Detection of a sudden change of the field time series based on the Lorenz system.

PubMed

Da, ChaoJiu; Li, Fang; Shen, BingLu; Yan, PengCheng; Song, Jian; Ma, DeShan

2017-01-01

We conducted an exploratory study of the detection of a sudden change of the field time series based on the numerical solution of the Lorenz system. First, the time when the Lorenz path jumped between the regions on the left and right of the equilibrium point of the Lorenz system was quantitatively marked and the sudden change time of the Lorenz system was obtained. Second, the numerical solution of the Lorenz system was regarded as a vector; thus, this solution could be considered as a vector time series. We transformed the vector time series into a time series using the vector inner product, considering the geometric and topological features of the Lorenz system path. Third, the sudden change of the resulting time series was detected using the sliding t-test method. Comparing the test results with the quantitatively marked time indicated that the method could detect every sudden change of the Lorenz path, thus the method is effective. Finally, we used the method to detect the sudden change of the pressure field time series and temperature field time series, and obtained good results for both series, which indicates that the method can apply to high-dimension vector time series. Mathematically, there is no essential difference between the field time series and vector time series; thus, we provide a new method for the detection of the sudden change of the field time series.

System Learning via Exploratory Data Analysis: Seeing Both the Forest and the Trees

NASA Astrophysics Data System (ADS)

Habash Krause, L.

2014-12-01

As the amount of observational Earth and Space Science data grows, so does the need for learning and employing data analysis techniques that can extract meaningful information from those data. Space-based and ground-based data sources from all over the world are used to inform Earth and Space environment models. However, with such a large amount of data comes a need to organize those data in a way such that trends within the data are easily discernible. This can be tricky due to the interaction between physical processes that lead to partial correlation of variables or multiple interacting sources of causality. With the suite of Exploratory Data Analysis (EDA) data mining codes available at MSFC, we have the capability to analyze large, complex data sets and quantitatively identify fundamentally independent effects from consequential or derived effects. We have used these techniques to examine the accuracy of ionospheric climate models with respect to trends in ionospheric parameters and space weather effects. In particular, these codes have been used to 1) Provide summary "at-a-glance" surveys of large data sets through categorization and/or evolution over time to identify trends, distribution shapes, and outliers, 2) Discern the underlying "latent" variables which share common sources of causality, and 3) Establish a new set of basis vectors by computing Empirical Orthogonal Functions (EOFs) which represent the maximum amount of variance for each principal component. Some of these techniques are easily implemented in the classroom using standard MATLAB functions, some of the more advanced applications require the statistical toolbox, and applications to unique situations require more sophisiticated levels of programming. This paper will present an overview of the range of tools available and how they might be used for a variety of time series Earth and Space Science data sets. Examples of feature recognition from both 1D and 2D (e.g. imagery) time series data sets will be presented.

Detection of a sudden change of the field time series based on the Lorenz system

PubMed Central

Li, Fang; Shen, BingLu; Yan, PengCheng; Song, Jian; Ma, DeShan

2017-01-01

We conducted an exploratory study of the detection of a sudden change of the field time series based on the numerical solution of the Lorenz system. First, the time when the Lorenz path jumped between the regions on the left and right of the equilibrium point of the Lorenz system was quantitatively marked and the sudden change time of the Lorenz system was obtained. Second, the numerical solution of the Lorenz system was regarded as a vector; thus, this solution could be considered as a vector time series. We transformed the vector time series into a time series using the vector inner product, considering the geometric and topological features of the Lorenz system path. Third, the sudden change of the resulting time series was detected using the sliding t-test method. Comparing the test results with the quantitatively marked time indicated that the method could detect every sudden change of the Lorenz path, thus the method is effective. Finally, we used the method to detect the sudden change of the pressure field time series and temperature field time series, and obtained good results for both series, which indicates that the method can apply to high-dimension vector time series. Mathematically, there is no essential difference between the field time series and vector time series; thus, we provide a new method for the detection of the sudden change of the field time series. PMID:28141832

Structured Sensory Trauma Interventions

ERIC Educational Resources Information Center

Steele, William; Kuban, Caelan

2010-01-01

This article features the National Institute of Trauma and Loss in Children (TLC), a program that has demonstrated via field testing, exploratory research, time series studies, and evidence-based research studies that its Structured Sensory Intervention for Traumatized Children, Adolescents, and Parents (SITCAP[R]) produces statistically…

MotionExplorer: exploratory search in human motion capture data based on hierarchical aggregation.

PubMed

Bernard, Jürgen; Wilhelm, Nils; Krüger, Björn; May, Thorsten; Schreck, Tobias; Kohlhammer, Jörn

2013-12-01

We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.

Rural counties chlamydia and gonorrhea rates in Pennsylvania among adolescents and young adults.

PubMed

Pinto, Casey N; Dorn, Lorah D; Chinchilli, Vernon M; Du, Ping; Chi, Guangqing

2017-09-01

American adolescents and young adults between the ages of 15 and 24 account for 50% of all sexually transmitted diseases (STDs) annually. Rural populations in this age group are often understudied, despite having factors that place them at higher risk for STDs. The purpose of this study was to evaluate the utility of time series analysis in the assessment of rural Pennsylvania county-level chlamydia and gonorrhea rates overtime (2004-2014) for 15- to 19- and 20- to 24-year-old age groups by gender. An exploratory analysis was completed using Pennsylvania STD surveillance case report and census data, to develop a linear mixed-effects model of the STD rate for each Pennsylvania county for the years 2004 through 2014 using 3-month increments. A cubic polynomial spline regression model was assumed over the 44 time points for each county to account for possible oscillations in the STD rate during the 11-year period. Eight out of 12 rural counties had a significant increase in chlamydia or gonorrhea rates, and five rural counties had significant decreases in chlamydia or gonorrhea rates from 2004 to 2014. Results from this study provide the first analysis of change in rates of STDs in rural settings and demonstrate the utility of time series analysis for populations with small sample sizes. Copyright © 2017 Elsevier Inc. All rights reserved.

LSAT Dimensionality Analysis for the December 1991, June 1992, and October 1992 Administrations. Statistical Report. LSAC Research Report Series.

ERIC Educational Resources Information Center

Douglas, Jeff; Kim, Hae-Rim; Roussos, Louis; Stout, William; Zhang, Jinming

An extensive nonparametric dimensionality analysis of latent structure was conducted on three forms of the Law School Admission Test (LSAT) (December 1991, June 1992, and October 1992) using the DIMTEST model in confirmatory analyses and using DIMTEST, FAC, DETECT, HCA, PROX, and a genetic algorithm in exploratory analyses. Results indicate that…

Welfare States, Labor Markets, Political Dynamics, and Population Health: A Time-Series Cross-Sectional Analysis Among East and Southeast Asian Nations.

PubMed

Ng, Edwin; Muntaner, Carles; Chung, Haejoo

2016-04-01

Recent scholarship offers different theories on how macrosocial determinants affect the population health of East and Southeast Asian nations. Dominant theories emphasize the effects of welfare regimes, welfare generosity, and labor market institutions. In this article, we conduct exploratory time-series cross-sectional analyses to generate new evidence on these theories while advancing a political explanation. Using unbalanced data of 7 East Asian countries and 11 Southeast Asian nations from 1960 to 2012, primary findings are 3-fold. First, welfare generosity measured as education and health spending has a positive impact on life expectancy, net of GDP. Second, life expectancy varies significantly by labor markets; however, these differences are explained by differences in welfare generosity. Third, as East and Southeast Asian countries become more democratic, welfare generosity increases, and population health improves. This study provides new evidence on the value of considering politics, welfare states, and labor markets within the same conceptual framework. © 2016 APJPH.

Data Acquisition and Preparation for Social Network Analysis Based on Email: Lessons Learned

DTIC Science & Technology

2009-06-01

Mrvar , A., and Batagelj , V. (2005), Exploratory Social Network Analysis with Pajek (Structural Analysis in the Social Sciences series). Cambridge, New...visualization of large networks. This program was developed by Vladimir Batagelj and Andrej Mrvar of the University of Ljubljana in Slovenia. Pajek evolved...theory, presumes Wasserman & Faust as foundation Amazon: 55% purchase rate among viewers 5. de Nooy, W., Mrvar , A., and Batagelj , V. (2005

Evidence for a Multidimensional Self-Efficacy for Exercise Scale

ERIC Educational Resources Information Center

Rodgers, W. M.; Wilson, P. M.; Hall, C. R.; Fraser, S. N.; Murray, T. C.

2008-01-01

This series of three studies considers the multidimensionality of exercise self-efficacy by examining the psychometric characteristics of an instrument designed to assess three behavioral subdomains: task, scheduling, and coping. In Study 1, exploratory factor analysis revealed the expected factor structure in a sample of 395 students.…

Nurse adoption of continuous patient monitoring on acute post-surgical units: managing technology implementation.

PubMed

Jeskey, Mary; Card, Elizabeth; Nelson, Donna; Mercaldo, Nathaniel D; Sanders, Neal; Higgins, Michael S; Shi, Yaping; Michaels, Damon; Miller, Anne

2011-10-01

To report an exploratory action-research process used during the implementation of continuous patient monitoring in acute post-surgical nursing units. Substantial US Federal funding has been committed to implementing new health care technology, but failure to manage implementation processes may limit successful adoption and the realisation of proposed benefits. Effective approaches for managing barriers to new technology implementation are needed. Continuous patient monitoring was implemented in three of 13 medical/surgical units. An exploratory action-feedback approach, using time-series nurse surveys, was used to identify barriers and develop and evaluate responses. Post-hoc interviews and document analysis were used to describe the change implementation process. Significant differences were identified in night- and dayshift nurses' perceptions of technology benefits. Research nurses' facilitated the change process by evolving 'clinical nurse implementation specialist' expertise. Health information technology (HIT)-related patient outcomes are mediated through nurses' acting on new information but HIT designed for critical care may not transfer to acute care settings. Exploratory action-feedback approaches can assist nurse managers in assessing and mitigating the real-world effects of HIT implementations. It is strongly recommended that nurse managers identify stakeholders and develop comprehensive plans for monitoring the effects of HIT in their units. © 2011 Blackwell Publishing Ltd.

The Multidimensional Structure of University Absenteeism: An Exploratory Study

ERIC Educational Resources Information Center

López-Bonilla, Jesús Manuel; López-Bonilla, Luis Miguel

2015-01-01

Absenteeism has been a common and very extended problem in university spheres for several years. This problem has become a permanent feature in academic studies in general, yet it has received scarce empirical research attention. This work is focused on the analysis of the factors that determine university absenteeism. It evaluates a series of…

An Exploratory Analysis of the Equity of Ohio School Funding

ERIC Educational Resources Information Center

Sweetland, Scott R.

2014-01-01

This research briefly summarizes a series of Ohio Supreme Court litigation known as "DeRolph v. State" and then measures the equality of expenditures among Ohio school districts. "DeRolph v. State" was a high-profile school finance adequacy case. Nevertheless, the high court continuously expressed concern for the financial…

Federal Programs Supporting Educational Change, Vol. 2: Factors Affecting Change Agent Projects.

ERIC Educational Resources Information Center

Berman, Paul; Pauly, Edward W.

This second volume in the change-agent series reports the interim results of an exploratory statistical analysis of a survey of a nationwide sample of 293 change-agent projects funded by four federal demonstration programs--Elementary Secondary Education Act (ESEA) Title III, Innovative Projects; ESEA Title VII, Bilingual Projects; Vocational…

Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation

ERIC Educational Resources Information Center

Hsieh, Fushing; Ferrer, Emilio; Chen, Shu-Chun; Chow, Sy-Miin

2010-01-01

In this article we present an exploratory tool for extracting systematic patterns from multivariate data. The technique, hierarchical segmentation (HS), can be used to group multivariate time series into segments with similar discrete-state recurrence patterns and it is not restricted by the stationarity assumption. We use a simulation study to…

The Organization of Exploratory Behaviors in Infant Locomotor Planning

ERIC Educational Resources Information Center

Kretch, Kari S.; Adolph, Karen E.

2017-01-01

How do infants plan and guide locomotion under challenging conditions? This experiment investigated the real-time process of visual and haptic exploration in 14-month-old infants as they decided whether and how to walk over challenging terrain--a series of bridges varying in width. Infants' direction of gaze was recorded with a head-mounted eye…

Bivariate autoregressive state-space modeling of psychophysiological time series data.

PubMed

Smith, Daniel M; Abtahi, Mohammadreza; Amiri, Amir Mohammad; Mankodiya, Kunal

2016-08-01

Heart rate (HR) and electrodermal activity (EDA) are often used as physiological measures of psychological arousal in various neuropsychology experiments. In this exploratory study, we analyze HR and EDA data collected from four participants, each with a history of suicidal tendencies, during a cognitive task known as the Paced Auditory Serial Addition Test (PASAT). A central aim of this investigation is to guide future research by assessing heterogeneity in the population of individuals with suicidal tendencies. Using a state-space modeling approach to time series analysis, we evaluate the effect of an exogenous input, i.e., the stimulus presentation rate which was increased systematically during the experimental task. Participants differed in several parameters characterizing the way in which psychological arousal was experienced during the task. Increasing the stimulus presentation rate was associated with an increase in EDA in participants 2 and 4. The effect on HR was positive for participant 2 and negative for participants 3 and 4. We discuss future directions in light of the heterogeneity in the population indicated by these findings.

Argument-Driven Inquiry as a Way to Help Undergraduate Students Write to Learn by Learning to Write in Chemistry

ERIC Educational Resources Information Center

Sampson, Victor; Walker, Joi Phelps

2012-01-01

This exploratory study examined how undergraduate students' ability to write in science changed over time as they completed a series of laboratory activities designed using a new instructional model called argument-driven inquiry. The study was conducted in a single section of an undergraduate general chemistry lab course offered at a large…

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.

Exploratory Bifactor Analysis of the WJ-III Cognitive in Adulthood via the Schmid-Leiman Procedure

ERIC Educational Resources Information Center

Dombrowski, Stefan C.

2014-01-01

The Woodcock-Johnson-III cognitive in the adult time period (age 20 to 90 plus) was analyzed using exploratory bifactor analysis via the Schmid-Leiman orthogonalization procedure. The results of this study suggested possible overfactoring, a different factor structure from that posited in the Technical Manual and a lack of invariance across both…

Forecast models for suicide: Time-series analysis with data from Italy.

PubMed

Preti, Antonio; Lentini, Gianluca

2016-01-01

The prediction of suicidal behavior is a complex task. To fine-tune targeted preventative interventions, predictive analytics (i.e. forecasting future risk of suicide) is more important than exploratory data analysis (pattern recognition, e.g. detection of seasonality in suicide time series). This study sets out to investigate the accuracy of forecasting models of suicide for men and women. A total of 101 499 male suicides and of 39 681 female suicides - occurred in Italy from 1969 to 2003 - were investigated. In order to apply the forecasting model and test its accuracy, the time series were split into a training set (1969 to 1996; 336 months) and a test set (1997 to 2003; 84 months). The main outcome was the accuracy of forecasting models on the monthly number of suicides. These measures of accuracy were used: mean absolute error; root mean squared error; mean absolute percentage error; mean absolute scaled error. In both male and female suicides a change in the trend pattern was observed, with an increase from 1969 onwards to reach a maximum around 1990 and decrease thereafter. The variances attributable to the seasonal and trend components were, respectively, 24% and 64% in male suicides, and 28% and 41% in female ones. Both annual and seasonal historical trends of monthly data contributed to forecast future trends of suicide with a margin of error around 10%. The finding is clearer in male than in female time series of suicide. The main conclusion of the study is that models taking seasonality into account seem to be able to derive information on deviation from the mean when this occurs as a zenith, but they fail to reproduce it when it occurs as a nadir. Preventative efforts should concentrate on the factors that influence the occurrence of increases above the main trend in both seasonal and cyclic patterns of suicides.

Assessment of indoor climate of Mogiła Abbey in Kraków (Poland) and the application of the analogues method to predict microclimate indoor conditions.

PubMed

Frasca, F; Siani, A M; Casale, G R; Pedone, M; Bratasz, Ł; Strojecki, M; Mleczkowska, A

2017-06-01

The microclimatic monitoring of the historic church of Mogiła Abbey (Kraków, Poland) was carried out to study the impact of the environmental parameters on the organic and hygroscopic artworks. Specific indexes were proposed to objectively assess the quality of time series of temperature (T), relative humidity (RH), and carbon dioxide (CO 2 ) before applying the exploratory data analysis. The series were used to define the historic environmental conditions as stated in the European Standard EN 15757:2010 and with the use of the climate evaluation chart (CEC). It was found that the percentage of time in which T and RH values are within the allowable limits of the ASHRAE (2011) Class B is more than 85 %. This means that, for about 15 % of the time, there is a high risk of mechanical damage to highly vulnerable objects mainly due to the RH variability. The environment at the chancel resulted moister than that at the cornice, and the fungal growth is possible. In addition, the time-weighted preservation index (TWPI) is computed to evaluate the life expectancy of the objects, taking into account the environmental conditions of the site under study. The method of analogues, developed to predict the evolution of a system given observations of the past and without the knowledge of any equation among variables, was proposed and applied to the time series of temperature, relative humidity, and carbon dioxide with a 1-h sampling time to avoid the influence of the autocorrelation.

Why Do High School Students Lack Motivation in the Classroom? Toward an Understanding of Academic Amotivation and the Role of Social Support

ERIC Educational Resources Information Center

Legault, Lisa; Green-Demers, Isabelle; Pelletier, Luc

2006-01-01

The present series of studies sought to develop and conceptually validate a taxonomy of reasons that give rise to academic amotivation and to investigate its social antecedents and academic consequences. In Study 1 (N = 351), an exploratory factor analysis offered preliminary support for an academic amotivation taxonomy comprising four dimensions:…

Smoke Signals: Adolescent Smoking and School Continuation. Working Papers Series. SAN06-05

ERIC Educational Resources Information Center

Cook, Philip J.; Hutchinson, Rebecca

2006-01-01

This paper presents an exploratory analysis using NLSY97 data of the relationship between the likelihood of school continuation and the choices of whether to smoke or drink. We demonstrate that in the United States as of the late 1990s, smoking in 11th-grade was a uniquely powerful predictor of whether the student finished high school, and if so…

An exploratory analysis of task-interspersal procedures while teaching object labels to children with autism.

PubMed

Volkert, Valerie M; Lerman, Dorothea C; Trosclair, Nicole; Addison, Laura; Kodak, Tiffany

2008-01-01

Research has demonstrated that interspersing mastered tasks with new tasks facilitates learning under certain conditions; however, little is known about factors that influence the effectiveness of this treatment strategy. The initial purpose of the current investigation was to evaluate the effects of similar versus dissimilar interspersed tasks while teaching object labels to children diagnosed with autism or developmental delays. We then conducted a series of exploratory analyses involving the type of reinforcer delivered for correct responses on trials with unknown or known object labels. Performance was enhanced under the interspersal condition only when either brief praise was delivered for all correct responses or presumably more preferred reinforcers were provided for performance on known trials rather than on unknown trials.

Optimizing Interactive Development of Data-Intensive Applications

PubMed Central

Interlandi, Matteo; Tetali, Sai Deep; Gulzar, Muhammad Ali; Noor, Joseph; Condie, Tyson; Kim, Miryung; Millstein, Todd

2017-01-01

Modern Data-Intensive Scalable Computing (DISC) systems are designed to process data through batch jobs that execute programs (e.g., queries) compiled from a high-level language. These programs are often developed interactively by posing ad-hoc queries over the base data until a desired result is generated. We observe that there can be significant overlap in the structure of these queries used to derive the final program. Yet, each successive execution of a slightly modified query is performed anew, which can significantly increase the development cycle. Vega is an Apache Spark framework that we have implemented for optimizing a series of similar Spark programs, likely originating from a development or exploratory data analysis session. Spark developers (e.g., data scientists) can leverage Vega to significantly reduce the amount of time it takes to re-execute a modified Spark program, reducing the overall time to market for their Big Data applications. PMID:28405637

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

NASA Astrophysics Data System (ADS)

Carranza, Coleen D. U.; van der Ploeg, Martine J.; Torfs, Paul J. J. F.

2018-04-01

Recent advances in radar remote sensing popularized the mapping of surface soil moisture at different spatial scales. Surface soil moisture measurements are used in combination with hydrological models to determine subsurface soil moisture values. However, variability of soil moisture across the soil column is important for estimating depth-integrated values, as decoupling between surface and subsurface can occur. In this study, we employ new methods to investigate the occurrence of (de)coupling between surface and subsurface soil moisture. Using time series datasets, lagged dependence was incorporated in assessing (de)coupling with the idea that surface soil moisture conditions will be reflected at the subsurface after a certain delay. The main approach involves the application of a distributed-lag nonlinear model (DLNM) to simultaneously represent both the functional relation and the lag structure in the time series. The results of an exploratory analysis using residuals from a fitted loess function serve as a posteriori information to determine (de)coupled values. Both methods allow for a range of (de)coupled soil moisture values to be quantified. Results provide new insights into the decoupled range as its occurrence among the sites investigated is not limited to dry conditions.

Comparative Racial Analysis of Enlisted Advancement Exams: Item- Difficulty.

DTIC Science & Technology

1975-07-01

11cm-ana lysis Promotion Racial comparison Equal opportunity 1 20. ABSTRACT (Continue on reveree aide 11 neceeemry mnd Identity by block...improving equal oppor- tunity in career growth for minority groups. The study of exam item- difficulty levels is the first of a series of technical reports...under Exploratory Development Task Area PF55.521.032 (Contemporary Social Issues). J. J. CLARKIN Commanding Officer SUMMARY Purpose A number of

Fuzzy cluster analysis of high-field functional MRI data.

PubMed

Windischberger, Christian; Barth, Markus; Lamm, Claus; Schroeder, Lee; Bauer, Herbert; Gur, Ruben C; Moser, Ewald

2003-11-01

Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver-operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes. We found that differentiation of true "neural" from false "vascular" activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales.

PubMed

Rueckl, Martin; Lenzi, Stephen C; Moreno-Velasquez, Laura; Parthier, Daniel; Schmitz, Dietmar; Ruediger, Sten; Johenning, Friedrich W

2017-01-01

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca 2+ -imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca 2+ imaging datasets, particularly when these have been acquired at different spatial scales.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales

PubMed Central

Rueckl, Martin; Lenzi, Stephen C.; Moreno-Velasquez, Laura; Parthier, Daniel; Schmitz, Dietmar; Ruediger, Sten; Johenning, Friedrich W.

2017-01-01

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca2+-imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca2+ imaging datasets, particularly when these have been acquired at different spatial scales. PMID:28706482

An exploratory data analysis of electroencephalograms using the functional boxplots approach

PubMed Central

Ngo, Duy; Sun, Ying; Genton, Marc G.; Wu, Jennifer; Srinivasan, Ramesh; Cramer, Steven C.; Ombao, Hernando

2015-01-01

Many model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEGs) in order to understand electrical neural data. In this work, we propose to use the functional boxplot (FBP) to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve—which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability, and detects potential outliers. By extending FBPs analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha (8–12 Hz) and beta (16–32 Hz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam. PMID:26347598

An Exploratory Analysis of Task-Interspersal Procedures While Teaching Object Labels to Children with Autism

PubMed Central

Volkert, Valerie M; Lerman, Dorothea C; Trosclair, Nicole; Addison, Laura; Kodak, Tiffany

2008-01-01

Research has demonstrated that interspersing mastered tasks with new tasks facilitates learning under certain conditions; however, little is known about factors that influence the effectiveness of this treatment strategy. The initial purpose of the current investigation was to evaluate the effects of similar versus dissimilar interspersed tasks while teaching object labels to children diagnosed with autism or developmental delays. We then conducted a series of exploratory analyses involving the type of reinforcer delivered for correct responses on trials with unknown or known object labels. Performance was enhanced under the interspersal condition only when either brief praise was delivered for all correct responses or presumably more preferred reinforcers were provided for performance on known trials rather than on unknown trials. PMID:18816973

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

GPS Position Time Series @ JPL

NASA Technical Reports Server (NTRS)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

Young Children's Views of the Technology Process: An Exploratory Study

ERIC Educational Resources Information Center

Milne, Louise; Edwards, Richard

2013-01-01

This paper describes an exploratory study of an aspect of the technological knowledge of two groups of five-year-old students in their first year at school. Their emerging understandings of the steps required to develop a new product were investigated through a series of interviews. A theoretical framework linking technological knowledge to "funds…

Lipopolysaccharide affects exploratory behaviors toward novel objects by impairing cognition and/or motivation in mice: Possible role of activation of the central amygdala.

PubMed

Haba, Ryota; Shintani, Norihito; Onaka, Yusuke; Wang, Hyper; Takenaga, Risa; Hayata, Atsuko; Baba, Akemichi; Hashimoto, Hitoshi

2012-03-17

Lipopolysaccharide (LPS) produces a series of systemic and psychiatric changes called sickness behavior. In the present study, we characterized the LPS-induced decrease in novel object exploratory behaviors in BALB/c mice. As already reported, LPS (0.3-5 μg/mouse) induced dose- and time-dependent decreases in locomotor activity, food intake, social interaction, and exploration for novel objects, and an increase in immobility in the forced-swim test. Although the decrease in locomotor activity was ameliorated by 10h postinjection, novel object exploratory behaviors remained decreased at 24h and were observed even with the lowest dose of LPS. In an object exploration test, LPS shortened object exploration time but did not affect moving time or the frequency of object exploration. Although pre-exposure to the same object markedly decreased the duration of exploration and LPS did not change this reduction, LPS significantly impaired the exploration of a novel object that replaced the familiar one. LPS did not affect anxiety-like behaviors in open-field and elevated plus-maze tests. An LPS-induced increase in the number of c-Fos-immunoreactive cells was observed in several brain regions within 6h of LPS administration, but the number of cells quickly returned to control levels, except in the central amygdala where the increase continued for 24h. These results suggest that LPS most prominently affects object exploratory behaviors by impairing cognition and/or motivation including continuous attention and curiosity toward objects, and that this may be associated with activation of brain nuclei such as the central amygdala. Copyright © 2012 Elsevier B.V. All rights reserved.

Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients.

PubMed

Steimer, Andreas; Müller, Michael; Schindler, Kaspar

2017-05-01

During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not. More specifically, for 5 out of 7 patients who obtained seizure freedom (= Engel class I) our method predicts the specific collection of brain areas that got actually resected during surgery to yield a markedly lower posterior probability for the seizure related clusters, when compared to the resection of random or empty collections. Conversely, for 4 out of 5 Engel class III/IV patients who still suffer from postsurgical seizures, performance of the actually resected collection is not significantly better than performances displayed by random or empty collections. As the number of possible collections ranges into billions and more, this is a substantial contribution to a problem that today is still solved by visual EEG inspection. Apart from epilepsy research, our clustering methodology is also of general interest for the analysis of multivariate time series and as a generative model for temporally evolving functional networks in the neurosciences and beyond. Hum Brain Mapp 38:2509-2531, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Fall in homicides in the City of São Paulo: an exploratory analysis of possible determinants

PubMed Central

Peres, Maria Fernanda Tourinho; de Almeida, Juliana Feliciano; Vicentin, Diego; Cerda, Magdalena; Cardia, Nancy; Adorno, Sérgio

2012-01-01

Throughout the first decade of the 2000s the homicide mortality rate (HMR) showed a significant reduction in the state and the city of São Paulo (MSP). The aim of this study is to describe the trend of HMR, socio-demographic indicators, and the investment in social and public security, and to analyze the correlation between HMR and independent variables in the MSP between 1996 and 2008. An exploratory time series ecological study was conducted. The following variables were included: HMR per 100,000 inhabitants, socio-demographic indicators, and investments in social and public security. The moving-averages for all variables were calculated and trends were analyzed through Simple Linear Regression models. Annual percentage changes, the average annual change and periodic percentage changes were calculated for all variables, and the associations between annual percentage changes were tested by Spearman’s correlation analysis. Correlations were found for the proportion of youth in the population (r = 0.69), unemployment rate (r = 0.60), State budget for education and culture (r = 0.87) and health and sanitation (r = 0.56), municipal (r = 0.68) and State (r = 0.53) budget for Public Security, firearms seized (r = 0.69) and the incarceration rate (r = 0.71). The results allow us to support the hypothesis that demographic changes, acceleration of the economy, in particular the fall in unemployment, investment in social policies and changes in public security policies act synergistically to reduce HMR in São Paulo. Complex models of analysis, incorporating the joint action of different potential explanatory variables, should be developed. PMID:22218669

Evolution of accesses to information on breast cancer and screening on the Brazilian National Cancer Institute website: an exploratory study.

PubMed

Vasconcellos-Silva, Paulo Roberto; Sormunen, Taina; Craftman, Åsa Gransjön

2018-04-01

Delays in diagnosis due to low Breast Cancer awareness are widespread in Brazil maybe owing to ineffective strategies to raise attention on early diagnosis. As a proxy of collective interest in BC screanning (BCS) we studied the monthly accesses to BC and BCS webpages in INCA's website along 48 months. A log analyzer built a time serie (2006-2009) of BC and BCS monthly means, which oscilations were studied by analysis of variance (ANOVA). We found significant increasing accesses to BC and transient "attention peaks". Enlargement in BC/BCS differences along all period were caused by increasing accesses to BC and decreasing/minor/stable oscillations to SBC pages. These results are consistent with previous reports on increasing interest to BC contrasting with indifference on BCS. In the context of an exploratory study, we discussed some aspects: weakness of a "prevention culture"; lack of confidence in health system and screening programs; "celebrity effect" in the context of media framing; collective perception of risks heightened by perception of social vulnerability. Findings suggest that culture-tailored communication strategies would be necessary to inform Brazilian people about BCS. Future research is needed to study social perceptions and constructions on BC topics.

Living Accommodation for Young People. Report of An Exploratory Review.

ERIC Educational Resources Information Center

Allen, Phyllis G.; Miller, A.

The Building Research Station has embarked on a series of case-studies on the provision of living accommodations for single young people in the 15 to 24 age group in England who live away from home because of education, training or employment. An exploratory review of the existing literature on the subject was made. Discussed are some of the…

HIV incidence and CDC's HIV prevention budget: an exploratory correlational analysis.

PubMed

Holtgrave, David R; Kates, Jennifer

2007-01-01

The central evaluative question about a national HIV prevention program is whether that program affects HIV incidence. Numerous factors may influence incidence, including public investment in HIV prevention. Few studies, however, have examined the relationship between public investment and the HIV epidemic in the United States. This 2006 exploratory analysis examined the period from 1978 through 2006 using a quantitative, lagged, correlational analysis to capture the relationship between national HIV incidence and Centers for Disease Control and Prevention's HIV prevention budget in the United States over time. The analyses suggest that early HIV incidence rose in advance of the nation's HIV prevention investment until the mid-1980s (1-year lag correlation, r=0.972, df=2, p <0.05). From that point on, it appears that the nation's investment in HIV prevention became a strong correlate of HIV incidence (1-year lag correlation, r=-0.905, df=18, p <0.05). This exploratory study provides correlational evidence of a relationship between U.S. HIV incidence and the federal HIV prevention budget over time, and calls for further analysis of the role of funding and other factors that may influence the direction of a nation's HIV epidemic.

Calculating system reliability with SRFYDO

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

Morzinski, Jerome; Anderson - Cook, Christine M; Klamann, Richard M

2010-01-01

SRFYDO is a process for estimating reliability of complex systems. Using information from all applicable sources, including full-system (flight) data, component test data, and expert (engineering) judgment, SRFYDO produces reliability estimates and predictions. It is appropriate for series systems with possibly several versions of the system which share some common components. It models reliability as a function of age and up to 2 other lifecycle (usage) covariates. Initial output from its Exploratory Data Analysis mode consists of plots and numerical summaries so that the user can check data entry and model assumptions, and help determine a final form for themore » system model. The System Reliability mode runs a complete reliability calculation using Bayesian methodology. This mode produces results that estimate reliability at the component, sub-system, and system level. The results include estimates of uncertainty, and can predict reliability at some not-too-distant time in the future. This paper presents an overview of the underlying statistical model for the analysis, discusses model assumptions, and demonstrates usage of SRFYDO.« less

Global Processing Speed in Children with Low Reading Ability and in Children and Adults with Typical Reading Ability: Exploratory Factor Analytic Models

ERIC Educational Resources Information Center

Peter, Beate; Matsushita, Mark; Raskind, Wendy H.

2011-01-01

Purpose: To investigate processing speed as a latent dimension in children with dyslexia and children and adults with typical reading skills. Method: Exploratory factor analysis (FA) was based on a sample of multigenerational families, each ascertained through a child with dyslexia. Eleven measures--6 of them timed--represented verbal and…

The Revised Child Anxiety and Depression Scale-Short Version: Scale Reduction via Exploratory Bifactor Modeling of the Broad Anxiety Factor

ERIC Educational Resources Information Center

Ebesutani, Chad; Reise, Steven P.; Chorpita, Bruce F.; Ale, Chelsea; Regan, Jennifer; Young, John; Higa-McMillan, Charmaine; Weisz, John R.

2012-01-01

Using a school-based (N = 1,060) and clinic-referred (N = 303) youth sample, the authors developed a 25-item shortened version of the Revised Child Anxiety and Depression Scale (RCADS) using Schmid-Leiman exploratory bifactor analysis to reduce client burden and administration time and thus improve the transportability characteristics of this…

Inter-subject phase synchronization for exploratory analysis of task-fMRI.

PubMed

Bolt, Taylor; Nomi, Jason S; Vij, Shruti G; Chang, Catie; Uddin, Lucina Q

2018-08-01

Analysis of task-based fMRI data is conventionally carried out using a hypothesis-driven approach, where blood-oxygen-level dependent (BOLD) time courses are correlated with a hypothesized temporal structure. In some experimental designs, this temporal structure can be difficult to define. In other cases, experimenters may wish to take a more exploratory, data-driven approach to detecting task-driven BOLD activity. In this study, we demonstrate the efficiency and power of an inter-subject synchronization approach for exploratory analysis of task-based fMRI data. Combining the tools of instantaneous phase synchronization and independent component analysis, we characterize whole-brain task-driven responses in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this framework to fMRI data collected during performance of a simple motor task and a social cognitive task. Analyses using an inter-subject phase synchronization approach revealed a large number of brain networks that dynamically synchronized to various features of the task, often not predicted by the hypothesized temporal structure of the task. We suggest that this methodological framework, along with readily available tools in the fMRI community, provides a powerful exploratory, data-driven approach for analysis of task-driven BOLD activity. Copyright © 2018 Elsevier Inc. All rights reserved.

Highly comparative time-series analysis: the empirical structure of time series and their methods.

PubMed

Fulcher, Ben D; Little, Max A; Jones, Nick S

2013-06-06

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Highly comparative time-series analysis: the empirical structure of time series and their methods

PubMed Central

Fulcher, Ben D.; Little, Max A.; Jones, Nick S.

2013-01-01

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines. PMID:23554344

Beyond Einstein: Exploring the Extreme Universe

NASA Technical Reports Server (NTRS)

Barbier, Louis M.

2005-01-01

This paper will give an overview of the NASA Universe Division Beyond Einstein program. The Beyond Einstein program consists of a series of exploratory missions to investigate some of the most important and pressing problems in modern-day astrophysics - including searches for Dark Energy and studies of the earliest times in the universe, during the inflationary period after the Big Bang. A variety of new technologies are being developed both in the science instrumentation these missions will carry and in the spacecraft that will carry those instruments.

Argument-Driven Inquiry as a Way to Help Students Learn How to Participate in Scientific Argumentation and Craft Written Arguments: An Exploratory Study

ERIC Educational Resources Information Center

Sampson, Victor; Grooms, Jonathon; Walker, Joi Phelps

2011-01-01

This exploratory study examines how a series of laboratory activities designed using a new instructional model, called Argument-Driven Inquiry (ADI), influences the ways students participate in scientific argumentation and the quality of the scientific arguments they craft as part of this process. The two outcomes of interest were assessed with a…

Interactive and coordinated visualization approaches for biological data analysis.

PubMed

Cruz, António; Arrais, Joel P; Machado, Penousal

2018-03-26

The field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein-protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.

Modeling vertebrate diversity in Oregon using satellite imagery

NASA Astrophysics Data System (ADS)

Cablk, Mary Elizabeth

Vertebrate diversity was modeled for the state of Oregon using a parametric approach to regression tree analysis. This exploratory data analysis effectively modeled the non-linear relationships between vertebrate richness and phenology, terrain, and climate. Phenology was derived from time-series NOAA-AVHRR satellite imagery for the year 1992 using two methods: principal component analysis and derivation of EROS data center greenness metrics. These two measures of spatial and temporal vegetation condition incorporated the critical temporal element in this analysis. The first three principal components were shown to contain spatial and temporal information about the landscape and discriminated phenologically distinct regions in Oregon. Principal components 2 and 3, 6 greenness metrics, elevation, slope, aspect, annual precipitation, and annual seasonal temperature difference were investigated as correlates to amphibians, birds, all vertebrates, reptiles, and mammals. Variation explained for each regression tree by taxa were: amphibians (91%), birds (67%), all vertebrates (66%), reptiles (57%), and mammals (55%). Spatial statistics were used to quantify the pattern of each taxa and assess validity of resulting predictions from regression tree models. Regression tree analysis was relatively robust against spatial autocorrelation in the response data and graphical results indicated models were well fit to the data.

Quadrivalent human papillomavirus vaccination in girls and the risk of autoimmune disorders: the Ontario Grade 8 HPV Vaccine Cohort Study.

PubMed

Liu, Erin Y; Smith, Leah M; Ellis, Anne K; Whitaker, Heather; Law, Barbara; Kwong, Jeffrey C; Farrington, Paddy; Lévesque, Linda E

2018-05-28

Despite demonstrated effectiveness in real-world settings, concerns persist regarding the safety of the quadrivalent human papillomavirus (HPV4) vaccine. We sought to assess the risk of autoimmune disorders following HPV4 vaccination among grade 8 girls eligible for Ontario's school-based HPV vaccination program. We undertook a population-based retrospective cohort study using Ontario's administrative health and vaccination databases from 2007 to 2013. The self-controlled case series method was used to compare the rate of a composite end point of autoimmune disorders diagnosed during days 7-60 post-vaccination ("exposed" follow-up) to that at any other time ("unexposed"). The analysis was repeated to assess the effect of a history of immune-mediated diseases and time since vaccination. We also conducted an exploratory analysis of individual autoimmune disorders. Rate ratios and 95% confidence intervals (CIs) were estimated using conditional Poisson regression, adjusted for age, seasonality, concomitant vaccinations and infections. The study cohort consisted of 290 939 girls aged 12-17 years who were eligible for vaccination between 2007 and 2013. There was no significant risk for developing an autoimmune disorder following HPV4 vaccination ( n = 681; rate ratio 1.12, 95% CI 0.85-1.47), and the association was unchanged by a history of immune-mediated disorders and time since vaccination. Exploratory analyses of individual autoimmune disorders found no significant risks, including for Bell palsy ( n = 65; rate ratio 1.73, 95% CI 0.77-3.89), optic neuritis ( n = 67; rate ratio 1.57, 95% CI 0.74-3.33) and Graves disease ( n = 47; rate ratio 1.55, 95% CI 0.92-2.63). We did not observe an increased risk of autoimmune disorders following HPV4 vaccination among teenaged girls. These findings should reassure parents and health care providers. © 2018 Joule Inc. or its licensors.

Quadrivalent human papillomavirus vaccination in girls and the risk of autoimmune disorders: the Ontario Grade 8 HPV Vaccine Cohort Study

PubMed Central

Liu, Erin Y.; Smith, Leah M.; Ellis, Anne K.; Whitaker, Heather; Law, Barbara; Kwong, Jeffrey C.; Farrington, Paddy

2018-01-01

BACKGROUND: Despite demonstrated effectiveness in real-world settings, concerns persist regarding the safety of the quadrivalent human papillomavirus (HPV4) vaccine. We sought to assess the risk of autoimmune disorders following HPV4 vaccination among grade 8 girls eligible for Ontario’s school-based HPV vaccination program. METHODS: We undertook a population-based retrospective cohort study using Ontario’s administrative health and vaccination databases from 2007 to 2013. The self-controlled case series method was used to compare the rate of a composite end point of autoimmune disorders diagnosed during days 7–60 post-vaccination (“exposed” follow-up) to that at any other time (“unexposed”). The analysis was repeated to assess the effect of a history of immune-mediated diseases and time since vaccination. We also conducted an exploratory analysis of individual autoimmune disorders. Rate ratios and 95% confidence intervals (CIs) were estimated using conditional Poisson regression, adjusted for age, seasonality, concomitant vaccinations and infections. RESULTS: The study cohort consisted of 290 939 girls aged 12–17 years who were eligible for vaccination between 2007 and 2013. There was no significant risk for developing an autoimmune disorder following HPV4 vaccination (n = 681; rate ratio 1.12, 95% CI 0.85–1.47), and the association was unchanged by a history of immune-mediated disorders and time since vaccination. Exploratory analyses of individual autoimmune disorders found no significant risks, including for Bell palsy (n = 65; rate ratio 1.73, 95% CI 0.77–3.89), optic neuritis (n = 67; rate ratio 1.57, 95% CI 0.74–3.33) and Graves disease (n = 47; rate ratio 1.55, 95% CI 0.92–2.63). INTERPRETATION: We did not observe an increased risk of autoimmune disorders following HPV4 vaccination among teenaged girls. These findings should reassure parents and health care providers. PMID:29807937

Exploratory Long-Range Models to Estimate Summer Climate Variability over Southern Africa.

