan business strategies in IT, we must understand overall situation around the target technology area besides technology itself. Especially it is crucial to understand overall situation as time series to know what will happen in the near future in the target area. For this purpose, we developed a method to generate Multiple-phased trend maps automatically based on the Internet content. Furthermore, we introduced quantitative indicators to analyze near future possible changes. According to the evaluation of this method we got successful and interesting results.

Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.

PubMed

Faes, Luca; Nollo, Giandomenico

2010-11-01

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.

Selecting and applying indicators of ecosystem collapse for risk assessments.

PubMed

Rowland, Jessica A; Nicholson, Emily; Murray, Nicholas J; Keith, David A; Lester, Rebecca E; Bland, Lucie M

2018-03-12

Ongoing ecosystem degradation and transformation are key threats to biodiversity. Measuring ecosystem change towards collapse relies on monitoring indicators that quantify key ecological processes. Yet little guidance is available on selecting and implementing indicators for ecosystem risk assessment. Here, we reviewed indicator use in ecological studies of decline towards collapse in marine pelagic and temperate forest ecosystems. We evaluated the use of indicator selection methods, indicator types (geographic distribution, abiotic, biotic), methods of assessing multiple indicators, and temporal quality of time series. We compared these ecological studies to risk assessments in the International Union for the Conservation of Nature Red List of Ecosystems (RLE), where indicators are used to estimate ecosystem collapse risk. We found that ecological studies and RLE assessments rarely reported how indicators were selected, particularly in terrestrial ecosystems. Few ecological studies and RLE assessments quantified ecosystem change with all three indicator types, and indicators types used varied between marine and terrestrial ecosystem. Several studies used indices or multivariate analyses to assess multiple indicators simultaneously, but RLE assessments did not, as RLE guidelines advise against them. Most studies and RLE assessments used time series spanning at least 30 years, increasing the chance of reliably detecting change. Limited use of indicator selection protocols and infrequent use of all three indicator types may hamper the ability to accurately detect changes. To improve the value of risk assessments for informing policy and management, we recommend using: (i) explicit protocols, including conceptual models, to identify and select indicators; (ii) a range of indicators spanning distributional, abiotic and biotic features; (iii) indices and multivariate analyses with extreme care until guidelines are developed; (iv) time series with sufficient data to increase ability to accurately diagnose directional change; (v) data from multiple sources to support assessments; and (vi) explicitly reporting steps in the assessment process. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Detecting time-specific differences between temporal nonlinear curves: Analyzing data from the visual world paradigm

PubMed Central

Oleson, Jacob J; Cavanaugh, Joseph E; McMurray, Bob; Brown, Grant

2015-01-01

In multiple fields of study, time series measured at high frequencies are used to estimate population curves that describe the temporal evolution of some characteristic of interest. These curves are typically nonlinear, and the deviations of each series from the corresponding curve are highly autocorrelated. In this scenario, we propose a procedure to compare the response curves for different groups at specific points in time. The method involves fitting the curves, performing potentially hundreds of serially correlated tests, and appropriately adjusting the overall alpha level of the tests. Our motivating application comes from psycholinguistics and the visual world paradigm. We describe how the proposed technique can be adapted to compare fixation curves within subjects as well as between groups. Our results lead to conclusions beyond the scope of previous analyses. PMID:26400088

Near Real-Time Event Detection & Prediction Using Intelligent Software Agents

DTIC Science & Technology

2006-03-01

value was 0.06743. Multiple autoregressive integrated moving average ( ARIMA ) models were then build to see if the raw data, differenced data, or...slight improvement. The best adjusted r^2 value was found to be 0.1814. Successful results were not expected from linear or ARIMA -based modelling ...appear, 2005. [63] Mora-Lopez, L., Mora, J., Morales-Bueno, R., et al. Modelling time series of climatic parameters with probabilistic finite

Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming.

PubMed

Ostrowski, M; Paulevé, L; Schaub, T; Siegel, A; Guziolowski, C

2016-11-01

Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions.

PubMed

Sharmin, Moushumi; Raij, Andrew; Epstien, David; Nahum-Shani, Inbal; Beck, J Gayle; Vhaduri, Sudip; Preston, Kenzie; Kumar, Santosh

2015-09-01

We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.

Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions

PubMed Central

Sharmin, Moushumi; Raij, Andrew; Epstien, David; Nahum-Shani, Inbal; Beck, J. Gayle; Vhaduri, Sudip; Preston, Kenzie; Kumar, Santosh

2015-01-01

We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs. PMID:26539566

EverVIEW: a visualization platform for hydrologic and Earth science gridded data

USGS Publications Warehouse

Romañach, Stephanie S.; McKelvy, James M.; Suir, Kevin J.; Conzelmann, Craig

2015-01-01

The EverVIEW Data Viewer is a cross-platform desktop application that combines and builds upon multiple open source libraries to help users to explore spatially-explicit gridded data stored in Network Common Data Form (NetCDF). Datasets are displayed across multiple side-by-side geographic or tabular displays, showing colorized overlays on an Earth globe or grid cell values, respectively. Time-series datasets can be animated to see how water surface elevation changes through time or how habitat suitability for a particular species might change over time under a given scenario. Initially targeted toward Florida's Everglades restoration planning, EverVIEW has been flexible enough to address the varied needs of large-scale planning beyond Florida, and is currently being used in biological planning efforts nationally and internationally.

Le Chatelier's principle with multiple relaxation channels

NASA Astrophysics Data System (ADS)

Gilmore, R.; Levine, R. D.

1986-05-01

Le Chatelier's principle is discussed within the constrained variational approach to thermodynamics. The formulation is general enough to encompass systems not in thermal (or chemical) equilibrium. Particular attention is given to systems with multiple constraints which can be relaxed. The moderation of the initial perturbation increases as additional constraints are removed. This result is studied in particular when the (coupled) relaxation channels have widely different time scales. A series of inequalities is derived which describes the successive moderation as each successive relaxation channel opens up. These inequalities are interpreted within the metric-geometry representation of thermodynamics.

Projection of landfill stabilization period by time series analysis of leachate quality and transformation trends of VOCs.

PubMed

Sizirici, Banu; Tansel, Berrin

2010-01-01

The purpose of this study was to evaluate suitability of using the time series analysis for selected leachate quantity and quality parameters to forecast the duration of post closure period of a closed landfill. Selected leachate quality parameters (i.e., sodium, chloride, iron, bicarbonate, total dissolved solids (TDS), and ammonium as N) and volatile organic compounds (VOCs) (i.e., vinyl chloride, 1,4-dichlorobenzene, chlorobenzene, benzene, toluene, ethyl benzene, xylenes, total BTEX) were analyzed by the time series multiplicative decomposition model to estimate the projected levels of the parameters. These parameters were selected based on their detection levels and consistency of detection in leachate samples. In addition, VOCs detected in leachate and their chemical transformations were considered in view of the decomposition stage of the landfill. Projected leachate quality trends were analyzed and compared with the maximum contaminant level (MCL) for the respective parameters. Conditions that lead to specific trends (i.e., increasing, decreasing, or steady) and interactions of leachate quality parameters were evaluated. Decreasing trends were projected for leachate quantity, concentrations of sodium, chloride, TDS, ammonia as N, vinyl chloride, 1,4-dichlorobenzene, benzene, toluene, ethyl benzene, xylenes, and total BTEX. Increasing trends were projected for concentrations of iron, bicarbonate, and chlorobenzene. Anaerobic conditions in landfill provide favorable conditions for corrosion of iron resulting in higher concentrations over time. Bicarbonate formation as a byproduct of bacterial respiration during waste decomposition and the lime rock cap system of the landfill contribute to the increasing levels of bicarbonate in leachate. Chlorobenzene is produced during anaerobic biodegradation of 1,4-dichlorobenzene, hence, the increasing trend of chlorobenzene may be due to the declining trend of 1,4-dichlorobenzene. The time series multiplicative decomposition model in general provides an adequate forecast for future planning purposes for the parameters monitored in leachate. The model projections for 1,4-dichlorobenzene were relatively less accurate in comparison to the projections for vinyl chloride and chlorobenzene. Based on the trends observed, future monitoring needs for the selected leachate parameters were identified.

Rating knowledge sharing in cross-domain collaborative filtering.

PubMed

Li, Bin; Zhu, Xingquan; Li, Ruijiang; Zhang, Chengqi

2015-05-01

Cross-domain collaborative filtering (CF) aims to share common rating knowledge across multiple related CF domains to boost the CF performance. In this paper, we view CF domains as a 2-D site-time coordinate system, on which multiple related domains, such as similar recommender sites or successive time-slices, can share group-level rating patterns. We propose a unified framework for cross-domain CF over the site-time coordinate system by sharing group-level rating patterns and imposing user/item dependence across domains. A generative model, say ratings over site-time (ROST), which can generate and predict ratings for multiple related CF domains, is developed as the basic model for the framework. We further introduce cross-domain user/item dependence into ROST and extend it to two real-world cross-domain CF scenarios: 1) ROST (sites) for alleviating rating sparsity in the target domain, where multiple similar sites are viewed as related CF domains and some items in the target domain depend on their correspondences in the related ones; and 2) ROST (time) for modeling user-interest drift over time, where a series of time-slices are viewed as related CF domains and a user at current time-slice depends on herself in the previous time-slice. All these ROST models are instances of the proposed unified framework. The experimental results show that ROST (sites) can effectively alleviate the sparsity problem to improve rating prediction performance and ROST (time) can clearly track and visualize user-interest drift over time.

COMPACT CASCADE IMPACTS

DOEpatents

Lippmann, M.

1964-04-01

A cascade particle impactor capable of collecting particles and distributing them according to size is described. In addition the device is capable of collecting on a pair of slides a series of different samples so that less time is required for the changing of slides. Other features of the device are its compactness and its ruggedness making it useful under field conditions. Essentially the unit consists of a main body with a series of transverse jets discharging on a pair of parallel, spaced glass plates. The plates are capable of being moved incremental in steps to obtain the multiple samples. (AEC)

Evaluation of the impact of fetal fibronectin test implementation on hospital admissions for preterm labour in Ontario: a multiple baseline time-series design.

PubMed

Fell, D B; Sprague, A E; Grimshaw, J M; Yasseen, A S; Coyle, D; Dunn, S I; Perkins, S L; Peterson, W E; Johnson, M; Bunting, P S; Walker, M C

2014-03-01

To determine the impact of a health system-wide fetal fibronectin (fFN) testing programme on the rates of hospital admission for preterm labour (PTL). Multiple baseline time-series design. Canadian province of Ontario. A retrospective population-based cohort of antepartum and delivered obstetrical admissions in all Ontario hospitals between 1 April 2002 and 31 March 2010. International Classification of Diseases codes in a health system-wide hospital administrative database were used to identify the study population and define the outcome measure. An aggregate time series of monthly rates of hospital admissions for PTL was analysed using segmented regression models after aligning the fFN test implementation date for each institution. Rate of obstetrical hospital admission for PTL. Estimated rates of hospital admission for PTL following fFN implementation were lower than predicted had pre-implementation trends prevailed. The reduction in the rate was modest, but statistically significant, when estimated at 12 months following fFN implementation (-0.96 hospital admissions for PTL per 100 preterm births; 95% confidence interval [CI], -1.02 to -0.90, P = 0.04). The statistically significant reduction was sustained at 24 and 36 months following implementation. Using a robust quasi-experimental study design to overcome confounding as a result of underlying secular trends or concurrent interventions, we found evidence of a small but statistically significant reduction in the health system-level rate of hospital admissions for PTL following implementation of fFN testing in a large Canadian province. © 2013 Royal College of Obstetricians and Gynaecologists.

Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring †

PubMed Central

Mao, Yingchi; Qi, Hai; Ping, Ping; Li, Xiaofang

2017-01-01

Time series data of multiple water quality parameters are obtained from the water sensor networks deployed in the agricultural water supply network. The accurate and efficient detection and warning of contamination events to prevent pollution from spreading is one of the most important issues when pollution occurs. In order to comprehensively reduce the event detection deviation, a spatial–temporal-based event detection approach with multivariate time-series data for water quality monitoring (M-STED) was proposed. The M-STED approach includes three parts. The first part is that M-STED adopts a Rule K algorithm to select backbone nodes as the nodes in the CDS, and forward the sensed data of multiple water parameters. The second part is to determine the state of each backbone node with back propagation neural network models and the sequential Bayesian analysis in the current timestamp. The third part is to establish a spatial model with Bayesian networks to estimate the state of the backbones in the next timestamp and trace the “outlier” node to its neighborhoods to detect a contamination event. The experimental results indicate that the average detection rate is more than 80% with M-STED and the false detection rate is lower than 9%, respectively. The M-STED approach can improve the rate of detection by about 40% and reduce the false alarm rate by about 45%, compared with the event detection with a single water parameter algorithm, S-STED. Moreover, the proposed M-STED can exhibit better performance in terms of detection delay and scalability. PMID:29207535

ePRISM: A case study in multiple proxy and mixed temporal resolution integration

USGS Publications Warehouse

Robinson, Marci M.; Dowsett, Harry J.

2010-01-01

As part of the Pliocene Research, Interpretation and Synoptic Mapping (PRISM) Project, we present the ePRISM experiment designed I) to provide climate modelers with a reconstruction of an early Pliocene warm period that was warmer than the PRISM interval (similar to 3.3 to 3.0 Ma), yet still similar in many ways to modern conditions and 2) to provide an example of how best to integrate multiple-proxy sea surface temperature (SST) data from time series with varying degrees of temporal resolution and age control as we begin to build the next generation of PRISM, the PRISM4 reconstruction, spanning a constricted time interval. While it is possible to tie individual SST estimates to a single light (warm) oxygen isotope event, we find that the warm peak average of SST estimates over a narrowed time interval is preferential for paleoclimate reconstruction as it allows for the inclusion of more records of multiple paleotemperature proxies.

Gravity Tides Extracted from Relative Gravimeter Data by Combining Empirical Mode Decomposition and Independent Component Analysis

NASA Astrophysics Data System (ADS)

Yu, Hongjuan; Guo, Jinyun; Kong, Qiaoli; Chen, Xiaodong

2018-04-01

The static observation data from a relative gravimeter contain noise and signals such as gravity tides. This paper focuses on the extraction of the gravity tides from the static relative gravimeter data for the first time applying the combined method of empirical mode decomposition (EMD) and independent component analysis (ICA), called the EMD-ICA method. The experimental results from the CG-5 gravimeter (SCINTREX Limited Ontario Canada) data show that the gravity tides time series derived by EMD-ICA are consistent with the theoretical reference (Longman formula) and the RMS of their differences only reaches 4.4 μGal. The time series of the gravity tides derived by EMD-ICA have a strong correlation with the theoretical time series and the correlation coefficient is greater than 0.997. The accuracy of the gravity tides estimated by EMD-ICA is comparable to the theoretical model and is slightly higher than that of independent component analysis (ICA). EMD-ICA could overcome the limitation of ICA having to process multiple observations and slightly improve the extraction accuracy and reliability of gravity tides from relative gravimeter data compared to that estimated with ICA.

Statistics for Time-Series Spatial Data: Applying Survival Analysis to Study Land-Use Change

ERIC Educational Resources Information Center

Wang, Ninghua Nathan

2013-01-01

Traditional spatial analysis and data mining methods fall short of extracting temporal information from data. This inability makes their use difficult to study changes and the associated mechanisms of many geographic phenomena of interest, for example, land-use. On the other hand, the growing availability of land-change data over multiple time…

78 FR 39260 - Applications for New Awards; Technical Assistance and Dissemination To Improve Services and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-01

... extend over a period of time, such as facilitating a series of conference calls on single or multiple... are supported by scientifically based research. Priority: In accordance with 34 CFR 75.105(b)(2)(v... (OSEP) has supported and other research, we know that direct and intensive supports and services are...

40 CFR 63.11511 - What definitions apply to this subpart?

Code of Federal Regulations, 2011 CFR

2011-07-01

... device is designed with multiple pads in series that are woven with layers of material with varying fiber... of time, during which none of the parts are removed from the tank and no other parts are added to the..., but is not limited to, the following components as applicable to a given capture system design: duct...

Testing and Evaluating C3I Systems That Employ AI. Volume 1. Handbook for Testing Expert Systems

DTIC Science & Technology

1991-01-31

Designs ....... ............. .. 6-29 Nonequivalent Control Group Design ...does not receive the system; and (c) nonequivalent (and nonrandomized) control group designs that rely on statistical techniques like analysis of...implementation); (b) multiple time-series designs using a control group ; and (c) nonequivalent control group designs that obtain pretest and

Market structure in U.S. southern pine roundwood

Treesearch

Matthew F. Bingham; Jeffrey P. Prestemon; Douglas J. MacNair; Robert C. Abt

2003-01-01

Time series of commodity prices from multiple locations can behave as if responding to forces of spatial arbitrage. cvcn while such prices may instead be responding similarly to common factors aside from spatial arbitrage. Hence, while the Law of One Price may hold as a statistical concept, its acceptance is not sufficient to conclude market integration. We tested...

Alcohol and liver cirrhosis mortality in the United States: comparison of methods for the analyses of time-series panel data models.

PubMed

Ye, Yu; Kerr, William C

2011-01-01

To explore various model specifications in estimating relationships between liver cirrhosis mortality rates and per capita alcohol consumption in aggregate-level cross-section time-series data. Using a series of liver cirrhosis mortality rates from 1950 to 2002 for 47 U.S. states, the effects of alcohol consumption were estimated from pooled autoregressive integrated moving average (ARIMA) models and 4 types of panel data models: generalized estimating equation, generalized least square, fixed effect, and multilevel models. Various specifications of error term structure under each type of model were also examined. Different approaches controlling for time trends and for using concurrent or accumulated consumption as predictors were also evaluated. When cirrhosis mortality was predicted by total alcohol, highly consistent estimates were found between ARIMA and panel data analyses, with an average overall effect of 0.07 to 0.09. Less consistent estimates were derived using spirits, beer, and wine consumption as predictors. When multiple geographic time series are combined as panel data, none of existent models could accommodate all sources of heterogeneity such that any type of panel model must employ some form of generalization. Different types of panel data models should thus be estimated to examine the robustness of findings. We also suggest cautious interpretation when beverage-specific volumes are used as predictors. Copyright © 2010 by the Research Society on Alcoholism.

High Spatial Resolution Forecasting of Long-Term Monthly Precipitation and Mean Temperature Trends in Data Scarce Regions

NASA Astrophysics Data System (ADS)

Mosier, T. M.; Hill, D. F.; Sharp, K. V.

2013-12-01

High spatial resolution time-series data are critical for many hydrological and earth science studies. Multiple groups have developed historical and forecast datasets of high-resolution monthly time-series for regions of the world such as the United States (e.g. PRISM for hindcast data and MACA for long-term forecasts); however, analogous datasets have not been available for most data scarce regions. The current work fills this data need by producing and freely distributing hindcast and forecast time-series datasets of monthly precipitation and mean temperature for all global land surfaces, gridded at a 30 arc-second resolution. The hindcast data are constructed through a Delta downscaling method, using as inputs 0.5 degree monthly time-series and 30 arc-second climatology global weather datasets developed by Willmott & Matsuura and WorldClim, respectively. The forecast data are formulated using a similar downscaling method, but with an additional step to remove bias from the climate variable's probability distribution over each region of interest. The downscaling package is designed to be compatible with a number of general circulation models (GCM) (e.g. with GCMs developed for the IPCC AR4 report and CMIP5), and is presently implemented using time-series data from the NCAR CESM1 model in conjunction with 30 arc-second future decadal climatologies distributed by the Consultative Group on International Agricultural Research. The resulting downscaled datasets are 30 arc-second time-series forecasts of monthly precipitation and mean temperature available for all global land areas. As an example of these data, historical and forecast 30 arc-second monthly time-series from 1950 through 2070 are created and analyzed for the region encompassing Pakistan. For this case study, forecast datasets corresponding to the future representative concentration pathways 45 and 85 scenarios developed by the IPCC are presented and compared. This exercise highlights a range of potential meteorological trends for the Pakistan region and more broadly serves to demonstrate the utility of the presented 30 arc-second monthly precipitation and mean temperature datasets for use in data scarce regions.

SparkClouds: visualizing trends in tag clouds.

PubMed

Lee, Bongshin; Riche, Nathalie Henry; Karlson, Amy K; Carpendale, Sheelash

2010-01-01

Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations—multiple line graphs and stacked bar charts—as well as Parallel Tag Clouds. Results show that SparkClouds ability to show trends compares favourably to the alternative visualizations.

Key Design Elements of a Data Utility for National Biosurveillance: Event-driven Architecture, Caching, and Web Service Model

PubMed Central

Tsui, Fu-Chiang; Espino, Jeremy U.; Weng, Yan; Choudary, Arvinder; Su, Hoah-Der; Wagner, Michael M.

2005-01-01

The National Retail Data Monitor (NRDM) has monitored over-the-counter (OTC) medication sales in the United States since December 2002. The NRDM collects data from over 18,600 retail stores and processes over 0.6 million sales records per day. This paper describes key architectural features that we have found necessary for a data utility component in a national biosurveillance system. These elements include event-driven architecture to provide analyses of data in near real time, multiple levels of caching to improve query response time, high availability through the use of clustered servers, scalable data storage through the use of storage area networks and a web-service function for interoperation with affiliated systems. The methods and architectural principles are relevant to the design of any production data utility for public health surveillance—systems that collect data from multiple sources in near real time for use by analytic programs and user interfaces that have substantial requirements for time-series data aggregated in multiple dimensions. PMID:16779138

Testing and analysis of flat and curved panels with multiple cracks

DOT National Transportation Integrated Search

1994-08-01

An experimental and analytical investigation of multiple cracking in various types of test specimens is described in this paper. The testing phase is comprised of a flat unstiffened panel series and curved stiffened and unstiffened panel series. The ...

GIAnT - Generic InSAR Analysis Toolbox

NASA Astrophysics Data System (ADS)

Agram, P.; Jolivet, R.; Riel, B. V.; Simons, M.; Doin, M.; Lasserre, C.; Hetland, E. A.

2012-12-01

We present a computing framework for studying the spatio-temporal evolution of ground deformation from interferometric synthetic aperture radar (InSAR) data. Several open-source tools including Repeat Orbit Interferometry PACkage (ROI-PAC) and InSAR Scientific Computing Environment (ISCE) from NASA-JPL, and Delft Object-oriented Repeat Interferometric Software (DORIS), have enabled scientists to generate individual interferograms from raw radar data with relative ease. Numerous computational techniques and algorithms that reduce phase information from multiple interferograms to a deformation time-series have been developed and verified over the past decade. However, the sharing and direct comparison of products from multiple processing approaches has been hindered by - 1) absence of simple standards for sharing of estimated time-series products, 2) use of proprietary software tools with license restrictions and 3) the closed source nature of the exact implementation of many of these algorithms. We have developed this computing framework to address all of the above issues. We attempt to take the first steps towards creating a community software repository for InSAR time-series analysis. To date, we have implemented the short baseline subset algorithm (SBAS), NSBAS and multi-scale interferometric time-series (MInTS) in this framework and the associated source code is included in the GIAnT distribution. A number of the associated routines have been optimized for performance and scalability with large data sets. Some of the new features in our processing framework are - 1) the use of daily solutions from continuous GPS stations to correct for orbit errors, 2) the use of meteorological data sets to estimate the tropospheric delay screen and 3) a data-driven bootstrapping approach to estimate the uncertainties associated with estimated time-series products. We are currently working on incorporating tidal load corrections for individual interferograms and propagation of noise covariance models through the processing chain for robust estimation of uncertainties in the deformation estimates. We will demonstrate the ease of use of our framework with results ranging from regional scale analysis around Long Valley, CA and Parkfield, CA to continental scale analysis in Western South America. We will also present preliminary results from a new time-series approach that simultaneously estimates deformation over the complete spatial domain at all time epochs on a distributed computing platform. GIAnT has been developed entirely using open source tools and uses Python as the underlying platform. We build on the extensive numerical (NumPy) and scientific (SciPy) computing Python libraries to develop an object-oriented, flexible and modular framework for time-series InSAR applications. The toolbox is currently configured to work with outputs from ROI-PAC, ISCE and DORIS, but can easily be extended to support products from other SAR/InSAR processors. The toolbox libraries include support for hierarchical data format (HDF5) memory mapped files, parallel processing with Python's multi-processing module and support for many convex optimization solvers like CSDP, CVXOPT etc. An extensive set of routines to deal with ASCII and XML files has also been included for controlling the processing parameters.

Category-Specific Comparison of Univariate Alerting Methods for Biosurveillance Decision Support

PubMed Central

Elbert, Yevgeniy; Hung, Vivian; Burkom, Howard

2013-01-01

Objective For a multi-source decision support application, we sought to match univariate alerting algorithms to surveillance data types to optimize detection performance. Introduction Temporal alerting algorithms commonly used in syndromic surveillance systems are often adjusted for data features such as cyclic behavior but are subject to overfitting or misspecification errors when applied indiscriminately. In a project for the Armed Forces Health Surveillance Center to enable multivariate decision support, we obtained 4.5 years of out-patient, prescription and laboratory test records from all US military treatment facilities. A proof-of-concept project phase produced 16 events with multiple evidence corroboration for comparison of alerting algorithms for detection performance. We used the representative streams from each data source to compare sensitivity of 6 algorithms to injected spikes, and we used all data streams from 16 known events to compare them for detection timeliness. Methods The six methods compared were: Holt-Winters generalized exponential smoothing method (1)automated choice between daily methods, regression and an exponential weighted moving average (2)adaptive daily Shewhart-type chartadaptive one-sided daily CUSUMEWMA applied to 7-day means with a trend correction; and7-day temporal scan statistic Sensitivity testing: We conducted comparative sensitivity testing for categories of time series with similar scales and seasonal behavior. We added multiples of the standard deviation of each time series as single-day injects in separate algorithm runs. For each candidate method, we then used as a sensitivity measure the proportion of these runs for which the output of each algorithm was below alerting thresholds estimated empirically for each algorithm using simulated data streams. We identified the algorithm(s) whose sensitivity was most consistently high for each data category. For each syndromic query applied to each data source (outpatient, lab test orders, and prescriptions), 502 authentic time series were derived, one for each reporting treatment facility. Data categories were selected in order to group time series with similar expected algorithm performance: Median > 100 < Median ≤ 10Median = 0Lag 7 Autocorrelation Coefficient ≥ 0.2Lag 7 Autocorrelation Coefficient < 0.2 Timeliness testing: For the timeliness testing, we avoided artificiality of simulated signals by measuring alerting detection delays in the 16 corroborated outbreaks. The multiple time series from these events gave a total of 141 time series with outbreak intervals for timeliness testing. The following measures were computed to quantify timeliness of detection: Median Detection Delay – median number of days to detect the outbreak.Penalized Mean Detection Delay –mean number of days to detect the outbreak with outbreak misses penalized as 1 day plus the maximum detection time. Results Based on the injection results, the Holt-Winters algorithm was most sensitive among time series with positive medians. The adaptive CUSUM and the Shewhart methods were most sensitive for data streams with median zero. Table 1 provides timeliness results using the 141 outbreak-associated streams on sparse (Median=0) and non-sparse data categories. [Insert table #1 here] Data median Detection Delay, days Holt-winters Regression EWMA Adaptive Shewhart Adaptive CUSUM 7-day Trend-adj. EWMA 7-day Temporal Scan Median 0 Median 3 2 4 2 4.5 2 Penalized Mean 7.2 7 6.6 6.2 7.3 7.6 Median >0 Median 2 2 2.5 2 6 4 Penalized Mean 6.1 7 7.2 7.1 7.7 6.6 The gray shading in the table 1 indicates methods with shortest detection delays for sparse and non-sparse data streams. The Holt-Winters method was again superior for non-sparse data. For data with median=0, the adaptive CUSUM was superior for a daily false alarm probability of 0.01, but the Shewhart method was timelier for more liberal thresholds. Conclusions Both kinds of detection performance analysis showed the method based on Holt-Winters exponential smoothing superior on non-sparse time series with day-of-week effects. The adaptive CUSUM and She-whart methods proved optimal on sparse data and data without weekly patterns.

Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models.

PubMed

Dean, Roger T; Dunsmuir, William T M

2016-06-01

Many articles on perception, performance, psychophysiology, and neuroscience seek to relate pairs of time series through assessments of their cross-correlations. Most such series are individually autocorrelated: they do not comprise independent values. Given this situation, an unfounded reliance is often placed on cross-correlation as an indicator of relationships (e.g., referent vs. response, leading vs. following). Such cross-correlations can indicate spurious relationships, because of autocorrelation. Given these dangers, we here simulated how and why such spurious conclusions can arise, to provide an approach to resolving them. We show that when multiple pairs of series are aggregated in several different ways for a cross-correlation analysis, problems remain. Finally, even a genuine cross-correlation function does not answer key motivating questions, such as whether there are likely causal relationships between the series. Thus, we illustrate how to obtain a transfer function describing such relationships, informed by any genuine cross-correlations. We illustrate the confounds and the meaningful transfer functions by two concrete examples, one each in perception and performance, together with key elements of the R software code needed. The approach involves autocorrelation functions, the establishment of stationarity, prewhitening, the determination of cross-correlation functions, the assessment of Granger causality, and autoregressive model development. Autocorrelation also limits the interpretability of other measures of possible relationships between pairs of time series, such as mutual information. We emphasize that further complexity may be required as the appropriate analysis is pursued fully, and that causal intervention experiments will likely also be needed.

The Decline in Diffuse Support for National Politics

PubMed Central

Jennings, Will; Clarke, Nick; Moss, Jonathan; Stoker, Gerry

2017-01-01

Abstract This research note considers how to track long-term trajectories of political discontent in Britain. Many accounts are confined to using either survey data drawn from recent decades or imperfect behavioral measures such as voting or party membership as indicators of political disengagement. We instead develop an approach that provides the long view on political disaffection. We first consider time-series data available from repeated survey measures. We next replicate historic survey questions to observe change in public opinion relative to earlier points in time. Finally, we use Stimson’s (1991) dyad-ratios algorithm to construct an over-time index of political discontent that combines data from multiple poll series. This reveals rising levels of political discontent for both specific and diffuse measures of mass opinion. Our method and findings offer insights into the rising tide of disillusionment afflicting many contemporary democracies. PMID:29731522

The Decline in Diffuse Support for National Politics: The Long View on Political Discontent in Britain.

PubMed

Jennings, Will; Clarke, Nick; Moss, Jonathan; Stoker, Gerry

2017-09-01

This research note considers how to track long-term trajectories of political discontent in Britain. Many accounts are confined to using either survey data drawn from recent decades or imperfect behavioral measures such as voting or party membership as indicators of political disengagement. We instead develop an approach that provides the long view on political disaffection. We first consider time-series data available from repeated survey measures. We next replicate historic survey questions to observe change in public opinion relative to earlier points in time. Finally, we use Stimson's (1991) dyad-ratios algorithm to construct an over-time index of political discontent that combines data from multiple poll series. This reveals rising levels of political discontent for both specific and diffuse measures of mass opinion. Our method and findings offer insights into the rising tide of disillusionment afflicting many contemporary democracies.

Application of process monitoring to anomaly detection in nuclear material processing systems via system-centric event interpretation of data from multiple sensors of varying reliability

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

Garcia, Humberto E.; Simpson, Michael F.; Lin, Wen-Chiao

In this paper, we apply an advanced safeguards approach and associated methods for process monitoring to a hypothetical nuclear material processing system. The assessment regarding the state of the processing facility is conducted at a systemcentric level formulated in a hybrid framework. This utilizes architecture for integrating both time- and event-driven data and analysis for decision making. While the time-driven layers of the proposed architecture encompass more traditional process monitoring methods based on time series data and analysis, the event-driven layers encompass operation monitoring methods based on discrete event data and analysis. By integrating process- and operation-related information and methodologiesmore » within a unified framework, the task of anomaly detection is greatly improved. This is because decision-making can benefit from not only known time-series relationships among measured signals but also from known event sequence relationships among generated events. This available knowledge at both time series and discrete event layers can then be effectively used to synthesize observation solutions that optimally balance sensor and data processing requirements. The application of the proposed approach is then implemented on an illustrative monitored system based on pyroprocessing and results are discussed.« less

Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series.

PubMed

Holmes, E A; Bonsall, M B; Hales, S A; Mitchell, H; Renner, F; Blackwell, S E; Watson, P; Goodwin, G M; Di Simplicio, M

2016-01-26

Treatment innovation for bipolar disorder has been hampered by a lack of techniques to capture a hallmark symptom: ongoing mood instability. Mood swings persist during remission from acute mood episodes and impair daily functioning. The last significant treatment advance remains Lithium (in the 1970s), which aids only the minority of patients. There is no accepted way to establish proof of concept for a new mood-stabilizing treatment. We suggest that combining insights from mood measurement with applied mathematics may provide a step change: repeated daily mood measurement (depression) over a short time frame (1 month) can create individual bipolar mood instability profiles. A time-series approach allows comparison of mood instability pre- and post-treatment. We test a new imagery-focused cognitive therapy treatment approach (MAPP; Mood Action Psychology Programme) targeting a driver of mood instability, and apply these measurement methods in a non-concurrent multiple baseline design case series of 14 patients with bipolar disorder. Weekly mood monitoring and treatment target data improved for the whole sample combined. Time-series analyses of daily mood data, sampled remotely (mobile phone/Internet) for 28 days pre- and post-treatment, demonstrated improvements in individuals' mood stability for 11 of 14 patients. Thus the findings offer preliminary support for a new imagery-focused treatment approach. They also indicate a step in treatment innovation without the requirement for trials in illness episodes or relapse prevention. Importantly, daily measurement offers a description of mood instability at the individual patient level in a clinically meaningful time frame. This costly, chronic and disabling mental illness demands innovation in both treatment approaches (whether pharmacological or psychological) and measurement tool: this work indicates that daily measurements can be used to detect improvement in individual mood stability for treatment innovation (MAPP).

Investigation of a long time series of CO2 from a tall tower using WRF-SPA

NASA Astrophysics Data System (ADS)

Smallman, Luke; Williams, Mathew; Moncrieff, John B.

2013-04-01

Atmospheric observations from tall towers are an important source of information about CO2 exchange at the regional scale. Here, we have used a forward running model, WRF-SPA, to generate a time series of CO2 at a tall tower for comparison with observations from Scotland over multiple years (2006-2008). We use this comparison to infer strength and distribution of sources and sinks of carbon and ecosystem process information at the seasonal scale. The specific aim of this research is to combine a high resolution (6 km) forward running meteorological model (WRF) with a modified version of a mechanistic ecosystem model (SPA). SPA provides surface fluxes calculated from coupled energy, hydrological and carbon cycles. This closely coupled representation of the biosphere provides realistic surface exchanges to drive mixing within the planetary boundary layer. The combined model is used to investigate the sources and sinks of CO2 and to explore which land surfaces contribute to a time series of hourly observations of atmospheric CO2 at a tall tower, Angus, Scotland. In addition to comparing the modelled CO2 time series to observations, modelled ecosystem specific (i.e. forest, cropland, grassland) CO2 tracers (e.g., assimilation and respiration) have been compared to the modelled land surface assimilation to investigate how representative tall tower observations are of land surface processes. WRF-SPA modelled CO2 time series compares well to observations (R2 = 0.67, rmse = 3.4 ppm, bias = 0.58 ppm). Through comparison of model-observation residuals, we have found evidence that non-cropped components of agricultural land (e.g., hedgerows and forest patches) likely contribute a significant and observable impact on regional carbon balance.

Bi-scale analysis of multitemporal land cover fractions for wetland vegetation mapping

NASA Astrophysics Data System (ADS)

Michishita, Ryo; Jiang, Zhiben; Gong, Peng; Xu, Bing

2012-08-01

Land cover fractions (LCFs) derived through spectral mixture analysis are useful in understanding sub-pixel information. However, few studies have been conducted on the analysis of time-series LCFs. Although multi-scale comparisons of spectral index, hard classification, and land surface temperature images have received attention, rarely have these approaches been applied to LCFs. This study compared the LCFs derived through Multiple Endmember Spectral Mixture Analysis (MESMA) using the time-series Landsat Thematic Mapper (TM) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired in the Poyang Lake area, China between 2004 and 2005. Specifically, we aimed to: (1) propose an approach for optimal endmember (EM) selection in time-series MESMA; (2) understand the trends in time-series LCFs derived from the TM and MODIS data; and (3) examine the trends in the correlation between the bi-scale LCFs derived from the time-series TM and MODIS data. Our results indicated: (1) the EM spectra chosen according to the proposed hierarchical three-step approach (overall, seasonal, and individual) accurately modeled the both the TM and MODIS images; (2) green vegetation (GV) and NPV/soil/impervious surface (N/S/I) classes followed sine curve trends in the overall area, while the two water classes displayed the water level change pattern in the areas primarily covered with wetland vegetation; and (3) GV, N/S/I, and bright water classes indicated a moderately high agreement between the TM and MODIS LCFs in the whole area (adjusted R2 ⩾ 0.6). However, low levels of correlations were found in the areas primarily dominated by wetland vegetation for all land cover classes.

Operator identities involving the bivariate Rogers-Szegö polynomials and their applications to the multiple q-series identities

NASA Astrophysics Data System (ADS)

Zhang, Zhizheng; Wang, Tianze

2008-07-01

In this paper, we first give several operator identities involving the bivariate Rogers-Szegö polynomials. By applying the technique of parameter augmentation to the multiple q-binomial theorems given by Milne [S.C. Milne, Balanced summation theorems for U(n) basic hypergeometric series, AdvE Math. 131 (1997) 93-187], we obtain several new multiple q-series identities involving the bivariate Rogers-Szegö polynomials. These include multiple extensions of Mehler's formula and Rogers's formula. Our U(n+1) generalizations are quite natural as they are also a direct and immediate consequence of their (often classical) known one-variable cases and Milne's fundamental theorem for An or U(n+1) basic hypergeometric series in Theorem 1E49 of [S.C. Milne, An elementary proof of the Macdonald identities for , Adv. Math. 57 (1985) 34-70], as rewritten in Lemma 7.3 on p. 163 of [S.C. Milne, Balanced summation theorems for U(n) basic hypergeometric series, Adv. Math. 131 (1997) 93-187] or Corollary 4.4 on pp. 768-769 of [S.C. Milne, M. Schlosser, A new An extension of Ramanujan's summation with applications to multilateral An series, Rocky Mountain J. Math. 32 (2002) 759-792].

Distributed Factorization Computation on Multiple Volunteered Mobile Resource to Break RSA Key

NASA Astrophysics Data System (ADS)

Jaya, I.; Hardi, S. M.; Tarigan, J. T.; Zamzami, E. M.; Sihombing, P.

2017-01-01

Similar to common asymmeric encryption, RSA can be cracked by usmg a series mathematical calculation. The private key used to decrypt the massage can be computed using the public key. However, finding the private key may require a massive amount of calculation. In this paper, we propose a method to perform a distributed computing to calculate RSA’s private key. The proposed method uses multiple volunteered mobile devices to contribute during the calculation process. Our objective is to demonstrate how the use of volunteered computing on mobile devices may be a feasible option to reduce the time required to break a weak RSA encryption and observe the behavior and running time of the application on mobile devices.

Technological innovation in spinal cord stimulation: use of a newly developed delivery device for introduction of spinal cord stimulation leads.

PubMed

Logé, David; De Coster, Olivier; Washburn, Stephanie

2012-07-01

The use of multiple cylindrical leads and multicolumn and single column paddle leads in spinal cord stimulation offers many advantages over the use of a single cylindrical lead. Despite these advantages, placement of multiple cylindrical leads or a paddle lead requires a more invasive surgical procedure. Thus, the ideal situation for lead delivery would be percutaneous insertion of a paddle lead or multiple cylindrical leads. This study evaluated the feasibility and safety of percutaneous delivery of S-Series paddle leads using a new delivery device called the Epiducer lead delivery system (all St. Jude Medical Neuromodulation Division, Plano, TX, USA). This uncontrolled, open-label, prospective, two-center study approved by the AZ St. Lucas (Ghent) Ethics Committee evaluated procedural aspects of implantation of an S-Series paddle lead using the Epiducer lead delivery system and any adverse events relating to the device. Efficacy data during the patent's 30-day trial also were collected.   Data from 34 patients were collected from two investigational sites. There were no adverse events related to the Epiducer lead delivery system. The device was inserted at an angle of either 20°-30° or 30°-40° and was entered into the epidural space at T12/L1 in most patients. The S-Series paddle lead was advanced four vertebral segments in more than 50% of patients. The average (±standard deviation [SD]) time it took to place the Epiducer lead delivery system was 8.7 (±5.0) min. The average (+SD) patient-reported pain relief was 78.8% (+24.1%). This study suggests the safe use of the Epiducer lead delivery system for percutaneous implantation and advancement of the S-Series paddle lead in 34 patients. © 2012 International Neuromodulation Society.

The annual cycles of phytoplankton biomass

USGS Publications Warehouse

Winder, M.; Cloern, J.E.

2010-01-01

Terrestrial plants are powerful climate sentinels because their annual cycles of growth, reproduction and senescence are finely tuned to the annual climate cycle having a period of one year. Consistency in the seasonal phasing of terrestrial plant activity provides a relatively low-noise background from which phenological shifts can be detected and attributed to climate change. Here, we ask whether phytoplankton biomass also fluctuates over a consistent annual cycle in lake, estuarine-coastal and ocean ecosystems and whether there is a characteristic phenology of phytoplankton as a consistent phase and amplitude of variability. We compiled 125 time series of phytoplankton biomass (chloro-phyll a concentration) from temperate and subtropical zones and used wavelet analysis to extract their dominant periods of variability and the recurrence strength at those periods. Fewer than half (48%) of the series had a dominant 12-month period of variability, commonly expressed as the canonical spring-bloom pattern. About 20 per cent had a dominant six-month period of variability, commonly expressed as the spring and autumn or winter and summer blooms of temperate lakes and oceans. These annual patterns varied in recurrence strength across sites, and did not persist over the full series duration at some sites. About a third of the series had no component of variability at either the six-or 12-month period, reflecting a series of irregular pulses of biomass. These findings show that there is high variability of annual phytoplankton cycles across ecosystems, and that climate-driven annual cycles can be obscured by other drivers of population variability, including human disturbance, aperiodic weather events and strong trophic coupling between phytoplankton and their consumers. Regulation of phytoplankton biomass by multiple processes operating at multiple time scales adds complexity to the challenge of detecting climate-driven trends in aquatic ecosystems where the noise to signal ratio is high. ?? 2010 The Royal Society.

The Effect of Talker Variability on Word Recognition in Preschool Children

PubMed Central

Ryalls, Brigette Oliver; Pisoni, David B.

2012-01-01

In a series of experiments, the authors investigated the effects of talker variability on children’s word recognition. In Experiment 1, when stimuli were presented in the clear, 3- and 5-year-olds were less accurate at identifying words spoken by multiple talkers than those spoken by a single talker when the multiple-talker list was presented first. In Experiment 2, when words were presented in noise, 3-, 4-, and 5-year-olds again performed worse in the multiple-talker condition than in the single-talker condition, this time regardless of order; processing multiple talkers became easier with age. Experiment 3 showed that both children and adults were slower to repeat words from multiple-talker than those from single-talker lists. More important, children (but not adults) matched acoustic properties of the stimuli (specifically, duration). These results provide important new information about the development of talker normalization in speech perception and spoken word recognition. PMID:9149923

College Preparatory Curriculum for All: Academic Consequences of Requiring Algebra and English I for Ninth Graders in Chicago

ERIC Educational Resources Information Center

Allensworth, Elaine; Nomi, Takako; Montgomery, Nicholas; Lee, Valerie E.

2009-01-01

There is a national movement to universalize the high school curriculum so that all students graduate prepared for college. The present work evaluates a policy in Chicago that ended remedial classes and mandated college preparatory course work for all students. Based on an interrupted time-series cohort design with multiple comparisons, this study…

The use of multiple imputation in the Southern Annual Forest Inventory System

Treesearch

Gregory A. Reams; Joseph M. McCollum

2000-01-01

The Southern Research Station is currently implementing an annual forest survey in 7 of the 13 States that it is responsible for surveying. The Southern Annual Forest Inventory System (SAFIS) sampling design is a systematic sample of five interpenetrating grids, whereby an equal number of plots are measured each year. The area-representative and time-series...

The use of multiple imputation in the Southern Annual Forest Inventory System

Treesearch

Gregory A. Reams; Joseph M. McCollum

2000-01-01

The Southern Research Station is currently implementing an annual forest survey in 7 of the 13 states that it is responsible for surveying. The Southern Annual Forest Inventory System (SAFIS) sampling design is a systematic sample of five interpenetrating grids, whereby an equal number of plots are measured each year. The area representative and time series nature of...

The Identity Construction Experiences of Early Career English Language Teachers in Hong Kong. Great Expectations and Practical Realities

ERIC Educational Resources Information Center

Trent, John

2016-01-01

This article reports the results of a multiple qualitative case study which investigated the challenges that seven early career English language teachers in Hong Kong confronted as they constructed their professional and personal identities. A series of in-depth interviews with participants during the entire first year of their full-time teaching…

Imputation of Test Scores in the National Education Longitudinal Study of 1988 (NELS:88). Working Paper Series.

ERIC Educational Resources Information Center

Bokossa, Maxime C.; Huang, Gary G.

This report describes the imputation procedures used to deal with missing data in the National Education Longitudinal Study of 1988 (NELS:88), the only current National Center for Education Statistics (NCES) dataset that contains scores from cognitive tests given the same set of students at multiple time points. As is inevitable, cognitive test…

Post-classification approaches to estimating change in forest area using remotely sense auxiliary data.

Treesearch

Ronald E. McRoberts

2014-01-01

Multiple remote sensing-based approaches to estimating gross afforestation, gross deforestation, and net deforestation are possible. However, many of these approaches have severe data requirements in the form of long time series of remotely sensed data and/or large numbers of observations of land cover change to train classifiers and assess the accuracy of...

Utilizing an Early Childhood Science Curriculum: Factors Influencing Implementation and How Variations Affect Students' Skills and Attitudes

ERIC Educational Resources Information Center

Shamas-Brandt, Ellen

2012-01-01

Early childhood is a ripe time for students to begin learning science, but due to certain constraints, this instruction is not happening as frequently as it should. This mixed-methods, multiple case study examined how two teachers implemented an early childhood science curriculum, the "Young Scientist Series." The teacher participants…

Comparison of an automated Most Probable Number (MPN) technique to traditional plating methods for estimating populations of total aerobes, coliforms and E. coli associated with freshly processed broiler chickens

USDA-ARS?s Scientific Manuscript database

Traditional microbiological techniques for estimating populations of viable bacteria can be laborious and time consuming. The Most Probable Number (MPN) technique is especially tedious as multiple series of tubes must be inoculated at several different dilutions. Recently, an instrument (TEMPOTM) ...

Decadal trends in Indian Ocean ambient sound.

PubMed

Miksis-Olds, Jennifer L; Bradley, David L; Niu, Xiaoyue Maggie

2013-11-01

The increase of ocean noise documented in the North Pacific has sparked concern on whether the observed increases are a global or regional phenomenon. This work provides evidence of low frequency sound increases in the Indian Ocean. A decade (2002-2012) of recordings made off the island of Diego Garcia, UK in the Indian Ocean was parsed into time series according to frequency band and sound level. Quarterly sound level comparisons between the first and last years were also performed. The combination of time series and temporal comparison analyses over multiple measurement parameters produced results beyond those obtainable from a single parameter analysis. The ocean sound floor has increased over the past decade in the Indian Ocean. Increases were most prominent in recordings made south of Diego Garcia in the 85-105 Hz band. The highest sound level trends differed between the two sides of the island; the highest sound levels decreased in the north and increased in the south. Rate, direction, and magnitude of changes among the multiple parameters supported interpretation of source functions driving the trends. The observed sound floor increases are consistent with concurrent increases in shipping, wind speed, wave height, and blue whale abundance in the Indian Ocean.

The Convergence Problems of Eigenfunction Expansions of Elliptic Differential Operators

NASA Astrophysics Data System (ADS)

Ahmedov, Anvarjon

2018-03-01

In the present research we investigate the problems concerning the almost everywhere convergence of multiple Fourier series summed over the elliptic levels in the classes of Liouville. The sufficient conditions for the almost everywhere convergence problems, which are most difficult problems in Harmonic analysis, are obtained. The methods of approximation by multiple Fourier series summed over elliptic curves are applied to obtain suitable estimations for the maximal operator of the spectral decompositions. Obtaining of such estimations involves very complicated calculations which depends on the functional structure of the classes of functions. The main idea on the proving the almost everywhere convergence of the eigenfunction expansions in the interpolation spaces is estimation of the maximal operator of the partial sums in the boundary classes and application of the interpolation Theorem of the family of linear operators. In the present work the maximal operator of the elliptic partial sums are estimated in the interpolation classes of Liouville and the almost everywhere convergence of the multiple Fourier series by elliptic summation methods are established. The considering multiple Fourier series as an eigenfunction expansions of the differential operators helps to translate the functional properties (for example smoothness) of the Liouville classes into Fourier coefficients of the functions which being expanded into such expansions. The sufficient conditions for convergence of the multiple Fourier series of functions from Liouville classes are obtained in terms of the smoothness and dimensions. Such results are highly effective in solving the boundary problems with periodic boundary conditions occurring in the spectral theory of differential operators. The investigations of multiple Fourier series in modern methods of harmonic analysis incorporates the wide use of methods from functional analysis, mathematical physics, modern operator theory and spectral decomposition. New method for the best approximation of the square-integrable function by multiple Fourier series summed over the elliptic levels are established. Using the best approximation, the Lebesgue constant corresponding to the elliptic partial sums is estimated. The latter is applied to obtain an estimation for the maximal operator in the classes of Liouville.

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.

An iterative solver for the 3D Helmholtz equation

NASA Astrophysics Data System (ADS)

Belonosov, Mikhail; Dmitriev, Maxim; Kostin, Victor; Neklyudov, Dmitry; Tcheverda, Vladimir

2017-09-01

We develop a frequency-domain iterative solver for numerical simulation of acoustic waves in 3D heterogeneous media. It is based on the application of a unique preconditioner to the Helmholtz equation that ensures convergence for Krylov subspace iteration methods. Effective inversion of the preconditioner involves the Fast Fourier Transform (FFT) and numerical solution of a series of boundary value problems for ordinary differential equations. Matrix-by-vector multiplication for iterative inversion of the preconditioned matrix involves inversion of the preconditioner and pointwise multiplication of grid functions. Our solver has been verified by benchmarking against exact solutions and a time-domain solver.

Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies.

PubMed

Goldman, Gretchen T; Mulholland, James A; Russell, Armistead G; Strickland, Matthew J; Klein, Mitchel; Waller, Lance A; Tolbert, Paige E

2011-06-22

Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta. Daily measures of twelve ambient air pollutants were analyzed: NO2, NOx, O3, SO2, CO, PM10 mass, PM2.5 mass, and PM2.5 components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits. Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed. For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.

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.

Detecting daily routines of older adults using sensor time series clustering.

PubMed

Hajihashemi, Zahra; Yefimova, Maria; Popescu, Mihail

2014-01-01

The aim of this paper is to develop an algorithm to identify deviations in patterns of day-to-day activities of older adults to generate alerts to the healthcare providers for timely interventions. Daily routines, such as bathroom visits, can be monitored by automated in-home sensor systems. We present a novel approach that finds periodicity in sensor time series data using clustering approach. For this study, we used data set from TigerPlace, a retirement community in Columbia, MO, where apartments are equipped with a network of motion, pressure and depth sensors. A retrospective multiple case study (N=3) design was used to quantify bathroom visits as parts of the older adult's daily routine, over a 10-day period. The distribution of duration, number, and average time between sensor hits was used to define the confidence level for routine visit extraction. Then, a hierarchical clustering was applied to extract periodic patterns. The performance of the proposed method was evaluated through experimental results.

The effect of nabilone on neuropsychological functions related to driving ability: an extended case series.

PubMed

Kurzthaler, Ilsemarie; Bodner, Thomas; Kemmler, Georg; Entner, Tanja; Wissel, Joerg; Berger, Thomas; Fleischhacker, W Wolfgang

2005-06-01

The primary goal of this prospective extended case series was to obtain the first data about the potential influence of nabilone intake on driving ability related neuropsychological functions. Six patients were investigated within a placebo controlled, double-blind crossover study of this synthetic cannabinoid (2 mg/day) in patients with multiple sclerosis and spasticity associated pain. Five neuropsychological functions (reaction time, working memory, divided attention, psychomotor speed and mental flexibility) were assessed. No indication was found of a deterioration of any of the five investigated neuropsychological functions during the 4-week treatment period with nabilone. Copyright 2005 John Wiley & Sons, Ltd.

