Academic Self-Concept and Achievement in Polish Primary Schools: Cross-Lagged Modelling and Gender-Specific Effects

ERIC Educational Resources Information Center

Grygiel, Pawel; Modzelewski, Michal; Pisarek, Jolanta

2017-01-01

This study reports relationships between general academic self-concept and achievement in grade 3 and grade 5. Gender-specific effects were investigated using a longitudinal, two-cycle, 3-year autoregressive cross-lagged panel design in a large, representative sample of Polish primary school pupils (N = 4,226). Analysis revealed (a) reciprocal…

Manual choice reaction times in the rate-domain

PubMed Central

Harris, Christopher M.; Waddington, Jonathan; Biscione, Valerio; Manzi, Sean

2014-01-01

Over the last 150 years, human manual reaction times (RTs) have been recorded countless times. Yet, our understanding of them remains remarkably poor. RTs are highly variable with positively skewed frequency distributions, often modeled as an inverse Gaussian distribution reflecting a stochastic rise to threshold (diffusion process). However, latency distributions of saccades are very close to the reciprocal Normal, suggesting that “rate” (reciprocal RT) may be the more fundamental variable. We explored whether this phenomenon extends to choice manual RTs. We recorded two-alternative choice RTs from 24 subjects, each with 4 blocks of 200 trials with two task difficulties (easy vs. difficult discrimination) and two instruction sets (urgent vs. accurate). We found that rate distributions were, indeed, very close to Normal, shifting to lower rates with increasing difficulty and accuracy, and for some blocks they appeared to become left-truncated, but still close to Normal. Using autoregressive techniques, we found temporal sequential dependencies for lags of at least 3. We identified a transient and steady-state component in each block. Because rates were Normal, we were able to estimate autoregressive weights using the Box-Jenkins technique, and convert to a moving average model using z-transforms to show explicit dependence on stimulus input. We also found a spatial sequential dependence for the previous 3 lags depending on whether the laterality of previous trials was repeated or alternated. This was partially dissociated from temporal dependency as it only occurred in the easy tasks. We conclude that 2-alternative choice manual RT distributions are close to reciprocal Normal and not the inverse Gaussian. This is not consistent with stochastic rise to threshold models, and we propose a simple optimality model in which reward is maximized to yield to an optimal rate, and hence an optimal time to respond. We discuss how it might be implemented. PMID:24959134

A Distributed Lag Autoregressive Model of Geostationary Relativistic Electron Fluxes: Comparing the Influences of Waves, Seed and Source Electrons, and Solar Wind Inputs

NASA Astrophysics Data System (ADS)

Simms, Laura; Engebretson, Mark; Clilverd, Mark; Rodger, Craig; Lessard, Marc; Gjerloev, Jesper; Reeves, Geoffrey

2018-05-01

Relativistic electron flux at geosynchronous orbit depends on enhancement and loss processes driven by ultralow frequency (ULF) Pc5, chorus, and electromagnetic ion cyclotron (EMIC) waves, seed electron flux, magnetosphere compression, the "Dst effect," and substorms, while solar wind inputs such as velocity, number density, and interplanetary magnetic field Bz drive these factors and thus correlate with flux. Distributed lag regression models show the time delay of highest influence of these factors on log10 high-energy electron flux (0.7-7.8 MeV, Los Alamos National Laboratory satellites). Multiple regression with an autoregressive term (flux persistence) allows direct comparison of the magnitude of each effect while controlling other correlated parameters. Flux enhancements due to ULF Pc5 and chorus waves are of equal importance. The direct effect of substorms on high-energy electron flux is strong, possibly due to injection of high-energy electrons by the substorms themselves. Loss due to electromagnetic ion cyclotron waves is less influential. Southward Bz shows only moderate influence when correlated processes are accounted for. Adding covariate compression effects (pressure and interplanetary magnetic field magnitude) allows wave-driven enhancements to be more clearly seen. Seed electrons (270 keV) are most influential at lower relativistic energies, showing that such a population must be available for acceleration. However, they are not accelerated directly to the highest energies. Source electrons (31.7 keV) show no direct influence when other factors are controlled. Their action appears to be indirect via the chorus waves they generate. Determination of specific effects of each parameter when studied in combination will be more helpful in furthering modeling work than studying them individually.

Marital satisfaction and maternal depressive symptoms among Korean mothers transitioning to parenthood.

PubMed

Choi, Eunsil

2016-06-01

Although many empirical findings support associations between marital satisfaction and depressive symptoms, gaps remain in our understanding of the magnitude and direction of the associations between marital satisfaction and depressive symptoms as well as the associations in a collectivistic culture. The present study examined autoregressive cross-lagged associations between marital satisfaction and maternal depressive symptoms across a 3-year investigation in a sample of Korean mothers transitioning to parenthood. The sample consisted of 2,078 mothers in the Panel Study of Korean Children. The mothers reported marital satisfaction and maternal depressive symptoms annually for 3 years. The results of an autoregressive cross-lagged model revealed bidirectional associations between marital satisfaction and maternal depressive symptoms. The findings provide evidence of an interactional model of depression in a sample of Korean mothers. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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.

Does export product quality matter for CO2 emissions? Evidence from China.

PubMed

Gozgor, Giray; Can, Muhlis

2017-01-01

This paper re-estimates the environmental Kuznets curve (EKC) in China. To this end, it uses the unit root tests with structural breaks and the autoregressive-distributed lag (ARDL) estimations over the period 1971-2010. The special role is given to the impact of export product quality on CO 2 emissions in the empirical models. The paper finds that the EKC hypothesis is applicable in China. It also observes the positive effect from energy consumption to CO 2 emissions. In addition, it finds that the export product quality is negatively associated with CO 2 emissions. The paper also argues potential implications.

Time series regression model for infectious disease and weather.

PubMed

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

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

PubMed

Hamaker, Ellen L; Grasman, Raoul P P P

2014-01-01

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

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

PubMed Central

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

2015-01-01

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

Tapered US carbon emissions during good times: what's old, what's new?

PubMed

Eng, Yoke-Kee; Wong, Chin-Yoong

2017-11-01

In light of a slow buildup in CO 2 emissions since the recovery, this paper revisits the relationship between CO 2 emissions and the US economy using a nonlinear autoregressive distributed lag model, in which the determinants are identified through an expanded real business cycle model. We find convincing evidence that CO 2 emissions decline more rapidly during recessions than increase during expansions over the long run. Of all determinants considered, long-run asymmetry is fostered once vehicle miles traveled is controlled. This calls for a greater attention to public transportation development and vehicle miles traveled tax for slowing down stock buildup of CO 2 emissions during good times.

Factors determining dengue outbreak in Malaysia.

PubMed

Ahmad, Rohani; Suzilah, Ismail; Wan Najdah, Wan Mohamad Ali; Topek, Omar; Mustafakamal, Ibrahim; Lee, Han Lim

2018-01-01

A large scale study was conducted to elucidate the true relationship among entomological, epidemiological and environmental factors that contributed to dengue outbreak in Malaysia. Two large areas (Selayang and Bandar Baru Bangi) were selected in this study based on five consecutive years of high dengue cases. Entomological data were collected using ovitraps where the number of larvae was used to reflect Aedes mosquito population size; followed by RT-PCR screening to detect and serotype dengue virus in mosquitoes. Notified cases, date of disease onset, and number and type of the interventions were used as epidemiological endpoint, while rainfall, temperature, relative humidity and air pollution index (API) were indicators for environmental data. The field study was conducted during 81 weeks of data collection. Correlation and Autoregressive Distributed Lag Model were used to determine the relationship. The study showed that, notified cases were indirectly related with the environmental data, but shifted one week, i.e. last 3 weeks positive PCR; last 4 weeks rainfall; last 3 weeks maximum relative humidity; last 3 weeks minimum and maximum temperature; and last 4 weeks air pollution index (API), respectively. Notified cases were also related with next week intervention, while conventional intervention only happened 4 weeks after larvae were found, indicating ample time for dengue transmission. Based on a significant relationship among the three factors (epidemiological, entomological and environmental), estimated Autoregressive Distributed Lag (ADL) model for both locations produced high accuracy 84.9% for Selayang and 84.1% for Bandar Baru Bangi in predicting the actual notified cases. Hence, such model can be used in forestalling dengue outbreak and acts as an early warning system. The existence of relationships among the entomological, epidemiological and environmental factors can be used to build an early warning system for the prediction of dengue outbreak so that preventive interventions can be taken early to avert the outbreaks.

Modeling the impacts of climate change and technical progress on the wheat yield in inland China: An autoregressive distributed lag approach.

PubMed

Zhai, Shiyan; Song, Genxin; Qin, Yaochen; Ye, Xinyue; Lee, Jay

2017-01-01

This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June) led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention.

Modeling the impacts of climate change and technical progress on the wheat yield in inland China: An autoregressive distributed lag approach

PubMed Central

Qin, Yaochen; Lee, Jay

2017-01-01

This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June) led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention. PMID:28950027

Mutual information estimation for irregularly sampled time series

NASA Astrophysics Data System (ADS)

Rehfeld, K.; Marwan, N.; Heitzig, J.; Kurths, J.

2012-04-01

For the automated, objective and joint analysis of time series, similarity measures are crucial. Used in the analysis of climate records, they allow for a complimentary, unbiased view onto sparse datasets. The irregular sampling of many of these time series, however, makes it necessary to either perform signal reconstruction (e.g. interpolation) or to develop and use adapted measures. Standard linear interpolation comes with an inevitable loss of information and bias effects. We have recently developed a Gaussian kernel-based correlation algorithm with which the interpolation error can be substantially lowered, but this would not work should the functional relationship in a bivariate setting be non-linear. We therefore propose an algorithm to estimate lagged auto and cross mutual information from irregularly sampled time series. We have extended the standard and adaptive binning histogram estimators and use Gaussian distributed weights in the estimation of the (joint) probabilities. To test our method we have simulated linear and nonlinear auto-regressive processes with Gamma-distributed inter-sampling intervals. We have then performed a sensitivity analysis for the estimation of actual coupling length, the lag of coupling and the decorrelation time in the synthetic time series and contrast our results to the performance of a signal reconstruction scheme. Finally we applied our estimator to speleothem records. We compare the estimated memory (or decorrelation time) to that from a least-squares estimator based on fitting an auto-regressive process of order 1. The calculated (cross) mutual information results are compared for the different estimators (standard or adaptive binning) and contrasted with results from signal reconstruction. We find that the kernel-based estimator has a significantly lower root mean square error and less systematic sampling bias than the interpolation-based method. It is possible that these encouraging results could be further improved by using non-histogram mutual information estimators, like k-Nearest Neighbor or Kernel-Density estimators, but for short (<1000 points) and irregularly sampled datasets the proposed algorithm is already a great improvement.

Reciprocal Influences between Parents' Marital Problems and Adolescent Internalizing and Externalizing Behavior

ERIC Educational Resources Information Center

Cui, Ming; Donnellan, M. Brent; Conger, Rand D.

2007-01-01

The present study examines reciprocal associations between marital functioning and adolescent maladjustment using cross-lagged autoregressive models. The research involved 451 early adolescents and their families and used a prospective, longitudinal research design with multi-informant methods. Results indicate that parental conflicts over child…

Experiences of Discrimination among Chinese American Adolescents and the Consequences for Socioemotional and Academic Development

ERIC Educational Resources Information Center

Benner, Aprile D.; Kim, Su Yeong

2009-01-01

This longitudinal study examined the influences of discrimination on socioemotional adjustment and academic performance for a sample of 444 Chinese American adolescents. Using autoregressive and cross-lagged techniques, the authors found that discrimination in early adolescence predicted depressive symptoms, alienation, school engagement, and…

Pathways between Self-Esteem and Depression in Couples

ERIC Educational Resources Information Center

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

2017-01-01

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

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

PubMed

Liu, Siwei; Molenaar, Peter C M

2014-12-01

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

Same- and Other-Sex Popularity and Preference during Early Adolescence

ERIC Educational Resources Information Center

Bowker, Julie C.; Adams, Ryan E.; Bowker, Matthew H.; Fisher, Carrie; Spencer, Sarah V.

2016-01-01

This study examined the longitudinal and bidirectional relations between same-sex (SS) and other-sex (OS) popularity and preference across one school year. Participants were 271 sixth-grade students who completed peer nomination measures at three time points in their schools. Tests of cross-lagged autoregressive models indicated that SS popularity…

Happiness Is the Way: Paths to Civic Engagement between Young Adulthood and Midlife

ERIC Educational Resources Information Center

Fang, Shichen; Galambos, Nancy L.; Johnson, Matthew D.; Krahn, Harvey J.

2018-01-01

Directional associations between civic engagement and happiness were explored with longitudinal data from a community sample surveyed four times from age 22 to 43 (n = 690). Autoregressive cross-lagged models, controlling for cross-time stabilities in happiness and civic engagement, examined whether happiness predicted future civic engagement,…

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

PubMed Central

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

2017-01-01

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

Predicting safe sex: Assessment of autoregressive and cross-lagged effects within the Theory of Planned Behavior.

PubMed

Eggers, Sander M; Taylor, Myra; Sathiparsad, Reshma; Bos, Arjan Er; de Vries, Hein

2015-11-01

Despite its popularity, few studies have assessed the temporal stability and cross-lagged effects of the Theory of Planned Behavior factors: Attitude, subjective norms and self-efficacy. For this study, 298 adolescent learners from KwaZulu-Natal, South Africa, filled out a Theory of Planned Behavior questionnaire on teenage pregnancy at baseline and after 6 months. Structural equation modeling showed that there were considerable cross-lagged effects between attitude and subjective norms. Temporal stability was moderate with test-retest correlations ranging from 0.37 to 0.51 and the model was able to predict intentions to have safe sex (R2 = 0.69) Implications for practice and future research are discussed. © The Author(s) 2013.

Epidemiological features and risk factors associated with the spatial and temporal distribution of human brucellosis in China

PubMed Central

2013-01-01

Background Human brucellosis incidence in China has been increasing dramatically since 1999. However, epidemiological features and potential factors underlying the re-emergence of the disease remain less understood. Methods Data on human and animal brucellosis cases at the county scale were collected for the year 2004 to 2010. Also collected were environmental and socioeconomic variables. Epidemiological features including spatial and temporal patterns of the disease were characterized, and the potential factors related to the spatial heterogeneity and the temporal trend of were analysed using Poisson regression analysis, Granger causality analysis, and autoregressive distributed lag (ADL) models, respectively. Results The epidemic showed a significantly higher spatial correlation with the number of sheep and goats than swine and cattle. The disease was most prevalent in grassland areas with elevation between 800–1,600 meters. The ADL models revealed that local epidemics were correlated with comparatively lower temperatures and less sunshine in winter and spring, with a 1–7 month lag before the epidemic peak in May. Conclusions Our findings indicate that human brucellosis tended to occur most commonly in grasslands at moderate elevation where sheep and goats were the predominant livestock, and in years with cooler winter and spring or less sunshine. PMID:24238301

The Relationship between Spirituality and Quality of Life among University Students: An Autoregressive Cross-Lagged Panel Analysis

ERIC Educational Resources Information Center

Lau, Wilfred W. F.; Hui, C. Harry; Lam, Jasmine; Lau, Esther Y. Y.; Cheung, Shu-Fai

2015-01-01

University represents a critical transition from secondary school. University students are exposed to many new opportunities and intellectual stimulations, and some may find university life stressful and demanding. The quality of life (QoL) of university students is thus an important topic for researchers and educators alike. Furthermore, many…

Lag-One Autocorrelation in Short Series: Estimation and Hypotheses Testing

ERIC Educational Resources Information Center

Solanas, Antonio; Manolov, Rumen; Sierra, Vicenta

2010-01-01

In the first part of the study, nine estimators of the first-order autoregressive parameter are reviewed and a new estimator is proposed. The relationships and discrepancies between the estimators are discussed in order to achieve a clear differentiation. In the second part of the study, the precision in the estimation of autocorrelation is…

Carbon dioxide emissions, output, and energy consumption categories in Algeria.

PubMed

Amri, Fethi

2017-06-01

This study examines the relation between CO 2 emissions, income, non-renewable, and renewable energy consumption in Algeria during the period extending from 1980 to 2011. Our work gives particular attention to the validity of environmental Kuznets curve (EKC) hypothesis. The autoregressive distributed lag (ARDL) with break point method outcome demonstrates the positive effect of non-renewable type of energy on CO 2 emissions consumption. On the contrary, the results reveal an insignificant effect of renewable energy on environment improvement. Moreover, the results accept the existence of EKC hypothesis but the highest gross domestic product value in logarithm scale of our data is inferior to the estimated turning point. Consequently, policy-makers in Algeria should expand the ratio of renewable energy and should decrease the quota of non-renewable energy consumption.

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

PubMed

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

2016-07-01

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

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

PubMed

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

2016-01-15

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

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

PubMed

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

2018-05-16

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

Comparison of estimators of standard deviation for hydrologic time series

USGS Publications Warehouse

Tasker, Gary D.; Gilroy, Edward J.

1982-01-01

Unbiasing factors as a function of serial correlation, ρ, and sample size, n for the sample standard deviation of a lag one autoregressive model were generated by random number simulation. Monte Carlo experiments were used to compare the performance of several alternative methods for estimating the standard deviation σ of a lag one autoregressive model in terms of bias, root mean square error, probability of underestimation, and expected opportunity design loss. Three methods provided estimates of σ which were much less biased but had greater mean square errors than the usual estimate of σ: s = (1/(n - 1) ∑ (xi −x¯)2)½. The three methods may be briefly characterized as (1) a method using a maximum likelihood estimate of the unbiasing factor, (2) a method using an empirical Bayes estimate of the unbiasing factor, and (3) a robust nonparametric estimate of σ suggested by Quenouille. Because s tends to underestimate σ, its use as an estimate of a model parameter results in a tendency to underdesign. If underdesign losses are considered more serious than overdesign losses, then the choice of one of the less biased methods may be wise.

Transactional Links between Teacher-Child Relationship Quality and Perceived versus Sociometric Popularity: A Three-Wave Longitudinal Study

ERIC Educational Resources Information Center

De Laet, Steven; Doumen, Sarah; Vervoort, Eleonora; Colpin, Hilde; Van Leeuwen, Karla; Goossens, Luc; Verschueren, Karine

2014-01-01

This study examined how peer relationships (i.e., sociometric and perceived popularity) and teacher--child relationships (i.e., support and conflict) impact one another throughout late childhood. The sample included 586 children (46% boys), followed annually from Grades 4 to 6 (Mage.wave1 = 9.26 years). Autoregressive cross-lagged modeling was…

Forecasting intentional wildfires using temporal and spatiotemporal autocorrelations

Treesearch

Jeffrey P. Prestemon; María L. Chas-Amil; Julia M. Touza; Scott L. Goodrick

2012-01-01

We report daily time series models containing both temporal and spatiotemporal lags, which are applied to forecasting intentional wildfires in Galicia, Spain. Models are estimated independently for each of the 19 forest districts in Galicia using a 1999–2003 training dataset and evaluated out-of-sample with a 2004–06 dataset. Poisson autoregressive models of order P –...

The asymmetric effect of coal price on the China's macro economy using NARDL model

NASA Astrophysics Data System (ADS)

Hou, J. C.; Yang, M. C.

2016-08-01

The present work endeavors to explore the asymmetric effect of coal price on the China's macro economy by applying nonlinear autoregressive distributed lag (NARDL) model for the period of January 2005 to June 2015. The obtained results indicate that the coal price has a strong asymmetric effect on China's macro economy in the long-run. Namely one percent increase in coal price leads to 0.6194 percent of the China's macro economy increase; and while the coal price is reduces by 1 percent, the China's macro economy will decrease by 0.008 percent. These data indicate that when coal price rises, the effect on China's macro economy is far greater than the price decline. In the short-run, coal price fluctuation has a positive effect on the China's macro economy.

Economic hardship and suicide mortality in Finland, 1875-2010.

PubMed

Korhonen, Marko; Puhakka, Mikko; Viren, Matti

2016-03-01

We investigate the determinants of suicide in Finland using annual data for consumption and suicides from 1860 to 2010. Instead of using some ad hoc measures of cyclical movements of the economy, we build our analysis on a more solid economic theory. A key feature is the habit persistence in preferences, which provides a way to measure individual well-being and predict suicide. We estimate time series of habit levels and develop an indicator (the hardship index) to describe the economic hardship of consumers. The higher the level of the index, the worse off consumers are. As a rational response to such a bad situation, some consumers might commit suicide. We employ the autoregressive distributed lags cointegration method and find that our index works well in explaining the long-term behavior of people committing suicide in Finland.

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

PubMed

Zerbo, Eléazar

2017-07-01

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

Urbanization and Greenhouse Gas Emissions from Industry

NASA Astrophysics Data System (ADS)

Didenko, N. I.; Skripnuk, D. F.; Mirolyubova, O. V.

2017-06-01

This article analyses the global environment. The article describes processes that characterize the global environment, specific indicators are suggested, that can be used to measure the change in the global environment. It is said that cities and all urbanized territories have a negative effect on the global environment. Originally, the authors wanted to call the article «City as a source of destruction of the global environment». But taking into account the fact that urbanization contributes to improving the economic efficiency of the state, cities are the centers of the economic, cultural and informational potential that provide a «breakthrough» into the development of the economy. The article assesses the impact of urbanization on the global environment. For the analysis of the impact of urbanization on the natural habitat, the autoregressive distributed lags (ADL-model) are chosen.

Examining spatiotemporal distribution and CPUE-environment relationships for the jumbo flying squid Dosidicus gigas offshore Peru based on spatial autoregressive model

NASA Astrophysics Data System (ADS)

Feng, Yongjiu; Chen, Xinjun; Liu, Yang

2017-09-01

The spatiotemporal distribution and relationship between nominal catch-per-unit-effort (CPUE) and environment for the jumbo flying squid (Dosidicus gigas) were examined in offshore Peruvian waters during 2009-2013. Three typical oceanographic factors affecting the squid habitat were investigated in this research, including sea surface temperature (SST), sea surface salinity (SSS) and sea surface height (SSH). We studied the CPUE-environment relationships for D. gigas using a spatially-lagged version of spatial autoregressive (SAR) model and a generalized additive model (GAM), with the latter for auxiliary and comparative purposes. The annual fishery centroids were distributed broadly in an area bounded by 79.5°-82.7°W and 11.9°-17.1°S, while the monthly fishery centroids were spatially close and lay in a smaller area bounded by 81.0°-81.2°W and 14.3°-15.4°S. Our results show that the preferred environmental ranges for D. gigas offshore Peru were 20.9°-21.9°C for SST, 35.16-35.32 for SSS and 27.2-31.5 cm for SSH in the areas bounded by 78°-80°W/82-84°W and 15°-18°S. Monthly spatial distributions during October to December were predicted using the calibrated GAM and SAR models and general similarities were found between the observed and predicted patterns for the nominal CPUE of D. gigas. The overall accuracies for the hotspots generated by the SAR model were much higher than those produced by the GAM model for all three months. Our results contribute to a better understanding of the spatiotemporal distributions of D. gigas offshore Peru, and offer a new SAR modeling method for advancing fishery science.

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets

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

Lu, Fengbin, E-mail: fblu@amss.ac.cn

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relationsmore » evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.« less

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets.

PubMed

Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze

2017-01-01

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spatial and temporal changes in the structure of groundwater nitrate concentration time series (1935 1999) as demonstrated by autoregressive modelling

NASA Astrophysics Data System (ADS)

Jones, A. L.; Smart, P. L.

2005-08-01

Autoregressive modelling is used to investigate the internal structure of long-term (1935-1999) records of nitrate concentration for five karst springs in the Mendip Hills. There is a significant short term (1-2 months) positive autocorrelation at three of the five springs due to the availability of sufficient nitrate within the soil store to maintain concentrations in winter recharge for several months. The absence of short term (1-2 months) positive autocorrelation in the other two springs is due to the marked contrast in land use between the limestone and swallet parts of the catchment, rapid concentrated recharge from the latter causing short term switching in the dominant water source at the spring and thus fluctuating nitrate concentrations. Significant negative autocorrelation is evident at lags varying from 4 to 7 months through to 14-22 months for individual springs, with positive autocorrelation at 19-20 months at one site. This variable timing is explained by moderation of the exhaustion effect in the soil by groundwater storage, which gives longer residence times in large catchments and those with a dominance of diffuse flow. The lags derived from autoregressive modelling may therefore provide an indication of average groundwater residence times. Significant differences in the structure of the autocorrelation function for successive 10-year periods are evident at Cheddar Spring, and are explained by the effect the ploughing up of grasslands during the Second World War and increased fertiliser usage on available nitrogen in the soil store. This effect is moderated by the influence of summer temperatures on rates of mineralization, and of both summer and winter rainfall on the timing and magnitude of nitrate leaching. The pattern of nitrate leaching also appears to have been perturbed by the 1976 drought.

Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province, Iran using seasonal ARIMA time series method.

PubMed

Sharafi, Mehdi; Ghaem, Haleh; Tabatabaee, Hamid Reza; Faramarzi, Hossein

2017-01-01

To predict the trend of cutaneous leishmaniasis and assess the relationship between the disease trend and weather variables in south of Fars province using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The trend of cutaneous leishmaniasis was predicted using Mini tab software and SARIMA model. Besides, information about the disease and weather conditions was collected monthly based on time series design during January 2010 to March 2016. Moreover, various SARIMA models were assessed and the best one was selected. Then, the model's fitness was evaluated based on normality of the residuals' distribution, correspondence between the fitted and real amounts, and calculation of Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). The study results indicated that SARIMA model (4,1,4)(0,1,0) (12) in general and SARIMA model (4,1,4)(0,1,1) (12) in below and above 15 years age groups could appropriately predict the disease trend in the study area. Moreover, temperature with a three-month delay (lag3) increased the disease trend, rainfall with a four-month delay (lag4) decreased the disease trend, and rainfall with a nine-month delay (lag9) increased the disease trend. Based on the results, leishmaniasis follows a descending trend in the study area in case drought condition continues, SARIMA models can suitably measure the disease trend, and the disease follows a seasonal trend. Copyright © 2017 Hainan Medical University. Production and hosting by Elsevier B.V. All rights reserved.

Assessing the impacts of Saskatchewan's minimum alcohol pricing regulations on alcohol-related crime.

PubMed

Stockwell, Tim; Zhao, Jinhui; Sherk, Adam; Callaghan, Russell C; Macdonald, Scott; Gatley, Jodi

2017-07-01

Saskatchewan's introduction in April 2010 of minimum prices graded by alcohol strength led to an average minimum price increase of 9.1% per Canadian standard drink (=13.45 g ethanol). This increase was shown to be associated with reduced consumption and switching to lower alcohol content beverages. Police also informally reported marked reductions in night-time alcohol-related crime. This study aims to assess the impacts of changes to Saskatchewan's minimum alcohol-pricing regulations between 2008 and 2012 on selected crime events often related to alcohol use. Data were obtained from Canada's Uniform Crime Reporting Survey. Auto-regressive integrated moving average time series models were used to test immediate and lagged associations between minimum price increases and rates of night-time and police identified alcohol-related crimes. Controls were included for simultaneous crime rates in the neighbouring province of Alberta, economic variables, linear trend, seasonality and autoregressive and/or moving-average effects. The introduction of increased minimum-alcohol prices was associated with an abrupt decrease in night-time alcohol-related traffic offences for men (-8.0%, P < 0.001), but not women. No significant immediate changes were observed for non-alcohol-related driving offences, disorderly conduct or violence. Significant monthly lagged effects were observed for violent offences (-19.7% at month 4 to -18.2% at month 6), which broadly corresponded to lagged effects in on-premise alcohol sales. Increased minimum alcohol prices may contribute to reductions in alcohol-related traffic-related and violent crimes perpetrated by men. Observed lagged effects for violent incidents may be due to a delay in bars passing on increased prices to their customers, perhaps because of inventory stockpiling. [Stockwell T, Zhao J, Sherk A, Callaghan RC, Macdonald S, Gatley J. Assessing the impacts of Saskatchewan's minimum alcohol pricing regulations on alcohol-related crime. Drug Alcohol Rev 2017;36:492-501]. © 2016 Australasian Professional Society on Alcohol and other Drugs.