NASA Astrophysics Data System (ADS)

Jury, Mark R.; Mulenga, Henry M.; Mason, Simon J.

1999-07-01

Teleconnection predictors are explored using multivariate regression models in an effort to estimate southern African summer rainfall and climate impacts one season in advance. The preliminary statistical formulations include many variables influenced by the El Niño-Southern Oscillation (ENSO) such as tropical sea surface temperatures (SST) in the Indian and Atlantic Oceans. Atmospheric circulation responses to ENSO include the alternation of tropical zonal winds over Africa and changes in convective activity within oceanic monsoon troughs. Numerous hemispheric-scale datasets are employed to extract predictors and include global indexes (Southern Oscillation index and quasi-biennial oscillation), SST principal component scores for the global oceans, indexes of tropical convection (outgoing longwave radiation), air pressure, and surface and upper winds over the Indian and Atlantic Oceans. Climatic targets include subseasonal, area-averaged rainfall over South Africa and the Zambezi river basin, and South Africa's annual maize yield. Predictors and targets overlap in the years 1971-93, the defined training period. Each target time series is fitted by an optimum group of predictors from the preceding spring, in a linear multivariate formulation. To limit artificial skill, predictors are restricted to three, providing 17 degrees of freedom. Models with colinear predictors are screened out, and persistence of the target time series is considered. The late summer rainfall models achieve a mean r2 fit of 72%, contributed largely through ENSO modulation. Early summer rainfall cross validation correlations are lower (61%). A conceptual understanding of the climate dynamics and ocean-atmosphere coupling processes inherent in the exploratory models is outlined.Seasonal outlooks based on the exploratory models could help mitigate the impacts of southern Africa's fluctuating climate. It is believed that an advance warning of drought risk and seasonal rainfall prospects will improve the economic growth potential of southern Africa and provide additional security for food and water supplies.

Polarisation Measurement with a Dual Beam Interferometer (CATSI). Exploratory Results and Preliminary Phenomenological Analysis

DTIC Science & Technology

2006-06-01

Polarisation measurement with a dual beam interferometer (CATSI) Exploratory results and preliminary phenomenological analysis H. Lavoie J.-M... Polarisation measurement with a dual beam interferometer (CATSI) Exploratory results and preliminary phenomenological analysis H. Lavoie J.-M. Thériault... Polarisation measurement with a dual beam interferometer (CATSI) - Exploratory results and preliminary phenomenological analysis. ECR 2004-372. DRDC Valcartier

Equivalent Dynamic Models.

PubMed

Molenaar, Peter C M

2017-01-01

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

A Brief History of the Philosophical Foundations of Exploratory Factor Analysis.

ERIC Educational Resources Information Center

Mulaik, Stanley A.

1987-01-01

Exploratory factor analysis derives its key ideas from many sources, including Aristotle, Francis Bacon, Descartes, Pearson and Yule, and Kant. The conclusions of exploratory factor analysis are never complete without subsequent confirmatory factor analysis. (Author/GDC)

Generating synthetic wave climates for coastal modelling: a linear mixed modelling approach

NASA Astrophysics Data System (ADS)

Thomas, C.; Lark, R. M.

2013-12-01

Numerical coastline morphological evolution models require wave climate properties to drive morphological change through time. Wave climate properties (typically wave height, period and direction) may be temporally fixed, culled from real wave buoy data, or allowed to vary in some way defined by a Gaussian or other pdf. However, to examine sensitivity of coastline morphologies to wave climate change, it seems desirable to be able to modify wave climate time series from a current to some new state along a trajectory, but in a way consistent with, or initially conditioned by, the properties of existing data, or to generate fully synthetic data sets with realistic time series properties. For example, mean or significant wave height time series may have underlying periodicities, as revealed in numerous analyses of wave data. Our motivation is to develop a simple methodology to generate synthetic wave climate time series that can change in some stochastic way through time. We wish to use such time series in a coastline evolution model to test sensitivities of coastal landforms to changes in wave climate over decadal and centennial scales. We have worked initially on time series of significant wave height, based on data from a Waverider III buoy located off the coast of Yorkshire, England. The statistical framework for the simulation is the linear mixed model. The target variable, perhaps after transformation (Box-Cox), is modelled as a multivariate Gaussian, the mean modelled as a function of a fixed effect, and two random components, one of which is independently and identically distributed (iid) and the second of which is temporally correlated. The model was fitted to the data by likelihood methods. We considered the option of a periodic mean, the period either fixed (e.g. at 12 months) or estimated from the data. We considered two possible correlation structures for the second random effect. In one the correlation decays exponentially with time. In the second (spherical) model, it cuts off at a temporal range. Having fitted the model, multiple realisations were generated; the random effects were simulated by specifying a covariance matrix for the simulated values, with the estimated parameters. The Cholesky factorisation of the covariance matrix was computed and realizations of the random component of the model generated by pre-multiplying a vector of iid standard Gaussian variables by the lower triangular factor. The resulting random variate was added to the mean value computed from the fixed effects, and the result back-transformed to the original scale of the measurement. Realistic simulations result from approach described above. Background exploratory data analysis was undertaken on 20-day sets of 30-minute buoy data, selected from days 5-24 of months January, April, July, October, 2011, to elucidate daily to weekly variations, and to keep numerical analysis tractable computationally. Work remains to be undertaken to develop suitable models for synthetic directional data. We suggest that the general principles of the method will have applications in other geomorphological modelling endeavours requiring time series of stochastically variable environmental parameters.

Analysis of Nonstationary Time Series for Biological Rhythms Research.

PubMed

Leise, Tanya L

2017-06-01

This article is part of a Journal of Biological Rhythms series exploring analysis and statistics topics relevant to researchers in biological rhythms and sleep research. The goal is to provide an overview of the most common issues that arise in the analysis and interpretation of data in these fields. In this article on time series analysis for biological rhythms, we describe some methods for assessing the rhythmic properties of time series, including tests of whether a time series is indeed rhythmic. Because biological rhythms can exhibit significant fluctuations in their period, phase, and amplitude, their analysis may require methods appropriate for nonstationary time series, such as wavelet transforms, which can measure how these rhythmic parameters change over time. We illustrate these methods using simulated and real time series.

Effects of beta-adrenergic antagonist, propranolol on spatial memory and exploratory behavior in mice.

PubMed

Sun, Huaying; Mao, Yu; Wang, Jianhong; Ma, Yuanye

2011-07-08

The beta-adrenergic system has been suggested to be involved in novelty detection and memory modulation. The present study aimed to investigate the role of beta-adrenergic receptors on novelty-based spatial recognition memory and exploratory behavior in mice using Y-maze test and open-field respectively. Mice were injected with three doses of beta-adrenergic receptor antagonist, propranolol (2, 10 and 20 mg/kg) or saline at three different time points (15 min prior to training, immediately after training and 15 min before test). The results showed that higher doses of propranolol (10 and 20 mg/kg) given before the training trial impaired spatial recognition memory while those injected at other two time points did not. A detailed analysis of exploratory behavior in open-field showed that lower dose (2 mg/kg) of propranolol reduced exploratory behavior of mice. Our findings indicate that higher dose of propranolol can impair acquisition of spatial information in the Y-maze without altering locomotion, suggesting that the beta-adrenergic system may be involved in modulating memory processes at the time of learning. Copyright © 2011. Published by Elsevier Ireland Ltd.

Videomicroscopic extraction of specific information on cell proliferation and migration in vitro

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

Debeir, Olivier; Megalizzi, Veronique; Warzee, Nadine

2008-10-01

In vitro cell imaging is a useful exploratory tool for cell behavior monitoring with a wide range of applications in cell biology and pharmacology. Combined with appropriate image analysis techniques, this approach has been shown to provide useful information on the detection and dynamic analysis of cell events. In this context, numerous efforts have been focused on cell migration analysis. In contrast, the cell division process has been the subject of fewer investigations. The present work focuses on this latter aspect and shows that, in complement to cell migration data, interesting information related to cell division can be extracted frommore » phase-contrast time-lapse image series, in particular cell division duration, which is not provided by standard cell assays using endpoint analyses. We illustrate our approach by analyzing the effects induced by two sigma-1 receptor ligands (haloperidol and 4-IBP) on the behavior of two glioma cell lines using two in vitro cell models, i.e., the low-density individual cell model and the high-density scratch wound model. This illustration also shows that the data provided by our approach are suggestive as to the mechanism of action of compounds, and are thus capable of informing the appropriate selection of further time-consuming and more expensive biological evaluations required to elucidate a mechanism.« less

Simulation of electricity demand in a remote island for optimal planning of a hybrid renewable energy system

NASA Astrophysics Data System (ADS)

Koskinas, Aristotelis; Zacharopoulou, Eleni; Pouliasis, George; Engonopoulos, Ioannis; Mavroyeoryos, Konstantinos; Deligiannis, Ilias; Karakatsanis, Georgios; Dimitriadis, Panayiotis; Iliopoulou, Theano; Koutsoyiannis, Demetris; Tyralis, Hristos

2017-04-01

We simulate the electrical energy demand in the remote island of Astypalaia. To this end we first obtain information regarding the local socioeconomic conditions and energy demand. Secondly, the available hourly demand data are analysed at various time scales (hourly, weekly, daily, seasonal). The cross-correlations between the electrical energy demand and the mean daily temperature as well as other climatic variables for the same time period are computed. Also, we investigate the cross-correlation between those climatic variables and other variables related to renewable energy resources from numerous observations around the globe in order to assess the impact of each one to a hybrid renewable energy system. An exploratory data analysis including all variables is performed with the purpose to find hidden relationships. Finally, the demand is simulated considering all the periodicities found in the analysis. The simulation time series will be used in the development of a framework for planning of a hybrid renewable energy system in Astypalaia. Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

EEG gamma coherence and other correlates of subjective reports during ayahuasca experiences.

PubMed

Stuckey, David E; Lawson, Robert; Luna, Luis Eduardo

2005-06-01

The current study examined QEEG power and coherence of ayahuasca experiences with two experienced participants in a Brazilian jungle setting. An exploratory case series design was adopted for naturalistic field research. EEGs recorded during visual imagery was compared to eyes-closed baselines. The most important findings were increases in global EEG coherence in the 36-44 Hz and 50-64 Hz frequency bands for both subjects. Widely distributed cortical hyper-coherence seems reasonable given the intense synesthesia during ayahuasca experiences. Other findings include increased modal EEG alpha frequency and global power decreases across the cortex in most frequency bands, which concur with the EEG of psychedelics literature. Exploratory analysis revealed the usefulness of analyzing single Hz bins over the standard wide-band analysis. The discovery-oriented naturalistic approach developed for this study resulted in potentially important findings. We believe that finding increases in global gamma coherence during peak psychedelic experiences might contribute to the discussion of binding theory. Also, in light of recent research with gamma coherence during advanced meditative conditions, our findings might further the comparison of shamanic psychedelic practices with meditation.

Non-negative Tensor Factorization for Robust Exploratory Big-Data Analytics

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

Alexandrov, Boian; Vesselinov, Velimir Valentinov; Djidjev, Hristo Nikolov

Currently, large multidimensional datasets are being accumulated in almost every field. Data are: (1) collected by distributed sensor networks in real-time all over the globe, (2) produced by large-scale experimental measurements or engineering activities, (3) generated by high-performance simulations, and (4) gathered by electronic communications and socialnetwork activities, etc. Simultaneous analysis of these ultra-large heterogeneous multidimensional datasets is often critical for scientific discoveries, decision-making, emergency response, and national and global security. The importance of such analyses mandates the development of the next-generation of robust machine learning (ML) methods and tools for bigdata exploratory analysis.

Assessing Change and Variability in First Flowering Dates: An Initial Look at Rescued Legacy Data from North Dakota and Kansas

NASA Astrophysics Data System (ADS)

Travers, S.; Henebry, G. M.

2010-12-01

Phenological data were collected by the occasional diligent observer in the USA over the past 100 years. Many of those data languish virtually forgotten in archive boxes, filing cabinets, or even articles in regional journals. With the recent establishment of the USA National Phenology Network, there has been a resurgence of interest in phenological observation and analysis. Here we present an exploratory analysis of five phenological datasets, three of which were recently rediscovered at the Kansas State University Herbarium and rescued into the digital age. The Hitchcock data covers first flowering dates (FFDs) of many species in vicinity of Manhattan, KS, for 1893-1898. The Crevecour dataset has FFDs in Onaga, KS, during two periods: 1910-1916 and 1920-1927. The Gates dataset has FFDs is also focused in Manhattan, KS, covering the period 1926-1955. The North Dakota data were collected by Stevens from 1910-1961 (Travers & Dunnell 2009), but he also published a series of articles in American Midland Naturalist that describe phenological data from Blue Rapids and Manhattan, KS, from 1904-1909. We contrast the Kansas FFD patterns with those in North Dakota; these locations fall roughly along the same meridian of longitude, but separated by more than 7 degrees of latitude. We also examine the time series of FFDs in relation to major climate modes.

Factors Affecting Female Teachers' Attitudes toward Help-Seeking or Help-Avoidance in Coping with Behavioral Problems

ERIC Educational Resources Information Center

Inbar-Furst, Hagit; Gumpel, Thomas P.

2015-01-01

Questionnaires were given to 392 elementary school teachers to examine help-seeking or help-avoidance in dealing with classroom behavioral problems. Scale validity was examined through a series of exploratory and confirmatory factor analyses. Using a series of multivariate regression analyses and structural equation modeling, we identified…

Lasers. Technology Learning Activity. Teacher Edition. Technology Education Series.

ERIC Educational Resources Information Center

Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This document contains the materials required for presenting an 8-day competency-based technology learning activity (TLA) designed to introduce students in grades 6-10 to advances and career opportunities in the field of laser technology. The guide uses a series of hands-on exploratory experiences into which activities to help students develop…

Reading Behaviors of Students in Kolej Datin Seri Endon (KDSE)

ERIC Educational Resources Information Center

Mohamed, Mohini; Rahman, Roshanida A.; Tin, Lee Chew; Hashim, Haslenda; Maarof, Hasmerya; Nasir, Noor Sharliana Mat; Zailani, Siti Nazrah; Esivan, Siti Marsilawati Mohamed; Jumari, Nur Fazirah

2012-01-01

Purpose: This is an exploratory study of reading behaviors and interest among students residing in a female residential college of Kolej Datin Seri Endon (KDSE), Universiti Teknologi Malaysia (UTM) and the use of reading stations (RS) placed at strategic locations throughout the main campus. The UTM's Vice Chancellor project of developing various…

An Exploratory Study to Analyze New Skill Content in Selected Occupations in Michigan and the Mechanism for its Translation into Vocational Education Curricula: Section Report on Automobile Engine Mechanics.

ERIC Educational Resources Information Center

Battelle Memorial Inst., Columbus, OH. Columbus Labs.

The report is one of a series which supplements the overall report "Exploratory Study to Analyze New Skill Content in Selected Occupations in Michigan and the Mechanism for its Translation into Vocational Education Curricula". The present report presents detailed task analyses and results of employers skill requirements for auto mechanics. The…

A Review of CEFA Software: Comprehensive Exploratory Factor Analysis Program

ERIC Educational Resources Information Center

Lee, Soon-Mook

2010-01-01

CEFA 3.02(Browne, Cudeck, Tateneni, & Mels, 2008) is a factor analysis computer program designed to perform exploratory factor analysis. It provides the main properties that are needed for exploratory factor analysis, namely a variety of factoring methods employing eight different discrepancy functions to be minimized to yield initial…

Exploratory Mediation Analysis via Regularization

PubMed Central

Serang, Sarfaraz; Jacobucci, Ross; Brimhall, Kim C.; Grimm, Kevin J.

2017-01-01

Exploratory mediation analysis refers to a class of methods used to identify a set of potential mediators of a process of interest. Despite its exploratory nature, conventional approaches are rooted in confirmatory traditions, and as such have limitations in exploratory contexts. We propose a two-stage approach called exploratory mediation analysis via regularization (XMed) to better address these concerns. We demonstrate that this approach is able to correctly identify mediators more often than conventional approaches and that its estimates are unbiased. Finally, this approach is illustrated through an empirical example examining the relationship between college acceptance and enrollment. PMID:29225454

[GABA-NO interaction in the N. Accumbens during danger-induced inhibition of exploratory behavior].

PubMed

Saul'skaia, N V; Terekhova, E A

2013-01-01

In Sprague-Dawley rats by means of in vivo microdialysis combined with HPLC analysis, it was shown that presentation to rats during exploratory activity of a tone previously pared with footshock inhibited the exploration and prevented the exploration-induced increase in extracellular levels of citrulline (an NO co-product) in the medial n. accumbens. Intra-accumbal infusions of 20 μM bicuculline, a GABA(A)-receptor antagonist, firstly, partially restored the exploration-induced increase of extracellular citrulline levels in this brain area, which was inhibited by presentation of the tone, previously paired with foot-shock and, secondly, prevented the inhibition of exploratory behavior produced by this sound signal of danger. The data obtained indicate for the first time that signals of danger inhibit exploratory behavior and exploration-induced activation of the accumbal nitrergic system via GABA(A)-receptor mechanisms.

Differences between Experts' and Students' Conceptual Images of the Mathematical Structure of Taylor Series Convergence

ERIC Educational Resources Information Center

Martin, Jason

2013-01-01

Taylor series convergence is a complicated mathematical structure which incorporates multiple concepts. Therefore, it can be very difficult for students to initially comprehend. How might students make sense of this structure? How might experts make sense of this structure? To answer these questions, an exploratory study was conducted using…

Duality between Time Series and Networks

PubMed Central

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

Introduction and application of the multiscale coefficient of variation analysis.

PubMed

Abney, Drew H; Kello, Christopher T; Balasubramaniam, Ramesh

2017-10-01

Quantifying how patterns of behavior relate across multiple levels of measurement typically requires long time series for reliable parameter estimation. We describe a novel analysis that estimates patterns of variability across multiple scales of analysis suitable for time series of short duration. The multiscale coefficient of variation (MSCV) measures the distance between local coefficient of variation estimates within particular time windows and the overall coefficient of variation across all time samples. We first describe the MSCV analysis and provide an example analytical protocol with corresponding MATLAB implementation and code. Next, we present a simulation study testing the new analysis using time series generated by ARFIMA models that span white noise, short-term and long-term correlations. The MSCV analysis was observed to be sensitive to specific parameters of ARFIMA models varying in the type of temporal structure and time series length. We then apply the MSCV analysis to short time series of speech phrases and musical themes to show commonalities in multiscale structure. The simulation and application studies provide evidence that the MSCV analysis can discriminate between time series varying in multiscale structure and length.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Monitoring stem cells in phase contrast imaging

NASA Astrophysics Data System (ADS)

Lam, K. P.; Dempsey, K. P.; Collins, D. J.; Richardson, J. B.

2016-04-01

Understanding the mechanisms behind the proliferation of Mesenchymal Stem cells (MSCs) can offer a greater insight into the behaviour of these cells throughout their life cycles. Traditional methods of determining the rate of MSC differentiation rely on population based studies over an extended time period. However, such methods can be inadequate as they are unable to track cells as they interact; for example, in autologous cell therapies for osteoarthritis, the development of biological assays that could predict in vivo functional activity and biological action are particularly challenging. Here further research is required to determine non-histochemical biomarkers which provide correlations between cell survival and predictive functional outcome. This paper proposes using a (previously developed) advanced texture-based analysis algorithm to facilitate in vitro cells tracking using time-lapsed microscopy. The technique was adopted to monitor stem cells in the context of unlabelled, phase contrast imaging, with the goal of examining the cell to cell interactions in both monoculture and co-culture systems. The results obtained are analysed using established exploratory procedures developed for time series data and compared with the typical fluorescent-based approach of cell labelling. A review of the progress and the lessons learned are also presented.

Association mining of dependency between time series

NASA Astrophysics Data System (ADS)

Hafez, Alaaeldin

2001-03-01

Time series analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Time series data is a sequence of observations collected over intervals of time. Each time series describes a phenomenon as a function of time. Analysis on time series data includes discovering trends (or patterns) in a time series sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In this paper, we adapt and innovate data mining techniques to analyze time series data. By using data mining techniques, maximal frequent patterns are discovered and used in predicting future sequences or trends, where trends describe the behavior of a sequence. In order to include different types of time series (e.g. irregular and non- systematic), we consider past frequent patterns of the same time sequences (local patterns) and of other dependent time sequences (global patterns). We use the word 'dependent' instead of the word 'similar' for emphasis on real life time series where two time series sequences could be completely different (in values, shapes, etc.), but they still react to the same conditions in a dependent way. In this paper, we propose the Dependence Mining Technique that could be used in predicting time series sequences. The proposed technique consists of three phases: (a) for all time series sequences, generate their trend sequences, (b) discover maximal frequent trend patterns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future time series sequences.

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Time averaging, ageing and delay analysis of financial time series

NASA Astrophysics Data System (ADS)

Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf

2017-06-01

We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.

Visibility Graph Based Time Series Analysis.

PubMed

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

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.…

Spectral analysis for GNSS coordinate time series using chirp Fourier transform

NASA Astrophysics Data System (ADS)

Feng, Shengtao; Bo, Wanju; Ma, Qingzun; Wang, Zifan

2017-12-01

Spectral analysis for global navigation satellite system (GNSS) coordinate time series provides a principal tool to understand the intrinsic mechanism that affects tectonic movements. Spectral analysis methods such as the fast Fourier transform, Lomb-Scargle spectrum, evolutionary power spectrum, wavelet power spectrum, etc. are used to find periodic characteristics in time series. Among spectral analysis methods, the chirp Fourier transform (CFT) with less stringent requirements is tested with synthetic and actual GNSS coordinate time series, which proves the accuracy and efficiency of the method. With the length of series only limited to even numbers, CFT provides a convenient tool for windowed spectral analysis. The results of ideal synthetic data prove CFT accurate and efficient, while the results of actual data show that CFT is usable to derive periodic information from GNSS coordinate time series.

The Measurand Framework: Scaling Exploratory Data Analysis

NASA Astrophysics Data System (ADS)

Schneider, D.; MacLean, L. S.; Kappler, K. N.; Bleier, T.

2017-12-01

Since 2005 QuakeFinder (QF) has acquired a unique dataset with outstanding spatial and temporal sampling of earth's time varying magnetic field along several active fault systems. This QF network consists of 124 stations in California and 45 stations along fault zones in Greece, Taiwan, Peru, Chile and Indonesia. Each station is equipped with three feedback induction magnetometers, two ion sensors, a 4 Hz geophone, a temperature sensor, and a humidity sensor. Data are continuously recorded at 50 Hz with GPS timing and transmitted daily to the QF data center in California for analysis. QF is attempting to detect and characterize anomalous EM activity occurring ahead of earthquakes. In order to analyze this sizable dataset, QF has developed an analytical framework to support processing the time series input data and hypothesis testing to evaluate the statistical significance of potential precursory signals. The framework was developed with a need to support legacy, in-house processing but with an eye towards big-data processing with Apache Spark and other modern big data technologies. In this presentation, we describe our framework, which supports rapid experimentation and iteration of candidate signal processing techniques via modular data transformation stages, tracking of provenance, and automatic re-computation of downstream data when upstream data is updated. Furthermore, we discuss how the processing modules can be ported to big data platforms like Apache Spark and demonstrate a migration path from local, in-house processing to cloud-friendly processing.

Non-parametric characterization of long-term rainfall time series

NASA Astrophysics Data System (ADS)

Tiwari, Harinarayan; Pandey, Brij Kishor

2018-03-01

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

Relationship-Enhancing Communication Skills in Prime-Time Family-Oriented Situation Comedies.

ERIC Educational Resources Information Center

Aust, Charles F.

Television situation comedies have been criticized for their portrayal of dysfunctional family behavior. An exploratory content analysis study assessed the extent of relationship-enhancing communication skills in family-oriented, prime-time situation comedies, a genre frequently targeted for both scorn and praise. Three episodes each of five shows…

The value of instructional communication in crisis situations: restoring order to chaos.

PubMed

Sellnow, Timothy L; Sellnow, Deanna D; Lane, Derek R; Littlefield, Robert S

2012-04-01

This article explores the nature of instructional communication in responding to crisis situations. Through the lens of chaos theory, the relevance of instructional messages in restoring order is established. This perspective is further advanced through an explanation of how various learning styles impact the receptivity of various instructional messages during the acute phase of crises. We then summarize an exploratory study focusing on the relationship between learning styles and the demands of instructional messages in crisis situations. We conclude the article with a series of conclusions and implications. © 2011 Society for Risk Analysis.

hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction.

PubMed

Fulcher, Ben D; Jones, Nick S

2017-11-22

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

A novel water quality data analysis framework based on time-series data mining.

PubMed

Deng, Weihui; Wang, Guoyin

2017-07-01

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance.

PubMed

Sacha, Dominik; Kraus, Matthias; Bernard, Jurgen; Behrisch, Michael; Schreck, Tobias; Asano, Yuki; Keim, Daniel A

2018-01-01

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

More on Time Series Designs: A Reanalysis of Mayer and Kozlow's Data.

ERIC Educational Resources Information Center

Willson, Victor L.

1982-01-01

Differentiating between time-series design and time-series analysis, examines design considerations and reanalyzes data previously reported by Mayer and Kozlow in this journal. The current analysis supports the analysis performed by Mayer and Kozlow but puts the results on a somewhat firmer statistical footing. (Author/JN)

A time-series approach to dynamical systems from classical and quantum worlds

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

Fossion, Ruben

2014-01-08

This contribution discusses some recent applications of time-series analysis in Random Matrix Theory (RMT), and applications of RMT in the statistial analysis of eigenspectra of correlation matrices of multivariate time series.

Assessment of time-series MODIS data for cropland mapping in the U.S. central Great Plains

NASA Astrophysics Data System (ADS)

Masialeti, Iwake

This study had three general objectives. First, to explore ways of creating and refining a reference data set when reference data set is unobtainable. Second, extend work previously done in Kansas by Wardlow et al. (2007) to Nebraska, several exploratory approaches were used to further investigate the potential of MODIS NDVI 250-m data in agricultural-related land cover research other parts of the Great Plains. The objective of this part of the research was to evaluate the applicability of time-series MODIS 250-m NDVI data for crop-type discrimination by spectrally characterizing and discriminating major crop types in Nebraska using the reference data set collected and refined under research performed for the first objective. Third, conduct an initial investigation into whether time-series NDVI response curves for crops over a growing season for one year could be used to classify crops for a different year. In this case, time-series NDVI response curves for 2001 and 2005 were investigated to ascertain whether or not the 2001 data set could be used to classify crops for 2005. GIS operations, and reference data refinement using clustering and visual assessment of each crop's NDVI cluster profiles in Nebraska, demonstrated that it is possible to devise an alternative reference data set and refinement plan that redresses the unexpected loss of training and validation data. The analysis enabled the identification and removal of crop pattern outliers and sites atypical of crop phenology under consideration, and after editing, a total of 1,288 field sites remained, which were used as a reference data set for classification of Nebraska crop types. A pixel-level analysis of the time-series MODIS 250-m NDVI for 1,288 field sites representing each of the eight cover types under investigation across Nebraska found that each crop type had a distinctive MODIS 250-m NDVI profile corresponding to the crop calendar. A visual and statistical comparison of the average NDVI profiles showed that the crop types were separable at different times of the growing season based on their phenology-driven spectral-temporal differences. Winter wheat and alfalfa, winter wheat and summer crops, and alfalfa and summer crops were clearly separable. Specific summer crop types were not easily distinguishable from each other due to their similar crop calendars. Their greatest separability however occurred during the initial spring green up and/or senescence plant growth phases. In Kansas, an initial investigation revealed that there was near-complete agreement between the winter wheat crop profiles but that there were some minor differences in the crop profiles for alfalfa and summer crops between 2001 and 2005. However, the profiles of summer crops---corn, grain sorghum, and soybeans---displayed a shift to the right by at least 1 composite date, indicative of possible late crop planting and emergence. Alfalfa and summer crops, seem to suggest that time series NDVI response curves for crops over a growing period for one year of valid ground reference data may not be used to map crops for a different year without taking into account the climatic and/or environmental conditions of each year.

Cause-effect relations between 55 kD soluble TNF receptor concentrations and specific and unspecific symptoms in a patient with mild SLE disease activity: an exploratory time series analysis study.

PubMed

Schubert, Christian; Haberkorn, Julia; Ocaña-Peinado, Francisco M; König, Paul; Sepp, Norbert; Schnapka-Köpf, Mirjam; Fuchs, Dietmar

2015-09-21

This integrative single-case study investigated the 12 h-to-12 h cause-effect relations between 55 kD soluble tumor necrosis factor receptor type 1 (sTNF-R55) and specific and unspecific symptoms in a 52-year-old Caucasian woman with mild systemic lupus erythematosus (SLE) disease activity. The patient collected her entire urine for 56 days in 12 h-intervals to determine sTNF-R55/creatinine and protein/creatinine levels (ELISA, HPLC). Additionally, twice a day, she took notes on oral ulceration and facial rash; answered questionnaires (VAS) on fatigue, weakness, and joint pain; and measured body temperature orally. Time series analysis consisted of ARIMA modeling and cross-correlational analyses (significance level = p < 0.05). Time series analysis revealed both a circadian and a circasemiseptan rhythm in the urinary sTNF-R55 data. Moreover, several significant lagged correlations between urinary sTNF-R55 concentrations and SLE symptoms in both directions of effect were identified. Specifically, increased urinary sTNF-R55 concentrations preceded decreased urinary protein levels by 36-48 h (r = -0.213) and, in the opposite direction of effect, increased protein levels preceded increased sTNF-R55 concentrations by 24-36 h (r = +0.202). In addition, increased urinary sTNF-R55 levels preceded increased oral ulcers by 36-48 h (r = +0.277) and, conversely, increased oral ulceration preceded decreased sTNF-R55 levels by 36-48 h (r = -0.313). Moreover, increased urinary sTNF-R55 levels preceded decreased facial rash by 36-48 h (r = -0.223) and followed increased body temperature after 36-48 h (r = +0.209). Weakness, fatigue and joint pain were not significantly correlated with urinary sTNF-R55 levels. This study gathered first evidence of real-life, long-term feedback loops between cytokines and SLE symptoms in mild SLE disease activity. Such insights into the potential role of sTNF-R55 in SLE would not have been possible had we applied a pre-post design group study. These findings require replication before firm conclusions can be drawn.

CADDIS Volume 4. Data Analysis: Exploratory Data Analysis

EPA Pesticide Factsheets

Intro to exploratory data analysis. Overview of variable distributions, scatter plots, correlation analysis, GIS datasets. Use of conditional probability to examine stressor levels and impairment. Exploring correlations among multiple stressors.

The method of trend analysis of parameters time series of gas-turbine engine state

NASA Astrophysics Data System (ADS)

Hvozdeva, I.; Myrhorod, V.; Derenh, Y.

2017-10-01

This research substantiates an approach to interval estimation of time series trend component. The well-known methods of spectral and trend analysis are used for multidimensional data arrays. The interval estimation of trend component is proposed for the time series whose autocorrelation matrix possesses a prevailing eigenvalue. The properties of time series autocorrelation matrix are identified.

Visibility Graph Based Time Series Analysis

PubMed Central

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it’s microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks. PMID:26571115

Nonlinear Dynamics, Poor Data, and What to Make of Them?

NASA Astrophysics Data System (ADS)

Ghil, M.; Zaliapin, I. V.

2005-12-01

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict variability in the geosciences. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this talk we will describe the connections between time series analysis and nonlinear dynamics, discuss signal-to-noise enhancement, and present some of the novel methods for spectral analysis. These fall into two broad categories: (i) methods that try to ferret out regularities of the time series; and (ii) methods aimed at describing the characteristics of irregular processes. The former include singular-spectrum analysis (SSA), the multi-taper method (MTM), and the maximum-entropy method (MEM). The various steps, as well as the advantages and disadvantages of these methods, will be illustrated by their application to several important climatic time series, such as the Southern Oscillation Index (SOI), paleoclimatic time series, and instrumental temperature time series. The SOI index captures major features of interannual climate variability and is used extensively in its prediction. The other time series cover interdecadal and millennial time scales. The second category includes the calculation of fractional dimension, leading Lyapunov exponents, and Hurst exponents. More recently, multi-trend analysis (MTA), binary-decomposition analysis (BDA), and related methods have attempted to describe the structure of time series that include both regular and irregular components. Within the time available, I will try to give a feeling for how these methods work, and how well.

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.

Regenerating time series from ordinal networks.

PubMed

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Regenerating time series from ordinal networks

NASA Astrophysics Data System (ADS)

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

FuryExplorer: visual-interactive exploration of horse motion capture data

NASA Astrophysics Data System (ADS)

Wilhelm, Nils; Vögele, Anna; Zsoldos, Rebeka; Licka, Theresia; Krüger, Björn; Bernard, Jürgen

2015-01-01

The analysis of equine motion has a long tradition in the past of mankind. Equine biomechanics aims at detecting characteristics of horses indicative of good performance. Especially, veterinary medicine gait analysis plays an important role in diagnostics and in the emerging research of long-term effects of athletic exercises. More recently, the incorporation of motion capture technology contributed to an easier and faster analysis, with a trend from mere observation of horses towards the analysis of multivariate time-oriented data. However, due to the novelty of this topic being raised within an interdisciplinary context, there is yet a lack of visual-interactive interfaces to facilitate time series data analysis and information discourse for the veterinary and biomechanics communities. In this design study, we bring visual analytics technology into the respective domains, which, to our best knowledge, was never approached before. Based on requirements developed in the domain characterization phase, we present a visual-interactive system for the exploration of horse motion data. The system provides multiple views which enable domain experts to explore frequent poses and motions, but also to drill down to interesting subsets, possibly containing unexpected patterns. We show the applicability of the system in two exploratory use cases, one on the comparison of different gait motions, and one on the analysis of lameness recovery. Finally, we present the results of a summative user study conducted in the environment of the domain experts. The overall outcome was a significant improvement in effectiveness and efficiency in the analytical workflow of the domain experts.

Clustering Financial Time Series by Network Community Analysis

NASA Astrophysics Data System (ADS)

Piccardi, Carlo; Calatroni, Lisa; Bertoni, Fabio

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

Time series analysis of InSAR data: Methods and trends

NASA Astrophysics Data System (ADS)

Osmanoğlu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cabral-Cano, Enrique

2016-05-01

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ;unwrapping; of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

Time Series Analysis of Insar Data: Methods and Trends

NASA Technical Reports Server (NTRS)

Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique

2015-01-01

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

Pedagogy and the PC: Trends in the AIS Curriculum

ERIC Educational Resources Information Center

Badua, Frank

2008-01-01

The author investigated the array of course topics in accounting information systems (AIS), as course syllabi embody. The author (a) used exploratory data analysis to determine the topics that AIS courses most frequently offered and (b) used descriptive statistics and econometric analysis to trace the diversity of course topics through time,…

Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

NASA Astrophysics Data System (ADS)

Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

2013-02-01

Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

Multifractal analysis of the Korean agricultural market

NASA Astrophysics Data System (ADS)

Kim, Hongseok; Oh, Gabjin; Kim, Seunghwan

2011-11-01

We have studied the long-term memory effects of the Korean agricultural market using the detrended fluctuation analysis (DFA) method. In general, the return time series of various financial data, including stock indices, foreign exchange rates, and commodity prices, are uncorrelated in time, while the volatility time series are strongly correlated. However, we found that the return time series of Korean agricultural commodity prices are anti-correlated in time, while the volatility time series are correlated. The n-point correlations of time series were also examined, and it was found that a multifractal structure exists in Korean agricultural market prices.

The Reliability and Validity of Zimbardo Time Perspective Inventory Scores in Academically Talented Adolescents

ERIC Educational Resources Information Center

Worrell, Frank C.; Mello, Zena R.

2007-01-01

In this study, the authors examined the reliability, structural validity, and concurrent validity of Zimbardo Time Perspective Inventory (ZTPI) scores in a group of 815 academically talented adolescents. Reliability estimates of the purported factors' scores were in the low to moderate range. Exploratory factor analysis supported a five-factor…

Multiple Indicator Stationary Time Series Models.

ERIC Educational Resources Information Center

Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

Interrupted Time Series Versus Statistical Process Control in Quality Improvement Projects.

PubMed

Andersson Hagiwara, Magnus; Andersson Gäre, Boel; Elg, Mattias

2016-01-01

To measure the effect of quality improvement interventions, it is appropriate to use analysis methods that measure data over time. Examples of such methods include statistical process control analysis and interrupted time series with segmented regression analysis. This article compares the use of statistical process control analysis and interrupted time series with segmented regression analysis for evaluating the longitudinal effects of quality improvement interventions, using an example study on an evaluation of a computerized decision support system.

Emergence of Exploratory, Technical and Tactical Behavior in Small-Sided Soccer Games when Manipulating the Number of Teammates and Opponents.

PubMed

Torrents, Carlota; Ric, Angel; Hristovski, Robert; Torres-Ronda, Lorena; Vicente, Emili; Sampaio, Jaime

2016-01-01

The effects that different constraints have on the exploratory behavior, measured by the variety and quantity of different responses within a game situation, is of the utmost importance for successful performance in team sports. The aim of this study was to determine how the number of teammates and opponents affects the exploratory behavior of both professional and amateur players in small-sided soccer games. Twenty-two professional (age 25.6 ± 4.9 years) and 22 amateur (age 23.1 ± 0.7 years) male soccer players played three small-sided game formats (4 vs. 3, 4 vs. 5, and 4 vs. 7). These trials were video-recorded and a systematic observation instrument was used to notate the actions, which were subsequently analyzed by means of a principal component analysis and the dynamic overlap order parameter (measure to identify the rate and breadth of exploratory behavior on different time scales). Results revealed that a higher the number of opponents required for more frequent ball controls. Moreover, with a higher number of teammates, there were more defensive actions focused on protecting the goal, with more players balancing. In relation to attack, an increase in the number of opponents produced a decrease in passing, driving and controlling actions, while an increase in the number of teammates led to more time being spent in attacking situations. A numerical advantage led to less exploratory behavior, an effect that was especially clear when playing within a team of seven players against four opponents. All teams showed strong effects of the number of teammates on the exploratory behavior when comparing 5 vs 7 or 3 vs 7 teammates. These results seem to be independent of the players' level.

In Situ Distribution Guided Analysis and Visualization of Transonic Jet Engine Simulations.

PubMed

Dutta, Soumya; Chen, Chun-Ming; Heinlein, Gregory; Shen, Han-Wei; Chen, Jen-Ping

2017-01-01

Study of flow instability in turbine engine compressors is crucial to understand the inception and evolution of engine stall. Aerodynamics experts have been working on detecting the early signs of stall in order to devise novel stall suppression technologies. A state-of-the-art Navier-Stokes based, time-accurate computational fluid dynamics simulator, TURBO, has been developed in NASA to enhance the understanding of flow phenomena undergoing rotating stall. Despite the proven high modeling accuracy of TURBO, the excessive simulation data prohibits post-hoc analysis in both storage and I/O time. To address these issues and allow the expert to perform scalable stall analysis, we have designed an in situ distribution guided stall analysis technique. Our method summarizes statistics of important properties of the simulation data in situ using a probabilistic data modeling scheme. This data summarization enables statistical anomaly detection for flow instability in post analysis, which reveals the spatiotemporal trends of rotating stall for the expert to conceive new hypotheses. Furthermore, the verification of the hypotheses and exploratory visualization using the summarized data are realized using probabilistic visualization techniques such as uncertain isocontouring. Positive feedback from the domain scientist has indicated the efficacy of our system in exploratory stall analysis.

Fighting Dark Networks: Using Social Network Analysis to Implement the Special Operations Targeting Process for Direct and Indirect Approaches

DTIC Science & Technology

2013-03-01

Wouter De Nooy, Andrej Mrvar and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek, (New York: Cambridge University Press, 2005), 5...Granovetter, “The Strength of Weak Ties,” 1350–1368. 151 de Nooy, Mrvar , and Batagelj , Exploratory Social Network Analysis with Pajek, 151. 152...Spacetime Wrinkles Exhibit (1995). de Nooy, Wouter, Andrej Mrvar , and Vladimir Batagelj . Exploratory Social Network Analysis with Pajek. Cambridge

A general framework for time series data mining based on event analysis: application to the medical domains of electroencephalography and stabilometry.