Single flux quantum voltage amplifiers

NASA Astrophysics Data System (ADS)

Golomidov, Vladimir; Kaplunenko, Vsevolod; Khabipov, Marat; Koshelets, Valery; Kaplunenko, Olga

The novel elements of the Rapid Single Flux Quantum (RSFQ) logic family — a Quasi Digital Voltage Parallel and Series Amplifiers (QDVA) have been computer simulated, designed and experimentally investigated. The Parallel QDVA consists of six stages and provides multiplication of the input voltage with factor five. The output resistance of the QDVA is five times larger than the input so this amplifier seems to be a good matching stage between RSFQL and usual semiconductor electronics. The series QDVA provides a gain factor four and involves two doublers connected by transmission line. The proposed parallel QDVA can be integrated on the same chip with a SQUID sensor.

Analysis of streamflow variability in Alpine catchments at multiple spatial and temporal scales

NASA Astrophysics Data System (ADS)

Pérez Ciria, T.; Chiogna, G.

2017-12-01

Alpine watersheds play a pivotal role in Europe for water provisioning and for hydropower production. In these catchments, temporal fluctuations of river discharge occur at multiple temporal scales due to natural as well as anthropogenic driving forces. In the last decades, modifications of the flow regime have been observed and their origin lies in the complex interplay between construction of dams for hydro power production, changes in water management policies and climatic changes. The alteration of the natural flow has negative impacts on the freshwater biodiversity and threatens the ecosystem integrity of the Alpine region. Therefore, understanding the temporal and spatial variability of river discharge has recently become a particular concern for environmental protection and represents a crucial contribution to achieve sustainable water resources management in the Alps. In this work, time series analysis is conducted for selected gauging stations in the Inn and the Adige catchments, which cover a large part of the central and eastern region of the Alps. We analyze the available time series using the continuous wavelet transform and change-point analyses for determining how and where changes have taken place. Although both catchments belong to different climatic zones of the Greater Alpine Region, streamflow properties share some similar characteristics. The comparison of the collected streamflow time series in the two catchments permits detecting gradients in the hydrological system dynamics that depend on station elevation, longitudinal location in the Alps and catchment area. This work evidences that human activities (e.g., water management practices and flood protection measures, changes in legislation and market regulation) have major impacts on streamflow and should be rigorously considered in hydrological models.

Kinematic Validation of a Multi-Kinect v2 Instrumented 10-Meter Walkway for Quantitative Gait Assessments.

PubMed

Geerse, Daphne J; Coolen, Bert H; Roerdink, Melvyn

2015-01-01

Walking ability is frequently assessed with the 10-meter walking test (10MWT), which may be instrumented with multiple Kinect v2 sensors to complement the typical stopwatch-based time to walk 10 meters with quantitative gait information derived from Kinect's 3D body point's time series. The current study aimed to evaluate a multi-Kinect v2 set-up for quantitative gait assessments during the 10MWT against a gold-standard motion-registration system by determining between-systems agreement for body point's time series, spatiotemporal gait parameters and the time to walk 10 meters. To this end, the 10MWT was conducted at comfortable and maximum walking speed, while 3D full-body kinematics was concurrently recorded with the multi-Kinect v2 set-up and the Optotrak motion-registration system (i.e., the gold standard). Between-systems agreement for body point's time series was assessed with the intraclass correlation coefficient (ICC). Between-systems agreement was similarly determined for the gait parameters' walking speed, cadence, step length, stride length, step width, step time, stride time (all obtained for the intermediate 6 meters) and the time to walk 10 meters, complemented by Bland-Altman's bias and limits of agreement. Body point's time series agreed well between the motion-registration systems, particularly so for body points in motion. For both comfortable and maximum walking speeds, the between-systems agreement for the time to walk 10 meters and all gait parameters except step width was high (ICC ≥ 0.888), with negligible biases and narrow limits of agreement. Hence, body point's time series and gait parameters obtained with a multi-Kinect v2 set-up match well with those derived with a gold standard in 3D measurement accuracy. Future studies are recommended to test the clinical utility of the multi-Kinect v2 set-up to automate 10MWT assessments, thereby complementing the time to walk 10 meters with reliable spatiotemporal gait parameters obtained objectively in a quick, unobtrusive and patient-friendly manner.

Kinematic Validation of a Multi-Kinect v2 Instrumented 10-Meter Walkway for Quantitative Gait Assessments

PubMed Central

Geerse, Daphne J.; Coolen, Bert H.; Roerdink, Melvyn

2015-01-01

Walking ability is frequently assessed with the 10-meter walking test (10MWT), which may be instrumented with multiple Kinect v2 sensors to complement the typical stopwatch-based time to walk 10 meters with quantitative gait information derived from Kinect’s 3D body point’s time series. The current study aimed to evaluate a multi-Kinect v2 set-up for quantitative gait assessments during the 10MWT against a gold-standard motion-registration system by determining between-systems agreement for body point’s time series, spatiotemporal gait parameters and the time to walk 10 meters. To this end, the 10MWT was conducted at comfortable and maximum walking speed, while 3D full-body kinematics was concurrently recorded with the multi-Kinect v2 set-up and the Optotrak motion-registration system (i.e., the gold standard). Between-systems agreement for body point’s time series was assessed with the intraclass correlation coefficient (ICC). Between-systems agreement was similarly determined for the gait parameters’ walking speed, cadence, step length, stride length, step width, step time, stride time (all obtained for the intermediate 6 meters) and the time to walk 10 meters, complemented by Bland-Altman’s bias and limits of agreement. Body point’s time series agreed well between the motion-registration systems, particularly so for body points in motion. For both comfortable and maximum walking speeds, the between-systems agreement for the time to walk 10 meters and all gait parameters except step width was high (ICC ≥ 0.888), with negligible biases and narrow limits of agreement. Hence, body point’s time series and gait parameters obtained with a multi-Kinect v2 set-up match well with those derived with a gold standard in 3D measurement accuracy. Future studies are recommended to test the clinical utility of the multi-Kinect v2 set-up to automate 10MWT assessments, thereby complementing the time to walk 10 meters with reliable spatiotemporal gait parameters obtained objectively in a quick, unobtrusive and patient-friendly manner. PMID:26461498

The effect of orthostatic stress on multiscale entropy of heart rate and blood pressure.

PubMed

Turianikova, Zuzana; Javorka, Kamil; Baumert, Mathias; Calkovska, Andrea; Javorka, Michal

2011-09-01

Cardiovascular control acts over multiple time scales, which introduces a significant amount of complexity to heart rate and blood pressure time series. Multiscale entropy (MSE) analysis has been developed to quantify the complexity of a time series over multiple time scales. In previous studies, MSE analyses identified impaired cardiovascular control and increased cardiovascular risk in various pathological conditions. Despite the increasing acceptance of the MSE technique in clinical research, information underpinning the involvement of the autonomic nervous system in the MSE of heart rate and blood pressure is lacking. The objective of this study is to investigate the effect of orthostatic challenge on the MSE of heart rate and blood pressure variability (HRV, BPV) and the correlation between MSE (complexity measures) and traditional linear (time and frequency domain) measures. MSE analysis of HRV and BPV was performed in 28 healthy young subjects on 1000 consecutive heart beats in the supine and standing positions. Sample entropy values were assessed on scales of 1-10. We found that MSE of heart rate and blood pressure signals is sensitive to changes in autonomic balance caused by postural change from the supine to the standing position. The effect of orthostatic challenge on heart rate and blood pressure complexity depended on the time scale under investigation. Entropy values did not correlate with the mean values of heart rate and blood pressure and showed only weak correlations with linear HRV and BPV measures. In conclusion, the MSE analysis of heart rate and blood pressure provides a sensitive tool to detect changes in autonomic balance as induced by postural change.

Multiple Solutions of Real-time Tsunami Forecasting Using Short-term Inundation Forecasting for Tsunamis Tool

NASA Astrophysics Data System (ADS)

Gica, E.

2016-12-01

The Short-term Inundation Forecasting for Tsunamis (SIFT) tool, developed by NOAA Center for Tsunami Research (NCTR) at the Pacific Marine Environmental Laboratory (PMEL), is used in forecast operations at the Tsunami Warning Centers in Alaska and Hawaii. The SIFT tool relies on a pre-computed tsunami propagation database, real-time DART buoy data, and an inversion algorithm to define the tsunami source. The tsunami propagation database is composed of 50×100km unit sources, simulated basin-wide for at least 24 hours. Different combinations of unit sources, DART buoys, and length of real-time DART buoy data can generate a wide range of results within the defined tsunami source. For an inexperienced SIFT user, the primary challenge is to determine which solution, among multiple solutions for a single tsunami event, would provide the best forecast in real time. This study investigates how the use of different tsunami sources affects simulated tsunamis at tide gauge locations. Using the tide gauge at Hilo, Hawaii, a total of 50 possible solutions for the 2011 Tohoku tsunami are considered. Maximum tsunami wave amplitude and root mean square error results are used to compare tide gauge data and the simulated tsunami time series. Results of this study will facilitate SIFT users' efforts to determine if the simulated tide gauge tsunami time series from a specific tsunami source solution would be within the range of possible solutions. This study will serve as the basis for investigating more historical tsunami events and tide gauge locations.

Charged particle tracking without magnetic field: Optimal measurement of track momentum by a Bayesian analysis of the multiple measurements of deflections due to multiple scattering

NASA Astrophysics Data System (ADS)

Frosini, Mikael; Bernard, Denis

2017-09-01

We revisit the precision of the measurement of track parameters (position, angle) with optimal methods in the presence of detector resolution, multiple scattering and zero magnetic field. We then obtain an optimal estimator of the track momentum by a Bayesian analysis of the filtering innovations of a series of Kalman filters applied to the track. This work could pave the way to the development of autonomous high-performance gas time-projection chambers (TPC) or silicon wafer γ-ray space telescopes and be a powerful guide in the optimization of the design of the multi-kilo-ton liquid argon TPCs that are under development for neutrino studies.

MICA: Multiple interval-based curve alignment

NASA Astrophysics Data System (ADS)

Mann, Martin; Kahle, Hans-Peter; Beck, Matthias; Bender, Bela Johannes; Spiecker, Heinrich; Backofen, Rolf

2018-01-01

MICA enables the automatic synchronization of discrete data curves. To this end, characteristic points of the curves' shapes are identified. These landmarks are used within a heuristic curve registration approach to align profile pairs by mapping similar characteristics onto each other. In combination with a progressive alignment scheme, this enables the computation of multiple curve alignments. Multiple curve alignments are needed to derive meaningful representative consensus data of measured time or data series. MICA was already successfully applied to generate representative profiles of tree growth data based on intra-annual wood density profiles or cell formation data. The MICA package provides a command-line and graphical user interface. The R interface enables the direct embedding of multiple curve alignment computation into larger analyses pipelines. Source code, binaries and documentation are freely available at https://github.com/BackofenLab/MICA

A new class of nitrosoureas. 4. Synthesis and antitumor activity of disaccharide derivatives of 3,3-disubstituted 1-(2-chloroethyl)-1-nitrosoureas.

PubMed

Tsujihara, K; Ozeki, M; Morikawa, T; Kawamori, M; Akaike, Y; Arai, Y

1982-04-01

A series of 33 N-(2-chloroethyl)-N-nitrosocarbamoyl derivatives of N-substituted glycosylamines has been prepared and tested for antitumor activities. The compounds were obtained by reaction of glycosylamines with isocyanate, followed by nitrosation with N2O4. Structure-activity relationships of these trisubstituted nitrosoureas were investigated by varying the N-substituents and disaccharide groups and by comparing them with the corresponding disubstituted analogues. A large number of the nitrosoureas bearing a maltosyl group exhibited strong antitumor activities against leukemia L1210 and Ehrlich ascites carcinoma, and 60-day survivors against leukemia L1210 were found at the optimal dose for these derivatives. In contrast, the lactosyl and the melibiosyl derivatives were almost inactive. The most interesting compound in this series, the 3-isobutyl-3-maltosyl derivative (37), was tested against leukemia L1210 by single and multiple treatment. Its therapeutic ratio (96.3) obtained by multiple treatment is 3 times larger than that (31.5) obtained by single treatment, suggesting a possible clinical utility of 37 by multiple treatment. The favorable effect of a maltosyl moiety in this class of compounds is discussed.

Multiple imputation of rainfall missing data in the Iberian Mediterranean context

NASA Astrophysics Data System (ADS)

Miró, Juan Javier; Caselles, Vicente; Estrela, María José

2017-11-01

Given the increasing need for complete rainfall data networks, in recent years have been proposed diverse methods for filling gaps in observed precipitation series, progressively more advanced that traditional approaches to overcome the problem. The present study has consisted in validate 10 methods (6 linear, 2 non-linear and 2 hybrid) that allow multiple imputation, i.e., fill at the same time missing data of multiple incomplete series in a dense network of neighboring stations. These were applied for daily and monthly rainfall in two sectors in the Júcar River Basin Authority (east Iberian Peninsula), which is characterized by a high spatial irregularity and difficulty of rainfall estimation. A classification of precipitation according to their genetic origin was applied as pre-processing, and a quantile-mapping adjusting as post-processing technique. The results showed in general a better performance for the non-linear and hybrid methods, highlighting that the non-linear PCA (NLPCA) method outperforms considerably the Self Organizing Maps (SOM) method within non-linear approaches. On linear methods, the Regularized Expectation Maximization method (RegEM) was the best, but far from NLPCA. Applying EOF filtering as post-processing of NLPCA (hybrid approach) yielded the best results.

Monitoring Global Food Security with New Remote Sensing Products and Tools

NASA Astrophysics Data System (ADS)

Budde, M. E.; Rowland, J.; Senay, G. B.; Funk, C. C.; Husak, G. J.; Magadzire, T.; Verdin, J. P.

2012-12-01

Global agriculture monitoring is a crucial aspect of monitoring food security in the developing world. The Famine Early Warning Systems Network (FEWS NET) has a long history of using remote sensing and crop modeling to address food security threats in the form of drought, floods, pests, and climate change. In recent years, it has become apparent that FEWS NET requires the ability to apply monitoring and modeling frameworks at a global scale to assess potential impacts of foreign production and markets on food security at regional, national, and local levels. Scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the University of California Santa Barbara (UCSB) Climate Hazards Group have provided new and improved data products as well as visualization and analysis tools in support of the increased mandate for remote monitoring. We present our monitoring products for measuring actual evapotranspiration (ETa), normalized difference vegetation index (NDVI) in a near-real-time mode, and satellite-based rainfall estimates and derivatives. USGS FEWS NET has implemented a Simplified Surface Energy Balance (SSEB) model to produce operational ETa anomalies for Africa and Central Asia. During the growing season, ETa anomalies express surplus or deficit crop water use, which is directly related to crop condition and biomass. We present current operational products and provide supporting validation of the SSEB model. The expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) production system provides FEWS NET with an improved NDVI dataset for crop and rangeland monitoring. eMODIS NDVI provides a reliable data stream with a relatively high spatial resolution (250-m) and short latency period (less than 12 hours) which allows for better operational vegetation monitoring. We provide an overview of these data and cite specific applications for crop monitoring. FEWS NET uses satellite rainfall estimates as inputs for monitoring agricultural food production and driving crop water balance models. We present a series of derived rainfall products and provide an update on efforts to improve satellite-based estimates. We also present advancements in monitoring tools, namely, the Early Warning eXplorer (EWX) and interactive rainfall and NDVI time series viewers. The EWX is a data analysis and visualization tool that allows users to rapidly visualize multiple remote sensing datasets and compare standardized anomaly maps and time series. The interactive time series viewers allow users to analyze rainfall and NDVI time series over multiple spatial domains. New and improved data products and more targeted analysis tools are a necessity as food security monitoring requirements expand and resources become limited.

ON THE GEOMETRY OF MEASURABLE SETS IN N-DIMENSIONAL SPACE ON WHICH GENERALIZED LOCALIZATION HOLDS FOR MULTIPLE FOURIER SERIES OF FUNCTIONS IN L_p, p>1

NASA Astrophysics Data System (ADS)

Bloshanskiĭ, I. L.

1984-02-01

The precise geometry is found of measurable sets in N-dimensional Euclidean space on which generalized localization almost everywhere holds for multiple Fourier series which are rectangularly summable.Bibliography: 14 titles.

Mapping Impervious Surface Expansion using Medium-resolution Satellite Image Time Series: A Case Study in the Yangtze River Delta, China

NASA Technical Reports Server (NTRS)

Gao, Feng; DeColstoun, Eric Brown; Ma, Ronghua; Weng, Qihao; Masek, Jeffrey G.; Chen, Jin; Pan, Yaozhong; Song, Conghe

2012-01-01

Cities have been expanding rapidly worldwide, especially over the past few decades. Mapping the dynamic expansion of impervious surface in both space and time is essential for an improved understanding of the urbanization process, land-cover and land-use change, and their impacts on the environment. Landsat and other medium-resolution satellites provide the necessary spatial details and temporal frequency for mapping impervious surface expansion over the past four decades. Since the US Geological Survey opened the historical record of the Landsat image archive for free access in 2008, the decades-old bottleneck of data limitation has gone. Remote-sensing scientists are now rich with data, and the challenge is how to make best use of this precious resource. In this article, we develop an efficient algorithm to map the continuous expansion of impervious surface using a time series of four decades of medium-resolution satellite images. The algorithm is based on a supervised classification of the time-series image stack using a decision tree. Each imerpervious class represents urbanization starting in a different image. The algorithm also allows us to remove inconsistent training samples because impervious expansion is not reversible during the study period. The objective is to extract a time series of complete and consistent impervious surface maps from a corresponding times series of images collected from multiple sensors, and with a minimal amount of image preprocessing effort. The approach was tested in the lower Yangtze River Delta region, one of the fastest urban growth areas in China. Results from nearly four decades of medium-resolution satellite data from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China-Brazil Earth Resources Satellite (CBERS) show a consistent urbanization process that is consistent with economic development plans and policies. The time-series impervious spatial extent maps derived from this study agree well with an existing urban extent polygon data set that was previously developed independently. The overall mapping accuracy was estimated at about 92.5% with 3% commission error and 12% omission error for the impervious type from all images regardless of image quality and initial spatial resolution.

NASA Satellite Monitoring of Water Clarity in Mobile Bay for Nutrient Criteria Development

NASA Technical Reports Server (NTRS)

Blonski, Slawomir; Holekamp, Kara; Spiering, Bruce A.

2009-01-01

This project has demonstrated feasibility of deriving from MODIS daily measurements time series of water clarity parameters that provide coverage of a specific location or an area of interest for 30-50% of days. Time series derived for estuarine and coastal waters display much higher variability than time series of ecological parameters (such as vegetation indices) derived for land areas. (Temporal filtering often applied in terrestrial studies cannot be used effectively in ocean color processing). IOP-based algorithms for retrieval of diffuse light attenuation coefficient and TSS concentration perform well for the Mobile Bay environment: only a minor adjustment was needed in the TSS algorithm, despite generally recognized dependence of such algorithms on local conditions. The current IOP-based algorithm for retrieval of chlorophyll a concentration has not performed as well: a more reliable algorithm is needed that may be based on IOPs at additional wavelengths or on remote sensing reflectance from multiple spectral bands. CDOM algorithm also needs improvement to provide better separation between effects of gilvin (gelbstoff) and detritus. (Identification or development of such algorithm requires more data from in situ measurements of CDOM concentration in Gulf of Mexico coastal waters (ongoing collaboration with the EPA Gulf Ecology Division))

Analysis of the impact of crude oil price fluctuations on China's stock market in different periods-Based on time series network model

NASA Astrophysics Data System (ADS)

An, Yang; Sun, Mei; Gao, Cuixia; Han, Dun; Li, Xiuming

2018-02-01

This paper studies the influence of Brent oil price fluctuations on the stock prices of China's two distinct blocks, namely, the petrochemical block and the electric equipment and new energy block, applying the Shannon entropy of information theory. The co-movement trend of crude oil price and stock prices is divided into different fluctuation patterns with the coarse-graining method. Then, the bivariate time series network model is established for the two blocks stock in five different periods. By joint analysis of the network-oriented metrics, the key modes and underlying evolutionary mechanisms were identified. The results show that the both networks have different fluctuation characteristics in different periods. Their co-movement patterns are clustered in some key modes and conversion intermediaries. The study not only reveals the lag effect of crude oil price fluctuations on the stock in Chinese industry blocks but also verifies the necessity of research on special periods, and suggests that the government should use different energy policies to stabilize market volatility in different periods. A new way is provided to study the unidirectional influence between multiple variables or complex time series.

A time series analysis of multiple ambient pollutants to investigate the underlying air pollution dynamics and interactions.

PubMed

Yu, Hwa-Lung; Lin, Yuan-Chien; Kuo, Yi-Ming

2015-09-01

Understanding the temporal dynamics and interactions of particulate matter (PM) concentration and composition is important for air quality control. This paper applied a dynamic factor analysis method (DFA) to reveal the underlying mechanisms of nonstationary variations in twelve ambient concentrations of aerosols and gaseous pollutants, and the associations with meteorological factors. This approach can consider the uncertainties and temporal dependences of time series data. The common trends of the yearlong and three selected diurnal variations were obtained to characterize the dominant processes occurring in general and specific scenarios in Taipei during 2009 (i.e., during Asian dust storm (ADS) events, rainfall, and under normal conditions). The results revealed the two distinct yearlong NOx transformation processes, and demonstrated that traffic emissions and photochemical reactions both critically influence diurnal variation, depending upon meteorological conditions. During an ADS event, transboundary transport and distinct weather conditions both influenced the temporal pattern of identified common trends. This study shows the DFA method can effectively extract meaningful latent processes of time series data and provide insights of the dominant associations and interactions in the complex air pollution processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis.

PubMed

Mohammed, Emad A; Naugler, Christopher

2017-01-01

Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. This tool will allow anyone with historic test volume data to model future demand.

Data-driven discovery of partial differential equations.

PubMed

Rudy, Samuel H; Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

2017-04-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg-de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis

PubMed Central

Mohammed, Emad A.; Naugler, Christopher

2017-01-01

Background: Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. Method: In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. Results: This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. Conclusion: This tool will allow anyone with historic test volume data to model future demand. PMID:28400996

A daily Azores-Iceland North Atlantic Oscillation index back to 1850.

PubMed

Cropper, Thomas; Hanna, Edward; Valente, Maria Antónia; Jónsson, Trausti

2015-07-01

We present the construction of a continuous, daily (09:00 UTC), station-based (Azores-Iceland) North Atlantic Oscillation (NAO) Index back to 1871 which is extended back to 1850 with additional daily mean data. The constructed index more than doubles the length of previously existing, widely available, daily NAO time series. The index is created using entirely observational sea-level pressure (SLP) data from Iceland and 73.5% of observational SLP data from the Azores - the remainder being filled in via reanalysis (Twentieth Century Reanalysis Project and European Mean Sea Level Pressure) SLP data. Icelandic data are taken from the Southwest Iceland pressure series. We construct and document a new Ponta Delgada SLP time series based on recently digitized and newly available data that extend back to 1872. The Ponta Delgada time series is created by splicing together several fractured records (from Ponta Delgada, Lajes, and Santa Maria) and filling in the major gaps (pre-1872, 1888-1905, and 1940-1941) and occasional days (145) with reanalysis data. Further homogeneity corrections are applied to the Azores record, and the daily (09:00 UTC) NAO index is then calculated. The resulting index, with its extended temporal length and daily resolution, is the first reconstruction of daily NAO back into the 19th Century and therefore is useful for researchers across multiple disciplines.

Classifying Human Voices by Using Hybrid SFX Time-Series Preprocessing and Ensemble Feature Selection

PubMed Central

Wong, Raymond

2013-01-01

Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person. Due to this uniqueness, voice classification has found useful applications in classifying speakers' gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth. In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series. Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination. An ensemble is utilized in selecting only the influential features to be used in classification model induction. We focus on the comparison of effects of various popular data mining algorithms on multiple datasets. Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition. The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC. PMID:24288684

Model calibration criteria for estimating ecological flow characteristics

USGS Publications Warehouse

Vis, Marc; Knight, Rodney; Poole, Sandra; Wolfe, William J.; Seibert, Jan; Breuer, Lutz; Kraft, Philipp

2016-01-01

Quantification of streamflow characteristics in ungauged catchments remains a challenge. Hydrological modeling is often used to derive flow time series and to calculate streamflow characteristics for subsequent applications that may differ from those envisioned by the modelers. While the estimation of model parameters for ungauged catchments is a challenging research task in itself, it is important to evaluate whether simulated time series preserve critical aspects of the streamflow hydrograph. To address this question, seven calibration objective functions were evaluated for their ability to preserve ecologically relevant streamflow characteristics of the average annual hydrograph using a runoff model, HBV-light, at 27 catchments in the southeastern United States. Calibration trials were repeated 100 times to reduce parameter uncertainty effects on the results, and 12 ecological flow characteristics were computed for comparison. Our results showed that the most suitable calibration strategy varied according to streamflow characteristic. Combined objective functions generally gave the best results, though a clear underprediction bias was observed. The occurrence of low prediction errors for certain combinations of objective function and flow characteristic suggests that (1) incorporating multiple ecological flow characteristics into a single objective function would increase model accuracy, potentially benefitting decision-making processes; and (2) there may be a need to have different objective functions available to address specific applications of the predicted time series.

Discovering significant evolution patterns from satellite image time series.

PubMed

Petitjean, François; Masseglia, Florent; Gançarski, Pierre; Forestier, Germain

2011-12-01

Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.

Working Memory and Aging: Separating the Effects of Content and Context

PubMed Central

Bopp, Kara L.; Verhaeghen, Paul

2009-01-01

In three experiments, we investigated the hypothesis that age-related differences in working memory might be due to the inability to bind content with context. Participants were required to find a repeating stimulus within a single series (no context memory required) or within multiple series (necessitating memory for context). Response time and accuracy were examined in two task domains: verbal and visuospatial. Binding content with context led to longer processing time and poorer accuracy in both age groups, even when working memory load was held constant. Although older adults were overall slower and less accurate than younger adults, the need for context memory did not differentially affect their performance. It is therefore unlikely that age differences in working memory are due to specific age-related problems with content-with-context binding. PMID:20025410

Program for the analysis of time series. [by means of fast Fourier transform algorithm

NASA Technical Reports Server (NTRS)

Brown, T. J.; Brown, C. G.; Hardin, J. C.

1974-01-01

A digital computer program for the Fourier analysis of discrete time data is described. The program was designed to handle multiple channels of digitized data on general purpose computer systems. It is written, primarily, in a version of FORTRAN 2 currently in use on CDC 6000 series computers. Some small portions are written in CDC COMPASS, an assembler level code. However, functional descriptions of these portions are provided so that the program may be adapted for use on any facility possessing a FORTRAN compiler and random-access capability. Properly formatted digital data are windowed and analyzed by means of a fast Fourier transform algorithm to generate the following functions: (1) auto and/or cross power spectra, (2) autocorrelations and/or cross correlations, (3) Fourier coefficients, (4) coherence functions, (5) transfer functions, and (6) histograms.