Time lag between immigration and tuberculosis rates in immigrants in the Netherlands: a time-series analysis.

PubMed

van Aart, C; Boshuizen, H; Dekkers, A; Korthals Altes, H

2017-05-01

In low-incidence countries, most tuberculosis (TB) cases are foreign-born. We explored the temporal relationship between immigration and TB in first-generation immigrants between 1995 and 2012 to assess whether immigration can be a predictor for TB in immigrants from high-incidence countries. We obtained monthly data on immigrant TB cases and immigration for the three countries of origin most frequently represented among TB cases in the Netherlands: Morocco, Somalia and Turkey. The best-fit seasonal autoregressive integrated moving average (SARIMA) model to the immigration time-series was used to prewhiten the TB time series. The cross-correlation function (CCF) was then computed on the residual time series to detect time lags between immigration and TB rates. We identified a 17-month lag between Somali immigration and Somali immigrant TB cases, but no time lag for immigrants from Morocco and Turkey. The absence of a lag in the Moroccan and Turkish population may be attributed to the relatively low TB prevalence in the countries of origin and an increased likelihood of reactivation TB in an ageing immigrant population. Understanding the time lag between Somali immigration and TB disease would benefit from a closer epidemiological analysis of cohorts of Somali cases diagnosed within the first years after entry.

Corrected goodness-of-fit test in covariance structure analysis.

PubMed

Hayakawa, Kazuhiko

2018-05-17

Many previous studies report simulation evidence that the goodness-of-fit test in covariance structure analysis or structural equation modeling suffers from the overrejection problem when the number of manifest variables is large compared with the sample size. In this study, we demonstrate that one of the tests considered in Browne (1974) can address this long-standing problem. We also propose a simple modification of Satorra and Bentler's mean and variance adjusted test for non-normal data. A Monte Carlo simulation is carried out to investigate the performance of the corrected tests in the context of a confirmatory factor model, a panel autoregressive model, and a cross-lagged panel (panel vector autoregressive) model. The simulation results reveal that the corrected tests overcome the overrejection problem and outperform existing tests in most cases. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A critique of the cross-lagged panel model.

PubMed

Hamaker, Ellen L; Kuiper, Rebecca M; Grasman, Raoul P P P

2015-03-01

The cross-lagged panel model (CLPM) is believed by many to overcome the problems associated with the use of cross-lagged correlations as a way to study causal influences in longitudinal panel data. The current article, however, shows that if stability of constructs is to some extent of a trait-like, time-invariant nature, the autoregressive relationships of the CLPM fail to adequately account for this. As a result, the lagged parameters that are obtained with the CLPM do not represent the actual within-person relationships over time, and this may lead to erroneous conclusions regarding the presence, predominance, and sign of causal influences. In this article we present an alternative model that separates the within-person process from stable between-person differences through the inclusion of random intercepts, and we discuss how this model is related to existing structural equation models that include cross-lagged relationships. We derive the analytical relationship between the cross-lagged parameters from the CLPM and the alternative model, and use simulations to demonstrate the spurious results that may arise when using the CLPM to analyze data that include stable, trait-like individual differences. We also present a modeling strategy to avoid this pitfall and illustrate this using an empirical data set. The implications for both existing and future cross-lagged panel research are discussed. (c) 2015 APA, all rights reserved).

A new approach to the renewable energy-growth nexus: evidence from the USA.

PubMed

Gozgor, Giray

2018-06-01

The climate change is one of the leading problems in the today's world. The rise of the renewable energy meets the sustainable growth objectives since it can decelerate the climate change. For this purpose, this paper investigates the relationship between the renewable energy consumption and the economic growth in the United States (USA). Theoretically, the paper constructs the growth model that captures the effects of the economic complexity indicator as a measure of capabilities and productivity for exporting the "complex" (high value-added) products. Empirically, the paper uses the Autoregressive Distributed Lag (ARDL) estimations and observes that both the economic complexity and the renewable energy consumption lead to a higher rate of economic growth in the USA for the period from 1965 to 2016. The paper also discusses the potential policy implications of the results for achieving the sustainable growth objectives.

Is trade openness good for environment in South Korea? The role of non-fossil electricity consumption.

PubMed

Zhang, Shun

2018-04-01

The paper investigates the linkage of carbon dioxide (CO 2 ) emissions, per capita real output, share of non-fossil electricity consumption, and trade openness in South Korea from 1971 to 2013. The empirical results indicate that the environmental Kuznets curve (EKC) is supported by autoregressive distributed lag (ARDL) test. Both short- and long-run estimates indicate that increasing non-fossil electricity consumption can mitigate environmental degradation, and increasing trade aggravates carbon dioxide emissions. By Granger causality, long-run causalities are found in both equations of CO 2 emissions and trade openness, as well as exports and imports. In the short-run, evidence indicates feedback linkage between output and trade, unidirectional linkages from trade to emissions, from emissions to output, and from output to non-fossil electricity use. Therefore, South Korea should strengthen the sustainable economy, consume clean energy, and develop green trade.

Effects of export concentration on CO2 emissions in developed countries: an empirical analysis.

PubMed

Apergis, Nicholas; Can, Muhlis; Gozgor, Giray; Lau, Chi Keung Marco

2018-03-08

This paper provides the evidence on the short- and the long-run effects of the export product concentration on the level of CO 2 emissions in 19 developed (high-income) economies, spanning the period 1962-2010. To this end, the paper makes use of the nonlinear panel unit root and cointegration tests with multiple endogenous structural breaks. It also considers the mean group estimations, the autoregressive distributed lag model, and the panel quantile regression estimations. The findings illustrate that the environmental Kuznets curve (EKC) hypothesis is valid in the panel dataset of 19 developed economies. In addition, it documents that a higher level of the product concentration of exports leads to lower CO 2 emissions. The results from the panel quantile regressions also indicate that the effect of the export product concentration upon the per capita CO 2 emissions is relatively high at the higher quantiles.

Economic and Sociological Correlates of Suicides: Multilevel Analysis of the Time Series Data in the United Kingdom.

PubMed

Sun, Bruce Qiang; Zhang, Jie

2016-03-01

For the effects of social integration on suicides, there have been different and even contradictive conclusions. In this study, the selected economic and social risks of suicide for different age groups and genders in the United Kingdom were identified and the effects were estimated by the multilevel time series analyses. To our knowledge, there exist no previous studies that estimated a dynamic model of suicides on the time series data together with multilevel analysis and autoregressive distributed lags. The investigation indicated that unemployment rate, inflation rate, and divorce rate are all significantly and positively related to the national suicide rates in the United Kingdom from 1981 to 2011. Furthermore, the suicide rates of almost all groups above 40 years are significantly associated with the risk factors of unemployment and inflation rate, in comparison with the younger groups. © 2016 American Academy of Forensic Sciences.

The dynamic relationship between health expenditure and economic growth: is the health-led growth hypothesis valid for Turkey?

PubMed

Atilgan, Emre; Kilic, Dilek; Ertugrul, Hasan Murat

2017-06-01

The well-known health-led growth hypothesis claims a positive correlation between health expenditure and economic growth. The aim of this paper is to empirically investigate the health-led growth hypothesis for the Turkish economy. The bound test approach, autoregressive-distributed lag approach (ARDL) and Kalman filter modeling are employed for the 1975-2013 period to examine the co-integration relationship between economic growth and health expenditure. The ARDL model is employed in order to investigate the long-term and short-term static relationship between health expenditure and economic growth. The results show that a 1 % increase in per-capita health expenditure will lead to a 0.434 % increase in per-capita gross domestic product. These findings are also supported by the Kalman filter model's results. Our findings show that the health-led growth hypothesis is supported for Turkey.

Impact of Foreign Direct Investment on the Unemployment Rate in Malaysia

NASA Astrophysics Data System (ADS)

Muhd Irpan, Hamidah; Mat Saad, Rosfadzimi; Nor, Abu Hassan Shaari Md; Noor, Abd Halim Md; Ibrahim, Noorazilah

2016-04-01

Malaysia as a developing country needs support from other countries for economic growth. This is done by receiving massive foreign direct investment (FDI) which contributes to a higher employment rate. Higher employment leads to a better living among Malaysians while increasing its gross domestic product (GDP). During 2009, Malaysia faced a downward trend on the FDI. In many studies, decreasing FDI affects employment rate significantly. This study focuses on the impact of FDI on employment rate in Malaysia. Other factors such as the number of foreign workers, gross domestic product (GDP) and exchange rate (EXCR) are also included in the study. Data used in the study is annual data spanning from 1980 to 2012. Autoregressive distributed lag (ARDL) model is used to determine the long run relationship between the variables. The study finds that FDI, number of foreign workers, and GDP significantly influence the unemployment rate in Malaysia.

Causal relationship between CO₂ emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia.

PubMed

Farhani, Sahbi; Ozturk, Ilhan

2015-10-01

The aim of this paper is to examine the causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia over the period of 1971-2012. The long-run relationship is investigated by the auto-regressive distributed lag (ARDL) bounds testing approach to cointegration and error correction method (ECM). The results of the analysis reveal a positive sign for the coefficient of financial development, suggesting that the financial development in Tunisia has taken place at the expense of environmental pollution. The Tunisian case also shows a positive monotonic relationship between real GDP and CO2 emissions. This means that the results do not support the validity of environmental Kuznets curve (EKC) hypothesis. In addition, the paper explores causal relationship between the variables by using Granger causality models and it concludes that financial development plays a vital role in the Tunisian economy.

Globalisation and its effect on pollution in Malaysia: the role of Trans-Pacific Partnership (TPP) agreement.

PubMed

Solarin, Sakiru Adebola; Al-Mulali, Usama; Sahu, Pritish Kumar

2017-10-01

The main objective of this study is to investigate the influence of the globalisation (Trans-Pacific Partnership (TPP) agreement in particular) on air pollution in Malaysia. To achieve this goal, the Autoregressive Distributed Lag (ARDL) model, Johansen cointegration test and fully modified ordinary least square (FMOLS) methods are utilised. CO 2 emission is used as an indicator of pollution while GDP per capita and urbanisation serve as its other determinants. In addition, this study uses Malaysia's total trade with 10 TPP members as an indicator of globalisation and analyse its effect on CO 2 emission in Malaysia. The outcome of this research shows that the variables are cointegrated. Additionally, GDP per capita, urbanisation and trade between Malaysia and its 10 TPP partners have a positive impact on CO 2 emissions in general. Based on the outcome of this research, important policy implications are provided for the investigated country.

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

PubMed

Moran, John L; Solomon, Patricia J

2011-02-01

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

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study.

PubMed

Hamaker, E L; Asparouhov, T; Brose, A; Schmiedek, F; Muthén, B

2018-04-06

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent-but mostly unresolved-issues in the area of dynamic multilevel modeling.

Impacts of Climatic Variability on Vibrio parahaemolyticus Outbreaks in Taiwan

PubMed Central

Hsiao, Hsin-I; Jan, Man-Ser; Chi, Hui-Ju

2016-01-01

This study aimed to investigate and quantify the relationship between climate variation and incidence of Vibrio parahaemolyticus in Taiwan. Specifically, seasonal autoregressive integrated moving average (ARIMA) models (including autoregression, seasonality, and a lag-time effect) were employed to predict the role of climatic factors (including temperature, rainfall, relative humidity, ocean temperature and ocean salinity) on the incidence of V. parahaemolyticus in Taiwan between 2000 and 2011. The results indicated that average temperature (+), ocean temperature (+), ocean salinity of 6 months ago (+), maximum daily rainfall (current (−) and one month ago (−)), and average relative humidity (current and 9 months ago (−)) had significant impacts on the incidence of V. parahaemolyticus. Our findings offer a novel view of the quantitative relationship between climate change and food poisoning by V. parahaemolyticus in Taiwan. An early warning system based on climate change information for the disease control management is required in future. PMID:26848675

Impacts of Climatic Variability on Vibrio parahaemolyticus Outbreaks in Taiwan.

PubMed

Hsiao, Hsin-I; Jan, Man-Ser; Chi, Hui-Ju

2016-02-03

This study aimed to investigate and quantify the relationship between climate variation and incidence of Vibrio parahaemolyticus in Taiwan. Specifically, seasonal autoregressive integrated moving average (ARIMA) models (including autoregression, seasonality, and a lag-time effect) were employed to predict the role of climatic factors (including temperature, rainfall, relative humidity, ocean temperature and ocean salinity) on the incidence of V. parahaemolyticus in Taiwan between 2000 and 2011. The results indicated that average temperature (+), ocean temperature (+), ocean salinity of 6 months ago (+), maximum daily rainfall (current (-) and one month ago (-)), and average relative humidity (current and 9 months ago (-)) had significant impacts on the incidence of V. parahaemolyticus. Our findings offer a novel view of the quantitative relationship between climate change and food poisoning by V. parahaemolyticus in Taiwan. An early warning system based on climate change information for the disease control management is required in future.

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

PubMed Central

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

2016-01-01

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

Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Manh, Cuong Do

2015-01-01

The Mekong Delta is highly vulnerable to climate change and a dengue endemic area in Vietnam. This study aims to examine the association between climate factors and dengue incidence and to identify the best climate prediction model for dengue incidence in Can Tho city, the Mekong Delta area in Vietnam. We used three different regression models comprising: standard multiple regression model (SMR), seasonal autoregressive integrated moving average model (SARIMA), and Poisson distributed lag model (PDLM) to examine the association between climate factors and dengue incidence over the period 2003-2010. We validated the models by forecasting dengue cases for the period of January-December, 2011 using the mean absolute percentage error (MAPE). Receiver operating characteristics curves were used to analyze the sensitivity of the forecast of a dengue outbreak. The results indicate that temperature and relative humidity are significantly associated with changes in dengue incidence consistently across the model methods used, but not cumulative rainfall. The Poisson distributed lag model (PDLM) performs the best prediction of dengue incidence for a 6, 9, and 12-month period and diagnosis of an outbreak however the SARIMA model performs a better prediction of dengue incidence for a 3-month period. The simple or standard multiple regression performed highly imprecise prediction of dengue incidence. We recommend a follow-up study to validate the model on a larger scale in the Mekong Delta region and to analyze the possibility of incorporating a climate-based dengue early warning method into the national dengue surveillance system. Copyright © 2014 Elsevier B.V. All rights reserved.

Vicious circle of perceived stigma, enacted stigma and depressive symptoms among children affected by HIV/AIDS in China.

PubMed

Chi, Peilian; Li, Xiaoming; Zhao, Junfeng; Zhao, Guoxiang

2014-06-01

Previous research has found a deleterious impact of stigma on the mental health of children affected by HIV/AIDS. Little is known about the longitudinal relationship of stigma and children's mental health. This study explores the longitudinal reciprocal effects of depressive symptoms and stigma, specifically enacted stigma and perceived stigma, among children affected by HIV/AIDS aged 6-12. Longitudinal data were collected from 272 children orphaned by AIDS and 249 children of HIV-positive parents in rural China. Cross-lagged panel analysis was conducted in the study. Results showed that the autoregressive effects were stable for depressive symptoms, perceived stigma and enacted stigma suggesting the substantially stable individual differences over time. The cross-lagged effects indicated a vicious circle among the three variables in an order of enacted stigma → depressive symptom → perceived stigma → enacted stigma. The possibility of employing equal constraints on cross-lagged paths suggested that the cross-lagged effects were repeatable over time. The dynamic interplay of enacted stigma, perceived stigma and depressive symptoms suggests the need of a multilevel intervention in stigma reduction programming to promote mental health of children affected by HIV/AIDS.

Climate variation and incidence of Ross river virus in Cairns, Australia: a time-series analysis.

PubMed Central

Tong, S; Hu, W

2001-01-01

In this study we assessed the impact of climate variability on the Ross River virus (RRv) transmission and validated an epidemic-forecasting model in Cairns, Australia. Data on the RRv cases recorded between 1985 and 1996 were obtained from the Queensland Department of Health. Climate and population data were supplied by the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. The cross-correlation function (CCF) showed that maximum temperature in the current month and rainfall and relative humidity at a lag of 2 months were positively and significantly associated with the monthly incidence of RRv, whereas relative humidity at a lag of 5 months was inversely associated with the RRv transmission. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1985 to 1994, and then validated the models using the data collected between 1995 and 1996. The results show that the relative humidity at a lag of 5 months (p < 0.001) and the rainfall at a lag of 2 months (p < 0.05) appeared to play significant roles in the transmission of RRv disease in Cairns. Furthermore, the regressive forecast curves were consistent with the pattern of actual values. PMID:11748035

Vicious Circle of Perceived Stigma, Enacted Stigma and Depressive Symptoms among Children affected by HIV/AIDS in China

PubMed Central

Chi, Peilian; Li, Xiaoming; Zhao, Junfeng; Zhao, Guoxiang

2013-01-01

Previous research has found a deleterious impact of stigma on the mental health of children affected by HIV/AIDS. Little is known about the longitudinal relationship of stigma and children’s mental health. This study explores the longitudinal reciprocal effects of depressive symptoms and stigma, specifically enacted stigma and perceived stigma, among children affected by HIV/AIDS aged 6 to 12. Longitudinal data were collected from 272 children orphaned by AIDS and 249 children of HIV-positive parents in rural China. Cross-lagged panel analysis was conducted in the study. Results showed that the autoregressive effects were stable for depressive symptoms, perceived stigma and enacted stigma suggesting the substantially stable individual differences over time. The cross-lagged effects indicated a vicious circle among the three variables in an order of enacted stigma→depressive symptom→perceived stigma→enacted stigma. The possibility of employing equal constraints on cross-lagged paths suggested that the cross-lagged effects were repeatable over time. The dynamic interplay of enacted stigma, perceived stigma and depressive symptoms suggests the need of a multilevel intervention in stigma reduction programming to promote mental health of children affected by HIV/AIDS. PMID:24158487

Modeling and roles of meteorological factors in outbreaks of highly pathogenic avian influenza H5N1.

PubMed

Biswas, Paritosh K; Islam, Md Zohorul; Debnath, Nitish C; Yamage, Mat

2014-01-01

The highly pathogenic avian influenza A virus subtype H5N1 (HPAI H5N1) is a deadly zoonotic pathogen. Its persistence in poultry in several countries is a potential threat: a mutant or genetically reassorted progenitor might cause a human pandemic. Its world-wide eradication from poultry is important to protect public health. The global trend of outbreaks of influenza attributable to HPAI H5N1 shows a clear seasonality. Meteorological factors might be associated with such trend but have not been studied. For the first time, we analyze the role of meteorological factors in the occurrences of HPAI outbreaks in Bangladesh. We employed autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to assess the roles of different meteorological factors in outbreaks of HPAI. Outbreaks were modeled best when multiplicative seasonality was incorporated. Incorporation of any meteorological variable(s) as inputs did not improve the performance of any multivariable models, but relative humidity (RH) was a significant covariate in several ARIMA and SARIMA models with different autoregressive and moving average orders. The variable cloud cover was also a significant covariate in two SARIMA models, but air temperature along with RH might be a predictor when moving average (MA) order at lag 1 month is considered.

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

PubMed

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

2016-04-01

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

School Attendance Problems and Youth Psychopathology: Structural Cross-Lagged Regression Models in Three Longitudinal Datasets

PubMed Central

Wood, Jeffrey J.; Lynne, Sarah D.; Langer, David A.; Wood, Patricia A.; Clark, Shaunna L.; Eddy, J. Mark; Ialongo, Nicholas

2011-01-01

This study tests a model of reciprocal influences between absenteeism and youth psychopathology using three longitudinal datasets (Ns= 20745, 2311, and 671). Participants in 1st through 12th grades were interviewed annually or bi-annually. Measures of psychopathology include self-, parent-, and teacher-report questionnaires. Structural cross-lagged regression models were tested. In a nationally representative dataset (Add Health), middle school students with relatively greater absenteeism at study year 1 tended towards increased depression and conduct problems in study year 2, over and above the effects of autoregressive associations and demographic covariates. The opposite direction of effects was found for both middle and high school students. Analyses with two regionally representative datasets were also partially supportive. Longitudinal links were more evident in adolescence than in childhood. PMID:22188462

International Trade, Pollution Accumulation and Sustainable Growth: A VAR Estimation from the Pearl River Delta Region

NASA Astrophysics Data System (ADS)

Zuo, Hui; Tian, Lu

2018-03-01

In order to investigate international trade influence in the regional environment. This paper constructs a vector auto-regression (VAR) model and estimates the equations with the environment and trade data of the Pearl River Delta Region. The major mechanisms to the lag are discussed and the fit simulation of the environmental change by the international impulse is given. The result shows that impulse of pollution-intensive export deteriorates the environment continuously and impulse of such import improves it. These effects on the environment are insignificantly correlated with contemporary regional income but significantly correlative to early-stage trade feature. To a typical trade-dependent economy, both export and import have hysteresis influence in the regional environment. The lagged impulse will change environmental development in the turning point, maximal pollution level and convergence.

Does national scale economic and environmental indicators spur logistics performance? Evidence from UK.

PubMed

Khan, Syed Abdul Rehman; Qianli, Dong

2017-12-01

The aim of this study is to examine the association between national economic and environmental indicators with green logistics performance in a time series data of UK since 1981 to 2016. The research used autoregressive distributed lag method to understand the long-run and short-run relationships of national scale economic (foreign direct investment (FDI) inflows, per capita income) and environmental indicators (total greenhouse gases, fossil fuel, and renewable energy) on green logistics. In the short run, the research findings indicate that the green logistics and renewable energy have positive relationship, while fossil fuel is negatively correlated with green logistics operations. On the other hand, in the long run, the results show that FDI inflows, renewable energy sources, and per capita income have statistically significant and positive association with green logistics activities, while foreign investments attracted by environmental friendly policies and practices adopted in global logistics operations, which not only increase the environmental sustainability but also enhance economic activities with greater export opportunities in the region.

An empirical analysis of financial development and energy demand: establishing the role of globalization.

PubMed

Saud, Shah; Danish; Chen, Songsheng

2018-06-14

The rapid mode of globalization is experienced in the last few years. The acceleration in globalization expands economic activities through a share of knowledge and transfer of technology which influence energy demand. So, the objective of this empirical work is to explore the impact of financial development on energy demand incorporating globalization. The empirical finding is based on autoregressive distributed lag (ARDL) bound testing approach from 1980 to 2016 in case of China. Overall, we infer that financial development increases energy demand in China. Furthermore, the finding shows that globalization has a negative and significant impact on energy demand. The additional determinants, such as economic growth, and urbanization stimulate energy consumption. Besides, energy consumption granger cause financial development in the long-run path. Similarly, unidirectional causality is detected between globalization and energy consumption. The result gives direction to policymakers to preserve as well as to enhance efficient energy consumption and sustain economic growth in China with acceleration in globalization.

Testing for Marshall-Lerner hypothesis: A panel approach

NASA Astrophysics Data System (ADS)

Azizan, Nur Najwa; Sek, Siok Kun

2014-12-01

The relationship between real exchange rate and trade balances are documented in many theories. One of the theories is the so-called Marshall-Lerner condition. In this study, we seek to test for the validity of Marshall-Lerner hypothesis, i.e. to reveal if the depreciation of real exchange rate leads to the improvement in trade balances. We focus our study in ASEAN-5 countries and their main trade partners of U.S., Japan and China. The dynamic panel data of pooled mean group (PMG) approach is used to detect the Marshall-Lerner hypothesis among ASEAN-5, between ASEAN-5 and U.S., between ASEAN-5 and Japan and between ASEAN-5 and China respectively. The estimation is based on the autoregressive Distributed Lag or ARDL model for the period of 1970-2012. The paper concludes that Marshal Lerner theory does not hold in bilateral trades in four groups of countries. The trade balances of ASEAN5 are mainly determined by the domestic income level and foreign production cost.

Are renewable energy policies upsetting carbon dioxide emissions? The case of Latin America countries.

PubMed

Fuinhas, José Alberto; Marques, António Cardoso; Koengkan, Matheus

2017-06-01

The impact of renewable energy policies in carbon dioxide emissions was analysed for a panel of ten Latin American countries, for the period from 1991 to 2012. Panel autoregressive distributed lag methodology was used to decompose the total effect of renewable energy policies on carbon dioxide emissions in its short- and long-run components. There is evidence for the presence of cross-sectional dependence, confirming that Latin American countries share spatial patterns. Heteroskedasticity, contemporaneous correlation, and first-order autocorrelation cross-sectional dependence are also present. To cope with these phenomena, the robust dynamic Driscoll-Kraay estimator, with fixed effects, was used. It was confirmed that the primary energy consumption per capita, in both the short- and long-run, contributes to an increase in carbon dioxide emissions, and also that renewable energy policies in the long-run, and renewable electricity generation per capita both in the short- and long-run, help to mitigate per capita carbon dioxide emissions.

Environmental Kuznets curve for EU agriculture: empirical evidence from new entrant EU countries.

PubMed

Zafeiriou, Eleni; Sofios, Spyros; Partalidou, Xanthi

2017-06-01

The present work examines the intertemporal causal relationship between environmental damage from carbon emissions released by agriculture per 1000 ha of utilized agriculture area and economic performance in the sector of agriculture as described by net value added per capita. The autoregressive distributed lag bounds testing approach is employed to examine this linkage, for three new entrant EU countries, namely, Bulgaria, Czech Republic, and Hungary. The environmental Kuznets hypothesis is confirmed in the long run for Bulgaria and Czech Republic while in the short run is validated only for the case of Czech Republic. The results indicate that the adoption of environment-friendly farming practices and crops' selection does not secure simultaneous high economic and environmental performance at least in the short run for our sample countries and also in the long run for Hungary necessitating the modification of the agro-environmental measures adopted to make those two targets complementary and not mutually exclusive for a farmer.

[Public spending on health and population health in Algeria: an econometric analysis].

PubMed

Messaili, Moussa; Kaïd Tlilane, Nouara

2017-07-10

Objective: The objective of this study was to estimate the impact of public spending on health, among other determinants of health, on the health of the population in Algeria, using life expectancy (men and women) and infant mortality rates as indicators of health status. Methods: We conducted a longitudinal study over the period from 1974 to 2010 using the ARDL (Autoregressive Distributed Lags) approach to co-integration to estimate the short-term and long-term relationship. Results: Public spending on health has a positive, but not statistically significant impact, in the long and short term, on life expectancy (men and women). However, public spending significantly reduces the infant mortality rate. The long-term impact of the number of hospital beds is significant for the life expectancy of men, but not for women and infant mortality, but is significant for all indicators in the short-term relationship. The most important variables in improving the health of the population are real GDP per capita and fertility rate.

Testing the EKC hypothesis by considering trade openness, urbanization, and financial development: the case of Turkey.