PubMed

Lara, Juan A; Lizcano, David; Pérez, Aurora; Valente, Juan P

2014-10-01

There are now domains where information is recorded over a period of time, leading to sequences of data known as time series. In many domains, like medicine, time series analysis requires to focus on certain regions of interest, known as events, rather than analyzing the whole time series. In this paper, we propose a framework for knowledge discovery in both one-dimensional and multidimensional time series containing events. We show how our approach can be used to classify medical time series by means of a process that identifies events in time series, generates time series reference models of representative events and compares two time series by analyzing the events they have in common. We have applied our framework on time series generated in the areas of electroencephalography (EEG) and stabilometry. Framework performance was evaluated in terms of classification accuracy, and the results confirmed that the proposed schema has potential for classifying EEG and stabilometric signals. The proposed framework is useful for discovering knowledge from medical time series containing events, such as stabilometric and electroencephalographic time series. These results would be equally applicable to other medical domains generating iconographic time series, such as, for example, electrocardiography (ECG). Copyright © 2014 Elsevier Inc. All rights reserved.

Time-series modeling of long-term weight self-monitoring data.

PubMed

Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka

2015-08-01

Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

A General Procedure to Assess the Internal Structure of a Noncognitive Measure--The Student360 Insight Program (S360) Time Management Scale. Research Report. ETS RR-11-42

ERIC Educational Resources Information Center

Ling, Guangming; Rijmen, Frank

2011-01-01

The factorial structure of the Time Management (TM) scale of the Student 360: Insight Program (S360) was evaluated based on a national sample. A general procedure with a variety of methods was introduced and implemented, including the computation of descriptive statistics, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA).…

SoccerStories: a kick-off for visual soccer analysis.

PubMed

Perin, Charles; Vuillemot, Romain; Fekete, Jean-Daniel

2013-12-01

This article presents SoccerStories, a visualization interface to support analysts in exploring soccer data and communicating interesting insights. Currently, most analyses on such data relate to statistics on individual players or teams. However, soccer analysts we collaborated with consider that quantitative analysis alone does not convey the right picture of the game, as context, player positions and phases of player actions are the most relevant aspects. We designed SoccerStories to support the current practice of soccer analysts and to enrich it, both in the analysis and communication stages. Our system provides an overview+detail interface of game phases, and their aggregation into a series of connected visualizations, each visualization being tailored for actions such as a series of passes or a goal attempt. To evaluate our tool, we ran two qualitative user studies on recent games using SoccerStories with data from one of the world's leading live sports data providers. The first study resulted in a series of four articles on soccer tactics, by a tactics analyst, who said he would not have been able to write these otherwise. The second study consisted in an exploratory follow-up to investigate design alternatives for embedding soccer phases into word-sized graphics. For both experiments, we received a very enthusiastic feedback and participants consider further use of SoccerStories to enhance their current workflow.

Differences in Spatio-Temporal Behavior of Zebrafish in the Open Tank Paradigm after a Short-Period Confinement into Dark and Bright Environments

PubMed Central

Rosemberg, Denis B.; Rico, Eduardo P.; Mussulini, Ben Hur M.; Piato, Ângelo L.; Calcagnotto, Maria E.; Bonan, Carla D.; Dias, Renato D.; Blaser, Rachel E.; Souza, Diogo O.; de Oliveira, Diogo L.

2011-01-01

The open tank paradigm, also known as novel tank diving test, is a protocol used to evaluate the zebrafish behavior. Several characteristics have been described for this species, including scototaxis, which is the natural preference for dark environments in detriment of bright ones. However, there is no evidence regarding the influence of “natural stimuli” in zebrafish subjected to novelty-based paradigms. In this report, we evaluated the spatio-temporal exploratory activity of the short-fin zebrafish phenotype in the open tank after a short-period confinement into dark/bright environments. A total of 44 animals were individually confined during a 10-min single session into one of three environments: black-painted, white-painted, and transparent cylinders (dark, bright, and transparent groups). Fish were further subjected to the novel tank test and their exploratory profile was recorded during a 15-min trial. The results demonstrated that zebrafish increased their vertical exploratory activity during the first 6-min, where the bright group spent more time and travelled a higher distance in the top area. Interestingly, all behavioral parameters measured for the dark group were similar to the transparent one. These data were confirmed by automated analysis of track and occupancy plots and also demonstrated that zebrafish display a classical homebase formation in the bottom area of the tank. A detailed spatio-temporal study of zebrafish exploratory behavior and the construction of representative ethograms showed that the experimental groups presented significant differences in the first 3-min vs. last 3-min of test. Although the main factors involved in these behavioral responses still remain ambiguous and require further investigation, the current report describes an alternative methodological approach for assessing the zebrafish behavior after a forced exposure to different environments. Additionally, the analysis of ethologically-relevant patterns across time could be a potential phenotyping tool to evaluate the zebrafish exploratory profile in the open tank task. PMID:21559304

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

PubMed

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Hidden Process Models

DTIC Science & Technology

2009-12-18

cannot be detected with univariate techniques, but require multivariate analysis instead (Kamitani and Tong [2005]). Two other time series analysis ...learning for time series analysis . The historical record of DBNs can be traced back to Dean and Kanazawa [1988] and Dean and Wellman [1991], with...Rev. 8-98) Prescribed by ANSI Std Z39-18 Keywords: Hidden Process Models, probabilistic time series modeling, functional Magnetic Resonance Imaging

Use of Time-Series, ARIMA Designs to Assess Program Efficacy.

ERIC Educational Resources Information Center

Braden, Jeffery P.; And Others

1990-01-01

Illustrates use of time-series designs for determining efficacy of interventions with fictitious data describing drug-abuse prevention program. Discusses problems and procedures associated with time-series data analysis using Auto Regressive Integrated Moving Averages (ARIMA) models. Example illustrates application of ARIMA analysis for…

The "Chaos Theory" and nonlinear dynamics in heart rate variability analysis: does it work in short-time series in patients with coronary heart disease?

PubMed

Krstacic, Goran; Krstacic, Antonija; Smalcelj, Anton; Milicic, Davor; Jembrek-Gostovic, Mirjana

2007-04-01

Dynamic analysis techniques may quantify abnormalities in heart rate variability (HRV) based on nonlinear and fractal analysis (chaos theory). The article emphasizes clinical and prognostic significance of dynamic changes in short-time series applied on patients with coronary heart disease (CHD) during the exercise electrocardiograph (ECG) test. The subjects were included in the series after complete cardiovascular diagnostic data. Series of R-R and ST-T intervals were obtained from exercise ECG data after sampling digitally. The range rescaled analysis method determined the fractal dimension of the intervals. To quantify fractal long-range correlation's properties of heart rate variability, the detrended fluctuation analysis technique was used. Approximate entropy (ApEn) was applied to quantify the regularity and complexity of time series, as well as unpredictability of fluctuations in time series. It was found that the short-term fractal scaling exponent (alpha(1)) is significantly lower in patients with CHD (0.93 +/- 0.07 vs 1.09 +/- 0.04; P < 0.001). The patients with CHD had higher fractal dimension in each exercise test program separately, as well as in exercise program at all. ApEn was significant lower in CHD group in both RR and ST-T ECG intervals (P < 0.001). The nonlinear dynamic methods could have clinical and prognostic applicability also in short-time ECG series. Dynamic analysis based on chaos theory during the exercise ECG test point out the multifractal time series in CHD patients who loss normal fractal characteristics and regularity in HRV. Nonlinear analysis technique may complement traditional ECG analysis.

Process fault detection and nonlinear time series analysis for anomaly detection in safeguards

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

Burr, T.L.; Mullen, M.F.; Wangen, L.E.

In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect lossmore » of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values.« less

Collaborative Research with Chinese, Indian, Filipino and North European Research Organizations on Infectious Disease Epidemics.

PubMed

Sumi, Ayako; Kobayashi, Nobumichi

2017-01-01

In this report, we present a short review of applications of time series analysis, which consists of spectral analysis based on the maximum entropy method in the frequency domain and the least squares method in the time domain, to the incidence data of infectious diseases. This report consists of three parts. First, we present our results obtained by collaborative research on infectious disease epidemics with Chinese, Indian, Filipino and North European research organizations. Second, we present the results obtained with the Japanese infectious disease surveillance data and the time series numerically generated from a mathematical model, called the susceptible/exposed/infectious/recovered (SEIR) model. Third, we present an application of the time series analysis to pathologic tissues to examine the usefulness of time series analysis for investigating the spatial pattern of pathologic tissue. It is anticipated that time series analysis will become a useful tool for investigating not only infectious disease surveillance data but also immunological and genetic tests.

The analysis of verbal interaction sequences in dyadic clinical communication: a review of methods.

PubMed

Connor, Martin; Fletcher, Ian; Salmon, Peter

2009-05-01

To identify methods available for sequential analysis of dyadic verbal clinical communication and to review their methodological and conceptual differences. Critical review, based on literature describing sequential analyses of clinical and other relevant social interaction. Dominant approaches are based on analysis of communication according to its precise position in the series of utterances that constitute event-coded dialogue. For practical reasons, methods focus on very short-term processes, typically the influence of one party's speech on what the other says next. Studies of longer-term influences are rare. Some analyses have statistical limitations, particularly in disregarding heterogeneity between consultations, patients or practitioners. Additional techniques, including ones that can use information about timing and duration of speech from interval-coding are becoming available. There is a danger that constraints of commonly used methods shape research questions and divert researchers from potentially important communication processes including ones that operate over a longer-term than one or two speech turns. Given that no one method can model the complexity of clinical communication, multiple methods, both quantitative and qualitative, are necessary. Broadening the range of methods will allow the current emphasis on exploratory studies to be balanced by tests of hypotheses about clinically important communication processes.

Quantitative analysis of NMR spectra with chemometrics

NASA Astrophysics Data System (ADS)

Winning, H.; Larsen, F. H.; Bro, R.; Engelsen, S. B.

2008-01-01

The number of applications of chemometrics to series of NMR spectra is rapidly increasing due to an emerging interest for quantitative NMR spectroscopy e.g. in the pharmaceutical and food industries. This paper gives an analysis of advantages and limitations of applying the two most common chemometric procedures, Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR), to a designed set of 231 simple alcohol mixture (propanol, butanol and pentanol) 1H 400 MHz spectra. The study clearly demonstrates that the major advantage of chemometrics is the visualisation of larger data structures which adds a new exploratory dimension to NMR research. While robustness and powerful data visualisation and exploration are the main qualities of the PCA method, the study demonstrates that the bilinear MCR method is an even more powerful method for resolving pure component NMR spectra from mixtures when certain conditions are met.

Lipophilicity of oils and fats estimated by TLC.

PubMed

Naşcu-Briciu, Rodica D; Sârbu, Costel

2013-04-01

A representative series of natural toxins belonging to alkaloids and mycotoxins classes was investigated by TLC on classical chemically bonded plates and also on oils- and fats-impregnated plates. Their lipophilicity indices are employed in the characterization and comparison of oils and fats. The retention results allowed an accurate indirect estimation of oils and fats lipophilicity. The investigated fats and oils near classical chemically bonded phases are classified and compared by means of multivariate exploratory techniques, such as cluster analysis, principal component analysis, or fuzzy-principal component analysis. Additionally, a concrete hierarchy of oils and fats derived from the observed lipophilic character is suggested. Human fat seems to be very similar to animal fats, but also possess RP-18, RP-18W, and RP-8. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Modeling of Engine Parameters for Condition-Based Maintenance of the MTU Series 2000 Diesel Engine

DTIC Science & Technology

2016-09-01

are suitable. To model the behavior of the engine, an autoregressive distributed lag (ARDL) time series model of engine speed and exhaust gas... time series model of engine speed and exhaust gas temperature is derived. The lag length for ARDL is determined by whitening of residuals using the...15 B. REGRESSION ANALYSIS ....................................................................15 1. Time Series Analysis

Integrating High-Dimensional Transcriptomics and Image Analysis Tools into Early Safety Screening: Proof of Concept for a New Early Drug Development Strategy.

PubMed

Verbist, Bie M P; Verheyen, Geert R; Vervoort, Liesbet; Crabbe, Marjolein; Beerens, Dominiek; Bosmans, Cindy; Jaensch, Steffen; Osselaer, Steven; Talloen, Willem; Van den Wyngaert, Ilse; Van Hecke, Geert; Wuyts, Dirk; Van Goethem, Freddy; Göhlmann, Hinrich W H

2015-10-19

During drug discovery and development, the early identification of adverse effects is expected to reduce costly late-stage failures of candidate drugs. As risk/safety assessment takes place rather late during the development process and due to the limited ability of animal models to predict the human situation, modern unbiased high-dimensional biology readouts are sought, such as molecular signatures predictive for in vivo response using high-throughput cell-based assays. In this theoretical proof of concept, we provide findings of an in-depth exploration of a single chemical core structure. Via transcriptional profiling, we identified a subset of close analogues that commonly downregulate multiple tubulin genes across cellular contexts, suggesting possible spindle poison effects. Confirmation via a qualified toxicity assay (in vitro micronucleus test) and the identification of a characteristic aggregate-formation phenotype via exploratory high-content imaging validated the initial findings. SAR analysis triggered the synthesis of a new set of compounds and allowed us to extend the series showing the genotoxic effect. We demonstrate the potential to flag toxicity issues by utilizing data from exploratory experiments that are typically generated for target evaluation purposes during early drug discovery. We share our thoughts on how this approach may be incorporated into drug development strategies.

A Study on Predictive Analytics Application to Ship Machinery Maintenance

DTIC Science & Technology

2013-09-01

Looking at the nature of the time series forecasting method , it would be better applied to offline analysis . The application for real- time online...other system attributes in future. Two techniques of statistical analysis , mainly time series models and cumulative sum control charts, are discussed in...statistical tool employed for the two techniques of statistical analysis . Both time series forecasting as well as CUSUM control charts are shown to be

A non linear analysis of human gait time series based on multifractal analysis and cross correlations

NASA Astrophysics Data System (ADS)

Muñoz-Diosdado, A.

2005-01-01

We analyzed databases with gait time series of adults and persons with Parkinson, Huntington and amyotrophic lateral sclerosis (ALS) diseases. We obtained the staircase graphs of accumulated events that can be bounded by a straight line whose slope can be used to distinguish between gait time series from healthy and ill persons. The global Hurst exponent of these series do not show tendencies, we intend that this is because some gait time series have monofractal behavior and others have multifractal behavior so they cannot be characterized with a single Hurst exponent. We calculated the multifractal spectra, obtained the spectra width and found that the spectra of the healthy young persons are almost monofractal. The spectra of ill persons are wider than the spectra of healthy persons. In opposition to the interbeat time series where the pathology implies loss of multifractality, in the gait time series the multifractal behavior emerges with the pathology. Data were collected from healthy and ill subjects as they walked in a roughly circular path and they have sensors in both feet, so we have one time series for the left foot and other for the right foot. First, we analyzed these time series separately, and then we compared both results, with direct comparison and with a cross correlation analysis. We tried to find differences in both time series that can be used as indicators of equilibrium problems.

Multiscale multifractal time irreversibility analysis of stock markets

NASA Astrophysics Data System (ADS)

Jiang, Chenguang; Shang, Pengjian; Shi, Wenbin

2016-11-01

Time irreversibility is one of the most important properties of nonstationary time series. Complex time series often demonstrate even multiscale time irreversibility, such that not only the original but also coarse-grained time series are asymmetric over a wide range of scales. We study the multiscale time irreversibility of time series. In this paper, we develop a method called multiscale multifractal time irreversibility analysis (MMRA), which allows us to extend the description of time irreversibility to include the dependence on the segment size and statistical moments. We test the effectiveness of MMRA in detecting multifractality and time irreversibility of time series generated from delayed Henon map and binomial multifractal model. Then we employ our method to the time irreversibility analysis of stock markets in different regions. We find that the emerging market has higher multifractality degree and time irreversibility compared with developed markets. In this sense, the MMRA method may provide new angles in assessing the evolution stage of stock markets.

Phase walk analysis of leptokurtic time series.

PubMed

Schreiber, Korbinian; Modest, Heike I; Räth, Christoph

2018-06-01

The Fourier phase information play a key role for the quantified description of nonlinear data. We present a novel tool for time series analysis that identifies nonlinearities by sensitively detecting correlations among the Fourier phases. The method, being called phase walk analysis, is based on well established measures from random walk analysis, which are now applied to the unwrapped Fourier phases of time series. We provide an analytical description of its functionality and demonstrate its capabilities on systematically controlled leptokurtic noise. Hereby, we investigate the properties of leptokurtic time series and their influence on the Fourier phases of time series. The phase walk analysis is applied to measured and simulated intermittent time series, whose probability density distribution is approximated by power laws. We use the day-to-day returns of the Dow-Jones industrial average, a synthetic time series with tailored nonlinearities mimicing the power law behavior of the Dow-Jones and the acceleration of the wind at an Atlantic offshore site. Testing for nonlinearities by means of surrogates shows that the new method yields strong significances for nonlinear behavior. Due to the drastically decreased computing time as compared to embedding space methods, the number of surrogate realizations can be increased by orders of magnitude. Thereby, the probability distribution of the test statistics can very accurately be derived and parameterized, which allows for much more precise tests on nonlinearities.

Longitudinal development of object permanence in mentally retarded children: an exploratory study.

PubMed

Wohlhueter, M J; Sindberg, R M

1975-03-01

Monthly testing on a series of Piaget object tasks was carried out on 1- to 6-year-old profoundly, severly, and moderately retarded children. Forty nine subjects were followed for 1 to 1.5 years or to criterion; 18 subjects were followed for shorter periods. Three general patterns occurred among the noncriterion subjects with approximately equeal frequency: (a) little or no change, (b) marked variability, and (c) relatively steady upward change from month to month. Criterion and upward subjects skipped certain substages about one-half of the time. Degree of retardation, CA, and diagnostic concomitants of these observations were discussed.

Fluctuations in Cerebral Hemodynamics

DTIC Science & Technology

2003-12-01

Determination of scaling properties Detrended Fluctuations Analysis (see (28) and references therein) is commonly used to determine scaling...pressure (averaged over a cardiac beat) of a healthy subject. First 1000 values of the time series are shown. (b) Detrended fluctuation analysis (DFA...1000 values of the time series are shown. (b) Detrended fluctuation analysis of the time series shown in (a). Fig . 3 Side-by-side boxplot for the

Gridded Surface Subsurface Hydrologic Analysis Modeling for Analysis of Flood Design Features at the Picayune Strand Restoration Project

DTIC Science & Technology

2016-08-01

the POI. ............................................................... 17  Figure 9. Discharge time series for the Miller pump system...2. In C2, the Miller Canal pump system was implicitly simulated by a time series of outflows assigned to model cells. This flow time series was...representative of how the pump system would operate during the storm events simulated in this work (USACE 2004). The outflow time series for the Miller

Caregiver’s feeding styles questionnaire - new factors and correlates

USDA-ARS?s Scientific Manuscript database

Study objectives were to conduct exploratory factor analysis (EFA) of Caregiver’s Feeding Styles Questionnaire (CFSQ) and evaluate correlations between factors and maternal feeding practices, attitudes, and perceptions. Mothers (N = 144) were 43% minority race/ethnicity, 24% full-time employed, 54% ...

Random forest meteorological normalisation models for Swiss PM10 trend analysis

NASA Astrophysics Data System (ADS)

Grange, Stuart K.; Carslaw, David C.; Lewis, Alastair C.; Boleti, Eirini; Hueglin, Christoph

2018-05-01

Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM10 trends ranged between -0.09 and -1.16 µg m-3 yr-1 with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at -0.77 µg m-3 yr-1. Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.

Extending nonlinear analysis to short ecological time series.

PubMed

Hsieh, Chih-hao; Anderson, Christian; Sugihara, George

2008-01-01

Nonlinearity is important and ubiquitous in ecology. Though detectable in principle, nonlinear behavior is often difficult to characterize, analyze, and incorporate mechanistically into models of ecosystem function. One obvious reason is that quantitative nonlinear analysis tools are data intensive (require long time series), and time series in ecology are generally short. Here we demonstrate a useful method that circumvents data limitation and reduces sampling error by combining ecologically similar multispecies time series into one long time series. With this technique, individual ecological time series containing as few as 20 data points can be mined for such important information as (1) significantly improved forecast ability, (2) the presence and location of nonlinearity, and (3) the effective dimensionality (the number of relevant variables) of an ecological system.

Empirical Identification of Hierarchies.

ERIC Educational Resources Information Center

McCormick, Douglas; And Others

Outlining a cluster procedure which maximizes specific criteria while building scales from binary measures using a sequential, agglomerative, overlapping, non-hierarchic method results in indices giving truer results than exploratory facotr analyses or multidimensional scaling. In a series of eleven figures, patterns within cluster histories…

Exploratory Model Analysis of the Space Based Infrared System (SBIRS) Low Global Scheduler Problem

DTIC Science & Technology

1999-12-01

solution. The non- linear least squares model is defined as Y = f{e,t) where: 0 =M-element parameter vector Y =N-element vector of all data t...NAVAL POSTGRADUATE SCHOOL Monterey, California THESIS EXPLORATORY MODEL ANALYSIS OF THE SPACE BASED INFRARED SYSTEM (SBIRS) LOW GLOBAL SCHEDULER...December 1999 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE EXPLORATORY MODEL ANALYSIS OF THE SPACE BASED INFRARED SYSTEM

Coupling detrended fluctuation analysis for analyzing coupled nonstationary signals.

PubMed

Hedayatifar, L; Vahabi, M; Jafari, G R

2011-08-01

When many variables are coupled to each other, a single case study could not give us thorough and precise information. When these time series are stationary, different methods of random matrix analysis and complex networks can be used. But, in nonstationary cases, the multifractal-detrended-cross-correlation-analysis (MF-DXA) method was introduced for just two coupled time series. In this article, we have extended the MF-DXA to the method of coupling detrended fluctuation analysis (CDFA) for the case when more than two series are correlated to each other. Here, we have calculated the multifractal properties of the coupled time series, and by comparing CDFA results of the original series with those of the shuffled and surrogate series, we can estimate the source of multifractality and the extent to which our series are coupled to each other. We illustrate the method by selected examples from air pollution and foreign exchange rates.

Coupling detrended fluctuation analysis for analyzing coupled nonstationary signals

NASA Astrophysics Data System (ADS)

Hedayatifar, L.; Vahabi, M.; Jafari, G. R.

2011-08-01

When many variables are coupled to each other, a single case study could not give us thorough and precise information. When these time series are stationary, different methods of random matrix analysis and complex networks can be used. But, in nonstationary cases, the multifractal-detrended-cross-correlation-analysis (MF-DXA) method was introduced for just two coupled time series. In this article, we have extended the MF-DXA to the method of coupling detrended fluctuation analysis (CDFA) for the case when more than two series are correlated to each other. Here, we have calculated the multifractal properties of the coupled time series, and by comparing CDFA results of the original series with those of the shuffled and surrogate series, we can estimate the source of multifractality and the extent to which our series are coupled to each other. We illustrate the method by selected examples from air pollution and foreign exchange rates.

Interactive Digital Signal Processor

NASA Technical Reports Server (NTRS)

Mish, W. H.

1985-01-01

Interactive Digital Signal Processor, IDSP, consists of set of time series analysis "operators" based on various algorithms commonly used for digital signal analysis. Processing of digital signal time series to extract information usually achieved by applications of number of fairly standard operations. IDSP excellent teaching tool for demonstrating application for time series operators to artificially generated signals.

G14A-06- Analysis of the DORIS, GNSS, SLR, VLBI and Gravimetric Time Series at the GGOS Core Sites

NASA Technical Reports Server (NTRS)

Moreaux, G.; Lemoine, F.; Luceri, V.; Pavlis, E.; MacMillan, D.; Bonvalot, S.; Saunier, J.

2017-01-01

Analysis of the time series at the 3-4 multi-technique GGOS sites to analyze and compare the spectral content of the space geodetic and gravity time series. Evaluate the level of agreement between the space geodesy measurements and the physical tie vectors.

Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space

PubMed Central

Zuo, Xi-Nian; Xu, Ting; Jiang, Lili; Yang, Zhi; Cao, Xiao-Yan; He, Yong; Zang, Yu-Feng; Castellanos, F. Xavier; Milham, Michael P.

2013-01-01

While researchers have extensively characterized functional connectivity between brain regions, the characterization of functional homogeneity within a region of the brain connectome is in early stages of development. Several functional homogeneity measures were proposed previously, among which regional homogeneity (ReHo) was most widely used as a measure to characterize functional homogeneity of resting state fMRI (R-fMRI) signals within a small region (Zang et al., 2004). Despite a burgeoning literature on ReHo in the field of neuroimaging brain disorders, its test–retest (TRT) reliability remains unestablished. Using two sets of public R-fMRI TRT data, we systematically evaluated the ReHo’s TRT reliability and further investigated the various factors influencing its reliability and found: 1) nuisance (head motion, white matter, and cerebrospinal fluid) correction of R-fMRI time series can significantly improve the TRT reliability of ReHo while additional removal of global brain signal reduces its reliability, 2) spatial smoothing of R-fMRI time series artificially enhances ReHo intensity and influences its reliability, 3) surface-based R-fMRI computation largely improves the TRT reliability of ReHo, 4) a scan duration of 5 min can achieve reliable estimates of ReHo, and 5) fast sampling rates of R-fMRI dramatically increase the reliability of ReHo. Inspired by these findings and seeking a highly reliable approach to exploratory analysis of the human functional connectome, we established an R-fMRI pipeline to conduct ReHo computations in both 3-dimensions (volume) and 2-dimensions (surface). PMID:23085497

Transformation-cost time-series method for analyzing irregularly sampled data

NASA Astrophysics Data System (ADS)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G. Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations—with associated costs—to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

Transformation-cost time-series method for analyzing irregularly sampled data.

PubMed

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

Providing web-based tools for time series access and analysis

NASA Astrophysics Data System (ADS)

Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane

2014-05-01

Time series information is widely used in environmental change analyses and is also an essential information for stakeholders and governmental agencies. However, a challenging issue is the processing of raw data and the execution of time series analysis. In most cases, data has to be found, downloaded, processed and even converted in the correct data format prior to executing time series analysis tools. Data has to be prepared to use it in different existing software packages. Several packages like TIMESAT (Jönnson & Eklundh, 2004) for phenological studies, BFAST (Verbesselt et al., 2010) for breakpoint detection, and GreenBrown (Forkel et al., 2013) for trend calculations are provided as open-source software and can be executed from the command line. This is needed if data pre-processing and time series analysis is being automated. To bring both parts, automated data access and data analysis, together, a web-based system was developed to provide access to satellite based time series data and access to above mentioned analysis tools. Users of the web portal are able to specify a point or a polygon and an available dataset (e.g., Vegetation Indices and Land Surface Temperature datasets from NASA MODIS). The data is then being processed and provided as a time series CSV file. Afterwards the user can select an analysis tool that is being executed on the server. The final data (CSV, plot images, GeoTIFFs) is visualized in the web portal and can be downloaded for further usage. As a first use case, we built up a complimentary web-based system with NASA MODIS products for Germany and parts of Siberia based on the Earth Observation Monitor (www.earth-observation-monitor.net). The aim of this work is to make time series analysis with existing tools as easy as possible that users can focus on the interpretation of the results. References: Jönnson, P. and L. Eklundh (2004). TIMESAT - a program for analysing time-series of satellite sensor data. Computers and Geosciences 30, 833-845. Verbesselt, J., R. Hyndman, G. Newnham and D. Culvenor (2010). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106-115. DOI: 10.1016/j.rse.2009.08.014 Forkel, M., N. Carvalhais, J. Verbesselt, M. Mahecha, C. Neigh and M. Reichstein (2013). Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology. Remote Sensing 5, 2113-2144.

The Social Networks of Small Arms Proliferation: Mapping an Aviation Enabled Supply Chain

DTIC Science & Technology

2007-12-01

each of the discrete arms 248 Wouter de Nooy, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social...303 Wouter de Nooy, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek, 101. 304 Ibid., 21. 93 entity. The data...305 Wouter de Nooy, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek, 101. 306 Linton C. Freeman, "Graphical

Exploratory Analysis of Supply Chains in the Defense Industrial Base

DTIC Science & Technology

2012-04-01

Instruments Industry Group 382: Laboratory Apparatus and Analytical, Optical, Measuring, and Controlling Instruments 3821 Laboratory Apparatus and Furniture ...I N S T I T U T E F O R D E F E N S E A N A LY S E S Exploratory Analysis of Supply Chains in the Defense Industrial Base James R. Dominy...contract DASW01-04-C-0003, AH-7-3315, “Exploratory Analysis of Supply Chains in the Defense Industrial Base,” for the Director, Industrial Policy. The

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

NASA Astrophysics Data System (ADS)

Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

2018-05-01

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

EMC: Air Quality Forecast Home page

Science.gov Websites

archive NAM Verification Meteorology Error Time Series EMC NAM Spatial Maps Real Time Mesoscale Analysis Precipitation verification NAQFC VERIFICATION CMAQ Ozone & PM Error Time Series AOD Error Time Series HYSPLIT Smoke forecasts vs GASP satellite Dust and Smoke Error Time Series HYSPLIT WCOSS Upgrade (July

Measuring Parent Time Scarcity and Fatigue as Barriers to Meal Planning and Preparation: Quantitative Scale Development

ERIC Educational Resources Information Center

Storfer-Isser, Amy; Musher-Eizenman, Dara

2013-01-01

Objective: To examine the psychometric properties of 9 quantitative items that assess time scarcity and fatigue as parent barriers to planning and preparing meals for their children. Methods: A convenience sample of 342 parents of children aged 2-6 years completed a 20-minute online survey. Exploratory factor analysis was used to examine the…

[Local fractal analysis of noise-like time series by all permutations method for 1-115 min periods].

PubMed

Panchelyuga, V A; Panchelyuga, M S

2015-01-01

Results of local fractal analysis of 329-per-day time series of 239Pu alpha-decay rate fluctuations by means of all permutations method (APM) are presented. The APM-analysis reveals in the time series some steady frequency set. The coincidence of the frequency set with the Earth natural oscillations was demonstrated. A short review of works by different authors who analyzed the time series of fluctuations in processes of different nature is given. We have shown that the periods observed in those works correspond to the periods revealed in our study. It points to a common mechanism of the phenomenon observed.

Automated Bayesian model development for frequency detection in biological time series.

PubMed

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.

Automated Bayesian model development for frequency detection in biological time series

PubMed Central

2011-01-01

Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure. PMID:21702910

Analysis of Time-Series Quasi-Experiments. Final Report.

ERIC Educational Resources Information Center

Glass, Gene V.; Maguire, Thomas O.

The objective of this project was to investigate the adequacy of statistical models developed by G. E. P. Box and G. C. Tiao for the analysis of time-series quasi-experiments: (1) The basic model developed by Box and Tiao is applied to actual time-series experiment data from two separate experiments, one in psychology and one in educational…

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

PubMed

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

PAPST, a User Friendly and Powerful Java Platform for ChIP-Seq Peak Co-Localization Analysis and Beyond.

PubMed

Bible, Paul W; Kanno, Yuka; Wei, Lai; Brooks, Stephen R; O'Shea, John J; Morasso, Maria I; Loganantharaj, Rasiah; Sun, Hong-Wei

2015-01-01

Comparative co-localization analysis of transcription factors (TFs) and epigenetic marks (EMs) in specific biological contexts is one of the most critical areas of ChIP-Seq data analysis beyond peak calling. Yet there is a significant lack of user-friendly and powerful tools geared towards co-localization analysis based exploratory research. Most tools currently used for co-localization analysis are command line only and require extensive installation procedures and Linux expertise. Online tools partially address the usability issues of command line tools, but slow response times and few customization features make them unsuitable for rapid data-driven interactive exploratory research. We have developed PAPST: Peak Assignment and Profile Search Tool, a user-friendly yet powerful platform with a unique design, which integrates both gene-centric and peak-centric co-localization analysis into a single package. Most of PAPST's functions can be completed in less than five seconds, allowing quick cycles of data-driven hypothesis generation and testing. With PAPST, a researcher with or without computational expertise can perform sophisticated co-localization pattern analysis of multiple TFs and EMs, either against all known genes or a set of genomic regions obtained from public repositories or prior analysis. PAPST is a versatile, efficient, and customizable tool for genome-wide data-driven exploratory research. Creatively used, PAPST can be quickly applied to any genomic data analysis that involves a comparison of two or more sets of genomic coordinate intervals, making it a powerful tool for a wide range of exploratory genomic research. We first present PAPST's general purpose features then apply it to several public ChIP-Seq data sets to demonstrate its rapid execution and potential for cutting-edge research with a case study in enhancer analysis. To our knowledge, PAPST is the first software of its kind to provide efficient and sophisticated post peak-calling ChIP-Seq data analysis as an easy-to-use interactive application. PAPST is available at https://github.com/paulbible/papst and is a public domain work.

PAPST, a User Friendly and Powerful Java Platform for ChIP-Seq Peak Co-Localization Analysis and Beyond

PubMed Central

Bible, Paul W.; Kanno, Yuka; Wei, Lai; Brooks, Stephen R.; O’Shea, John J.; Morasso, Maria I.; Loganantharaj, Rasiah; Sun, Hong-Wei

2015-01-01

Comparative co-localization analysis of transcription factors (TFs) and epigenetic marks (EMs) in specific biological contexts is one of the most critical areas of ChIP-Seq data analysis beyond peak calling. Yet there is a significant lack of user-friendly and powerful tools geared towards co-localization analysis based exploratory research. Most tools currently used for co-localization analysis are command line only and require extensive installation procedures and Linux expertise. Online tools partially address the usability issues of command line tools, but slow response times and few customization features make them unsuitable for rapid data-driven interactive exploratory research. We have developed PAPST: Peak Assignment and Profile Search Tool, a user-friendly yet powerful platform with a unique design, which integrates both gene-centric and peak-centric co-localization analysis into a single package. Most of PAPST’s functions can be completed in less than five seconds, allowing quick cycles of data-driven hypothesis generation and testing. With PAPST, a researcher with or without computational expertise can perform sophisticated co-localization pattern analysis of multiple TFs and EMs, either against all known genes or a set of genomic regions obtained from public repositories or prior analysis. PAPST is a versatile, efficient, and customizable tool for genome-wide data-driven exploratory research. Creatively used, PAPST can be quickly applied to any genomic data analysis that involves a comparison of two or more sets of genomic coordinate intervals, making it a powerful tool for a wide range of exploratory genomic research. We first present PAPST’s general purpose features then apply it to several public ChIP-Seq data sets to demonstrate its rapid execution and potential for cutting-edge research with a case study in enhancer analysis. To our knowledge, PAPST is the first software of its kind to provide efficient and sophisticated post peak-calling ChIP-Seq data analysis as an easy-to-use interactive application. PAPST is available at https://github.com/paulbible/papst and is a public domain work. PMID:25970601

Exploratory Analysis in Learning Analytics

ERIC Educational Resources Information Center

Gibson, David; de Freitas, Sara

2016-01-01

This article summarizes the methods, observations, challenges and implications for exploratory analysis drawn from two learning analytics research projects. The cases include an analysis of a games-based virtual performance assessment and an analysis of data from 52,000 students over a 5-year period at a large Australian university. The complex…

Beyond linear methods of data analysis: time series analysis and its applications in renal research.

PubMed

Gupta, Ashwani K; Udrea, Andreea

2013-01-01

Analysis of temporal trends in medicine is needed to understand normal physiology and to study the evolution of disease processes. It is also useful for monitoring response to drugs and interventions, and for accountability and tracking of health care resources. In this review, we discuss what makes time series analysis unique for the purposes of renal research and its limitations. We also introduce nonlinear time series analysis methods and provide examples where these have advantages over linear methods. We review areas where these computational methods have found applications in nephrology ranging from basic physiology to health services research. Some examples include noninvasive assessment of autonomic function in patients with chronic kidney disease, dialysis-dependent renal failure and renal transplantation. Time series models and analysis methods have been utilized in the characterization of mechanisms of renal autoregulation and to identify the interaction between different rhythms of nephron pressure flow regulation. They have also been used in the study of trends in health care delivery. Time series are everywhere in nephrology and analyzing them can lead to valuable knowledge discovery. The study of time trends of vital signs, laboratory parameters and the health status of patients is inherent to our everyday clinical practice, yet formal models and methods for time series analysis are not fully utilized. With this review, we hope to familiarize the reader with these techniques in order to assist in their proper use where appropriate.

Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

NASA Astrophysics Data System (ADS)

zhang, L.

2011-12-01

Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be studied through the copula theory. As to the parameter estimation, the maximum likelihood estimation (MLE) will be applied. To illustrate the method, the univariate time series model and the dependence structure will be determined and tested using the monthly discharge time series of Cuyahoga River Basin.

'Lonely within the mother': An exploratory study of first-time mothers' experiences of loneliness.

PubMed

Lee, Katharine; Vasileiou, Konstantina; Barnett, Julie

2017-08-01

Loneliness is associated with life transitions such as new motherhood, yet there are few studies investigating the issue in this population. Using data from semi-structured interviews and an interpretative phenomenological analysis, this exploratory study sought to understand seven new mothers' experiences of loneliness. Experiences were organised around three themes, reflecting loneliness arising from making unfavourable self-comparisons with perceived mothering 'norms', from reduced social contact and relationships lacking in empathy. Accounts were homogeneous and point to potential solutions to ameliorate loneliness in new mothers; encouraging empathy in new mothers' partners and countering prevalent unrealistic representations of motherhood with more pragmatic accounts.

Hispanic Vocational Exploration Project. Final Report.

ERIC Educational Resources Information Center

Centro De La Comunidad, Inc., New London, CT.

During its second year, the Hispanic Vocational Exploration Project recruited eighth and ninth grade Hispanic youth for a four-week cycle, after-school, career exploratory program at Southeastern Regional Vocational Technical School, Groton, Connecticut. A series of career education workshops was the other major project activity. Supportive…

Nonstationary time series prediction combined with slow feature analysis

NASA Astrophysics Data System (ADS)

Wang, G.; Chen, X.

2015-07-01

Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

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.

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

PubMed

Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

2014-01-01

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

A comparison between nailfold capillaroscopy patterns in adulthood in juvenile and adult-onset systemic sclerosis: A EUSTAR exploratory study.

PubMed

Ingegnoli, Francesca; Boracchi, Patrizia; Gualtierotti, Roberta; Smith, Vanessa; Cutolo, Maurizio; Foeldvari, Ivan

2015-11-01

Qualitative capillaroscopy patterns in juvenile- and adult-onset systemic sclerosis (SSc) were studied in adulthood using data from the EULAR Scleroderma Trials and Research (EUSTAR) database. Data collected between June 2004 and April 2013 were examined with focus on capillaroscopy. In this retrospective exploratory study, series of patients with juvenile-onset SSc were matched with series of adult-onset SSc having the same gender and autoantibody profile. 30 of 123 patients with juvenile-onset and 2108 of 7133 with adult-onset SSc had data on capillaroscopy. Juvenile-onset SSc showed scleroderma pattern more frequently than adult-onset SSc (93.3% and 88%). The OR was 2.44 and 95% CI 0.57-10.41. An active scleroderma pattern was present in 58% of juvenile- and 61% of adult-onset SSc. The OR was 0.91 and 95% CI 0.28-2.93. The late scleroderma pattern was present in 61% of juvenile- and 55.5% of adult-onset SSc. The OR was 1.06 and 95% CI 0.34-3.56. This is the first exploratory study on the comparison of capillaroscopy between juvenile- and adult-onset SSc in adulthood. Juvenile-onset SSc had an increase prevalence of scleroderma pattern, but a similar distribution of the three patterns was suggested. Further studies are needed to define this issue. Copyright © 2015 Elsevier Inc. All rights reserved.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates.

PubMed

Xia, Li C; Steele, Joshua A; Cram, Jacob A; Cardon, Zoe G; Simmons, Sheri L; Vallino, Joseph J; Fuhrman, Jed A; Sun, Fengzhu

2011-01-01

The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

PubMed Central

2011-01-01

Background The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. Results We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. Conclusions The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa. PMID:22784572

On the equivalence of case-crossover and time series methods in environmental epidemiology.

PubMed

Lu, Yun; Zeger, Scott L

2007-04-01

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.

Defense Applications of Signal Processing

DTIC Science & Technology

1999-08-27

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

An Observation Analysis Tool for time-series analysis and sensor management in the FREEWAT GIS environment for water resources management

NASA Astrophysics Data System (ADS)

Cannata, Massimiliano; Neumann, Jakob; Cardoso, Mirko; Rossetto, Rudy; Foglia, Laura; Borsi, Iacopo

2017-04-01

In situ time-series are an important aspect of environmental modelling, especially with the advancement of numerical simulation techniques and increased model complexity. In order to make use of the increasing data available through the requirements of the EU Water Framework Directive, the FREEWAT GIS environment incorporates the newly developed Observation Analysis Tool for time-series analysis. The tool is used to import time-series data into QGIS from local CSV files, online sensors using the istSOS service, or MODFLOW model result files and enables visualisation, pre-processing of data for model development, and post-processing of model results. OAT can be used as a pre-processor for calibration observations, integrating the creation of observations for calibration directly from sensor time-series. The tool consists in an expandable Python library of processing methods and an interface integrated in the QGIS FREEWAT plug-in which includes a large number of modelling capabilities, data management tools and calibration capacity.