Raising the legal drinking age in Maine: impact on traffic accidents among young drivers.

PubMed

Wagenaar, A C

1983-04-01

The minimum legal age for purchase and consumption of alcoholic beverages continues to be a controversial issue in North America as numerous jurisdictions that lowered the legal age in the early 1970s are returning to higher drinking ages. Monthly frequencies of motor vehicle crashes among drivers aged 18-45 in the states of Maine and Pennsylvania from 1972 through 1979 were examined using a multiple time series design. Controlling for the effects of long-term trends, seasonal cycles, and other factors with Box-Jenkins time series models, a significant 17-21% reduction in alcohol-related property damage crash involvement among drivers aged 18-19 is attributable to Maine's increase in drinking age. No demonstrable effect of the raised drinking age on the incidence of injury and fatal crashes was found.

Schedule Optimization of Imaging Missions for Multiple Satellites and Ground Stations Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Lee, Junghyun; Kim, Heewon; Chung, Hyun; Kim, Haedong; Choi, Sujin; Jung, Okchul; Chung, Daewon; Ko, Kwanghee

2018-04-01

In this paper, we propose a method that uses a genetic algorithm for the dynamic schedule optimization of imaging missions for multiple satellites and ground systems. In particular, the visibility conflicts of communication and mission operation using satellite resources (electric power and onboard memory) are integrated in sequence. Resource consumption and restoration are considered in the optimization process. Image acquisition is an essential part of satellite missions and is performed via a series of subtasks such as command uplink, image capturing, image storing, and image downlink. An objective function for optimization is designed to maximize the usability by considering the following components: user-assigned priority, resource consumption, and image-acquisition time. For the simulation, a series of hypothetical imaging missions are allocated to a multi-satellite control system comprising five satellites and three ground stations having S- and X-band antennas. To demonstrate the performance of the proposed method, simulations are performed via three operation modes: general, commercial, and tactical.

Bayesian correlated clustering to integrate multiple datasets

PubMed Central

Kirk, Paul; Griffin, Jim E.; Savage, Richard S.; Ghahramani, Zoubin; Wild, David L.

2012-01-01

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct—but often complementary—information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets. Results: Using a set of six artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real Saccharomyces cerevisiae datasets. In the two-dataset case, we show that MDI’s performance is comparable with the present state-of-the-art. We then move beyond the capabilities of current approaches and integrate gene expression, chromatin immunoprecipitation–chip and protein–protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques—as well as to non-integrative approaches—demonstrate that MDI is competitive, while also providing information that would be difficult or impossible to extract using other methods. Availability: A Matlab implementation of MDI is available from http://www2.warwick.ac.uk/fac/sci/systemsbiology/research/software/. Contact: D.L.Wild@warwick.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23047558

Monitoring Urban Greenness Dynamics Using Multiple Endmember Spectral Mixture Analysis

PubMed Central

Gan, Muye; Deng, Jinsong; Zheng, Xinyu; Hong, Yang; Wang, Ke

2014-01-01

Urban greenness is increasingly recognized as an essential constituent of the urban environment and can provide a range of services and enhance residents’ quality of life. Understanding the pattern of urban greenness and exploring its spatiotemporal dynamics would contribute valuable information for urban planning. In this paper, we investigated the pattern of urban greenness in Hangzhou, China, over the past two decades using time series Landsat-5 TM data obtained in 1990, 2002, and 2010. Multiple endmember spectral mixture analysis was used to derive vegetation cover fractions at the subpixel level. An RGB-vegetation fraction model, change intensity analysis and the concentric technique were integrated to reveal the detailed, spatial characteristics and the overall pattern of change in the vegetation cover fraction. Our results demonstrated the ability of multiple endmember spectral mixture analysis to accurately model the vegetation cover fraction in pixels despite the complex spectral confusion of different land cover types. The integration of multiple techniques revealed various changing patterns in urban greenness in this region. The overall vegetation cover has exhibited a drastic decrease over the past two decades, while no significant change occurred in the scenic spots that were studied. Meanwhile, a remarkable recovery of greenness was observed in the existing urban area. The increasing coverage of small green patches has played a vital role in the recovery of urban greenness. These changing patterns were more obvious during the period from 2002 to 2010 than from 1990 to 2002, and they revealed the combined effects of rapid urbanization and greening policies. This work demonstrates the usefulness of time series of vegetation cover fractions for conducting accurate and in-depth studies of the long-term trajectories of urban greenness to obtain meaningful information for sustainable urban development. PMID:25375176

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool.

PubMed

Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

2007-10-01

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.

Introduction of the ASGARD Code

NASA Technical Reports Server (NTRS)

Bethge, Christian; Winebarger, Amy; Tiwari, Sanjiv; Fayock, Brian

2017-01-01

ASGARD stands for 'Automated Selection and Grouping of events in AIA Regional Data'. The code is a refinement of the event detection method in Ugarte-Urra & Warren (2014). It is intended to automatically detect and group brightenings ('events') in the AIA EUV channels, to record event parameters, and to find related events over multiple channels. Ultimately, the goal is to automatically determine heating and cooling timescales in the corona and to significantly increase statistics in this respect. The code is written in IDL and requires the SolarSoft library. It is parallelized and can run with multiple CPUs. Input files are regions of interest (ROIs) in time series of AIA images from the JSOC cutout service (http://jsoc.stanford.edu/ajax/exportdata.html). The ROIs need to be tracked, co-registered, and limited in time (typically 12 hours).

Automated high-throughput flow-through real-time diagnostic system

DOEpatents

Regan, John Frederick

2012-10-30

An automated real-time flow-through system capable of processing multiple samples in an asynchronous, simultaneous, and parallel fashion for nucleic acid extraction and purification, followed by assay assembly, genetic amplification, multiplex detection, analysis, and decontamination. The system is able to hold and access an unlimited number of fluorescent reagents that may be used to screen samples for the presence of specific sequences. The apparatus works by associating extracted and purified sample with a series of reagent plugs that have been formed in a flow channel and delivered to a flow-through real-time amplification detector that has a multiplicity of optical windows, to which the sample-reagent plugs are placed in an operative position. The diagnostic apparatus includes sample multi-position valves, a master sample multi-position valve, a master reagent multi-position valve, reagent multi-position valves, and an optical amplification/detection system.

Extending Research on a Math Fluency Building Intervention: Applying Taped Problems in a Second-Grade Classroom

ERIC Educational Resources Information Center

Windingstad, Sunny; Skinner, Christopher H.; Rowland, Emily; Cardin, Elizabeth; Fearrington, Jamie Y.

2009-01-01

A multiple-baseline, across-tasks design was used to extend research on the taped-problems (TP) intervention with an intact, rural, second-grade classroom. During TP sessions an audio recording paced the class through a series of 15 or 16 addition facts four times. Problems and answers were read and students were instructed to attempt to provide…

The Missing Treatment Design Element: Continuity of Treatment When Multiple Postobservations Are Used in Time-Series and Repeated Measures Study Designs

ERIC Educational Resources Information Center

Barnette, J. Jackson; Wallis, Anne Baber

2005-01-01

We rely a great deal on the schematic descriptions that represent experimental and quasi-experimental design arrangements, as well as the discussions of threats to validity associated with these, provided by Campbell and his associates: Stanley, Cook, and Shadish. Some of these designs include descriptions of treatments removed, removed and then…

Pan-European stochastic flood event set

NASA Astrophysics Data System (ADS)

Kadlec, Martin; Pinto, Joaquim G.; He, Yi; Punčochář, Petr; Kelemen, Fanni D.; Manful, Desmond; Palán, Ladislav

2017-04-01

Impact Forecasting (IF), the model development center of Aon Benfield, has been developing a large suite of catastrophe flood models on probabilistic bases for individual countries in Europe. Such natural catastrophes do not follow national boundaries: for example, the major flood in 2016 was responsible for the Europe's largest insured loss of USD3.4bn and affected Germany, France, Belgium, Austria and parts of several other countries. Reflecting such needs, IF initiated a pan-European flood event set development which combines cross-country exposures with country based loss distributions to provide more insightful data to re/insurers. Because the observed discharge data are not available across the whole Europe in sufficient quantity and quality to permit a detailed loss evaluation purposes, a top-down approach was chosen. This approach is based on simulating precipitation from a GCM/RCM model chain followed by a calculation of discharges using rainfall-runoff modelling. IF set up this project in a close collaboration with Karlsruhe Institute of Technology (KIT) regarding the precipitation estimates and with University of East Anglia (UEA) in terms of the rainfall-runoff modelling. KIT's main objective is to provide high resolution daily historical and stochastic time series of key meteorological variables. A purely dynamical downscaling approach with the regional climate model COSMO-CLM (CCLM) is used to generate the historical time series, using re-analysis data as boundary conditions. The resulting time series are validated against the gridded observational dataset E-OBS, and different bias-correction methods are employed. The generation of the stochastic time series requires transfer functions between large-scale atmospheric variables and regional temperature and precipitation fields. These transfer functions are developed for the historical time series using reanalysis data as predictors and bias-corrected CCLM simulated precipitation and temperature as predictands. Finally, the transfer functions are applied to a large ensemble of GCM simulations with forcing corresponding to present day climate conditions to generate highly resolved stochastic time series of precipitation and temperature for several thousand years. These time series form the input for the rainfall-runoff model developed by the UEA team. It is a spatially distributed model adapted from the HBV model and will be calibrated for individual basins using historical discharge data. The calibrated model will be driven by the precipitation time series generated by the KIT team to simulate discharges at a daily time step. The uncertainties in the simulated discharges will be analysed using multiple model parameter sets. A number of statistical methods will be used to assess return periods, changes in the magnitudes, changes in the characteristics of floods such as time base and time to peak, and spatial correlations of large flood events. The Pan-European flood stochastic event set will permit a better view of flood risk for market applications.

Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

NASA Astrophysics Data System (ADS)

Niazmardi, S.; Safari, A.; Homayouni, S.

2017-09-01

Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

A 40 Year Time Series of SBUV Observations: the Version 8.6 Processing

NASA Technical Reports Server (NTRS)

McPeters, Richard; Bhartia, P. K.; Flynn, L.

2012-01-01

Under a NASA program to produce long term data records from instruments on multiple satellites (MEaSUREs), data from a series of eight SBUV and SBUV 12 instruments have been reprocessed to create a 40 year long ozone time series. Data from the Nimbus 4 BUV, Nimbus 7 SBUV, and SBUV/2 instruments on NOAA 9, 11, 14, 16, 17, and 18 were used covering the period 1970 to 1972 and 1979 to the present. In past analyses an ozone time series was created from these instruments by adjusting ozone itself, instrument by instrument, for consistency during overlap periods. In the version 8.6 processing adjustments were made to the radiance calibration of each instrument to maintain a consistent calibration over the entire time series. Data for all eight instruments were then reprocessed using the adjusted radiances. Reprocessing is necessary to produce an accurate latitude dependence. Other improvements incorporated in version 8.6 included the use of the ozone cross sections of Brion, Daumont, and Malicet, and the use of a cloud height climatology derived from Aura OMI measurements. The new cross sections have a more accurate temperature dependence than the cross sections previously used. The OMI-based cloud heights account for the penetration of UV into the upper layers of clouds. The consistency of the version 8.6 time series was evaluated by intra-instrument comparisons during overlap periods, comparisons with ground-based instruments, and comparisons with measurements made by instruments on other satellites such as SAGE II and UARS MLS. These comparisons show that for the instruments on NOAA 16, 17 and 18, the instrument calibrations were remarkably stable and consistent from instrument to instrument. The data record from the Nimbus 7 SBUV was also very stable, and SAGE and ground-based comparisons show that the' calibration was consistent with measurements made years laterby the NOAA 16 instrument. The calibrations of the SBUV/2 instruments on NOAA 9, 11, and 14 were more of a problem. The rapidly drifting orbits of these satellites resulted in relative time and altitude dependent differences that are significant. Despite these problems, total column ozone appears to be consistent to better than 1% over the entire time series, while the ozone vertical distribution is consistent to approximately 5%.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

PubMed

Aragri, Adriana; Scarabino, Tommaso; Seifritz, Erich; Comani, Silvia; Cirillo, Sossio; Tedeschi, Gioacchino; Esposito, Fabrizio; Di Salle, Francesco

2006-09-01

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity. (c) 2006 Wiley-Liss, Inc.

Linear and nonlinear frequency- and time-domain spectroscopy with multiple frequency combs.

PubMed

Bennett, Kochise; Rouxel, Jeremy R; Mukamel, Shaul

2017-09-07

Two techniques that employ equally spaced trains of optical pulses to map an optical high frequency into a low frequency modulation of the signal that can be detected in real time are compared. The development of phase-stable optical frequency combs has opened up new avenues to metrology and spectroscopy. The ability to generate a series of frequency spikes with precisely controlled separation permits a fast, highly accurate sampling of the material response. Recently, pairs of frequency combs with slightly different repetition rates have been utilized to down-convert material susceptibilities from the optical to microwave regime where they can be recorded in real time. We show how this one-dimensional dual comb technique can be extended to multiple dimensions by using several combs. We demonstrate how nonlinear susceptibilities can be quickly acquired using this technique. In a second class of techniques, sequences of ultrafast mode locked laser pulses are used to recover pathways of interactions contributing to nonlinear susceptibilities by using a photo-acoustic modulation varying along the sequences. We show that these techniques can be viewed as a time-domain analog of the multiple frequency comb scheme.

Guiding Principles for a Pediatric Neurology ICU (neuroPICU) Bedside Multimodal Monitor

PubMed Central

Eldar, Yonina C.; Gopher, Daniel; Gottlieb, Amihai; Lammfromm, Rotem; Mangat, Halinder S; Peleg, Nimrod; Pon, Steven; Rozenberg, Igal; Schiff, Nicholas D; Stark, David E; Yan, Peter; Pratt, Hillel; Kosofsky, Barry E

2016-01-01

Summary Background Physicians caring for children with serious acute neurologic disease must process overwhelming amounts of physiological and medical information. Strategies to optimize real time display of this information are understudied. Objectives Our goal was to engage clinical and engineering experts to develop guiding principles for creating a pediatric neurology intensive care unit (neuroPICU) monitor that integrates and displays data from multiple sources in an intuitive and informative manner. Methods To accomplish this goal, an international group of physicians and engineers communicated regularly for one year. We integrated findings from clinical observations, interviews, a survey, signal processing, and visualization exercises to develop a concept for a neuroPICU display. Results Key conclusions from our efforts include: (1) A neuroPICU display should support (a) rapid review of retrospective time series (i.e. cardiac, pulmonary, and neurologic physiology data), (b) rapidly modifiable formats for viewing that data according to the specialty of the reviewer, and (c) communication of the degree of risk of clinical decline. (2) Specialized visualizations of physiologic parameters can highlight abnormalities in multivariable temporal data. Examples include 3-D stacked spider plots and color coded time series plots. (3) Visual summaries of EEG with spectral tools (i.e. hemispheric asymmetry and median power) can highlight seizures via patient-specific “fingerprints.” (4) Intuitive displays should emphasize subsets of physiology and processed EEG data to provide a rapid gestalt of the current status and medical stability of a patient. Conclusions A well-designed neuroPICU display must present multiple datasets in dynamic, flexible, and informative views to accommodate clinicians from multiple disciplines in a variety of clinical scenarios. PMID:27437048

Guiding Principles for a Pediatric Neurology ICU (neuroPICU) Bedside Multimodal Monitor: Findings from an International Working Group.

PubMed

Grinspan, Zachary M; Eldar, Yonina C; Gopher, Daniel; Gottlieb, Amihai; Lammfromm, Rotem; Mangat, Halinder S; Peleg, Nimrod; Pon, Steven; Rozenberg, Igal; Schiff, Nicholas D; Stark, David E; Yan, Peter; Pratt, Hillel; Kosofsky, Barry E

2016-01-01

Physicians caring for children with serious acute neurologic disease must process overwhelming amounts of physiological and medical information. Strategies to optimize real time display of this information are understudied. Our goal was to engage clinical and engineering experts to develop guiding principles for creating a pediatric neurology intensive care unit (neuroPICU) monitor that integrates and displays data from multiple sources in an intuitive and informative manner. To accomplish this goal, an international group of physicians and engineers communicated regularly for one year. We integrated findings from clinical observations, interviews, a survey, signal processing, and visualization exercises to develop a concept for a neuroPICU display. Key conclusions from our efforts include: (1) A neuroPICU display should support (a) rapid review of retrospective time series (i.e. cardiac, pulmonary, and neurologic physiology data), (b) rapidly modifiable formats for viewing that data according to the specialty of the reviewer, and (c) communication of the degree of risk of clinical decline. (2) Specialized visualizations of physiologic parameters can highlight abnormalities in multivariable temporal data. Examples include 3-D stacked spider plots and color coded time series plots. (3) Visual summaries of EEG with spectral tools (i.e. hemispheric asymmetry and median power) can highlight seizures via patient-specific "fingerprints." (4) Intuitive displays should emphasize subsets of physiology and processed EEG data to provide a rapid gestalt of the current status and medical stability of a patient. A well-designed neuroPICU display must present multiple datasets in dynamic, flexible, and informative views to accommodate clinicians from multiple disciplines in a variety of clinical scenarios.

Multivariate statistical analysis of wildfires in Portugal

NASA Astrophysics Data System (ADS)

Costa, Ricardo; Caramelo, Liliana; Pereira, Mário

2013-04-01

Several studies demonstrate that wildfires in Portugal present high temporal and spatial variability as well as cluster behavior (Pereira et al., 2005, 2011). This study aims to contribute to the characterization of the fire regime in Portugal with the multivariate statistical analysis of the time series of number of fires and area burned in Portugal during the 1980 - 2009 period. The data used in the analysis is an extended version of the Rural Fire Portuguese Database (PRFD) (Pereira et al, 2011), provided by the National Forest Authority (Autoridade Florestal Nacional, AFN), the Portuguese Forest Service, which includes information for more than 500,000 fire records. There are many multiple advanced techniques for examining the relationships among multiple time series at the same time (e.g., canonical correlation analysis, principal components analysis, factor analysis, path analysis, multiple analyses of variance, clustering systems). This study compares and discusses the results obtained with these different techniques. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005: "Synoptic patterns associated with large summer forest fires in Portugal". Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 This work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project FCOMP-01-0124-FEDER-022692, the project FLAIR (PTDC/AAC-AMB/104702/2008) and the EU 7th Framework Program through FUME (contract number 243888).

Intensive (Daily) Behavior Therapy for School Refusal: A Multiple Baseline Case Series

ERIC Educational Resources Information Center

Tolin, David F.; Whiting, Sara; Maltby, Nicholas; Diefenbach, Gretchen J.; Lothstein, Mary Anne; Hardcastle, Surrey; Catalano, Amy; Gray, Krista

2009-01-01

The following multiple baseline case series examines school refusal behavior in 4 male adolescents. School refusal symptom presentation was ascertained utilizing a functional analysis from the School Refusal Assessment Scale (Kearney, 2002). For the majority of cases, treatment was conducted within a 15-session intensive format over a 3-week…

Forward Period Analysis Method of the Periodic Hamiltonian System.

PubMed

Wang, Pengfei

2016-01-01

Using the forward period analysis (FPA), we obtain the period of a Morse oscillator and mathematical pendulum system, with the accuracy of 100 significant digits. From these results, the long-term [0, 1060] (time unit) solutions, ranging from the Planck time to the age of the universe, are computed reliably and quickly with a parallel multiple-precision Taylor series (PMT) scheme. The application of FPA to periodic systems can greatly reduce the computation time of long-term reliable simulations. This scheme provides an efficient way to generate reference solutions, against which long-term simulations using other schemes can be tested.

Identification and classification of transient pulses observed in magnetometer array data by time-domain principal component analysis filtering

NASA Astrophysics Data System (ADS)

Kappler, Karl N.; Schneider, Daniel D.; MacLean, Laura S.; Bleier, Thomas E.

2017-08-01

A method for identification of pulsations in time series of magnetic field data which are simultaneously present in multiple channels of data at one or more sensor locations is described. Candidate pulsations of interest are first identified in geomagnetic time series by inspection. Time series of these "training events" are represented in matrix form and transpose-multiplied to generate time-domain covariance matrices. The ranked eigenvectors of this matrix are stored as a feature of the pulsation. In the second stage of the algorithm, a sliding window (approximately the width of the training event) is moved across the vector-valued time-series comprising the channels on which the training event was observed. At each window position, the data covariance matrix and associated eigenvectors are calculated. We compare the orientation of the dominant eigenvectors of the training data to those from the windowed data and flag windows where the dominant eigenvectors directions are similar. This was successful in automatically identifying pulses which share polarization and appear to be from the same source process. We apply the method to a case study of continuously sampled (50 Hz) data from six observatories, each equipped with three-component induction coil magnetometers. We examine a 90-day interval of data associated with a cluster of four observatories located within 50 km of Napa, California, together with two remote reference stations-one 100 km to the north of the cluster and the other 350 km south. When the training data contains signals present in the remote reference observatories, we are reliably able to identify and extract global geomagnetic signals such as solar-generated noise. When training data contains pulsations only observed in the cluster of local observatories, we identify several types of non-plane wave signals having similar polarization.

Versatile utilization of real-time intraoperative contrast-enhanced ultrasound in cranial neurosurgery: technical note and retrospective case series.

PubMed

Lekht, Ilya; Brauner, Noah; Bakhsheshian, Joshua; Chang, Ki-Eun; Gulati, Mittul; Shiroishi, Mark S; Grant, Edward G; Christian, Eisha; Zada, Gabriel

2016-03-01

Intraoperative contrast-enhanced ultrasound (iCEUS) offers dynamic imaging and provides functional data in real time. However, no standardized protocols or validated quantitative data exist to guide its routine use in neurosurgery. The authors aimed to provide further clinical data on the versatile application of iCEUS through a technical note and illustrative case series. Five patients undergoing craniotomies for suspected tumors were included. iCEUS was performed using a contrast agent composed of lipid shell microspheres enclosing perflutren (octafluoropropane) gas. Perfusion data were acquired through a time-intensity curve analysis protocol obtained using iCEUS prior to biopsy and/or resection of all lesions. Three primary tumors (gemistocytic astrocytoma, glioblastoma multiforme, and meningioma), 1 metastatic lesion (melanoma), and 1 tumefactive demyelinating lesion (multiple sclerosis) were assessed using real-time iCEUS. No intraoperative complications occurred following multiple administrations of contrast agent in all cases. In all neoplastic cases, iCEUS replicated enhancement patterns observed on preoperative Gd-enhanced MRI, facilitated safe tumor debulking by differentiating neoplastic tissue from normal brain parenchyma, and helped identify arterial feeders and draining veins in and around the surgical cavity. Intraoperative CEUS was also useful in guiding a successful intraoperative needle biopsy of a cerebellar tumefactive demyelinating lesion obtained during real-time perfusion analysis. Intraoperative CEUS has potential for safe, real-time, dynamic contrast-based imaging for routine use in neurooncological surgery and image-guided biopsy. Intraoperative CEUS eliminates the effect of anatomical distortions associated with standard neuronavigation and provides quantitative perfusion data in real time, which may hold major implications for intraoperative diagnosis, tissue differentiation, and quantification of extent of resection. Further prospective studies will help standardize the role of iCEUS in neurosurgery.

The relevance of time series in molecular ecology and conservation biology.

PubMed

Habel, Jan C; Husemann, Martin; Finger, Aline; Danley, Patrick D; Zachos, Frank E

2014-05-01

The genetic structure of a species is shaped by the interaction of contemporary and historical factors. Analyses of individuals from the same population sampled at different points in time can help to disentangle the effects of current and historical forces and facilitate the understanding of the forces driving the differentiation of populations. The use of such time series allows for the exploration of changes at the population and intraspecific levels over time. Material from museum collections plays a key role in understanding and evaluating observed population structures, especially if large numbers of individuals have been sampled from the same locations at multiple time points. In these cases, changes in population structure can be assessed empirically. The development of new molecular markers relying on short DNA fragments (such as microsatellites or single nucleotide polymorphisms) allows for the analysis of long-preserved and partially degraded samples. Recently developed techniques to construct genome libraries with a reduced complexity and next generation sequencing and their associated analysis pipelines have the potential to facilitate marker development and genotyping in non-model species. In this review, we discuss the problems with sampling and available marker systems for historical specimens and demonstrate that temporal comparative studies are crucial for the estimation of important population genetic parameters and to measure empirically the effects of recent habitat alteration. While many of these analyses can be performed with samples taken at a single point in time, the measurements are more robust if multiple points in time are studied. Furthermore, examining the effects of habitat alteration, population declines, and population bottlenecks is only possible if samples before and after the respective events are included. © 2013 The Authors. Biological Reviews © 2013 Cambridge Philosophical Society.

[Investigation of RNA viral genome amplification by multiple displacement amplification technique].

PubMed

Pang, Zheng; Li, Jian-Dong; Li, Chuan; Liang, Mi-Fang; Li, De-Xin

2013-06-01

In order to facilitate the detection of newly emerging or rare viral infectious diseases, a negative-strand RNA virus-severe fever with thrombocytopenia syndrome bunyavirus, and a positive-strand RNA virus-dengue virus, were used to investigate RNA viral genome unspecific amplification by multiple displacement amplification technique from clinical samples. Series of 10-fold diluted purified viral RNA were utilized as analog samples with different pathogen loads, after a series of reactions were sequentially processed, single-strand cDNA, double-strand cDNA, double-strand cDNA treated with ligation without or with supplemental RNA were generated, then a Phi29 DNA polymerase depended isothermal amplification was employed, and finally the target gene copies were detected by real time PCR assays to evaluate the amplification efficiencies of various methods. The results showed that multiple displacement amplification effects of single-strand or double-strand cDNA templates were limited, while the fold increases of double-strand cDNA templates treated with ligation could be up to 6 X 10(3), even 2 X 10(5) when supplemental RNA existed, and better results were obtained when viral RNA loads were lower. A RNA viral genome amplification system using multiple displacement amplification technique was established in this study and effective amplification of RNA viral genome with low load was achieved, which could provide a tool to synthesize adequate viral genome for multiplex pathogens detection.

Outcomes of reintervention after failed urethroplasty.