PubMed

Ozatac, Nesrin; Gokmenoglu, Korhan K; Taspinar, Nigar

2017-07-01

This study investigates the environmental Kuznets curve (EKC) hypothesis for the case of Turkey from 1960 to 2013 by considering energy consumption, trade, urbanization, and financial development variables. Although previous literature examines various aspects of the EKC hypothesis for the case of Turkey, our model augments the basic model with several covariates to develop a better understanding of the relationship among the variables and to refrain from omitted variable bias. The results of the bounds test and the error correction model under autoregressive distributed lag mechanism suggest long-run relationships among the variables as well as proof of the EKC and the scale effect in Turkey. A conditional Granger causality test reveals that there are causal relationships among the variables. Our findings can have policy implications including the imposition of a "polluter pays" mechanism, such as the implementation of a carbon tax for pollution trading, to raise the urban population's awareness about the importance of adopting renewable energy and to support clean, environmentally friendly technology.

The effect of urbanization and industrialization on carbon emissions in Turkey: evidence from ARDL bounds testing procedure.

PubMed

Pata, Ugur Korkut

2018-03-01

This paper examines the dynamic short- and long-term relationship between per capita GDP, per capita energy consumption, financial development, urbanization, industrialization, and per capita carbon dioxide (CO 2 ) emissions within the framework of the environmental Kuznets curve (EKC) hypothesis for Turkey covering the period from 1974 to 2013. According to the results of the autoregressive distributed lag bounds testing approach, an increase in per capita GDP, per capita energy consumption, financial development, urbanization, and industrialization has a positive effect on per capita CO 2 emissions in the long term, and also the variables other than urbanization increase per capita CO 2 emissions in the short term. In addition, the findings support the validity of the EKC hypothesis for Turkey in the short and long term. However, the turning points obtained from long-term regressions lie outside the sample period. Therefore, as the per capita GDP increases in Turkey, per capita CO 2 emissions continue to increase.

Economic growth, energy consumption and CO2 emissions in India: a disaggregated causal analysis

NASA Astrophysics Data System (ADS)

Nain, Md Zulquar; Ahmad, Wasim; Kamaiah, Bandi

2017-09-01

This study examines the long-run and short-run causal relationships among energy consumption, real gross domestic product (GDP) and CO2 emissions using aggregate and disaggregate (sectoral) energy consumption measures utilising annual data from 1971 to 2011. The autoregressive distributed lag bounds test reveals that there is a long-run relationship among the variables concerned at both aggregate and disaggregate levels. The Toda-Yamamoto causality tests, however, reveal that the long-run as well short-run causal relationship among the variables is not uniform across sectors. The weight of evidences of the study indicates that there is short-run causality from electricity consumption to economic growth, and to CO2 emissions. The results suggest that India should take appropriate cautious steps to sustain high growth rate and at the same time to control emissions of CO2. Further, energy and environmental policies should acknowledge the sectoral differences in the relationship between energy consumption and real gross domestic product.

Longitudinal Relationship between Self-efficacy and Posttraumatic Stress Symptoms 8 Years after a Violent Assault: An Autoregressive Cross-Lagged Model

PubMed Central

Nygaard, Egil; Johansen, Venke A.; Siqveland, Johan; Hussain, Ajmal; Heir, Trond

2017-01-01

Self-efficacy is assumed to promote posttraumatic adaption, and several cross-sectional studies support this notion. However, there is a lack of prospective longitudinal studies to further illuminate the temporal relationship between self-efficacy and posttraumatic stress symptoms. Thus, an important unresolved research question is whether posttraumatic stress disorder (PTSD) symptoms affect the level of self-efficacy or vice versa or whether they mutually influence each other. The present prospective longitudinal study investigated the reciprocal relationship between general self-efficacy (GSE) and posttraumatic stress symptoms in 143 physical assault victims. We used an autoregressive cross-lagged model across four assessment waves: within 4 months after the assault (T1) and then 3 months (T2), 12 months (T3) and 8 years (T4) after the first assessment. Stress symptoms at T1 and T2 predicted subsequent self-efficacy, while self-efficacy at T1 and T2 was not related to subsequent stress symptoms. These relationships were reversed after T3; higher levels of self-efficacy at T3 predicted lower levels of posttraumatic stress symptoms at T4, while posttraumatic tress symptoms at T3 did not predict self-efficacy at T4. In conclusion, posttraumatic stress symptoms may have a deteriorating effect on self-efficacy in the early phase after physical assault, whereas self-efficacy may promote recovery from posttraumatic stress symptoms over the long term. PMID:28620334

Longitudinal Relationship between Self-efficacy and Posttraumatic Stress Symptoms 8 Years after a Violent Assault: An Autoregressive Cross-Lagged Model.

PubMed

Nygaard, Egil; Johansen, Venke A; Siqveland, Johan; Hussain, Ajmal; Heir, Trond

2017-01-01

Self-efficacy is assumed to promote posttraumatic adaption, and several cross-sectional studies support this notion. However, there is a lack of prospective longitudinal studies to further illuminate the temporal relationship between self-efficacy and posttraumatic stress symptoms. Thus, an important unresolved research question is whether posttraumatic stress disorder (PTSD) symptoms affect the level of self-efficacy or vice versa or whether they mutually influence each other. The present prospective longitudinal study investigated the reciprocal relationship between general self-efficacy (GSE) and posttraumatic stress symptoms in 143 physical assault victims. We used an autoregressive cross-lagged model across four assessment waves: within 4 months after the assault (T1) and then 3 months (T2), 12 months (T3) and 8 years (T4) after the first assessment. Stress symptoms at T1 and T2 predicted subsequent self-efficacy, while self-efficacy at T1 and T2 was not related to subsequent stress symptoms. These relationships were reversed after T3; higher levels of self-efficacy at T3 predicted lower levels of posttraumatic stress symptoms at T4, while posttraumatic tress symptoms at T3 did not predict self-efficacy at T4. In conclusion, posttraumatic stress symptoms may have a deteriorating effect on self-efficacy in the early phase after physical assault, whereas self-efficacy may promote recovery from posttraumatic stress symptoms over the long term.

Relations between Precipitation and Shallow Groundwater in Illinois.

NASA Astrophysics Data System (ADS)

Changnon, Stanley A.; Huff, Floyd A.; Hsu, Chin-Fei

1988-12-01

The statistical relationships between monthly precipitation (P) and shallow groundwater levels (GW) in 20 wells scattered across Illinois with data for 1960-84 were defined using autoregressive integrated moving average (ARIMA) modeling. A lag of 1 month between P to GW was the strongest temporal relationship found across Illinois, followed by no (0) lag in the northern two-thirds of Illinois where mollisols predominate, and a lag of 2 months in the alfisols of southern Illinois. Spatial comparison of the 20 P-GW correlations with several physical conditions (aquifer types, soils, and physiography) revealed that the parent soil materials of outwash alluvium, glacial till, thick loess (2.1 m), and thin loess (>2.1) best defined regional relationships for drought assessment.Equations developed from ARTMA using 1960-79 data for each region were used to estimate GW levels during the 1980-81 drought, and estimates averaged between 25 to 45 cm of actual levels. These estimates are considered adequate to allow a useful assessment of drought onset, severity, and termination in other parts of the state. The techniques and equations should be transferrable to regions of comparable soils and climate.

Interpersonal Conflicts and Development of Self-Esteem from Adolescence to Mid-Adulthood. A 26-Year Follow-Up

PubMed Central

Kiviruusu, Olli; Berg, Noora; Huurre, Taina; Aro, Hillevi; Marttunen, Mauri; Haukkala, Ari

2016-01-01

This study investigated the association between interpersonal conflicts and the trajectory of self-esteem from adolescence to mid-adulthood. The directionality of effects between self-esteem and interpersonal conflicts was also studied. Participants of a Finnish cohort study in 1983 at age 16 (N = 2194) were followed up at ages 22 (N = 1656), 32 (N = 1471) and 42 (N = 1334) using postal questionnaires. Measures covered self-esteem and interpersonal conflicts including, conflicts with parents, friends, colleagues, superiors, partners, break-ups with girl/boyfriends, and divorces. Participants were grouped using latent profile analysis to those having “consistently low”, “decreasing”, or “increasing” number of interpersonal conflicts from adolescence to adulthood. Analyses were done using latent growth curve models and autoregressive cross-lagged models. Among both females and males the self-esteem growth trajectory was most favorable in the group with a consistently low number of interpersonal conflicts. Compared to the low group, the group with a decreasing number of interpersonal conflicts had a self-esteem trajectory that started and remained at a lower level throughout the study period. The group with an increasing number of interpersonal conflicts had a significantly slower self-esteem growth rate compared to the other groups, and also the lowest self-esteem level at the end of the study period. Cross-lagged autoregressive models indicated small, but significant lagged effects from low self-esteem to later interpersonal conflicts, although only among males. There were no effects to the opposite direction among either gender. Our results show that those reporting more and an increasing number of interpersonal conflicts have a lower and more slowly developing self-esteem trajectory from adolescence to mid-adulthood. While the result was expected, it does not seem to imply an effect from interpersonal conflicts to low self-esteem. Rather, if anything, our results seem to suggest that those with low self-esteem are more prone to later interpersonal conflicts. PMID:27755568

Interpersonal Conflicts and Development of Self-Esteem from Adolescence to Mid-Adulthood. A 26-Year Follow-Up.

PubMed

Kiviruusu, Olli; Berg, Noora; Huurre, Taina; Aro, Hillevi; Marttunen, Mauri; Haukkala, Ari

2016-01-01

This study investigated the association between interpersonal conflicts and the trajectory of self-esteem from adolescence to mid-adulthood. The directionality of effects between self-esteem and interpersonal conflicts was also studied. Participants of a Finnish cohort study in 1983 at age 16 (N = 2194) were followed up at ages 22 (N = 1656), 32 (N = 1471) and 42 (N = 1334) using postal questionnaires. Measures covered self-esteem and interpersonal conflicts including, conflicts with parents, friends, colleagues, superiors, partners, break-ups with girl/boyfriends, and divorces. Participants were grouped using latent profile analysis to those having "consistently low", "decreasing", or "increasing" number of interpersonal conflicts from adolescence to adulthood. Analyses were done using latent growth curve models and autoregressive cross-lagged models. Among both females and males the self-esteem growth trajectory was most favorable in the group with a consistently low number of interpersonal conflicts. Compared to the low group, the group with a decreasing number of interpersonal conflicts had a self-esteem trajectory that started and remained at a lower level throughout the study period. The group with an increasing number of interpersonal conflicts had a significantly slower self-esteem growth rate compared to the other groups, and also the lowest self-esteem level at the end of the study period. Cross-lagged autoregressive models indicated small, but significant lagged effects from low self-esteem to later interpersonal conflicts, although only among males. There were no effects to the opposite direction among either gender. Our results show that those reporting more and an increasing number of interpersonal conflicts have a lower and more slowly developing self-esteem trajectory from adolescence to mid-adulthood. While the result was expected, it does not seem to imply an effect from interpersonal conflicts to low self-esteem. Rather, if anything, our results seem to suggest that those with low self-esteem are more prone to later interpersonal conflicts.

Prediction of municipal solid waste generation using nonlinear autoregressive network.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A

2015-12-01

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

A multivariate auto-regressive combined-harmonics analysis and its application to ozone time series data

NASA Astrophysics Data System (ADS)

Yang, Eun-Su

2001-07-01

A new statistical approach is used to analyze Dobson Umkehr layer-ozone measurements at Arosa for 1979-1996 and Total Ozone Mapping Spectrometer (TOMS) Version 7 zonal mean ozone for 1979-1993, accounting for stratospheric aerosol optical depth (SAOD), quasi-biennial oscillation (QBO), and solar flux effects. A stepwise regression scheme selects statistically significant periodicities caused by season, SAOD, QBO, and solar variations and filters them out. Auto-regressive (AR) terms are included in ozone residuals and time lags are assumed for the residuals of exogenous variables. Then, the magnitudes of responses of ozone to the SAOD, QBO, and solar index (SI) series are derived from the stationary time series of the residuals. These Multivariate Auto-Regressive Combined Harmonics (MARCH) processes possess the following significant advantages: (1)the ozone trends are estimated more precisely than the previous methods; (2)the influences of the exogenous SAOD, QBO, and solar variations are clearly separated at various time lags; (3)the collinearity of the exogenous variables in the regression is significantly reduced; and (4)the probability of obtaining misleading correlations between ozone and exogenous times series is reduced. The MARCH results indicate that the Umkehr ozone response to SAOD (not a real ozone response but rather an optical interference effect), QBO, and solar effects is driven by combined dynamical radiative-chemical processes. These results are independently confirmed using the revised Standard models that include aerosol and solar forcing mechanisms with all possible time lags but not by the Standard model when restricted to a zero time lag in aerosol and solar ozone forcings. As for Dobson Umkehr ozone measurements at Arosa, the aerosol effects are most significant in layers 8, 7, and 6 with no time lag, as is to be expected due to the optical contamination of Umkehr measurements by SAOD. The QBO and solar UV effects appear in all layers 4-8, and in total ozone. In order to account for annual modulation of the equatorial winds that affects ozone at midlatitudes, a new QBO proxy is selected and applied to the Dobson Umkehr measurements at Arosa. The QBO proxy turns out to be more effective to filter the modulated ozone signals at midlatitudes than the mostly used QBO proxy, the Singapore winds at 30 mb. A statistically significant negative phase relationship is found between solar UV variation and ozone response, especially in layer 4, implying dynamical effects of solar variations on ozone at midlatitudes. Linear negative trends in ozone of -7.8 +/- 1.1 and -5.2 +/- 1.4 [%/decade +/- 2σ] are calculated in layers 7 (~35 km) and 8 (~40 km), respectively, for the period of 1979-1996, with smaller trends of -2.2 +/- 1.0, 1.8 +/- 0.9, and -1.4 +/- 1.1 in layers 6 (~30 km), 5 (~25 km), and 4 (~20 km), respectively. A trend in total ozone (layers 1 through 10) of -2.9 +/- 1.2 [%/decade +/- 2σ] is found over this same period. The aerosol effects obtained from the TOMS zonal means become significant at midlatitudes. QBO ozone contributes to the TOMS zonal means by +/-2 to 4% of their means. The negative solar ozone responses are also found at midlatitudes from the TOMS measurements. The most negative trends from TOMS zonal means are about -6.3 +/- 0.6%/decade at 40-50°N.

Kumaraswamy autoregressive moving average models for double bounded environmental data

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Determinants of Rotavirus Transmission: A Lag Nonlinear Time Series Analysis.

PubMed

van Gaalen, Rolina D; van de Kassteele, Jan; Hahné, Susan J M; Bruijning-Verhagen, Patricia; Wallinga, Jacco

2017-07-01

Rotavirus is a common viral infection among young children. As in many countries, the infection dynamics of rotavirus in the Netherlands are characterized by an annual winter peak, which was notably low in 2014. Previous study suggested an association between weather factors and both rotavirus transmission and incidence. From epidemic theory, we know that the proportion of susceptible individuals can affect disease transmission. We investigated how these factors are associated with rotavirus transmission in the Netherlands, and their impact on rotavirus transmission in 2014. We used available data on birth rates and rotavirus laboratory reports to estimate rotavirus transmission and the proportion of individuals susceptible to primary infection. Weather data were directly available from a central meteorological station. We developed an approach for detecting determinants of seasonal rotavirus transmission by assessing nonlinear, delayed associations between each factor and rotavirus transmission. We explored relationships by applying a distributed lag nonlinear regression model with seasonal terms. We corrected for residual serial correlation using autoregressive moving average errors. We inferred the relationship between different factors and the effective reproduction number from the most parsimonious model with low residual autocorrelation. Higher proportions of susceptible individuals and lower temperatures were associated with increases in rotavirus transmission. For 2014, our findings suggest that relatively mild temperatures combined with the low proportion of susceptible individuals contributed to lower rotavirus transmission in the Netherlands. However, our model, which overestimated the magnitude of the peak, suggested that other factors were likely instrumental in reducing the incidence that year.

Do drug seizures predict drug-related emergency department presentations or arrests for drug use and possession?

PubMed

Wan, Wai-Yin; Weatherburn, Don; Wardlaw, Grant; Sarafidis, Vasilis; Sara, Grant

2016-01-01

Direct evidence of the effect of drug seizures on drug use and drug-related harm is fairly sparse. The aim of this study was to see whether seizures of heroin, cocaine and ATS predict the number of people arrested for use and possession of these drugs and the number overdosing on them. We examined the effect of seizure frequency and seizure weight on arrests for drug use and possession and on the frequency of drug overdose with autoregressive distributed lag (ARDL) models. Granger causality tests were used to test for simultaneity. Over the short term (i.e. up to 4 months), increases in the intensity of high-level drug law enforcement (as measured by seizure weight and frequency) directed at ATS, cocaine and heroin did not appear to have any suppression effect on emergency department (ED) presentations relating to ATS, cocaine and heroin, or on arrests for use and/or possession of these drugs. A significant negative contemporaneous relationship was found between the heroin seizure weight and arrests for use and/or possession of heroin. However no evidence emerged of a contemporaneous or lagged relationship between heroin seizures and heroin ED presentations. The balance of evidence suggests that, in the Australian context, increases in the monthly seizure frequency and quantity of ATS, cocaine and heroin are signals of increased rather than reduced supply. Copyright © 2015 Elsevier B.V. All rights reserved.

Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time-series and ARIMAX analyses.

PubMed

Chadsuthi, Sudarat; Modchang, Charin; Lenbury, Yongwimon; Iamsirithaworn, Sopon; Triampo, Wannapong

2012-07-01

To study the number of leptospirosis cases in relations to the seasonal pattern, and its association with climate factors. Time series analysis was used to study the time variations in the number of leptospirosis cases. The Autoregressive Integrated Moving Average (ARIMA) model was used in data curve fitting and predicting the next leptospirosis cases. We found that the amount of rainfall was correlated to leptospirosis cases in both regions of interest, namely the northern and northeastern region of Thailand, while the temperature played a role in the northeastern region only. The use of multivariate ARIMA (ARIMAX) model showed that factoring in rainfall (with an 8 months lag) yields the best model for the northern region while the model, which factors in rainfall (with a 10 months lag) and temperature (with an 8 months lag) was the best for the northeastern region. The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions. The models can also be used to predict the next seasonal peak quite accurately. Copyright © 2012 Hainan Medical College. Published by Elsevier B.V. All rights reserved.

Understanding performance measures of reservoirs

NASA Astrophysics Data System (ADS)

McMahon, Thomas A.; Adeloye, Adebayo J.; Zhou, Sen-Lin

2006-06-01

This paper examines 10 reservoir performance metrics including time and volume based reliability, several measures of resilience and vulnerability, drought risk index and sustainability. Both historical and stochastically generated streamflows are considered as inflows to a range of hypothetical storage on four rivers—Earn river in the United Kingdom, Hatchie river in the United States, Richmond river in Australia and the Vis river in South Africa. The monthly stochastic sequences were generated applying an autoregressive lag one model to Box-Cox transformed annual streamflows incorporating parameter uncertainty by the Stedinger-Taylor method and the annual flows disaggregated by the method of fragments.

On the Feed-back Mechanism of Chinese Stock Markets

NASA Astrophysics Data System (ADS)

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

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

Medical Mondays: ED Utilization for Medicaid Recipients Depends on the Day of the Week, Season, and Holidays.

PubMed

Castner, Jessica; Yin, Yong; Loomis, Dianne; Hewner, Sharon

2016-07-01

The purpose of this study is to describe and explain the temporal and seasonal trends in ED utilization for a low-income population. A retrospective analysis of 66,487 ED Medicaid-insured health care claims in 2009 was conducted for 2 Western New York Counties using time-series analysis with autoregressive moving average (ARMA) models. The final ARMA (2,0) model indicated an autoregressive structure with up to a 2-day lag. ED volume is lower on weekends than on weekdays, and the highest volumes are on Mondays. Summer and fall seasons demonstrated higher volumes, whereas lower volume outliers were associated with holidays. Day of the week was an influential predictor of ED utilization in low-income persons. Season and holidays are also predictors of ED utilization. These calendar-based patterns support the need for ongoing and future emergency leaders' collaborations in community-based care system redesign to meet the health care access needs of low-income persons. Copyright © 2016 Emergency Nurses Association. Published by Elsevier Inc. All rights reserved.

Modeling the impact of transport energy consumption on CO2 emission in Pakistan: Evidence from ARDL approach.

PubMed

Danish; Baloch, Muhammad Awais; Suad, Shah

2018-04-01

The objective of this research is to examine the relationship between transport energy consumption, economic growth, and carbon dioxide emission (CO 2 ) from transport sector incorporating foreign direct investment and urbanization. This study is carried out in Pakistan by applying autoregressive distributive lag (ARDL) and vector error correction model (VECM) over 1990-2015. The empirical results indicate a strong significant impact of transport energy consumption on CO 2 emissions from the transportation sector. Furthermore, foreign direct investment also contributes to CO 2 emission. Interestingly, the impact of economic growth and urbanization on transport CO 2 emission is statistically insignificant. Overall, transport energy consumption and foreign direct investment are not environmentally friendly. The new empirical evidence from this study provides a complete picture of the determinants of emissions from the transport sector and these novel findings not only help to advance the existing literature but also can be of special interest to the country's policymakers. So, we urge that government needs to focus on promoting the energy efficient means of transportation to improve environmental quality with less adverse influence on economic growth.

Examining the reaction of monetary policy to exchange rate changes: A nonlinear ARDL approach

NASA Astrophysics Data System (ADS)

Manogaran, Lavaneesvari; Sek, Siok Kun

2017-04-01

Previous studies showed the exchange rate changes can have significant impacts on macroeconomic performance. Over fluctuation of exchange rate may lead to economic instability. Hence, monetary policy rule tends to react to exchange rate changes. Especially, in emerging economies where the policy-maker tends to limit the exchange rate movement through interventions. In this study, we seek to investigate how the monetary policy rule reacts to exchange rate changes. The nonlinear autoregressive distributed lag (NARDL) model is applied to capture the asymmetric effect of exchange rate changes on monetary policy reaction function (interest rate). We focus the study in ASEAN5 countries (Indonesia, Malaysia, Philippines, Thailand and Singapore). The results indicated the existence of asymmetric effect of exchange rates changes on the monetary reaction function for all ASEAN5 countries in the long-run. Where, in majority of the cases the monetary policy is reacting to the appreciation and depreciation of exchange rate by raising the policy rate. This affirms the intervention of policymakers with the `fear of floating' behavior.

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

PubMed

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

2017-09-18

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

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

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

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

1995-10-01

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

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

PubMed

Faes, Luca; Nollo, Giandomenico

2010-11-01

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

Behavioral Exchanges between Teachers and Children Over the Course of a Typical Preschool Day: Testing Bidirectional Associations

PubMed Central

Downer, Jason T.; Booren, Leslie

2014-01-01

In the present study, 314 preschool classrooms and 606 children were observed to understand the behavioral exchanges between teachers and children. Teachers’ emotionally and organizationally supportive behaviors and children’s engagement were explored for longitudinal associations throughout a day. Observations were conducted in each classroom wherein emotional and organizational supports were assessed, followed by observations of two children’s positive engagement with teachers, tasks, and peers as well as negative classroom engagement. Cross-lagged autoregressive models were used to test for time-lagged associations which could be unidirectional or bidirectional. Results indicated teachers’ emotionally and organizationally supportive behaviors were related to later child engagement in seven of eight models. Furthermore, in two of those seven models, we found evidence of bidirectional associations whereby children’s engagement was associated with later teacher emotional and organizational supports. Findings are discussed in terms of understanding classroom processes over the course of a day in preschool. PMID:26722153

Inter-annual cascade effect on marine food web: A benthic pathway lagging nutrient supply to pelagic fish stock

PubMed Central

Fernandes, Lohengrin Dias de Almeida; Fagundes Netto, Eduardo Barros; Coutinho, Ricardo

2017-01-01

Currently, spatial and temporal changes in nutrients availability, marine planktonic, and fish communities are best described on a shorter than inter-annual (seasonal) scale, primarily because the simultaneous year-to-year variations in physical, chemical, and biological parameters are very complex. The limited availability of time series datasets furnishing simultaneous evaluations of temperature, nutrients, plankton, and fish have limited our ability to describe and to predict variability related to short-term process, as species-specific phenology and environmental seasonality. In the present study, we combine a computational time series analysis on a 15-year (1995–2009) weekly-sampled time series (high-resolution long-term time series, 780 weeks) with an Autoregressive Distributed Lag Model to track non-seasonal changes in 10 potentially related parameters: sea surface temperature, nutrient concentrations (NO2, NO3, NH4 and PO4), phytoplankton biomass (as in situ chlorophyll a biomass), meroplankton (barnacle and mussel larvae), and fish abundance (Mugil liza and Caranx latus). Our data demonstrate for the first time that highly intense and frequent upwelling years initiate a huge energy flux that is not fully transmitted through classical size-structured food web by bottom-up stimulus but through additional ontogenetic steps. A delayed inter-annual sequential effect from phytoplankton up to top predators as carnivorous fishes is expected if most of energy is trapped into benthic filter feeding organisms and their larval forms. These sequential events can explain major changes in ecosystem food web that were not predicted in previous short-term models. PMID:28886162

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

PubMed

Moran, John L; Solomon, Patricia J

2013-05-24

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

Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012).

PubMed

Yan, Long; Wang, Hong; Zhang, Xuan; Li, Ming-Yue; He, Juan

2017-01-01

Influence of meteorological variables on the transmission of bacillary dysentery (BD) is under investigated topic and effective forecasting models as public health tool are lacking. This paper aimed to quantify the relationship between meteorological variables and BD cases in Beijing and to establish an effective forecasting model. A time series analysis was conducted in the Beijing area based upon monthly data on weather variables (i.e. temperature, rainfall, relative humidity, vapor pressure, and wind speed) and on the number of BD cases during the period 1970-2012. Autoregressive integrated moving average models with explanatory variables (ARIMAX) were built based on the data from 1970 to 2004. Prediction of monthly BD cases from 2005 to 2012 was made using the established models. The prediction accuracy was evaluated by the mean square error (MSE). Firstly, temperature with 2-month and 7-month lags and rainfall with 12-month lag were found positively correlated with the number of BD cases in Beijing. Secondly, ARIMAX model with covariates of temperature with 7-month lag (β = 0.021, 95% confidence interval(CI): 0.004-0.038) and rainfall with 12-month lag (β = 0.023, 95% CI: 0.009-0.037) displayed the highest prediction accuracy. The ARIMAX model developed in this study showed an accurate goodness of fit and precise prediction accuracy in the short term, which would be beneficial for government departments to take early public health measures to prevent and control possible BD popularity.

Carbon financial markets: A time-frequency analysis of CO2 prices

NASA Astrophysics Data System (ADS)

Sousa, Rita; Aguiar-Conraria, Luís; Soares, Maria Joana

2014-11-01

We characterize the interrelation of CO2 prices with energy prices (electricity, gas and coal), and with economic activity. Previous studies have relied on time-domain techniques, such as Vector Auto-Regressions. In this study, we use multivariate wavelet analysis, which operates in the time-frequency domain. Wavelet analysis provides convenient tools to distinguish relations at particular frequencies and at particular time horizons. Our empirical approach has the potential to identify relations getting stronger and then disappearing over specific time intervals and frequencies. We are able to examine the coherency of these variables and lead-lag relations at different frequencies for the time periods in focus.