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

NASA Astrophysics Data System (ADS)

Galiana-Merino, J. J.; Pla, C.; Fernandez-Cortes, A.; Cuezva, S.; Ortiz, J.; Benavente, D.

2014-10-01

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time-period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results.

Enabling Web-Based Analysis of CUAHSI HIS Hydrologic Data Using R and Web Processing Services

NASA Astrophysics Data System (ADS)

Ames, D. P.; Kadlec, J.; Bayles, M.; Seul, M.; Hooper, R. P.; Cummings, B.

2015-12-01

The CUAHSI Hydrologic Information System (CUAHSI HIS) provides open access to a large number of hydrological time series observation and modeled data from many parts of the world. Several software tools have been designed to simplify searching and access to the CUAHSI HIS datasets. These software tools include: Desktop client software (HydroDesktop, HydroExcel), developer libraries (WaterML R Package, OWSLib, ulmo), and the new interactive search website, http://data.cuahsi.org. An issue with using the time series data from CUAHSI HIS for further analysis by hydrologists (for example for verification of hydrological and snowpack models) is the large heterogeneity of the time series data. The time series may be regular or irregular, contain missing data, have different time support, and be recorded in different units. R is a widely used computational environment for statistical analysis of time series and spatio-temporal data that can be used to assess fitness and perform scientific analyses on observation data. R includes the ability to record a data analysis in the form of a reusable script. The R script together with the input time series dataset can be shared with other users, making the analysis more reproducible. The major goal of this study is to examine the use of R as a Web Processing Service for transforming time series data from the CUAHSI HIS and sharing the results on the Internet within HydroShare. HydroShare is an online data repository and social network for sharing large hydrological data sets such as time series, raster datasets, and multi-dimensional data. It can be used as a permanent cloud storage space for saving the time series analysis results. We examine the issues associated with running R scripts online: including code validation, saving of outputs, reporting progress, and provenance management. An explicit goal is that the script which is run locally should produce exactly the same results as the script run on the Internet. Our design can be used as a model for other studies that need to run R scripts on the web.

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen

2015-11-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

HydroClimATe: hydrologic and climatic analysis toolkit

USGS Publications Warehouse

Dickinson, Jesse; Hanson, Randall T.; Predmore, Steven K.

2014-01-01

The potential consequences of climate variability and climate change have been identified as major issues for the sustainability and availability of the worldwide water resources. Unlike global climate change, climate variability represents deviations from the long-term state of the climate over periods of a few years to several decades. Currently, rich hydrologic time-series data are available, but the combination of data preparation and statistical methods developed by the U.S. Geological Survey as part of the Groundwater Resources Program is relatively unavailable to hydrologists and engineers who could benefit from estimates of climate variability and its effects on periodic recharge and water-resource availability. This report documents HydroClimATe, a computer program for assessing the relations between variable climatic and hydrologic time-series data. HydroClimATe was developed for a Windows operating system. The software includes statistical tools for (1) time-series preprocessing, (2) spectral analysis, (3) spatial and temporal analysis, (4) correlation analysis, and (5) projections. The time-series preprocessing tools include spline fitting, standardization using a normal or gamma distribution, and transformation by a cumulative departure. The spectral analysis tools include discrete Fourier transform, maximum entropy method, and singular spectrum analysis. The spatial and temporal analysis tool is empirical orthogonal function analysis. The correlation analysis tools are linear regression and lag correlation. The projection tools include autoregressive time-series modeling and generation of many realizations. These tools are demonstrated in four examples that use stream-flow discharge data, groundwater-level records, gridded time series of precipitation data, and the Multivariate ENSO Index.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

The Timeseries Toolbox - A Web Application to Enable Accessible, Reproducible Time Series Analysis

NASA Astrophysics Data System (ADS)

Veatch, W.; Friedman, D.; Baker, B.; Mueller, C.

2017-12-01

The vast majority of data analyzed by climate researchers are repeated observations of physical process or time series data. This data lends itself of a common set of statistical techniques and models designed to determine trends and variability (e.g., seasonality) of these repeated observations. Often, these same techniques and models can be applied to a wide variety of different time series data. The Timeseries Toolbox is a web application designed to standardize and streamline these common approaches to time series analysis and modeling with particular attention to hydrologic time series used in climate preparedness and resilience planning and design by the U. S. Army Corps of Engineers. The application performs much of the pre-processing of time series data necessary for more complex techniques (e.g. interpolation, aggregation). With this tool, users can upload any dataset that conforms to a standard template and immediately begin applying these techniques to analyze their time series data.

Advanced spectral methods for climatic time series

USGS Publications Warehouse

Ghil, M.; Allen, M.R.; Dettinger, M.D.; Ide, K.; Kondrashov, D.; Mann, M.E.; Robertson, A.W.; Saunders, A.; Tian, Y.; Varadi, F.; Yiou, P.

2002-01-01

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal- to-noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review.

The Infinitesimal Jackknife with Exploratory Factor Analysis

ERIC Educational Resources Information Center

Zhang, Guangjian; Preacher, Kristopher J.; Jennrich, Robert I.

2012-01-01

The infinitesimal jackknife, a nonparametric method for estimating standard errors, has been used to obtain standard error estimates in covariance structure analysis. In this article, we adapt it for obtaining standard errors for rotated factor loadings and factor correlations in exploratory factor analysis with sample correlation matrices. Both…

Assessing Stress in Cancer Patients: A Second-Order Factor Analysis Model for the Perceived Stress Scale

ERIC Educational Resources Information Center

Golden-Kreutz, Deanna M.; Browne, Michael W.; Frierson, Georita M.; Andersen, Barbara L.

2004-01-01

Using the Perceived Stress Scale (PSS), perceptions of global stress were assessed in 111women following breast cancer surgery and at 12 and 24 months later. This is the first study to factor analyze the PSS. The PSS data were factor analyzed each time using exploratory factor analysis with oblique direct quartimin rotation. Goodness-of-fit…

Multifractal analysis of visibility graph-based Ito-related connectivity time series.

PubMed

Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

2016-02-01

In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

Internal and External Constraints on Teenage Mothering.

ERIC Educational Resources Information Center

Mercer, Ramona T.

This paper reports findings of an exploratory field study of the teenager's first year of motherhood. Twelve subjects, aged 14-19, were interviewed a number of times during that year. Interviews were largely unstructured, allowing mothers to express their concerns and feelings. Data for analysis were: (1) narrative style protocols that were…

Measuring and Monitoring Conceptions of Research

ERIC Educational Resources Information Center

Zhang, Ran; Zwaal, Wichard; Otting, Hans

2018-01-01

This study assessed the validity and reliability of the Meyer, Shanahan, and Laugksch's Conceptions of Research Inventory using data collected from 227 undergraduate hotel management students in the Netherlands. The results of a series of exploratory and confirmatory factor analyses showed substantial empirical support for the five-factor…

Frequency-phase analysis of resting-state functional MRI

PubMed Central

Goelman, Gadi; Dan, Rotem; Růžička, Filip; Bezdicek, Ondrej; Růžička, Evžen; Roth, Jan; Vymazal, Josef; Jech, Robert

2017-01-01

We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience. PMID:28272522

Recurrence plots and recurrence quantification analysis of human motion data

NASA Astrophysics Data System (ADS)

Josiński, Henryk; Michalczuk, Agnieszka; Świtoński, Adam; Szczesna, Agnieszka; Wojciechowski, Konrad

2016-06-01

The authors present exemplary application of recurrence plots, cross recurrence plots and recurrence quantification analysis for the purpose of exploration of experimental time series describing selected aspects of human motion. Time series were extracted from treadmill gait sequences which were recorded in the Human Motion Laboratory (HML) of the Polish-Japanese Academy of Information Technology in Bytom, Poland by means of the Vicon system. Analysis was focused on the time series representing movements of hip, knee, ankle and wrist joints in the sagittal plane.

Analysis of Site Position Time Series Derived From Space Geodetic Solutions

NASA Astrophysics Data System (ADS)

Angermann, D.; Meisel, B.; Kruegel, M.; Tesmer, V.; Miller, R.; Drewes, H.

2003-12-01

This presentation deals with the analysis of station coordinate time series obtained from VLBI, SLR, GPS and DORIS solutions. We also present time series for the origin and scale derived from these solutions and discuss their contribution to the realization of the terrestrial reference frame. For these investigations we used SLR and VLBI solutions computed at DGFI with the software systems DOGS (SLR) and OCCAM (VLBI). The GPS and DORIS time series were obtained from weekly station coordinates solutions provided by the IGS, and from the joint DORIS analysis center (IGN-JPL). We analysed the time series with respect to various aspects, such as non-linear motions, periodic signals and systematic differences (biases). A major focus is on a comparison of the results at co-location sites in order to identify technique- and/or solution related problems. This may also help to separate and quantify possible effects, and to understand the origin of still existing discrepancies. Technique-related systematic effects (biases) should be reduced to the highest possible extent, before using the space geodetic solutions for a geophysical interpretation of seasonal signals in site position time series.

77 FR 47383 - Annual Assessment of the Status of Competition in the Market for the Delivery of Video Programming

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-08

... monitor trends on an annual basis. To continue our time-series analysis, we request data as of June 30... information and time- series data we should collect for the analysis of various MVPD performance metrics. In... revenues, cash flows, and margins. To the extent possible, we seek five-year time-series data to allow us...

Nonstationary time series prediction combined with slow feature analysis

NASA Astrophysics Data System (ADS)

Wang, G.; Chen, X.

2015-01-01

Almost all climate time series have some degree of nonstationarity due to external driving forces perturbations of the observed system. Therefore, these external driving forces should be taken into account when reconstructing the climate dynamics. This paper presents a new technique of combining the driving force of a time series obtained using the Slow Feature Analysis (SFA) approach, then introducing the driving force into a predictive model to predict non-stationary time series. In essence, the main idea of the technique is to consider the driving forces as state variables and incorporate them into the prediction model. To test the method, experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted. The results showed improved and effective prediction skill.

Nonlinear Analysis of Surface EMG Time Series

NASA Astrophysics Data System (ADS)

Zurcher, Ulrich; Kaufman, Miron; Sung, Paul

2004-04-01

Applications of nonlinear analysis of surface electromyography time series of patients with and without low back pain are presented. Limitations of the standard methods based on the power spectrum are discussed.

Stochastic nature of Landsat MSS data

NASA Technical Reports Server (NTRS)

Labovitz, M. L.; Masuoka, E. J.

1987-01-01

A multiple series generalization of the ARIMA models is used to model Landsat MSS scan lines as sequences of vectors, each vector having four elements (bands). The purpose of this work is to investigate if Landsat scan lines can be described by a general multiple series linear stochastic model and if the coefficients of such a model vary as a function of satellite system and target attributes. To accomplish this objective, an exploratory experimental design was set up incorporating six factors, four representing target attributes - location, cloud cover, row (within location), and column (within location) - and two factors representing system attributes - satellite number and detector bank. Each factor was included in the design at two levels and, with two replicates per treatment, 128 scan lines were analyzed. The results of the analysis suggests that a multiple AR(4) model is an adequate representation across all scan lines. Furthermore, the coefficients of the AR(4) model vary with location, particularly changes in physiography (slope regimes), and with percent cloud cover, but are insensitive to changes in system attributes.

Using Horn's Parallel Analysis Method in Exploratory Factor Analysis for Determining the Number of Factors

ERIC Educational Resources Information Center

Çokluk, Ömay; Koçak, Duygu

2016-01-01

In this study, the number of factors obtained from parallel analysis, a method used for determining the number of factors in exploratory factor analysis, was compared to that of the factors obtained from eigenvalue and scree plot--two traditional methods for determining the number of factors--in terms of consistency. Parallel analysis is based on…

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.

Staying on track: a cluster randomized controlled trial of automated reminders aimed at increasing human papillomavirus vaccine completion.

PubMed

Patel, Ashlesha; Stern, Lisa; Unger, Zoe; Debevec, Elie; Roston, Alicia; Hanover, Rita; Morfesis, Johanna

2014-05-01

To evaluate whether automated reminders increase on-time completion of the three-dose human papillomavirus (HPV) vaccine series. Ten reproductive health centers enrolled 365 women aged 19-26 to receive dose one of the HPV vaccine. Health centers were matched and randomized so that participants received either routine follow-up (control) or automated reminder messages for vaccine doses two and three (intervention). Intervention participants selected their preferred method of reminders - text, e-mail, phone, private Facebook message, or standard mail. We compared vaccine completion rates between groups over a period of 32 weeks. The reminder system did not increase completion rates, which overall were low at 17.2% in the intervention group and 18.9% in the control group (p=0.881). Exploratory analyses revealed that participants who completed the series on-time were more likely to be older (OR=1.15, 95% CI 1.01-1.31), report having completed a four-year college degree or more (age-adjusted OR=2.51, 95% CI 1.29-4.90), and report three or more lifetime sexual partners (age-adjusted OR=3.45, 95% CI 1.20-9.92). The study intervention did not increase HPV vaccine series completion. Despite great public health interest in HPV vaccine completion and reminder technologies, completion rates remain low. Copyright © 2014 Elsevier Ltd. All rights reserved.

Robust extrema features for time-series data analysis.

PubMed

Vemulapalli, Pramod K; Monga, Vishal; Brennan, Sean N

2013-06-01

The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.

Adaptive time-variant models for fuzzy-time-series forecasting.

PubMed

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Does intraoperative low arterial partial pressure of oxygen increase the risk of surgical site infection following emergency exploratory laparotomy in horses?

PubMed

Costa-Farré, Cristina; Prades, Marta; Ribera, Thaïs; Valero, Oliver; Taurà, Pilar

2014-04-01

Decreased tissue oxygenation is a critical factor in the development of wound infection as neutrophil mediated oxidative killing is an essential mechanism against surgical pathogens. The objective of this prospective case series was to assess the impact of intraoperative arterial partial pressure of oxygen (PaO2) on surgical site infection (SSI) in horses undergoing emergency exploratory laparotomy for acute gastrointestinal disease. The anaesthetic and antibiotic protocol was standardised. Demographic data, surgical potential risk factors and PaO2, obtained 1h after induction of anaesthesia were recorded. Surgical wounds were assessed daily for infection during hospitalisation and follow up information was obtained after discharge. A total of 84 adult horses were included. SSI developed in 34 (40.4%) horses. Multivariate logistic regression showed that PaO2, anaesthetic time and subcutaneous suture material were predictors of SSI (AUC=0.76, sensitivity=71%, specificity=65%). The use of polyglycolic acid sutures increased the risk and horses with a PaO2 value < 80 mm Hg [10.6 kPa] and anaesthetic time >2h had the highest risk of developing SSI (OR=9.01; 95% CI 2.28-35.64). The results of this study confirm the hypothesis that low intraoperative PaO2 contributes to the development of SSI following colic surgery. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed

Kis, Maria

2005-01-01

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

Technology for the Organic Chemist: Three Exploratory Modules

ERIC Educational Resources Information Center

Esteb, John J.; McNulty, LuAnne M.; Magers, John; Morgan, Paul; Wilson, Anne M.

2010-01-01

The ability to use computer-based technology is an essential skill set for students majoring in chemistry. This exercise details the introduction of appropriate uses for this technology in the organic chemistry series. The incorporation of chemically appropriate online resources (module 1), scientific databases (module 2), and the use of a…

Art & Music Appreciation. A to Z Active Learning Series.

ERIC Educational Resources Information Center

Forte, Imogene; Schurr, Sandra

This workbook includes high-interest activities, lessons, and projects to further students' interest in and understanding of important exploratory and enrichment topics essential to a balanced middle grades program. The workbook includes lessons and activities that encourage students to learn more about the arts. Instructional strategies are…

Voice Blog: An Exploratory Study of Language Learning

ERIC Educational Resources Information Center

Sun, Yu-Chih

2009-01-01

This study uses voice blogs as a platform for an extensive study of language learners' speaking skills. To triangulate the findings, the study collected data by surveying the learners' blogging processes, investigating learning strategies, and conducting retrospective interviews. The results revealed that students (a) developed a series of…

Careers in Construction: Construction Industry Series: Student Manual and Instructor's Guide.

ERIC Educational Resources Information Center

Texas Education Agency, Austin. Dept. of Occupational Education and Technology.

The guide for instructors of construction occupations provides instructional suggestions and informational sources for structuring an exploratory program. The program is divided into the following blocks, representing different experiences in construction: (1) wood; (2) finishing; (3) engineering, support, and management services; (4) metal; (5)…

Departmental Dialogues: Facilitating Positive Academic Climates to Improve Equity in STEM Disciplines

ERIC Educational Resources Information Center

Holmes, Maja Husar; Jackson, J. Kasi; Stoiko, Rachel

2016-01-01

This exploratory qualitative study examined faculty responses to a collegiality-building process called Dialogues. The process used a series of discussions and activities to guide faculty members toward a common, mutually beneficially goal, while changing patterns of interaction. The responses revealed how faculty members experienced…

Instructional Leadership in Greek and English Outstanding Schools

ERIC Educational Resources Information Center

Kaparou, Maria; Bush, Tony

2016-01-01

Purpose: The purpose of this paper is to examine instructional leadership (IL) in outstanding secondary schools within a centralised (Greece) and a partially decentralised (England) education context. Design/methodology/approach: Since the purpose of the study is exploratory, the researchers adopt a qualitative approach, employing a series of four…

The Effects of Children's Criminality on Mothers of Offenders

ERIC Educational Resources Information Center

Sturges, Judith E.; Hanrahan, Kathleen J.

2011-01-01

This exploratory study sought to understand the effects of criminality on mothers of offenders. Semistructured in-depth interviews were used to gather data from 27 mothers. Respondents reported that their children's criminality leads to a series of complications and stressors in mothers' lives, including physical, psychological, relational,…

Initial Development and Validation of the Global Citizenship Scale

ERIC Educational Resources Information Center

Morais, Duarte B.; Ogden, Anthony C.

2011-01-01

The purpose of this article is to report on the initial development of a theoretically grounded and empirically validated scale to measure global citizenship. The methodology employed is multi-faceted, including two expert face validity trials, extensive exploratory and confirmatory factor analyses with multiple datasets, and a series of three…

Breaking the Sound Barrier

ERIC Educational Resources Information Center

Brown, Tom; Boehringer, Kim

2007-01-01

Students in a fourth-grade class participated in a series of dynamic sound learning centers followed by a dramatic capstone event--an exploration of the amazing Trashcan Whoosh Waves. It's a notoriously difficult subject to teach, but this hands-on, exploratory approach ignited student interest in sound, promoted language acquisition, and built…

Exploratory Evaluation of a School-Based Child Sexual Abuse Prevention Program

ERIC Educational Resources Information Center

Barron, Ian G.; Topping, Keith J.

2013-01-01

Internationally, efficacy studies of school-based child sexual abuse prevention programs display a series of methodological shortcomings. Few studies include adolescent participants, recording of disclosures has been inconsistent, and no studies to date have assessed presenter adherence to program protocols or summated the costs of program…

Information retrieval for nonstationary data records

NASA Technical Reports Server (NTRS)

Su, M. Y.

1971-01-01

A review and a critical discussion are made on the existing methods for analysis of nonstationary time series, and a new algorithm for splitting nonstationary time series, is applied to the analysis of sunspot data.

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik; Marwan, Norbert; Dijkstra, Henk; Kurths, Jürgen

2016-04-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. pyunicorn is available online at https://github.com/pik-copan/pyunicorn. Reference: J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), DOI: 10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].

Mitragynine Attenuates Withdrawal Syndrome in Morphine-Withdrawn Zebrafish

PubMed Central

Khor, Beng-Siang; Amar Jamil, Mohd Fadzly; Adenan, Mohamad Ilham; Chong Shu-Chien, Alexander

2011-01-01

A major obstacle in treating drug addiction is the severity of opiate withdrawal syndrome, which can lead to unwanted relapse. Mitragynine is the major alkaloid compound found in leaves of Mitragyna speciosa, a plant widely used by opiate addicts to mitigate the harshness of drug withdrawal. A series of experiments was conducted to investigate the effect of mitragynine on anxiety behavior, cortisol level and expression of stress pathway related genes in zebrafish undergoing morphine withdrawal phase. Adult zebrafish were subjected to two weeks chronic morphine exposure at 1.5 mg/L, followed by withdrawal for 24 hours prior to tests. Using the novel tank diving tests, we first showed that morphine-withdrawn zebrafish display anxiety-related swimming behaviors such as decreased exploratory behavior and increased erratic movement. Morphine withdrawal also elevated whole-body cortisol levels, which confirms the phenotypic stress-like behaviors. Exposing morphine-withdrawn fish to mitragynine however attenuates majority of the stress-related swimming behaviors and concomitantly lower whole-body cortisol level. Using real-time PCR gene expression analysis, we also showed that mitragynine reduces the mRNA expression of corticotropin releasing factor receptors and prodynorphin in zebrafish brain during morphine withdrawal phase, revealing for the first time a possible link between mitragynine's ability to attenuate anxiety during opiate withdrawal with the stress-related corticotropin pathway. PMID:22205946

Persistence analysis of extreme CO, NO2 and O3 concentrations in ambient air of Delhi

NASA Astrophysics Data System (ADS)

Chelani, Asha B.

2012-05-01

Persistence analysis of air pollutant concentration and corresponding exceedance time series is carried out to examine for temporal evolution. For this purpose, air pollutant concentrations, namely, CO, NO2 and O3 observed during 2000-2009 at a traffic site in Delhi are analyzed using detrended fluctuation analysis. Two types of extreme values are analyzed; exceeded concentrations to a threshold provided by national pollution controlling agency and time interval between two exceedances. The time series of three pollutants is observed to possess persistence property whereas the extreme value time series of only primary pollutant concentrations is found to be persistent. Two time scaling regions are observed to be significant in extreme time series of CO and NO2, mainly attributed to implementation of CNG in vehicles. The presence of persistence in three pollutant concentration time series is linked to the property of self-organized criticality. The observed persistence in the time interval between two exceeded levels is a matter of concern as persistent high concentrations can trigger health problems.

Exploratory Climate Data Visualization and Analysis Using DV3D and UVCDAT

NASA Technical Reports Server (NTRS)

Maxwell, Thomas

2012-01-01

Earth system scientists are being inundated by an explosion of data generated by ever-increasing resolution in both global models and remote sensors. Advanced tools for accessing, analyzing, and visualizing very large and complex climate data are required to maintain rapid progress in Earth system research. To meet this need, NASA, in collaboration with the Ultra-scale Visualization Climate Data Analysis Tools (UVCOAT) consortium, is developing exploratory climate data analysis and visualization tools which provide data analysis capabilities for the Earth System Grid (ESG). This paper describes DV3D, a UV-COAT package that enables exploratory analysis of climate simulation and observation datasets. OV3D provides user-friendly interfaces for visualization and analysis of climate data at a level appropriate for scientists. It features workflow inte rfaces, interactive 40 data exploration, hyperwall and stereo visualization, automated provenance generation, and parallel task execution. DV30's integration with CDAT's climate data management system (COMS) and other climate data analysis tools provides a wide range of high performance climate data analysis operations. DV3D expands the scientists' toolbox by incorporating a suite of rich new exploratory visualization and analysis methods for addressing the complexity of climate datasets.

Cognitive and Personality Components Underlying Spoken Idiom Comprehension in Context. An Exploratory Study.

PubMed

Cacciari, Cristina; Corrardini, Paola; Ferlazzo, Fabio

2018-01-01

In this exploratory study, we investigated whether and to what extent individual differences in cognitive and personality variables are associated with spoken idiom comprehension in context. Language unimpaired participants were enrolled in a cross-modal lexical decision study in which semantically ambiguous Italian idioms (i.e., strings with both a literal and an idiomatic interpretation as, for instance, break the ice ), predictable or unpredictable before the string offset, were embedded in idiom-biasing contexts. To explore the contributions of different cognitive and personality components, participants also completed a series of tests respectively assessing general speed, inhibitory control, short-term and working memory, cognitive flexibility, crystallized and fluid intelligence, and personality. Stepwise regression analyses revealed that online idiom comprehension was associated with the participants' working memory, inhibitory control and crystallized verbal intelligence, an association modulated by idiom type. Also personality-related variables (State Anxiety and Openness to Experience) were associated with idiom comprehension, although in marginally significant ways. These results contribute to the renewed interest on how individual variability modulates language comprehension, and for the first time document contributions of individual variability on lexicalized, high frequency multi-word expressions as idioms adding new knowledge to the existing evidence on metaphor and sarcasm.

Estimating short-run and long-run interaction mechanisms in interictal state.

PubMed

Ozkaya, Ata; Korürek, Mehmet

2010-04-01

We address the issue of analyzing electroencephalogram (EEG) from seizure patients in order to test, model and determine the statistical properties that distinguish between EEG states (interictal, pre-ictal, ictal) by introducing a new class of time series analysis methods. In the present study: firstly, we employ statistical methods to determine the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals that are deemed non-stationary we suggest the concept of Autoregressive Integrated Moving Average (ARIMA) process modelling, well known in time series analysis. We finally address the queries of causal relationships between epileptic states and between brain areas during epileptiform activity. We estimate the interaction between different EEG series (channels) in short time intervals by performing Granger-causality analysis and also estimate such interaction in long time intervals by employing Cointegration analysis, both analysis methods are well-known in econometrics. Here we find: first, that the causal relationship between neuronal assemblies can be identified according to the duration and the direction of their possible mutual influences; second, that although the estimated bidirectional causality in short time intervals yields that the neuronal ensembles positively affect each other, in long time intervals neither of them is affected (increasing amplitudes) from this relationship. Moreover, Cointegration analysis of the EEG series enables us to identify whether there is a causal link from the interictal state to ictal state.

Group Time in Early Childhood Centers: An Exploratory Study.

ERIC Educational Resources Information Center

McAfee, Oralie

To investigate the current status of group time in early childhood centers, a small-scale exploratory study was designed and executed. Results of interviews with 35 teachers and observations in five classrooms serving children ages 2 1/2 through kindergarten revealed that all classrooms had at least one group time or circle time, usually in the…

Entropic Analysis of Electromyography Time Series

NASA Astrophysics Data System (ADS)

Kaufman, Miron; Sung, Paul

2005-03-01

We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.

Exploratory Bi-Factor Analysis: The Oblique Case

ERIC Educational Resources Information Center

Jennrich, Robert I.; Bentler, Peter M.

2012-01-01

Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…

Multimedia Exploratory Data Analysis for Geospatial Data Mining: The Case for Augmented Seriation.

ERIC Educational Resources Information Center

Gluck, Myke

2001-01-01

Reviews the role of exploratory data analysis (EDA) for spatial data mining and presents a case study addressing environmental risk assessments in New York State to illustrate the feasibility and usability of augmenting seriation for spatial data analysis. Describes augmentation with multimedia tools to understand relationships among spatial,…

Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability.

PubMed

Krafty, Robert T

2016-07-01

Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same group. Although a number of frequency domain methods for the discriminant analysis of time series have been explored, there is a dearth of models and methods that account for within-group spectral variability. This article proposes a model for groups of time series in which transfer functions are modeled as stochastic variables that can account for both between-group and within-group differences in spectra that are identified from individual replicates. An ensuing discriminant analysis of stochastic cepstra under this model is developed to obtain parsimonious measures of relative power that optimally separate groups in the presence of within-group spectral variability. The approach possess favorable properties in classifying new observations and can be consistently estimated through a simple discriminant analysis of a finite number of estimated cepstral coefficients. Benefits in accounting for within-group spectral variability are empirically illustrated in a simulation study and through an analysis of gait variability.

The physiology analysis system: an integrated approach for warehousing, management and analysis of time-series physiology data.

PubMed

McKenna, Thomas M; Bawa, Gagandeep; Kumar, Kamal; Reifman, Jaques

2007-04-01

The physiology analysis system (PAS) was developed as a resource to support the efficient warehousing, management, and analysis of physiology data, particularly, continuous time-series data that may be extensive, of variable quality, and distributed across many files. The PAS incorporates time-series data collected by many types of data-acquisition devices, and it is designed to free users from data management burdens. This Web-based system allows both discrete (attribute) and time-series (ordered) data to be manipulated, visualized, and analyzed via a client's Web browser. All processes occur on a server, so that the client does not have to download data or any application programs, and the PAS is independent of the client's computer operating system. The PAS contains a library of functions, written in different computer languages that the client can add to and use to perform specific data operations. Functions from the library are sequentially inserted into a function chain-based logical structure to construct sophisticated data operators from simple function building blocks, affording ad hoc query and analysis of time-series data. These features support advanced mining of physiology data.

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

Financial Time-series Analysis: a Brief Overview

NASA Astrophysics Data System (ADS)

Chakraborti, A.; Patriarca, M.; Santhanam, M. S.

Prices of commodities or assets produce what is called time-series. Different kinds of financial time-series have been recorded and studied for decades. Nowadays, all transactions on a financial market are recorded, leading to a huge amount of data available, either for free in the Internet or commercially. Financial time-series analysis is of great interest to practitioners as well as to theoreticians, for making inferences and predictions. Furthermore, the stochastic uncertainties inherent in financial time-series and the theory needed to deal with them make the subject especially interesting not only to economists, but also to statisticians and physicists [1]. While it would be a formidable task to make an exhaustive review on the topic, with this review we try to give a flavor of some of its aspects.

A comparative analysis of spectral exponent estimation techniques for 1/fβ processes with applications to the analysis of stride interval time series

PubMed Central

Schaefer, Alexander; Brach, Jennifer S.; Perera, Subashan; Sejdić, Ervin

2013-01-01

Background The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f) = 1/fβ. The scaling exponent β is thus often interpreted as a “biomarker” of relative health and decline. New Method This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. Results The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Comparison with Existing Methods: Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. Conclusions The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series. PMID:24200509

A comparative analysis of spectral exponent estimation techniques for 1/f(β) processes with applications to the analysis of stride interval time series.

PubMed

Schaefer, Alexander; Brach, Jennifer S; Perera, Subashan; Sejdić, Ervin

2014-01-30

The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f)=1/f(β). The scaling exponent β is thus often interpreted as a "biomarker" of relative health and decline. This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series. Copyright © 2013 Elsevier B.V. All rights reserved.

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

ERIC Educational Resources Information Center

Koopmans, Matthijs

2011-01-01

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

Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space.

PubMed

Zuo, Xi-Nian; Xu, Ting; Jiang, Lili; Yang, Zhi; Cao, Xiao-Yan; He, Yong; Zang, Yu-Feng; Castellanos, F Xavier; Milham, Michael P

2013-01-15

While researchers have extensively characterized functional connectivity between brain regions, the characterization of functional homogeneity within a region of the brain connectome is in early stages of development. Several functional homogeneity measures were proposed previously, among which regional homogeneity (ReHo) was most widely used as a measure to characterize functional homogeneity of resting state fMRI (R-fMRI) signals within a small region (Zang et al., 2004). Despite a burgeoning literature on ReHo in the field of neuroimaging brain disorders, its test-retest (TRT) reliability remains unestablished. Using two sets of public R-fMRI TRT data, we systematically evaluated the ReHo's TRT reliability and further investigated the various factors influencing its reliability and found: 1) nuisance (head motion, white matter, and cerebrospinal fluid) correction of R-fMRI time series can significantly improve the TRT reliability of ReHo while additional removal of global brain signal reduces its reliability, 2) spatial smoothing of R-fMRI time series artificially enhances ReHo intensity and influences its reliability, 3) surface-based R-fMRI computation largely improves the TRT reliability of ReHo, 4) a scan duration of 5 min can achieve reliable estimates of ReHo, and 5) fast sampling rates of R-fMRI dramatically increase the reliability of ReHo. Inspired by these findings and seeking a highly reliable approach to exploratory analysis of the human functional connectome, we established an R-fMRI pipeline to conduct ReHo computations in both 3-dimensions (volume) and 2-dimensions (surface). Copyright © 2012 Elsevier Inc. All rights reserved.

Daily rainfall forecasting for one year in a single run using Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Unnikrishnan, Poornima; Jothiprakash, V.

2018-06-01

Effective modelling and prediction of smaller time step rainfall is reported to be very difficult owing to its highly erratic nature. Accurate forecast of daily rainfall for longer duration (multi time step) may be exceptionally helpful in the efficient planning and management of water resources systems. Identification of inherent patterns in a rainfall time series is also important for an effective water resources planning and management system. In the present study, Singular Spectrum Analysis (SSA) is utilized to forecast the daily rainfall time series pertaining to Koyna watershed in Maharashtra, India, for 365 days after extracting various components of the rainfall time series such as trend, periodic component, noise and cyclic component. In order to forecast the time series for longer time step (365 days-one window length), the signal and noise components of the time series are forecasted separately and then added together. The results of the study show that the method of SSA could extract the various components of the time series effectively and could also forecast the daily rainfall time series for longer duration such as one year in a single run with reasonable accuracy.

Drunk driving detection based on classification of multivariate time series.

PubMed

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

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.

Replication Analysis in Exploratory Factor Analysis: What It Is and Why It Makes Your Analysis Better

ERIC Educational Resources Information Center

Osborne, Jason W.; Fitzpatrick, David C.

2012-01-01

Exploratory Factor Analysis (EFA) is a powerful and commonly-used tool for investigating the underlying variable structure of a psychometric instrument. However, there is much controversy in the social sciences with regard to the techniques used in EFA (Ford, MacCallum, & Tait, 1986; Henson & Roberts, 2006) and the reliability of the outcome.…

Falcon: A Temporal Visual Analysis System

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

Steed, Chad A.

2016-09-05

Flexible visible exploration of long, high-resolution time series from multiple sensor streams is a challenge in several domains. Falcon is a visual analytics approach that helps researchers acquire a deep understanding of patterns in log and imagery data. Falcon 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 with multiple levels of detail. These capabilities are applicable to the analysis of any quantitative time series.

Growth and Performance of Fully Online and Blended K-12 Public Schools

ERIC Educational Resources Information Center

Gulosino, Charisse; Miron, Gary

2017-01-01

This study provides a census of full-time virtual schools and blended schools from 35 states. Specifically, it utilizes data visualization and exploratory data analysis to examine student demographics and school performance measures of virtual schools and blended schools operating in the 2014-15 school year. The school achievement measures for…

Single-Case Time Series with Bayesian Analysis: A Practitioner's Guide.

ERIC Educational Resources Information Center

Jones, W. Paul

2003-01-01

This article illustrates a simplified time series analysis for use by the counseling researcher practitioner in single-case baseline plus intervention studies with a Bayesian probability analysis to integrate findings from replications. The C statistic is recommended as a primary analysis tool with particular relevance in the context of actual…

Time series regression studies in environmental epidemiology.

PubMed

Bhaskaran, Krishnan; Gasparrini, Antonio; Hajat, Shakoor; Smeeth, Liam; Armstrong, Ben

2013-08-01

Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed ('lagged') associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model.

Multiresolution analysis of Bursa Malaysia KLCI time series

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

Symplectic geometry spectrum regression for prediction of noisy time series

NASA Astrophysics Data System (ADS)

Xie, Hong-Bo; Dokos, Socrates; Sivakumar, Bellie; Mengersen, Kerrie

2016-05-01

We present the symplectic geometry spectrum regression (SGSR) technique as well as a regularized method based on SGSR for prediction of nonlinear time series. The main tool of analysis is the symplectic geometry spectrum analysis, which decomposes a time series into the sum of a small number of independent and interpretable components. The key to successful regularization is to damp higher order symplectic geometry spectrum components. The effectiveness of SGSR and its superiority over local approximation using ordinary least squares are demonstrated through prediction of two noisy synthetic chaotic time series (Lorenz and Rössler series), and then tested for prediction of three real-world data sets (Mississippi River flow data and electromyographic and mechanomyographic signal recorded from human body).

To Explore the Experiences of Women on Reasons in Initiating and Maintaining Breastfeeding in Urban Area of Karachi, Pakistan: An Exploratory Study

PubMed Central

Shaheen Premani, Zahra; Kurji, Zohra; Mithani, Yasmin

2011-01-01

This is an exploratory study that explores the experiences of lactating women in initiating, continuing, or discontinuing breastfeeding in an urban area of Karachi, Pakistan. Objectives. To explore the experiences of lactating women and to understand their support and hindering mechanisms in initiating and maintaining breastfeeding. Methods. This is an exploratory design assisting in exploring the participant's experiences of initiating and maintaining breastfeeding to better understand their world. Purposive sampling was used, and data was analyzed through manual thematic analysis. Results. The data revealed that mother's knowledge, sociocultural environment, breastfeeding decision, and self- and professional support acted as driving forces for the participants. However, sociocultural environment, physiological changes, time management, and being a housewife to breastfeed their children were all challenges and barriers that the participants thought hindered their breastfeeding initiation and maintenance. Conclusion. Breastfeeding is a natural but taxing phenomenon, and breastfeeding mothers experience supporting and hindering factors in initiating and maintaining breastfeeding. PMID:22389780

[Mediolateral gradient of the nucleus accumbens nitrergic activation during exploratory behavior].

PubMed

Saul'skaia, N B; Sudorgina, P V

2012-04-01

In Sprague-Dawley rats, by means of in vivo microdialysis combined with HPLC analysis it has been shown that an exploratory behavior in a new environment is accompanied by a rise in extracellular levels of citrulline (an NO co-product) in the mediolateral regions of the n. accumbens with the maximum observed in the medial n. accumbens. Infusions of 7-nitroindazole (0.5 mM), a neuronal NO synthase inhibitor, into the medial n. accumbens prevented the exploration-induced rise of extracellular citrulline levels in this area. The second presentation of the same chamber did not produce any significant changes of extracellular citrulline levels in the medial n. accumbens, although there was a tendency of a small increase. The presentation of a familiar chamber did not affect citrulline extracellular levels in this area. The data obtained indicate for the first time that exploratory activity in a new environment is accompanied by the nitrergic activation in the entire n. accumbens with the maximal activation in the medial part of this brain area.

Exploratory of society

NASA Astrophysics Data System (ADS)

Cederman, L.-E.; Conte, R.; Helbing, D.; Nowak, A.; Schweitzer, F.; Vespignani, A.

2012-11-01

A huge flow of quantitative social, demographic and behavioral data is becoming available that traces the activities and interactions of individuals, social patterns, transportation infrastructures and travel fluxes. This has caused, together with innovative computational techniques and methods for modeling social actions in hybrid (natural and artificial) societies, a qualitative change in the ways we model socio-technical systems. For the first time, society can be studied in a comprehensive fashion that addresses social and behavioral complexity. In other words we are in the position to envision the development of large data and computational cyber infrastructure defining an exploratory of society that provides quantitative anticipatory, explanatory and scenario analysis capabilities ranging from emerging infectious disease to conflict and crime surges. The goal of the exploratory of society is to provide the basic infrastructure embedding the framework of tools and knowledge needed for the design of forecast/anticipatory/crisis management approaches to socio technical systems, supporting future decision making procedures by accelerating the scientific cycle that goes from data generation to predictions.

Macroscopic Spatial Complexity of the Game of Life Cellular Automaton: A Simple Data Analysis

NASA Astrophysics Data System (ADS)

Hernández-Montoya, A. R.; Coronel-Brizio, H. F.; Rodríguez-Achach, M. E.

In this chapter we present a simple data analysis of an ensemble of 20 time series, generated by averaging the spatial positions of the living cells for each state of the Game of Life Cellular Automaton (GoL). We show that at the macroscopic level described by these time series, complexity properties of GoL are also presented and the following emergent properties, typical of data extracted complex systems such as financial or economical come out: variations of the generated time series following an asymptotic power law distribution, large fluctuations tending to be followed by large fluctuations, and small fluctuations tending to be followed by small ones, and fast decay of linear correlations, however, the correlations associated to their absolute variations exhibit a long range memory. Finally, a Detrended Fluctuation Analysis (DFA) of the generated time series, indicates that the GoL spatial macro states described by the time series are not either completely ordered or random, in a measurable and very interesting way.

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

NASA Astrophysics Data System (ADS)

Imani, Farhad; Daoud, Mohammad; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin

2011-03-01

Time series analysis of ultrasound radio-frequency (RF) signals has been shown to be an effective tissue classification method. Previous studies of this method for tissue differentiation at high and clinical-frequencies have been reported. In this paper, analysis of RF time series is extended to improve tissue classification at the clinical frequencies by including novel features extracted from the time series spectrum. The primary feature examined is the Mean Central Frequency (MCF) computed for regions of interest (ROIs) in the tissue extending along the axial axis of the transducer. In addition, the intercept and slope of a line fitted to the MCF-values of the RF time series as a function of depth have been included. To evaluate the accuracy of the new features, an in vitro animal study is performed using three tissue types: bovine muscle, bovine liver, and chicken breast, where perfect two-way classification is achieved. The results show statistically significant improvements over the classification accuracies with previously reported features.