PubMed

Ekerhult, Teresa Olsen; Lindqvist, Klas; Peeker, Ralph; Grenabo, Lars

2017-02-01

Urethroplasty is a procedure that has a high success rate. However, there exists a small subgroup of patients who require multiple procedures to achieve an acceptable result. This study analyses the outcomes of a series of patients with failed urethroplasty. This is a retrospective review of 82 failures out of 407 patients who underwent urethroplasty due to urethral stricture during the period 1999-2013. Failure was defined as the need for an additional surgical procedure. Of the failures, 26 patients had penile strictures and 56 had bulbar strictures. Meatal strictures were not included. The redo procedures included one or multiple direct vision internal urethrotomies, dilatations or new urethroplasties, all with a long follow-up time. The patients underwent one to seven redo surgeries (mean 2.4 procedures per patient). In the present series of patients, endourological procedures cured 34% (28/82) of the patients. Ten patients underwent multiple redo urethroplasties until a satisfactory outcome was achieved; the penile strictures were the most difficult to cure. In patients with bulbar strictures, excision with anastomosis and substitution urethroplasty were equally successful. Nevertheless, 18 patients were defined as treatment failures. Of these patients, nine ended up with clean intermittent self-dilatation as a final solution, five had perineal urethrostomy and four are awaiting a new reintervention. Complicated cases need centralized professional care. Despite the possibility of needing multiple reinterventions, the majority of patients undergoing urethroplasty have a good chance of successful treatment.

Bipolar Electrode Array Embedded in a Polymer Light-Emitting Electrochemical Cell.

PubMed

Gao, Jun; Chen, Shulun; AlTal, Faleh; Hu, Shiyu; Bouffier, Laurent; Wantz, Guillaume

2017-09-20

A linear array of aluminum discs is deposited between the driving electrodes of an extremely large planar polymer light-emitting electrochemical cell (PLEC). The planar PLEC is then operated at a constant bias voltage of 100 V. This promotes in situ electrochemical doping of the luminescent polymer from both the driving electrodes and the aluminum discs. These aluminum discs function as discrete bipolar electrodes (BPEs) that can drive redox reactions at their extremities. Time-lapse fluorescence imaging reveals that p- and n-doping that originated from neighboring BPEs can interact to form multiple light-emitting p-n junctions in series. This provides direct evidence of the working principle of bulk homojunction PLECs. The propagation of p-doping is faster from the BPEs than from the positive driving electrode due to electric field enhancement at the extremities of BPEs. The effect of field enhancement and the fact that the doping fronts only need to travel the distance between the neighboring BPEs to form a light-emitting junction greatly reduce the response time for electroluminescence in the region containing the BPE array. The near simultaneous formation of multiple light-emitting p-n junctions in series causes a measurable increase in cell current. This indicates that the region containing a BPE is much more conductive than the rest of the planar cell despite the latter's greater width. The p- and n-doping originating from the BPEs is initially highly confined. Significant expansion and divergence of doping occurred when the region containing the BPE array became more conductive. The shape and direction of expanded doping strongly suggest that the multiple light-emitting p-n junctions, formed between and connected by the array of metal BPEs, have functioned as a single rod-shaped BPE. This represents a new type of BPE that is formed in situ and as a combination of metal, doped polymers, and forward-biased p-n junctions connected in series.

Accuracy of time-domain and frequency-domain methods used to characterize catchment transit time distributions

NASA Astrophysics Data System (ADS)

Godsey, S. E.; Kirchner, J. W.

2008-12-01

The mean residence time - the average time that it takes rainfall to reach the stream - is a basic parameter used to characterize catchment processes. Heterogeneities in these processes lead to a distribution of travel times around the mean residence time. By examining this travel time distribution, we can better predict catchment response to contamination events. A catchment system with shorter residence times or narrower distributions will respond quickly to contamination events, whereas systems with longer residence times or longer-tailed distributions will respond more slowly to those same contamination events. The travel time distribution of a catchment is typically inferred from time series of passive tracers (e.g., water isotopes or chloride) in precipitation and streamflow. Variations in the tracer concentration in streamflow are usually damped compared to those in precipitation, because precipitation inputs from different storms (with different tracer signatures) are mixed within the catchment. Mathematically, this mixing process is represented by the convolution of the travel time distribution and the precipitation tracer inputs to generate the stream tracer outputs. Because convolution in the time domain is equivalent to multiplication in the frequency domain, it is relatively straightforward to estimate the parameters of the travel time distribution in either domain. In the time domain, the parameters describing the travel time distribution are typically estimated by maximizing the goodness of fit between the modeled and measured tracer outputs. In the frequency domain, the travel time distribution parameters can be estimated by fitting a power-law curve to the ratio of precipitation spectral power to stream spectral power. Differences between the methods of parameter estimation in the time and frequency domain mean that these two methods may respond differently to variations in data quality, record length and sampling frequency. Here we evaluate how well these two methods of travel time parameter estimation respond to different sources of uncertainty and compare the methods to one another. We do this by generating synthetic tracer input time series of different lengths, and convolve these with specified travel-time distributions to generate synthetic output time series. We then sample both the input and output time series at various sampling intervals and corrupt the time series with realistic error structures. Using these 'corrupted' time series, we infer the apparent travel time distribution, and compare it to the known distribution that was used to generate the synthetic data in the first place. This analysis allows us to quantify how different record lengths, sampling intervals, and error structures in the tracer measurements affect the apparent mean residence time and the apparent shape of the travel time distribution.

Urban Land: Study of Surface Run-off Composition and Its Dynamics

NASA Astrophysics Data System (ADS)

Palagin, E. D.; Gridneva, M. A.; Bykova, P. G.

2017-11-01

The qualitative composition of urban land surface run-off is liable to significant variations. To study surface run-off dynamics, to examine its behaviour and to discover reasons of these variations, it is relevant to use the mathematical apparatus technique of time series analysis. A seasonal decomposition procedure was applied to a temporary series of monthly dynamics with the annual frequency of seasonal variations in connection with a multiplicative model. The results of the quantitative chemical analysis of surface wastewater of the 22nd Partsjezd outlet in Samara for the period of 2004-2016 were used as basic data. As a result of the analysis, a seasonal pattern of variations in the composition of surface run-off in Samara was identified. Seasonal indices upon 15 waste-water quality indicators were defined. BOD (full), suspended materials, mineralization, chlorides, sulphates, ammonium-ion, nitrite-anion, nitrate-anion, phosphates (phosphorus), iron general, copper, zinc, aluminium, petroleum products, synthetic surfactants (anion-active). Based on the seasonal decomposition of the time series data, the contribution of trends, seasonal and accidental components of the variability of the surface run-off indicators was estimated.

Investigation of multifractality in the Brazilian stock market

NASA Astrophysics Data System (ADS)

Maganini, Natália Diniz; Da Silva Filho, Antônio Carlos; Lima, Fabiano Guasti

2018-05-01

Many studies point to a possible new stylized fact for financial time series: the multifractality. Several authors have already detected this characteristic in multiple time series in several countries. With that in mind and based on Multifractal Detrended Fluctuation Analysis (MFDFA) method, this paper analyzes the multifractality in the Brazilian market. This analysis is performed with daily data from IBOVESPA index (Brazilian stock exchange's main index) and other four highly marketable stocks in the Brazilian market (VALE5, ITUB4, BBDC4 and CIEL3), which represent more than 25% of the index composition, making up 1961 observations for each asset in the period from June 26 2009 to May 31 2017. We found that the studied stock prices and Brazilian index are multifractal, but that the multifractality degree is not the same for all the assets. The use of shuffled and surrogated series indicates that for the period and the actions considered the long-range correlations do not strongly influence the multifractality, but the distribution (fat tails) exerts a possible influence on IBOVESPA and CIEL3.

Disk Disruptions and X-ray Intensity Excursions in Cyg X-2, LMC X-3 and Cyg X-3

NASA Astrophysics Data System (ADS)

Boyd, P. T.; Smale, A. P.

2001-05-01

The RXTE All Sky Monitor soft X-ray light curves of many X-ray binaries show long-term intensity variations (a.k.a "superorbital periodicities") that have been ascribed to precession of a warped, tilted accretion disk around the X-ray source. We have found that the excursion times between X-ray minima in Cyg X-2 can be characterized as a series of integer multiples of the 9.8 binary orbital period, (as opposed to the previously reported stable 77.7 day single periodicity, or a single modulation whose period changes slowly with time). While the data set is too short for a proper statistical analysis, it is clear that the length of any given intensity excursion cannot be used to predict the next (integer) excursion length in the series. In the black hole candidate system LMC X-3, the excursion times are shown to be related to each other by rational fractions. We find that the long term light curve of the unusual galactic X-ray jet source Cyg X-3 can also be described as a series of intensity excursions related to each other by integer multiples of a fundamental underlying clock. In the latter cases, the clock is apparently not related to the known binary periods. A unified physical model, involving both an inclined accretion disk and a fixed-probability disk disruption mechanism is presented, and compared with three-body scattering results. Each time the disk passes through the orbital plane it experiences a fixed probability P that it will disrupt. This model has testable predictions---the distribution of integers should resemble that of an atomic process with a characteristic half life. Further analysis can support or refute the model, and shed light on what system parameters effectively set the value of P.

Comparison of Interferometric Time-Series Analysis Techniques with Implications for Future Mission Design

NASA Astrophysics Data System (ADS)

Werner, C. L.; Wegmuller, U.; Strozzi, T.; Wiesmann, A.

2006-12-01

Principle contributors to the noise in differential SAR interferograms are temporal phase stability of the surface, geometry relating to baseline and surface slope, and propagation path delay variations due to tropospheric water vapor and the ionosphere. Time series analysis of multiple interferograms generated from a stack of SAR SLC images seeks to determine the deformation history of the surface while reducing errors. Only those scatterers within a resolution element that are stable and coherent for each interferometric pair contribute to the desired deformation signal. Interferograms with baselines exceeding 1/3 the critical baseline have substantial geometrical decorrelation for distributed targets. Short baseline pairs with multiple reference scenes can be combined using least-squares estimation to obtain a global deformation solution. Alternately point-like persistent scatterers can be identified in scenes that do not exhibit geometrical decorrelation associated with large baselines. In this approach interferograms are formed from a stack of SAR complex images using a single reference scene. Stable distributed scatter pixels are excluded however due to the presence of large baselines. We apply both point- based and short-baseline methodologies and compare results for a stack of fine-beam Radarsat data acquired in 2002-2004 over a rapidly subsiding oil field near Lost Hills, CA. We also investigate the density of point-like scatters with respect to image resolution. The primary difficulty encountered when applying time series methods is phase unwrapping errors due to spatial and temporal gaps. Phase unwrapping requires sufficient spatial and temporal sampling. Increasing the SAR range bandwidth increases the range resolution as well as increasing the critical interferometric baseline that defines the required satellite orbital tube diameter. Sufficient spatial sampling also permits unwrapping because of the reduced phase/pixel gradient. Short time intervals further reduce the differential phase due to deformation when the deformation is continuous. Lower frequency systems (L- vs. C-Band) substantially improve the ability to unwrap the phase correctly by directly reducing both interferometric phase amplitude and temporal decorrelation.

Sidebands alongside the diurnal frequencies of radon time series in a simulation experiment -- an indication for a direct association with the earth-sun system

NASA Astrophysics Data System (ADS)

Steinitz, Gideon; Sturrock, Peter A.; Piatibratova, Oksana; Kotlarsky, Peter

2015-04-01

A radon simulation experiment using a confined mode is operating at GSI since 2007 at a time resolution of 15-minutes [1]. The nuclear radiation from radon in the confined air is measured using internal alpha and gamma sensors, and external gamma sensors. Detailed analysis [1, 2] demonstrated that the variation patterns cannot be ascribed to local environmental influences. On the other hand the specific features and relation led to the suggestion that a component in solar radiation is driving the signals. Prominent periodicities dominate the variation in the annual and diurnal frequency bands. The primary periodicity in the diurnal band has a frequency of 1 CPD (S1). Significant multiples occur at 2 CPD (S2), 3 CPD (S3) and also at 4 CPD (S4). The S2 and S3 constituents are clearly observed in the time domain in addition to the primary S1 periodicity. The measured signal is detrended by removing the large annual variation. Spectral analysis (FFT) of the residual time series reveals sidebands (Sb) alongside and on both sides of the S1 frequency in the time series of the alpha and gamma sensors. The lower sideband (LSb) occurs at a frequency close to the astronomical sidereal frequency (0.9972696 CPD). The upper sideband (USb) occurs at a symmetric frequency relative to S1. The four sensors (alpha and gamma)exhibit the LSb, S1, and USb at the following frequencies (CPD): Gamma-C: 0.99739; 0.99989; 1.00275 Gamma-W: 0.99717; 0.99986; 1.00257 Alpha-H: 0.99710; 0.99992; 1.00269 Alpha-L: 0.99719; 0.99991 Multiples of LSb and USb are observed around the S1 periodicity. Similar features of Sb and multiples occur also around S2, S3, and S4. The development of the specific Sb around the diurnal periodicities may be attributed to a driver composed of two waveforms having periodicities of 1 day and 365.25 days, which interacts in a non-linear mode with radon inside the confined volume. The pattern of the alpha and gamma emission of the decaying radon is reflecting this non-linear interaction. The observed patterns of diurnal periodicities together with the associated Sb and their multiples can be demonstrated by statistical simulation using polynomial combinations of these sinusoidal waveforms. Notwithstanding, at this stage the identification of the underlying physical and geophysical processes remains open. The observation of sidebands around S1 at the specific periodicities indicates that the periodic signals in the radon time series of the experiment are directly related to the cyclic rotational relations in the earth-sun system. This in turn is an independent confirmation of the notion that these signals are influenced by a component in solar radiation [1, 2]. 1. Steinitz, G., Piatibratova, O., Kotlarsky, P., 2011. Possible effect of solar tides on radon signals. Journal of Environmental Radioactivity, 102, 749-765. doi: 10.1016/j.jenvrad.2011.04.002. 2. Sturrock, P.A., Steinitz, G., Fischbach, E., Javorsek, D. and Jenkins, J.H., 2012. Analysis of Gamma Radiation from a Radon Source: Indications of a Solar Influence. Astroparticle Physics, 36/1, 18-26.

Data Rods: High Speed, Time-Series Analysis of Massive Cryospheric Data Sets Using Object-Oriented Database Methods

NASA Astrophysics Data System (ADS)

Liang, Y.; Gallaher, D. W.; Grant, G.; Lv, Q.

2011-12-01

Change over time, is the central driver of climate change detection. The goal is to diagnose the underlying causes, and make projections into the future. In an effort to optimize this process we have developed the Data Rod model, an object-oriented approach that provides the ability to query grid cell changes and their relationships to neighboring grid cells through time. The time series data is organized in time-centric structures called "data rods." A single data rod can be pictured as the multi-spectral data history at one grid cell: a vertical column of data through time. This resolves the long-standing problem of managing time-series data and opens new possibilities for temporal data analysis. This structure enables rapid time- centric analysis at any grid cell across multiple sensors and satellite platforms. Collections of data rods can be spatially and temporally filtered, statistically analyzed, and aggregated for use with pattern matching algorithms. Likewise, individual image pixels can be extracted to generate multi-spectral imagery at any spatial and temporal location. The Data Rods project has created a series of prototype databases to store and analyze massive datasets containing multi-modality remote sensing data. Using object-oriented technology, this method overcomes the operational limitations of traditional relational databases. To demonstrate the speed and efficiency of time-centric analysis using the Data Rods model, we have developed a sea ice detection algorithm. This application determines the concentration of sea ice in a small spatial region across a long temporal window. If performed using traditional analytical techniques, this task would typically require extensive data downloads and spatial filtering. Using Data Rods databases, the exact spatio-temporal data set is immediately available No extraneous data is downloaded, and all selected data querying occurs transparently on the server side. Moreover, fundamental statistical calculations such as running averages are easily implemented against the time-centric columns of data.

0.1 Trend analysis of δ18O composition of precipitation in Germany: Combining Mann-Kendall trend test and ARIMA models to correct for higher order serial correlation

NASA Astrophysics Data System (ADS)

Klaus, Julian; Pan Chun, Kwok; Stumpp, Christine

2015-04-01

Spatio-temporal dynamics of stable oxygen (18O) and hydrogen (2H) isotopes in precipitation can be used as proxies for changing hydro-meteorological and regional and global climate patterns. While spatial patterns and distributions gained much attention in recent years the temporal trends in stable isotope time series are rarely investigated and our understanding of them is still limited. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here we make use of an extensive data set of stable isotope in German precipitation. In this study we investigate temporal trends of δ18O in precipitation at 17 observation station in Germany between 1978 and 2009. For that we test different approaches for proper trend detection, accounting for first and higher order serial correlation. We test if significant trends in the isotope time series based on different models can be observed. We apply the Mann-Kendall trend tests on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models which account for first and higher order serial correlations. With the approach we can also account for the effects of temperature, precipitation amount on the trend. Further we investigate the role of geographic parameters on isotope trends. To benchmark our proposed approach, the ARIMA results are compared to a trend-free prewhiting (TFPW) procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we explore whether higher order serial correlations in isotope series affects our trend results. The results show that three out of the 17 stations have significant changes when higher order autocorrelation are adjusted, and four stations show a significant trend when temperature and precipitation effects are considered. Significant trends in the isotope time series are generally observed at low elevation stations (≤315 m a.s.l.). Higher order autoregressive processes are important in the isotope time series analysis. Our results show that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative.

Intereruptive deformation at Three Sisters volcano, Oregon, USA: a strategy for traking volume changes through coupled hydraulic-viscoelastic modeling

NASA Astrophysics Data System (ADS)

Charco, M.; Rodriguez Molina, S.; Gonzalez, P. J.; Negredo, A. M.; Poland, M. P.; Schmidt, D. A.

2017-12-01

The Three Sisters volcanic region Oregon (USA) is one of the most active volcanic areas in the Cascade Range and is densely populated with eruptive vents. An extensive area just west of South Sister volcano has been actively uplifting since about 1998. InSAR data from 1992 through 2001 showed an uplift rate in the area of 3-4 cm/yr. Then the deformation rate considerably decreased between 2004 and 2006 as shown by both InSAR and continuous GPS measurements. Once magmatic system geometry and location are determined, a linear inversion of all GPS and InSAR data available is performed in order to estimate the volume changes of the source along the analyzed time interval. For doing so, we applied a technique based on the Truncated Singular Value Decomposition (TSVD) of the Green's function matrix representing the linear inversion. Here, we develop a strategy to provide a cut-off for truncation removing the smallest singular values without too much loose of data resolution against the stability of the method. Furthermore, the strategy will give us a quantification of the uncertainty of the volume change time series. The strength of the methodology resides in allowing the joint inversion of InSAR measurements from multiple tracks with different look angles and three component GPS measurements from multiple sites.Finally, we analyze the temporal behavior of the source volume changes using a new analytical model that describes the process of injecting magma into a reservoir surrounded by a viscoelastic shell. This dynamic model is based on Hagen-Poiseuille flow through a vertical conduit that leads to an increase in pressure within a spherical reservoir and time-dependent surface deformation. The volume time series are compared to predictions from the dynamic model to constrain model parameters, namely characteristic Poiseuille and Maxwell time scales, inlet and outlet injection pressure, and source and shell geometries. The modeling approach used here could be used to develop a mathematically rigorous strategy for including time-series deformation data in the interpretation of volcanic unrest.

Tot Graeci Tot Sententiae: Astronomical Perspective Multiplicity in Ancient Greece

NASA Astrophysics Data System (ADS)

Longo, O.

2011-06-01

Ancient Greece was made of a multiplicity of thinking heads, in an atmosphere of (relative) freedom of opinions, in every field of knowledge. then we should not wonder if many astronomical and cosmological theories, survived until our 17th century, had already been formulated by different philosophers and in different regions, cities and periods of Greek history. Geocentric and heliocentric theories, as well as an atomistic theory of an infinite universe (with infinite worlds), could survive without crashing with one another. In the same time, religious opinions regarding the planets and Sun as a series of gods were present, however not on a scientific ground.

Crime prediction modeling

NASA Technical Reports Server (NTRS)

1971-01-01

A study of techniques for the prediction of crime in the City of Los Angeles was conducted. Alternative approaches to crime prediction (causal, quasicausal, associative, extrapolative, and pattern-recognition models) are discussed, as is the environment within which predictions were desired for the immediate application. The decision was made to use time series (extrapolative) models to produce the desired predictions. The characteristics of the data and the procedure used to choose equations for the extrapolations are discussed. The usefulness of different functional forms (constant, quadratic, and exponential forms) and of different parameter estimation techniques (multiple regression and multiple exponential smoothing) are compared, and the quality of the resultant predictions is assessed.

Measuring Multiple Resistances Using Single-Point Excitation

NASA Technical Reports Server (NTRS)

Hall, Dan; Davies, Frank

2009-01-01

In a proposed method of determining the resistances of individual DC electrical devices connected in a series or parallel string, no attempt would be made to perform direct measurements on individual devices. Instead, (1) the devices would be instrumented by connecting reactive circuit components in parallel and/or in series with the devices, as appropriate; (2) a pulse or AC voltage excitation would be applied at a single point on the string; and (3) the transient or AC steady-state current response of the string would be measured at that point only. Each reactive component(s) associated with each device would be distinct in order to associate a unique time-dependent response with that device.

Concussion symptoms and neurocognitive performance of high school and college athletes who incur multiple concussions.

PubMed

Covassin, Tracey; Moran, Ryan; Wilhelm, Kristyn

2013-12-01

Multiple concussions have been associated with prolonged symptoms, recovery time, and risk for future concussions. However, very few studies have examined the effect of multiple concussions on neurocognitive performance and the recently revised symptom clusters using a large database. To examine concussed athletes with a history of 0, 1, 2, or ≥3 concussions on neurocognitive performance and the recently revised symptom clusters. Cohort study (prognosis); Level of evidence, 2. The independent variables were concussion group (0, 1, 2, and ≥3 concussions) and time (baseline, 3 days, and 8 days). The dependent variables were neurocognitive test scores as measured by the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) neurocognitive test battery (verbal and visual memory, processing speed, and reaction time) and 4 concussion symptom clusters (migraine-cognitive-fatigue, affective, somatic, and sleep). All concussed athletes (n = 596) were administered the ImPACT test at a mean 2.67 ± 1.98 and 7.95 ± 4.46 days after injury. A series of 4 (concussion group) × 3 (time) repeated-measures analyses of covariance (age = covariate) were performed on ImPACT composite scores and symptom clusters. Concussed athletes with ≥3 concussions were still impaired 8 days after a concussion compared with baseline scores on verbal memory (P < .001), reaction time (P < .001), and migraine-cognitive-fatigue symptoms (P < .001). There were no significant findings on the remaining dependent variables. Concussed athletes with a history of ≥3 concussions take longer to recover than athletes with 1 or no previous concussion. Future research should concentrate on validating the new symptom clusters on multiple concussed athletes, examining longer recovery times (ie, >8 days) among athletes with multiple concussions.

Multifractality of cerebral blood flow

NASA Astrophysics Data System (ADS)

West, Bruce J.; Latka, Miroslaw; Glaubic-Latka, Marta; Latka, Dariusz

2003-02-01

Scale invariance, the property relating time series across multiple scales, has provided a new perspective of physiological phenomena and their underlying control systems. The traditional “signal plus noise” paradigm of the engineer was first replaced with a model in which biological time series have a fractal structure in time (Fractal Physiology, Oxford University Press, Oxford, 1994). This new paradigm was subsequently shown to be overly restrictive when certain physiological signals were found to be characterized by more than one scaling parameter and therefore to belong to a class of more complex processes known as multifractals (Fractals, Plenum Press, New York, 1988). Here we demonstrate that in addition to heart rate (Nature 399 (1999) 461) and human gait (Phys. Rev. E, submitted for publication), the nonlinear control system for cerebral blood flow (CBF) (Phys. Rev. Lett., submitted for publication; Phys. Rev. E 59 (1999) 3492) is multifractal. We also find that this multifractality is greatly reduced for subjects with “serious” migraine and we present a simple model for the underlying control process to describe this effect.

Comparison and covalidation of ozone anomalies and variability observed in SBUV(/2) and Umkehr northern midlatitude ozone profile estimates

NASA Astrophysics Data System (ADS)

Petropavlovskikh, I.; Ahn, Changwoo; Bhartia, P. K.; Flynn, L. E.

2005-03-01

This analysis presents comparisons of upper-stratosphere ozone information observed by two independent systems: the Solar Backscatter UltraViolet (SBUV and SBUV/2) satellite instruments, and ground-based Dobson spectrophotometers. Both the new SBUV Version 8 and the new UMK04 profile retrieval algorithms are optimized for studying long-term variability and trends in ozone. Trend analyses of the ozone time series from the SBUV(/2) data set are complex because of the multiple instruments involved, changes in the instruments' geo-location, and short periods of overlaps for inter-calibrations among different instruments. Three northern middle latitudes Dobson ground stations (Arosa, Boulder, and Tateno) are used in this analysis to validate the trend quality of the combined 25-year SBUV/2 time series, 1979 to 2003. Generally, differences between the satellite and ground-based data do not suggest any significant time-dependent shifts or trends. The shared features confirm the value of these data sets for studies of ozone variability.

The Superstatistical Nature and Interoccurrence Time of Atmospheric Mercury Concentration Fluctuations

NASA Astrophysics Data System (ADS)

Carbone, F.; Bruno, A. G.; Naccarato, A.; De Simone, F.; Gencarelli, C. N.; Sprovieri, F.; Hedgecock, I. M.; Landis, M. S.; Skov, H.; Pfaffhuber, K. A.; Read, K. A.; Martin, L.; Angot, H.; Dommergue, A.; Magand, O.; Pirrone, N.

2018-01-01

The probability density function (PDF) of the time intervals between subsequent extreme events in atmospheric Hg0 concentration data series from different latitudes has been investigated. The Hg0 dynamic possesses a long-term memory autocorrelation function. Above a fixed threshold Q in the data, the PDFs of the interoccurrence time of the Hg0 data are well described by a Tsallis q-exponential function. This PDF behavior has been explained in the framework of superstatistics, where the competition between multiple mesoscopic processes affects the macroscopic dynamics. An extensive parameter μ, encompassing all possible fluctuations related to mesoscopic phenomena, has been identified. It follows a χ2 distribution, indicative of the superstatistical nature of the overall process. Shuffling the data series destroys the long-term memory, the distributions become independent of Q, and the PDFs collapse on to the same exponential distribution. The possible central role of atmospheric turbulence on extreme events in the Hg0 data is highlighted.

Approximation-based adaptive tracking control of pure-feedback nonlinear systems with multiple unknown time-varying delays.