Effect of lag time distribution on the lag phase of bacterial growth - a Monte Carlo analysis

USDA-ARS?s Scientific Manuscript database

The objective of this study is to use Monte Carlo simulation to evaluate the effect of lag time distribution of individual bacterial cells incubated under isothermal conditions on the development of lag phase. The growth of bacterial cells of the same initial concentration and mean lag phase durati...

Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses.

PubMed

Chadsuthi, Sudarat; Iamsirithaworn, Sopon; Triampo, Wannapong; Modchang, Charin

2015-01-01

Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region.

Measurement error, time lag, unmeasured confounding: Considerations for longitudinal estimation of the effect of a mediator in randomised clinical trials.

PubMed

Goldsmith, K A; Chalder, T; White, P D; Sharpe, M; Pickles, A

2018-06-01

Clinical trials are expensive and time-consuming and so should also be used to study how treatments work, allowing for the evaluation of theoretical treatment models and refinement and improvement of treatments. These treatment processes can be studied using mediation analysis. Randomised treatment makes some of the assumptions of mediation models plausible, but the mediator-outcome relationship could remain subject to bias. In addition, mediation is assumed to be a temporally ordered longitudinal process, but estimation in most mediation studies to date has been cross-sectional and unable to explore this assumption. This study used longitudinal structural equation modelling of mediator and outcome measurements from the PACE trial of rehabilitative treatments for chronic fatigue syndrome (ISRCTN 54285094) to address these issues. In particular, autoregressive and simplex models were used to study measurement error in the mediator, different time lags in the mediator-outcome relationship, unmeasured confounding of the mediator and outcome, and the assumption of a constant mediator-outcome relationship over time. Results showed that allowing for measurement error and unmeasured confounding were important. Contemporaneous rather than lagged mediator-outcome effects were more consistent with the data, possibly due to the wide spacing of measurements. Assuming a constant mediator-outcome relationship over time increased precision.

Measurement error, time lag, unmeasured confounding: Considerations for longitudinal estimation of the effect of a mediator in randomised clinical trials

PubMed Central

Goldsmith, KA; Chalder, T; White, PD; Sharpe, M; Pickles, A

2016-01-01

Clinical trials are expensive and time-consuming and so should also be used to study how treatments work, allowing for the evaluation of theoretical treatment models and refinement and improvement of treatments. These treatment processes can be studied using mediation analysis. Randomised treatment makes some of the assumptions of mediation models plausible, but the mediator–outcome relationship could remain subject to bias. In addition, mediation is assumed to be a temporally ordered longitudinal process, but estimation in most mediation studies to date has been cross-sectional and unable to explore this assumption. This study used longitudinal structural equation modelling of mediator and outcome measurements from the PACE trial of rehabilitative treatments for chronic fatigue syndrome (ISRCTN 54285094) to address these issues. In particular, autoregressive and simplex models were used to study measurement error in the mediator, different time lags in the mediator–outcome relationship, unmeasured confounding of the mediator and outcome, and the assumption of a constant mediator–outcome relationship over time. Results showed that allowing for measurement error and unmeasured confounding were important. Contemporaneous rather than lagged mediator–outcome effects were more consistent with the data, possibly due to the wide spacing of measurements. Assuming a constant mediator–outcome relationship over time increased precision. PMID:27647810

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

PubMed

Monserud, R A; Marshall, J D

2001-09-01

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

Equilibrium Policy Proposals with Abstentions.

DTIC Science & Technology

1981-05-01

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

Questioning the sustainable palm oil demand: case study from French-Indonesia supply chain

NASA Astrophysics Data System (ADS)

Chalil, D.; Barus, R.

2018-02-01

Sustainable palm oil has been widely debated. Consuming countries insist certified sustainable produces palm oil, but in fact the absorption of the certified palm oil is still less than 60%. This raise questions about the sustainable palm oil demand. In this study, such a condition will be analysed in French-Indonesia supply chain case. Using monthly and quarterly data from 2010 to 2016 with Autoregressive Distributed Lag (ARDL) approach and Error Correction Model, demand influencing factors and price integration in each market of the supply chain is estimated. Two scenarios namely re-export and direct export models are considered in the Error Correction Model. The results show that France Gross Domestic Product, prices of France palm oil import from Indonesia, Malaysia, and Germany, and price of France groundnut import significantly influence the France palm oil import volume from Indonesia. Prices in each market along palm oil re-export France-Indonesia supply chain are co-integrated and converge towards long-run equilibrium, but not in the direct export supply chain. This leads to a conclusion that France market preferences in specific and EU market preferences in general need to be considered by Indonesian palm oil decision makers.

Renewable energy consumption and economic growth in nine OECD countries: bounds test approach and causality analysis.

PubMed

Hung-Pin, Lin

2014-01-01

The purpose of this paper is to investigate the short-run and long-run causality between renewable energy (RE) consumption and economic growth (EG) in nine OECD countries from the period between 1982 and 2011. To examine the linkage, this paper uses the autoregressive distributed lag (ARDL) bounds testing approach of cointegration test and vector error-correction models to test the causal relationship between variables. The co-integration and causal relationships are found in five countries-United States of America (USA), Japan, Germany, Italy, and United Kingdom (UK). The overall results indicate that (1) a short-run unidirectional causality runs from EG to RE in Italy and UK; (2) long-run unidirectional causalities run from RE to EG for Germany, Italy, and UK; (3) a long-run unidirectional causality runs from EG to RE in USA, and Japan; (4) both long-run and strong unidirectional causalities run from RE to EG for Germany and UK; and (5) Finally, both long-run and strong unidirectional causalities run from EG to RE in only USA. Further evidence reveals that policies for renewable energy conservation may have no impact on economic growth in France, Denmark, Portugal, and Spain.

Carbon dioxide emissions, economic growth, energy use, and urbanization in Saudi Arabia: evidence from the ARDL approach and impulse saturation break tests.

PubMed

Raggad, Bechir

2018-05-01

This study investigates the existence of long-run relationship between CO 2 emissions, economic growth, energy use, and urbanization in Saudi Arabia over the period 1971-2014. The autoregressive distributed lag (ARDL) approach with structural breaks, where structural breaks are identified with the recently impulse saturation break tests, is applied to conduct the analysis. The bounds test result supports the existence of long-run relationship among the variables. The existence of environmental Kuznets curve (EKC) hypothesis has also been tested. The results reveal the non-validity of the EKC hypothesis for Saudi Arabia as the relationship between GDP and pollution is positive in both the short and the long run. Moreover, energy use increases pollution both in short and long run in the country. On the contrary, the results show a negative and significant impact of urbanization on carbon emissions in Saudi Arabia, which means that urban development is not an obstacle to the improvement of environmental quality. Consequently, policy-makers in Saudi Arabia should consider the efficiency enhancement, frugality in energy consumption, and especially increase the share of renewable energies in the total energy mix.

Economic growth and environmental pollution in Myanmar: an analysis of environmental Kuznets curve.

PubMed

Aung, Thiri Shwesin; Saboori, Behnaz; Rasoulinezhad, Ehsan

2017-09-01

This empirical study examines the short- and long-run relationship between GDP as an economic growth indicator and CO 2 emissions as an environmental pollution indicator in Myanmar by using annual time series data over the period of 1970-2014. It also carefully considered other proxies, such as trade openness, financial openness and urbanization, and structural breaks in the country. The fundamental objective of this study is to test the validity of environmental Kuznets curve (EKC) in the context of Myanmar. The dynamic estimates of the long- and short-term relationship among greenhouse gases (CO 2 , CH 4 , N 2 O), GDP, trade intensity, financial openness, and urbanization growth are built through an autoregressive distributed lag (ARDL) model. The empirical findings indicate that there is positive short- and long-run relationship between CO 2 and GDP and thus, no evidence of EKC hypothesis is found for CO 2 in Myanmar. Nevertheless, the existence of the EKC is observed for CH 4 and N 2 O. On the other hand, trade and financial openness have inverse relationship with CO 2 emissions. These results demonstrate that trade liberalization and financial openness will improve the environment quality in Myanmar in the long run.

Renewable Energy Consumption and Economic Growth in Nine OECD Countries: Bounds Test Approach and Causality Analysis

PubMed Central

Hung-Pin, Lin

2014-01-01

The purpose of this paper is to investigate the short-run and long-run causality between renewable energy (RE) consumption and economic growth (EG) in nine OECD countries from the period between 1982 and 2011. To examine the linkage, this paper uses the autoregressive distributed lag (ARDL) bounds testing approach of cointegration test and vector error-correction models to test the causal relationship between variables. The co-integration and causal relationships are found in five countries—United States of America (USA), Japan, Germany, Italy, and United Kingdom (UK). The overall results indicate that (1) a short-run unidirectional causality runs from EG to RE in Italy and UK; (2) long-run unidirectional causalities run from RE to EG for Germany, Italy, and UK; (3) a long-run unidirectional causality runs from EG to RE in USA, and Japan; (4) both long-run and strong unidirectional causalities run from RE to EG for Germany and UK; and (5) Finally, both long-run and strong unidirectional causalities run from EG to RE in only USA. Further evidence reveals that policies for renewable energy conservation may have no impact on economic growth in France, Denmark, Portugal, and Spain. PMID:24558343

Urbanization and carbon dioxide emissions in Singapore: evidence from the ARDL approach.

PubMed

Ali, Hamisu Sadi; Abdul-Rahim, A S; Ribadu, Mohammed Bashir

2017-01-01

The main aim of this article is to examine empirically the impact of urbanization on carbon dioxide emissions in Singapore from 1970 to 2015. The autoregressive distributed lags (ARDL) approach is applied within the analysis. The main finding reveals a negative and significant impact of urbanization on carbon emissions in Singapore, which means that urban development in Singapore is not a barrier to the improvement of environmental quality. Thus, urbanization enhances environmental quality by reducing carbon emissions in the sample country. The result also highlighted that economic growth has a positive and significant impact on carbon emissions, which suggests that economic growth reduces environmental quality through its direct effect of increasing carbon emissions in the country. Despite the high level of urbanization in Singapore, which shows that 100 % of the populace is living in the urban center, it does not lead to more environmental degradation. Hence, urbanization will not be considered an obstacle when initiating policies that will be used to reduce environmental degradation in the country. Policy makers should consider the country's level of economic growth instead of urbanization when formulating policies to reduce environmental degradation, due to its direct impact on increasing carbon dioxide emissions.

Testing the relationships between energy consumption, CO2 emissions, and economic growth in 24 African countries: a panel ARDL approach.

PubMed

Asongu, Simplice; El Montasser, Ghassen; Toumi, Hassen

2016-04-01

This study complements existing literature by examining the nexus between energy consumption (EC), CO2 emissions (CE), and economic growth (GDP; gross domestic product) in 24 African countries using a panel autoregressive distributed lag (ARDL) approach. The following findings are established. First, there is a long-run relationship between EC, CE, and GDP. Second, a long-term effect from CE to GDP and EC is apparent, with reciprocal paths. Third, the error correction mechanisms are consistently stable. However, in cases of disequilibrium, only EC can be significantly adjusted to its long-run relationship. Fourth, there is a long-run causality running from GDP and CE to EC. Fifth, we find causality running from either CE or both CE and EC to GDP, and inverse causal paths are observable. Causality from EC to GDP is not strong, which supports the conservative hypothesis. Sixth, the causal direction from EC to GDP remains unobservable in the short term. By contrast, the opposite path is observable. There are also no short-run causalities from GDP, or EC, or EC, and GDP to EC. Policy implications are discussed.

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

PubMed

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

2013-03-01

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

The influence of chronic health problems on work ability and productivity at work: a longitudinal study among older employees.

PubMed

Leijten, Fenna R M; van den Heuvel, Swenne G; Ybema, Jan Fekke; van der Beek, Allard J; Robroek, Suzan J W; Burdorf, Alex

2014-09-01

This study aimed to assess the influence of chronic health problems on work ability and productivity at work among older employees using different methodological approaches in the analysis of longitudinal studies. Data from employees, aged 45-64, of the longitudinal Study on Transitions in Employment, Ability and Motivation was used (N=8411). Using three annual online questionnaires, we assessed the presence of seven chronic health problems, work ability (scale 0-10), and productivity at work (scale 0-10). Three linear regression generalized estimating equations were used. The time-lag model analyzed the relation of health problems with work ability and productivity at work after one year; the autoregressive model adjusted for work ability and productivity in the preceding year; and the third model assessed the relation of incidence and recovery with changes in work ability and productivity at work within the same year. Workers with health problems had lower work ability at one-year follow-up than workers without these health problems, varying from a 2.0% reduction with diabetes mellitus to a 9.5% reduction with psychological health problems relative to the overall mean (time-lag). Work ability of persons with health problems decreased slightly more during one-year follow-up than that of persons without these health problems, ranging from 1.4% with circulatory to 5.9% with psychological health problems (autoregressive). Incidence related to larger decreases in work ability, from 0.6% with diabetes mellitus to 19.0% with psychological health problems, than recovery related to changes in work ability, from a 1.8% decrease with circulatory to an 8.5% increase with psychological health problems (incidence-recovery). Only workers with musculoskeletal and psychological health problems had lower productivity at work at one-year follow-up than workers without those health problems (1.2% and 5.6%, respectively, time-lag). All methodological approaches indicated that chronic health problems were associated with decreased work ability and, to a much lesser extent, lower productivity at work. The choice for a particular methodological approach considerably influenced the strength of the associations, with the incidence of health problems resulting in the largest decreases in work ability and productivity at work.

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

PubMed

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

2018-03-01

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

Assessing protein conformational sampling methods based on bivariate lag-distributions of backbone angles

PubMed Central

Maadooliat, Mehdi; Huang, Jianhua Z.

2013-01-01

Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence–structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu.edu/∼madoliat/LagSVD) that can be used to produce informative animations. PMID:22926831

Individual cell lag time distributions of Cronobacter (Enterobacter sakazakii) and impact of pooling samples on its detection in powdered infant formula.

PubMed

Miled, Rabeb Bennour; Guillier, Laurent; Neves, Sandra; Augustin, Jean-Christophe; Colin, Pierre; Besse, Nathalie Gnanou

2011-06-01

Cells of six strains of Cronobacter were subjected to dry stress and stored for 2.5 months at ambient temperature. The individual cell lag time distributions of recovered cells were characterized at 25 °C and 37 °C in non-selective broth. The individual cell lag times were deduced from the times taken by cultures from individual cells to reach an optical density threshold. In parallel, growth curves for each strain at high contamination levels were determined in the same growth conditions. In general, the extreme value type II distribution with a shape parameter fixed to 5 (EVIIb) was the most effective at describing the 12 observed distributions of individual cell lag times. Recently, a model for characterizing individual cell lag time distribution from population growth parameters was developed for other food-borne pathogenic bacteria such as Listeria monocytogenes. We confirmed this model's applicability to Cronobacter by comparing the mean and the standard deviation of individual cell lag times to populational lag times observed with high initial concentration experiments. We also validated the model in realistic conditions by studying growth in powdered infant formula decimally diluted in Buffered Peptone Water, which represents the first enrichment step of the standard detection method for Cronobacter. Individual lag times and the pooling of samples significantly affect detection performances. Copyright © 2010 Elsevier Ltd. All rights reserved.

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

PubMed Central

2013-01-01

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

Unintentional Interpersonal Synchronization Represented as a Reciprocal Visuo-Postural Feedback System: A Multivariate Autoregressive Modeling Approach.

PubMed

Okazaki, Shuntaro; Hirotani, Masako; Koike, Takahiko; Bosch-Bayard, Jorge; Takahashi, Haruka K; Hashiguchi, Maho; Sadato, Norihiro

2015-01-01

People's behaviors synchronize. It is difficult, however, to determine whether synchronized behaviors occur in a mutual direction--two individuals influencing one another--or in one direction--one individual leading the other, and what the underlying mechanism for synchronization is. To answer these questions, we hypothesized a non-leader-follower postural sway synchronization, caused by a reciprocal visuo-postural feedback system operating on pairs of individuals, and tested that hypothesis both experimentally and via simulation. In the behavioral experiment, 22 participant pairs stood face to face either 20 or 70 cm away from each other wearing glasses with or without vision blocking lenses. The existence and direction of visual information exchanged between pairs of participants were systematically manipulated. The time series data for the postural sway of these pairs were recorded and analyzed with cross correlation and causality. Results of cross correlation showed that postural sway of paired participants was synchronized, with a shorter time lag when participant pairs could see one another's head motion than when one of the participants was blindfolded. In addition, there was less of a time lag in the observed synchronization when the distance between participant pairs was smaller. As for the causality analysis, noise contribution ratio (NCR), the measure of influence using a multivariate autoregressive model, was also computed to identify the degree to which one's postural sway is explained by that of the other's and how visual information (sighted vs. blindfolded) interacts with paired participants' postural sway. It was found that for synchronization to take place, it is crucial that paired participants be sighted and exert equal influence on one another by simultaneously exchanging visual information. Furthermore, a simulation for the proposed system with a wider range of visual input showed a pattern of results similar to the behavioral results.

Unintentional Interpersonal Synchronization Represented as a Reciprocal Visuo-Postural Feedback System: A Multivariate Autoregressive Modeling Approach

PubMed Central

Okazaki, Shuntaro; Hirotani, Masako; Koike, Takahiko; Bosch-Bayard, Jorge; Takahashi, Haruka K.; Hashiguchi, Maho; Sadato, Norihiro

2015-01-01

People’s behaviors synchronize. It is difficult, however, to determine whether synchronized behaviors occur in a mutual direction—two individuals influencing one another—or in one direction—one individual leading the other, and what the underlying mechanism for synchronization is. To answer these questions, we hypothesized a non-leader-follower postural sway synchronization, caused by a reciprocal visuo-postural feedback system operating on pairs of individuals, and tested that hypothesis both experimentally and via simulation. In the behavioral experiment, 22 participant pairs stood face to face either 20 or 70 cm away from each other wearing glasses with or without vision blocking lenses. The existence and direction of visual information exchanged between pairs of participants were systematically manipulated. The time series data for the postural sway of these pairs were recorded and analyzed with cross correlation and causality. Results of cross correlation showed that postural sway of paired participants was synchronized, with a shorter time lag when participant pairs could see one another’s head motion than when one of the participants was blindfolded. In addition, there was less of a time lag in the observed synchronization when the distance between participant pairs was smaller. As for the causality analysis, noise contribution ratio (NCR), the measure of influence using a multivariate autoregressive model, was also computed to identify the degree to which one’s postural sway is explained by that of the other’s and how visual information (sighted vs. blindfolded) interacts with paired participants’ postural sway. It was found that for synchronization to take place, it is crucial that paired participants be sighted and exert equal influence on one another by simultaneously exchanging visual information. Furthermore, a simulation for the proposed system with a wider range of visual input showed a pattern of results similar to the behavioral results. PMID:26398768

Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China.

PubMed

Du, Zhicheng; Xu, Lin; Zhang, Wangjian; Zhang, Dingmei; Yu, Shicheng; Hao, Yuantao

2017-10-06

Hand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD. Ecological study. Information on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011-2014. Analyses were conducted at aggregate level and no confidential information was involved. A seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI. A high correlation between HFMD incidence and BDI ( r =0.794, p<0.001) or temperature ( r =0.657, p<0.001) was observed using both time series plot and correlation matrix. A linear effect of BDI (without lag) and non-linear effect of temperature (1 week lag) on HFMD incidence were found in a distributed lag non-linear model. Compared with the model based on surveillance data only, the ARIMAX model including BDI reached the best goodness-of-fit with an Akaike information criterion (AIC) value of -345.332, whereas the model including both BDI and temperature had the most accurate prediction in terms of the mean absolute percentage error (MAPE) of 101.745%. An ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China

PubMed Central

Du, Zhicheng; Xu, Lin; Zhang, Wangjian; Zhang, Dingmei; Yu, Shicheng; Hao, Yuantao

2017-01-01

Objectives Hand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD. Design Ecological study. Setting and participants Information on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011–2014. Analyses were conducted at aggregate level and no confidential information was involved. Outcome measures A seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI. Results A high correlation between HFMD incidence and BDI (r=0.794, p<0.001) or temperature (r=0.657, p<0.001) was observed using both time series plot and correlation matrix. A linear effect of BDI (without lag) and non-linear effect of temperature (1 week lag) on HFMD incidence were found in a distributed lag non-linear model. Compared with the model based on surveillance data only, the ARIMAX model including BDI reached the best goodness-of-fit with an Akaike information criterion (AIC) value of −345.332, whereas the model including both BDI and temperature had the most accurate prediction in terms of the mean absolute percentage error (MAPE) of 101.745%. Conclusions An ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings. PMID:28988169

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

PubMed

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

2018-01-01

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

Dealing with Multiple Solutions in Structural Vector Autoregressive Models.

PubMed

Beltz, Adriene M; Molenaar, Peter C M

2016-01-01

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

GSTARI model of BPR assets in West Java, Central Java, and East Java

NASA Astrophysics Data System (ADS)

Susanti, Susi; Sulistijowati Handajani, Sri; Indriati, Diari

2018-05-01

Bank Perkreditan Rakyat (BPR) is a financial institution in Indonesia dealing with Micro, Small, and Medium Enterprises (MSMEs). Though limited to MSMEs, the development of the BPR industry continues to increase. West Java, Central Java, and East Java have high BPR asset development are suspected to be interconnected because of their economic activities as a neighboring provincies. BPR assets are nonstationary time series data that follow the uptrend pattern. Therefore, the suitable model with the data is generalized space time autoregressive integrated (GSTARI) which considers the spatial and time interrelationships. GSTARI model used spatial order 1 and the autoregressive order is obtained of optimal lag which has the smallest value of Akaike information criterion corrected. The correlation test results showed that the location used in this study had a close relationship. Based on the results of model identification, the best model obtained is GSTAR(31)-I(1). The parameter estimation used the ordinary least squares with the selection of significant variables used the stepwise method and the normalization cross correlation weighting. The residual model fulfilled the assumption of white noise and normal multivariate, so the model was appropriate. The average RMSE and MAPE values of the model were 498.75 and 2.48%.

Modelling of capital asset pricing by considering the lagged effects

NASA Astrophysics Data System (ADS)

Sukono; Hidayat, Y.; Bon, A. Talib bin; Supian, S.

2017-01-01

In this paper the problem of modelling the Capital Asset Pricing Model (CAPM) with the effect of the lagged is discussed. It is assumed that asset returns are analysed influenced by the market return and the return of risk-free assets. To analyse the relationship between asset returns, the market return, and the return of risk-free assets, it is conducted by using a regression equation of CAPM, and regression equation of lagged distributed CAPM. Associated with the regression equation lagged CAPM distributed, this paper also developed a regression equation of Koyck transformation CAPM. Results of development show that the regression equation of Koyck transformation CAPM has advantages, namely simple as it only requires three parameters, compared with regression equation of lagged distributed CAPM.

Relations Among Intimate Partner Violence, Maternal Depressive Symptoms, and Maternal Parenting Behaviors

PubMed Central

Gustafsson, Hanna C.; Cox, Martha J.

2013-01-01

The authors examined the relations among intimate partner violence (IPV), maternal depressive symptoms, and maternal harsh intrusive parenting. Using a cross-lagged, autoregressive path model, they sought to clarify the directionality of the relations among these 3 variables over the first 2 years of the child’s life. The results indicated that, in this diverse sample of families living in predominantly low-income rural communities (N = 705), higher levels of early IPV were associated with increases in maternal depressive symptoms, which in turn were associated with increases in maternal harsh intrusive parenting behaviors. These findings suggest that interventions aimed at improving the parenting of women exposed to domestic violence may want to simultaneously target IPV and depressive symptomatology. PMID:23869110

Relationships among Energy Price Shocks, Stock Market, and the Macroeconomy: Evidence from China

PubMed Central

Cong, Rong-Gang; Shen, Shaochuan

2013-01-01

This paper investigates the interactive relationships among China energy price shocks, stock market, and the macroeconomy using multivariate vector autoregression. The results indicate that there is a long cointegration among them. A 1% rise in the energy price index can depress the stock market index by 0.54% and the industrial value-adding growth by 0.037%. Energy price shocks also cause inflation and have a 5-month lag effect on stock market, which may result in the stock market “underreacting.” The energy price can explain stock market fluctuations better than the interest rate over a longer time period. Consequently, investors should pay greater attention to the long-term effect of energy on the stock market. PMID:23690737

Influenza newspaper reports and the influenza epidemic: an observational study in Fukuoka City, Japan.

PubMed

Hagihara, Akihito; Onozuka, Daisuke; Miyazaki, Shougo; Abe, Takeru

2015-12-30

We examined whether the weekly number of newspaper articles reporting on influenza was related to the incidence of influenza in a large city. Prospective, non-randomised, observational study. Registry data of influenza cases in Fukuoka City, Japan. A total of 83,613 cases of influenza cases that occurred between October 1999 and March 2007 in Fukuoka City, Japan. A linear model with autoregressive time series errors was fitted to time series data on the incidence of influenza and the accumulated number of influenza-related newspaper articles with different time lags in Fukuoka City, Japan. In order to obtain further evidence that the number of newspaper articles a week with specific time lags is related to the incidence of influenza, Granger causality was also tested. Of the 16 models including 'number of newspaper articles' with different time lags between 2 and 17 weeks (xt-2 to t-17), the β coefficients of 'number of newspaper articles' at time lags between t-5 and t-13 were significant. However, the β coefficients of 'number of newspaper articles' that are significant with respect to the Granger causality tests (p<0.05) were the weekly number of newspaper articles at time lags between t-6 and t-10 (time shift of 10 weeks, β=-0.301, p<0.01; time shift of 9 weeks, β=-0.200, p<0.01; time shift of 8 weeks, β=-0.156, p<0.01; time shift of 7 weeks, β=-0.122, p<0.05; time shift of 6 weeks, β=-0.113, p<0.05). We found that the number of newspaper articles reporting on influenza in a week was related to the incidence of influenza 6-10 weeks after media coverage in a large city in Japan. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Influenza newspaper reports and the influenza epidemic: an observational study in Fukuoka City, Japan

PubMed Central

Hagihara, Akihito; Onozuka, Daisuke; Miyazaki, Shougo; Abe, Takeru

2015-01-01

Objectives We examined whether the weekly number of newspaper articles reporting on influenza was related to the incidence of influenza in a large city. Design Prospective, non-randomised, observational study. Setting Registry data of influenza cases in Fukuoka City, Japan. Participants A total of 83 613 cases of influenza cases that occurred between October 1999 and March 2007 in Fukuoka City, Japan. Main outcome measure A linear model with autoregressive time series errors was fitted to time series data on the incidence of influenza and the accumulated number of influenza-related newspaper articles with different time lags in Fukuoka City, Japan. In order to obtain further evidence that the number of newspaper articles a week with specific time lags is related to the incidence of influenza, Granger causality was also tested. Results Of the 16 models including ‘number of newspaper articles’ with different time lags between 2 and 17 weeks (xt-2 to t-17), the β coefficients of ‘number of newspaper articles’ at time lags between t-5 and t-13 were significant. However, the β coefficients of ‘number of newspaper articles’ that are significant with respect to the Granger causality tests (p<0.05) were the weekly number of newspaper articles at time lags between t-6 and t-10 (time shift of 10 weeks, β=−0.301, p<0.01; time shift of 9 weeks, β=−0.200, p<0.01; time shift of 8 weeks, β=−0.156, p<0.01; time shift of 7 weeks, β=−0.122, p<0.05; time shift of 6 weeks, β=−0.113, p<0.05). Conclusions We found that the number of newspaper articles reporting on influenza in a week was related to the incidence of influenza 6–10 weeks after media coverage in a large city in Japan. PMID:26719323

Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models

NASA Astrophysics Data System (ADS)

Dettmer, Jan; Molnar, Sheri; Steininger, Gavin; Dosso, Stan E.; Cassidy, John F.