Memory and betweenness preference in temporal networks induced from time series

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Multiscale multifractal detrended cross-correlation analysis of financial time series

NASA Astrophysics Data System (ADS)

Shi, Wenbin; Shang, Pengjian; Wang, Jing; Lin, Aijing

2014-06-01

In this paper, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). The method allows us to extend the description of the cross-correlation properties between two time series. MM-DCCA may provide new ways of measuring the nonlinearity of two signals, and it helps to present much richer information than multifractal detrended cross-correlation analysis (MF-DCCA) by sweeping all the range of scale at which the multifractal structures of complex system are discussed. Moreover, to illustrate the advantages of this approach we make use of the MM-DCCA to analyze the cross-correlation properties between financial time series. We show that this new method can be adapted to investigate stock markets under investigation. It can provide a more faithful and more interpretable description of the dynamic mechanism between financial time series than traditional MF-DCCA. We also propose to reduce the scale ranges to analyze short time series, and some inherent properties which remain hidden when a wide range is used may exhibit perfectly in this way.

Towards human-computer synergetic analysis of large-scale biological data.

PubMed

Singh, Rahul; Yang, Hui; Dalziel, Ben; Asarnow, Daniel; Murad, William; Foote, David; Gormley, Matthew; Stillman, Jonathan; Fisher, Susan

2013-01-01

Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user-directed information visualization, data exploration, and hypotheses formulation. Second, to illustrate the proposed design paradigm and measure its efficacy, we describe two prototype web applications. The first, called XMAS (Experiential Microarray Analysis System) is designed for analysis of time-series transcriptional data. The second system, called PSPACE (Protein Space Explorer) is designed for holistic analysis of structural and structure-function relationships using interactive low-dimensional maps of the protein structure space. Both these systems promote and facilitate human-computer synergy, where cognitive elements such as domain knowledge, contextual reasoning, and purpose-driven exploration, are integrated with a host of powerful algorithmic operations that support large-scale data analysis, multifaceted data visualization, and multi-source information integration. The proposed design philosophy, combines visualization, algorithmic components and cognitive expertise into a seamless processing-analysis-exploration framework that facilitates sense-making, exploration, and discovery. Using XMAS, we present case studies that analyze transcriptional data from two highly complex domains: gene expression in the placenta during human pregnancy and reaction of marine organisms to heat stress. With PSPACE, we demonstrate how complex structure-function relationships can be explored. These results demonstrate the novelty, advantages, and distinctions of the proposed paradigm. Furthermore, the results also highlight how domain insights can be combined with algorithms to discover meaningful knowledge and formulate evidence-based hypotheses during the data analysis process. Finally, user studies against comparable systems indicate that both XMAS and PSPACE deliver results with better interpretability while placing lower cognitive loads on the users. XMAS is available at: http://tintin.sfsu.edu:8080/xmas. PSPACE is available at: http://pspace.info/.

Towards human-computer synergetic analysis of large-scale biological data

PubMed Central

2013-01-01

Background Advances in technology have led to the generation of massive amounts of complex and multifarious biological data in areas ranging from genomics to structural biology. The volume and complexity of such data leads to significant challenges in terms of its analysis, especially when one seeks to generate hypotheses or explore the underlying biological processes. At the state-of-the-art, the application of automated algorithms followed by perusal and analysis of the results by an expert continues to be the predominant paradigm for analyzing biological data. This paradigm works well in many problem domains. However, it also is limiting, since domain experts are forced to apply their instincts and expertise such as contextual reasoning, hypothesis formulation, and exploratory analysis after the algorithm has produced its results. In many areas where the organization and interaction of the biological processes is poorly understood and exploratory analysis is crucial, what is needed is to integrate domain expertise during the data analysis process and use it to drive the analysis itself. Results In context of the aforementioned background, the results presented in this paper describe advancements along two methodological directions. First, given the context of biological data, we utilize and extend a design approach called experiential computing from multimedia information system design. This paradigm combines information visualization and human-computer interaction with algorithms for exploratory analysis of large-scale and complex data. In the proposed approach, emphasis is laid on: (1) allowing users to directly visualize, interact, experience, and explore the data through interoperable visualization-based and algorithmic components, (2) supporting unified query and presentation spaces to facilitate experimentation and exploration, (3) providing external contextual information by assimilating relevant supplementary data, and (4) encouraging user-directed information visualization, data exploration, and hypotheses formulation. Second, to illustrate the proposed design paradigm and measure its efficacy, we describe two prototype web applications. The first, called XMAS (Experiential Microarray Analysis System) is designed for analysis of time-series transcriptional data. The second system, called PSPACE (Protein Space Explorer) is designed for holistic analysis of structural and structure-function relationships using interactive low-dimensional maps of the protein structure space. Both these systems promote and facilitate human-computer synergy, where cognitive elements such as domain knowledge, contextual reasoning, and purpose-driven exploration, are integrated with a host of powerful algorithmic operations that support large-scale data analysis, multifaceted data visualization, and multi-source information integration. Conclusions The proposed design philosophy, combines visualization, algorithmic components and cognitive expertise into a seamless processing-analysis-exploration framework that facilitates sense-making, exploration, and discovery. Using XMAS, we present case studies that analyze transcriptional data from two highly complex domains: gene expression in the placenta during human pregnancy and reaction of marine organisms to heat stress. With PSPACE, we demonstrate how complex structure-function relationships can be explored. These results demonstrate the novelty, advantages, and distinctions of the proposed paradigm. Furthermore, the results also highlight how domain insights can be combined with algorithms to discover meaningful knowledge and formulate evidence-based hypotheses during the data analysis process. Finally, user studies against comparable systems indicate that both XMAS and PSPACE deliver results with better interpretability while placing lower cognitive loads on the users. XMAS is available at: http://tintin.sfsu.edu:8080/xmas. PSPACE is available at: http://pspace.info/. PMID:24267485

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

Comparison of detrending methods for fluctuation analysis in hydrology

NASA Astrophysics Data System (ADS)

Zhang, Qiang; Zhou, Yu; Singh, Vijay P.; Chen, Yongqin David

2011-03-01

SummaryTrends within a hydrologic time series can significantly influence the scaling results of fluctuation analysis, such as rescaled range (RS) analysis and (multifractal) detrended fluctuation analysis (MF-DFA). Therefore, removal of trends is important in the study of scaling properties of the time series. In this study, three detrending methods, including adaptive detrending algorithm (ADA), Fourier-based method, and average removing technique, were evaluated by analyzing numerically generated series and observed streamflow series with obvious relative regular periodic trend. Results indicated that: (1) the Fourier-based detrending method and ADA were similar in detrending practices, and given proper parameters, these two methods can produce similarly satisfactory results; (2) detrended series by Fourier-based detrending method and ADA lose the fluctuation information at larger time scales, and the location of crossover points is heavily impacted by the chosen parameters of these two methods; and (3) the average removing method has an advantage over the other two methods, i.e., the fluctuation information at larger time scales is kept well-an indication of relatively reliable performance in detrending. In addition, the average removing method performed reasonably well in detrending a time series with regular periods or trends. In this sense, the average removing method should be preferred in the study of scaling properties of the hydrometeorolgical series with relative regular periodic trend using MF-DFA.

Validating the Farsi version of the Pregnancy Worries and Stress Questionnaire (PWSQ): An exploratory factor analysis.

PubMed

Navidpour, Fariba; Dolatian, Mahrokh; Shishehgar, Sara; Yaghmaei, Farideh; Majd, Hamid Alavi; Hashemi, Seyed Saeed

2016-10-01

Biological, environmental, inter- and intrapersonal changes during the antenatal period can result in anxiety and stress in pregnant women. It is pivotal to identify potential stressors and prevent their foetal and maternal consequences. The present study was conducted to validate and examine the factor structure of the Farsi version of the Pregnancy Worries and Stress Questionnaire (PWSQ). In 2015, 502 Iranian healthy pregnant women, referred to selected hospitals in Tehran for prenatal care at 8-39 weeks of pregnancy, were recruited through a randomized cluster sampling. The PWSQ was translated into Farsi, and its validity and reliability were examined using exploratory factor analysis by SPSS version 21. The content validity of items on the PWSQ was between 0.63-1. The content validity index for relevance, clarity and simplicity were 0.92, 0.98, and 0.98, respectively, with a mean of 0.94. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.863. Test-retest reliability showed high internal consistency (α=0.89; p<0.0001). The psychometric evaluation and exploratory factor analysis showed that the translated questionnaire is a valid and reliable tool to identify stress in Iranian pregnant women. Application of the questionnaire can facilitate the diagnosis of stress in pregnant women and assist health care providers in providing timely support and minimizing negative outcomes of stress and anxiety in pregnant women and their infants.

The Sensitivity of the Crayfish Reward System to Mammalian Drugs of Abuse.

PubMed

Shipley, Adam T; Imeh-Nathaniel, Adebobola; Orfanakos, Vasiliki B; Wormack, Leah N; Huber, Robert; Nathaniel, Thomas I

2017-01-01

The idea that addiction occurs when the brain is not able to differentiate whether specific reward circuits were triggered by adaptive natural rewards or falsely activated by addictive drugs exist in several models of drug addiction. The suitability of crayfish ( Orconectes rusticus ) for drug addiction research arises from developmental variation of growth, life span, reproduction, behavior and some quantitative traits, especially among isogenic mates reared in the same environment. This broad spectrum of traits makes it easier to analyze the effect of mammalian drugs of abuse in shaping behavioral phenotype. Moreover, the broad behavioral repertoire allows the investigation of self-reinforcing circuitries involving appetitive and exploratory motor behavior, while the step-wise alteration of the phenotype by metamorphosis allows accurate longitudinal analysis of different behavioral states. This paper reviews a series of recent experimental findings that evidence the suitability of crayfish as an invertebrate model system for the study of drug addiction. Results from these studies reveal that unconditioned exposure to mammalian drugs of abuse produces a variety of stereotyped behaviors. Moreover, if presented in the context of novelty, drugs directly stimulate exploration and appetitive motor patterns along with molecular processes for drug conditioned reward. Findings from these studies indicate the existence of drug sensitive circuitry in crayfish that facilitates exploratory behavior and appetitive motor patterns via increased incentive salience of environmental stimuli or by increasing exploratory motor patterns. This work demonstrates the potential of crayfish as a model system for research into the neural mechanisms of addiction, by contributing an evolutionary, comparative context to our understanding of natural reward as an important life-sustaining process.

Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics.

PubMed

Gavrishchaka, Valeriy; Senyukova, Olga; Davis, Kristina

2015-01-01

Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.

Influence of a component of solar irradiance on radon signals at 1000 meter depth at the Gran Sasso Laboratory, Italy

NASA Astrophysics Data System (ADS)

Gazit-Yaari (Charit-Yaari), N.; Steinitz, G.; Piatibratova, O.

2012-04-01

Exploratory monitoring of radon is conducted at one site at the deep underground Gran Sasso National Laboratory (LNGS; 1,000m below the surface). Monitoring is performed in a small secluded space separated by a sealed partition from the entirety of the laboratory environment in air in contact with the exposed surrounding calcareous country rock. Overall radon levels are low (0.45 kBq/m3). Utilizing both alpha and gamma-ray detectors measurements (15-minute resolution) cover a time span of ca. 600 days. Systematic and recurring radon signals are recorded consisting of two primary signal types: a) non-periodic Multi-Day (MD) signals lasting 2-10 days, and b) Daily Radon (DR) signals - which are of a periodic nature exhibiting a primary 24-hour cycle. Temperature in the closed enclosure is stable (11.5±0.3 °C) and pressure reflects above surface barometric variations. Analysis and comparison in the time and frequency domains (FFT) of local environmental data (P, T) indicates that these do not drive radon variation in air at the site. The phenomenology of the MD and DR signals is similar to situations encountered at other locations where radon is monitored with a high time resolution in geogas at upper crustal levels. Using the Continuous Wavelet Transform analysis tool a different variation pattern is observed for time series consisting of day-time and night-time measurement of the gamma radiation from radon progeny. Applying the same analysis to the time series of local air pressure does not reveal a day-time and night-time difference. The observation of a differing day/night pattern in the gamma radiation from radon at LNGS is similar to further occurrences at other subsurface locations. Production of a day/night pattern must be related to rotation of Earth around its axis. This phenomenon is a further confirmation of the recent proposition as to the influence of a component of solar irradiance on the nuclear radiation from radon in air. The occurrence of these radon signals in the 1 km deep low radiation underground geological environment of LNGS provides new information on the time variation of the local radiation environment. The observations and results place the LNGS facility as a high priority location for performing advanced investigations of these geophysical phenomena, due to its location and its infrastructure. New multi disciplinary prospects for the research are indicated in terms of a) the radioactive behavior of radon in above and subsurface air, b) an above surface geophysical driver for this behavior and, c) the influence of a component of solar irradiation.

Comparisons of Exploratory and Confirmatory Factor Analysis.

ERIC Educational Resources Information Center

Daniel, Larry G.

Historically, most researchers conducting factor analysis have used exploratory methods. However, more recently, confirmatory factor analytic methods have been developed that can directly test theory either during factor rotation using "best fit" rotation methods or during factor extraction, as with the LISREL computer programs developed…

Long Term Precipitation Pattern Identification and Derivation of Non Linear Precipitation Trend in a Catchment using Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Unnikrishnan, Poornima; Jothiprakash, Vinayakam

2017-04-01

Precipitation is the major component in the hydrologic cycle. Awareness of not only the total amount of rainfall pertaining to a catchment, but also the pattern of its spatial and temporal distribution are equally important in the management of water resources systems in an efficient way. Trend is the long term direction of a time series; it determines the overall pattern of a time series. Singular Spectrum Analysis (SSA) is a time series analysis technique that decomposes the time series into small components (eigen triples). This property of the method of SSA has been utilized to extract the trend component of the rainfall time series. In order to derive trend from the rainfall time series, we need to select components corresponding to trend from the eigen triples. For this purpose, periodogram analysis of the eigen triples have been proposed to be coupled with SSA, in the present study. In the study, seasonal data of England and Wales Precipitation (EWP) for a time period of 1766-2013 have been analyzed and non linear trend have been derived out of the precipitation data. In order to compare the performance of SSA in deriving trend component, Mann Kendall (MK) test is also used to detect trends in EWP seasonal series and the results have been compared. The result showed that the MK test could detect the presence of positive or negative trend for a significance level, whereas the proposed methodology of SSA could extract the non-linear trend present in the rainfall series along with its shape. We will discuss further the comparison of both the methodologies along with the results in the presentation.

Improving Skill Development: An Exploratory Study Comparing a Philosophical and an Applied Ethical Analysis Technique

ERIC Educational Resources Information Center

Al-Saggaf, Yeslam; Burmeister, Oliver K.

2012-01-01

This exploratory study compares and contrasts two types of critical thinking techniques; one is a philosophical and the other an applied ethical analysis technique. The two techniques analyse an ethically challenging situation involving ICT that a recent media article raised to demonstrate their ability to develop the ethical analysis skills of…

Factor Retention in Exploratory Factor Analysis: A Comparison of Alternative Methods.

ERIC Educational Resources Information Center

Mumford, Karen R.; Ferron, John M.; Hines, Constance V.; Hogarty, Kristine Y.; Kromrey, Jeffery D.

This study compared the effectiveness of 10 methods of determining the number of factors to retain in exploratory common factor analysis. The 10 methods included the Kaiser rule and a modified Kaiser criterion, 3 variations of parallel analysis, 4 regression-based variations of the scree procedure, and the minimum average partial procedure. The…

Seeing the Forest Despite the Trees: The Benefit of Exploratory Data Analysis to Program Evaluation Research.

ERIC Educational Resources Information Center

Sinacore, James M.; And Others

1992-01-01

It is argued that there is a benefit to applying techniques of exploratory data analysis (EDA) to program evaluation. The evaluation of a rehabilitation program for people with rheumatoid arthritis (20 subjects and 21 comparisons) through EDA supports the argument, indicating outcomes more precisely than conventional analysis of variance. (SLD)

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.

PubMed

Marken, John P; Halleran, Andrew D; Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C; Golino, Caroline A; Kemper, Peter; Saha, Margaret S

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.

The parametric modified limited penetrable visibility graph for constructing complex networks from time series

NASA Astrophysics Data System (ADS)

Li, Xiuming; Sun, Mei; Gao, Cuixia; Han, Dun; Wang, Minggang

2018-02-01

This paper presents the parametric modified limited penetrable visibility graph (PMLPVG) algorithm for constructing complex networks from time series. We modify the penetrable visibility criterion of limited penetrable visibility graph (LPVG) in order to improve the rationality of the original penetrable visibility and preserve the dynamic characteristics of the time series. The addition of view angle provides a new approach to characterize the dynamic structure of the time series that is invisible in the previous algorithm. The reliability of the PMLPVG algorithm is verified by applying it to three types of artificial data as well as the actual data of natural gas prices in different regions. The empirical results indicate that PMLPVG algorithm can distinguish the different time series from each other. Meanwhile, the analysis results of natural gas prices data using PMLPVG are consistent with the detrended fluctuation analysis (DFA). The results imply that the PMLPVG algorithm may be a reasonable and significant tool for identifying various time series in different fields.

Characterizing Time Series Data Diversity for Wind Forecasting: Preprint

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

Hodge, Brian S; Chartan, Erol Kevin; Feng, Cong

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the importantmore » information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.« less

77 FR 76135 - Self-Regulatory Organizations; Chicago Board Options Exchange, Incorporated; Notice of Filing of...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-26

... 500 Index option series in the pilot: (1) A time series analysis of open interest; and (2) an analysis... issue's total market share value, which is the share price times the number of shares outstanding. These... other series. Strike price intervals would be set no less than 5 points apart. Consistent with existing...

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

Kamışlıoğlu, Miraç, E-mail: m.kamislioglu@gmail.com; Külahcı, Fatih, E-mail: fatihkulahci@firat.edu.tr

Nonlinear time series analysis techniques have large application areas on the geoscience and geophysics fields. Modern nonlinear methods are provided considerable evidence for explain seismicity phenomena. In this study nonlinear time series analysis, fractal analysis and spectral analysis have been carried out for researching the chaotic behaviors of release radon gas ({sup 222}Rn) concentration occurring during seismic events. Nonlinear time series analysis methods (Lyapunov exponent, Hurst phenomenon, correlation dimension and false nearest neighbor) were applied for East Anatolian Fault Zone (EAFZ) Turkey and its surroundings where there are about 35,136 the radon measurements for each region. In this paper weremore » investigated of {sup 222}Rn behavior which it’s used in earthquake prediction studies.« less

Time Series Analysis Based on Running Mann Whitney Z Statistics

USDA-ARS?s Scientific Manuscript database

A sensitive and objective time series analysis method based on the calculation of Mann Whitney U statistics is described. This method samples data rankings over moving time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-...

Aboriginal users of Canadian quitlines: an exploratory analysis

PubMed Central

Hayward, Lynda M; Campbell, H Sharon; Sutherland‐Brown, Carol

2007-01-01

Objectives To conduct an exploratory, comparative study of the utilisation and effectiveness of tobacco cessation quitlines among aboriginal and non‐aboriginal Canadian smokers. Setting Population based quitlines that provide free cessation information, advice and counselling to Canadian smokers. Subjects First time quitline callers, age 18 years of age and over, who called the quitline between August 2001 and December 2005 and who completed the evaluation and provided data on their ethnic status (n = 7082). Main measures Demographic characteristics and tobacco behaviours of participants at intake and follow‐up; reasons for calling; actions taken toward quitting, and 6‐month follow‐up quit rates. Results 7% of evaluation participants in the time period reported aboriginal origins. Aboriginal participants were younger than non‐aboriginals but had similar smoking status and level of addiction at intake. Concern about future health and current health problems were the most common reasons aboriginal participants called. Six months after intake aboriginals and non‐aboriginals had taken similar actions with 57% making a 24‐hour quit attempt. Quit rates were higher for aboriginals than non‐aboriginals, particularly for men. The 6‐month prolonged abstinence rate for aboriginal men was 16.7% compared with 7.2% for aboriginal women and 9.4% and 8.3% for non‐aboriginal men and women, respectively. Conclusions This exploratory analysis showed that even without targeted promotion, aboriginal smokers do call Canadian quitlines, primarily for health related reasons. We also showed that the quitlines are effective at helping them to quit. As a population focused intervention, quitlines can reach a large proportion of smokers in a cost efficient manner. In aboriginal communities where smoking rates exceed 50% and multiple health risks and chronic diseases already exist, eliminating non‐ceremonial tobacco use must be a priority. Our results, although exploratory, suggest quitlines can be an effective addition to aboriginal tobacco cessation strategies. PMID:18048634

Indole RSK inhibitors. Part 1: discovery and initial SAR.

PubMed

Boyer, Stephen J; Burke, Jennifer; Guo, Xin; Kirrane, Thomas M; Snow, Roger J; Zhang, Yunlong; Sarko, Chris; Soleymanzadeh, Lida; Swinamer, Alan; Westbrook, John; Dicapua, Frank; Padyana, Anil; Cogan, Derek; Gao, Amy; Xiong, Zhaoming; Madwed, Jeffrey B; Kashem, Mohammed; Kugler, Stanley; O'Neill, Margaret M

2012-01-01

A series of inhibitors for the 90 kDa ribosomal S6 kinase (RSK) based on an 1-oxo-2,3,4,5-tetrahydro-1H-[1,4]diazepino[1,2-a]indole-8-carboxamide scaffold were identified through high throughput screening. An RSK crystal structure and exploratory SAR were used to define the series pharmacophore. Compounds with good cell potency, such as compounds 43, 44, and 55 were identified, and form the basis for subsequent kinase selectivity optimization. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C ( τ ), as opposed to standard methods that decompose the time series, X ( t ), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

Using machine learning to identify structural breaks in single-group interrupted time series designs.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

Analysis and generation of groundwater concentration time series

NASA Astrophysics Data System (ADS)

Crăciun, Maria; Vamoş, Călin; Suciu, Nicolae

2018-01-01

Concentration time series are provided by simulated concentrations of a nonreactive solute transported in groundwater, integrated over the transverse direction of a two-dimensional computational domain and recorded at the plume center of mass. The analysis of a statistical ensemble of time series reveals subtle features that are not captured by the first two moments which characterize the approximate Gaussian distribution of the two-dimensional concentration fields. The concentration time series exhibit a complex preasymptotic behavior driven by a nonstationary trend and correlated fluctuations with time-variable amplitude. Time series with almost the same statistics are generated by successively adding to a time-dependent trend a sum of linear regression terms, accounting for correlations between fluctuations around the trend and their increments in time, and terms of an amplitude modulated autoregressive noise of order one with time-varying parameter. The algorithm generalizes mixing models used in probability density function approaches. The well-known interaction by exchange with the mean mixing model is a special case consisting of a linear regression with constant coefficients.

Mental Toughness in Education: Exploring Relationships with Attainment, Attendance, Behaviour and Peer Relationships

ERIC Educational Resources Information Center

St Clair-Thompson, Helen; Bugler, Myfanwy; Robinson, Jamey; Clough, Peter; McGeown, Sarah P.; Perry, John

2015-01-01

Mental toughness has frequently been associated with successful performance in sport; however, recent research suggests that it may also be related to academic performance in Higher Education. In a series of three exploratory studies, we examined the relationship between mental toughness and different aspects of educational performance in…

Establishing a Common Language: The Meaning of Research-Based and Evidence-Based Programming (in the Human Sciences)

ERIC Educational Resources Information Center

Sellers, Debra M.; Schainker, Lisa M.; Lockhart, Peggy; Yeh, Hsiu Chen

2017-01-01

This article describes the development, implementation, and exploratory evaluation of a professional development series that addressed educators' knowledge and use of the terms "research-based" and "evidence-based" within Human Sciences Extension and Outreach at one university. Respondents to a follow-up survey were more likely…

Best Practices for Teaching Those Afraid in Water

ERIC Educational Resources Information Center

Stillwell, Belinda E.

2011-01-01

The primary purpose of this study is to share what has been found to work well in professional practice based on a series of exploratory scholarly studies as well as information gathered informally from students and through specialized aquatic workshops, conferences and seminars. Research has shown that there is an existing population of at-risk…

Photography. Technology Learning Activity. Teacher Edition. Technology Education Series.

ERIC Educational Resources Information Center

Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This curriculum guide provides technology learning activities designed to prepare students in grades 6-10 to work in the world of the future. The 8-day course provides exploratory, hands-on learning activities and information that can enhance the education of students of all types in an integrated curriculum that provides practical applications of…

An Exploratory Study of Software Cost Estimating at the Electronic Systems Division.

DTIC Science & Technology

1976-07-01

action’. to improve the software cost Sestimating proces., While thin research was limited to the M.nD onvironment, the same types of problema may exist...Methods in Social Science. Now York: Random House, 1969. 57. Smith, Ronald L. Structured Programming Series (Vol. XI) - Estimating Software Project

Robotics-Control Technology. Technology Learning Activity. Teacher Edition. Technology Education Series.

ERIC Educational Resources Information Center

Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This document contains the materials required for presenting an 8-day competency-based technology learning activity (TLA) designed to introduce students in grades 6-10 to advances and career opportunities in the field of robotics-control technology. The guide uses hands-on exploratory experiences into which activities to help students develop…

Exploring Internet Visibility of Eminent Alumni: Variables and Correlates.

ERIC Educational Resources Information Center

Ridley, Dennis R.; Matveev, Alexei G.; Cuevas, Nuria M.

This paper reports an exploratory study that is the second in a series concerned with measuring Internet visibility as it influences colleges and universities. The purpose was to examine one possible source of Internet visibility, the association of eminent alumni with particular colleges and universities. College-educated U.S. presidents were…

Experimental fungicidal control of blister rust on sugar pine in California

Treesearch

Clarence R. Quick

1964-01-01

Parallel series of exploratory experiments with antifungal antibiotics and conventional chemical fungicides for control of blister rust on sugar pine were started in northern California in 1959. Several fungicides, both antibiotic and conventional, appear slightly systemic, but all tested materials are more effective when sprayed directly on infected tissues....

Outsourcing of Instruction at Community Colleges

ERIC Educational Resources Information Center

Bailey, Thomas; Jacobs, James; Jenkins, Davis

2004-01-01

This report presents the findings of exploratory research designed to identify the characteristics of the outsourcing of instruction at community colleges and the forces that promote or block its spread. It is the second in a series of reports by the National Center for Postsecondary Improvement and the Community College Research Center on the…

The Impact of Commercially Promoted Vocational Degrees on the Student Experience

ERIC Educational Resources Information Center

Molesworth, Mike; Scullion, Richard

2005-01-01

Exploratory focus group research with undergraduate students reveals a series of related tensions that students experience about vocational marketing and communication degrees that have been promoted to them primarily on the basis of job prospects and university location. We summarise these tensions in six themes: short versus long-term goals;…

Tecemotide in unresectable stage III non-small-cell lung cancer in the phase III START study: updated overall survival and biomarker analyses.

PubMed

Mitchell, P; Thatcher, N; Socinski, M A; Wasilewska-Tesluk, E; Horwood, K; Szczesna, A; Martín, C; Ragulin, Y; Zukin, M; Helwig, C; Falk, M; Butts, C; Shepherd, F A

2015-06-01

Tecemotide is a MUC1-antigen-specific cancer immunotherapy. The phase III START study did not meet its primary end point but reported notable survival benefit with tecemotide versus placebo in an exploratory analysis of the predefined patient subgroup treated with concurrent chemoradiotherapy. Here, we attempted to gain further insight into the effects of tecemotide in START. START recruited patients who did not progress following frontline chemoradiotherapy for unresectable stage III non-small-cell lung cancer. We present updated overall survival (OS) data and exploratory analyses of OS for baseline biomarkers: soluble MUC1 (sMUC1), antinuclear antibodies (ANA), neutrophil/lymphocyte ratio (NLR), lymphocyte count, and HLA type. Updated OS data are consistent with the primary analysis: median 25.8 months (tecemotide) versus 22.4 months (placebo) (HR 0.89, 95% CI 0.77-1.03, P = 0.111), with ∼20 months additional median follow-up time compared with the primary analysis. Exploratory analysis of the predefined subgroup treated with concurrent chemoradiotherapy revealed clinically relevant prolonged OS with tecemotide versus placebo (29.4 versus 20.8 months; HR 0.81, 95% CI 0.68-0.98, P = 0.026). No improvement was seen with sequential chemoradiotherapy. High sMUC1 and ANA correlated with a possible survival benefit with tecemotide (interaction P = 0.0085 and 0.0022) and might have future value as biomarkers. Interactions between lymphocyte count, NLR, or prespecified HLA alleles and treatment effect were not observed. Updated OS data support potential treatment benefit with tecemotide in patients treated with concurrent chemoradiotherapy. Exploratory biomarker analyses suggest that elevated sMUC1 or ANA levels correlate with tecemotide benefit. NCT00409188. © The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

On statistical inference in time series analysis of the evolution of road safety.

PubMed

Commandeur, Jacques J F; Bijleveld, Frits D; Bergel-Hayat, Ruth; Antoniou, Constantinos; Yannis, George; Papadimitriou, Eleonora

2013-11-01

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Modeling the outcomes of nursing home care.

PubMed

Rohrer, J E; Hogan, A J

1987-01-01

In this exploratory analysis using data on 290 patients, we use regression analysis to model patient outcomes in two Veterans Administration nursing homes. We find resource use, as measured with minutes of nursing time, to be associated with outcomes when case mix is controlled. Our results suggest that, under case-based reimbursement systems, nursing homes could increase their revenues by withholding unskilled and psychosocial care and discouraging physicians' visits. Implications for nursing home policy are discussed.

Modelling short time series in metabolomics: a functional data analysis approach.

PubMed

Montana, Giovanni; Berk, Maurice; Ebbels, Tim

2011-01-01

Metabolomics is the study of the complement of small molecule metabolites in cells, biofluids and tissues. Many metabolomic experiments are designed to compare changes observed over time under two or more experimental conditions (e.g. a control and drug-treated group), thus producing time course data. Models from traditional time series analysis are often unsuitable because, by design, only very few time points are available and there are a high number of missing values. We propose a functional data analysis approach for modelling short time series arising in metabolomic studies which overcomes these obstacles. Our model assumes that each observed time series is a smooth random curve, and we propose a statistical approach for inferring this curve from repeated measurements taken on the experimental units. A test statistic for detecting differences between temporal profiles associated with two experimental conditions is then presented. The methodology has been applied to NMR spectroscopy data collected in a pre-clinical toxicology study.

Identifying arsenic trioxide (ATO) functions in leukemia cells by using time series gene expression profiles.

PubMed

Yang, Hong; Lin, Shan; Cui, Jingru

2014-02-10

Arsenic trioxide (ATO) is presently the most active single agent in the treatment of acute promyelocytic leukemia (APL). In order to explore the molecular mechanism of ATO in leukemia cells with time series, we adopted bioinformatics strategy to analyze expression changing patterns and changes in transcription regulation modules of time series genes filtered from Gene Expression Omnibus database (GSE24946). We totally screened out 1847 time series genes for subsequent analysis. The KEGG (Kyoto encyclopedia of genes and genomes) pathways enrichment analysis of these genes showed that oxidative phosphorylation and ribosome were the top 2 significantly enriched pathways. STEM software was employed to compare changing patterns of gene expression with assigned 50 expression patterns. We screened out 7 significantly enriched patterns and 4 tendency charts of time series genes. The result of Gene Ontology showed that functions of times series genes mainly distributed in profiles 41, 40, 39 and 38. Seven genes with positive regulation of cell adhesion function were enriched in profile 40, and presented the same first increased model then decreased model as profile 40. The transcription module analysis showed that they mainly involved in oxidative phosphorylation pathway and ribosome pathway. Overall, our data summarized the gene expression changes in ATO treated K562-r cell lines with time and suggested that time series genes mainly regulated cell adhesive. Furthermore, our result may provide theoretical basis of molecular biology in treating acute promyelocytic leukemia. Copyright © 2013 Elsevier B.V. All rights reserved.

P-MartCancer-Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets.

PubMed

Webb-Robertson, Bobbie-Jo M; Bramer, Lisa M; Jensen, Jeffrey L; Kobold, Markus A; Stratton, Kelly G; White, Amanda M; Rodland, Karin D

2017-11-01

P-MartCancer is an interactive web-based software environment that enables statistical analyses of peptide or protein data, quantitated from mass spectrometry-based global proteomics experiments, without requiring in-depth knowledge of statistical programming. P-MartCancer offers a series of statistical modules associated with quality assessment, peptide and protein statistics, protein quantification, and exploratory data analyses driven by the user via customized workflows and interactive visualization. Currently, P-MartCancer offers access and the capability to analyze multiple cancer proteomic datasets generated through the Clinical Proteomics Tumor Analysis Consortium at the peptide, gene, and protein levels. P-MartCancer is deployed as a web service (https://pmart.labworks.org/cptac.html), alternatively available via Docker Hub (https://hub.docker.com/r/pnnl/pmart-web/). Cancer Res; 77(21); e47-50. ©2017 AACR . ©2017 American Association for Cancer Research.

Exploratory and Higher-Order Factor Analyses of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) Adolescent Subsample

ERIC Educational Resources Information Center

Canivez, Gary L.; Watkins, Marley W.

2010-01-01

The factor structure of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV; Wechsler, 2008a) with the adolescent participants (ages 16-19 years; N = 400) in the standardization sample was assessed using exploratory factor analysis, multiple factor extraction criteria, and higher-order exploratory factor analyses. Results from…

Influence analysis for high-dimensional time series with an application to epileptic seizure onset zone detection

PubMed Central

Flamm, Christoph; Graef, Andreas; Pirker, Susanne; Baumgartner, Christoph; Deistler, Manfred

2013-01-01

Granger causality is a useful concept for studying causal relations in networks. However, numerical problems occur when applying the corresponding methodology to high-dimensional time series showing co-movement, e.g. EEG recordings or economic data. In order to deal with these shortcomings, we propose a novel method for the causal analysis of such multivariate time series based on Granger causality and factor models. We present the theoretical background, successfully assess our methodology with the help of simulated data and show a potential application in EEG analysis of epileptic seizures. PMID:23354014

Analysis of Land Subsidence Monitoring in Mining Area with Time-Series Insar Technology

NASA Astrophysics Data System (ADS)

Sun, N.; Wang, Y. J.

2018-04-01

Time-series InSAR technology has become a popular land subsidence monitoring method in recent years, because of its advantages such as high accuracy, wide area, low expenditure, intensive monitoring points and free from accessibility restrictions. In this paper, we applied two kinds of satellite data, ALOS PALSAR and RADARSAT-2, to get the subsidence monitoring results of the study area in two time periods by time-series InSAR technology. By analyzing the deformation range, rate and amount, the time-series analysis of land subsidence in mining area was realized. The results show that InSAR technology could be used to monitor land subsidence in large area and meet the demand of subsidence monitoring in mining area.

The use of exploratory analyses within the National Institute for Health and Care Excellence single technology appraisal process: an evaluation and qualitative analysis.

PubMed

Kaltenthaler, Eva; Carroll, Christopher; Hill-McManus, Daniel; Scope, Alison; Holmes, Michael; Rice, Stephen; Rose, Micah; Tappenden, Paul; Woolacott, Nerys

2016-04-01

As part of the National Institute for Health and Care Excellence (NICE) single technology appraisal (STA) process, independent Evidence Review Groups (ERGs) critically appraise the company submission. During the critical appraisal process the ERG may undertake analyses to explore uncertainties around the company's model and their implications for decision-making. The ERG reports are a central component of the evidence considered by the NICE Technology Appraisal Committees (ACs) in their deliberations. The aim of this research was to develop an understanding of the number and type of exploratory analyses undertaken by the ERGs within the STA process and to understand how these analyses are used by the NICE ACs in their decision-making. The 100 most recently completed STAs with published guidance were selected for inclusion in the analysis. The documents considered were ERG reports, clarification letters, the first appraisal consultation document and the final appraisal determination. Over 400 documents were assessed in this study. The categories of types of exploratory analyses included fixing errors, fixing violations, addressing matters of judgement and the ERG-preferred base case. A content analysis of documents (documentary analysis) was undertaken to identify and extract relevant data, and narrative synthesis was then used to rationalise and present these data. The level and type of detail in ERG reports and clarification letters varied considerably. The vast majority (93%) of ERG reports reported one or more exploratory analyses. The most frequently reported type of analysis in these 93 ERG reports related to the category 'matters of judgement', which was reported in 83 (89%) reports. The category 'ERG base-case/preferred analysis' was reported in 45 (48%) reports, the category 'fixing errors' was reported in 33 (35%) reports and the category 'fixing violations' was reported in 17 (18%) reports. The exploratory analyses performed were the result of issues raised by an ERG in its critique of the submitted economic evidence. These analyses had more influence on recommendations earlier in the STA process than later on in the process. The descriptions of analyses undertaken were often highly specific to a particular STA and could be inconsistent across ERG reports and thus difficult to interpret. Evidence Review Groups frequently conduct exploratory analyses to test or improve the economic evaluations submitted by companies as part of the STA process. ERG exploratory analyses often have an influence on the recommendations produced by the ACs. More in-depth analysis is needed to understand how ERGs make decisions regarding which exploratory analyses should be undertaken. More research is also needed to fully understand which types of exploratory analyses are most useful to ACs in their decision-making. The National Institute for Health Research Health Technology Assessment programme.

Mobile Visualization and Analysis Tools for Spatial Time-Series Data

NASA Astrophysics Data System (ADS)

Eberle, J.; Hüttich, C.; Schmullius, C.

2013-12-01

The Siberian Earth System Science Cluster (SIB-ESS-C) provides access and analysis services for spatial time-series data build on products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and climate data from meteorological stations. Until now a webportal for data access, visualization and analysis with standard-compliant web services was developed for SIB-ESS-C. As a further enhancement a mobile app was developed to provide an easy access to these time-series data for field campaigns. The app sends the current position from the GPS receiver and a specific dataset (like land surface temperature or vegetation indices) - selected by the user - to our SIB-ESS-C web service and gets the requested time-series data for the identified pixel back in real-time. The data is then being plotted directly in the app. Furthermore the user has possibilities to analyze the time-series data for breaking points and other phenological values. These processings are executed on demand of the user on our SIB-ESS-C web server and results are transfered to the app. Any processing can also be done at the SIB-ESS-C webportal. The aim of this work is to make spatial time-series data and analysis functions available for end users without the need of data processing. In this presentation the author gives an overview on this new mobile app, the functionalities, the technical infrastructure as well as technological issues (how the app was developed, our made experiences).

Connectivism in Postsecondary Online Courses: An Exploratory Factor Analysis

ERIC Educational Resources Information Center

Hogg, Nanette; Lomicky, Carol S.

2012-01-01

This study explores 465 postsecondary students' experiences in online classes through the lens of connectivism. Downes' 4 properties of connectivism (diversity, autonomy, interactivity, and openness) were used as the study design. An exploratory factor analysis was performed. This study found a 4-factor solution. Subjects indicated that autonomy…

Bootstrap Confidence Intervals for Ordinary Least Squares Factor Loadings and Correlations in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Zhang, Guangjian; Preacher, Kristopher J.; Luo, Shanhong

2010-01-01

This article is concerned with using the bootstrap to assign confidence intervals for rotated factor loadings and factor correlations in ordinary least squares exploratory factor analysis. Coverage performances of "SE"-based intervals, percentile intervals, bias-corrected percentile intervals, bias-corrected accelerated percentile…

Establishing Evidence for Internal Structure Using Exploratory Factor Analysis

ERIC Educational Resources Information Center

Watson, Joshua C.

2017-01-01

Exploratory factor analysis (EFA) is a data reduction technique used to condense data into smaller sets of summary variables by identifying underlying factors potentially accounting for patterns of collinearity among said variables. Using an illustrative example, the 5 general steps of EFA are described with best practices for decision making…

Unmanned Multiple Exploratory Probe System (MEPS) for Mars observation. Volume 1: Trade analysis and design

NASA Technical Reports Server (NTRS)

Adams, Daniel E.; Crumbly, Christopher M.; Delp, Steve E.; Guidry, Michelle A.; Lisano, Michael E.; Packard, James D.; Striepe, Scott A.

1988-01-01

This report presents the unmanned Multiple Exploratory Probe Systems (MEPS), a space vehicle designed to observe the planet Mars in preparation for manned missions. The options considered for each major element are presented as a trade analysis, and the final vehicle design is defined.

Model-free fMRI group analysis using FENICA.