PubMed

Wang, Min; Ge, Shuzhi Sam; Hong, Keum-Shik

2010-11-01

This paper presents adaptive neural tracking control for a class of non-affine pure-feedback systems with multiple unknown state time-varying delays. To overcome the design difficulty from non-affine structure of pure-feedback system, mean value theorem is exploited to deduce affine appearance of state variables x(i) as virtual controls α(i), and of the actual control u. The separation technique is introduced to decompose unknown functions of all time-varying delayed states into a series of continuous functions of each delayed state. The novel Lyapunov-Krasovskii functionals are employed to compensate for the unknown functions of current delayed state, which is effectively free from any restriction on unknown time-delay functions and overcomes the circular construction of controller caused by the neural approximation of a function of u and [Formula: see text] . Novel continuous functions are introduced to overcome the design difficulty deduced from the use of one adaptive parameter. To achieve uniformly ultimate boundedness of all the signals in the closed-loop system and tracking performance, control gains are effectively modified as a dynamic form with a class of even function, which makes stability analysis be carried out at the present of multiple time-varying delays. Simulation studies are provided to demonstrate the effectiveness of the proposed scheme.

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

New methods for the numerical integration of ordinary differential equations and their application to the equations of motion of spacecraft

NASA Technical Reports Server (NTRS)

Banyukevich, A.; Ziolkovski, K.

1975-01-01

A number of hybrid methods for solving Cauchy problems are described on the basis of an evaluation of advantages of single and multiple-point numerical integration methods. The selection criterion is the principle of minimizing computer time. The methods discussed include the Nordsieck method, the Bulirsch-Stoer extrapolation method, and the method of recursive Taylor-Steffensen power series.

Evaluation of Growing Season Milestones, Using Eddy Covariance Time-Series of Net Ecosystem Exchange

NASA Astrophysics Data System (ADS)

Pastorello, G.; Faybishenko, B.; Poindexter, C.; Menzer, O.; Agarwal, D.; Papale, D.; Baldocchi, D. D.

2014-12-01

Common methods for determining timing of plants' developmental events, such as direct observation and remote sensing of NDVI, usually produce data of temporal resolution on the order of one week or more. This limitation can make observing subtle trends across years difficult. The goal of this presentation is to demonstrate a conceptual approach and a computational technique to quantify seasonal, annual and long-term phenological indices and patterns, based on continuous eddy covariance measurements of net ecosystem exchange (NEE) measured at eddy covariance towers in the AmeriFlux network. Using a comprehensive time series analysis of NEE fluxes in different climatic zones, we determined multiple characteristics (and their confidence intervals) of the growing season including: the initiation day—the day when canopy photosynthesis development starts, the photosynthesis stabilization day - the day when the development process of canopy photosynthesis starts to slow down and gradually moves toward stabilization, and the growing season effective termination day. We also determined the spring photosynthetic development velocity and the fall photosynthetic development velocity. The results of calculations using NEE were compared with those from temperature and precipitation data measured at the same AmeriFlux tower stations, as well as with the in-situ directly observed phenological records. The results of calculations of phenological indices from the NEE time-series collected at AmeriFlux sites can be used to constrain the application of other time- and labor-intensive sensing methods and to reduce the uncertainty in identifying trends in the timing of phenological indices.

Intrinsic vs. spurious long-range memory in high-frequency records of environmental radioactivity. Critical re-assessment and application to indoor 222Rn concentrations from Coimbra, Portugal

NASA Astrophysics Data System (ADS)

Donner, R. V.; Potirakis, S. M.; Barbosa, S. M.; Matos, J. A. O.; Pereira, A. J. S. C.; Neves, L. J. P. F.

2015-05-01

The presence or absence of long-range correlations in the environmental radioactivity fluctuations has recently attracted considerable interest. Among a multiplicity of practically relevant applications, identifying and disentangling the environmental factors controlling the variable concentrations of the radioactive noble gas radon is important for estimating its effect on human health and the efficiency of possible measures for reducing the corresponding exposition. In this work, we present a critical re-assessment of a multiplicity of complementary methods that have been previously applied for evaluating the presence of long-range correlations and fractal scaling in environmental radon variations with a particular focus on the specific properties of the underlying time series. As an illustrative case study, we subsequently re-analyze two high-frequency records of indoor radon concentrations from Coimbra, Portugal, each of which spans several weeks of continuous measurements at a high temporal resolution of five minutes.Our results reveal that at the study site, radon concentrations exhibit complex multi-scale dynamics with qualitatively different properties at different time-scales: (i) essentially white noise in the high-frequency part (up to time-scales of about one hour), (ii) spurious indications of a non-stationary, apparently long-range correlated process (at time scales between some hours and one day) arising from marked periodic components, and (iii) low-frequency variability indicating a true long-range dependent process. In the presence of such multi-scale variability, common estimators of long-range memory in time series are prone to fail if applied to the raw data without previous separation of time-scales with qualitatively different dynamics.

The mass spectral density in quantitative time-of-flight mass spectrometry of polymers

NASA Astrophysics Data System (ADS)

Tate, Ranjeet S.; Ebeling, Dan; Smith, Lloyd M.

2001-03-01

Time-of-flight mass spectrometry (TOF-MS) is being increasingly used for the study of polymers, for example to obtain the distribution of molecular masses for polymer samples. Serious efforts have also been underway to use TOF-MS for DNA sequencing. In TOF-MS the data is obtained in the form of a time-series that represents the distribution in arrival times of ions of various m/z ratios. This time-series data is then converted to a "mass-spectrum" via a coordinate transformation from the arrival time (t) to the corresponding mass-to-charge ratio (m/z = const. t^2). In this transformation, it is important to keep in mind that spectra are distributions, or densities of weight +1, and thus do not transform as functions. To obtain the mass-spectral density, it is necessary to include a multiplicative factor of √m/z. Common commercial instruments do not take this factor into account. Dropping this factor has no effect on qualitative analysis (detection) or local quantitative measurements, since S/N or signal-to-baseline ratios are unaffected for peaks with small dispersions. However, there are serious consequences for general quantitative analyses. In DNA sequencing applications, loss of signal intensity is in part attributed to multiple charging; however, since the √m/z factor is not taken into account, this conclusion is based on an overestimate (by a factor of √z) of the relative amount of the multiply charged species. In the study of polymers, the normalized dispersion is underestimated by approximately (M_w/Mn -1)/2. In terms of M_w/Mn itself, for example, a M_w/M_n=1.5 calculated without the √m factor corresponds in fact to a M_w/M_n=1.88.

Spectral Analysis: From Additive Perspective to Multiplicative Perspective

NASA Astrophysics Data System (ADS)

Wu, Z.

2017-12-01

The early usage of trigonometric functions can be traced back to at least 17th century BC. It was Bhaskara II of the 12th century CE who first proved the mathematical equivalence between the sum of two trigonometric functions of any given angles and the product of two trigonometric functions of related angles, which has been taught these days in middle school classroom. The additive perspective of trigonometric functions led to the development of the Fourier transform that is used to express any functions as the sum of a set of trigonometric functions and opened a new mathematical field called harmonic analysis. Unfortunately, Fourier's sum cannot directly express nonlinear interactions between trigonometric components of different periods, and thereby lacking the capability of quantifying nonlinear interactions in dynamical systems. In this talk, the speaker will introduce the Huang transform and Holo-spectrum which were pioneered by Norden Huang and emphasizes the multiplicative perspective of trigonometric functions in expressing any function. Holo-spectrum is a multi-dimensional spectral expression of a time series that explicitly identifies the interactions among different scales and quantifies nonlinear interactions hidden in a time series. Along with this introduction, the developing concepts of physical, rather than mathematical, analysis of data will be explained. Various enlightening applications of Holo-spectrum analysis in atmospheric and climate studies will also be presented.

Population cycles are highly correlated over long time series and large spatial scales in two unrelated species: Greater sage-grouse and cottontail rabbits

USGS Publications Warehouse

Fedy, B.C.; Doherty, K.E.

2011-01-01

Animal species across multiple taxa demonstrate multi-annual population cycles, which have long been of interest to ecologists. Correlated population cycles between species that do not share a predator-prey relationship are particularly intriguing and challenging to explain. We investigated annual population trends of greater sage-grouse (Centrocercus urophasianus) and cottontail rabbits (Sylvilagus sp.) across Wyoming to explore the possibility of correlations between unrelated species, over multiple cycles, very large spatial areas, and relatively southern latitudes in terms of cycling species. We analyzed sage-grouse lek counts and annual hunter harvest indices from 1982 to 2007. We show that greater sage-grouse, currently listed as warranted but precluded under the US Endangered Species Act, and cottontails have highly correlated cycles (r = 0. 77). We explore possible mechanistic hypotheses to explain the synchronous population cycles. Our research highlights the importance of control populations in both adaptive management and impact studies. Furthermore, we demonstrate the functional value of these indices (lek counts and hunter harvest) for tracking broad-scale fluctuations in the species. This level of highly correlated long-term cycling has not previously been documented between two non-related species, over a long time-series, very large spatial scale, and within more southern latitudes. ?? 2010 US Government.

Was That Assumption Necessary? Reconsidering Boundary Conditions for Analytical Solutions to Estimate Streambed Fluxes

NASA Astrophysics Data System (ADS)

Luce, Charles H.; Tonina, Daniele; Applebee, Ralph; DeWeese, Timothy

2017-11-01

Two common refrains about using the one-dimensional advection diffusion equation to estimate fluid fluxes and thermal conductivity from temperature time series in streambeds are that the solution assumes that (1) the surface boundary condition is a sine wave or nearly so, and (2) there is no gradient in mean temperature with depth. Although the mathematical posing of the problem in the original solution to the problem might lead one to believe these constraints exist, the perception that they are a source of error is a fallacy. Here we develop a mathematical proof demonstrating the equivalence of the solution as developed based on an arbitrary (Fourier integral) surface temperature forcing when evaluated at a single given frequency versus that derived considering a single frequency from the beginning. The implication is that any single frequency can be used in the frequency-domain solutions to estimate thermal diffusivity and 1-D fluid flux in streambeds, even if the forcing has multiple frequencies. This means that diurnal variations with asymmetric shapes or gradients in the mean temperature with depth are not actually assumptions, and deviations from them should not cause errors in estimates. Given this clarification, we further explore the potential for using information at multiple frequencies to augment the information derived from time series of temperature.

Seismic joint analysis for non-destructive testing of asphalt and concrete slabs

USGS Publications Warehouse

Ryden, N.; Park, C.B.

2005-01-01

A seismic approach is used to estimate the thickness and elastic stiffness constants of asphalt or concrete slabs. The overall concept of the approach utilizes the robustness of the multichannel seismic method. A multichannel-equivalent data set is compiled from multiple time series recorded from multiple hammer impacts at progressively different offsets from a fixed receiver. This multichannel simulation with one receiver (MSOR) replaces the true multichannel recording in a cost-effective and convenient manner. A recorded data set is first processed to evaluate the shear wave velocity through a wave field transformation, normally used in the multichannel analysis of surface waves (MASW) method, followed by a Lambwave inversion. Then, the same data set is used to evaluate compression wave velocity from a combined processing of the first-arrival picking and a linear regression. Finally, the amplitude spectra of the time series are used to evaluate the thickness by following the concepts utilized in the Impact Echo (IE) method. Due to the powerful signal extraction capabilities ensured by the multichannel processing schemes used, the entire procedure for all three evaluations can be fully automated and results can be obtained directly in the field. A field data set is used to demonstrate the proposed approach.

Time-series RNA-seq analysis package (TRAP) and its application to the analysis of rice, Oryza sativa L. ssp. Japonica, upon drought stress.

PubMed

Jo, Kyuri; Kwon, Hawk-Bin; Kim, Sun

2014-06-01

Measuring expression levels of genes at the whole genome level can be useful for many purposes, especially for revealing biological pathways underlying specific phenotype conditions. When gene expression is measured over a time period, we have opportunities to understand how organisms react to stress conditions over time. Thus many biologists routinely measure whole genome level gene expressions at multiple time points. However, there are several technical difficulties for analyzing such whole genome expression data. In addition, these days gene expression data is often measured by using RNA-sequencing rather than microarray technologies and then analysis of expression data is much more complicated since the analysis process should start with mapping short reads and produce differentially activated pathways and also possibly interactions among pathways. In addition, many useful tools for analyzing microarray gene expression data are not applicable for the RNA-seq data. Thus a comprehensive package for analyzing time series transcriptome data is much needed. In this article, we present a comprehensive package, Time-series RNA-seq Analysis Package (TRAP), integrating all necessary tasks such as mapping short reads, measuring gene expression levels, finding differentially expressed genes (DEGs), clustering and pathway analysis for time-series data in a single environment. In addition to implementing useful algorithms that are not available for RNA-seq data, we extended existing pathway analysis methods, ORA and SPIA, for time series analysis and estimates statistical values for combined dataset by an advanced metric. TRAP also produces visual summary of pathway interactions. Gene expression change labeling, a practical clustering method used in TRAP, enables more accurate interpretation of the data when combined with pathway analysis. We applied our methods on a real dataset for the analysis of rice (Oryza sativa L. Japonica nipponbare) upon drought stress. The result showed that TRAP was able to detect pathways more accurately than several existing methods. TRAP is available at http://biohealth.snu.ac.kr/software/TRAP/. Copyright © 2014 Elsevier Inc. All rights reserved.

Performance of vegetation indices from Landsat time series in deforestation monitoring

NASA Astrophysics Data System (ADS)

Schultz, Michael; Clevers, Jan G. P. W.; Carter, Sarah; Verbesselt, Jan; Avitabile, Valerio; Quang, Hien Vu; Herold, Martin

2016-10-01

The performance of Landsat time series (LTS) of eight vegetation indices (VIs) was assessed for monitoring deforestation across the tropics. Three sites were selected based on differing remote sensing observation frequencies, deforestation drivers and environmental factors. The LTS of each VI was analysed using the Breaks For Additive Season and Trend (BFAST) Monitor method to identify deforestation. A robust reference database was used to evaluate the performance regarding spatial accuracy, sensitivity to observation frequency and combined use of multiple VIs. The canopy cover sensitive Normalized Difference Fraction Index (NDFI) was the most accurate. Among those tested, wetness related VIs (Normalized Difference Moisture Index (NDMI) and the Tasselled Cap wetness (TCw)) were spatially more accurate than greenness related VIs (Normalized Difference Vegetation Index (NDVI) and Tasselled Cap greenness (TCg)). When VIs were fused on feature level, spatial accuracy was improved and overestimation of change reduced. NDVI and NDFI produced the most robust results when observation frequency varies.

Temporal abstraction for the analysis of intensive care information

NASA Astrophysics Data System (ADS)

Hadad, Alejandro J.; Evin, Diego A.; Drozdowicz, Bartolomé; Chiotti, Omar

2007-11-01

This paper proposes a scheme for the analysis of time-stamped series data from multiple monitoring devices of intensive care units, using Temporal Abstraction concepts. This scheme is oriented to obtain a description of the patient state evolution in an unsupervised way. The case of study is based on a dataset clinically classified with Pulmonary Edema. For this dataset a trends based Temporal Abstraction mechanism is proposed, by means of a Behaviours Base of time-stamped series and then used in a classification step. Combining this approach with the introduction of expert knowledge, using Fuzzy Logic, and multivariate analysis by means of Self-Organizing Maps, a states characterization model is obtained. This model is feasible of being extended to different patients groups and states. The proposed scheme allows to obtain intermediate states descriptions through which it is passing the patient and that could be used to anticipate alert situations.

ESO/ST-ECF Data Analysis Workshop, 5th, Garching, Germany, Apr. 26, 27, 1993, Proceedings

NASA Astrophysics Data System (ADS)

Grosbol, Preben; de Ruijsscher, Resy

1993-01-01

Various papers on astronomical data analysis are presented. Individual optics addressed include: surface photometry of early-type galaxies, wavelet transform and adaptive filtering, package for surface photometry of galaxies, calibration of large-field mosaics, surface photometry of galaxies with HST, wavefront-supported image deconvolution, seeing effects on elliptical galaxies, multiple algorithms deconvolution program, enhancement of Skylab X-ray images, MIDAS procedures for the image analysis of E-S0 galaxies, photometric data reductions under MIDAS, crowded field photometry with deconvolved images, the DENIS Deep Near Infrared Survey. Also discussed are: analysis of astronomical time series, detection of low-amplitude stellar pulsations, new SOT method for frequency analysis, chaotic attractor reconstruction and applications to variable stars, reconstructing a 1D signal from irregular samples, automatic analysis for time series with large gaps, prospects for content-based image retrieval, redshift survey in the South Galactic Pole Region.

Reproducibility of geochemical and climatic signals in the Atlantic coral Montastraea faveolata

USGS Publications Warehouse

Smith, Joseph M.; Quinn, T.M.; Helmle, K.P.; Halley, R.B.

2006-01-01

Monthly resolved, 41-year-long stable isotopic and elemental ratio time series were generated from two separate heads of Montastraea faveolata from Looe Key, Florida, to assess the fidelity of using geochemical variations in Montastraea, the dominant reef-building coral of the Atlantic, to reconstruct sea surface environmental conditions at this site. The stable isotope time series of the two corals replicate well; mean values of ??18O and ??13C are indistinguishable between cores (compare 0.70??? versus 0.68??? for ??13C and -3.90??? versus - 3.94??? for ??18O). Mean values from the Sr/Ca time series differ by 0.037 mmol/mol, which is outside of analytical error and indicates that nonenvironmental factors are influencing the coral Sr/ Ca records at Looe Key. We have generated significant ?? 18O-sea surface temperature (SST) (R = -0.84) and Sr/ Ca-SST (R = -0.86) calibration equations at Looe Key; however, these equations are different from previously published equations for Montastraea. Variations in growth parameters or kinetic effects are not sufficient to explain either the observed differences in the mean offset between Sr/Ca time series or the disagreement between previous calibrations and our calculated ??18O-SST and Sr/Ca-SST relationships. Calibration differences are most likely due to variations in seawater chemistry in the continentally influenced waters at Looe Key. Additional geochemical replication studies of Montastraea are needed and should include multiple coral heads from open ocean localities complemented whenever possible by seawater chemistry determinations. Copyright 2006 by the American Geophysical Union.

Functional quantitative susceptibility mapping (fQSM).

PubMed

Balla, Dávid Z; Sanchez-Panchuelo, Rosa M; Wharton, Samuel J; Hagberg, Gisela E; Scheffler, Klaus; Francis, Susan T; Bowtell, Richard

2014-10-15

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is a powerful technique, typically based on the statistical analysis of the magnitude component of the complex time-series. Here, we additionally interrogated the phase data of the fMRI time-series and used quantitative susceptibility mapping (QSM) in order to investigate the potential of functional QSM (fQSM) relative to standard magnitude BOLD fMRI. High spatial resolution data (1mm isotropic) were acquired every 3 seconds using zoomed multi-slice gradient-echo EPI collected at 7 T in single orientation (SO) and multiple orientation (MO) experiments, the latter involving 4 repetitions with the subject's head rotated relative to B0. Statistical parametric maps (SPM) were reconstructed for magnitude, phase and QSM time-series and each was subjected to detailed analysis. Several fQSM pipelines were evaluated and compared based on the relative number of voxels that were coincidentally found to be significant in QSM and magnitude SPMs (common voxels). We found that sensitivity and spatial reliability of fQSM relative to the magnitude data depended strongly on the arbitrary significance threshold defining "activated" voxels in SPMs, and on the efficiency of spatio-temporal filtering of the phase time-series. Sensitivity and spatial reliability depended slightly on whether MO or SO fQSM was performed and on the QSM calculation approach used for SO data. Our results present the potential of fQSM as a quantitative method of mapping BOLD changes. We also critically discuss the technical challenges and issues linked to this intriguing new technique. Copyright © 2014 Elsevier Inc. All rights reserved.

AI-based (ANN and SVM) statistical downscaling methods for precipitation estimation under climate change scenarios

NASA Astrophysics Data System (ADS)

Mehrvand, Masoud; Baghanam, Aida Hosseini; Razzaghzadeh, Zahra; Nourani, Vahid

2017-04-01

Since statistical downscaling methods are the most largely used models to study hydrologic impact studies under climate change scenarios, nonlinear regression models known as Artificial Intelligence (AI)-based models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used to spatially downscale the precipitation outputs of Global Climate Models (GCMs). The study has been carried out using GCM and station data over GCM grid points located around the Peace-Tampa Bay watershed weather stations. Before downscaling with AI-based model, correlation coefficient values have been computed between a few selected large-scale predictor variables and local scale predictands to select the most effective predictors. The selected predictors are then assessed considering grid location for the site in question. In order to increase AI-based downscaling model accuracy pre-processing has been developed on precipitation time series. In this way, the precipitation data derived from various GCM data analyzed thoroughly to find the highest value of correlation coefficient between GCM-based historical data and station precipitation data. Both GCM and station precipitation time series have been assessed by comparing mean and variances over specific intervals. Results indicated that there is similar trend between GCM and station precipitation data; however station data has non-stationary time series while GCM data does not. Finally AI-based downscaling model have been applied to several GCMs with selected predictors by targeting local precipitation time series as predictand. The consequences of recent step have been used to produce multiple ensembles of downscaled AI-based models.

Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: a case series

PubMed Central

Holmes, E A; Bonsall, M B; Hales, S A; Mitchell, H; Renner, F; Blackwell, S E; Watson, P; Goodwin, G M; Di Simplicio, M

2016-01-01

Treatment innovation for bipolar disorder has been hampered by a lack of techniques to capture a hallmark symptom: ongoing mood instability. Mood swings persist during remission from acute mood episodes and impair daily functioning. The last significant treatment advance remains Lithium (in the 1970s), which aids only the minority of patients. There is no accepted way to establish proof of concept for a new mood-stabilizing treatment. We suggest that combining insights from mood measurement with applied mathematics may provide a step change: repeated daily mood measurement (depression) over a short time frame (1 month) can create individual bipolar mood instability profiles. A time-series approach allows comparison of mood instability pre- and post-treatment. We test a new imagery-focused cognitive therapy treatment approach (MAPP; Mood Action Psychology Programme) targeting a driver of mood instability, and apply these measurement methods in a non-concurrent multiple baseline design case series of 14 patients with bipolar disorder. Weekly mood monitoring and treatment target data improved for the whole sample combined. Time-series analyses of daily mood data, sampled remotely (mobile phone/Internet) for 28 days pre- and post-treatment, demonstrated improvements in individuals' mood stability for 11 of 14 patients. Thus the findings offer preliminary support for a new imagery-focused treatment approach. They also indicate a step in treatment innovation without the requirement for trials in illness episodes or relapse prevention. Importantly, daily measurement offers a description of mood instability at the individual patient level in a clinically meaningful time frame. This costly, chronic and disabling mental illness demands innovation in both treatment approaches (whether pharmacological or psychological) and measurement tool: this work indicates that daily measurements can be used to detect improvement in individual mood stability for treatment innovation (MAPP). PMID:26812041

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

Common trends in elements? Within- and between-tree variations of wood-chemistry measured by X-ray fluorescence - A dendrochemical study.

PubMed

Scharnweber, Tobias; Hevia, Andrea; Buras, Allan; van der Maaten, Ernst; Wilmking, Martin

2016-10-01

Element composition of annually resolved tree-rings constitutes a promising biological proxy for reconstructions of environmental conditions and pollution history. However, several methodological and physiological issues have to be addressed before sound conclusions can be drawn from dendrochemical time series. For example, radial and vertical translocation processes of elements in the wood might blur or obscure any dendrochemical signal. In this study, we tested the degree of synchronism of elemental time series within and between trees of one coniferous (Pinus sylvestris L.) and one broadleaf (Castanea sativa Mill.) species growing in conventionally managed forests without direct pollution sources in their surroundings. Micro X-ray fluorescence (μXRF) analysis was used to establish time series of relative concentrations of multiple elements (Mg, Al, P, Cl, K, Ca, Cr, Mn, Fe and Ni) for different stem heights and stem exposures. We found a common long-term (decadal) trend for most elements in both species, but only little coherence in the high frequency domain (inter-annual variations). Aligning the element curves by cambial age instead of year of ring formation reduced the standard deviations between the single measurements. This points at an influence of age on longer term trends and would require a detrending in order to extract any environmental signal from dendrochemical time series. The common signal was stronger for pine than for chestnut. In pine, many elements show a concentration gradient with higher values towards the tree crown. Mobility of elements in the stem leading to high within- and between-tree variability, as well as a potential age-trend apparently complicate the establishment of reliable dendrochemical chronologies. For future wood-chemical studies, we recommend to work with element ratios instead of single element time series, to consider potential age trends and to analyze more than one sample per tree to account for internal variability. Copyright © 2016 Elsevier B.V. All rights reserved.

Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge.

PubMed

Leifeld, Thomas; Zhang, Zhihua; Zhang, Ping

2018-01-01

Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components. Since length of a time series and number of replicates is limited by the cost of experiments, Boolean networks as a structurally simple and parameter-free logical model for gene regulatory networks have attracted interests of many scientists. In order to fit into the biological contexts and to lower the data requirements, biological prior knowledge is taken into consideration during the inference procedure. In the literature, the existing identification approaches can only deal with a subset of possible types of prior knowledge. Results: We propose a new approach to identify Boolean networks from time series data incorporating prior knowledge, such as partial network structure, canalizing property, positive and negative unateness. Using vector form of Boolean variables and applying a generalized matrix multiplication called the semi-tensor product (STP), each Boolean function can be equivalently converted into a matrix expression. Based on this, the identification problem is reformulated as an integer linear programming problem to reveal the system matrix of Boolean model in a computationally efficient way, whose dynamics are consistent with the important dynamics captured in the data. By using prior knowledge the number of candidate functions can be reduced during the inference. Hence, identification incorporating prior knowledge is especially suitable for the case of small size time series data and data without sufficient stimuli. The proposed approach is illustrated with the help of a biological model of the network of oxidative stress response. Conclusions: The combination of efficient reformulation of the identification problem with the possibility to incorporate various types of prior knowledge enables the application of computational model inference to systems with limited amount of time series data. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research.

A Methodological Framework for Model Selection in Interrupted Time Series Studies.

PubMed

Lopez Bernal, J; Soumerai, S; Gasparrini, A

2018-06-06

Interrupted time series is a powerful and increasingly popular design for evaluating public health and health service interventions. The design involves analysing trends in the outcome of interest and estimating the change in trend following an intervention relative to the counterfactual (the expected ongoing trend if the intervention had not occurred). There are two key components to modelling this effect: first, defining the counterfactual; second, defining the type of effect that the intervention is expected to have on the outcome, known as the impact model. The counterfactual is defined by extrapolating the underlying trends observed before the intervention to the post-intervention period. In doing this, authors must consider the pre-intervention period that will be included, any time varying confounders, whether trends may vary within different subgroups of the population and whether trends are linear or non-linear. Defining the impact model involves specifying the parameters that model the intervention, including for instance whether to allow for an abrupt level change or a gradual slope change, whether to allow for a lag before any effect on the outcome, whether to allow a transition period during which the intervention is being implemented and whether a ceiling or floor effect might be expected. Inappropriate model specification can bias the results of an interrupted time series analysis and using a model that is not closely tailored to the intervention or testing multiple models increases the risk of false positives being detected. It is important that authors use substantive knowledge to customise their interrupted time series model a priori to the intervention and outcome under study. Where there is uncertainty in model specification, authors should consider using separate data sources to define the intervention, running limited sensitivity analyses or undertaking initial exploratory studies. Copyright © 2018. Published by Elsevier Inc.