2012-02-01

This paper applies a general trans-dimensional Bayesian inference methodology and hierarchical autoregressive data-error models to the inversion of microtremor array dispersion data for shear wave velocity (vs) structure. This approach accounts for the limited knowledge of the optimal earth model parametrization (e.g. the number of layers in the vs profile) and of the data-error statistics in the resulting vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the indexing parameter) are considered in the results. The earth model is parametrized in terms of a partition model with interfaces given over a depth-range of interest. In this work, the number of interfaces (layers) in the partition model represents the trans-dimensional model indexing. In addition, serial data-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate data-error statistics, and have no requirement for computing the inverse or determinant of a data-error covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the state space that spans multiple subspaces of different dimensionalities. The order of the autoregressive process required to fit the data is determined here by posterior residual-sample examination and statistical tests. Inference for earth model parameters is carried out on the trans-dimensional posterior probability distribution by considering ensembles of parameter vectors. In particular, vs uncertainty estimates are obtained by marginalizing the trans-dimensional posterior distribution in terms of vs-profile marginal distributions. The methodology is applied to microtremor array dispersion data collected at two sites with significantly different geology in British Columbia, Canada. At both sites, results show excellent agreement with estimates from invasive measurements.

Climate variables as predictors for seasonal forecast of dengue occurrence in Chennai, Tamil Nadu

NASA Astrophysics Data System (ADS)

Subash Kumar, D. D.; Andimuthu, R.

2013-12-01

Background Dengue is a recently emerging vector borne diseases in Chennai. As per the WHO report in 2011 dengue is one of eight climate sensitive disease of this century. Objective Therefore an attempt has been made to explore the influence of climate parameters on dengue occurrence and use for forecasting. Methodology Time series analysis has been applied to predict the number of dengue cases in Chennai, a metropolitan city which is the capital of Tamil Nadu, India. Cross correlation of the climate variables with dengue cases revealed that the most influential parameters were monthly relative humidity, minimum temperature at 4 months lag and rainfall at one month lag (Table 1). However due to intercorrelation of relative humidity and rainfall was high and therefore for predictive purpose the rainfall at one month lag was used for the model development. Autoregressive Integrated Moving Average (ARIMA) models have been applied to forecast the occurrence of dengue. Results and Discussion The best fit model was ARIMA (1,0,1). It was seen that the monthly minimum temperature at four months lag (β= 3.612, p = 0.02) and rainfall at one month lag (β= 0.032, p = 0.017) were associated with dengue occurrence and they had a very significant effect. Mean Relative Humidity had a directly significant positive correlation at 99% confidence level, but the lagged effect was not prominent. The model predicted dengue cases showed significantly high correlation of 0.814(Figure 1) with the observed cases. The RMSE of the model was 18.564 and MAE was 12.114. The model is limited by the scarcity of the dataset. Inclusion of socioeconomic conditions and population offset are further needed to be incorporated for effective results. Conclusion Thus it could be claimed that the change in climatic parameters is definitely influential in increasing the number of dengue occurrence in Chennai. The climate variables therefore can be used for seasonal forecasting of dengue with rise in minimum temperature and rainfall at a city level. Table 1. Cross correlation of climate variables with dengue cases in Chennai ** p<0.01,*p<0.05

The relationship between CO2 emission, energy consumption and economic growth in Malaysia: a three-way linkage approach.

PubMed

Sulaiman, Chindo; Abdul-Rahim, A S

2017-11-01

This study examines the three-way linkage relationships between CO 2 emission, energy consumption and economic growth in Malaysia, covering the 1975-2015 period. An autoregressive distributed lag approach was employed to achieve the objective of the study and gauged by dynamic ordinary least squares. Additionally, vector error correction model, variance decompositions and impulse response functions were employed to further examine the relationship between the interest variables. The findings show that economic growth is neither influenced by energy consumption nor by CO 2 emission. Energy consumption is revealed to be an increasing function of CO 2 emission. Whereas, CO 2 emission positively and significantly depends on energy consumption and economic growth. This implies that CO 2 emission increases with an increase in both energy consumption and economic growth. Conclusively, the main drivers of CO 2 emission in Malaysia are proven to be energy consumption and economic growth. Therefore, renewable energy sources ought to be considered by policy makers to curb emission from the current non-renewable sources. Wind and biomass can be explored as they are viable sources. Energy efficiency and savings should equally be emphasised and encouraged by policy makers. Lastly, growth-related policies that target emission reduction are also recommended.

Investigating the asymmetric relationship between inflation-output growth exchange rate changes

NASA Astrophysics Data System (ADS)

Chu, Jenq Fei; Sek, Siok Kun

2017-08-01

The relationship between inflation-output growth or output variation has long been studied. In this study, we extend the investigation under two exchange rate flexibility/regime in four Asian countries (Indonesia, Korea, Philippines and Thailand) that have experienced drastic exchange rate regime changes aftermath the financial crisis of 1997. These countries have switched from fixed/rigid exchange rate regime to flexible exchange rate and inflation targeting (IT) regime after the crisis. Our main objective is to compare the inflation-output trade-off relationship in the pre-IT and post-IT periods as a tool to evaluate the efficiency of monetary policy. A nonlinear autoregressive distributed lags (NARDL) model is applied to capture the asymmetric effects of exchange rate changes (increases and decreases). The data ranging from 1981M1 onwards till 2016M3. Our results show that exchange rate has asymmetric effect on inflation both short-run and long-run with larger impact in the post-IT period under flexible regime. Depreciation of exchange rate has leads to higher inflation. Furthermore, we find evidences on the relationship between inflation and growth in both short-run and long-run, but the trade-off only detected in the short run both in the pre- and post-IT periods.

Towards simplification of hydrologic modeling: Identification of dominant processes

USGS Publications Warehouse

Markstrom, Steven; Hay, Lauren E.; Clark, Martyn P.

2016-01-01

The Precipitation–Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many

Dynamic impact of urbanization, economic growth, energy consumption, and trade openness on CO 2 emissions in Nigeria.

PubMed

Ali, Hamisu Sadi; Law, Siong Hook; Zannah, Talha Ibrahim

2016-06-01

The objective of this paper is to examine the dynamic impact of urbanization, economic growth, energy consumption, and trade openness on CO 2 emissions in Nigeria based on autoregressive distributed lags (ARDL) approach for the period of 1971-2011. The result shows that variables were cointegrated as null hypothesis was rejected at 1 % level of significance. The coefficients of long-run result reveal that urbanization does not have any significant impact on CO 2 emissions in Nigeria, economic growth, and energy consumption has a positive and significant impact on CO 2 emissions. However, trade openness has negative and significant impact on CO 2 emissions. Consumption of energy is among the main determinant of CO 2 emissions which is directly linked to the level of income. Despite the high level of urbanization in the country, consumption of energy still remains low due to lower income of the majority populace and this might be among the reasons why urbanization does not influence emissions of CO 2 in the country. Initiating more open economy policies will be welcoming in the Nigerian economy as the openness leads to the reduction of pollutants from the environment particularly CO 2 emissions which is the major gases that deteriorate physical environment.

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality

NASA Astrophysics Data System (ADS)

Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B. M. J.

2018-07-01

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship.

Order-Constrained Reference Priors with Implications for Bayesian Isotonic Regression, Analysis of Covariance and Spatial Models

NASA Astrophysics Data System (ADS)

Gong, Maozhen

Selecting an appropriate prior distribution is a fundamental issue in Bayesian Statistics. In this dissertation, under the framework provided by Berger and Bernardo, I derive the reference priors for several models which include: Analysis of Variance (ANOVA)/Analysis of Covariance (ANCOVA) models with a categorical variable under common ordering constraints, the conditionally autoregressive (CAR) models and the simultaneous autoregressive (SAR) models with a spatial autoregression parameter rho considered. The performances of reference priors for ANOVA/ANCOVA models are evaluated by simulation studies with comparisons to Jeffreys' prior and Least Squares Estimation (LSE). The priors are then illustrated in a Bayesian model of the "Risk of Type 2 Diabetes in New Mexico" data, where the relationship between the type 2 diabetes risk (through Hemoglobin A1c) and different smoking levels is investigated. In both simulation studies and real data set modeling, the reference priors that incorporate internal order information show good performances and can be used as default priors. The reference priors for the CAR and SAR models are also illustrated in the "1999 SAT State Average Verbal Scores" data with a comparison to a Uniform prior distribution. Due to the complexity of the reference priors for both CAR and SAR models, only a portion (12 states in the Midwest) of the original data set is considered. The reference priors can give a different marginal posterior distribution compared to a Uniform prior, which provides an alternative for prior specifications for areal data in Spatial statistics.

Using the Quantile Mapping to improve a weather generator

NASA Astrophysics Data System (ADS)

Chen, Y.; Themessl, M.; Gobiet, A.

2012-04-01

We developed a weather generator (WG) by using statistical and stochastic methods, among them are quantile mapping (QM), Monte-Carlo, auto-regression, empirical orthogonal function (EOF). One of the important steps in the WG is using QM, through which all the variables, no matter what distribution they originally are, are transformed into normal distributed variables. Therefore, the WG can work on normally distributed variables, which greatly facilitates the treatment of random numbers in the WG. Monte-Carlo and auto-regression are used to generate the realization; EOFs are employed for preserving spatial relationships and the relationships between different meteorological variables. We have established a complete model named WGQM (weather generator and quantile mapping), which can be applied flexibly to generate daily or hourly time series. For example, with 30-year daily (hourly) data and 100-year monthly (daily) data as input, the 100-year daily (hourly) data would be relatively reasonably produced. Some evaluation experiments with WGQM have been carried out in the area of Austria and the evaluation results will be presented.

Testing Alternative Explanations for the Associations Between Parenting and Adolescent Suicidal Problems

PubMed Central

Boeninger, Daria K.; Masyn, Katherine E.; Conger, Rand D.

2012-01-01

Although studies have established associations between parenting characteristics and adolescent suicidality, the strength of the evidence for these links remains unclear, largely because of methodological limitations, including lack of accounting for possible child effects on parenting. This study addresses these issues by using autoregressive cross-lag models with data on 802 adolescents and their parents across 5 years. Observed parenting behaviors predicted change in adolescent suicidal problems across one-year intervals even after controlling for adolescents’ effects on parenting. Nurturant-involved parenting continued to demonstrate salutary effects after controlling for adolescent and parent internalizing psychopathology: over time, observed nurturant-involved parenting reduced the likelihood of adolescent suicidal problems. This study increases the empirical support implicating parenting behaviors in the developmental course of adolescent suicidality. PMID:24244079

Attribution of precipitation changes on ground-air temperature offset: Granger causality analysis

NASA Astrophysics Data System (ADS)

Cermak, Vladimir; Bodri, Louise

2018-01-01

This work examines the causal relationship between the value of the ground-air temperature offset and the precipitation changes for monitored 5-min data series together with their hourly and daily averages obtained at the Sporilov Geophysical Observatory (Prague). Shallow subsurface soil temperatures were monitored under four different land cover types (bare soil, sand, short-cut grass and asphalt). The ground surface temperature (GST) and surface air temperature (SAT) offset, Δ T(GST-SAT), is defined as the difference between the temperature measured at the depth of 2 cm below the surface and the air temperature measured at 5 cm above the surface. The results of the Granger causality test did not reveal any evidence of Granger causality for precipitation to ground-air temperature offsets on the daily scale of aggregation except for the asphalt pavement. On the contrary, a strong evidence of Granger causality for precipitation to the ground-air temperature offsets was found on the hourly scale of aggregation for all land cover types except for the sand surface cover. All results are sensitive to the lag choice of the autoregressive model. On the whole, obtained results contain valuable information on the delay time of Δ T(GST-SAT) caused by the rainfall events and confirmed the importance of using autoregressive models to understand the ground-air temperature relationship.

Water balance models in one-month-ahead streamflow forecasting

USGS Publications Warehouse

Alley, William M.

1985-01-01

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

Nanoflare vs Footpoint Heating : Observational Signatures

NASA Technical Reports Server (NTRS)

Winebarger, Amy; Alexander, Caroline; Lionello, Roberto; Linker, Jon; Mikic, Zoran; Downs, Cooper

2015-01-01

Time lag analysis shows very long time lags between all channel pairs. Impulsive heating cannot address these long time lags. 3D Simulations of footpoint heating shows a similar pattern of time lags (magnitude and distribution) to observations. Time lags and relative peak intensities may be able to differentiate between TNE and impulsive heating solutions. Adding a high temperature channel (like XRT Be-­thin) may improve diagnostics.

Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis.

PubMed

Tohidinik, Hamid Reza; Mohebali, Mehdi; Mansournia, Mohammad Ali; Niakan Kalhori, Sharareh R; Ali-Akbarpour, Mohsen; Yazdani, Kamran

2018-05-22

To predict the occurrence of zoonotic cutaneous leishmaniasis (ZCL) and evaluate the effect of climatic variables on disease incidence in the east of Fars province, Iran using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The Box-Jenkins approach was applied to fit the SARIMA model for ZCL incidence from 2004 to 2015. Then the model was used to predict the number of ZCL cases for the year 2016. Finally, we assessed the relation of meteorological variables (rainfall, rainy days, temperature, hours of sunshine and relative humidity) with ZCL incidence. SARIMA(2,0,0) (2,1,0)12 was the preferred model for predicting ZCL incidence in the east of Fars province (validation Root Mean Square Error, RMSE = 0.27). It showed that ZCL incidence in a given month can be estimated by the number of cases occurring 1 and 2 months, as well as 12 and 24 months earlier. The predictive power of SARIMA models was improved by the inclusion of rainfall at a lag of 2 months (β = -0.02), rainy days at a lag of 2 months (β = -0.09) and relative humidity at a lag of 8 months (β = 0.13) as external regressors (P-values < 0.05). The latter was the best climatic variable for predicting ZCL cases (validation RMSE = 0.26). Time series models can be useful tools to predict the trend of ZCL in Fars province, Iran; thus, they can be used in the planning of public health programmes. Introducing meteorological variables into the models may improve their precision. © 2018 John Wiley & Sons Ltd.

Burst Statistics Using the Lag-Luminosity Relationship

NASA Technical Reports Server (NTRS)

Band, D. L.; Norris, J. P.; Bonnell, J. T.

2003-01-01

Using the lag-luminosity relation and various BATSE catalogs we create a large catalog of burst redshifts, peak luminosities and emitted energies. These catalogs permit us to evaluate the lag-luminosity relation, and to study the burst energy distribution. We find that this distribution can be described as a power law with an index of alpha = 1.76 +/- 0.05 (95% confidence), close to the alpha = 2 predicted by the original quasi-universal jet model.

Estimation of daily reference evapotranspiration (ETo) using artificial intelligence methods: Offering a new approach for lagged ETo data-based modeling

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid

2018-04-01

Evapotranspiration (ET) is considered as a key factor in hydrological and climatological studies, agricultural water management, irrigation scheduling, etc. It can be directly measured using lysimeters. Moreover, other methods such as empirical equations and artificial intelligence methods can be used to model ET. In the recent years, artificial intelligence methods have been widely utilized to estimate reference evapotranspiration (ETo). In the present study, local and external performances of multivariate adaptive regression splines (MARS) and gene expression programming (GEP) were assessed for estimating daily ETo. For this aim, daily weather data of six stations with different climates in Iran, namely Urmia and Tabriz (semi-arid), Isfahan and Shiraz (arid), Yazd and Zahedan (hyper-arid) were employed during 2000-2014. Two types of input patterns consisting of weather data-based and lagged ETo data-based scenarios were considered to develop the models. Four statistical indicators including root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE) were used to check the accuracy of models. The local performance of models revealed that the MARS and GEP approaches have the capability to estimate daily ETo using the meteorological parameters and the lagged ETo data as inputs. Nevertheless, the MARS had the best performance in the weather data-based scenarios. On the other hand, considerable differences were not observed in the models' accuracy for the lagged ETo data-based scenarios. In the innovation of this study, novel hybrid models were proposed in the lagged ETo data-based scenarios through combination of MARS and GEP models with autoregressive conditional heteroscedasticity (ARCH) time series model. It was concluded that the proposed novel models named MARS-ARCH and GEP-ARCH improved the performance of ETo modeling compared to the single MARS and GEP. In addition, the external analysis of the performance of models at stations with similar climatic conditions denoted the applicability of nearby station' data for estimation of the daily ETo at target station.

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

Santini, Danilo J.; Poyer, David A.

Vector error correction (VEC) was used to test the importance of a theoretical causal chain from transportation fuel cost to vehicle sales to macroeconomic activity. Real transportation fuel cost was broken into two cost components: real gasoline price (rpgas) and real personal consumption of gasoline and other goods (gas). Real personal consumption expenditure on vehicles (RMVE) represented vehicle sales. Real gross domestic product (rGDP) was used as the measure of macroeconomic activity. The VEC estimates used quarterly data from the third quarter of 1952 to the first quarter of 2014. Controlling for the financial causes of the recent Great Recession,more » real homeowners’ equity (equity) and real credit market instruments liability (real consumer debt, rcmdebt) were included. Results supported the primary hypothesis of the research, but also introduced evidence that another financial path through equity is important, and that use of the existing fleet of vehicles (not just sales of vehicles) is an important transport-related contributor to macroeconomic activity. Consumer debt reduction is estimated to be a powerful short-run force reducing vehicle sales. Findings are interpreted in the context of the recent Greene, Lee, and Hopson (2012) (hereafter GLH) estimation of the magnitude of three distinct macroeconomic damage effects that result from dependence on imported oil, the price of which is manipulated by the Organization of Petroleum Exporting Countries (OPEC). The three negative macroeconomic impacts are due to (1) dislocation (positive oil price shock), (2) high oil price levels, and (3) a high value of the quantity of oil imports times an oil price delta (cartel price less competitive price). The third of these is the wealth effect. The VEC model addresses the first two, but the software output from the model (impulse response plots) does not isolate them. Nearly all prior statistical tests in the literature have used vector autoregression (VAR) and autoregressive distributed lag models that considered effects of oil price changes, but did not account for effects of oil price levels. Gasoline prices were rarely examined. The tests conducted in this report evaluate gasoline instead of oil.« less

Modeling Seasonality in Carbon Dioxide Emissions From Fossil Fuel Consumption

NASA Astrophysics Data System (ADS)

Gregg, J. S.; Andres, R. J.

2004-05-01

Using United States data, a method is developed to estimate the monthly consumption of solid, liquid and gaseous fossil fuels using monthly sales data to estimate the relative monthly proportions of the total annual national fossil fuel use. These proportions are then used to estimate the total monthly carbon dioxide emissions for each state. From these data, the goal is to develop mathematical models that describe the seasonal flux in consumption for each type of fuel, as well as the total emissions for the nation. The time series models have two components. First, the general long-term yearly trend is determined with regression models for the annual totals. After removing the general trend, two alternatives are considered for modeling the seasonality. The first alternative uses the mean of the monthly proportions to predict the seasonal distribution. Because the seasonal patterns are fairly consistent in the United States, this is an effective modeling technique. Such regularity, however, may not be present with data from other nations. Therefore, as a second alternative, an ordinary least squares autoregressive model is used. This model is chosen for its ability to accurately describe dependent data and for its predictive capacity. It also has a meaningful interpretation, as each coefficient in the model quantifies the dependency for each corresponding time lag. Most importantly, it is dynamic, and able to adapt to anomalies and changing patterns. The order of the autoregressive model is chosen by the Akaike Information Criterion (AIC), which minimizes the predicted variance for all models of increasing complexity. To model the monthly fuel consumption, the annual trend is combined with the seasonal model. The models for each fuel type are then summed together to predict the total carbon dioxide emissions. The prediction error is estimated with the root mean square error (RMSE) from the actual estimated emission values. Overall, the models perform very well, with relative RMSE less than 10% for all fuel types, and under 5% for the national total emissions. Development of successful models is important to better understand and predict global environmental impacts from fossil fuel consumption.

Longitudinal associations between adult children's relations with parents and intimate partners.

PubMed

Johnson, Matthew D; Galovan, Adam M; Horne, Rebecca M; Min, Joohong; Walper, Sabine

2017-10-01

Drawing on 5 waves of multiple-informant data gathered from focal participants and their parents and intimate partners (n = 360 families) who completed annual surveys in the German Family Panel (pairfam) study, the present investigation examined bidirectional associations between the development of adults' conflictual and intimate interactions with their parents and intimate partners. Autoregressive cross-lagged latent change score modeling results revealed a robust pattern of coordinated development between parent-adult child and couple conflictual and intimate interactions: increases in conflict and intimacy in one relationship were contemporaneously intertwined with changes in the other relationship. Additionally, prior couple intimacy and conflict predicted future parent-adult child relations in 7 out of 14 cross-lagged pathways examined, but parent-adult child conflict and intimacy was only associated with future couple interactions in 1 pathway. These associations were not moderated by the gender of parents or the adult child or whether the adult child was a young adult or nearing midlife. Frequency of contact between parents and the adult child moderated some associations. Adults simultaneously juggle ties with parents and intimate partners, and this study provides strong evidence supporting the coordinated development of conflictual and intimate patterns of interaction in each relationship. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Perceived Control and Social Activity in Midlife and Older Age: A Reciprocal Association? Findings From the German Ageing Survey.

PubMed

Curtis, Rachel G; Huxhold, Oliver; Windsor, Tim D

2018-06-14

Perceived control may promote social activity in older adults because individuals with greater perceived control have greater confidence in their ability to achieve outcomes and are more likely to choose difficult activities, show persistence, and employ strategies to overcome challenges. Cross-sectional research has linked perceived control with social activity in life span and older adult samples but provides little insight into the direction of influence. We examined reciprocal associations between perceived control and social activity in order to establish temporal sequencing, which is one prerequisite for determining potential causation. Participants were 14,126 midlife and older adults from the German Ageing Survey. Using cross-lagged autoregressive modeling with age as the time metric (40-87 years), we examined reciprocal 3-year lagged associations between perceived control and social activity, while controlling for concurrent associations. Perceived control significantly predicted social activity 3 years later. Reciprocally, social activity significantly predicted perceived control 3 years later. The influence of perceived control on social activity was greater than the influence of social activity on perceived control. The finding that perceived control significantly predicts future social activity has potential implications for developing interventions aimed at promoting social activity in midlife and older adults.

Gender, space, and the location changes of jobs and people: a spatial simultaneous equations analysis.

PubMed

Hoogstra, Gerke J

2012-01-01

This article summarizes a spatial econometric analysis of local population and employment growth in the Netherlands, with specific reference to impacts of gender and space. The simultaneous equations model used distinguishes between population- and gender-specific employment groups, and includes autoregressive and cross-regressive spatial lags to detect relations both within and among these groups. Spatial weights matrices reflecting different bands of travel times are used to calculate the spatial lags and to gauge the spatial nature of these relations. The empirical results show that although population–employment interaction is more localized for women's employment, no gender difference exists in the direction of interaction. Employment growth for both men and women is more influenced by population growth than vice versa. The interaction within employment groups is even more important than population growth. Women's, and especially men's, local employment growth mostly benefits from the same employment growth in neighboring locations. Finally, interaction between these groups is practically absent, although men's employment growth may have a negative impact on women's employment growth within small geographic areas. In summary, the results confirm the crucial roles of gender and space, and offer important insights into possible relations within and among subgroups of jobs and people.

How much does the time lag between wildlife field-data collection and LiDAR-data acquisition matter for studies of animal distributions? A case study using bird communities

Treesearch

Kerri T. Vierling; Charles E. Swift; Andrew T. Hudak; Jody C. Vogeler; Lee A. Vierling

2014-01-01

Vegetation structure quantified by light detection and ranging (LiDAR) can improve understanding of wildlife occupancy and species-richness patterns. However, there is often a time lag between the collection of LiDAR data and wildlife data. We investigated whether a time lag between the LiDAR acquisition and field-data acquisition affected mapped wildlife distributions...

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-01-01

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

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.

Implications of Lag-Luminosity Relationship for Unified GRB Paradigms

NASA Technical Reports Server (NTRS)

Norris, J. P.; White, Nicholas E. (Technical Monitor)

2002-01-01

Spectral lags (tau(sub lag)) are deduced for 1437 long (T(sub 90) greater than 2 s) BATSE gamma-ray bursts (GRBs) with peak flux F(sub p) greater than 0.25 photons cm(sup -2)/s, near to the BATSE trigger threshold. The lags are modeled to approximate the observed distribution in the F(sub p)-T(sub lag) plane, realizing a noise-free representation. Assuming a two-branch lag-luminosity relationship, the lags are self-consistently corrected for cosmological effects to yield distributions in luminosity, distance, and redshift. The results have several consequences for GRB populations and for unified gamma-ray/afterglow scenarios which would account for afterglow break times and gamma-ray spectral evolution in terms of jet opening angle, viewing angle, or a profiled jet with variable Lorentz factor: A component of the burst sample is identified - those with few, wide pulses, lags of a few tenths to several seconds, and soft spectra - whose Log[N]-Log[F(sub p)] distribution approximates a -3/2 power-law, suggesting homogeneity and thus relatively nearby sources. The proportion of these long-lag bursts increases from negligible among bright BATSE bursts to approx. 50% at trigger threshold. Bursts with very long lags, approx. 1-2 less than tau(sub lag) (S) less than 10, show a tendency to concentrate near the Supergalactic Plane with a quadrupole moment of approx. -0.10 +/- 0.04. GRB 980425 (SN 1998bw) is a member of this subsample of approx. 90 bursts with estimated distances less than 100 Mpc. The frequency of the observed ultra-low luminosity bursts is approx. 1/4 that of SNe Ib/c within the same volume. If truly nearby, the core-collapse events associated with these GRBs might produce gravitational radiation detectable by LIGO-II. Such nearby bursts might also help explain flattening of the cosmic ray spectrum at ultra-high energies, as observed by AGASA.

Determinant of Road Traffic Crash Fatalities in Iran: A Longitudinal Econometric Analysis.

PubMed

Rezaei, Satar; Bagheri Lankarani, Kamran; Karami Matin, Behzad; Bazyar, Mohammad; Hamzeh, Behrooz; Najafi, Farid

2015-01-01

Injuries and deaths from road traffic crashes are one of the main public health problems throughout the world. This study aimed to identify determinants of fatality traffic accident in Iran for the twenty-span year from 1991 to 2011. A time series analysis (1991-2011) was used to examine the effects of some of the key explanatory factors (GDP per capita, number of doctors per 10,000 populations, degree of urbanization, unemployment rate and motorization rate) on deaths from road traffic in Iran. In order to examine long- and short-run effects of variables, we employed autoregressive distributed lag (ARDL) approach and error correction method (ECM). The data for the study was obtained from the Central Bank of Iran (CBI), Iranian Statistical Center (ISC) and Legal medical organizations (LMO). GDP per capita, doctor per 10,000 populations, degree of urbanization and motorization rate had a significant impact on fatality from road traffic in Iran. We did not observe any short- and long-term effects of the unemployment rate on fatality from road traffic. GDP per capita, doctor per 10,000 populations, degree of urbanization and motorization rate were identified as main determinant of fatality from road traffic accidents in Iran. We hope the results of the current study enable health policy-makers to understand better the factors affecting deaths from road traffic accidents in the country.

Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-07-01

In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future.

Decoupling emissions of greenhouse gas, urbanization, energy and income: analysis from the economy of China.

PubMed

Wang, Tianqiong; Riti, Joshua Sunday; Shu, Yang

2018-05-08

The adoption and ratification of relevant policies, particularly the household enrolment system metamorphosis in China, led to rising urbanization growth. As the leading developing economy, China has experienced a drastic and rapid increase in the rate of urbanization, energy use, economic growth and greenhouse gas (GHG) pollution for the past 30 years. The knowledge of the dynamic interrelationships among these trends has a plethora of implications ranging from demographic, energy, and environmental and sustainable development policies. This study analyzes the role of urbanization in decoupling GHG emissions, energy, and income in China while considering the critical contribution of energy use. As a contribution to the extant body of literature, the present research introduces a new phenomenon called "the environmental urbanization Kuznets curve" (EUKC), which shows that at the early stage of urbanization, the environment degrades however, after a threshold point the technique effects surface and environmental degradation reduces with rise in urbanization. Applying the autoregressive distributed lag model and the vector error correction model, the paper finds the presence of inverted U-shaped curve between urbanization and GHG emission of CO 2 , while the same hypothesis cannot be found between income and GHG emission of CO 2 . Energy use in all the models contributes to GHG emission of CO 2 . In decoupling greenhouse gas emissions, urbanization, energy, and income, articulated and well-implemented energy and urbanization policies should be considered.

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality.

PubMed

Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B M J

2018-07-01

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship. Copyright © 2018. Published by Elsevier B.V.

Optimal and centralized reservoir management for drought and flood protection via Stochastic Dual Dynamic Programming on the Upper Seine-Aube River system

NASA Astrophysics Data System (ADS)

Chiavico, Mattia; Raso, Luciano; Dorchies, David; Malaterre, Pierre-Olivier

2015-04-01

Seine river region is an extremely important logistic and economic junction for France and Europe. The hydraulic protection of most part of the region relies on four controlled reservoirs, managed by EPTB Seine-Grands Lacs. Presently, reservoirs operation is not centrally coordinated, and release rules are based on empirical filling curves. In this study, we analyze how a centralized release policy can face flood and drought risks, optimizing water system efficiency. The optimal and centralized decisional problem is solved by Stochastic Dual Dynamic Programming (SDDP) method, minimizing an operational indicator for each planning objective. SDDP allows us to include into the system: 1) the hydrological discharge, specifically a stochastic semi-distributed auto-regressive model, 2) the hydraulic transfer model, represented by a linear lag and route model, and 3) reservoirs and diversions. The novelty of this study lies on the combination of reservoir and hydraulic models in SDDP for flood and drought protection problems. The study case covers the Seine basin until the confluence with Aube River: this system includes two reservoirs, the city of Troyes, and the Nuclear power plant of Nogent-Sur-Seine. The conflict between the interests of flood protection, drought protection, water use and ecology leads to analyze the environmental system in a Multi-Objective perspective.

Decomposing the trade-environment nexus for Malaysia: what do the technique, scale, composition, and comparative advantage effect indicate?

PubMed

Ling, Chong Hui; Ahmed, Khalid; Binti Muhamad, Rusnah; Shahbaz, Muhammad

2015-12-01

This paper investigates the impact of trade openness on CO2 emissions using time series data over the period of 1970QI-2011QIV for Malaysia. We disintegrate the trade effect into scale, technique, composition, and comparative advantage effects to check the environmental consequence of trade at four different transition points. To achieve the purpose, we have employed augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests in order to examine the stationary properties of the variables. Later, the long-run association among the variables is examined by applying autoregressive distributed lag (ARDL) bounds testing approach to cointegration. Our results confirm the presence of cointegration. Further, we find that scale effect has positive and technique effect has negative impact on CO2 emissions after threshold income level and form inverted U-shaped relationship-hence validates the environmental Kuznets curve hypothesis. Energy consumption adds in CO2 emissions. Trade openness and composite effect improve environmental quality by lowering CO2 emissions. The comparative advantage effect increases CO2 emissions and impairs environmental quality. The results provide the innovative approach to see the impact of trade openness in four sub-dimensions of trade liberalization. Hence, this study attributes more comprehensive policy tool for trade economists to better design environmentally sustainable trade rules and agreements.

Analysis of asymmetries in air pollution with water resources, and energy consumption in Iran.

PubMed

Ashouri, Mohammad Javad; Rafei, Meysam

2018-04-17

Iran should pay special attention to its excessive consumption of energy and air pollution due to the limited availability of water resources. This study explores the effects of the consumption of energy and water resources on air pollution in Iran from 1971 to 2014. It utilizes the non-linear autoregressive distributed lag approach to establish a robust relationship between the variables which show that both long- and short-run coefficients are asymmetrical. The positive and negative aspects of the long-run coefficients of energy consumption and water resources were found to be 0.19, - 1.63, 0.18, and 2.36, respectively, while only the negative ones were significant for energy consumption. Based on the cumulative effects, it can be established that there are important and significant differences in the responses of air pollution to positive and negative changes in water productivity and energy consumption. In particular, CO 2 gas emissions are affected by negative changes in H 2 O productivity both in terms of the total and the GDP per unit of energy use in Iran. In regard to short-run results, considerable asymmetric effects occur on all the variables for CO 2 emissions. Based on the results obtained, some recommendations are presented, which policymakers can adopt in efforts to address the issues of pollution and consumption.

Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models.

PubMed

Baquero, Oswaldo Santos; Santana, Lidia Maria Reis; Chiaravalloti-Neto, Francisco

2018-01-01

Globally, the number of dengue cases has been on the increase since 1990 and this trend has also been found in Brazil and its most populated city-São Paulo. Surveillance systems based on predictions allow for timely decision making processes, and in turn, timely and efficient interventions to reduce the burden of the disease. We conducted a comparative study of dengue predictions in São Paulo city to test the performance of trained seasonal autoregressive integrated moving average models, generalized additive models and artificial neural networks. We also used a naïve model as a benchmark. A generalized additive model with lags of the number of cases and meteorological variables had the best performance, predicted epidemics of unprecedented magnitude and its performance was 3.16 times higher than the benchmark and 1.47 higher that the next best performing model. The predictive models captured the seasonal patterns but differed in their capacity to anticipate large epidemics and all outperformed the benchmark. In addition to be able to predict epidemics of unprecedented magnitude, the best model had computational advantages, since its training and tuning was straightforward and required seconds or at most few minutes. These are desired characteristics to provide timely results for decision makers. However, it should be noted that predictions are made just one month ahead and this is a limitation that future studies could try to reduce.

Landscape genetic structure of coastal tailed frogs (Ascaphus truei) in protected vs. managed forests.

PubMed

Spear, Stephen F; Storfer, Andrew

2008-11-01

Habitat loss and fragmentation are the leading causes of species' declines and extinctions. A key component of studying population response to habitat alteration is to understand how fragmentation affects population connectivity in disturbed landscapes. We used landscape genetic analyses to determine how habitat fragmentation due to timber harvest affects genetic population connectivity of the coastal tailed frog (Ascaphus truei), a forest-dwelling, stream-breeding amphibian. We compared rates of gene flow across old-growth (Olympic National Park) and logged landscapes (Olympic National Forest) and used spatial autoregression to estimate the effect of landscape variables on genetic structure. We detected higher overall genetic connectivity across the managed forest, although this was likely a historical signature of continuous forest before timber harvest began. Gene flow also occurred terrestrially, as connectivity was high across unconnected river basins. Autoregressive models demonstrated that closed forest and low solar radiation were correlated with increased gene flow. In addition, there was evidence for a temporal lag in the correlation of decreased gene flow with harvest, suggesting that the full genetic impact may not appear for several generations. Furthermore, we detected genetic evidence of population bottlenecks across the Olympic National Forest, including at sites that were within old-growth forest but surrounded by harvested patches. Collectively, this research suggests that absence of forest (whether due to natural or anthropogenic changes) is a key restrictor of genetic connectivity and that intact forested patches in the surrounding environment are necessary for continued gene flow and population connectivity.

Distributed lags time series analysis versus linear correlation analysis (Pearson's r) in identifying the relationship between antipseudomonal antibiotic consumption and the susceptibility of Pseudomonas aeruginosa isolates in a single Intensive Care Unit of a tertiary hospital.

PubMed

Erdeljić, Viktorija; Francetić, Igor; Bošnjak, Zrinka; Budimir, Ana; Kalenić, Smilja; Bielen, Luka; Makar-Aušperger, Ksenija; Likić, Robert

2011-05-01

The relationship between antibiotic consumption and selection of resistant strains has been studied mainly by employing conventional statistical methods. A time delay in effect must be anticipated and this has rarely been taken into account in previous studies. Therefore, distributed lags time series analysis and simple linear correlation were compared in their ability to evaluate this relationship. Data on monthly antibiotic consumption for ciprofloxacin, piperacillin/tazobactam, carbapenems and cefepime as well as Pseudomonas aeruginosa susceptibility were retrospectively collected for the period April 2006 to July 2007. Using distributed lags analysis, a significant temporal relationship was identified between ciprofloxacin, meropenem and cefepime consumption and the resistance rates of P. aeruginosa isolates to these antibiotics. This effect was lagged for ciprofloxacin and cefepime [1 month (R=0.827, P=0.039) and 2 months (R=0.962, P=0.001), respectively] and was simultaneous for meropenem (lag 0, R=0.876, P=0.002). Furthermore, a significant concomitant effect of meropenem consumption on the appearance of multidrug-resistant P. aeruginosa strains (resistant to three or more representatives of classes of antibiotics) was identified (lag 0, R=0.992, P<0.001). This effect was not delayed and it was therefore identified both by distributed lags analysis and the Pearson's correlation coefficient. Correlation coefficient analysis was not able to identify relationships between antibiotic consumption and bacterial resistance when the effect was delayed. These results indicate that the use of diverse statistical methods can yield significantly different results, thus leading to the introduction of possibly inappropriate infection control measures. Copyright © 2010 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.

The longitudinal associations between substance use, crime, and social risk among emerging adults: A longitudinal within and between-person latent variables analysis.

PubMed

Merrin, Gabriel J; Davis, Jordan P; Berry, Daniel; D'Amico, Elizabeth J; Dumas, Tara M

2016-08-01

The reciprocal relationship between crime and substance use is well known. However, when examining this relationship, no study to date has disaggregated between- and within-person effects, which represents a more methodologically sound and developmentally-appropriate analytic approach. Further, few studies have considered the role of social risk (e.g., deviant peers, high-risk living situations) in the aforementioned relationship. We examined these associations in a group of individuals with heightened vulnerability to substance use, crime and social risk: emerging adults (aged 18-25 years) in substance use treatment. Participants were 3479 emerging adults who had entered treatment. We used auto-regressive latent growth models with structured residuals (ALT-SR) to examine the within-person cross-lagged association between crime and substance use and whether social risk contributed to this association. A taxonomy of nested models was used to determine the structural form of the data, within-person cross-lagged associations, and between-person associations. In contrast to the extant literature on cross-lagged relations between crime and substance use, we found little evidence of such relations once between- and within-person relations were plausibly disaggregated. Yet, our results indicated that within-person increases in social risk were predictive of subsequent increases in crime and substance use. Post-hoc analyses revealed a mediation effect of social risk between crime and substance use. Findings suggest the need to re-think the association between crime and substance use among emerging adults. Individuals that remain connected to high-risk social environments after finishing treatment may represent a group that could use more specialized, tailored treatments. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Short-term effects of meteorological factors on hand, foot and mouth disease among children in Shenzhen, China: Non-linearity, threshold and interaction.

PubMed

Zhang, Zhen; Xie, Xu; Chen, Xiliang; Li, Yuan; Lu, Yan; Mei, Shujiang; Liao, Yuxue; Lin, Hualiang

2016-01-01

Various meteorological factors have been associated with hand, foot and mouth disease (HFMD) among children; however, fewer studies have examined the non-linearity and interaction among the meteorological factors. A generalized additive model with a log link allowing Poisson auto-regression and over-dispersion was applied to investigate the short-term effects daily meteorological factors on children HFMD with adjustment of potential confounding factors. We found positive effects of mean temperature and wind speed, the excess relative risk (ERR) was 2.75% (95% CI: 1.98%, 3.53%) for one degree increase in daily mean temperature on lag day 6, and 3.93% (95% CI: 2.16% to 5.73%) for 1m/s increase in wind speed on lag day 3. We found a non-linear effect of relative humidity with thresholds with the low threshold at 45% and high threshold at 85%, within which there was positive effect, the ERR was 1.06% (95% CI: 0.85% to 1.27%) for 1 percent increase in relative humidity on lag day 5. No significant effect was observed for rainfall and sunshine duration. For the interactive effects, we found a weak additive interaction between mean temperature and relative humidity, and slightly antagonistic interaction between mean temperature and wind speed, and between relative humidity and wind speed in the additive models, but the interactions were not statistically significant. This study suggests that mean temperature, relative humidity and wind speed might be risk factors of children HFMD in Shenzhen, and the interaction analysis indicates that these meteorological factors might have played their roles individually. Copyright © 2015 Elsevier B.V. All rights reserved.

Level shift two-components autoregressive conditional heteroscedasticity modelling for WTI crude oil market

NASA Astrophysics Data System (ADS)

Sin, Kuek Jia; Cheong, Chin Wen; Hooi, Tan Siow

2017-04-01

This study aims to investigate the crude oil volatility using a two components autoregressive conditional heteroscedasticity (ARCH) model with the inclusion of abrupt jump feature. The model is able to capture abrupt jumps, news impact, clustering volatility, long persistence volatility and heavy-tailed distributed error which are commonly observed in the crude oil time series. For the empirical study, we have selected the WTI crude oil index from year 2000 to 2016. The results found that by including the multiple-abrupt jumps in ARCH model, there are significant improvements of estimation evaluations as compared with the standard ARCH models. The outcomes of this study can provide useful information for risk management and portfolio analysis in the crude oil markets.

Experiences of Discrimination among Chinese American Adolescents and the Consequences for Socioemotional and Academic Development

PubMed Central

Benner, Aprile D.; Kim, Su Yeong

2009-01-01

This longitudinal study examined the influences of discrimination on socioemotional adjustment and academic performance for a sample of 444 Chinese American adolescents. Using autoregressive and cross-lagged techniques, results indicate that discrimination in early adolescence predicted depressive symptoms, alienation, school engagement, and grades in middle adolescence, but early socioemotional adjustment and academic performance did not predict later experiences of discrimination. Further, our investigation of whether earlier or contemporaneous experiences of discrimination influenced developmental outcomes in middle adolescence indicated differential effects, with contemporaneous experiences of discrimination affecting socioemotional adjustment, while earlier discrimination was more influential for academic performance. Finally, we found a persistent negative effect of acculturation on the link between discrimination and adolescents’ developmental outcomes, such that those adolescents who were more acculturated (in this case, higher in American orientation) experienced more deleterious effects of discrimination on both socioemotional and academic outcomes. PMID:19899924

Adolescent Dating Violence Stability and Mutuality: A 4-Year Longitudinal Study.

PubMed

Fernández-González, Liria; Calvete, Esther; Orue, Izaskun

2017-03-01

This 4-year longitudinal study explored the stability of dating violence (DV) during adolescence and the reciprocal associations between perpetration and victimization over time. Participants were 991 high school students (52.4% females; mean age at baseline = 14.80 years) from Bizkaia (Spain), who completed a measure of DV perpetration and victimization at four measurement points spaced 1 year apart. Findings evidenced stability of teen perpetration and victimization of DV, which appears to increase in late adolescence. Moreover, longitudinal reciprocal influences were demonstrated, but in general, the cross-lagged paths from one's partner's aggression to one's own perpetration and vice versa were lower than the autoregressive paths obtained from stability. The model showed an adequate fit for both females and males, although some paths were significantly higher for the females than for the males. Preventive interventions should consider these findings about stability and longitudinal reciprocal associations of DV during adolescence.

A longitudinal analysis of burnout in middle and high school Korean teachers.

PubMed

Park, Yang Min; Lee, Sang Min

2013-12-01

This study examines longitudinal relationships among three burnout dimensions in middle and high school teachers. For this study, 419 middle and high school teachers participated in a panel survey, which was conducted in three waves. Using Amos 7.0, we performed autoregressive cross-lagged modeling to obtain a complete picture of the longitudinal relationships among the three factors of the Maslach Burnout Inventory-Educator Survey. Results indicated that the paths from emotional exhaustion at Time1 and Time2 to depersonalization at Time2 and Time3 were statistically significant. In addition, the paths from personal accomplishment at Time1 and Time2 to depersonalization at Time2 and Time3 were also statistically significant. Empirically identifying the process by which burnout occurs could help practitioners and policy makers to design burnout prevention strategies. Copyright © 2012 John Wiley & Sons, Ltd.

Transactional links between teacher-child relationship quality and perceived versus sociometric popularity: a three-wave longitudinal study.

PubMed

De Laet, Steven; Doumen, Sarah; Vervoort, Eleonora; Colpin, Hilde; Van Leeuwen, Karla; Goossens, Luc; Verschueren, Karine

2014-01-01

This study examined how peer relationships (i.e., sociometric and perceived popularity) and teacher-child relationships (i.e., support and conflict) impact one another throughout late childhood. The sample included 586 children (46% boys), followed annually from Grades 4 to 6 (M(age.wave1) = 9.26 years). Autoregressive cross-lagged modeling was applied. Results stress the importance of peer relationships in shaping teacher-child relationships and vice versa. Higher sociometric popularity predicted more teacher-child support, which in turn predicted higher sociometric popularity, beyond changes in children's prosocial behavior. Higher perceived popularity predicted more teacher-child conflict (driven by children's aggressive behavior), which, in turn and in itself, predicted higher perceived popularity. The influence of the "invisible hand" of both teachers and peers in classrooms has been made visible. © 2014 The Authors. Child Development © 2014 Society for Research in Child Development, Inc.

Behavioral Engagement, Peer Status, and Teacher-Student Relationships in Adolescence: A Longitudinal Study on Reciprocal Influences.

PubMed

Engels, Maaike C; Colpin, Hilde; Van Leeuwen, Karla; Bijttebier, Patricia; Van Den Noortgate, Wim; Claes, Stephan; Goossens, Luc; Verschueren, Karine

2016-06-01

Although teachers and peers play an important role in shaping students' engagement, no previous study has directly investigated transactional associations of these classroom-based relationships in adolescence. This study investigated the transactional associations between adolescents' behavioral engagement, peer status (likeability and popularity), and (positive and negative) teacher-student relationships during secondary education. A large sample of adolescents was followed from Grade 7 to 11 (N = 1116; 49 % female; M age = 13.79 years). Multivariate autoregressive cross-lagged modeling revealed only unidirectional effects from teacher-student relationships and peer status on students' behavioral engagement. Positive teacher-student relationships were associated with more behavioral engagement over time, whereas negative teacher-student relationships, higher likeability and higher popularity were related to less behavioral engagement over time. We conclude that teachers and peers constitute different sources of influence, and play independent roles in adolescents' behavioral engagement.

Temporality of couple conflict and relationship perceptions.

PubMed

Johnson, Matthew D; Horne, Rebecca M; Hardy, Nathan R; Anderson, Jared R

2018-05-03

Using 5 waves of longitudinal survey data gathered from 3,405 couples, the present study investigates the temporal associations between self-reported couple conflict (frequency and each partner's constructive and withdrawing behaviors) and relationship perceptions (satisfaction and perceived instability). Autoregressive cross-lagged model results revealed couple conflict consistently predicted future relationship perceptions: More frequent conflict and withdrawing behaviors and fewer constructive behaviors foretold reduced satisfaction and conflict frequency and withdrawal heightened perceived instability. Relationship perceptions also shaped future conflict, but in surprising ways: Perceptions of instability were linked with less frequent conflict, and male partner instability predicted fewer withdrawing behaviors for female partners. Higher satisfaction from male partners also predicted more frequent and less constructive conflict behavior in the future. These findings illustrate complex bidirectional linkages between relationship perceptions and couple conflict behaviors in the development of couple relations. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The Interplay Among Children's Negative Family Representations, Visual Processing of Negative Emotions, and Externalizing Symptoms.

PubMed

Davies, Patrick T; Coe, Jesse L; Hentges, Rochelle F; Sturge-Apple, Melissa L; van der Kloet, Erika

2018-03-01

This study examined the transactional interplay among children's negative family representations, visual processing of negative emotions, and externalizing symptoms in a sample of 243 preschool children (M age  = 4.60 years). Children participated in three annual measurement occasions. Cross-lagged autoregressive models were conducted with multimethod, multi-informant data to identify mediational pathways. Consistent with schema-based top-down models, negative family representations were associated with attention to negative faces in an eye-tracking task and their externalizing symptoms. Children's negative representations of family relationships specifically predicted decreases in their attention to negative emotions, which, in turn, was associated with subsequent increases in their externalizing symptoms. Follow-up analyses indicated that the mediational role of diminished attention to negative emotions was particularly pronounced for angry faces. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

Crime Modeling using Spatial Regression Approach

NASA Astrophysics Data System (ADS)

Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.

2018-01-01

Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.

Are U.S. Military Interventions Contagious over Time? Intervention Timing and Its Implications for Force Planning

DTIC Science & Technology

2013-01-01

29 3.5. ARIMA Models , Temporal Clustering of Conflicts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.6...39 3.9. ARIMA Models ...variance across a distribution. Autoregressive integrated moving average ( ARIMA ) models are used with time-series data sets and are designed to capture

The Measurement of Pressure Through Tubes in Pressure Distribution Tests

NASA Technical Reports Server (NTRS)

Hemke, Paul E

1928-01-01

The tests described in this report were made to determine the error caused by using small tubes to connect orifices on the surface of aircraft to central pressure capsules in making pressure distribution tests. Aluminum tubes of 3/16-inch inside diameter were used to determine this error. Lengths from 20 feet to 226 feet and pressures whose maxima varied from 2 inches to 140 inches of water were used. Single-pressure impulses for which the time of rise of pressure from zero to a maximum varied from 0.25 second to 3 seconds were investigated. The results show that the pressure recorded at the capsule on the far end of the tube lags behind the pressure at the orifice end and experiences also a change in magnitude. For the values used in these tests the time lag and pressure change vary principally with the time of rise of pressure from zero to a maximum and the tube length. Curves are constructed showing the time lag and pressure change. Empirical formulas are also given for computing the time lag. Analysis of pressure distribution tests made on airplanes in flight shows that the recorded pressures are slightly higher than the pressures at the orifice and that the time lag is negligible. The apparent increase in pressure is usually within the experimental error, but in the case of the modern pursuit type of airplane the pressure increase may be 5 per cent. For pressure-distribution tests on airships the analysis shows that the time lag and pressure change may be neglected.

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations

NASA Astrophysics Data System (ADS)

Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza

2018-03-01

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

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

PubMed Central

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

2014-01-01

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

Relation between germination and mycelium growth of individual fungal spores.

PubMed

Gougouli, Maria; Koutsoumanis, Konstantinos P

2013-02-15

The relation between germination time and lag time of mycelium growth of individual spores was studied by combining microscopic and macroscopic techniques. The radial growth of a large number (100-200) of Penicillium expansum and Aspergillus niger mycelia originating from single spores was monitored macroscopically at isothermal conditions ranging from 0 to 30°C and 10 to 41.5°C, respectively. The radial growth curve for each mycelium was fitted to a linear model for the estimation of mycelium lag time. The results showed that the lag time varied significantly among single spores. The cumulative frequency distributions of the lag times were fitted to the modified Gompertz model and compared with the respective distributions for the germination time, which were obtained microscopically. The distributions of the measured mycelium lag time were found to be similar to the germination time distributions under the same conditions but shifted in time with the lag times showing a significant delay compared to germination times. A numerical comparison was also performed based on the distribution parameters λ(m) and λ(g), which indicate the time required from the spores to start the germination process and the completion of the lag phase, respectively. The relative differences %(λ(m)-λ(g))/λ(m) were not found to be significantly affected by temperatures tested with mean values of 72.5±5.1 and 60.7±2.1 for P. expansum for A. niger, respectively. In order to investigate the source of the above difference, a time-lapse microscopy method was developed providing videos with the behavior of single fungal spore from germination until mycelium formation. The distances of the apexes of the first germ tubes that emerged from the swollen spore were measured in each frame of the videos and these data were expressed as a function of time. The results showed that in the early hyphal development, the measured radii appear to increase exponentially, until a certain time, where growth becomes linear. The two phases of hyphal development can explain the difference between germination and lag time. Since the lag time is estimated from the extrapolation of the regression line of the linear part of the graph only, its value is significantly higher than the germination time, t(G). The relation of germination and lag time was further investigated by comparing their temperature dependence using the Cardinal Model with Inflection. The estimated values of the cardinal parameters (T(min), T(opt), and T(max)) for 1/λ(g) were found to be very close to the respective values for 1/λ(m), indicating similar temperature dependence between them. Copyright © 2012 Elsevier B.V. All rights reserved.

Repetition and lag effects in movement recognition.

PubMed

Hall, C R; Buckolz, E

1982-03-01

Whether repetition and lag improve the recognition of movement patterns was investigated. Recognition memory was tested for one repetition, two-repetitions massed, and two-repetitions distributed with movement patterns at lags of 3, 5, 7, and 13. Recognition performance was examined both immediately afterwards and following a 48 hour delay. Both repetition and lag effects failed to be demonstrated, providing some support for the claim that memory is unaffected by repetition at a constant level of processing (Craik & Lockhart, 1972). There was, as expected, a significant decrease in recognition memory following the retention interval, but this appeared unrelated to repetition or lag.

Quantifying the effect of water activity and storage temperature on single spore lag times of three moulds isolated from spoiled bakery products.

PubMed

Dagnas, Stéphane; Gougouli, Maria; Onno, Bernard; Koutsoumanis, Konstantinos P; Membré, Jeanne-Marie

2017-01-02

The inhibitory effect of water activity (a w ) and storage temperature on single spore lag times of Aspergillus niger, Eurotium repens (Aspergillus pseudoglaucus) and Penicillium corylophilum strains isolated from spoiled bakery products, was quantified. A full factorial design was set up for each strain. Data were collected at levels of a w varying from 0.80 to 0.98 and temperature from 15 to 35°C. Experiments were performed on malt agar, at pH5.5. When growth was observed, ca 20 individual growth kinetics per condition were recorded up to 35days. Radius of the colony vs time was then fitted with the Buchanan primary model. For each experimental condition, a lag time variability was observed, it was characterized by its mean, standard deviation (sd) and 5 th percentile, after a Normal distribution fit. As the environmental conditions became stressful (e.g. storage temperature and a w lower), mean and sd of single spore lag time distribution increased, indicating longer lag times and higher variability. The relationship between mean and sd followed a monotonous but not linear pattern, identical whatever the species. Next, secondary models were deployed to estimate the cardinal values (minimal, optimal and maximal temperatures, minimal water activity where no growth is observed anymore) for the three species. That enabled to confirm the observation made based on raw data analysis: concerning the temperature effect, A. niger behaviour was significantly different from E. repens and P. corylophilum: T opt of 37.4°C (standard deviation 1.4°C) instead of 27.1°C (1.4°C) and 25.2°C (1.2°C), respectively. Concerning the a w effect, from the three mould species, E. repens was the species able to grow at the lowest a w (aw min estimated to 0.74 (0.02)). Finally, results obtained with single spores were compared to findings from a previous study carried out at the population level (Dagnas et al., 2014). For short lag times (≤5days), there was no difference between lag time of the population (ca 2000 spores inoculated in one spot) and mean (nor 5 th percentile) of single spore lag time distribution. In contrast, when lag time was longer, i.e. under more stressful conditions, there was a discrepancy between individual and population lag times (population lag times shorter than 5 th percentiles of single spore lag time distribution), confirming a stochastic process. Finally, the temperature cardinal values estimated with single spores were found to be similar to those obtained at the population level, whatever the species. All these findings will be used to describe better mould spore lag time variability and then to predict more accurately bakery product shelf-life. Copyright © 2016 Elsevier B.V. All rights reserved.