PubMed

Schöpf, V; Windischberger, C; Robinson, S; Kasess, C H; Fischmeister, F PhS; Lanzenberger, R; Albrecht, J; Kleemann, A M; Kopietz, R; Wiesmann, M; Moser, E

2011-03-01

Exploratory analysis of functional MRI data allows activation to be detected even if the time course differs from that which is expected. Independent Component Analysis (ICA) has emerged as a powerful approach, but current extensions to the analysis of group studies suffer from a number of drawbacks: they can be computationally demanding, results are dominated by technical and motion artefacts, and some methods require that time courses be the same for all subjects or that templates be defined to identify common components. We have developed a group ICA (gICA) method which is based on single-subject ICA decompositions and the assumption that the spatial distribution of signal changes in components which reflect activation is similar between subjects. This approach, which we have called Fully Exploratory Network Independent Component Analysis (FENICA), identifies group activation in two stages. ICA is performed on the single-subject level, then consistent components are identified via spatial correlation. Group activation maps are generated in a second-level GLM analysis. FENICA is applied to data from three studies employing a wide range of stimulus and presentation designs. These are an event-related motor task, a block-design cognition task and an event-related chemosensory experiment. In all cases, the group maps identified by FENICA as being the most consistent over subjects correspond to task activation. There is good agreement between FENICA results and regions identified in prior GLM-based studies. In the chemosensory task, additional regions are identified by FENICA and temporal concatenation ICA that we show is related to the stimulus, but exhibit a delayed response. FENICA is a fully exploratory method that allows activation to be identified without assumptions about temporal evolution, and isolates activation from other sources of signal fluctuation in fMRI. It has the advantage over other gICA methods that it is computationally undemanding, spotlights components relating to activation rather than artefacts, allows the use of familiar statistical thresholding through deployment of a higher level GLM analysis and can be applied to studies where the paradigm is different for all subjects. Copyright © 2010 Elsevier Inc. All rights reserved.

76 FR 12775 - Self-Regulatory Organizations; C2 Options Exchange, Incorporated; Notice of Filing of a Proposed...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-08

... series in the pilot: (1) A time series analysis of open interest; and (2) An analysis of the distribution... times the number of shares outstanding. These are summed for all 500 stocks and divided by a... below $3.00 and $0.10 for all other series. Strike price intervals would be set no less than 5 points...

Time series behaviour of the number of Air Asia passengers: A distributional approach

NASA Astrophysics Data System (ADS)

Asrah, Norhaidah Mohd; Djauhari, Maman Abdurachman

2013-09-01

The common practice to time series analysis is by fitting a model and then further analysis is conducted on the residuals. However, if we know the distributional behavior of time series, the analyses in model identification, parameter estimation, and model checking are more straightforward. In this paper, we show that the number of Air Asia passengers can be represented as a geometric Brownian motion process. Therefore, instead of using the standard approach in model fitting, we use an appropriate transformation to come up with a stationary, normally distributed and even independent time series. An example in forecasting the number of Air Asia passengers will be given to illustrate the advantages of the method.

Regional Landslide Mapping Aided by Automated Classification of SqueeSAR™ Time Series (Northern Apennines, Italy)

NASA Astrophysics Data System (ADS)

Iannacone, J.; Berti, M.; Allievi, J.; Del Conte, S.; Corsini, A.

2013-12-01

Space borne InSAR has proven to be very valuable for landslides detection. In particular, extremely slow landslides (Cruden and Varnes, 1996) can be now clearly identified, thanks to the millimetric precision reached by recent multi-interferometric algorithms. The typical approach in radar interpretation for landslides mapping is based on average annual velocity of the deformation which is calculated over the entire times series. The Hotspot and Cluster Analysis (Lu et al., 2012) and the PSI-based matrix approach (Cigna et al., 2013) are examples of landslides mapping techniques based on average annual velocities. However, slope movements can be affected by non-linear deformation trends, (i.e. reactivation of dormant landslides, deceleration due to natural or man-made slope stabilization, seasonal activity, etc). Therefore, analyzing deformation time series is crucial in order to fully characterize slope dynamics. While this is relatively simple to be carried out manually when dealing with small dataset, the time series analysis over regional scale dataset requires automated classification procedures. Berti et al. (2013) developed an automatic procedure for the analysis of InSAR time series based on a sequence of statistical tests. The analysis allows to classify the time series into six distinctive target trends (0=uncorrelated; 1=linear; 2=quadratic; 3=bilinear; 4=discontinuous without constant velocity; 5=discontinuous with change in velocity) which are likely to represent different slope processes. The analysis also provides a series of descriptive parameters which can be used to characterize the temporal changes of ground motion. All the classification algorithms were integrated into a Graphical User Interface called PSTime. We investigated an area of about 2000 km2 in the Northern Apennines of Italy by using SqueeSAR™ algorithm (Ferretti et al., 2011). Two Radarsat-1 data stack, comprising of 112 scenes in descending orbit and 124 scenes in ascending orbit, were processed. The time coverage lasts from April 2003 to November 2012, with an average temporal frequency of 1 scene/month. Radar interpretation has been carried out by considering average annual velocities as well as acceleration/deceleration trends evidenced by PSTime. Altogether, from ascending and descending geometries respectively, this approach allowed detecting of 115 and 112 potential landslides on the basis of average displacement rate and 77 and 79 landslides on the basis of acceleration trends. In conclusion, time series analysis resulted to be very valuable for landslide mapping. In particular it highlighted areas with marked acceleration in a specific period in time while still being affected by low average annual velocity over the entire analysis period. On the other hand, even in areas with high average annual velocity, time series analysis was of primary importance to characterize the slope dynamics in terms of acceleration events.

Sexual Harassment Retaliation Climate DEOCS 4.1 Construct Validity Summary

DTIC Science & Technology

2017-08-01

exploratory factor analysis, and bivariate correlations (sample 1) 2) To determine the factor structure of the remaining (final) questions via...statistics, reliability analysis, exploratory factor analysis, and bivariate correlations of the prospective Sexual Harassment Retaliation Climate...reported by the survey requester). For information regarding the composition of sample, refer to Table 1. Table 1. Sample 1 Demographics n

Designing an Exploratory Text Analysis Tool for Humanities and Social Sciences Research

ERIC Educational Resources Information Center

Shrikumar, Aditi

2013-01-01

This dissertation presents a new tool for exploratory text analysis that attempts to improve the experience of navigating and exploring text and its metadata. The design of the tool was motivated by the unmet need for text analysis tools in the humanities and social sciences. In these fields, it is common for scholars to have hundreds or thousands…

Toward Reflective Judgment in Exploratory Factor Analysis Decisions: Determining the Extraction Method and Number of Factors To Retain.

ERIC Educational Resources Information Center

Knight, Jennifer L.

This paper considers some decisions that must be made by the researcher conducting an exploratory factor analysis. The primary purpose is to aid the researcher in making informed decisions during the factor analysis instead of relying on defaults in statistical programs or traditions of previous researchers. Three decision areas are addressed.…

Data Science Programs in U.S. Higher Education: An Exploratory Content Analysis of Program Description, Curriculum Structure, and Course Focus

ERIC Educational Resources Information Center

Tang, Rong; Sae-Lim, Watinee

2016-01-01

In this study, an exploratory content analysis of 30 randomly selected Data Science (DS) programs from eight disciplines revealed significant gaps in current DS education in the United States. The analysis centers on linguistic patterns of program descriptions, curriculum requirements, and DS course focus as pertaining to key skills and domain…

A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades

PubMed Central

Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd

2017-01-01

The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter. PMID:28813566

A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades.

PubMed

Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd

2017-08-01

The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter.

Time Series Econometrics for the 21st Century

ERIC Educational Resources Information Center

Hansen, Bruce E.

2017-01-01

The field of econometrics largely started with time series analysis because many early datasets were time-series macroeconomic data. As the field developed, more cross-sectional and longitudinal datasets were collected, which today dominate the majority of academic empirical research. In nonacademic (private sector, central bank, and governmental)…

Comparative Fatigue Lives of Rubber and PVC Wiper Cylindrical Coatings

NASA Technical Reports Server (NTRS)

Vlcek, Brian L.; Hendricks, Robert C.; Zaretsky, Erwin V.; Savage, Michael

2002-01-01

Three coating materials for rotating cylindrical-coated wiping rollers were fatigue tested in 2 Intaglio printing presses. The coatings were a hard, cross-linked, plasticized PVC thermoset (P-series); a plasticized PVC (A-series); and a hard, nitryl rubber (R-series). Both 2- and 3-parameter Weibull analyses as well as a cost-benefit analysis were performed. The mean value of life for the R-series coating is 24 and 9 times longer than the P- and A-series coatings, respectively. Both the cost and replacement rate for the R-series coating was significantly less than those for the P- and A-series coatings. At a very high probability of survival the R-series coating is approximately 2 and 6 times the lives of the P- and A-series, respectively, before the first failure occurs. Where all coatings are run to failure, using the mean (life) time between removal (MTBR) for each coating to calculate the number of replacements and costs provides qualitatively similar results to those using a Weibull analysis.

Spatio-temporal patterns of Campylobacter colonization in Danish broilers.

PubMed

Chowdhury, S; Themudo, G E; Sandberg, M; Ersbøll, A K

2013-05-01

Despite a number of risk-factor studies in different countries, the epidemiology of Campylobacter colonization in broilers, particularly spatial dependencies, is still not well understood. A series of analyses (visualization and exploratory) were therefore conducted in order to obtain a better understanding of the spatial and temporal distribution of Campylobacter in the Danish broiler population. In this study, we observed a non-random temporal occurrence of Campylobacter, with high prevalence during summer and low during winter. Significant spatio-temporal clusters were identified in the same areas in the summer months from 2007 to 2009. Range of influence between broiler farms were estimated at distances of 9.6 km and 13.5 km in different years. Identification of areas and time with greater risk indicates variable presence of risk factors with space and time. Implementation of safety measures on farms within high-risk clusters during summer could have an impact in reducing prevalence.

Finding One's Place: A Case Study of a Music Atelierista

ERIC Educational Resources Information Center

Bond, Vanessa L.

2018-01-01

The purpose of this exploratory case study was to document one teacher's journey as he negotiated the role of music atelierista (music studio teacher) in a school inspired by the Reggio Emilia approach (REA). Through the collection of qualitative data over a one-year time period and subsequent analysis, the author identified and described the…

College Student Perceptions and Ideals of Advising: An Exploratory Analysis

ERIC Educational Resources Information Center

Christian, Tiffany Y.; Sprinkle, Julie E.

2013-01-01

Student advising has been a staple of the college experience for decades. However, the importance of advising differs greatly through the lens of the observer. Students may feel that advising is a "waste of time" or that they already know what they need to take to meet degree requirements. Conversely, other students may want the added…

Stochastic modeling of hourly rainfall times series in Campania (Italy)

NASA Astrophysics Data System (ADS)

Giorgio, M.; Greco, R.

2009-04-01

Occurrence of flowslides and floods in small catchments is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Consequently, landslide risk assessment procedures and early warning systems still rely on simple empirical models based on correlation between recorded rainfall data and observed landslides and/or river discharges. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction, which can allow gaining larger lead-times. Analysis of on-site recorded rainfall height time series represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR, ARMA, ARX, ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted, in which the sequence of rain storms and dry intervals constituting the structure of rainfall time series is modeled as an alternating renewal process. Final aim of the study is to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil protection agency meteorological warning network. ACKNOWLEDGEMENTS The research was co-financed by the Italian Ministry of University, by means of the PRIN 2006 PRIN program, within the research project entitled ‘Definition of critical rainfall thresholds for destructive landslides for civil protection purposes'. REFERENCES Cowpertwait, P.S.P., Kilsby, C.G. and O'Connell, P.E., 2002. A space-time Neyman-Scott model of rainfall: Empirical analysis of extremes, Water Resources Research, 38(8):1-14. Salas, J.D., 1992. Analysis and modeling of hydrological time series, in D.R. Maidment, ed., Handbook of Hydrology, McGraw-Hill, New York. Heneker, T.M., Lambert, M.F. and Kuczera G., 2001. A point rainfall model for risk-based design, Journal of Hydrology, 247(1-2):54-71.

Forecasting and analyzing high O3 time series in educational area through an improved chaotic approach

NASA Astrophysics Data System (ADS)

Hamid, Nor Zila Abd; Adenan, Nur Hamiza; Noorani, Mohd Salmi Md

2017-08-01

Forecasting and analyzing the ozone (O3) concentration time series is important because the pollutant is harmful to health. This study is a pilot study for forecasting and analyzing the O3 time series in one of Malaysian educational area namely Shah Alam using chaotic approach. Through this approach, the observed hourly scalar time series is reconstructed into a multi-dimensional phase space, which is then used to forecast the future time series through the local linear approximation method. The main purpose is to forecast the high O3 concentrations. The original method performed poorly but the improved method addressed the weakness thereby enabling the high concentrations to be successfully forecast. The correlation coefficient between the observed and forecasted time series through the improved method is 0.9159 and both the mean absolute error and root mean squared error are low. Thus, the improved method is advantageous. The time series analysis by means of the phase space plot and Cao method identified the presence of low-dimensional chaotic dynamics in the observed O3 time series. Results showed that at least seven factors affect the studied O3 time series, which is consistent with the listed factors from the diurnal variations investigation and the sensitivity analysis from past studies. In conclusion, chaotic approach has been successfully forecast and analyzes the O3 time series in educational area of Shah Alam. These findings are expected to help stakeholders such as Ministry of Education and Department of Environment in having a better air pollution management.

Application of the Allan Variance to Time Series Analysis in Astrometry and Geodesy: A Review.

PubMed

Malkin, Zinovy

2016-04-01

The Allan variance (AVAR) was introduced 50 years ago as a statistical tool for assessing the frequency standards deviations. For the past decades, AVAR has increasingly been used in geodesy and astrometry to assess the noise characteristics in geodetic and astrometric time series. A specific feature of astrometric and geodetic measurements, as compared with clock measurements, is that they are generally associated with uncertainties; thus, an appropriate weighting should be applied during data analysis. In addition, some physically connected scalar time series naturally form series of multidimensional vectors. For example, three station coordinates time series X, Y, and Z can be combined to analyze 3-D station position variations. The classical AVAR is not intended for processing unevenly weighted and/or multidimensional data. Therefore, AVAR modifications, namely weighted AVAR (WAVAR), multidimensional AVAR (MAVAR), and weighted multidimensional AVAR (WMAVAR), were introduced to overcome these deficiencies. In this paper, a brief review is given of the experience of using AVAR and its modifications in processing astrogeodetic time series.

Analysis of Zenith Tropospheric Delay above Europe based on long time series derived from the EPN data

NASA Astrophysics Data System (ADS)

Baldysz, Zofia; Nykiel, Grzegorz; Figurski, Mariusz; Szafranek, Karolina; Kroszczynski, Krzysztof; Araszkiewicz, Andrzej

2015-04-01

In recent years, the GNSS system began to play an increasingly important role in the research related to the climate monitoring. Based on the GPS system, which has the longest operational capability in comparison with other systems, and a common computational strategy applied to all observations, long and homogeneous ZTD (Zenith Tropospheric Delay) time series were derived. This paper presents results of analysis of 16-year ZTD time series obtained from the EPN (EUREF Permanent Network) reprocessing performed by the Military University of Technology. To maintain the uniformity of data, analyzed period of time (1998-2013) is exactly the same for all stations - observations carried out before 1998 were removed from time series and observations processed using different strategy were recalculated according to the MUT LAC approach. For all 16-year time series (59 stations) Lomb-Scargle periodograms were created to obtain information about the oscillations in ZTD time series. Due to strong annual oscillations which disturb the character of oscillations with smaller amplitude and thus hinder their investigation, Lomb-Scargle periodograms for time series with the deleted annual oscillations were created in order to verify presence of semi-annual, ter-annual and quarto-annual oscillations. Linear trend and seasonal components were estimated using LSE (Least Square Estimation) and Mann-Kendall trend test were used to confirm the presence of linear trend designated by LSE method. In order to verify the effect of the length of time series on the estimated size of the linear trend, comparison between two different length of ZTD time series was performed. To carry out a comparative analysis, 30 stations which have been operating since 1996 were selected. For these stations two periods of time were analyzed: shortened 16-year (1998-2013) and full 18-year (1996-2013). For some stations an additional two years of observations have significant impact on changing the size of linear trend - only for 4 stations the size of linear trend was exactly the same for two periods of time. In one case, the nature of the trend has changed from negative (16-year time series) for positive (18-year time series). The average value of a linear trends for 16-year time series is 1,5 mm/decade, but their spatial distribution is not uniform. The average value of linear trends for all 18-year time series is 2,0 mm/decade, with better spatial distribution and smaller discrepancies.

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

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.

Modified cross sample entropy and surrogate data analysis method for financial time series

NASA Astrophysics Data System (ADS)

Yin, Yi; Shang, Pengjian

2015-09-01

For researching multiscale behaviors from the angle of entropy, we propose a modified cross sample entropy (MCSE) and combine surrogate data analysis with it in order to compute entropy differences between original dynamics and surrogate series (MCSDiff). MCSDiff is applied to simulated signals to show accuracy and then employed to US and Chinese stock markets. We illustrate the presence of multiscale behavior in the MCSDiff results and reveal that there are synchrony containing in the original financial time series and they have some intrinsic relations, which are destroyed by surrogate data analysis. Furthermore, the multifractal behaviors of cross-correlations between these financial time series are investigated by multifractal detrended cross-correlation analysis (MF-DCCA) method, since multifractal analysis is a multiscale analysis. We explore the multifractal properties of cross-correlation between these US and Chinese markets and show the distinctiveness of NQCI and HSI among the markets in their own region. It can be concluded that the weaker cross-correlation between US markets gives the evidence for the better inner mechanism in the US stock markets than that of Chinese stock markets. To study the multiscale features and properties of financial time series can provide valuable information for understanding the inner mechanism of financial markets.

A better understanding of long-range temporal dependence of traffic flow time series

NASA Astrophysics Data System (ADS)

Feng, Shuo; Wang, Xingmin; Sun, Haowei; Zhang, Yi; Li, Li

2018-02-01

Long-range temporal dependence is an important research perspective for modelling of traffic flow time series. Various methods have been proposed to depict the long-range temporal dependence, including autocorrelation function analysis, spectral analysis and fractal analysis. However, few researches have studied the daily temporal dependence (i.e. the similarity between different daily traffic flow time series), which can help us better understand the long-range temporal dependence, such as the origin of crossover phenomenon. Moreover, considering both types of dependence contributes to establishing more accurate model and depicting the properties of traffic flow time series. In this paper, we study the properties of daily temporal dependence by simple average method and Principal Component Analysis (PCA) based method. Meanwhile, we also study the long-range temporal dependence by Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). The results show that both the daily and long-range temporal dependence exert considerable influence on the traffic flow series. The DFA results reveal that the daily temporal dependence creates crossover phenomenon when estimating the Hurst exponent which depicts the long-range temporal dependence. Furthermore, through the comparison of the DFA test, PCA-based method turns out to be a better method to extract the daily temporal dependence especially when the difference between days is significant.

Detecting PM2.5's Correlations between Neighboring Cities Using a Time-Lagged Cross-Correlation Coefficient.

PubMed

Wang, Fang; Wang, Lin; Chen, Yuming

2017-08-31

In order to investigate the time-dependent cross-correlations of fine particulate (PM2.5) series among neighboring cities in Northern China, in this paper, we propose a new cross-correlation coefficient, the time-lagged q-L dependent height crosscorrelation coefficient (denoted by p q (τ, L)), which incorporates the time-lag factor and the fluctuation amplitude information into the analogous height cross-correlation analysis coefficient. Numerical tests are performed to illustrate that the newly proposed coefficient ρ q (τ, L) can be used to detect cross-correlations between two series with time lags and to identify different range of fluctuations at which two series possess cross-correlations. Applying the new coefficient to analyze the time-dependent cross-correlations of PM2.5 series between Beijing and the three neighboring cities of Tianjin, Zhangjiakou, and Baoding, we find that time lags between the PM2.5 series with larger fluctuations are longer than those between PM2.5 series withsmaller fluctuations. Our analysis also shows that cross-correlations between the PM2.5 series of two neighboring cities are significant and the time lags between two PM2.5 series of neighboring cities are significantly non-zero. These findings providenew scientific support on the view that air pollution in neighboring cities can affect one another not simultaneously but with a time lag.

Multiscale structure of time series revealed by the monotony spectrum.

PubMed

Vamoş, Călin

2017-03-01

Observation of complex systems produces time series with specific dynamics at different time scales. The majority of the existing numerical methods for multiscale analysis first decompose the time series into several simpler components and the multiscale structure is given by the properties of their components. We present a numerical method which describes the multiscale structure of arbitrary time series without decomposing them. It is based on the monotony spectrum defined as the variation of the mean amplitude of the monotonic segments with respect to the mean local time scale during successive averagings of the time series, the local time scales being the durations of the monotonic segments. The maxima of the monotony spectrum indicate the time scales which dominate the variations of the time series. We show that the monotony spectrum can correctly analyze a diversity of artificial time series and can discriminate the existence of deterministic variations at large time scales from the random fluctuations. As an application we analyze the multifractal structure of some hydrological time series.

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)

Multidimensional stock network analysis: An Escoufier's RV coefficient approach

NASA Astrophysics Data System (ADS)

Lee, Gan Siew; Djauhari, Maman A.

2013-09-01

The current practice of stocks network analysis is based on the assumption that the time series of closed stock price could represent the behaviour of the each stock. This assumption leads to consider minimal spanning tree (MST) and sub-dominant ultrametric (SDU) as an indispensible tool to filter the economic information contained in the network. Recently, there is an attempt where researchers represent stock not only as a univariate time series of closed price but as a bivariate time series of closed price and volume. In this case, they developed the so-called multidimensional MST to filter the important economic information. However, in this paper, we show that their approach is only applicable for that bivariate time series only. This leads us to introduce a new methodology to construct MST where each stock is represented by a multivariate time series. An example of Malaysian stock exchange will be presented and discussed to illustrate the advantages of the method.

Programmable Logic Application Notes

NASA Technical Reports Server (NTRS)

Katz, Richard

2000-01-01

This column will be provided each quarter as a source for reliability, radiation results, NASA capabilities, and other information on programmable logic devices and related applications. This quarter will continue a series of notes concentrating on analysis techniques with this issue's section discussing: Digital Timing Analysis Tools and Techniques. Articles in this issue include: SX and SX-A Series Devices Power Sequencing; JTAG and SXISX-AISX-S Series Devices; Analysis Techniques (i.e., notes on digital timing analysis tools and techniques); Status of the Radiation Hard reconfigurable Field Programmable Gate Array Program, Input Transition Times; Apollo Guidance Computer Logic Study; RT54SX32S Prototype Data Sets; A54SX32A - 0.22 micron/UMC Test Results; Ramtron FM1608 FRAM; and Analysis of VHDL Code and Synthesizer Output.

Weighted combination of LOD values oa splitted into frequency windows

NASA Astrophysics Data System (ADS)

Fernandez, L. I.; Gambis, D.; Arias, E. F.

In this analysis a one-day combined time series of LOD(length-of-day) estimates is presented. We use individual data series derived by 7 GPS and 3 SLR analysis centers, which routinely contribute to the IERS database over a recent 27-month period (Jul 1996 - Oct 1998). The result is compared to the multi-technique combined series C04 produced by the Central Bureau of the IERS that is commonly used as a reference for the study of the phenomena of Earth rotation variations. The Frequency Windows Combined Series procedure brings out a time series, which is close to C04 but shows an amplitude difference that might explain the evident periodic behavior present in the differences of these two combined series. This method could be useful to generate a new time series to be used as a reference in the high frequency variations of the Earth rotation studies.

Adventures in Modern Time Series Analysis: From the Sun to the Crab Nebula and Beyond

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey

2014-01-01

With the generation of long, precise, and finely sampled time series the Age of Digital Astronomy is uncovering and elucidating energetic dynamical processes throughout the Universe. Fulfilling these opportunities requires data effective analysis techniques rapidly and automatically implementing advanced concepts. The Time Series Explorer, under development in collaboration with Tom Loredo, provides tools ranging from simple but optimal histograms to time and frequency domain analysis for arbitrary data modes with any time sampling. Much of this development owes its existence to Joe Bredekamp and the encouragement he provided over several decades. Sample results for solar chromospheric activity, gamma-ray activity in the Crab Nebula, active galactic nuclei and gamma-ray bursts will be displayed.

"Exploratory experimentation" as a probe into the relation between historiography and philosophy of science.

PubMed

Schickore, Jutta

2016-02-01

This essay utilizes the concept "exploratory experimentation" as a probe into the relation between historiography and philosophy of science. The essay traces the emergence of the historiographical concept "exploratory experimentation" in the late 1990s. The reconstruction of the early discussions about exploratory experimentation shows that the introduction of the concept had unintended consequences: Initially designed to debunk philosophical ideas about theory testing, the concept "exploratory experimentation" quickly exposed the poverty of our conceptual tools for the analysis of experimental practice. Looking back at a number of detailed analyses of experimental research, we can now appreciate that the concept of exploratory experimentation is too vague and too elusive to fill the desideratum whose existence it revealed. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Space Affine Matching Approach to fMRI Time Series Analysis.

PubMed

Chen, Liang; Zhang, Weishi; Liu, Hongbo; Feng, Shigang; Chen, C L Philip; Wang, Huili

2016-07-01

For fMRI time series analysis, an important challenge is to overcome the potential delay between hemodynamic response signal and cognitive stimuli signal, namely the same frequency but different phase (SFDP) problem. In this paper, a novel space affine matching feature is presented by introducing the time domain and frequency domain features. The time domain feature is used to discern different stimuli, while the frequency domain feature to eliminate the delay. And then we propose a space affine matching (SAM) algorithm to match fMRI time series by our affine feature, in which a normal vector is estimated using gradient descent to explore the time series matching optimally. The experimental results illustrate that the SAM algorithm is insensitive to the delay between the hemodynamic response signal and the cognitive stimuli signal. Our approach significantly outperforms GLM method while there exists the delay. The approach can help us solve the SFDP problem in fMRI time series matching and thus of great promise to reveal brain dynamics.

A novel weight determination method for time series data aggregation

NASA Astrophysics Data System (ADS)

Xu, Paiheng; Zhang, Rong; Deng, Yong

2017-09-01

Aggregation in time series is of great importance in time series smoothing, predicting and other time series analysis process, which makes it crucial to address the weights in times series correctly and reasonably. In this paper, a novel method to obtain the weights in time series is proposed, in which we adopt induced ordered weighted aggregation (IOWA) operator and visibility graph averaging (VGA) operator and linearly combine the weights separately generated by the two operator. The IOWA operator is introduced to the weight determination of time series, through which the time decay factor is taken into consideration. The VGA operator is able to generate weights with respect to the degree distribution in the visibility graph constructed from the corresponding time series, which reflects the relative importance of vertices in time series. The proposed method is applied to two practical datasets to illustrate its merits. The aggregation of Construction Cost Index (CCI) demonstrates the ability of proposed method to smooth time series, while the aggregation of The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) illustrate how proposed method maintain the variation tendency of original data.

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series

PubMed Central

Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C.; Golino, Caroline A.; Kemper, Peter; Saha, Margaret S.

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features. PMID:27977764

Attitudes Toward Transgender Men and Women: Development and Validation of a New Measure

PubMed Central

Billard, Thomas J

2018-01-01

A series of three studies were conducted to generate, develop, and validate the Attitudes toward Transgender Men and Women (ATTMW) scale. In Study 1, 120 American adults responded to an open-ended questionnaire probing various dimensions of their perceptions of transgender individuals and identity. Qualitative thematic analysis generated 200 items based on their responses. In Study 2, 238 American adults completed a questionnaire consisting of the generated items. Exploratory factor analysis (EFA) revealed two non-identical 12-item subscales (ATTM and ATTW) of the full 24-item scale. In Study 3, 150 undergraduate students completed a survey containing the ATTMW and a number of validity-testing variables. Confirmatory factor analysis (CFA) verified the single-factor structures of the ATTM and ATTW subscales, and the convergent, discriminant, predictive, and concurrent validities of the ATTMW were also established. Together, our results demonstrate that the ATTMW is a reliable and valid measure of attitudes toward transgender individuals. PMID:29666595

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.

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.

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%.

Attitudes toward abortion among students at the University of Cape Coast, Ghana.

PubMed

Rominski, Sarah D; Darteh, Eugene; Dickson, Kwamena Sekyi; Munro-Kramer, Michelle

2017-03-01

This study aimed to describe the attitudes toward abortion of Ghanaian University students and to determine factors which are associated with supporting a woman's right to an abortion. This cross-sectional survey was administered to residential students at the University of Cape Coast. Participants were posed a series of 26 statements to determine to what extent they were supportive of abortion as a woman's right. An exploratory factor analysis was used to create a scale with the pertinent factors that relate to abortion attitudes and a multivariable linear regression model explored the relationships among significant variables noted during exploratory factor analysis. 1038 students completed the survey and these students had a generally negative view of abortion. Two factors emerged: (1) the Abortion as a Right scale consisted of five questions (α = .755) and (2) the Moral Objection to Abortion scale consisted of three questions (α = .740). In linear regression, being older (β = 1.9), sexually experienced (β = 1.2), having a boyfriend/girlfriend (β = 1.4), and knowing someone who has terminated a pregnancy (β = 1.1) were significantly associated with a more liberal view of a right to an abortion. This work supports the idea that students who have personal exposure to an abortion experience hold more liberal views on abortion than those who have not had a similar exposure. Copyright © 2016 Elsevier B.V. All rights reserved.

Wavelet application to the time series analysis of DORIS station coordinates

NASA Astrophysics Data System (ADS)

Bessissi, Zahia; Terbeche, Mekki; Ghezali, Boualem

2009-06-01

The topic developed in this article relates to the residual time series analysis of DORIS station coordinates using the wavelet transform. Several analysis techniques, already developed in other disciplines, were employed in the statistical study of the geodetic time series of stations. The wavelet transform allows one, on the one hand, to provide temporal and frequential parameter residual signals, and on the other hand, to determine and quantify systematic signals such as periodicity and tendency. Tendency is the change in short or long term signals; it is an average curve which represents the general pace of the signal evolution. On the other hand, periodicity is a process which is repeated, identical to itself, after a time interval called the period. In this context, the topic of this article consists, on the one hand, in determining the systematic signals by wavelet analysis of time series of DORIS station coordinates, and on the other hand, in applying the denoising signal to the wavelet packet, which makes it possible to obtain a well-filtered signal, smoother than the original signal. The DORIS data used in the treatment are a set of weekly residual time series from 1993 to 2004 from eight stations: DIOA, COLA, FAIB, KRAB, SAKA, SODB, THUB and SYPB. It is the ign03wd01 solution expressed in stcd format, which is derived by the IGN/JPL analysis center. Although these data are not very recent, the goal of this study is to detect the contribution of the wavelet analysis method on the DORIS data, compared to the other analysis methods already studied.

Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction

NASA Astrophysics Data System (ADS)

Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan

2017-04-01

Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.

Supporting cognition in systems biology analysis: findings on users' processes and design implications.

PubMed

Mirel, Barbara

2009-02-13

Current usability studies of bioinformatics tools suggest that tools for exploratory analysis support some tasks related to finding relationships of interest but not the deep causal insights necessary for formulating plausible and credible hypotheses. To better understand design requirements for gaining these causal insights in systems biology analyses a longitudinal field study of 15 biomedical researchers was conducted. Researchers interacted with the same protein-protein interaction tools to discover possible disease mechanisms for further experimentation. Findings reveal patterns in scientists' exploratory and explanatory analysis and reveal that tools positively supported a number of well-structured query and analysis tasks. But for several of scientists' more complex, higher order ways of knowing and reasoning the tools did not offer adequate support. Results show that for a better fit with scientists' cognition for exploratory analysis systems biology tools need to better match scientists' processes for validating, for making a transition from classification to model-based reasoning, and for engaging in causal mental modelling. As the next great frontier in bioinformatics usability, tool designs for exploratory systems biology analysis need to move beyond the successes already achieved in supporting formulaic query and analysis tasks and now reduce current mismatches with several of scientists' higher order analytical practices. The implications of results for tool designs are discussed.

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.

NASA standard: Trend analysis techniques

NASA Technical Reports Server (NTRS)

1990-01-01

Descriptive and analytical techniques for NASA trend analysis applications are presented in this standard. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. This document should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend analysis is neither a precise term nor a circumscribed methodology: it generally connotes quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this document. The basic ideas needed for qualitative and quantitative assessment of trends along with relevant examples are presented.

Application of Local Linear Embedding to Nonlinear Exploratory Latent Structure Analysis

ERIC Educational Resources Information Center

Wang, Haonan; Iyer, Hari

2007-01-01

In this paper we discuss the use of a recent dimension reduction technique called Locally Linear Embedding, introduced by Roweis and Saul, for performing an exploratory latent structure analysis. The coordinate variables from the locally linear embedding describing the manifold on which the data reside serve as the latent variable scores. We…

The School Counselor Leadership Survey: Instrument Development and Exploratory Factor Analysis

ERIC Educational Resources Information Center

Young, Anita; Bryan, Julia

2015-01-01

This study examined the factor structure of the School Counselor Leadership Survey (SCLS). Survey development was a threefold process that resulted in a 39-item survey of 801 school counselors and school counselor supervisors. The exploratory factor analysis indicated a five-factor structure that revealed five key dimensions of school counselor…

Exploratory Factor Analysis with Small Sample Sizes

ERIC Educational Resources Information Center

de Winter, J. C. F.; Dodou, D.; Wieringa, P. A.

2009-01-01

Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes ("N"), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for "N" below 50. Simulations were carried out to estimate the minimum required "N" for different…

A Psychometric Investigation of the Multicultural and Special Education Survey: An Exploratory Factor Analysis

ERIC Educational Resources Information Center

Utley, Cheryl A.

2011-01-01

An exploratory factorial analysis of the Multicultural and Special Education Survey (MSES) evaluated the professional development training needs of general and special educators in a midwestern state. Survey items were selected from the culturally and linguistically diverse multicultural, bilingual and special education literature bases (CLD). The…

An Exploratory Analysis of TPACK Perceptions of Pre-Service Science Teachers: A Regional Australian Perspective

ERIC Educational Resources Information Center

Reyes, Vicente Chua, Jr.; Rizk, Nadya; Gregory, Sue; Doyle, Helen

2016-01-01

Four distinct constructs were identified from a survey of a sample of pre-service science teachers at a regional Australian University. The constructs emerged after employing Exploratory Factor Analysis (EFA) on respondents' perceptions of pedagogical practices incorporating the use of Information Communication and Technology (ICT). The key…

Likelihood-Based Confidence Intervals in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Oort, Frans J.

2011-01-01

In exploratory or unrestricted factor analysis, all factor loadings are free to be estimated. In oblique solutions, the correlations between common factors are free to be estimated as well. The purpose of this article is to show how likelihood-based confidence intervals can be obtained for rotated factor loadings and factor correlations, by…

Exploratory and Confirmatory Analysis of the Trauma Practices Questionnaire

ERIC Educational Resources Information Center

Craig, Carlton D.; Sprang, Ginny

2009-01-01

Objective: The present study provides psychometric data for the Trauma Practices Questionnaire (TPQ). Method: A nationally randomized sample of 2,400 surveys was sent to self-identified trauma treatment specialists, and 711 (29.6%) were returned. Results: An exploratory factor analysis (N = 319) conducted on a randomly split sample (RSS) revealed…

High-Dimensional Exploratory Item Factor Analysis by a Metropolis-Hastings Robbins-Monro Algorithm

ERIC Educational Resources Information Center

Cai, Li

2010-01-01

A Metropolis-Hastings Robbins-Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. The sequence of estimates from the MH-RM algorithm converges with probability one to the maximum likelihood solution. Details on the computer implementation of this algorithm are provided. The…

Mission Attachment and Job Satisfaction among Emergency Shelter and Transitional Housing Service Professionals: An Exploratory Analysis

ERIC Educational Resources Information Center

Fermin, Caroline P.

2017-01-01

This research study was an exploratory analysis to determine if a relationship existed between mission attachment and job satisfaction of emergency nonprofit domestic violence shelter/transitional housing workers. The study examined if the perceptions, beliefs, and attitudes were different between entry-level, middle-level, and senior-level…

An Exploratory Case Study of PBIS Implementation Using Social Network Analysis

ERIC Educational Resources Information Center

Whitcomb, Sara A.; Woodland, Rebecca H.; Barry, Shannon K.

2017-01-01

An exploratory case study is presented in which social network analysis (SNA) was used to explore how school teaming structures influence the implementation of School-Wide Positive Behavioral Interventions and Supports (PBIS). The authors theorized that PBIS leadership teams that include members with connections to all other information-sharing…

What Is Rotating in Exploratory Factor Analysis?

ERIC Educational Resources Information Center

Osborne, Jason W.

2015-01-01

Exploratory factor analysis (EFA) is one of the most commonly-reported quantitative methodology in the social sciences, yet much of the detail regarding what happens during an EFA remains unclear. The goal of this brief technical note is to explore what "rotation" is, what exactly is rotating, and why we use rotation when performing…

What Time-Series Designs May Have to Offer Educational Researchers.

ERIC Educational Resources Information Center

Kratochwill, Thomas R.; Levin, Joel R.

1978-01-01

The promise of time-series designs for educational research and evaluation is reviewed. Ten time-series designs are presented and discussed in the context of threats to internal and external validity. The advantages and disadvantages of various visual and statistical data-analysis techniques are presented. A bibliography is appended. (Author/RD)

FATS: Feature Analysis for Time Series

NASA Astrophysics Data System (ADS)

Nun, Isadora; Protopapas, Pavlos; Sim, Brandon; Zhu, Ming; Dave, Rahul; Castro, Nicolas; Pichara, Karim

2017-11-01

FATS facilitates and standardizes feature extraction for time series data; it quickly and efficiently calculates a compilation of many existing light curve features. Users can characterize or analyze an astronomical photometric database, though this library is not necessarily restricted to the astronomical domain and can also be applied to any kind of time series data.

The Prediction of Teacher Turnover Employing Time Series Analysis.

ERIC Educational Resources Information Center

Costa, Crist H.

The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…

Information and Complexity Measures Applied to Observed and Simulated Soil Moisture Time Series

USDA-ARS?s Scientific Manuscript database

Time series of soil moisture-related parameters provides important insights in functioning of soil water systems. Analysis of patterns within these time series has been used in several studies. The objective of this work was to compare patterns in observed and simulated soil moisture contents to u...

A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history

EPA Science Inventory

A time series intervention analysis (TSIA) of dendrochronological data to infer the tree growth-climate-disturbance relations and forest disturbance history is described. Maximum likelihood is used to estimate the parameters of a structural time series model with components for ...

Testing for nonlinearity in non-stationary physiological time series.

PubMed

Guarín, Diego; Delgado, Edilson; Orozco, Álvaro

2011-01-01

Testing for nonlinearity is one of the most important preprocessing steps in nonlinear time series analysis. Typically, this is done by means of the linear surrogate data methods. But it is a known fact that the validity of the results heavily depends on the stationarity of the time series. Since most physiological signals are non-stationary, it is easy to falsely detect nonlinearity using the linear surrogate data methods. In this document, we propose a methodology to extend the procedure for generating constrained surrogate time series in order to assess nonlinearity in non-stationary data. The method is based on the band-phase-randomized surrogates, which consists (contrary to the linear surrogate data methods) in randomizing only a portion of the Fourier phases in the high frequency domain. Analysis of simulated time series showed that in comparison to the linear surrogate data method, our method is able to discriminate between linear stationarity, linear non-stationary and nonlinear time series. Applying our methodology to heart rate variability (HRV) records of five healthy patients, we encountered that nonlinear correlations are present in this non-stationary physiological signals.

Foreign Language Exploratory (French, German, Spanish), (6-8), Resource Guide and Handbook.

ERIC Educational Resources Information Center

Kennedy, Dora F.; And Others

The guide focuses on a rationale for exploratory foreign language courses in middle or junior high school, and on the goals and objectives for such courses. An exploratory course may serve a number of purposes regardless of whether or not a pupil elects a foreign language at a later time. These purposes include: (1) acquainting pupils with a…

The Effect on Non-Normal Distributions on the Integrated Moving Average Model of Time-Series Analysis.

ERIC Educational Resources Information Center

Doerann-George, Judith

The Integrated Moving Average (IMA) model of time series, and the analysis of intervention effects based on it, assume random shocks which are normally distributed. To determine the robustness of the analysis to violations of this assumption, empirical sampling methods were employed. Samples were generated from three populations; normal,…

Teaching Earth Signals Analysis Using the Java-DSP Earth Systems Edition: Modern and Past Climate Change

ERIC Educational Resources Information Center

Ramamurthy, Karthikeyan Natesan; Hinnov, Linda A.; Spanias, Andreas S.