Persistent threats to validity in single-group interrupted time series analysis with a cross over design.

PubMed

Linden, Ariel

2017-04-01

The basic single-group interrupted time series analysis (ITSA) design has been shown to be susceptible to the most common threat to validity-history-the possibility that some other event caused the observed effect in the time series. A single-group ITSA with a crossover design (in which the intervention is introduced and withdrawn 1 or more times) should be more robust. In this paper, we describe and empirically assess the susceptibility of this design to bias from history. Time series data from 2 natural experiments (the effect of multiple repeals and reinstatements of Louisiana's motorcycle helmet law on motorcycle fatalities and the association between the implementation and withdrawal of Gorbachev's antialcohol campaign with Russia's mortality crisis) are used to illustrate that history remains a threat to ITSA validity, even in a crossover design. Both empirical examples reveal that the single-group ITSA with a crossover design may be biased because of history. In the case of motorcycle fatalities, helmet laws appeared effective in reducing mortality (while repealing the law increased mortality), but when a control group was added, it was shown that this trend was similar in both groups. In the case of Gorbachev's antialcohol campaign, only when contrasting the results against those of a control group was the withdrawal of the campaign found to be the more likely culprit in explaining the Russian mortality crisis than the collapse of the Soviet Union. Even with a robust crossover design, single-group ITSA models remain susceptible to bias from history. Therefore, a comparable control group design should be included, whenever possible. © 2016 John Wiley & Sons, Ltd.

Geostatistical Analysis of Surface Temperature and In-Situ Soil Moisture Using LST Time-Series from Modis

NASA Astrophysics Data System (ADS)

Sohrabinia, M.; Rack, W.; Zawar-Reza, P.

2012-07-01

The objective of this analysis is to provide a quantitative estimate of the fluctuations of land surface temperature (LST) with varying near surface soil moisture (SM) on different land-cover (LC) types. The study area is located in the Canterbury Plains in the South Island of New Zealand. Time series of LST from the MODerate resolution Imaging Spectro-radiometer (MODIS) have been analysed statistically to study the relationship between the surface skin temperature and near-surface SM. In-situ measurements of the skin temperature and surface SM with a quasi-experimental design over multiple LC types are used for validation. Correlations between MODIS LST and in-situ SM, as well as in-situ surface temperature and SM are calculated. The in-situ measurements and MODIS data are collected from various LC types. Pearson's r correlation coefficient and linear regression are used to fit the MODIS LST and surface skin temperature with near-surface SM. There was no significant correlation between time-series of MODIS LST and near-surface SM from the initial analysis, however, careful analysis of the data showed significant correlation between the two parameters. Night-time series of the in-situ surface temperature and SM from a 12 hour period over Irrigated-Crop, Mixed-Grass, Forest, Barren and Open- Grass showed inverse correlations of -0.47, -0.68, -0.74, -0.88 and -0.93, respectively. These results indicated that the relationship between near-surface SM and LST in short-terms (12 to 24 hours) is strong, however, remotely sensed LST with higher temporal resolution is required to establish this relationship in such time-scales. This method can be used to study near-surface SM using more frequent LST observations from a geostationary satellite over the study area.

Mapping croplands, cropping patterns, and crop types using MODIS time-series data

NASA Astrophysics Data System (ADS)

Chen, Yaoliang; Lu, Dengsheng; Moran, Emilio; Batistella, Mateus; Dutra, Luciano Vieira; Sanches, Ieda Del'Arco; da Silva, Ramon Felipe Bicudo; Huang, Jingfeng; Luiz, Alfredo José Barreto; de Oliveira, Maria Antonia Falcão

2018-07-01

The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.

Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

NASA Astrophysics Data System (ADS)

Zheng, Jinde; Pan, Haiyang; Cheng, Junsheng

2017-02-01

To timely detect the incipient failure of rolling bearing and find out the accurate fault location, a novel rolling bearing fault diagnosis method is proposed based on the composite multiscale fuzzy entropy (CMFE) and ensemble support vector machines (ESVMs). Fuzzy entropy (FuzzyEn), as an improvement of sample entropy (SampEn), is a new nonlinear method for measuring the complexity of time series. Since FuzzyEn (or SampEn) in single scale can not reflect the complexity effectively, multiscale fuzzy entropy (MFE) is developed by defining the FuzzyEns of coarse-grained time series, which represents the system dynamics in different scales. However, the MFE values will be affected by the data length, especially when the data are not long enough. By combining information of multiple coarse-grained time series in the same scale, the CMFE algorithm is proposed in this paper to enhance MFE, as well as FuzzyEn. Compared with MFE, with the increasing of scale factor, CMFE obtains much more stable and consistent values for a short-term time series. In this paper CMFE is employed to measure the complexity of vibration signals of rolling bearings and is applied to extract the nonlinear features hidden in the vibration signals. Also the physically meanings of CMFE being suitable for rolling bearing fault diagnosis are explored. Based on these, to fulfill an automatic fault diagnosis, the ensemble SVMs based multi-classifier is constructed for the intelligent classification of fault features. Finally, the proposed fault diagnosis method of rolling bearing is applied to experimental data analysis and the results indicate that the proposed method could effectively distinguish different fault categories and severities of rolling bearings.

Chaotic examination

NASA Astrophysics Data System (ADS)

Bildirici, Melike; Sonustun, Fulya Ozaksoy; Sonustun, Bahri

2018-01-01

In the regards of chaos theory, new concepts such as complexity, determinism, quantum mechanics, relativity, multiple equilibrium, complexity, (continuously) instability, nonlinearity, heterogeneous agents, irregularity were widely questioned in economics. It is noticed that linear models are insufficient for analyzing unpredictable, irregular and noncyclical oscillations of economies, and for predicting bubbles, financial crisis, business cycles in financial markets. Therefore, economists gave great consequence to use appropriate tools for modelling non-linear dynamical structures and chaotic behaviors of the economies especially in macro and the financial economy. In this paper, we aim to model the chaotic structure of exchange rates (USD-TL and EUR-TL). To determine non-linear patterns of the selected time series, daily returns of the exchange rates were tested by BDS during the period from January 01, 2002 to May 11, 2017 which covers after the era of the 2001 financial crisis. After specifying the non-linear structure of the selected time series, it was aimed to examine the chaotic characteristic for the selected time period by Lyapunov Exponents. The findings verify the existence of the chaotic structure of the exchange rate returns in the analyzed time period.

The Version 8.6 SBUV Ozone Data Record: An Overview

NASA Technical Reports Server (NTRS)

McPeters, Richard D.; Bhartia, P. K.; Haffner, D.; Labow, Gordon J.; Flynn, Larry

2013-01-01

Under a NASA program to produce long-term data records from instruments on multiple satellites, data from a series of nine Solar Backscatter Ultraviolet (SBUV and SBUV2) instruments have been re-processed to create a coherent ozone time series. Data from the BUV instrument on Nimbus 4, SBUV on Nimbus 7, and SBUV2 instruments on NOAA 9, 11, 14, 16, 17, 18, and 19 covering the period 1970-1972 and 1979-2011 were used to create a long-term data set. The goal is an ozone Earth Science Data Record - a consistent, calibrated ozone time series that can be used for trend analyses and other studies. In order to create this ozone data set, the radiances were adjusted and used to re-process the entire data records for each of the nine instruments. Inter-instrument comparisons during periods of overlap as well as comparisons with data from other satellite and ground-based instruments were used to evaluate the consistency of the record and make calibration adjustments as needed. Additional improvements in this version 8.6 processing included the use of the Brion, Daumont, and Malicet ozone cross sections, and a cloud-height climatology derived from Aura OMI measurements. Validation of the re-processed ozone shows that total column ozone is consistent with the Brewer Dobson network to within about 1 for the new time series. Comparisons with MLS, SAGE, sondes, and lidar show that ozone at individual levels in the stratosphere is generally consistent to within 5 percent.

Time Series Reconstruction of Surface Flow Velocity on Marine-terminating Outlet Glaciers

NASA Astrophysics Data System (ADS)

Jeong, Seongsu

The flow velocity of glacier and its fluctuation are valuable data to study the contribution of sea level rise of ice sheet by understanding its dynamic structure. Repeat-image feature tracking (RIFT) is a platform-independent, feature tracking-based velocity measurement methodology effective for building a time series of velocity maps from optical images. However, limited availability of perfectly-conditioned images motivated to improve robustness of the algorithm. With this background, we developed an improved RIFT algorithm based on multiple-image multiple-chip algorithm presented in Ahn and Howat (2011). The test results affirm improvement in the new RIFT algorithm in avoiding outlier, and the analysis of the multiple matching results clarified that each individual matching results worked in complementary manner to deduce the correct displacements. LANDSAT 8 is a new satellite in LANDSAT program that has begun its operation since 2013. The improved radiometric performance of OLI aboard the satellite is expected to enable better velocity mapping results than ETM+ aboard LANDSAT 7. However, it was not yet well studied that in what cases the new will sensor will be beneficial, and how much the improvement will be obtained. We carried out a simulation-based comparison between ETM+ and OLI and confirmed OLI outperforms ETM+ especially in low contrast conditions, especially in polar night, translucent cloud covers, and bright upglacier with less texture. We have identified a rift on ice shelf of Pine island glacier located in western Antarctic ice sheet. Unlike the previous events, the evolution of the current started from the center of the ice shelf. In order to analyze this unique event, we utilized the improved RIFT algorithm to its OLI images to retrieve time series of velocity maps. We discovered from the analyses that the part of ice shelf below the rift is changing its speed, and shifting of splashing crevasses on shear margin is migrating to the center of the shelf. Concerning the concurrent disintegration of ice melange on its western part of the terminus, we postulate that change in flow regime attributes to loss of resistance force exerted by the melange. There are several topics that need to be addressed for further improve the RIFT algorithm. As coregistration error is significant contributor to the velocity measurement, a method to mitigate that error needs to be devised. Also, considering that the domain of RIFT product spans not only in space but also in time, its regridding and gap filling work will benefit from extending its domain to both space and time.

Electric properties and carrier multiplication in breakdown sites in multi-crystalline silicon solar cells

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

Schneemann, Matthias; Carius, Reinhard; Rau, Uwe

2015-05-28

This paper studies the effective electrical size and carrier multiplication of breakdown sites in multi-crystalline silicon solar cells. The local series resistance limits the current of each breakdown site and is thereby linearizing the current-voltage characteristic. This fact allows the estimation of the effective electrical diameters to be as low as 100 nm. Using a laser beam induced current (LBIC) measurement with a high spatial resolution, we find carrier multiplication factors on the order of 30 (Zener-type breakdown) and 100 (avalanche breakdown) as new lower limits. Hence, we prove that also the so-called Zener-type breakdown is followed by avalanche multiplication. Wemore » explain that previous measurements of the carrier multiplication using thermography yield results higher than unity, only if the spatial defect density is high enough, and the illumination intensity is lower than what was used for the LBIC method. The individual series resistances of the breakdown sites limit the current through these breakdown sites. Therefore, the measured multiplication factors depend on the applied voltage as well as on the injected photocurrent. Both dependencies are successfully simulated using a series-resistance-limited diode model.« less

Wrong Answers on Multiple-Choice Achievement Tests: Blind Guesses or Systematic Choices?.

ERIC Educational Resources Information Center

Powell, J. C.

A multi-faceted model for the selection of answers for multiple-choice tests was developed from the findings of a series of exploratory studies. This model implies that answer selection should be curvilinear. A series of models were tested for fit using the chi square procedure. Data were collected from 359 elementary school students ages 9-12.…

The Representation of Multiple Intelligences Types in the Top-Notch Series: A Textbook Evaluation

ERIC Educational Resources Information Center

Razmjoo, Seyyed Ayatollah; Jozaghi, Zahra

2010-01-01

This study aims at evaluating Top-Notch series through a checklist devised by the researchers based on the elements of the Multiple Intelligences (MI) theory proposed by Gardner (1998). With the shift from teacher-centered classrooms to learner-centered one, more and more research is needed to be done in the realm of students' need analysis. One…

GPS data exploration for seismologists and geodesists

NASA Astrophysics Data System (ADS)

Webb, F.; Bock, Y.; Kedar, S.; Dong, D.; Jamason, P.; Chang, R.; Prawirodirdjo, L.; MacLeod, I.; Wadsworth, G.

2007-12-01

Over the past decade, GPS and seismic networks spanning the western US plate boundaries have produced vast amounts of data that need to be made accessible to both the geodesy and seismology communities. Unlike seismic data, raw geodetic data requires significant processing before geophysical interpretations can be made. This requires the generation of data-products (time series, velocities and strain maps) and dissemination strategies to bridge these differences and assure efficient use of data across traditionally separate communities. "GPS DATA PRODUCTS FOR SOLID EARTH SCIENCE" (GDPSES) is a multi-year NASA funded project, designed to produce and deliver high quality GPS time series, velocities, and strain fields, derived from multiple GPS networks along the western US plate boundary, and to make these products easily accessible to geophysicists. Our GPS product dissemination is through modern web-based IT methodology. Product browsing is facilitated through a web tool known as GPS Explorer and continuous streams of GPS time series are provided using web services to the seismic archive, where it can be accessed by seismologists using traditional seismic data viewing and manipulation tools. GPS-Explorer enables users to efficiently browse several layers of data products from raw data through time series, velocities and strain by providing the user with a web interface, which seamlessly interacts with a continuously updated database of these data products through the use of web-services. The current archive contains GDPSES data products beginning in 1995, and includes observations from GPS stations in EarthScope's Plate Boundary Observatory (PBO), as well as from real-time real-time CGPS stations. The generic, standards-based approach used in this project enables GDPSES to seamlessly expand indefinitely to include other space-time-dependent data products from additional GPS networks. The prototype GPS-Explorer provides users with a personalized working environment in which the user may zoom in and access subsets of the data via web services. It provides users with a variety of interactive web tools interconnected in a portlet environment to explore and save datasets of interest to return to at a later date. At the same time the GPS time series are also made available through the seismic data archive, where the GPS networks are treated as regular seismic networks, whose data is made available in data formats used by seismic utilities such as SEED readers and SAC. A key challenge, stemming from the fundamental differences between seismic and geodetic time series, is the representation of reprocessed of GPS data in the seismic archive. As GPS processing algorithms evolve and their accuracy increases, a periodic complete recreation of the the GPS time series archive is necessary.

Feeder Voltage Regulation with High-Penetration PV Using Advanced Inverters and a Distribution Management System: A Duke Energy Case Study

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

Palmintier, Bryan; Giraldez, Julieta; Gruchalla, Kenny

2016-11-01

Duke Energy, Alstom Grid, and the National Renewable Energy Laboratory teamed up to better understand the impacts of solar photovoltaics (PV) on distribution system operations. The core goal of the project is to compare the operational - specifically, voltage regulation - impacts of three classes of PV inverter operations: 1.) Active power only (Baseline); 2.) Local inverter control (e.g., PF...not equal...1, Q(V), etc.); and 3.) Integrated volt-VAR control (centralized through the distribution management system). These comparisons were made using multiple approaches, each of which represents an important research-and-development effort on its own: a) Quasi-steady-state time-series modeling for approximately 1 yearmore » of operations using the Alstom eTerra (DOTS) system as a simulation engine, augmented by Python scripting for scenario and time-series control and using external models for an advanced inverter; b) Power-hardware-in-the-loop (PHIL) testing of a 500-kVA-class advanced inverter and traditional voltage regulating equipment. This PHIL testing used cosimulation to link full-scale feeder simulation using DOTS in real time to hardware testing; c) Advanced visualization to provide improved insights into time-series results and other PV operational impacts; and d) Cost-benefit analysis to compare the financial and business-model impacts of each integration approach.« less

Scaling analysis of bilateral hand tremor movements in essential tremor patients.

PubMed

Blesic, S; Maric, J; Dragasevic, N; Milanovic, S; Kostic, V; Ljubisavljevic, Milos

2011-08-01

Recent evidence suggests that the dynamic-scaling behavior of the time-series of signals extracted from separate peaks of tremor spectra may reveal existence of multiple independent sources of tremor. Here, we have studied dynamic characteristics of the time-series of hand tremor movements in essential tremor (ET) patients using the detrended fluctuation analysis method. Hand accelerometry was recorded with (500 g) and without weight loading under postural conditions in 25 ET patients and 20 normal subjects. The time-series comprising peak-to-peak (PtP) intervals were extracted from regions around the first three main frequency components of power spectra (PwS) of the recorded tremors. The data were compared between the load and no-load condition on dominant (related to tremor severity) and non-dominant tremor side and with the normal (physiological) oscillations in healthy subjects. Our analysis shows that, in ET, the dynamic characteristics of the main frequency component of recorded tremors exhibit scaling behavior. Furthermore, they show that the two main components of ET tremor frequency spectra, otherwise indistinguishable without load, become significantly different after inertial loading and that they differ between the tremor sides (related to tremor severity). These results show that scaling, a time-domain analysis, helps revealing tremor features previously not revealed by frequency-domain analysis and suggest that distinct oscillatory central circuits may generate the tremor in ET patients.

A time series model of the occurrence of gastric dilatation-volvulus in a population of dogs

PubMed Central

Levine, Michael; Moore, George E

2009-01-01

Background Gastric dilatation-volvulus (GDV) is a life-threatening condition of mammals, with increased risk in large breed dogs. The study of its etiological factors is difficult due to the variety of possible living conditions. The association between meteorological events and the occurrence of GDV has been postulated but remains unclear. This study introduces the binary time series approach to the investigation of the possible meteorological risk factors for GDV. The data collected in a population of high-risk working dogs in Texas was used. Results Minimum and maximum daily atmospheric pressure on the day of GDV event and the maximum daily atmospheric pressure on the day before the GDV event were positively associated with the probability of GDV. All of the odds/multiplicative factors of a day being GDV day were interpreted conditionally on the past GDV occurrences. There was minimal difference between the binary and Poisson general linear models. Conclusion Time series modeling provided a novel method for evaluating the association between meteorological variables and GDV in a large population of dogs. Appropriate application of this method was enhanced by a common environment for the dogs and availability of meteorological data. The potential interaction between weather changes and patient risk factors for GDV deserves further investigation. PMID:19368730

A time series model of the occurrence of gastric dilatation-volvulus in a population of dogs.

PubMed

Levine, Michael; Moore, George E

2009-04-15

Gastric dilatation-volvulus (GDV) is a life-threatening condition of mammals, with increased risk in large breed dogs. The study of its etiological factors is difficult due to the variety of possible living conditions. The association between meteorological events and the occurrence of GDV has been postulated but remains unclear. This study introduces the binary time series approach to the investigation of the possible meteorological risk factors for GDV. The data collected in a population of high-risk working dogs in Texas was used. Minimum and maximum daily atmospheric pressure on the day of GDV event and the maximum daily atmospheric pressure on the day before the GDV event were positively associated with the probability of GDV. All of the odds/multiplicative factors of a day being GDV day were interpreted conditionally on the past GDV occurrences. There was minimal difference between the binary and Poisson general linear models. Time series modeling provided a novel method for evaluating the association between meteorological variables and GDV in a large population of dogs. Appropriate application of this method was enhanced by a common environment for the dogs and availability of meteorological data. The potential interaction between weather changes and patient risk factors for GDV deserves further investigation.

Data-driven discovery of partial differential equations

PubMed Central

Rudy, Samuel H.; Brunton, Steven L.; Proctor, Joshua L.; Kutz, J. Nathan

2017-01-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg–de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable. PMID:28508044

Application of the Karhunen-Loeve transform temporal image filter to reduce noise in real-time cardiac cine MRI

NASA Astrophysics Data System (ADS)

Ding, Yu; Chung, Yiu-Cho; Raman, Subha V.; Simonetti, Orlando P.

2009-06-01

Real-time dynamic magnetic resonance imaging (MRI) typically sacrifices the signal-to-noise ratio (SNR) to achieve higher spatial and temporal resolution. Spatial and/or temporal filtering (e.g., low-pass filtering or averaging) of dynamic images improves the SNR at the expense of edge sharpness. We describe the application of a temporal filter for dynamic MR image series based on the Karhunen-Loeve transform (KLT) to remove random noise without blurring stationary or moving edges and requiring no training data. In this paper, we present several properties of this filter and their effects on filter performance, and propose an automatic way to find the filter cutoff based on the autocorrelation of the eigenimages. Numerical simulation and in vivo real-time cardiac cine MR image series spanning multiple cardiac cycles acquired using multi-channel sensitivity-encoded MRI, i.e., parallel imaging, are used to validate and demonstrate these properties. We found that in this application, the noise standard deviation was reduced to 42% of the original with no apparent image blurring by using the proposed filter cutoff. Greater noise reduction can be achieved by increasing the length of the image series. This advantage of KLT filtering provides flexibility in the form of another scan parameter to trade for SNR.

Modeling influenza-like illnesses through composite compartmental models

NASA Astrophysics Data System (ADS)

Levy, Nir; , Michael, Iv; Yom-Tov, Elad

2018-03-01

Epidemiological models for the spread of pathogens in a population are usually only able to describe a single pathogen. This makes their application unrealistic in cases where multiple pathogens with similar symptoms are spreading concurrently within the same population. Here we describe a method which makes possible the application of multiple single-strain models under minimal conditions. As such, our method provides a bridge between theoretical models of epidemiology and data-driven approaches for modeling of influenza and other similar viruses. Our model extends the Susceptible-Infected-Recovered model to higher dimensions, allowing the modeling of a population infected by multiple viruses. We further provide a method, based on an overcomplete dictionary of feasible realizations of SIR solutions, to blindly partition the time series representing the number of infected people in a population into individual components, each representing the effect of a single pathogen. We demonstrate the applicability of our proposed method on five years of seasonal influenza-like illness (ILI) rates, estimated from Twitter data. We demonstrate that our method describes, on average, 44% of the variance in the ILI time series. The individual infectious components derived from our model are matched to known viral profiles in the populations, which we demonstrate matches that of independently collected epidemiological data. We further show that the basic reproductive numbers (R 0) of the matched components are in range known for these pathogens. Our results suggest that the proposed method can be applied to other pathogens and geographies, providing a simple method for estimating the parameters of epidemics in a population.

Influence of atmospheric transport on ozone and trace- level toxic air contaminants over the northeastern United States

NASA Astrophysics Data System (ADS)

Brankov, Elvira

This thesis presents a methodology for examining the relationship between synoptic-scale atmospheric transport patterns and observed pollutant concentration levels. It involves calculating a large number of back-trajectories from the observational site and subjecting them to cluster analysis. The pollutant concentration data observed at that site are then segregated according to the back-trajectory clusters. If the pollutant observations extend over several seasons, it is important to filter out seasonal and long-term components from the time series data before pollutant cluster-segregation, because only the short-term component of the time series data is related to the synoptic-scale transport. Multiple comparison procedures are used to test for significant differences in the chemical composition of pollutant data associated with each cluster. This procedure is useful in indicating potential pollutant source regions and isolating meteorological regimes associated with pollutant transport from those regions. If many observational sites are available, the spatial and temporal scales of the pollution transport from a given direction can be extracted through the time-lagged inter- site correlation analysis of pollutant concentrations. The proposed methodology is applicable to any pollutant at any site if sufficiently abundant data set is available. This is illustrated through examination of five-year long time series data of ozone concentrations at several sites in the Northeast. The results provide evidence of ozone transport to these sites, revealing the characteristic spatial and temporal scales involved in the transport and identifying source regions for this pollutant. Problems related to statistical analyses of censored data are addressed in the second half of this thesis. Although censoring (reporting concentrations in a non-quantitative way) is typical for trace-level measurements, methods for statistical analysis, inference and interpretation of such data are complex and still under development. In this study, multiple comparison of censored data sets was required in order to examine the influence of synoptic- scale circulations on concentration levels of several trace-level toxic pollutants observed in the Northeast (e.g., As, Se, Mn, V, etc.). Since the traditional multiple comparison procedures are not readily applicable to such data sets, a Monte Carlo simulation study was performed to assess several nonparametric methods for multiple comparison of censored data sets. Application of an appropriate comparison procedure to clusters of toxic trace elements observed in the Northeast led to the identification of potential source regions and atmospheric patterns associated with the long-range transport of these pollutants. A method for comparison of proportions and elemental ratio calculations were used to confirm/clarify these inferences with a greater degree of confidence.

TWO CRITERIA FOR WEAK GENERALIZED LOCALIZATION FOR MULTIPLE TRIGONOMETRIC FOURIER SERIES OF FUNCTIONS IN L_p, p \\ge 1

NASA Astrophysics Data System (ADS)

Bloshanskiĭ, I. L.

1986-04-01

The concept of weak generalized localization almost everywhere is introduced. For the multiple Fourier series of a function f, weak generalized localization almost everywhere holds on the set E (E is an arbitrary set of positive measure E \\subset T^N = \\lbrack- \\pi, \\pi\\rbrack^N) if the condition f(x) \\in L_p(T^N), p \\ge 1, f = 0 on E implies that the indicated series converges almost everywhere on some subset E_1 \\subset E of positive measure. For a large class of sets \\{ E \\}, E \\subset T^N, a number of propositions are proved showing that weak localization of rectangular sums holds on the set E in the classes L_p, p \\ge 1, if and only if the set E has certain specific properties. In the course of the proof the precise geometry and structure of the subset E_1 of E on which the multiple Fourier series converges almost everywhere to zero are determined. Bibliography: 13 titles.

Long-term forecasting of internet backbone traffic.

PubMed

Papagiannaki, Konstantina; Taft, Nina; Zhang, Zhi-Li; Diot, Christophe

2005-09-01

We introduce a methodology to predict when and where link additions/upgrades have to take place in an Internet protocol (IP) backbone network. Using simple network management protocol (SNMP) statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent points of presence (PoPs) and look at its evolution at time scales larger than 1 h. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis (MRA) and linear time series models. Using wavelet MRA, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12-h time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12-h time scale yields accurate estimates for at least 6 months in the future.

Local to Global Scale Time Series Analysis of US Dryland Degradation Using Landsat, AVHRR, and MODIS

NASA Astrophysics Data System (ADS)

Washington-Allen, R. A.; Ramsey, R. D.; West, N. E.; Kulawardhana, W.; Reeves, M. C.; Mitchell, J. E.; Van Niel, T. G.