Monitoring seasonal bat activity on a coastal barrier island in Maryland, USA.

PubMed

Johnson, Joshua B; Gates, J Edward; Zegre, Nicolas P

2011-02-01

Research on effects of wind turbines on bats has increased dramatically in recent years because of significant numbers of bats killed by rotating wind turbine blades. Whereas most research has focused on the Midwest and inland portions of eastern North America, bat activity and migration on the Atlantic Coast has largely been unexamined. We used three long-term acoustic monitoring stations to determine seasonal bat activity patterns on the Assateague Island National Seashore, a barrier island off the coast of Maryland, from 2005 to 2006. We recorded five species, including eastern red bats (Lasiurus borealis), big brown bats (Eptesicus fuscus), hoary bats (Lasiurus cinereus), tri-colored bats (Perimyotis subflavus), and silver-haired bats (Lasionycteris noctivagans). Seasonal bat activity (number of bat passes recorded) followed a cosine function and gradually increased beginning in April, peaked in August, and declined gradually until cessation in December. Based on autoregressive models, inter-night bat activity was autocorrelated for lags of seven nights or fewer but varied among acoustic monitoring stations. Higher nightly temperatures and lower wind speeds positively affected bat activity. When autoregressive model predictions were fitted to the observed nightly bat pass totals, model residuals>2 standard deviations from the mean existed only during migration periods, indicating that periodic increases in bat activity could not be accounted for by seasonal trends and weather variables alone. Rather, the additional bat passes were attributable to migrating bats. We conclude that bats, specifically eastern red, hoary, and silver-haired bats, use this barrier island during migration and that this phenomenon may have implications for the development of near and offshore wind energy.

Nowcasting influenza outbreaks using open-source media report.

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

Ray, Jaideep; Brownstein, John S.

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

Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers.

PubMed

Briët, Olivier J T; Amerasinghe, Priyanie H; Vounatsou, Penelope

2013-01-01

With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions' impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during "consolidation" and "pre-elimination" phases. Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low.

Generalized Seasonal Autoregressive Integrated Moving Average Models for Count Data with Application to Malaria Time Series with Low Case Numbers

PubMed Central

Briët, Olivier J. T.; Amerasinghe, Priyanie H.; Vounatsou, Penelope

2013-01-01

Introduction With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions’ impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during “consolidation” and “pre-elimination” phases. Methods Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. Results The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. Conclusions G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low. PMID:23785448

Causality links among renewable energy consumption, CO2 emissions, and economic growth in Africa: evidence from a panel ARDL-PMG approach.

PubMed

Attiaoui, Imed; Toumi, Hassen; Ammouri, Bilel; Gargouri, Ilhem

2017-05-01

This research examines the causality (For the remainder of the paper, the notion of causality refers to Granger causality.) links among renewable energy consumption (REC), CO 2 emissions (CE), non-renewable energy consumption (NREC), and economic growth (GDP) using an autoregressive distributed lag model based on the pooled mean group estimation (ARDL-PMG) and applying Granger causality tests for a panel consisting of 22 African countries for the period between 1990 and 2011. There is unidirectional and irreversible short-run causality from CE to GDP. The causal direction between CE and REC is unobservable over the short-term. Moreover, we find unidirectional, short-run causality from REC to GDP. When testing per pair of variables, there are short-run bidirectional causalities among REC, CE, and GDP. However, if we add CE to the variables REC and NREC, the causality to GDP is observable, and causality from the pair REC and NREC to economic growth is neutral. Likewise, if we add NREC to the variables GDP and REC, there is causality. There are bidirectional long-run causalities among REC, CE, and GDP, which supports the feedback assumption. Causality from GDP to REC is not strong for the panel. If we test per pair of variables, the strong causality from GDP and CE to REC is neutral. The long-run PMG estimates show that NREC and gross domestic product increase CE, whereas REC decreases CE.

Efficient hierarchical trans-dimensional Bayesian inversion of magnetotelluric data

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

European regional efficiency and geographical externalities: a spatial nonparametric frontier analysis

NASA Astrophysics Data System (ADS)

Ramajo, Julián; Cordero, José Manuel; Márquez, Miguel Ángel

2017-10-01

This paper analyses region-level technical efficiency in nine European countries over the 1995-2007 period. We propose the application of a nonparametric conditional frontier approach to account for the presence of heterogeneous conditions in the form of geographical externalities. Such environmental factors are beyond the control of regional authorities, but may affect the production function. Therefore, they need to be considered in the frontier estimation. Specifically, a spatial autoregressive term is included as an external conditioning factor in a robust order- m model. Thus we can test the hypothesis of non-separability (the external factor impacts both the input-output space and the distribution of efficiencies), demonstrating the existence of significant global interregional spillovers into the production process. Our findings show that geographical externalities affect both the frontier level and the probability of being more or less efficient. Specifically, the results support the fact that the spatial lag variable has an inverted U-shaped non-linear impact on the performance of regions. This finding can be interpreted as a differential effect of interregional spillovers depending on the size of the neighboring economies: positive externalities for small values, possibly related to agglomeration economies, and negative externalities for high values, indicating the possibility of production congestion. Additionally, evidence of the existence of a strong geographic pattern of European regional efficiency is reported and the levels of technical efficiency are acknowledged to have converged during the period under analysis.

Is rapid growth in Internet usage environmentally sustainable for Australia? An empirical investigation.

PubMed

Salahuddin, Mohammad; Alam, Khorshed; Ozturk, Ilhan

2016-03-01

This study estimates the short- and long-run effects of Internet usage and economic growth on carbon dioxide (CO2) emissions using annual time series macro data for Australia for the period 1985-2012. Autoregressive distributive lag (ARDL) bounds and Gregory-Hansen structural break cointegration tests are applied. ARDL estimates indicate no significant long-run relationship between Internet usage and CO2 emissions, which implies that the rapid growth in Internet usage is still not an environmental threat for Australia. The study further indicates that higher level of economic growth is associated with lower level of CO2 emissions; however, Internet usage and economic growth have no significant short-run relationship with CO2 emissions. Financial development has both short-run and long-run significant positive association with CO2 emissions. The findings offer support in favor of energy efficiency gains and a reduction in energy intensity in Australia. However, impulse response and variance decomposition analysis suggest that Internet usage, economic growth and financial development will continue to impact CO2 emissions in the future, and as such, this study recommends that in addition to the existing measures to combat CO2 emissions, Australia needs to exploit the potential of the Internet not only to reduce its own carbon footprint but also to utilize information and communication technology (ICT)-enabled emissions abatement potential to reduce emissions in various other sectors across the economy, such as, power, renewable energy especially in solar and wind energy, agriculture, transport and service.

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

ERIC Educational Resources Information Center

Bockenholt, Ulf

1999-01-01

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

Short term effect of air pollution, noise and heat waves on preterm births in Madrid (Spain).

PubMed

Arroyo, Virginia; Díaz, Julio; Ortiz, Cristina; Carmona, Rocío; Sáez, Marc; Linares, Cristina

2016-02-01

Preterm birth (PTB) refers to delivery before 37 weeks of gestation and represents the leading cause of early-life mortality and morbidity in developed countries. PTB can lead to serious infant health outcomes. The etiology of PTB remains uncertain, but epidemiologic studies have consistently shown elevated risks with different environmental variables as traffic-related air pollution (TRAP). The aim of the study was to evaluate with time series methodology the short-term effect of air pollutants, noise levels and ambient temperature on the number of births and preterm births occurred in Madrid City during the 2001-2009 period. A time-series analysis was performed to assess the short term impact of daily mean concentrations (µg/m(3)) of PM2.5 and PM10, O3 and NO2. Measurements of Acoustic Pollution in dB(A) analyzed were: Leqd, equivalent diurnal noise level and Leqn, equivalent nocturnal noise level. Maximum and Minimum daily temperature (°C), mean Humidity in the air (%) and Atmospheric Pressure (HPa), were included too. Linear trends, seasonality, as well as the autoregressive nature of the series itself were controlled. We added as covariate the day of the week too. Autoregressive over-dispersed Poisson regression models were performed and the environmental variables were included with short-term lags (from 0 to 7 days) in reference to the date of birth. Firstly, simple models for the total number of births and preterm births were done separately. In a second stage, a model for total births adjusted for preterm births was performed. A total of 298,705 births were analyzed. The results of the final models were expressed in relative risks (RRs) for interquartile increase. We observed evidence of a short term effect at Lag 0, for the following environmental variables analyzed, PM2.5 (RR: 1.020; 95% CI:(1.008 1.032)) and O3 (RR: 1.012; 95% CI:(1.002 1.022)) concentrations and Leqd (RR: 1.139; 95% CI:( (1.124 1.154)) for the total number of births, and besides these, heat temperatures at Lag 1 (RR: 1.055; 95% CI:( (1.018 1.092)) on preterm births in Madrid City during the studied period. In the model adjusted for preterm births, similar RR was obtained for the same environmental variables. Especially PM2.5, diurnal noise levels and O3 have a short-term impact on total births and heat temperatures on preterm births in Madrid City during the studied period. Our results suggest that, given the widespread exposure of the population to the environmental factors analyzed and the possible effects on long-term health associated to low birth weight. There is a clear need to minimize this exposure through the decrease of air pollution and noise levels and through the behavior modification of the mothers. Copyright © 2015 Elsevier Inc. All rights reserved.

Modeling of photocurrent and lag signals in amorphous selenium x-ray detectors

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

Siddiquee, Sinchita; Kabir, M. Z., E-mail: kabir@encs.concordia.ca

2015-07-15

A mathematical model for transient photocurrent and lag signal in x-ray imaging detectors has been developed by considering charge carrier trapping and detrapping in the energy distributed defect states under exponentially distributed carrier generation across the photoconductor. The model for the transient and steady-state carrier distributions and hence the photocurrent has been developed by solving the carrier continuity equation for both holes and electrons. The residual (commonly known as lag signal) current is modeled by solving the trapping rate equations considering the thermal release and trap filling effects. The model is applied to amorphous selenium (a-Se) detectors for both chestmore » radiography and mammography. The authors analyze the dependence of the residual current on various factors, such as x-ray exposure, applied electric field, and temperature. The electron trapping and detrapping mostly determines the residual current in a-Se detectors. The lag signal is more prominent in chest radiographic detector than in mammographic detectors. The model calculations are compared with the published experimental data and show a very good agreement.« less

Durability of classification and action learning: differences revealed using ex-Gaussian distribution analysis.

PubMed

Moutsopoulou, Karolina; Waszak, Florian

2013-05-01

It has been shown that in associative learning it is possible to disentangle the effects caused on behaviour by the associations between a stimulus and a classification (S-C) and the associations between a stimulus and the action performed towards it (S-A). Such evidence has been provided using ex-Gaussian distribution analysis to show that different parameters of the reaction time distribution reflect the different processes. Here, using this method, we investigate another difference between these two types of associations: What is the relative durability of these associations across time? Using a task-switching paradigm and by manipulating the lag between the point of the creation of the associations and the test phase, we show that S-A associations have stronger effects on behaviour when the lag between the two repetitions of a stimulus is short. However, classification learning affects behaviour not only in short-term lags but also (and equally so) when the lag between prime and probe is long and the same stimuli are repeatedly presented within a different classification task, demonstrating a remarkable durability of S-C associations.

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

NASA Astrophysics Data System (ADS)

Sato, Aki-Hiro

2004-04-01

Autoregressive conditional duration (ACD) processes, which have the potential to be applied to power law distributions of complex systems found in natural science, life science, and social science, are analyzed both numerically and theoretically. An ACD(1) process exhibits the singular second order moment, which suggests that its probability density function (PDF) has a power law tail. It is verified that the PDF of the ACD(1) has a power law tail with an arbitrary exponent depending on a model parameter. On the basis of theory of the random multiplicative process a relation between the model parameter and the power law exponent is theoretically derived. It is confirmed that the relation is valid from numerical simulations. An application of the ACD(1) to intervals between two successive transactions in a foreign currency market is shown.

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

PubMed

Sato, Aki-Hiro

2004-04-01

Autoregressive conditional duration (ACD) processes, which have the potential to be applied to power law distributions of complex systems found in natural science, life science, and social science, are analyzed both numerically and theoretically. An ACD(1) process exhibits the singular second order moment, which suggests that its probability density function (PDF) has a power law tail. It is verified that the PDF of the ACD(1) has a power law tail with an arbitrary exponent depending on a model parameter. On the basis of theory of the random multiplicative process a relation between the model parameter and the power law exponent is theoretically derived. It is confirmed that the relation is valid from numerical simulations. An application of the ACD(1) to intervals between two successive transactions in a foreign currency market is shown.

The lag effects and vulnerabilities of temperature effects on cardiovascular disease mortality in a subtropical climate zone in China.

PubMed

Huang, Jixia; Wang, Jinfeng; Yu, Weiwei

2014-04-11

This research quantifies the lag effects and vulnerabilities of temperature effects on cardiovascular disease in Changsha--a subtropical climate zone of China. A Poisson regression model within a distributed lag nonlinear models framework was used to examine the lag effects of cold- and heat-related CVD mortality. The lag effect for heat-related CVD mortality was just 0-3 days. In contrast, we observed a statistically significant association with 10-25 lag days for cold-related CVD mortality. Low temperatures with 0-2 lag days increased the mortality risk for those ≥65 years and females. For all ages, the cumulative effects of cold-related CVD mortality was 6.6% (95% CI: 5.2%-8.2%) for 30 lag days while that of heat-related CVD mortality was 4.9% (95% CI: 2.0%-7.9%) for 3 lag days. We found that in Changsha city, the lag effect of hot temperatures is short while the lag effect of cold temperatures is long. Females and older people were more sensitive to extreme hot and cold temperatures than males and younger people.

Air Pollution and Deaths among Elderly Residents of São Paulo, Brazil: An Analysis of Mortality Displacement.

PubMed

Costa, Amine Farias; Hoek, Gerard; Brunekreef, Bert; Ponce de Leon, Antônio C M

2017-03-01

Evaluation of short-term mortality displacement is essential to accurately estimate the impact of short-term air pollution exposure on public health. We quantified mortality displacement by estimating single-day lag effects and cumulative effects of air pollutants on mortality using distributed lag models. We performed a daily time series of nonaccidental and cause-specific mortality among elderly residents of São Paulo, Brazil, between 2000 and 2011. Effects of particulate matter smaller than 10 μm (PM 10 ), nitrogen dioxide (NO 2 ) and carbon monoxide (CO) were estimated in Poisson generalized additive models. Single-day lag effects of air pollutant exposure were estimated for 0-, 1- and 2-day lags. Distributed lag models with lags of 0-10, 0-20 and 0-30 days were used to assess mortality displacement and potential cumulative exposure effects. PM 10 , NO 2 and CO were significantly associated with nonaccidental and cause-specific deaths in both single-day lag and cumulative lag models. Cumulative effect estimates for 0-10 days were larger than estimates for single-day lags. Cumulative effect estimates for 0-30 days were essentially zero for nonaccidental and circulatory deaths but remained elevated for respiratory and cancer deaths. We found evidence of mortality displacement within 30 days for nonaccidental and circulatory deaths in elderly residents of São Paulo. We did not find evidence of mortality displacement within 30 days for respiratory or cancer deaths. Citation: Costa AF, Hoek G, Brunekreef B, Ponce de Leon AC. 2017. Air pollution and deaths among elderly residents of São Paulo, Brazil: an analysis of mortality displacement. Environ Health Perspect 125:349-354; http://dx.doi.org/10.1289/EHP98.

Measuring Treasury Bond Portfolio Risk and Portfolio Optimization with a Non-Gaussian Multivariate Model

NASA Astrophysics Data System (ADS)

Dong, Yijun

The research about measuring the risk of a bond portfolio and the portfolio optimization was relatively rare previously, because the risk factors of bond portfolios are not very volatile. However, this condition has changed recently. The 2008 financial crisis brought high volatility to the risk factors and the related bond securities, even if the highly rated U.S. treasury bonds. Moreover, the risk factors of bond portfolios show properties of fat-tailness and asymmetry like risk factors of equity portfolios. Therefore, we need to use advanced techniques to measure and manage risk of bond portfolios. In our paper, we first apply autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model with multivariate normal tempered stable (MNTS) distribution innovations to predict risk factors of U.S. treasury bonds and statistically demonstrate that MNTS distribution has the ability to capture the properties of risk factors based on the goodness-of-fit tests. Then based on empirical evidence, we find that the VaR and AVaR estimated by assuming normal tempered stable distribution are more realistic and reliable than those estimated by assuming normal distribution, especially for the financial crisis period. Finally, we use the mean-risk portfolio optimization to minimize portfolios' potential risks. The empirical study indicates that the optimized bond portfolios have better risk-adjusted performances than the benchmark portfolios for some periods. Moreover, the optimized bond portfolios obtained by assuming normal tempered stable distribution have improved performances in comparison to the optimized bond portfolios obtained by assuming normal distribution.

Distributed lag effects and vulnerable groups of floods on bacillary dysentery in Huaihua, China

PubMed Central

Liu, Zhi-Dong; Li, Jing; Zhang, Ying; Ding, Guo-Yong; Xu, Xin; Gao, Lu; Liu, Xue-Na; Liu, Qi-Yong; Jiang, Bao-Fa

2016-01-01

Understanding the potential links between floods and bacillary dysentery in China is important to develop appropriate intervention programs after floods. This study aimed to explore the distributed lag effects of floods on bacillary dysentery and to identify the vulnerable groups in Huaihua, China. Weekly number of bacillary dysentery cases from 2005–2011 were obtained during flood season. Flood data and meteorological data over the same period were obtained from the China Meteorological Data Sharing Service System. To examine the distributed lag effects, a generalized linear mixed model combined with a distributed lag non-linear model were developed to assess the relationship between floods and bacillary dysentery. A total of 3,709 cases of bacillary dysentery were notified over the study period. The effects of floods on bacillary dysentery continued for approximately 3 weeks with a cumulative risk ratio equal to 1.52 (95% CI: 1.08–2.12). The risks of bacillary dysentery were higher in females, farmers and people aged 15–64 years old. This study suggests floods have increased the risk of bacillary dysentery with 3 weeks’ effects, especially for the vulnerable groups identified. Public health programs should be taken to prevent and control a potential risk of bacillary dysentery after floods. PMID:27427387

Distributed lag effects and vulnerable groups of floods on bacillary dysentery in Huaihua, China.

PubMed

Liu, Zhi-Dong; Li, Jing; Zhang, Ying; Ding, Guo-Yong; Xu, Xin; Gao, Lu; Liu, Xue-Na; Liu, Qi-Yong; Jiang, Bao-Fa

2016-07-18

Understanding the potential links between floods and bacillary dysentery in China is important to develop appropriate intervention programs after floods. This study aimed to explore the distributed lag effects of floods on bacillary dysentery and to identify the vulnerable groups in Huaihua, China. Weekly number of bacillary dysentery cases from 2005-2011 were obtained during flood season. Flood data and meteorological data over the same period were obtained from the China Meteorological Data Sharing Service System. To examine the distributed lag effects, a generalized linear mixed model combined with a distributed lag non-linear model were developed to assess the relationship between floods and bacillary dysentery. A total of 3,709 cases of bacillary dysentery were notified over the study period. The effects of floods on bacillary dysentery continued for approximately 3 weeks with a cumulative risk ratio equal to 1.52 (95% CI: 1.08-2.12). The risks of bacillary dysentery were higher in females, farmers and people aged 15-64 years old. This study suggests floods have increased the risk of bacillary dysentery with 3 weeks' effects, especially for the vulnerable groups identified. Public health programs should be taken to prevent and control a potential risk of bacillary dysentery after floods.

Distributed lag effects and vulnerable groups of floods on bacillary dysentery in Huaihua, China

NASA Astrophysics Data System (ADS)

Liu, Zhi-Dong; Li, Jing; Zhang, Ying; Ding, Guo-Yong; Xu, Xin; Gao, Lu; Liu, Xue-Na; Liu, Qi-Yong; Jiang, Bao-Fa

2016-07-01

Understanding the potential links between floods and bacillary dysentery in China is important to develop appropriate intervention programs after floods. This study aimed to explore the distributed lag effects of floods on bacillary dysentery and to identify the vulnerable groups in Huaihua, China. Weekly number of bacillary dysentery cases from 2005-2011 were obtained during flood season. Flood data and meteorological data over the same period were obtained from the China Meteorological Data Sharing Service System. To examine the distributed lag effects, a generalized linear mixed model combined with a distributed lag non-linear model were developed to assess the relationship between floods and bacillary dysentery. A total of 3,709 cases of bacillary dysentery were notified over the study period. The effects of floods on bacillary dysentery continued for approximately 3 weeks with a cumulative risk ratio equal to 1.52 (95% CI: 1.08-2.12). The risks of bacillary dysentery were higher in females, farmers and people aged 15-64 years old. This study suggests floods have increased the risk of bacillary dysentery with 3 weeks’ effects, especially for the vulnerable groups identified. Public health programs should be taken to prevent and control a potential risk of bacillary dysentery after floods.

Response of spectral vegetation indices to soil moisture in grasslands and shrublands

USGS Publications Warehouse

Zhang, Li; Ji, Lei; Wylie, Bruce K.

2011-01-01

The relationships between satellite-derived vegetation indices (VIs) and soil moisture are complicated because of the time lag of the vegetation response to soil moisture. In this study, we used a distributed lag regression model to evaluate the lag responses of VIs to soil moisture for grasslands and shrublands at Soil Climate Analysis Network sites in the central and western United States. We examined the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS)-derived VIs and soil moisture measurements. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) showed significant lag responses to soil moisture. The lag length varies from 8 to 56 days for NDVI and from 16 to 56 days for NDWI. However, the lag response of NDVI and NDWI to soil moisture varied among the sites. Our study suggests that the lag effect needs to be taken into consideration when the VIs are used to estimate soil moisture.

Two dynamic regimes in the human gut microbiome

PubMed Central

Smillie, Chris S.; Alm, Eric J.

2017-01-01

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

Two dynamic regimes in the human gut microbiome.

PubMed

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

2017-02-01

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

Forecast of Frost Days Based on Monthly Temperatures

NASA Astrophysics Data System (ADS)

Castellanos, M. T.; Tarquis, A. M.; Morató, M. C.; Saa-Requejo, A.

2009-04-01

Although frost can cause considerable crop damage and mitigation practices against forecasted frost exist, frost forecasting technologies have not changed for many years. The paper reports a new method to forecast the monthly number of frost days (FD) for several meteorological stations at Community of Madrid (Spain) based on successive application of two models. The first one is a stochastic model, autoregressive integrated moving average (ARIMA), that forecasts monthly minimum absolute temperature (tmin) and monthly average of minimum temperature (tminav) following Box-Jenkins methodology. The second model relates these monthly temperatures to minimum daily temperature distribution during one month. Three ARIMA models were identified for the time series analyzed with a stational period correspondent to one year. They present the same stational behavior (moving average differenced model) and different non-stational part: autoregressive model (Model 1), moving average differenced model (Model 2) and autoregressive and moving average model (Model 3). At the same time, the results point out that minimum daily temperature (tdmin), for the meteorological stations studied, followed a normal distribution each month with a very similar standard deviation through years. This standard deviation obtained for each station and each month could be used as a risk index for cold months. The application of Model 1 to predict minimum monthly temperatures showed the best FD forecast. This procedure provides a tool for crop managers and crop insurance companies to asses the risk of frost frequency and intensity, so that they can take steps to mitigate against frost damage and estimated the damage that frost would cost. This research was supported by Comunidad de Madrid Research Project 076/92. The cooperation of the Spanish National Meteorological Institute and the Spanish Ministerio de Agricultura, Pesca y Alimentation (MAPA) is gratefully acknowledged.

Leading indicators of community-based violent events among adults with mental illness.

PubMed

Van Dorn, R A; Grimm, K J; Desmarais, S L; Tueller, S J; Johnson, K L; Swartz, M S

2017-05-01

The public health, public safety and clinical implications of violent events among adults with mental illness are significant; however, the causes and consequences of violence and victimization among adults with mental illness are complex and not well understood, which limits the effectiveness of clinical interventions and risk management strategies. This study examined interrelationships between violence, victimization, psychiatric symptoms, substance use, homelessness and in-patient treatment over time. Available data were integrated from four longitudinal studies of adults with mental illness. Assessments took place at baseline, and at 1, 3, 6, 9, 12, 15, 18, 24, 30 and 36 months, depending on the parent studies' protocol. Data were analysed with the autoregressive cross-lag model. Violence and victimization were leading indicators of each other and affective symptoms were a leading indicator of both. Drug and alcohol use were leading indicators of violence and victimization, respectively. All psychiatric symptom clusters - affective, positive, negative, disorganized cognitive processing - increased the likelihood of experiencing at least one subsequent symptom cluster. Sensitivity analyses identified few group-based differences in the magnitude of effects in this heterogeneous sample. Violent events demonstrated unique and shared indicators and consequences over time. Findings indicate mechanisms for reducing violent events, including trauma-informed therapy, targeting internalizing and externalizing affective symptoms with cognitive-behavioral and psychopharmacological interventions, and integrating substance use and psychiatric care. Finally, mental illness and violence and victimization research should move beyond demonstrating concomitant relationships and instead focus on lagged effects with improved spatio-temporal contiguity.

Reciprocal associations between depressive symptoms and disordered eating among adolescent girls and boys: a multiwave, prospective study.

PubMed

Ferreiro, Fátima; Wichstrøm, Lars; Seoane, Gloria; Senra, Carmen

2014-01-01

Symptoms of depression and eating disorders increase during adolescence, particularly among girls, and they tend to co-occur. Despite this evidence, there is meager research on whether depression increases the risk of future eating pathology, or vice versa, and we do not know whether these processes are different for adolescent girls and boys. Accordingly, this study explored the prospective reciprocal associations between depressive symptoms and disordered eating at different time points from preadolescence to mid-adolescence and tested the moderator effect of gender on these associations. A community-based sample of Spanish youth (N = 942, 49 % female) was assessed at ages of approximately 10-11 (T1), 12-13 (T2), 14-15 (T3), and 16-17 (T4) years. The bidirectional relationships between depressive symptoms and disordered eating were estimated in an autoregressive cross-lagged model with latent variables. A unidirectional, age-specific association between depressive symptoms at T1 and disordered eating at T2 was found. No other significant cross-lagged effect emerged, but the stability of the constructs was considerable. Gender did not moderate any of the links examined. Regardless of gender, the transition from childhood to adolescence appears to be a key period when depressive symptoms foster the development of disordered eating. These findings suggest that early prevention and treatment of depression targeting both girls and boys may result in lower levels of depressive symptoms and disordered eating in adolescence.

Temporal dynamics of physical activity and affect in depressed and nondepressed individuals.