2014-01-01

Modern data collection in the Earth Sciences has propelled the need for understanding signal processing and time-series analysis techniques. However, there is an educational disconnect in the lack of instruction of time-series analysis techniques in many Earth Science academic departments. Furthermore, there are no platform-independent freeware…

A new methodological approach for worldwide beryllium-7 time series analysis

NASA Astrophysics Data System (ADS)

Bianchi, Stefano; Longo, Alessandro; Plastino, Wolfango

2018-07-01

Time series analyses of cosmogenic radionuclide 7Be and 22Na atmospheric activity concentrations and meteorological data observed at twenty-five International Monitoring System (IMS) stations of the Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO) have shown great variability in terms of noise structures, harmonic content, cross-correlation patterns and local Hurst exponent behaviour. Noise content and its structure has been extracted and characterised for the two radionuclides time series. It has been found that the yearly component, which is present in most of the time series, is not stationary, but has a percentage weight that varies with time. Analysis of atmospheric activity concentrations of 7Be, measured at IMS stations, has shown them to be influenced by distinct meteorological patterns, mainly by atmospheric pressure and temperature.

Integrated method for chaotic time series analysis

DOEpatents

Hively, Lee M.; Ng, Esmond G.

1998-01-01

Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated.

Design Considerations of Help Options in Computer-Based L2 Listening Materials Informed by Participatory Design

ERIC Educational Resources Information Center

Cárdenas-Claros, Mónica Stella

2015-01-01

This paper reports on the findings of two qualitative exploratory studies that sought to investigate design features of help options in computer-based L2 listening materials. Informed by principles of participatory design, language learners, software designers, language teachers, and a computer programmer worked collaboratively in a series of…

Invention and Innovation: A Standards-Based Middle School Model Course Guide. Advancing Technological Literacy: ITEA Professional Series

ERIC Educational Resources Information Center

International Technology Education Association (ITEA), 2005

2005-01-01

This guide presents a model for a standards-based contemporary technology education course for the middle school. This model course guide features an exploratory curriculum thrust for a cornerstone middle level course. It provides teachers with an overview of the concept, suggestions for planning the course, and ideas for developing…

Urban metabolism in Syracuse, NY – introduction

Treesearch

David J. Nowak

2016-01-01

This special issue of Urban Ecosystems contains a series of papers related to assessing urban metabolism in Syracuse, NY. These papers were developed under the Urban Long-Term Research Area Exploratory Awards Program funded by the National Science Foundation. Objectives of this two-year project (2009–2011) in Syracuse, NY were to investigate: a)...

"More Aware of Everything": Exploring the Returnee Experience in American Higher Education

ERIC Educational Resources Information Center

Haines, David

2013-01-01

At the intersection of the topics of migration and diversity in higher education lies the experience of people who grow up overseas, or who go overseas for education or military service, and then return as college students. This article addresses their experience, drawing from a series of exploratory interviews conducted--as part of a broader…

Problem Solving. Technology Learning Activity. Teacher Edition. Technology Education Series.

ERIC Educational Resources Information Center

Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This curriculum guide provides technology learning activities designed to prepare students in grades 6-10 to work in the world of the future. The 8-day course provides exploratory, hands-on learning activities and information that can enhance the education of students of all types in an integrated curriculum that provides practical applications of…

Structural Engineering. Technology Learning Activity. Teacher Edition. Technology Education Series.

ERIC Educational Resources Information Center

Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This curriculum guide provides technology learning activities designed to prepare students in grades 6-10 to work in the world of the future. The 8-day course provides exploratory, hands-on learning activities and information that can enhance the education of students of all types in an integrated curriculum that provides practical applications of…

Thermal Screen Printing. Technology Learning Activity. Teacher Edition. Technology Education Series.

ERIC Educational Resources Information Center

Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This curriculum guide provides technology learning activities designed to prepare students in grades 6-10 to work in the world of the future. The 8-day course provides exploratory, hands-on learning activities and information that can enhance the education of students of all types in an integrated curriculum that provides practical applications of…

Research and Design. Technology Learning Activity. Teacher Edition. Technology Education Series.

ERIC Educational Resources Information Center

Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This curriculum guide provides technology learning activities designed to prepare students in grades 6-10 to work in the world of the future. The 8-day course provides exploratory, hands-on learning activities and information that can enhance the education of students of all types in an integrated curriculum that provides practical applications of…

Boredom in Achievement Settings: Exploring Control-Value Antecedents and Performance Outcomes of a Neglected Emotion

ERIC Educational Resources Information Center

Pekrun, Reinhard; Goetz, Thomas; Daniels, Lia M.; Stupnisky, Robert H.; Perry, Raymond P.

2010-01-01

The linkages of achievement-related boredom with students' appraisals and performance outcomes were examined in a series of 5 exploratory, cross-sectional, and predictive investigations. Studies 1 and 2 assessed students' boredom in a single achievement episode (i.e., state achievement boredom); Studies 3, 4, and 5 focused on their habitual…

What Does the Eye See? Reading Online Primary Source Photographs in History

ERIC Educational Resources Information Center

Levesque, Stephane; Ng-A-Fook, Nicholas; Corrigan, Julie

2014-01-01

This exploratory study looks at how a sample of preservice teachers and historians read visuals in the context of school history. The participants used eye tracking technology and think-aloud protocol, as they examined a series of online primary source photographs from a virtual exhibit. Voluntary participants (6 students and 2 professional…

Parameter motivated mutual correlation analysis: Application to the study of currency exchange rates based on intermittency parameter and Hurst exponent

NASA Astrophysics Data System (ADS)

Cristescu, Constantin P.; Stan, Cristina; Scarlat, Eugen I.; Minea, Teofil; Cristescu, Cristina M.

2012-04-01

We present a novel method for the parameter oriented analysis of mutual correlation between independent time series or between equivalent structures such as ordered data sets. The proposed method is based on the sliding window technique, defines a new type of correlation measure and can be applied to time series from all domains of science and technology, experimental or simulated. A specific parameter that can characterize the time series is computed for each window and a cross correlation analysis is carried out on the set of values obtained for the time series under investigation. We apply this method to the study of some currency daily exchange rates from the point of view of the Hurst exponent and the intermittency parameter. Interesting correlation relationships are revealed and a tentative crisis prediction is presented.

A study of sound generation in subsonic rotors, volume 2

NASA Technical Reports Server (NTRS)

Chalupnik, J. D.; Clark, L. T.

1975-01-01

Computer programs were developed for use in the analysis of sound generation by subsonic rotors. Program AIRFOIL computes the spectrum of radiated sound from a single airfoil immersed in a laminar flow field. Program ROTOR extends this to a rotating frame, and provides a model for sound generation in subsonic rotors. The program also computes tone sound generation due to steady state forces on the blades. Program TONE uses a moving source analysis to generate a time series for an array of forces moving in a circular path. The resultant time series are than Fourier transformed to render the results in spectral form. Program SDATA is a standard time series analysis package. It reads in two discrete time series and forms auto and cross covariances and normalizes these to form correlations. The program then transforms the covariances to yield auto and cross power spectra by means of a Fourier transformation.

Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature.

PubMed

Bhandari, Siddhartha; Bergmann, Neil; Jurdak, Raja; Kusy, Branislav

2017-05-26

Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of measured temperature time series. Unlike previous works which have concentrated on suppressing the transmission of some data samples by time-series analysis but still maintaining high sampling rates, this work investigates reducing the sampling rate (and sensor wake up rate) and looks at the effects on accuracy. Results show that the sampling period of the sensor can be increased up to one hour while still allowing intermediate and future states to be estimated with interpolation RMSE less than 0.2 °C and forecasting RMSE less than 1 °C.

On the characterization of vegetation recovery after fire disturbance using Fisher-Shannon analysis and SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series

NASA Astrophysics Data System (ADS)

Lasaponara, Rosa; Lanorte, Antonio; Lovallo, Michele; Telesca, Luciano

2015-04-01

Time series can fruitfully support fire monitoring and management from statistical analysis of fire occurrence (Tuia et al. 2008) to danger estimation (lasaponara 2005), damage evaluation (Lanorte et al 2014) and post fire recovery (Lanorte et al. 2014). In this paper, the time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher-Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher-Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1 km × 1 km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers. Reference Lanorte A, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbanceInternational Journal of Applied Earth Observation and Geoinformation 26 441-446 Lanorte A, M Danese, R Lasaponara, B Murgante 2014 Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis International Journal of Applied Earth Observation and Geoinformation 20, 42-51 Tuia D, F Ratle, R Lasaponara, L Telesca, M Kanevski 2008 Scan statistics analysis of forest fire clusters Communications in Nonlinear Science and Numerical Simulation 13 (8), 1689-1694 Telesca L, R Lasaponara 2006 Pre and post fire behavioral trends revealed in satellite NDVI time series Geophysical Research Letters 33 (14) Lasaponara R 2005 Intercomparison of AVHRR based fire susceptibility indicators for the Mediterranean ecosystems of southern Italy International Journal of Remote Sensing 26 (5), 853-870

Sports and Recreational Injuries in Relation to Lost Duty Time Among Deployed U.S. Marine Corps Personnel

DTIC Science & Technology

2011-06-07

Lost Duty Time 6 standardized residuals of cells were examined to determine which cells had observed counts sizably different from expected counts...exploratory analysis) was used as the criterion to indicate that a cell had more (positive residual) or less (negative residual) observed events than...and supplies. These activities require lower body strength, stamina , and core strength that would be impaired by injuries to the lower extremities

Allan deviation analysis of financial return series

NASA Astrophysics Data System (ADS)

Hernández-Pérez, R.

2012-05-01

We perform a scaling analysis for the return series of different financial assets applying the Allan deviation (ADEV), which is used in the time and frequency metrology to characterize quantitatively the stability of frequency standards since it has demonstrated to be a robust quantity to analyze fluctuations of non-stationary time series for different observation intervals. The data used are opening price daily series for assets from different markets during a time span of around ten years. We found that the ADEV results for the return series at short scales resemble those expected for an uncorrelated series, consistent with the efficient market hypothesis. On the other hand, the ADEV results for absolute return series for short scales (first one or two decades) decrease following approximately a scaling relation up to a point that is different for almost each asset, after which the ADEV deviates from scaling, which suggests that the presence of clustering, long-range dependence and non-stationarity signatures in the series drive the results for large observation intervals.

Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Kalantari, Mahdi; Yarmohammadi, Masoud; Hassani, Hossein; Silva, Emmanuel Sirimal

Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the L1 norm-based version of Singular Spectrum Analysis (SSA), namely L1-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially L1-SSA can provide better imputation in comparison to other methods.

Functional exploratory data analysis for high-resolution measurements of urban particulate matter.

PubMed

Ranalli, M Giovanna; Rocco, Giorgia; Jona Lasinio, Giovanna; Moroni, Beatrice; Castellini, Silvia; Crocchianti, Stefano; Cappelletti, David

2016-09-01

In this work we propose the use of functional data analysis (FDA) to deal with a very large dataset of atmospheric aerosol size distribution resolved in both space and time. Data come from a mobile measurement platform in the town of Perugia (Central Italy). An OPC (Optical Particle Counter) is integrated on a cabin of the Minimetrò, an urban transportation system, that moves along a monorail on a line transect of the town. The OPC takes a sample of air every six seconds and counts the number of particles of urban aerosols with a diameter between 0.28 μm and 10 μm and classifies such particles into 21 size bins according to their diameter. Here, we adopt a 2D functional data representation for each of the 21 spatiotemporal series. In fact, space is unidimensional since it is measured as the distance on the monorail from the base station of the Minimetrò. FDA allows for a reduction of the dimensionality of each dataset and accounts for the high space-time resolution of the data. Functional cluster analysis is then performed to search for similarities among the 21 size channels in terms of their spatiotemporal pattern. Results provide a good classification of the 21 size bins into a relatively small number of groups (between three and four) according to the season of the year. Groups including coarser particles have more similar patterns, while those including finer particles show a more different behavior according to the period of the year. Such features are consistent with the physics of atmospheric aerosol and the highlighted patterns provide a very useful ground for prospective model-based studies. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

How would peak rainfall intensity affect runoff predictions using conceptual water balance models?

NASA Astrophysics Data System (ADS)

Yu, B.

2015-06-01

Most hydrological models use continuous daily precipitation and potential evapotranspiration for streamflow estimation. With the projected increase in mean surface temperature, hydrological processes are set to intensify irrespective of the underlying changes to the mean precipitation. The effect of an increase in rainfall intensity on the long-term water balance is, however, not adequately accounted for in the commonly used hydrological models. This study follows from a previous comparative analysis of a non-stationary daily series of stream flow of a forested watershed (River Rimbaud) in the French Alps (area = 1.478 km2) (1966-2006). Non-stationarity in the recorded stream flow occurred as a result of a severe wild fire in 1990. Two daily models (AWBM and SimHyd) were initially calibrated for each of three distinct phases in relation to the well documented land disturbance. At the daily and monthly time scales, both models performed satisfactorily with the Nash-Sutcliffe coefficient of efficiency (NSE) varying from 0.77 to 0.92. When aggregated to the annual time scale, both models underestimated the flow by about 22% with a reduced NSE at about 0.71. Exploratory data analysis was undertaken to relate daily peak hourly rainfall intensity to the discrepancy between the observed and modelled daily runoff amount. Preliminary results show that the effect of peak hourly rainfall intensity on runoff prediction is insignificant, and model performance is unlikely to improve when peak daily precipitation is included. Trend analysis indicated that the large decrease of precipitation when daily precipitation amount exceeded 10-20 mm may have contributed greatly to the decrease in stream flow of this forested watershed.

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

Mining concepts of health responsibility using text mining and exploratory graph analysis.

PubMed

Kjellström, Sofia; Golino, Hudson

2018-05-24

Occupational therapists need to know about people's beliefs about personal responsibility for health to help them pursue everyday activities. The study aims to employ state-of-the-art quantitative approaches to understand people's views of health and responsibility at different ages. A mixed method approach was adopted, using text mining to extract information from 233 interviews with participants aged 5 to 96 years, and then exploratory graph analysis to estimate the number of latent variables. The fit of the structure estimated via the exploratory graph analysis was verified using confirmatory factor analysis. Exploratory graph analysis estimated three dimensions of health responsibility: (1) creating good health habits and feeling good; (2) thinking about one's own health and wanting to improve it; and 3) adopting explicitly normative attitudes to take care of one's health. The comparison between the three dimensions among age groups showed, in general, that children and adolescents, as well as the old elderly (>73 years old) expressed ideas about personal responsibility for health less than young adults, adults and young elderly. Occupational therapists' knowledge of the concepts of health responsibility is of value when working with a patient's health, but an identified challenge is how to engage children and older persons.

Simultaneous determination of radionuclides separable into natural decay series by use of time-interval analysis.

PubMed

Hashimoto, Tetsuo; Sanada, Yukihisa; Uezu, Yasuhiro

2004-05-01

A delayed coincidence method, time-interval analysis (TIA), has been applied to successive alpha- alpha decay events on the millisecond time-scale. Such decay events are part of the (220)Rn-->(216)Po ( T(1/2) 145 ms) (Th-series) and (219)Rn-->(215)Po ( T(1/2) 1.78 ms) (Ac-series). By using TIA in addition to measurement of (226)Ra (U-series) from alpha-spectrometry by liquid scintillation counting (LSC), two natural decay series could be identified and separated. The TIA detection efficiency was improved by using the pulse-shape discrimination technique (PSD) to reject beta-pulses, by solvent extraction of Ra combined with simple chemical separation, and by purging the scintillation solution with dry N(2) gas. The U- and Th-series together with the Ac-series were determined, respectively, from alpha spectra and TIA carried out immediately after Ra-extraction. Using the (221)Fr-->(217)At ( T(1/2) 32.3 ms) decay process as a tracer, overall yields were estimated from application of TIA to the (225)Ra (Np-decay series) at the time of maximum growth. The present method has proven useful for simultaneous determination of three radioactive decay series in environmental samples.

Radiocarbon dating uncertainty and the reliability of the PEWMA method of time-series analysis for research on long-term human-environment interaction

PubMed Central

Carleton, W. Christopher; Campbell, David

2018-01-01

Statistical time-series analysis has the potential to improve our understanding of human-environment interaction in deep time. However, radiocarbon dating—the most common chronometric technique in archaeological and palaeoenvironmental research—creates challenges for established statistical methods. The methods assume that observations in a time-series are precisely dated, but this assumption is often violated when calibrated radiocarbon dates are used because they usually have highly irregular uncertainties. As a result, it is unclear whether the methods can be reliably used on radiocarbon-dated time-series. With this in mind, we conducted a large simulation study to investigate the impact of chronological uncertainty on a potentially useful time-series method. The method is a type of regression involving a prediction algorithm called the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed for use with count time-series data, which makes it applicable to a wide range of questions about human-environment interaction in deep time. Our simulations suggest that the PEWMA method can often correctly identify relationships between time-series despite chronological uncertainty. When two time-series are correlated with a coefficient of 0.25, the method is able to identify that relationship correctly 20–30% of the time, providing the time-series contain low noise levels. With correlations of around 0.5, it is capable of correctly identifying correlations despite chronological uncertainty more than 90% of the time. While further testing is desirable, these findings indicate that the method can be used to test hypotheses about long-term human-environment interaction with a reasonable degree of confidence. PMID:29351329

Radiocarbon dating uncertainty and the reliability of the PEWMA method of time-series analysis for research on long-term human-environment interaction.

PubMed

Carleton, W Christopher; Campbell, David; Collard, Mark

2018-01-01

Statistical time-series analysis has the potential to improve our understanding of human-environment interaction in deep time. However, radiocarbon dating-the most common chronometric technique in archaeological and palaeoenvironmental research-creates challenges for established statistical methods. The methods assume that observations in a time-series are precisely dated, but this assumption is often violated when calibrated radiocarbon dates are used because they usually have highly irregular uncertainties. As a result, it is unclear whether the methods can be reliably used on radiocarbon-dated time-series. With this in mind, we conducted a large simulation study to investigate the impact of chronological uncertainty on a potentially useful time-series method. The method is a type of regression involving a prediction algorithm called the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed for use with count time-series data, which makes it applicable to a wide range of questions about human-environment interaction in deep time. Our simulations suggest that the PEWMA method can often correctly identify relationships between time-series despite chronological uncertainty. When two time-series are correlated with a coefficient of 0.25, the method is able to identify that relationship correctly 20-30% of the time, providing the time-series contain low noise levels. With correlations of around 0.5, it is capable of correctly identifying correlations despite chronological uncertainty more than 90% of the time. While further testing is desirable, these findings indicate that the method can be used to test hypotheses about long-term human-environment interaction with a reasonable degree of confidence.

A systems relations model for Tier 2 early intervention child mental health services with schools: an exploratory study.

PubMed

van Roosmalen, Marc; Gardner-Elahi, Catherine; Day, Crispin

2013-01-01

Over the last 15 years, policy initiatives have aimed at the provision of more comprehensive Child and Adolescent Mental Health care. These presented a series of new challenges in organising and delivering Tier 2 child mental health services, particularly in schools. This exploratory study aimed to examine and clarify the service model underpinning a Tier 2 child mental health service offering school-based mental health work. Using semi-structured interviews, clinician descriptions of operational experiences were gathered. These were analysed using grounded theory methods. Analysis was validated by respondents at two stages. A pathway for casework emerged that included a systemic consultative function, as part of an overall three-function service model, which required: (1) activity as a member of the multi-agency system; (2) activity to improve the system working around a particular child; and (3) activity to universally develop a Tier 1 workforce confident in supporting children at risk of or experiencing mental health problems. The study challenged the perception of such a service serving solely a Tier 2 function, the requisite workforce to deliver the service model, and could give service providers a rationale for negotiating service models that include an explicit focus on improving the children's environments.

Visualization of Space-Time Ambiguities to be Explored by the NASA GEC Mission with a Critique of Synthesized Measurements for Different GEC Mission Scenarios

NASA Technical Reports Server (NTRS)

Sojka, Jan J.; Zhu, Lie; Fuller-Rowell, Timothy J.

2005-01-01

The objective of this grant was to study how a multi-satellite mission configuration can be optimized for maximum exploratory scientific return. NASA's Solar Terrestrial Probe (STP) concept mission Geospace Electrodynamic Connections (GEC) was the target mission for this pilot study. GEC prime mission characteristics were two fold: (i) a series of three satellites in the same orbit plane with differential spacing, and (ii) a deep-dipping phase in which these satellites could dip to altitudes as low as 130 km to explore the lower ionosphere and thermosphere. Each satellite would carry a full suite of plasma and neutral in-situ sensors and have the same dipping capability. This latter aspect would be envisaged as a series, up to 10, of deep-dipping campaigns, each lasting 10 days during which the perigee would be lowered to the desired probing depth. The challenge in optimization is to establish the scientific problems that can best be addressed by varying or selecting satellite spacing during a two-year mission while also interspersing, in this two year time frame, the deep-dipping campaigns. Although this sounds like a straightforward trade-off situation, it is complicated by the orbit precession in local time, the location of perigee, and that even the dipping campaigns will have preferred satellite spacing requirements.

Unveiling signatures of interdecadal climate changes by Hilbert analysis

NASA Astrophysics Data System (ADS)

Zappalà, Dario; Barreiro, Marcelo; Masoller, Cristina

2017-04-01

A recent study demonstrated that, in a class of networks of oscillators, the optimal network reconstruction from dynamics is obtained when the similarity analysis is performed not on the original dynamical time series, but on transformed series obtained by Hilbert transform. [1] That motivated us to use Hilbert transform to study another kind of (in a broad sense) "oscillating" series, such as the series of temperature. Actually, we found that Hilbert analysis of SAT (Surface Air Temperature) time series uncovers meaningful information about climate and is therefore a promising tool for the study of other climatological variables. [2] In this work we analysed a large dataset of SAT series, performing Hilbert transform and further analysis with the goal of finding signs of climate change during the analysed period. We used the publicly available ERA-Interim dataset, containing reanalysis data. [3] In particular, we worked on daily SAT time series, from year 1979 to 2015, in 16380 points arranged over a regular grid on the Earth surface. From each SAT time series we calculate the anomaly series and also, by using the Hilbert transform, we calculate the instantaneous amplitude and instantaneous frequency series. Our first approach is to calculate the relative variation: the difference between the average value on the last 10 years and the average value on the first 10 years, divided by the average value over all the analysed period. We did this calculations on our transformed series: frequency and amplitude, both with average values and standard deviation values. Furthermore, to have a comparison with an already known analysis methods, we did these same calculations on the anomaly series. We plotted these results as maps, where the colour of each site indicates the value of its relative variation. Finally, to gain insight in the interpretation of our results over real SAT data, we generated synthetic sinusoidal series with various levels of additive noise. By applying Hilbert analysis to the synthetic data, we uncovered a clear trend between mean amplitude and mean frequency: as the noise level grows, the amplitude increases while the frequency decreases. Research funded in part by AGAUR (Generalitat de Catalunya), EU LINC project (Grant No. 289447) and Spanish MINECO (FIS2015-66503-C3-2-P).

A multi-center screening trial of rasagiline in patients with amyotrophic lateral sclerosis: Possible mitochondrial biomarker target engagement

PubMed Central

Macchi, Zachary; Wang, Yunxia; Moore, Dan; Katz, Jonathan; Saperstein, David; Walk, David; Simpson, Ericka; Genge, Angela; Bertorini, Tulio; Fernandes, J Americo; Swenson, Andrea; Elman, Lauren; Dimachkie, Mazen; Herbelin, Laura; Miller, Joann; Lu, Jianghua; Wilkins, Heather; Swerdlow, Russell H; Statland, Jeffrey; Barohn, Richard

2015-01-01

OBJECTIVE Rasagiline, a monoamine oxidase B inhibitor, slowed disease progression in the SOD1 mouse, and in a case series of patients with amyotrophic lateral sclerosis (ALS). Here we determine whether rasagiline is safe and effective in ALS compared to historical placebo controls, and whether it alters mitochondrial biomarkers. METHODS We performed a prospective open-label, multicenter screening trial of 36 ALS patients treated with 2mg oral rasagiline daily for 12 months. Outcomes included the slope of deterioration of the revised ALS Functional Rating Scale (ALSFRS-R), adverse event monitoring, time to treatment failure, and exploratory biomarkers. RESULTS Participants experienced no serious drug-related adverse events, and the most common adverse event was nausea (11.1%). Rasagiline did not improve the rate of decline in the ALSFRS-R; however, differences in symptom duration compared to historical placebo controls differentially affected ALSFRS-R slope estimates. Rasagiline changed biomarkers over 12 months, such that the mitochondrial membrane potential increased (JC-1 red/green fluorescent ratio 1.92, P=0.0001) and apoptosis markers decreased (Bcl-2/Bax ratio 0.24, P<0.0001). CONCLUSION Engagement of exploratory biomarkers and questions about comparability of baseline characteristics lead us to recommend a further placebo-controlled trial. PMID:25832828

Cognitive and Personality Components Underlying Spoken Idiom Comprehension in Context. An Exploratory Study

PubMed Central

Cacciari, Cristina; Corrardini, Paola; Ferlazzo, Fabio

2018-01-01

In this exploratory study, we investigated whether and to what extent individual differences in cognitive and personality variables are associated with spoken idiom comprehension in context. Language unimpaired participants were enrolled in a cross-modal lexical decision study in which semantically ambiguous Italian idioms (i.e., strings with both a literal and an idiomatic interpretation as, for instance, break the ice), predictable or unpredictable before the string offset, were embedded in idiom-biasing contexts. To explore the contributions of different cognitive and personality components, participants also completed a series of tests respectively assessing general speed, inhibitory control, short-term and working memory, cognitive flexibility, crystallized and fluid intelligence, and personality. Stepwise regression analyses revealed that online idiom comprehension was associated with the participants' working memory, inhibitory control and crystallized verbal intelligence, an association modulated by idiom type. Also personality-related variables (State Anxiety and Openness to Experience) were associated with idiom comprehension, although in marginally significant ways. These results contribute to the renewed interest on how individual variability modulates language comprehension, and for the first time document contributions of individual variability on lexicalized, high frequency multi-word expressions as idioms adding new knowledge to the existing evidence on metaphor and sarcasm. PMID:29765350

"Observation Obscurer" - Time Series Viewer, Editor and Processor

NASA Astrophysics Data System (ADS)

Andronov, I. L.

The program is described, which contains a set of subroutines suitable for East viewing and interactive filtering and processing of regularly and irregularly spaced time series. Being a 32-bit DOS application, it may be used as a default fast viewer/editor of time series in any compute shell ("commander") or in Windows. It allows to view the data in the "time" or "phase" mode, to remove ("obscure") or filter outstanding bad points; to make scale transformations and smoothing using few methods (e.g. mean with phase binning, determination of the statistically opti- mal number of phase bins; "running parabola" (Andronov, 1997, As. Ap. Suppl, 125, 207) fit and to make time series analysis using some methods, e.g. correlation, autocorrelation and histogram analysis: determination of extrema etc. Some features have been developed specially for variable star observers, e.g. the barycentric correction, the creation and fast analysis of "OC" diagrams etc. The manual for "hot keys" is presented. The computer code was compiled with a 32-bit Free Pascal (www.freepascal.org).

Hybrid Wavelet De-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

NASA Astrophysics Data System (ADS)

WANG, D.; Wang, Y.; Zeng, X.

2017-12-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, Wavelet De-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

An Energy-Based Similarity Measure for Time Series

NASA Astrophysics Data System (ADS)

Boudraa, Abdel-Ouahab; Cexus, Jean-Christophe; Groussat, Mathieu; Brunagel, Pierre

2007-12-01

A new similarity measure, called SimilB, for time series analysis, based on the cross-[InlineEquation not available: see fulltext.]-energy operator (2004), is introduced. [InlineEquation not available: see fulltext.] is a nonlinear measure which quantifies the interaction between two time series. Compared to Euclidean distance (ED) or the Pearson correlation coefficient (CC), SimilB includes the temporal information and relative changes of the time series using the first and second derivatives of the time series. SimilB is well suited for both nonstationary and stationary time series and particularly those presenting discontinuities. Some new properties of [InlineEquation not available: see fulltext.] are presented. Particularly, we show that [InlineEquation not available: see fulltext.] as similarity measure is robust to both scale and time shift. SimilB is illustrated with synthetic time series and an artificial dataset and compared to the CC and the ED measures.

Relating interesting quantitative time series patterns with text events and text features

NASA Astrophysics Data System (ADS)

Wanner, Franz; Schreck, Tobias; Jentner, Wolfgang; Sharalieva, Lyubka; Keim, Daniel A.

2013-12-01

In many application areas, the key to successful data analysis is the integrated analysis of heterogeneous data. One example is the financial domain, where time-dependent and highly frequent quantitative data (e.g., trading volume and price information) and textual data (e.g., economic and political news reports) need to be considered jointly. Data analysis tools need to support an integrated analysis, which allows studying the relationships between textual news documents and quantitative properties of the stock market price series. In this paper, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which reflect quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a-priori method. First, based on heuristics we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a-priori method supports the discovery of such sequential temporal patterns. Then, various text features like the degree of sentence nesting, noun phrase complexity, the vocabulary richness, etc. are extracted from the news to obtain meta patterns. Meta patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time-, cluster- and sequence visualization and analysis functionality. We provide two case studies, showing the effectiveness of our combined quantitative and textual analysis work flow. The workflow can also be generalized to other application domains such as data analysis of smart grids, cyber physical systems or the security of critical infrastructure, where the data consists of a combination of quantitative and textual time series data.

Exploratory analysis of rainfall events in Coimbra, Portugal: variability of raindrop characteristics

NASA Astrophysics Data System (ADS)

Carvalho, S. C. P.; de Lima, M. I. P.; de Lima, J. L. M. P.

2012-04-01

Laser disdrometers can monitor efficiently rainfall characteristics at small temporal scales, providing data on rain intensity, raindrop diameter and fall speed, and raindrop counts over time. This type of data allows for the increased understanding of the rainfall structure at small time scales. Of particular interest for many hydrological applications is the characterization of the properties of extreme events, including the intra-event variability, which are affected by different factors (e.g. geographical location, rainfall generating mechanisms). These properties depend on the microphysical, dynamical and kinetic processes that interact to produce rain. In this study we explore rainfall data obtained during two years with a laser disdrometer installed in the city of Coimbra, in the centre region of mainland Portugal. The equipment was developed by Thies Clima. The data temporal resolution is one-minute. Descriptive statistics of time series of raindrop diameter (D), fall speed, kinetic energy, and rain rate were studied at the event scale; for different variables, the average, maximum, minimum, median, variance, standard deviation, quartile, coefficient of variation, skewness and kurtosis were determined. The empirical raindrop size distribution, N(D), was also calculated. Additionally, the parameterization of rainfall was attempted by investigating the applicability of different theoretical statistical distributions to fit the empirical data (e.g. exponential, gamma and lognormal distributions). As expected, preliminary results show that rainfall properties and structure vary with rainfall type and weather conditions over the year. Although only two years were investigated, already some insight into different rain events' structure was obtained.

Stress-tension reduction in the treatment of sexually tortured women--an exploratory study.

PubMed

Larsen, H; Pagaduan-Lopez, J

1987-01-01

Three women who had been tortured and sexually abused during imprisonment as political prisoners during the dictatorship in the Philippines were treated with a nonverbal, manual stress-tension reduction therapy (STRT). All women had difficulty in sexual and social relationships and a series of unspecific complaints such as headache, dizziness, irritability, aggressiveness toward their own children, etc. The therapy is described. A series of four sessions was given each woman, followed by group training. A remarkable improvement was noted, and it is suggested that STRT may be use in other sexological disorders.

Terrain Dynamics Analysis Using Space-Time Domain Hypersurfaces and Gradient Trajectories Derived From Time Series of 3D Point Clouds

DTIC Science & Technology

2015-08-01

optimized space-time interpolation method. Tangible geospatial modeling system was further developed to support the analysis of changing elevation surfaces...Evolution Mapped by Terrestrial Laser Scanning, talk, AGU Fall 2012 *Hardin E, Mitas L, Mitasova H., Simulation of Wind -Blown Sand for...Geomorphological Applications: A Smoothed Particle Hydrodynamics Approach, GSA 2012 *Russ, E. Mitasova, H., Time series and space-time cube analyses on

Multifractal detrended cross-correlation analysis on gold, crude oil and foreign exchange rate time series

NASA Astrophysics Data System (ADS)

Pal, Mayukha; Madhusudana Rao, P.; Manimaran, P.

2014-12-01

We apply the recently developed multifractal detrended cross-correlation analysis method to investigate the cross-correlation behavior and fractal nature between two non-stationary time series. We analyze the daily return price of gold, West Texas Intermediate and Brent crude oil, foreign exchange rate data, over a period of 18 years. The cross correlation has been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the results, the existence of multifractal cross-correlation between all of these time series is found. We also found that the cross correlation between gold and oil prices possess uncorrelated behavior and the remaining bivariate time series possess persistent behavior. It was observed for five bivariate series that the cross-correlation exponents are less than the calculated average generalized Hurst exponents (GHE) for q<0 and greater than GHE when q>0 and for one bivariate series the cross-correlation exponent is greater than GHE for all q values.

Emerging spectra of singular correlation matrices under small power-map deformations

NASA Astrophysics Data System (ADS)

Vinayak; Schäfer, Rudi; Seligman, Thomas H.

2013-09-01

Correlation matrices are a standard tool in the analysis of the time evolution of complex systems in general and financial markets in particular. Yet most analysis assume stationarity of the underlying time series. This tends to be an assumption of varying and often dubious validity. The validity of the assumption improves as shorter time series are used. If many time series are used, this implies an analysis of highly singular correlation matrices. We attack this problem by using the so-called power map, which was introduced to reduce noise. Its nonlinearity breaks the degeneracy of the zero eigenvalues and we analyze the sensitivity of the so-emerging spectra to correlations. This sensitivity will be demonstrated for uncorrelated and correlated Wishart ensembles.

Emerging spectra of singular correlation matrices under small power-map deformations.

PubMed

Vinayak; Schäfer, Rudi; Seligman, Thomas H

2013-09-01

Correlation matrices are a standard tool in the analysis of the time evolution of complex systems in general and financial markets in particular. Yet most analysis assume stationarity of the underlying time series. This tends to be an assumption of varying and often dubious validity. The validity of the assumption improves as shorter time series are used. If many time series are used, this implies an analysis of highly singular correlation matrices. We attack this problem by using the so-called power map, which was introduced to reduce noise. Its nonlinearity breaks the degeneracy of the zero eigenvalues and we analyze the sensitivity of the so-emerging spectra to correlations. This sensitivity will be demonstrated for uncorrelated and correlated Wishart ensembles.

Inhomogeneous scaling behaviors in Malaysian foreign currency exchange rates

NASA Astrophysics Data System (ADS)

Muniandy, S. V.; Lim, S. C.; Murugan, R.

2001-12-01

In this paper, we investigate the fractal scaling behaviors of foreign currency exchange rates with respect to Malaysian currency, Ringgit Malaysia. These time series are examined piecewise before and after the currency control imposed in 1st September 1998 using the monofractal model based on fractional Brownian motion. The global Hurst exponents are determined using the R/ S analysis, the detrended fluctuation analysis and the method of second moment using the correlation coefficients. The limitation of these monofractal analyses is discussed. The usual multifractal analysis reveals that there exists a wide range of Hurst exponents in each of the time series. A new method of modelling the multifractal time series based on multifractional Brownian motion with time-varying Hurst exponents is studied.

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.

The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series

NASA Astrophysics Data System (ADS)

Du, Kongchang; Zhao, Ying; Lei, Jiaqiang

2017-09-01

In hydrological time series prediction, singular spectrum analysis (SSA) and discrete wavelet transform (DWT) are widely used as preprocessing techniques for artificial neural network (ANN) and support vector machine (SVM) predictors. These hybrid or ensemble models seem to largely reduce the prediction error. In current literature researchers apply these techniques to the whole observed time series and then obtain a set of reconstructed or decomposed time series as inputs to ANN or SVM. However, through two comparative experiments and mathematical deduction we found the usage of SSA and DWT in building hybrid models is incorrect. Since SSA and DWT adopt 'future' values to perform the calculation, the series generated by SSA reconstruction or DWT decomposition contain information of 'future' values. These hybrid models caused incorrect 'high' prediction performance and may cause large errors in practice.

78 FR 40104 - Endangered and Threatened Wildlife; 12-Month Finding on Petitions To List the Northeastern...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-03

...). Across the entire time series of available logbook data (1981-2011), CPUE in this fishery appears to have... abundance over this period. The time series of annual abundance estimates from this analysis showed there... been documented at Southeast Farallon Island since the 1980s, providing a relatively long time series...

A Computer Evolution in Teaching Undergraduate Time Series

ERIC Educational Resources Information Center

Hodgess, Erin M.

2004-01-01

In teaching undergraduate time series courses, we have used a mixture of various statistical packages. We have finally been able to teach all of the applied concepts within one statistical package; R. This article describes the process that we use to conduct a thorough analysis of a time series. An example with a data set is provided. We compare…

Analysis of Complex Intervention Effects in Time-Series Experiments.

ERIC Educational Resources Information Center

Bower, Cathleen

An iterative least squares procedure for analyzing the effect of various kinds of intervention in time-series data is described. There are numerous applications of this design in economics, education, and psychology, although until recently, no appropriate analysis techniques had been developed to deal with the model adequately. This paper…

A Comparison of Missing-Data Procedures for Arima Time-Series Analysis

ERIC Educational Resources Information Center

Velicer, Wayne F.; Colby, Suzanne M.

2005-01-01

Missing data are a common practical problem for longitudinal designs. Time-series analysis is a longitudinal method that involves a large number of observations on a single unit. Four different missing-data methods (deletion, mean substitution, mean of adjacent observations, and maximum likelihood estimation) were evaluated. Computer-generated…

Interrupted Time Series Analysis: A Research Technique for Evaluating Social Programs for the Elderly

ERIC Educational Resources Information Center

Calsyn, Robert J.; And Others

1977-01-01

After arguing that treatment programs for the elderly need to be evaluated with better research designs, the authors illustrate how interrupted time series analysis can be used to evaluate programs for the elderly when random assignment to experimental and control groups is not possible. (Author)

Time series analysis of monthly pulpwood use in the Northeast

Treesearch

James T. Bones

1980-01-01

Time series analysis was used to develop a model that depicts pulpwood use in the Northeast. The model is useful in forecasting future pulpwood requirements (short term) or monitoring pulpwood-use activity in relation to past use patterns. The model predicted a downturn in use during 1980.

Nonlinear Analysis of Surface EMG Time Series of Back Muscles

NASA Astrophysics Data System (ADS)

Dolton, Donald C.; Zurcher, Ulrich; Kaufman, Miron; Sung, Paul

2004-10-01

A nonlinear analysis of surface electromyography time series of subjects with and without low back pain is presented. The mean-square displacement and entropy shows anomalous diffusive behavior on intermediate time range 10 ms < t < 1 s. This behavior implies the presence of correlations in the signal. We discuss the shape of the power spectrum of the signal.

Integrated method for chaotic time series analysis

DOEpatents

Hively, L.M.; Ng, E.G.

1998-09-29

Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data are disclosed. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated. 8 figs.

On the Likelihood Ratio Test for the Number of Factors in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Hayashi, Kentaro; Bentler, Peter M.; Yuan, Ke-Hai

2007-01-01

In the exploratory factor analysis, when the number of factors exceeds the true number of factors, the likelihood ratio test statistic no longer follows the chi-square distribution due to a problem of rank deficiency and nonidentifiability of model parameters. As a result, decisions regarding the number of factors may be incorrect. Several…

A Note on Procrustean Rotation in Exploratory Factor Analysis: A Computer Intensive Approach to Goodness-of-Fit Evaluation.

ERIC Educational Resources Information Center

Raykov, Tenko; Little, Todd D.

1999-01-01

Describes a method for evaluating results of Procrustean rotation to a target factor pattern matrix in exploratory factor analysis. The approach, based on the bootstrap method, yields empirical approximations of the sampling distributions of: (1) differences between target elements and rotated factor pattern matrices; and (2) the overall…

An Exploratory Factor Analysis of the Sexual Orientation Counselor Competency Scale: Examining the Variable of Experience

ERIC Educational Resources Information Center

Ali, Shainna; Lambie, Glenn; Bloom, Zachary D.

2017-01-01

The Sexual Orientation Counselor Competency Scale (SOCCS), developed by Bidell in 2005, measures counselors' levels of skills, awareness, and knowledge in assisting lesbian, gay, or bisexual (LGB) clients. In an effort to gain an increased understanding of the construct validity of the SOCCS, researchers performed an exploratory factor analysis on…

Technical Communications in Aeronautics: Results of an Exploratory Study. An Analysis of Managers' and Nonmanagers' Responses. NASA Technical Memorandum 101625.