2011-12-01

Drylands cover 41% of the terrestrial land surface and annually generate $1 trillion in ecosystem goods and services for 38% of the global population, yet estimates of the global extent of Dryland degradation is uncertain with a range of 10 - 80%. It is currently understood that Drylands exhibit topological complexity including self-organization of parameters of different levels-of-organization, e.g., ecosystem and landscape parameters such as soil and vegetation pattern and structure, that gradually or discontinuously shift to multiple basins of attraction in response to herbivory, fire, and climatic drivers at multiple spatial and temporal scales. Our research has shown that at large geographic scales, contemporaneous time series of 10 to 20 years for response and driving variables across two or more spatial scales is required to replicate and differentiate between the impact of climate and land use activities such as commercial grazing. For example, the Pacific Decadal Oscillation (PDO) is a major driver of Dryland net primary productivity (NPP), biodiversity, and ecological resilience with a 10-year return interval, thus 20 years of data are required to replicate its impact. Degradation is defined here as a change in physiognomic composition contrary to management goals, a persistent reduction in vegetation response, e.g., NPP, accelerated soil erosion, a decline in soil quality, and changes in landscape configuration and structure that lead to a loss of ecosystem function. Freely available Landsat, Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradimeter (MODIS) archives of satellite imagery exist that provide local to global spatial coverage and time series between 1972 to the present from which proxies of land degradation can be derived. This paper presents time series assessments between 1972 and 2011 of US Dryland degradation including early detection of dynamic regime shifts in the Mojave and landscape pattern and erosion state changes in the Intermountain region in response to the "Great North American Drought" in 1988, PDO and El Niño Southern Oscillation (ENSO) and commercial grazing. Additionally, we will show the discoveries in the last 10-years that US Drylands are "greening" despite the severe Southwestern drought and that commercial livestock are a driver of this response with an annual appropriation of some 58% of NPP.

Exploring Low-Amplitude, Long-Duration Deformational Transients on the Cascadia Subduction Zone

NASA Astrophysics Data System (ADS)

Nuyen, C.; Schmidt, D. A.

2017-12-01

The absence of long-term slow slip events (SSEs) in Cascadia is enigmatic on account of the diverse group of subduction zone systems that do experience long-term SSEs. In particular, southwest Japan, Alaska, New Zealand and Mexico have observed long-term SSEs, with some of the larger events exhibiting centimeter-scale surface displacements over the course of multiple years. The conditions that encourage long-term slow slip are not well established due to the variability in thermal parameter and plate dip amongst subduction zones that host long-term events. The Cascadia Subduction Zone likely has the capacity to host long-term SSEs, and the lack of such events motivates further exploration of the observational data. In order to search for the existence of long-duration transients in surface displacements, we examine Cascadia GPS time series from PANGA and PBO to determine whether or not Cascadia has hosted a long-term slow slip event in the past 20 years. A careful review of the time series does not reveal any large-scale multi-year transients. In order to more clearly recognize possible small amplitude long-term SSEs in Cascadia, the GPS time series are reduced with two separate methods. The first method involves manually removing (1) continental water loading terms, (2) transient displacements of known short-term SSEs, and (3) common mode signals that span the network. The second method utilizes a seasonal-trend decomposition procedure (STL) to extract a long-term trend from the GPS time-series. Manual inspection of both of these products reveals intriguing long-term changes in the longitudinal component of several GPS stations in central Cascadia. To determine whether these shifts could be due to long-term slow slip, we invert the reduced surface displacement time series for fault slip using a principle component analysis-based inversion method. We also utilize forward fault models of various synthetic long-term SSEs to better understand how these events may appear in the time series for a range of magnitudes and durations. Results from this research have direct implications for the possible slip modes in Cascadia and how variations in slip over time can impact stress and strain accumulations along the margin.

Using syllable-timed speech to treat preschool children who stutter: a multiple baseline experiment.

PubMed

Trajkovski, Natasha; Andrews, Cheryl; Onslow, Mark; Packman, Ann; O'Brian, Sue; Menzies, Ross

2009-03-01

This report presents the results of an experimental investigation of the effects of a syllable-timed speech treatment on three stuttering preschool children. Syllable-timed speech involves speaking with minimal differentiation in linguistic stress across syllables. Three children were studied in a multiple baseline across participants design, with percent syllables stuttered (%SS) as the dependent variable. In the week following the initial clinic visit, each child decreased their beyond-clinic stuttering by 40%, 49% and 32%, respectively. These reductions are only evident in the time series after the introduction of the syllable-timed speech treatment procedure. Participants required a mean of six clinic visits, of approximately 30-60 min in duration, to reach and sustain a beyond-clinic %SS below 1.0. The results suggest that clinical trials of the treatment are warranted. The reader will be able to summarize, discuss and evaluate: (1) The nature, impact and treatment options available for early stuttering. (2) The syllable-timed speech treatment protocol administered. (3) The advantages of syllable-timed speech treatment for early stuttering. (4) The questions that further research needs to answer about the syllable-timed speech treatment.

Detecting multiple moving objects in crowded environments with coherent motion regions

DOEpatents

Cheriyadat, Anil M.; Radke, Richard J.

2013-06-11

Coherent motion regions extend in time as well as space, enforcing consistency in detected objects over long time periods and making the algorithm robust to noisy or short point tracks. As a result of enforcing the constraint that selected coherent motion regions contain disjoint sets of tracks defined in a three-dimensional space including a time dimension. An algorithm operates directly on raw, unconditioned low-level feature point tracks, and minimizes a global measure of the coherent motion regions. At least one discrete moving object is identified in a time series of video images based on the trajectory similarity factors, which is a measure of a maximum distance between a pair of feature point tracks.

Multiple disturbances classifier for electric signals using adaptive structuring neural networks

NASA Astrophysics Data System (ADS)

Lu, Yen-Ling; Chuang, Cheng-Long; Fahn, Chin-Shyurng; Jiang, Joe-Air

2008-07-01

This work proposes a novel classifier to recognize multiple disturbances for electric signals of power systems. The proposed classifier consists of a series of pipeline-based processing components, including amplitude estimator, transient disturbance detector, transient impulsive detector, wavelet transform and a brand-new neural network for recognizing multiple disturbances in a power quality (PQ) event. Most of the previously proposed methods usually treated a PQ event as a single disturbance at a time. In practice, however, a PQ event often consists of various types of disturbances at the same time. Therefore, the performances of those methods might be limited in real power systems. This work considers the PQ event as a combination of several disturbances, including steady-state and transient disturbances, which is more analogous to the real status of a power system. Six types of commonly encountered power quality disturbances are considered for training and testing the proposed classifier. The proposed classifier has been tested on electric signals that contain single disturbance or several disturbances at a time. Experimental results indicate that the proposed PQ disturbance classification algorithm can achieve a high accuracy of more than 97% in various complex testing cases.

Characteristics, location and origin of flare activity in a complex active region

NASA Technical Reports Server (NTRS)

Machado, M. E.; Gary, G. A.; Hagyard, M. J.; Hernandez, A. M.; Rovira, M. G.

1986-01-01

The observational characteristics of series of multiple-loop flares from a complex active region are summarized. The location of the highest observed photospheric magnetic shear is found to be the commonly observed site of flare onset, but not, in many cases, the magnetic region where the largest time-integrated energy release is observed. The observations thus reveal a consistent pattern of energy-release processes related to the magnetic-field topology.

Influence of the Tumor Microenvironment on Genomic Changes Conferring Chemoresistance in Breast Cancer

DTIC Science & Technology

2013-04-01

tumor microenvironment on clonal selection using intravital microscopy Jae-Hyun Park 1 , Miriam R. Fein 1 , Mikala Egeblad 1 1 Cold Spring Harbor...used surgically implanted mammary imaging windows in immunocompetent mice and injected “brainbow” expressing, syngeneic 4T1 breast carcinoma cells...under the windows. This allowed us to acquire multiple time- lapse imaging series by spinning disk confocal microscopy of the same tumor, done about 3

Sociological and economic theories of suicide: a comparison of the U.S.A. and Taiwan.

PubMed

Yang, B; Lester, D; Yang, C H

1992-02-01

Time-series analyses were carried out to explore the importance of sociological and economic variables in accounting for the suicide rate in the U.S.A. and in Taiwan for 1952-1984. Sociological variables (divorce and female labor force participation) played similar roles in the multiple regressions for both nations while economic variables (GNP per capita/growth and unemployment) played a role only in the U.S.A.

An assessment of the validity and discrimination of the intensive time-series design by monitoring learning differences between students with different cognitive tendencies

NASA Astrophysics Data System (ADS)

Farnsworth, Carolyn H.; Mayer, Victor J.

Intensive time-series designs for classroom investigations have been under development since 1975. Studies have been conducted to determine their feasibility (Mayer & Lewis, 1979), their potential for monitoring knowledge acquisition (Mayer & Kozlow, 1980), and the potential threat to validity of the frequency of testing inherent in the design (Mayer & Rojas, 1982). This study, an extension of those previous studies, is an attempt to determine the degree of discrimination the design allows in collecting data on achievement. It also serves as a replication of the Mayer and Kozlow study, an attempt to determine design validity for collecting achievement data. The investigator used her eighth-grade earth science students, from a suburban Columbus (Ohio) junior high school. A multiple-group single intervention time-series design (Glass, Willson, & Gottman, 1975) was adapted to the collection of daily data on achievement in the topic of the intervention, a unit on plate tectonics. Single multiple-choice items were randomly assigned to each of three groups of students, identified on the basis of their ranking on a written test of cognitive level (Lawson, 1978). The top third, or those with formal cognitive tendencies, were compared on the basis of knowledge achievement and understanding achievement with the lowest third of the students, or those with concrete cognitive tendencies, to determine if the data collected in the design would discriminate between the two groups. Several studies (Goodstein & Howe, 1978; Lawson & Renner, 1975) indicated that students with formal cognitive tendencies should learn a formal concept such as plate tectonics with greater understanding than should students with concrete cognitive tendencies. Analyses used were a comparison of regression lines in each of the three study stages: baseline, intervention, and follow-up; t-tests of means of days summed across each stage; and a time-series analysis program. Statistically significant differences were found between the two groups both in slopes of regression lines (0.0001) and in t-tests (0.0005) on both knowledge and understanding levels of learning. These differences confirm the discrimination of the intensive time-series design in showing that it can distinguish differences in learning between students with formal cognitive tendencies and those with concrete cognitive tendencies. The time-series analysis model with a trend in the intervention was better than a model with no trend for both groups of students, in that it accounted for a greater amount of variance in the data from both knowledge and understanding levels of learning. This finding adds additional confidence in the validity of the design for obtaining achievement data. When the analysis model with trend was used on data from the group with formal cognitive tendencies, it accounted for a greater degree of variance than the same model applied to the data from the group with concrete cognitive tendencies. This more conservative analysis, therefor, gave results consistent with those from the more usual linear regression techniques and t-tests, further adding to the confidence in the discrimination of the design.

Stacked Transformer for Driver Gain and Receive Signal Splitting

NASA Technical Reports Server (NTRS)

Driscoll, Kevin R.

2013-01-01

In a high-speed signal transmission system that uses transformer coupling, there is a need to provide increased transmitted signal strength without adding active components. This invention uses additional transformers to achieve the needed gain. The prior art uses stronger drivers (which require an IC redesign and a higher power supply voltage), or the addition of another active component (which can decrease reliability, increase power consumption, reduce the beneficial effect of serializer/deserializer preemphasis or deemphasis, and/or interfere with fault containment mechanisms), or uses a different transformer winding ratio (which requires redesign of the transformer and may not be feasible with high-speed signals that require a 1:1 winding ratio). This invention achieves the required gain by connecting the secondaries of multiple transformers in series. The primaries of these transformers are currently either connected in parallel or are connected to multiple drivers. There is also a need to split a receive signal to multiple destinations with minimal signal loss. Additional transformers can achieve the split. The prior art uses impedance-matching series resistors that cause a loss of signal. Instead of causing a loss, most instantiations of this invention would actually provide gain. Multiple transformers are used instead of multiple windings on a single transformer because multiple windings on the same transformer would require a redesign of the transformer, and may not be feasible with high-speed transformers that usually require a bifilar winding with a 1:1 ratio. This invention creates the split by connecting the primaries of multiple transformers in series. The secondary of each transformer is connected to one of the intended destinations without the use of impedance-matching series resistors.

Open area 2 × 2 MIMO channel model for 2 GHz low-elevation links with diversity and capacity applications

NASA Astrophysics Data System (ADS)

Zelený, J.; Pérez-Fontán, F.; Pechac, P.; Mariño-Espiñeira, P.

2017-05-01

In civil surveillance applications, unmanned aerial vehicles (UAV) are being increasingly used in floods, fires, and law enforcement scenarios. In order to transfer large amounts of information from UAV-mounted cameras, relays, or sensors, large bandwidths are needed in comparison to those required for remotely commanding the UAV. This demands the use of higher-frequency bands, in all probability in the vicinity of 2 or 5 GHz. Novel hardware developments need propagation channel models for the ample range of operational scenarios envisaged, including multiple-input, multiple-output (MIMO) deployments. These configurations may enable a more robust transmission by increasing either the carrier-to-noise ratio statistics or the achievable capacity. In this paper, a 2 × 2 MIMO propagation channel model for an open-field environment capable of synthesizing a narrowband time series at 2 GHz is described. Maximal ratio combining diversity and capacity improvements are also evaluated through synthetic series and compared with measurement results. A simple flat, open scenario was evaluated based on which other, more complex environments can be modeled.

Ensemble Bayesian forecasting system Part I: Theory and algorithms

NASA Astrophysics Data System (ADS)

Herr, Henry D.; Krzysztofowicz, Roman

2015-05-01

The ensemble Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input ensemble forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an ensemble of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an ensemble of time series of outputs, which is next transformed stochastically by the HUP into an ensemble of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the ensemble Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple ensemble members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input ensemble and makes it operationally feasible to generate a Bayesian ensemble forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of predictand, possesses a Bayesian coherence property, constitutes a random sample of the predictand, and has an acceptable sampling error-which makes it suitable for rational decision making under uncertainty.

Multiple causes of nonstationarity in the Weihe annual low-flow series

NASA Astrophysics Data System (ADS)

Xiong, Bin; Xiong, Lihua; Chen, Jie; Xu, Chong-Yu; Li, Lingqi

2018-02-01

Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

Subtle Nonlinearity in Popular Album Charts

NASA Astrophysics Data System (ADS)

Bentley, R. Alexander; Maschner, Herbert D. G.

Large-scale patterns of culture change may be explained by models of self organized criticality, or alternatively, by multiplicative processes. We speculate that popular album activity may be similar to critical models of extinction in that interconnected agents compete to survive within a limited space. Here we investigate whether popular music albums as listed on popular album charts display evidence of self-organized criticality, including a self-affine time series of activity and power-law distributions of lifetimes and exit activity in the chart. We find it difficult to distinguish between multiplicative growth and critical model hypotheses for these data. However, aspects of criticality may be masked by the selective sampling that a "Top 200" listing necessarily implies.

On the use of Different Methodologies in Cognitive Neuropsychology: Drink Deep and from Several Sources

PubMed Central

Nickels, Lyndsey; Howard, David; Best, Wendy

2012-01-01

Cognitive neuropsychology has championed the use of single-case research design. Recently, however, case series designs that employ multiple single cases have been increasingly utilized to address theoretical issues using data from neuropsychological populations. In this paper, we examine these methodologies, focusing on a number of points in particular. First we discuss the use of dissociations and associations, often thought of as a defining feature of cognitive neuropsychology, and argue that they are better viewed as part of a spectrum of methods that aim to explain and predict behaviour. We also raise issues regarding case series design in particular, arguing that selection of an appropriate sample, including controlling degree of homogeneity, is critical and constrains the theoretical claims that can be made on the basis of the data. We discuss the possible interpretation of “outliers” in a case series, suggesting that while they may reflect “noise” caused by variability in performance due to factors that are not of relevance to the theoretical claims, they may also reflect the presence of patterns that are critical to test, refine, and potentially falsify our theories. The role of case series in treatment research is also raised, in light of the fact that, despite their status as gold standard, randomized controlled trials cannot provide answers to many crucial theoretical and clinical questions. Finally, we stress the importance of converging evidence: We propose that it is conclusions informed by multiple sources of evidence that are likely to best inform theory and stand the test of time. PMID:22746689

Comparison of Co-Temporal Modeling Algorithms on Sparse Experimental Time Series Data Sets.

PubMed

Allen, Edward E; Norris, James L; John, David J; Thomas, Stan J; Turkett, William H; Fetrow, Jacquelyn S

2010-01-01

Multiple approaches for reverse-engineering biological networks from time-series data have been proposed in the computational biology literature. These approaches can be classified by their underlying mathematical algorithms, such as Bayesian or algebraic techniques, as well as by their time paradigm, which includes next-state and co-temporal modeling. The types of biological relationships, such as parent-child or siblings, discovered by these algorithms are quite varied. It is important to understand the strengths and weaknesses of the various algorithms and time paradigms on actual experimental data. We assess how well the co-temporal implementations of three algorithms, continuous Bayesian, discrete Bayesian, and computational algebraic, can 1) identify two types of entity relationships, parent and sibling, between biological entities, 2) deal with experimental sparse time course data, and 3) handle experimental noise seen in replicate data sets. These algorithms are evaluated, using the shuffle index metric, for how well the resulting models match literature models in terms of siblings and parent relationships. Results indicate that all three co-temporal algorithms perform well, at a statistically significant level, at finding sibling relationships, but perform relatively poorly in finding parent relationships.

Techniques for efficient, real-time, 3D visualization of multi-modality cardiac data using consumer graphics hardware.

PubMed

Levin, David; Aladl, Usaf; Germano, Guido; Slomka, Piotr

2005-09-01

We exploit consumer graphics hardware to perform real-time processing and visualization of high-resolution, 4D cardiac data. We have implemented real-time, realistic volume rendering, interactive 4D motion segmentation of cardiac data, visualization of multi-modality cardiac data and 3D display of multiple series cardiac MRI. We show that an ATI Radeon 9700 Pro can render a 512x512x128 cardiac Computed Tomography (CT) study at 0.9 to 60 frames per second (fps) depending on rendering parameters and that 4D motion based segmentation can be performed in real-time. We conclude that real-time rendering and processing of cardiac data can be implemented on consumer graphics cards.

How to estimate exposure when studying the temperature-mortality relationship? A case study of the Paris area.

PubMed

Schaeffer, Laura; de Crouy-Chanel, Perrine; Wagner, Vérène; Desplat, Julien; Pascal, Mathilde

2016-01-01

Time series studies assessing the effect of temperature on mortality generally use temperatures measured by a single weather station. In the Paris region, there is a substantial measurement network, and a variety of exposure indicators created from multiple stations can be tested. The aim of this study is to test the influence of exposure indicators on the temperature-mortality relationship in the Paris region. The relationship between temperature and non-accidental mortality was assessed based on a time series analysis using Poisson regression and a generalised additive model. Twenty-five stations in Paris and its three neighbouring departments were used to create four exposure indicators. These indicators were (1) the temperature recorded by one reference station, (2) a simple average of the temperatures of all stations, (3) an average weighted on the departmental population and (4) a classification of the stations based on land use and an average weighted on the population in each class. The relative risks and the Akaike criteria were similar for all the exposure indicators. The estimated temperature-mortality relationship therefore did not appear to be significantly affected by the indicator used, regardless of study zone (departments or region) or age group. The increase in temperatures from the 90(th) to the 99(th) percentile of the temperature distribution led to a significant increase in mortality over 75 years (RR = 1.10 [95% CI, 1.07; 1.14]). Conversely, the decrease in temperature between the 10(th) and 1(st) percentile had a significant effect on the mortality under 75 years (RR = 1.04 [95% CI, 1.01; 1.06]). In the Paris area, there is no added value in taking multiple climatic stations into account when estimating exposure in time series studies. Methods to better represent the subtle temperature variations in densely populated areas in epidemiological studies are needed.

Multiple timescales of cyclical behaviour observed at two dome-forming eruptions

NASA Astrophysics Data System (ADS)

Lamb, Oliver D.; Varley, Nick R.; Mather, Tamsin A.; Pyle, David M.; Smith, Patrick J.; Liu, Emma J.

2014-09-01

Cyclic behaviour over a range of timescales is a well-documented feature of many dome-forming volcanoes, but has not previously been identified in high resolution seismic data from Volcán de Colima (Mexico). Using daily seismic count datasets from Volcán de Colima and Soufrière Hills volcano (Montserrat), this study explores parallels in the long-term behaviour of seismicity at two long-lived systems. Datasets are examined using multiple techniques, including Fast-Fourier Transform, Detrended Fluctuation Analysis and Probabilistic Distribution Analysis, and the comparison of results from two systems reveals interesting parallels in sub-surface processes operating at both systems. Patterns of seismicity at both systems reveal complex but broadly similar long-term temporal patterns with cycles on the order of ~ 50- to ~ 200-days. These patterns are consistent with previously published spectral analyses of SO2 flux time-series at Soufrière Hills volcano, and are attributed to variations in the movement of magma in each system. Detrended Fluctuation Analysis determined that both volcanic systems showed a systematic relationship between the number of seismic events and the relative ‘roughness' of the time-series, and explosions at Volcán de Colima showed a 1.5-2 year cycle; neither observation has a clear explanatory mechanism. At Volcán de Colima, analysis of repose intervals between seismic events shows long-term behaviour that responds to changes in activity at the system. Similar patterns for both volcanic systems suggest a common process or processes driving the observed signal but it is not clear from these results alone what those processes may be. Further attempts to model conduit processes at each volcano must account for the similarities and differences in activity within each system. The identification of some commonalities in the patterns of behaviour during long-lived dome-forming eruptions at andesitic volcanoes provides a motivation for investigating further use of time-series analysis as a monitoring tool.

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis.

PubMed

Baglietto, Gabriel; Gigante, Guido; Del Giudice, Paolo

2017-01-01

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.

Broadband piezoelectric energy harvesting devices using multiple bimorphs with different operating frequencies.

PubMed

Xue, Huan; Hu, Yuantai; Wang, Qing-Ming

2008-09-01

This paper presents a novel approach for designing broadband piezoelectric harvesters by integrating multiple piezoelectric bimorphs (PBs) with different aspect ratios into a system. The effect of 2 connecting patterns among PBs, in series and in parallel, on improving energy harvesting performance is discussed. It is found for multifrequency spectra ambient vibrations: 1) the operating frequency band (OFB) of a harvesting structure can be widened by connecting multiple PBs with different aspect ratios in series; 2) the OFB of a harvesting structure can be shifted to the dominant frequency domain of the ambient vibrations by increasing or decreasing the number of PBs in parallel. Numerical results show that the OFB of the piezoelectric energy harvesting devices can be tailored by the connection patterns (i.e., in series and in parallel) among PBs.

Photoconductivity response time in amorphous semiconductors

NASA Astrophysics Data System (ADS)

Adriaenssens, G. J.; Baranovskii, S. D.; Fuhs, W.; Jansen, J.; Öktü, Ö.

1995-04-01

The photoconductivity response time of amorphous semiconductors is examined theoretically on the basis of standard definitions for free- and trapped-carrier lifetimes, and experimentally for a series of a-Si1-xCx:H alloys with x<0.1. Particular attention is paid to its dependence on carrier generation rate and temperature. As no satisfactory agreement between models and experiments emerges, a simple theory is developed that can account for the experimental observations on the basis of the usual multiple-trappping ideas, provided a small probability of direct free-carrier recombination is included. The theory leads to a stretched-exponential photocurrent decay.

Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study

PubMed Central

Fang, Xin; Li, Runkui; Kan, Haidong; Bottai, Matteo; Fang, Fang

2016-01-01

Objective To demonstrate an application of Bayesian model averaging (BMA) with generalised additive mixed models (GAMM) and provide a novel modelling technique to assess the association between inhalable coarse particles (PM10) and respiratory mortality in time-series studies. Design A time-series study using regional death registry between 2009 and 2010. Setting 8 districts in a large metropolitan area in Northern China. Participants 9559 permanent residents of the 8 districts who died of respiratory diseases between 2009 and 2010. Main outcome measures Per cent increase in daily respiratory mortality rate (MR) per interquartile range (IQR) increase of PM10 concentration and corresponding 95% confidence interval (CI) in single-pollutant and multipollutant (including NOx, CO) models. Results The Bayesian model averaged GAMM (GAMM+BMA) and the optimal GAMM of PM10, multipollutants and principal components (PCs) of multipollutants showed comparable results for the effect of PM10 on daily respiratory MR, that is, one IQR increase in PM10 concentration corresponded to 1.38% vs 1.39%, 1.81% vs 1.83% and 0.87% vs 0.88% increase, respectively, in daily respiratory MR. However, GAMM+BMA gave slightly but noticeable wider CIs for the single-pollutant model (−1.09 to 4.28 vs −1.08 to 3.93) and the PCs-based model (−2.23 to 4.07 vs −2.03 vs 3.88). The CIs of the multiple-pollutant model from two methods are similar, that is, −1.12 to 4.85 versus −1.11 versus 4.83. Conclusions The BMA method may represent a useful tool for modelling uncertainty in time-series studies when evaluating the effect of air pollution on fatal health outcomes. PMID:27531727

Modeling multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth-observation and river flow data

NASA Astrophysics Data System (ADS)

Heimhuber, V.; Tulbure, M. G.; Broich, M.

2017-02-01

Periodically inundated floodplain areas are hot spots of biodiversity and provide a broad range of ecosystem services but have suffered alarming declines in recent history. Despite their importance, their long-term surface water (SW) dynamics and hydroclimatic drivers remain poorly quantified on continental scales. In this study, we used a 26 year time series of Landsat-derived SW maps in combination with river flow data from 68 gauges and spatial time series of rainfall, evapotranspiration and soil moisture to statistically model SW dynamics as a function of key drivers across Australia's Murray-Darling Basin (˜1 million km2). We fitted generalized additive models for 18,521 individual modeling units made up of 10 × 10 km grid cells, each split into floodplain, floodplain-lake, and nonfloodplain area. Average goodness of fit of models was high across floodplains and floodplain-lakes (r2 > 0.65), which were primarily driven by river flow, and was lower for nonfloodplain areas (r2 > 0.24), which were primarily driven by rainfall. Local climate conditions were more relevant for SW dynamics in the northern compared to the southern basin and had the highest influence in the least regulated and most extended floodplains. We further applied the models of two contrasting floodplain areas to predict SW extents of cloud-affected time steps in the Landsat series during the large 2010 floods with high validated accuracy (r2 > 0.97). Our framework is applicable to other complex river basins across the world and enables a more detailed quantification of large floods and drivers of SW dynamics compared to existing methods.

Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models.

PubMed

Luo, Li; Luo, Le; Zhang, Xinli; He, Xiaoli

2017-07-10

Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration. We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead. The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better. Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.