ERIC Educational Resources Information Center

Pinelli, Thomas E.; And Others

Data collected from an exploratory study concerned with the technical communications practices of aerospace engineers and scientists were analyzed to test the primary assumption that aerospace managers and nonmanagers have different technical communications practices. Five secondary assumptions were established for the analysis: (1) that the…

Graphical and Numerical Descriptive Analysis: Exploratory Tools Applied to Vietnamese Data

ERIC Educational Resources Information Center

Haughton, Dominique; Phong, Nguyen

2004-01-01

This case study covers several exploratory data analysis ideas, the histogram and boxplot, kernel density estimates, the recently introduced bagplot--a two-dimensional extension of the boxplot--as well as the violin plot, which combines a boxplot with a density shape plot. We apply these ideas and demonstrate how to interpret the output from these…

Validation of the Adolescent Concerns Measure (ACM): Evidence from Exploratory and Confirmatory Factor Analysis

ERIC Educational Resources Information Center

Ang, Rebecca P.; Chong, Wan Har; Huan, Vivien S.; Yeo, Lay See

2007-01-01

This article reports the development and initial validation of scores obtained from the Adolescent Concerns Measure (ACM), a scale which assesses concerns of Asian adolescent students. In Study 1, findings from exploratory factor analysis using 619 adolescents suggested a 24-item scale with four correlated factors--Family Concerns (9 items), Peer…

An Exploratory Factor Analysis of the URICA among Couple Therapy Participants

ERIC Educational Resources Information Center

Tambling, Rachel B.; Johnson, Lee N.

2012-01-01

Assessing and measuring client motivation to change has been of great interest to therapists and researchers in a variety of fields. This article presents the results of an exploratory factor analysis of the University of Rhode Island Change Assessment (URICA), a measure of motivation to change, in a sample of individuals in couple therapy. Four…

Improving Your Exploratory Factor Analysis for Ordinal Data: A Demonstration Using FACTOR

ERIC Educational Resources Information Center

Baglin, James

2014-01-01

Exploratory factor analysis (EFA) methods are used extensively in the field of assessment and evaluation. Due to EFA's widespread use, common methods and practices have come under close scrutiny. A substantial body of literature has been compiled highlighting problems with many of the methods and practices used in EFA, and, in response, many…

The School Counseling Program Implementation Survey: Initial Instrument Development and Exploratory Factor Analysis

ERIC Educational Resources Information Center

Clemens, Elysia V.; Carey, John C.; Harrington, Karen M.

2010-01-01

This article details the initial development of the School Counseling Program Implementation Survey and psychometric results including reliability and factor structure. An exploratory factor analysis revealed a three-factor model that accounted for 54% of the variance of the intercorrelation matrix and a two-factor model that accounted for 47% of…

Exploratory and Confirmatory Factor Analyses of the WISC-IV with Gifted Students

ERIC Educational Resources Information Center

Rowe, Ellen W.; Dandridge, Jessica; Pawlush, Alexandra; Thompson, Dawna F.; Ferrier, David E.

2014-01-01

These 2 studies investigated the factor structure of the Wechsler Intelligence Scale for Children-4th edition (WISC-IV; Wechsler, 2003a) with exploratory factor analysis (EFA; Study 1) and confirmatory factor analysis (CFA; Study 2) among 2 independent samples of gifted students. The EFA sample consisted of 225 children who were referred for a…

FACTOR 9.2: A Comprehensive Program for Fitting Exploratory and Semiconfirmatory Factor Analysis and IRT Models

ERIC Educational Resources Information Center

Lorenzo-Seva, Urbano; Ferrando, Pere J.

2013-01-01

FACTOR 9.2 was developed for three reasons. First, exploratory factor analysis (FA) is still an active field of research although most recent developments have not been incorporated into available programs. Second, there is now renewed interest in semiconfirmatory (SC) solutions as suitable approaches to the complex structures are commonly found…

Ordinary Least Squares Estimation of Parameters in Exploratory Factor Analysis with Ordinal Data

ERIC Educational Resources Information Center

Lee, Chun-Ting; Zhang, Guangjian; Edwards, Michael C.

2012-01-01

Exploratory factor analysis (EFA) is often conducted with ordinal data (e.g., items with 5-point responses) in the social and behavioral sciences. These ordinal variables are often treated as if they were continuous in practice. An alternative strategy is to assume that a normally distributed continuous variable underlies each ordinal variable.…

An Exploratory Factor Analysis and Reliability Analysis of the Student Online Learning Readiness (SOLR) Instrument

ERIC Educational Resources Information Center

Yu, Taeho; Richardson, Jennifer C.

2015-01-01

The purpose of this study was to develop an effective instrument to measure student readiness in online learning with reliable predictors of online learning success factors such as learning outcomes and learner satisfaction. The validity and reliability of the Student Online Learning Readiness (SOLR) instrument were tested using exploratory factor…

Development of an Instrument to Measure Student Use of Academic Success Skills: An Exploratory Factor Analysis

ERIC Educational Resources Information Center

Carey, John; Brigman, Greg; Webb, Linda; Villares, Elizabeth; Harrington, Karen

2014-01-01

This article describes the development of the Student Engagement in School Success Skills instrument including item development and exploratory factor analysis. The instrument was developed to measure student use of the skills and strategies identified as most critical for long-term school success that are typically taught by school counselors.

An Exploratory Analysis of a Middle School Science Curriculum: Implications for Students with Learning Disabilities

ERIC Educational Resources Information Center

Taylor, Gregory S.; Hord, Casey

2016-01-01

An exploratory study of a middle school curriculum directly aligned with the Next Generation Science Standards was conducted with a focus on how the curriculum addresses the instructional needs of students with learning disabilities. A descriptive analysis of a lesson on speed and velocity was conducted and implications discussed for students with…

Battle of Narratives

DTIC Science & Technology

2012-06-01

18 De Nooy, Wouter, Andrej Mrvar , and Vladimir Batagelj , Exploratory Social Network Analysis with Pajek (New York: Cambridge University Press, 2005... Mrvar , and Vladimir Batagelj . Exploratory Social Network Analysis with Pajek. New York: Cambridge University Press, 2005. Democratic National...Review 54(1):33-48; Brian Uzzi. 1996 . "The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect

Using Exploratory Spatial Data Analysis to Leverage Social Indicator Databases: The Discovery of Interesting Patterns

ERIC Educational Resources Information Center

Anselin, Luc; Sridharan, Sanjeev; Gholston, Susan

2007-01-01

With the proliferation of social indicator databases, the need for powerful techniques to study patterns of change has grown. In this paper, the utility of spatial data analytical methods such as exploratory spatial data analysis (ESDA) is suggested as a means to leverage the information contained in social indicator databases. The principles…

An exploratory analysis of the relationship between ambient ozone and particulate matter concentrations during early pregnancy and selected birth defects in Texas

EPA Science Inventory

Background: Associations between ozone (O3) and fine particulate matter (PM2.5) concentrations and birth outcomes have been previously demonstrated. We perform an exploratory analysis of O3 and PM2.5 concentrations during early pregnancy and multiple types of birth defects. Met...

Exploratory Two-Level Analysis of Individual- and School-Level Factors on Truant Youth Emotional/Psychological Functioning

ERIC Educational Resources Information Center

Dembo, Richard; Wareham, Jennifer; Schmeidler, James; Winters, Ken C.

2016-01-01

Research on samples of truant adolescents is limited, with little known about mental health problems among truant youths. This study provided an exploratory, multilevel examination of mental health problems for a sample of 300 truant adolescents. Confirmatory factor analysis indicated a single factor of multiple mental health problems at the…

Interrupted time series regression for the evaluation of public health interventions: a tutorial.

PubMed

Bernal, James Lopez; Cummins, Steven; Gasparrini, Antonio

2017-02-01

Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.

Interrupted time series regression for the evaluation of public health interventions: a tutorial

PubMed Central

Bernal, James Lopez; Cummins, Steven; Gasparrini, Antonio

2017-01-01

Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design. PMID:27283160

Control order and visuomotor strategy development for joystick-steered underground shuttle cars.

PubMed

Cloete, Steven; Zupanc, Christine; Burgess-Limerick, Robin; Wallis, Guy

2014-09-01

In this simulator-based study, we aimed to quantify performance differences between joystick steering systems using first-order and second-order control, which are used in underground coal mining shuttle cars. In addition, we conducted an exploratory analysis of how users of the more difficult, second-order system changed their behavior over time. Evidence from the visuomotor control literature suggests that higher-order control devices are not intuitive, which could pose a significant risk to underground mine personnel, equipment, and infrastructure. Thirty-six naive participants were randomly assigned to first- and second-order conditions and completed three experimental trials comprising sequences of 90 degrees turns in a virtual underground mine environment, with velocity held constant at 9 km/h(-1). Performance measures were lateral deviation, steering angle variability, high-frequency steering content, joystick activity, and cumulative time in collision with the virtual mine wall. The second-order control group exhibited significantly poorer performance for all outcome measures. In addition, a series of correlation analyses revealed that changes in strategy were evident in the second-order group but not the first-order group. Results were consistent with previous literature indicating poorer performance with higher-order control devices and caution against the adoption of the second-order joystick system for underground shuttle cars. Low-cost, portable simulation platforms may provide an effective basis for operator training and recruitment.

Long-range memory and multifractality in gold markets

NASA Astrophysics Data System (ADS)

Mali, Provash; Mukhopadhyay, Amitabha

2015-03-01

Long-range correlation and fluctuation in the gold market time series of the world's two leading gold consuming countries, namely China and India, are studied. For both the market series during the period 1985-2013 we observe a long-range persistence of memory in the sequences of maxima (minima) of returns in successive time windows of fixed length, but the series, as a whole, are found to be uncorrelated. Multifractal analysis for these series as well as for the sequences of maxima (minima) is carried out in terms of the multifractal detrended fluctuation analysis (MF-DFA) method. We observe a weak multifractal structure for the original series that mainly originates from the fat-tailed probability distribution function of the values, and the multifractal nature of the original time series is enriched into their sequences of maximal (minimal) returns. A quantitative measure of multifractality is provided by using a set of ‘complexity parameters’.

Information mining over heterogeneous and high-dimensional time-series data in clinical trials databases.

PubMed

Altiparmak, Fatih; Ferhatosmanoglu, Hakan; Erdal, Selnur; Trost, Donald C

2006-04-01

An effective analysis of clinical trials data involves analyzing different types of data such as heterogeneous and high dimensional time series data. The current time series analysis methods generally assume that the series at hand have sufficient length to apply statistical techniques to them. Other ideal case assumptions are that data are collected in equal length intervals, and while comparing time series, the lengths are usually expected to be equal to each other. However, these assumptions are not valid for many real data sets, especially for the clinical trials data sets. An addition, the data sources are different from each other, the data are heterogeneous, and the sensitivity of the experiments varies by the source. Approaches for mining time series data need to be revisited, keeping the wide range of requirements in mind. In this paper, we propose a novel approach for information mining that involves two major steps: applying a data mining algorithm over homogeneous subsets of data, and identifying common or distinct patterns over the information gathered in the first step. Our approach is implemented specifically for heterogeneous and high dimensional time series clinical trials data. Using this framework, we propose a new way of utilizing frequent itemset mining, as well as clustering and declustering techniques with novel distance metrics for measuring similarity between time series data. By clustering the data, we find groups of analytes (substances in blood) that are most strongly correlated. Most of these relationships already known are verified by the clinical panels, and, in addition, we identify novel groups that need further biomedical analysis. A slight modification to our algorithm results an effective declustering of high dimensional time series data, which is then used for "feature selection." Using industry-sponsored clinical trials data sets, we are able to identify a small set of analytes that effectively models the state of normal health.

NASA standard: Trend analysis techniques

NASA Technical Reports Server (NTRS)

1988-01-01

This Standard presents descriptive and analytical techniques for NASA trend analysis applications. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. Use of this Standard is not mandatory; however, it should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend Analysis is neither a precise term nor a circumscribed methodology, but rather connotes, generally, quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this Standard. The document presents the basic ideas needed for qualitative and quantitative assessment of trends, together with relevant examples. A list of references provides additional sources of information.

Genotype evaluation of cowpea seeds (Vigna unguiculata) using 1H qNMR combined with exploratory tools and solid-state NMR.

PubMed

Alves Filho, Elenilson G; Silva, Lorena M A; Teofilo, Elizita M; Larsen, Flemming H; de Brito, Edy S

2017-01-01

The ultimate aim of this study was to apply a non-targeted chemometric analysis (principal component analysis and hierarchical clustering analysis using the heat map approach) of NMR data to investigate the variability of organic compounds in nine genotype cowpea seeds, without any complex pre-treatment. In general, both exploratory tools show that Tvu 233, CE-584, and Setentão genotypes presented higher amount mainly of raffinose and Tvu 382 presented the highest content of choline and least content of raffinose. The evaluation of the aromatic region showed the Setentão genotype with highest content of niacin/vitamin B3 whereas Tvu 382 with lowest amount. To investigate rigid and mobile components in the seeds cotyledon, 13 C CP and SP/MAS solid-state NMR experiments were performed. The cotyledon of the cowpea comprised a rigid part consisting of starch as well as a soft portion made of starch, fatty acids, and protein. The variable contact time experiment suggests the presence of lipid-amylose complexes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of Hexaconazole Concentration in the Top Most Layer of Oil Palm Plantation Soil Using Exploratory Data Analysis (EDA)

PubMed Central

Maznah, Zainol; Halimah, Muhamad; Shitan, Mahendran; Kumar Karmokar, Provash; Najwa, Sulaiman

2017-01-01

Ganoderma boninense is a fungus that can affect oil palm trees and cause a serious disease called the basal stem root (BSR). This disease causes the death of more than 80% of oil palm trees midway through their economic life and hexaconazole is one of the particular fungicides that can control this fungus. Hexaconazole can be applied by the soil drenching method and it will be of interest to know the concentration of the residue in the soil after treatment with respect to time. Hence, a field study was conducted in order to determine the actual concentration of hexaconazole in soil. In the present paper, a new approach that can be used to predict the concentration of pesticides in the soil is proposed. The statistical analysis revealed that the Exploratory Data Analysis (EDA) techniques would be appropriate in this study. The EDA techniques were used to fit a robust resistant model and predict the concentration of the residue in the topmost layer of the soil. PMID:28060816

Prediction of Hexaconazole Concentration in the Top Most Layer of Oil Palm Plantation Soil Using Exploratory Data Analysis (EDA).

PubMed

Maznah, Zainol; Halimah, Muhamad; Shitan, Mahendran; Kumar Karmokar, Provash; Najwa, Sulaiman

2017-01-01

Ganoderma boninense is a fungus that can affect oil palm trees and cause a serious disease called the basal stem root (BSR). This disease causes the death of more than 80% of oil palm trees midway through their economic life and hexaconazole is one of the particular fungicides that can control this fungus. Hexaconazole can be applied by the soil drenching method and it will be of interest to know the concentration of the residue in the soil after treatment with respect to time. Hence, a field study was conducted in order to determine the actual concentration of hexaconazole in soil. In the present paper, a new approach that can be used to predict the concentration of pesticides in the soil is proposed. The statistical analysis revealed that the Exploratory Data Analysis (EDA) techniques would be appropriate in this study. The EDA techniques were used to fit a robust resistant model and predict the concentration of the residue in the topmost layer of the soil.

Psychometric evaluation of the Dutch version of the Subjective Opiate Withdrawal Scale (SOWS).

PubMed

Dijkstra, Boukje A G; Krabbe, Paul F M; Riezebos, Truus G M; van der Staak, Cees P F; De Jong, Cor A J

2007-01-01

To evaluate the psychometric properties of the Dutch version of the 16-item Subjective Opiate Withdrawal Scale (SOWS). The SOWS measures withdrawal symptoms at the time of assessment. The Dutch SOWS was repeatedly administered to a sample of 272 opioid-dependent inpatients of four addiction treatment centers during rapid detoxification with or without general anesthesia. Examination of the psychometric properties of the SOWS included exploratory factor analysis, internal consistency, test-retest reliability, and criterion validity. Exploratory factor analysis of the SOWS revealed a general pattern of four factors with three items not always clustered in the same factors at different points of measurement. After excluding these items from factor analysis four factors were identified during detoxification (temperature dysregulation, tractus locomotorius, tractus gastro-intestinalis and facial disinhibition). The 13-item SOWS shows high internal consistency and test-retest reliability and good validity at different stages of withdrawal. The 13-item SOWS is a reliable and valid instrument to assess opioid withdrawal during rapid detoxification. Three items were deleted because their content does not correspond directly with opioid withdrawal symptoms. Copyright (c) 2007 S. Karger AG, Basel.

Combined Vocal Exercises for Rehabilitation After Supracricoid Laryngectomy: Evaluation of Different Execution Times.

PubMed

Silveira, Hevely Saray Lima; Simões-Zenari, Marcia; Kulcsar, Marco Aurélio; Cernea, Claudio Roberto; Nemr, Kátia

2017-10-27

The supracricoid partial laryngectomy allows the preservation of laryngeal functions with good local cancer control. To assess laryngeal configuration and voice analysis data following the performance of a combination of two vocal exercises: the prolonged /b/vocal exercise combined with the vowel /e/ using chest and arm pushing with different durations among individuals who have undergone supracricoid laryngectomy. Eleven patients undergoing partial laryngectomy supracricoid with cricohyoidoepiglottopexy (CHEP) were evaluated using voice recording. Four judges performed separately a perceptive-vocal analysis of hearing voices, with random samples. For the analysis of intrajudge reliability, repetitions of 70% of the voices were done. Intraclass correlation coefficient was used to analyze the reliability of the judges. For an analysis of each judge to the comparison between zero time (time point 0), after the first series of exercises (time point 1), after the second series (time point 2), after the third series (time point 3), after the fourth series (time point 4), and after the fifth and final series (time point 5), the Friedman test was used with a significance level of 5%. The data relative to the configuration of the larynx were subjected to a descriptive analysis. In the evaluation, were considered the judge results 1 which have greater reliability. There was an improvement in the general level of vocal, roughness, and breathiness deviations from time point 4 [T4]. The prolonged /b/vocal exercise, combined with the vowel /e/ using chest- and arm-pushing exercises, was associated with an improvement in the overall grade of vocal deviation, roughness, and breathiness starting at minute 4 among patients who had undergone supracricoid laryngectomy with CHEP reconstruction. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Towards viable, useful and usable human factors design guidance.

PubMed

Burns, C M; Vicente, K J; Christoffersen, K; Pawlak, W S

1997-01-01

This paper investigates the factors relevant to producing effective human factors design guidance, using the Engineering Data Compendium (EDC) as a research vehicle. A series of three exploratory experiments focusing on the factors that affect the usability, usefulness and viability of human factors handbooks was conducted. The results of these studies were interpreted in the context of the process by which the EDC was developed, leading to the following recommendations: (a) human factors guidance should be organized in a manner that is stepped in context; (b) human factors guidance should be based on an explicit requirements analysis; (c) the calibration of designers' perceptions of the cost of obtaining human factors information must be improved; (d) organizational policies must be changed to induce more effective information search behaviour.

Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data

PubMed Central

Takayasu, Hideki; Takayasu, Misako

2017-01-01

We extend the concept of statistical symmetry as the invariance of a probability distribution under transformation to analyze binary sign time series data of price difference from the foreign exchange market. We model segments of the sign time series as Markov sequences and apply a local hypothesis test to evaluate the symmetries of independence and time reversion in different periods of the market. For the test, we derive the probability of a binary Markov process to generate a given set of number of symbol pairs. Using such analysis, we could not only segment the time series according the different behaviors but also characterize the segments in terms of statistical symmetries. As a particular result, we find that the foreign exchange market is essentially time reversible but this symmetry is broken when there is a strong external influence. PMID:28542208

Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains.

PubMed

Xia, Li C; Ai, Dongmei; Cram, Jacob A; Liang, Xiaoyi; Fuhrman, Jed A; Sun, Fengzhu

2015-09-21

Local trend (i.e. shape) analysis of time series data reveals co-changing patterns in dynamics of biological systems. However, slow permutation procedures to evaluate the statistical significance of local trend scores have limited its applications to high-throughput time series data analysis, e.g., data from the next generation sequencing technology based studies. By extending the theories for the tail probability of the range of sum of Markovian random variables, we propose formulae for approximating the statistical significance of local trend scores. Using simulations and real data, we show that the approximate p-value is close to that obtained using a large number of permutations (starting at time points >20 with no delay and >30 with delay of at most three time steps) in that the non-zero decimals of the p-values obtained by the approximation and the permutations are mostly the same when the approximate p-value is less than 0.05. In addition, the approximate p-value is slightly larger than that based on permutations making hypothesis testing based on the approximate p-value conservative. The approximation enables efficient calculation of p-values for pairwise local trend analysis, making large scale all-versus-all comparisons possible. We also propose a hybrid approach by integrating the approximation and permutations to obtain accurate p-values for significantly associated pairs. We further demonstrate its use with the analysis of the Polymouth Marine Laboratory (PML) microbial community time series from high-throughput sequencing data and found interesting organism co-occurrence dynamic patterns. The software tool is integrated into the eLSA software package that now provides accelerated local trend and similarity analysis pipelines for time series data. The package is freely available from the eLSA website: http://bitbucket.org/charade/elsa.

Policy and Persistence: An Exploratory Mixed Methods Case Study of "Last Mile" Students at Portland State University

ERIC Educational Resources Information Center

Wubbold, Joseph Mark

2012-01-01

In an extension of educational attainment research, this exploratory mixed- methods case study examines the influence of institutional policies on the behavior of five cohorts (n = 925) of traditional first time, full time (FTFT) freshmen--called "Last Mile" students--at one urban research university located in the Pacific Northwest.…

Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory

NASA Astrophysics Data System (ADS)

Wang, Na; Li, Dong; Wang, Qiwen

2012-12-01

The visibility graph approach and complex network theory provide a new insight into time series analysis. The inheritance of the visibility graph from the original time series was further explored in the paper. We found that degree distributions of visibility graphs extracted from Pseudo Brownian Motion series obtained by the Frequency Domain algorithm exhibit exponential behaviors, in which the exponential exponent is a binomial function of the Hurst index inherited in the time series. Our simulations presented that the quantitative relations between the Hurst indexes and the exponents of degree distribution function are different for different series and the visibility graph inherits some important features of the original time series. Further, we convert some quarterly macroeconomic series including the growth rates of value-added of three industry series and the growth rates of Gross Domestic Product series of China to graphs by the visibility algorithm and explore the topological properties of graphs associated from the four macroeconomic series, namely, the degree distribution and correlations, the clustering coefficient, the average path length, and community structure. Based on complex network analysis we find degree distributions of associated networks from the growth rates of value-added of three industry series are almost exponential and the degree distributions of associated networks from the growth rates of GDP series are scale free. We also discussed the assortativity and disassortativity of the four associated networks as they are related to the evolutionary process of the original macroeconomic series. All the constructed networks have “small-world” features. The community structures of associated networks suggest dynamic changes of the original macroeconomic series. We also detected the relationship among government policy changes, community structures of associated networks and macroeconomic dynamics. We find great influences of government policies in China on the changes of dynamics of GDP and the three industries adjustment. The work in our paper provides a new way to understand the dynamics of economic development.

Characterization of chaotic attractors under noise: A recurrence network perspective

NASA Astrophysics Data System (ADS)

Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.

2016-12-01

We undertake a detailed numerical investigation to understand how the addition of white and colored noise to a chaotic time series changes the topology and the structure of the underlying attractor reconstructed from the time series. We use the methods and measures of recurrence plot and recurrence network generated from the time series for this analysis. We explicitly show that the addition of noise obscures the property of recurrence of trajectory points in the phase space which is the hallmark of every dynamical system. However, the structure of the attractor is found to be robust even upto high noise levels of 50%. An advantage of recurrence network measures over the conventional nonlinear measures is that they can be applied on short and non stationary time series data. By using the results obtained from the above analysis, we go on to analyse the light curves from a dominant black hole system and show that the recurrence network measures are capable of identifying the nature of noise contamination in a time series.

Investigation of the 16-year and 18-year ZTD Time Series Derived from GPS Data Processing

NASA Astrophysics Data System (ADS)

Bałdysz, Zofia; Nykiel, Grzegorz; Figurski, Mariusz; Szafranek, Karolina; KroszczyńSki, Krzysztof

2015-08-01

The GPS system can play an important role in activities related to the monitoring of climate. Long time series, coherent strategy, and very high quality of tropospheric parameter Zenith Tropospheric Delay (ZTD) estimated on the basis of GPS data analysis allows to investigate its usefulness for climate research as a direct GPS product. This paper presents results of analysis of 16-year time series derived from EUREF Permanent Network (EPN) reprocessing performed by the Military University of Technology. For 58 stations Lomb-Scargle periodograms were performed in order to obtain information about the oscillations in ZTD time series. Seasonal components and linear trend were estimated using Least Square Estimation (LSE) and Mann—Kendall trend test was used to confirm the presence of a linear trend designated by LSE method. In order to verify the impact of the length of time series on trend value, comparison between 16 and 18 years were performed.

Multiscale entropy-based methods for heart rate variability complexity analysis

NASA Astrophysics Data System (ADS)

Silva, Luiz Eduardo Virgilio; Cabella, Brenno Caetano Troca; Neves, Ubiraci Pereira da Costa; Murta Junior, Luiz Otavio

2015-03-01

Physiologic complexity is an important concept to characterize time series from biological systems, which associated to multiscale analysis can contribute to comprehension of many complex phenomena. Although multiscale entropy has been applied to physiological time series, it measures irregularity as function of scale. In this study we purpose and evaluate a set of three complexity metrics as function of time scales. Complexity metrics are derived from nonadditive entropy supported by generation of surrogate data, i.e. SDiffqmax, qmax and qzero. In order to access accuracy of proposed complexity metrics, receiver operating characteristic (ROC) curves were built and area under the curves was computed for three physiological situations. Heart rate variability (HRV) time series in normal sinus rhythm, atrial fibrillation, and congestive heart failure data set were analyzed. Results show that proposed metric for complexity is accurate and robust when compared to classic entropic irregularity metrics. Furthermore, SDiffqmax is the most accurate for lower scales, whereas qmax and qzero are the most accurate when higher time scales are considered. Multiscale complexity analysis described here showed potential to assess complex physiological time series and deserves further investigation in wide context.

Measuring precarious employment in times of crisis: the revised Employment Precariousness Scale (EPRES) in Spain.

PubMed

Vives, Alejandra; González, Francisca; Moncada, Salvador; Llorens, Clara; Benach, Joan

2015-01-01

This study examines the psychometric properties of the revised Employment Precariousness Scale (EPRES-2010) in a context of economic crisis and growing unemployment. Data correspond to salaried workers with a contract (n=4,750) from the second Psychosocial Work Environment Survey (Spain, 2010). Analyses included acceptability, scale score distributions, Cronbach's alpha coefficient and exploratory factor analysis. Response rates were 80% or above, scores were widely distributed with reductions in floor effects for temporariness among permanent workers and for vulnerability. Cronbach's alpha coefficients were 0.70 or above; exploratory factor analysis confirmed the theoretical allocation of 21 out of 22 items. The revised version of the EPRES demonstrated good metric properties and improved sensitivity to worker vulnerability and employment instability among permanent workers. Furthermore, it was sensitive to increased levels of precariousness in some dimensions despite decreases in others, demonstrating responsiveness to the context of the economic crisis affecting the Spanish labour market. Copyright © 2015 SESPAS. Published by Elsevier Espana. All rights reserved.

Modeling BAS Dysregulation in Bipolar Disorder.

PubMed

Hamaker, Ellen L; Grasman, Raoul P P P; Kamphuis, Jan Henk

2016-08-01

Time series analysis is a technique that can be used to analyze the data from a single subject and has great potential to investigate clinically relevant processes like affect regulation. This article uses time series models to investigate the assumed dysregulation of affect that is associated with bipolar disorder. By formulating a number of alternative models that capture different kinds of theoretically predicted dysregulation, and by comparing these in both bipolar patients and controls, we aim to illustrate the heuristic potential this method of analysis has for clinical psychology. We argue that, not only can time series analysis elucidate specific maladaptive dynamics associated with psychopathology, it may also be clinically applied in symptom monitoring and the evaluation of therapeutic interventions.

Characterization of cracking behavior using posttest fractographic analysis

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

Kobayashi, T.; Shockey, D.A.

A determination of time to initiation of stress corrosion cracking in structures and test specimens is important for performing structural failure analysis and for setting inspection intervals. Yet it is seldom possible to establish how much of a component's lifetime represents the time to initiation of fracture and how much represents postinitiation crack growth. This exploratory research project was undertaken to examine the feasibility of determining crack initiation times and crack growth rates from posttest examination of fracture surfaces of constant-extension-rate-test (CERT) specimens by using the fracture reconstruction applying surface topography analysis (FRASTA) technique. The specimens used in this studymore » were Type 304 stainless steel fractured in several boiling water reactor (BWR) aqueous environments. 2 refs., 25 figs., 2 tabs.« less

Sensitivity analysis of machine-learning models of hydrologic time series

NASA Astrophysics Data System (ADS)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

Using exploratory data analysis to identify and predict patterns of human Lyme disease case clustering within a multistate region, 2010-2014.

PubMed

Hendricks, Brian; Mark-Carew, Miguella

2017-02-01

Lyme disease is the most commonly reported vectorborne disease in the United States. The objective of our study was to identify patterns of Lyme disease reporting after multistate inclusion to mitigate potential border effects. County-level human Lyme disease surveillance data were obtained from Kentucky, Maryland, Ohio, Pennsylvania, Virginia, and West Virginia state health departments. Rate smoothing and Local Moran's I was performed to identify clusters of reporting activity and identify spatial outliers. A logistic generalized estimating equation was performed to identify significant associations in disease clustering over time. Resulting analyses identified statistically significant (P=0.05) clusters of high reporting activity and trends over time. High reporting activity aggregated near border counties in high incidence states, while low reporting aggregated near shared county borders in non-high incidence states. Findings highlight the need for exploratory surveillance approaches to describe the extent to which state level reporting affects accurate estimation of Lyme disease progression. Copyright © 2017 Elsevier Ltd. All rights reserved.

Clashing Values: A Longitudinal, Exploratory Study of Student Beliefs about General Education, Vocationalism, and Transfer of Learning

ERIC Educational Resources Information Center

Driscoll, Dana Lynn

2014-01-01

One challenge with general education is the often- clashing goal of vocationalism, or educating for the purpose a specific careers or professions. Through a series of longitudinal interviews spanning a group of 14 students' second and fourth semesters at a public, regional research university, the author examines the intersection of beliefs and…

Dubbing Projects for the Language Learner: A Framework for Integrating Audiovisual Translation into Task-Based Instruction

ERIC Educational Resources Information Center

Danan, Martine

2010-01-01

This article describes a series of exploratory L1 to L2 dubbing projects for which students translated and used editing software to dub short American film and TV clips into their target language. Translating and dubbing into the target language involve students in multifaceted, high-level language production tasks that lead to enhanced vocabulary…

Development of a Design-Based Learning Curriculum through Design-Based Research for a Technology-Enabled Science Classroom

ERIC Educational Resources Information Center

Kim, Paul; Suh, Esther; Song, Donggil

2015-01-01

This exploratory study provides a deeper look into the aspects of students' experience from design-based learning (DBL) activities for fifth grade students. Using design-based research (DBR), this study was conducted on a series of science learning activities leveraging mobile phones with relevant applications and sensors. We observed 3 different…

Exploratory Analyses of the Long-Term Effects of Improving Behavior, Attendance, and Educational Achievement in Grades 1-6 and 8-12. ACT Research Report Series, 2012 (3)

ERIC Educational Resources Information Center

Sawyer, Richard; Gibson, Neal

2012-01-01

We studied relationships among background characteristics, behavioral infractions, punishments, attendance, and educational achievement, using longitudinal data of students in grades 1-6 and 8-12. We estimated how much hypothesized early improvements in educational achievement or sustained improvements in behavior and attendance might ultimately…

Keyboarding and Computer Application. Technology Learning Activity. Teacher Edition. Technology Education Series.

ERIC Educational Resources Information Center

Oklahoma State Dept. of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This curriculum guide is designed to prepare students in grades 6-10 to work in the world of the future. The 8-day course provides exploratory, hands-on learning activities and information that can enhance the education of students of all types in an integrated curriculum that provides practical applications of academic skills. The guide contains…

Characterizations of Social-Based and Self-Based Contexts Associated with Students' Awareness, Evaluation, and Regulation of Their Thinking during Small-Group Mathematical Modeling

ERIC Educational Resources Information Center

Magiera, Marta T.; Zawojewski, Judith S.

2011-01-01

This exploratory study focused on characterizing problem-solving situations associated with spontaneous metacognitive activity. The results came from connected case studies of a group of 3 purposefully selected 9th-grade students working collaboratively on a series of 5 modeling problems. Students' descriptions of their own thinking during…

The Effects of High School Organization on Dropping Out: An Exploratory Investigation. CPRE Research Report Series.

ERIC Educational Resources Information Center

Bryk, Anthony S.; Thum, Yeow Meng

Examined in this paper are the effects of school characteristics on both the probability of dropping out and absenteeism as the strongest predictor of dropping out. The project employed a subsample from the High School and Beyond database that contains results from questionnaires and achievement tests given in 1980 to approximately 30,000…

Diagnosis of the Initial State of Formation of Research Competence of a Future Social Pedagogue

ERIC Educational Resources Information Center

Zhexembinova, Ainur K.; Shah, Saeeda; Taubayeva, Sharkul T.

2016-01-01

The article presents the results of the first series of practical research within the scope of an adopted program of pilot testing on "The Technology of Formation of Exploratory Competence in Future Social Teachers within the System of University Education." A set of questionnaires offered to students made it possible to identify the…

Skype-Based English Activities: A Case for Compelling Input? Correlational Changes before and after Skype Exchanges

ERIC Educational Resources Information Center

Ockert, David

2015-01-01

This paper reports the results of a small, longitudinal study involving a group of Japanese elementary school students (N = 29) involved in exploratory research using foreign language activities, including two Skype exchanges between these students and students in Australia. The purpose of the research was to test for the impact of a series of…

Affordable and Open Textbooks: An Exploratory Study of Faculty Attitudes. Research & Occasional Paper Series. CSHE.9.09

ERIC Educational Resources Information Center

Harley, Diane; Lawrence, Shannon; Acord, Sophia Krzys; Dixson, Jason

2009-01-01

The Student Public Interest Research Groups (Student PIRGs)--who have been at the forefront of raising awareness about textbook affordability for much of the past decade--launched a two-year campaign (MakeTextbooksAffordable.org/statement) in 2007 to drive mainstream faculty's acceptance of open textbooks and other affordable alternatives in place…

76 FR 25345 - Annual Assessment of the Status of Competition in the Market for the Delivery of Video Programming

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-04

... as of June 30 of the relevant year to monitor trends on an annual basis. To continue our time-series... video programming? 24. MVPD Performance. We seek comment on the information and time- series data we... Television Performance. We seek information and time- series data for the analysis of various performance...

EO-1 Hyperion reflectance time series at calibration and validation sites: stability and sensitivity to seasonal dynamics

Treesearch

Petya K. Entcheva Campbell; Elizabeth M. Middleton; Kurt J. Thome; Raymond F. Kokaly; Karl Fred Huemmrich; David Lagomasino; Kimberly A. Novick; Nathaniel A. Brunsell

2013-01-01

This study evaluated Earth Observing 1 (EO-1) Hyperion reflectance time series at established calibration sites to assess the instrument stability and suitability for monitoring vegetation functional parameters. Our analysis using three pseudo-invariant calibration sites in North America indicated that the reflectance time series are devoid of apparent spectral trends...

A comparison of classroom and online asynchronous problem-based learning for students undertaking statistics training as part of a Public Health Masters degree.

PubMed

de Jong, N; Verstegen, D M L; Tan, F E S; O'Connor, S J

2013-05-01

This case-study compared traditional, face-to-face classroom-based teaching with asynchronous online learning and teaching methods in two sets of students undertaking a problem-based learning module in the multilevel and exploratory factor analysis of longitudinal data as part of a Masters degree in Public Health at Maastricht University. Students were allocated to one of the two study variants on the basis of their enrolment status as full-time or part-time students. Full-time students (n = 11) followed the classroom-based variant and part-time students (n = 12) followed the online asynchronous variant which included video recorded lectures and a series of asynchronous online group or individual SPSS activities with synchronous tutor feedback. A validated student motivation questionnaire was administered to both groups of students at the start of the study and a second questionnaire was administered at the end of the module. This elicited data about student satisfaction with the module content, teaching and learning methods, and tutor feedback. The module coordinator and problem-based learning tutor were also interviewed about their experience of delivering the experimental online variant and asked to evaluate its success in relation to student attainment of the module's learning outcomes. Student examination results were also compared between the two groups. Asynchronous online teaching and learning methods proved to be an acceptable alternative to classroom-based teaching for both students and staff. Educational outcomes were similar for both groups, but importantly, there was no evidence that the asynchronous online delivery of module content disadvantaged part-time students in comparison to their full-time counterparts.

Analysis in natural time domain of geoelectric time series monitored prior two strong earthquakes occurred in Mexico

NASA Astrophysics Data System (ADS)

Ramírez-Rojas, A.; Flores-Marquez, L. E.

2009-12-01

The short-time prediction of seismic phenomena is currently an important problem in the scientific community. In particular, the electromagnetic processes associated with seismic events take in great interest since the VAN method was implemented. The most important features of this methodology are the seismic electrical signals (SES) observed prior to strong earthquakes. SES has been observed in the electromagnetic series linked to EQs in Greece, Japan and Mexico. By mean of the so-called natural time domain, introduced by Varotsos et al. (2001), they could characterize signals of dichotomic nature observed in different systems, like SES and ionic current fluctuations in membrane channels. In this work we analyze SES observed in geoelectric time series monitored in Guerrero, México. Our analysis concern with two strong earthquakes occurred, on October 24, 1993 (M=6.6) and September 14, 1995 (M=7.3). The time series of the first one displayed a seismic electric signal six days before the main shock and for the second case the time series displayed dichotomous-like fluctuations some months before the EQ. In this work we present the first results of the analysis in natural time domain for the two cases which seems to be agreeing with the results reported by Varotsos. P. Varotsos, N. Sarlis, and E. Skordas, Practica of the Athens Academy 76, 388 (2001).

Mathematical Sciences Division 1992 Programs

DTIC Science & Technology

1992-10-01

statistical theory that underlies modern signal analysis . There is a strong emphasis on stochastic processes and time series , particularly those which...include optimal resource planning and real- time scheduling of stochastic shop-floor processes. Scheduling systems will be developed that can adapt to...make forecasts for the length-of-service time series . Protocol analysis of these sessions will be used to idenify relevant contextual features and to

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.

The Relationship of Bureaucratic Structure to School Climate: An Exploratory Factor Analysis of Construct Validity

ERIC Educational Resources Information Center

Lennon, Patricia A.

2010-01-01

This researcher examined the relationship of bureaucratic structure to school climate by means of an exploratory factor analysis of a measure of bureaucracy developed by Hoy and Sweetland (2000) and the four dimensional measure of climate developed by Hoy, Smith, and Sweetland (2002). Since there had been no other empirical studies whose authors…

Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations

ERIC Educational Resources Information Center

Schmitt, Thomas A.; Sass, Daniel A.

2011-01-01

Exploratory factor analysis (EFA) has long been used in the social sciences to depict the relationships between variables/items and latent traits. Researchers face many choices when using EFA, including the choice of rotation criterion, which can be difficult given that few research articles have discussed and/or demonstrated their differences.…

Rotation to a Partially Specified Target Matrix in Exploratory Factor Analysis: How Many Targets?

ERIC Educational Resources Information Center

Myers, Nicholas D.; Ahn, Soyeon; Jin, Ying

2013-01-01

The purpose of this study was to explore the influence of the number of targets specified on the quality of exploratory factor analysis solutions with a complex underlying structure and incomplete substantive measurement theory. Three Monte Carlo studies were performed based on the ratio of the number of observed variables to the number of…

SOCR Motion Charts: An Efficient, Open-Source, Interactive and Dynamic Applet for Visualizing Longitudinal Multivariate Data

PubMed Central

Al-Aziz, Jameel; Christou, Nicolas; Dinov, Ivo D.

2011-01-01

The amount, complexity and provenance of data have dramatically increased in the past five years. Visualization of observed and simulated data is a critical component of any social, environmental, biomedical or scientific quest. Dynamic, exploratory and interactive visualization of multivariate data, without preprocessing by dimensionality reduction, remains a nearly insurmountable challenge. The Statistics Online Computational Resource (www.SOCR.ucla.edu) provides portable online aids for probability and statistics education, technology-based instruction and statistical computing. We have developed a new Java-based infrastructure, SOCR Motion Charts, for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. We validated this new visualization paradigm using several publicly available multivariate datasets including Ice-Thickness, Housing Prices, Consumer Price Index, and California Ozone Data. SOCR Motion Charts is designed using object-oriented programming, implemented as a Java Web-applet and is available to the entire community on the web at www.socr.ucla.edu/SOCR_MotionCharts. It can be used as an instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis. PMID:21479108

Quantifying memory in complex physiological time-series.

PubMed

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Quantifying Memory in Complex Physiological Time-Series

PubMed Central

Shirazi, Amir H.; Raoufy, Mohammad R.; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R.; Amodio, Piero; Jafari, G. Reza; Montagnese, Sara; Mani, Ali R.

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of “memory length” was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are ‘forgotten’ quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations. PMID:24039811

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.