PubMed

Stavrakakis, Nikolaos; Booij, Sanne H; Roest, Annelieke M; de Jonge, Peter; Oldehinkel, Albertine J; Bos, Elisabeth H

2015-12-01

The association between physical activity and affect found in longitudinal observational studies is generally small to moderate. It is unknown how this association generalizes to individuals. The aim of the present study was to investigate interindividual differences in the bidirectional dynamic relationship between physical activity and affect, in depressed and nondepressed individuals, using time-series analysis. A pair-matched sample of 10 depressed and 10 nondepressed participants (mean age = 36.6, SD = 8.9, 30% males) wore accelerometers and completed electronic questionnaires 3 times a day for 30 days. Physical activity was operationalized as the total energy expenditure (EE) per day segment (i.e., 6 hr). The multivariate time series (T = 90) of every individual were analyzed using vector autoregressive modeling (VAR), with the aim to assess direct as well as lagged (i.e., over 1 day) effects of EE on positive and negative affect, and vice versa. Large interindividual differences in the strength, direction and temporal aspects of the relationship between physical activity and positive and negative affect were observed. An exception was the direct (but not the lagged) effect of physical activity on positive affect, which was positive in nearly all individuals. This study showed that the association between physical activity and affect varied considerably across individuals. Thus, while at the group level the effect of physical activity on affect may be small, in some individuals the effect may be clinically relevant. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

A time-series analysis of the impact of heavy drinking on homicide and suicide mortality in Russia, 1956–2002*

PubMed Central

Pridemore, William Alex; Chamlin, Mitchell B.

2008-01-01

Aim Assess the impact of heavy drinking on homicide and suicide mortality in Russia between 1956 and 2002. Measures and design Alcohol-related mortality was used as a proxy for heavy drinking. We used autoregressive integrated moving average techniques to model total and sex-specific alcohol—homicide and alcohol—suicide relationships at the population level. Findings We found a positive and significant contemporaneous association between alcohol and homicide and between alcohol and suicide. We found no evidence of lagged relationships. These results held for overall and sex-specific associations. Conclusion Our results lend convergent validity to the alcohol—suicide link in Russia found by Nemtsov and to the alcohol—homicide associations found in cross-sectional analyses of Russia. Levels of alcohol consumption, homicide and suicide in Russia are among the highest in the world, and the mounting evidence of the damaging effects of consumption on the social fabric of the country reveals the need for intervention at multiple levels. PMID:17156171

The Interplay Between Child and Maternal Health: Reciprocal Relationships and Cumulative Disadvantage During Childhood and Adolescence*

PubMed Central

Garbarski, Dana

2015-01-01

While many studies use parental socioeconomic status and health to predict children's health, this study examines the interplay over time between child and maternal health across childhood and adolescence. Using data from women in the National Longitudinal Study of Youth 1979 cohort and their children (N = 2,225), autoregressive cross-lagged models demonstrate that at particular points during childhood and adolescence, there are direct effects of child activity limitations on maternal health limitations two years later and direct effects of maternal health limitations on child activity limitations two years later, net of a range of health-relevant time-varying and time-invariant covariates. Furthermore, there are indirect effects of child activity limitations on subsequent maternal health limitations and indirect effects of maternal health limitations on subsequent child activity limitations via intervening health statuses. This study examines how the interplay between child and maternal health unfolds over time and describes how these interdependent statuses jointly experience health disadvantages. PMID:24578398

Reciprocal relations between body satisfaction and self-esteem: A large 13-year prospective study of adolescents.

PubMed

Wichstrøm, Lars; von Soest, Tilmann

2016-02-01

Previous research has demonstrated that body satisfaction and self-esteem are highly correlated in adolescence, but reasons are poorly understood. We tested three explanations: (i) the two constructs are actually one; (ii) the correlation is explained by a third factor; (iii) there are prospective relationships between body satisfaction and self-esteem. A population based sample of Norwegian adolescents (n = 3251) was examined four times over a 13-year period. Confirmatory factor analysis showed that body satisfaction and self-esteem were separate constructs and the correlation between them was not attenuated when adjusting for 3rd variables. Autoregressive cross-lagged analysis showed reciprocal relations between body satisfaction and self-esteem. The prospective relationship between body satisfaction during adolescence and self-esteem in late adolescence and emerging adulthood was stronger than at later stages. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

The Multiple Faces of Interparental Conflict: Implications for Cascades of Children’s Insecurity and Externalizing Problems

PubMed Central

Davies, Patrick T.; Hentges, Rochelle F.; Coe, Jesse L.; Martin, Meredith J.; Sturge-Apple, Melissa L.; Cummings, E. Mark

2016-01-01

This multi-study paper examined the relative strength of mediational pathways involving hostile, disengaged, and uncooperative forms of interparental conflict, children’s emotional insecurity, and their externalizing problems across two longitudinal studies. Participants in Study 1 consisted of 243 preschool children (M age = 4.60 years) and their parents, whereas Study 2 consisted of 263 adolescents (M age = 12.62 years) and their parents. Both studies utilized multi-method, multi-informant assessment batteries within a longitudinal design with three measurement occasions. Across both studies, lagged, autoregressive tests of the mediational paths revealed that interparental hostility was a significantly stronger predictor of the prospective cascade of children’s insecurity and externalizing problems than interparental disengagement and low levels of interparental cooperation. Findings further indicated that interparental disengagement was a stronger predictor of the insecurity pathway than was low interparental cooperation for the sample of adolescents in Study 2. Results are discussed in relation to how they inform and advance developmental models of family risk. PMID:27175983

Meteorological variables and bacillary dysentery cases in Changsha City, China.

PubMed

Gao, Lu; Zhang, Ying; Ding, Guoyong; Liu, Qiyong; Zhou, Maigeng; Li, Xiujun; Jiang, Baofa

2014-04-01

This study aimed to investigate the association between meteorological-related risk factors and bacillary dysentery in a subtropical inland Chinese area: Changsha City. The cross-correlation analysis and the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model were used to quantify the relationship between meteorological factors and the incidence of bacillary dysentery. Monthly mean temperature, mean relative humidity, mean air pressure, mean maximum temperature, and mean minimum temperature were significantly correlated with the number of bacillary dysentery cases with a 1-month lagged effect. The ARIMAX models suggested that a 1°C rise in mean temperature, mean maximum temperature, and mean minimum temperature might lead to 14.8%, 12.9%, and 15.5% increases in the incidence of bacillary dysentery disease, respectively. Temperature could be used as a forecast factor for the increase of bacillary dysentery in Changsha. More public health actions should be taken to prevent the increase of bacillary dysentery disease with consideration of local climate conditions, especially temperature.

Meteorological Variables and Bacillary Dysentery Cases in Changsha City, China

PubMed Central

Gao, Lu; Zhang, Ying; Ding, Guoyong; Liu, Qiyong; Zhou, Maigeng; Li, Xiujun; Jiang, Baofa

2014-01-01

This study aimed to investigate the association between meteorological-related risk factors and bacillary dysentery in a subtropical inland Chinese area: Changsha City. The cross-correlation analysis and the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model were used to quantify the relationship between meteorological factors and the incidence of bacillary dysentery. Monthly mean temperature, mean relative humidity, mean air pressure, mean maximum temperature, and mean minimum temperature were significantly correlated with the number of bacillary dysentery cases with a 1-month lagged effect. The ARIMAX models suggested that a 1°C rise in mean temperature, mean maximum temperature, and mean minimum temperature might lead to 14.8%, 12.9%, and 15.5% increases in the incidence of bacillary dysentery disease, respectively. Temperature could be used as a forecast factor for the increase of bacillary dysentery in Changsha. More public health actions should be taken to prevent the increase of bacillary dysentery disease with consideration of local climate conditions, especially temperature. PMID:24591435

Cycle of Violence in Schools: Longitudinal Reciprocal Relationship Between Student's Aggression and Teacher's Use of Corporal Punishment.

PubMed

Choi, Boungho

2017-11-01

The purpose of this study is to examine the longitudinal reciprocal relationship between student's aggression and teacher's use of corporal punishment. An autoregressive cross-lagged model was analyzed with the data drawn from 4,051 Korean secondary students (male = 2,084, female = 1,967), in Gyeonggi Education Panel Study for three waves (seventh-ninth grades). Results revealed that student's aggression provoke teacher's use of corporal punishment and also teacher's use of corporal punishment provokes student's aggression. It is important in that it suggests the cycle of violence with the reciprocal relationship between student's aggression and teacher's use of corporal punishment, rather than positing the unidirectional effects. Practically, teachers should keep in mind that corporal punishments, which are at least partially attributable to student's aggression, actually worsen the problem and lead to a cycle of violence in schools. Accordingly, they should instead respond with alternative disciplinary strategies or direct interventions dealing with the causes of aggression.

A worthy self is a caring self: Examining the developmental relations between self-esteem and self-compassion in adolescents.

PubMed

Donald, James N; Ciarrochi, Joseph; Parker, Philip D; Sahdra, Baljinder K; Marshall, Sarah L; Guo, Jiesi

2017-08-18

Self-compassion has been framed as a healthy alternative to self-esteem, as it is nonevaluative. However, rather than being alternatives, it may be that the two constructs develop in a mutually reinforcing way. The present study tested this possibility among adolescents. A large adolescent sample (N = 2,809; 49.8% female) reported levels of trait self-esteem and self-compassion annually for 4 years. Autoregressive cross-lagged structural equation models were used to estimate the reciprocal longitudinal relations between the two constructs. Self-esteem consistently predicted changes in self-compassion across the 4 years of the study, but not vice versa. Self-esteem appears to be an important antecedent of the development of self-compassion, perhaps because the capacity to extend compassion toward the self depends on one's appraisals of worthiness. These findings add important insights to our theoretical understanding of the development of self-compassion. © 2017 Wiley Periodicals, Inc.

Teacher Support, Peer Acceptance, and Engagement in the Classroom: A Three-Wave Longitudinal Study in Late Childhood.

PubMed

Weyns, Tessa; Colpin, Hilde; De Laet, Steven; Engels, Maaike; Verschueren, Karine

2018-06-01

Although research has examined the bivariate effects of teacher support, peer acceptance, and engagement, it remains unclear how these key classroom experiences evolve together, especially in late childhood. This study aims to provide a detailed picture of their transactional relations in late childhood. A sample of 586 children (M age  = 9.26 years, 47.1% boys) was followed from fourth to sixth grade. Teacher support and engagement were student-reported and peer acceptance was peer-reported. Autoregressive cross-lagged models revealed unique longitudinal effects of both peer acceptance and teacher support on engagement, and of peer acceptance on teacher support. No reverse effects of engagement on peer acceptance or teacher support were found. The study underscores the importance of examining the relative contribution of several social actors in the classroom. Regarding interventions, improving both peer acceptance and teacher support can increase children's engagement, and augmenting peer acceptance can help to increase teacher support.

Religiousness, spiritual seeking, and personality: findings from a longitudinal study.

PubMed

Wink, Paul; Ciciolla, Lucia; Dillon, Michele; Tracy, Allison

2007-10-01

The hypothesis that personality characteristics in adolescence can be used to predict religiousness and spiritual seeking in late adulthood was tested using a structural equation modeling framework to estimate cross-lagged and autoregressive effects in a two-wave panel design. The sample consisted of 209 men and women participants in the Berkeley Guidance and Oakland Growth studies. In late adulthood, religiousness was positively related to Conscientiousness and Agreeableness, and spiritual seeking was related to Openness to Experience. Longitudinal models indicated that Conscientiousness in adolescence significantly predicted religiousness in late adulthood above and beyond adolescent religiousness. Similarly, Openness in adolescence predicted spiritual seeking in late adulthood. The converse effect, adolescent religiousness to personality in late adulthood, was not significant in either model. Among women, adolescent Agreeableness predicted late-life religiousness and adolescent religiousness predicted late-life Agreeableness; both these effects were absent among men. Adolescent personality appears to shape late-life religiousness and spiritual seeking independent of early religious socialization.

Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

NASA Astrophysics Data System (ADS)

Khan, Muhammad Yousaf; Mittnik, Stefan

2018-01-01

In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

Radio pulsar glitches as a state-dependent Poisson process

NASA Astrophysics Data System (ADS)

Fulgenzi, W.; Melatos, A.; Hughes, B. D.

2017-10-01

Gross-Pitaevskii simulations of vortex avalanches in a neutron star superfluid are limited computationally to ≲102 vortices and ≲102 avalanches, making it hard to study the long-term statistics of radio pulsar glitches in realistically sized systems. Here, an idealized, mean-field model of the observed Gross-Pitaevskii dynamics is presented, in which vortex unpinning is approximated as a state-dependent, compound Poisson process in a single random variable, the spatially averaged crust-superfluid lag. Both the lag-dependent Poisson rate and the conditional distribution of avalanche-driven lag decrements are inputs into the model, which is solved numerically (via Monte Carlo simulations) and analytically (via a master equation). The output statistics are controlled by two dimensionless free parameters: α, the glitch rate at a reference lag, multiplied by the critical lag for unpinning, divided by the spin-down rate; and β, the minimum fraction of the lag that can be restored by a glitch. The system evolves naturally to a self-regulated stationary state, whose properties are determined by α/αc(β), where αc(β) ≈ β-1/2 is a transition value. In the regime α ≳ αc(β), one recovers qualitatively the power-law size and exponential waiting-time distributions observed in many radio pulsars and Gross-Pitaevskii simulations. For α ≪ αc(β), the size and waiting-time distributions are both power-law-like, and a correlation emerges between size and waiting time until the next glitch, contrary to what is observed in most pulsars. Comparisons with astrophysical data are restricted by the small sample sizes available at present, with ≤35 events observed per pulsar.

Spatial pattern of diarrhea based on regional economic and environment by spatial autoregressive model

NASA Astrophysics Data System (ADS)

Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy

2014-10-01

The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.

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

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

PubMed

Porto, Markus; Roman, H Eduardo

2002-04-01

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

Association of Interleukin-17 polymorphism (-197G/A) in chronic and localized aggressive periodontitis.

PubMed

Chaudhari, Harshal Liladhar; Warad, Shivaraj; Ashok, Nipun; Baroudi, Kusai; Tarakji, Bassel

2016-01-01

Interleukin 17(IL-17) is a pro-inflammatory cytokine produced mainly by Th17 cells. The present study was undertaken to investigate a possible association between IL-17 A genetic polymorphism at (-197A/G) and susceptibility to chronic and localized aggressive periodontitis (LAgP) in an Indian population. The study was carried out on 105 subjects, which included 35 LAgP patients, 35 chronic periodontitis patients and 35 healthy controls. Blood samples were drawn from the subjects and analyzed for IL-17 genetic polymorphism at (-197A/G), by using the polymerase chain reaction-restriction fragment length polymorphism method. A statistically significant difference was seen in the genotype distribution among chronic periodontitis patients, LAgP patients and healthy subjects. There was a significant difference in the distribution of alleles among chronic periodontitis patients, LAgP patients and healthy subjects. The odds ratio for A allele versus G allele was 5.1 between chronic periodontitis patients and healthy controls, and 5.1 between LAgp patients and healthy controls. Our study concluded that IL-17 A gene polymorphism at (-197A/G) is linked to chronic periodontitis and LAgP in Indian population. The presence of allele A in the IL-17 gene polymorphism (-197A/G) can be considered a risk factor for chronic periodontitis and LAgP.

Determinants of healthcare expenditures in Iran: evidence from a time series analysis

PubMed Central

Rezaei, Satar; Fallah, Razieh; Kazemi Karyani, Ali; Daroudi, Rajabali; Zandiyan, Hamed; Hajizadeh, Mohammad

2016-01-01

Background: A dramatic increase in healthcare expenditures is a major health policy concern worldwide. Understanding factors that underlie the growth in healthcare expenditures is essential to assist decision-makers in finding best policies to manage healthcare costs. We aimed to examine the determinants of healthcare spending in Iran over the periods of 1978-2011. Methods: A time series analysis was used to examine the effect of selected socio-economic, demographic and health service input on per capita healthcare expenditures (HCE) in Iran from 1978 to 2011. Data were retrieved from the Central Bank of Iran, Iranian Statistical Center and World Bank. Autoregressive distributed lag approach and error correction method were employed to examine long- and short-run effects of covariates. Results: Our findings indicated that the GDP per capita, degree of urbanization and illiteracy rate increase healthcare expenditures, while physician per 10,000 populations and proportion of population aged≥ 65 years decrease healthcare expenditures. In addition, we found that healthcare spending is a "necessity good" with long- and short-run income (GDP per capita), elasticities of 0.46 (p<0.01) and 0.67 (p = 0.01), respectively. Conclusion: Our analysis identified GDP per capita, illiteracy rate, degree of urbanization and number of physicians as some of the driving forces behind the persistent increase in HCE in Iran. These findings provide important insights into the growth in HCE in Iran. In addition, since we found that health spending is a "necessity good" in Iran, healthcare services should thus be the object of public funding and government intervention PMID:27390683

The dynamic relationship between structural change and CO2 emissions in Malaysia: a cointegrating approach.

PubMed

Ali, Wajahat; Abdullah, Azrai; Azam, Muhammad

2017-05-01

The current study investigates the dynamic relationship between structural changes, real GDP per capita, energy consumption, trade openness, population density, and carbon dioxide (CO 2 ) emissions within the EKC framework over a period 1971-2013. The study used the autoregressive distributed lagged (ARDL) approach to investigate the long-run relationship between the selected variables. The study also employed the dynamic ordinary least squared (DOLS) technique to obtain the robust long-run estimates. Moreover, the causal relationship between the variables is explored using the VECM Granger causality test. Empirical results reveal a negative relationship between structural change and CO 2 emissions in the long run. The results indicate a positive relationship between energy consumption, trade openness, and CO 2 emissions. The study applied the turning point formula of Itkonen (2012) rather than the conventional formula of the turning point. The empirical estimates of the study do not support the presence of the EKC relationship between income and CO 2 emissions. The Granger causality test indicates the presence of long-run bidirectional causality between energy consumption, structural change, and CO 2 emissions in the long run. Economic growth, openness to trade, and population density unidirectionally cause CO 2 emissions. These results suggest that the government should focus more on information-based services rather than energy-intensive manufacturing activities. The feedback relationship between energy consumption and CO 2 emissions suggests that there is an ominous need to refurbish the energy-related policy reforms to ensure the installations of some energy-efficient modern technologies.

The dynamic interaction between combustible renewables and waste consumption and international tourism: the case of Tunisia.

PubMed

Ben Jebli, Mehdi; Ben Youssef, Slim; Apergis, Nicholas

2015-08-01

This paper employs the autoregressive distributed lag (ARDL) bounds methodological approach to investigate the relationship between economic growth, combustible renewables and waste consumption, carbon dioxide (CO2) emissions, and international tourism for the case of Tunisia spanning the period 1990-2010. The results from the Fisher statistic of both the Wald test and the Johansen test confirm the presence of a long-run relationship among the variables under investigation. The stability of estimated parameters has been tested, while Granger causality tests recommend a short-run unidirectional causality running from economic growth and combustible renewables and waste consumption to CO2 emissions, a bidirectional causality between economic growth and combustible renewables and waste consumption and unidirectional causality running from economic growth and combustible renewables and waste consumption to international tourism. In the long-run, the error correction terms confirm the presence of bidirectional causality relationships between economic growth, CO2 emissions, combustible renewables and waste consumption, and international tourism. Our long-run estimates show that combustible renewables and waste consumption increases international tourism, and both renewables and waste consumption and international tourism increase CO2 emissions and output. We recommend that (i) Tunisia should use more combustible renewables and waste energy as this eliminates wastes from touristic zones and increases the number of tourist arrivals, leading to economic growth, and (ii) a fraction of this economic growth generated by the increase in combustible renewables and waste consumption should be invested in clean renewable energy production (i.e., solar, wind, geothermal) and energy efficiency projects.

Effects of Air Pollution on Public and Private Health Expenditures in Iran: A Time Series Study (1972-2014)

PubMed Central

Mousavi, Abdoreza; Khodabakhshzadeh, Saeed

2018-01-01

Objectives Environmental pollution is a negative consequence of the development process, and many countries are grappling with this phenomenon. As a developing country, Iran is not exempt from this rule, and Iran pays huge expenditures for the consequences of pollution. The aim of this study was to analyze the long- and short-run impact of air pollution, along with other health indicators, on private and public health expenditures. Methods This study was an applied and developmental study. Autoregressive distributed lag estimating models were used for the period of 1972 to 2014. In order to determine the co-integration between health expenditures and the infant mortality rate, fertility rate, per capita income, and pollution, we used the Wald test in Microfit version 4.1. We then used Eviews version 8 to evaluate the stationarity of the variables and to estimate the long- and short-run relationships. Results Long-run air pollution had a positive and significant effect on health expenditures, so that a 1.00% increase in the index of carbon dioxide led to an increase of 3.32% and 1.16% in public and private health expenditures, respectively. Air pollution also had a greater impact on health expenditures in the long term than in the short term. Conclusions The findings of this study indicate that among the factors affecting health expenditures, environmental quality and contaminants played the most important role. Therefore, in order to reduce the financial burden of health expenditures in Iran, it is essential to reduce air pollution by enacting and implementing laws that protect the environment. PMID:29886709

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.

Does agricultural ecosystem cause environmental pollution in Pakistan? Promise and menace.

PubMed

Ullah, Arif; Khan, Dilawar; Khan, Imran; Zheng, Shaofeng

2018-05-01

The increasing trend of atmospheric carbon dioxide (CO 2 ) is the main cause of harmful anthropogenic greenhouse gas emissions, which may result in environmental pollution, global warming, and climate change. These issues are expected to adversely affect the agricultural ecosystem and well-being of the society. In order to minimize food insecurity and prevent hunger, a timely adaptation is desirable to reduce potential losses and to seek alternatives for promoting a global knowledge system for agricultural sustainability. This paper examines the causal relationship between agricultural ecosystem and CO 2 emissions as an environmental pollution indicator in Pakistan from the period 1972 to 2014 by employing Johansen cointegration, autoregressive distributed lag (ARDL) model, and Granger causality approach. The Johansen cointegration results show that there is a significant long-run relationship between the agricultural ecosystem and the CO 2 emissions. The long-run relationship shows that a 1% increase in biomass burned crop residues, emissions of CO 2 equivalent of nitrous oxide (N 2 O) from synthetic fertilizers, stock of livestock, agricultural machinery, cereal production, and other crop productions will increase CO 2 emissions by 1.29, 0.05, 0.45, 0.05, 0.03, and 0.65%, respectively. Further, our finding detects that there is a bidirectional causality of CO 2 emissions with rice area paddy harvested, cereal production, and other crop productions. The impulse response function analysis displays that biomass-burned crop residues, stock of livestock, agriculture machinery, cereal production, and other crop productions are significantly contributing to CO 2 emissions in Pakistan.

Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination.

PubMed

Chuang, Ting-Wu; Soble, Adam; Ntshalintshali, Nyasatu; Mkhonta, Nomcebo; Seyama, Eric; Mthethwa, Steven; Pindolia, Deepa; Kunene, Simon

2017-06-01

Swaziland aims to eliminate malaria by 2020. However, imported cases from neighbouring endemic countries continue to sustain local parasite reservoirs and initiate transmission. As certain weather and climatic conditions may trigger or intensify malaria outbreaks, identification of areas prone to these conditions may aid decision-makers in deploying targeted malaria interventions more effectively. Malaria case-surveillance data for Swaziland were provided by Swaziland's National Malaria Control Programme. Climate data were derived from local weather stations and remote sensing images. Climate parameters and malaria cases between 2001 and 2015 were then analysed using seasonal autoregressive integrated moving average models and distributed lag non-linear models (DLNM). The incidence of malaria in Swaziland increased between 2005 and 2010, especially in the Lubombo and Hhohho regions. A time-series analysis indicated that warmer temperatures and higher precipitation in the Lubombo and Hhohho administrative regions are conducive to malaria transmission. DLNM showed that the risk of malaria increased in Lubombo when the maximum temperature was above 30 °C or monthly precipitation was above 5 in. In Hhohho, the minimum temperature remaining above 15 °C or precipitation being greater than 10 in. might be associated with malaria transmission. This study provides a preliminary assessment of the impact of short-term climate variations on malaria transmission in Swaziland. The geographic separation of imported and locally acquired malaria, as well as population behaviour, highlight the varying modes of transmission, part of which may be relevant to climate conditions. Thus, the impact of changing climate conditions should be noted as Swaziland moves toward malaria elimination.

Effects of Air Pollution on Public and Private Health Expenditures in Iran: A Time Series Study (1972-2014).

PubMed

Raeissi, Pouran; Harati-Khalilabad, Touraj; Rezapour, Aziz; Hashemi, Seyed Yaser; Mousavi, Abdoreza; Khodabakhshzadeh, Saeed

2018-05-01

Environmental pollution is a negative consequence of the development process, and many countries are grappling with this phenomenon. As a developing country, Iran is not exempt from this rule, and Iran pays huge expenditures for the consequences of pollution. The aim of this study was to analyze the long- and short-run impact of air pollution, along with other health indicators, on private and public health expenditures. This study was an applied and developmental study. Autoregressive distributed lag estimating models were used for the period of 1972 to 2014. In order to determine the co-integration between health expenditures and the infant mortality rate, fertility rate, per capita income, and pollution, we used the Wald test in Microfit version 4.1. We then used Eviews version 8 to evaluate the stationarity of the variables and to estimate the long- and short-run relationships. Long-run air pollution had a positive and significant effect on health expenditures, so that a 1.00% increase in the index of carbon dioxide led to an increase of 3.32% and 1.16% in public and private health expenditures, respectively. Air pollution also had a greater impact on health expenditures in the long term than in the short term. The findings of this study indicate that among the factors affecting health expenditures, environmental quality and contaminants played the most important role. Therefore, in order to reduce the financial burden of health expenditures in Iran, it is essential to reduce air pollution by enacting and implementing laws that protect the environment.

Vector Autoregressive Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example.

PubMed

Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M

Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity, (b) appropriate lag was determined using a lag-length selection criteria, (c) the VAR model was constructed, (d) residual autocorrelation was assessed with the Lagrange Multiplier test, (e) stability of the VAR system was checked, and (f) Granger causality was evaluated in the final stable model. The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes tend to occur before changes in HR and SpO2. These findings suggest that contextual assessment of RR changes as the earliest sign of CRI is warranted. Use of VAR modeling may be helpful in other nursing research applications based on time series data.

Exposure-lag-response in Longitudinal Studies: Application of Distributed Lag Non-linear Models in an Occupational Cohort.

PubMed

Neophytou, Andreas M; Picciotto, Sally; Brown, Daniel M; Gallagher, Lisa E; Checkoway, Harvey; Eisen, Ellen A; Costello, Sadie

2018-02-13

Prolonged exposures can have complex relationships with health outcomes, as timing, duration, and intensity of exposure are all potentially relevant. Summary measures such as cumulative exposure or average intensity of exposure may not fully capture these relationships. We applied penalized and unpenalized distributed lag non-linear models (DLNMs) with flexible exposure-response and lag-response functions in order to examine the association between crystalline silica exposure and mortality from lung cancer and non-malignant respiratory disease in a cohort study of 2,342 California diatomaceous earth workers, followed 1942-2011. We also assessed associations using simple measures of cumulative exposure assuming linear exposure-response and constant lag-response. Measures of association from DLNMs were generally higher than from simpler models. Rate ratios from penalized DLNMs corresponding to average daily exposures of 0.4 mg/m3 during lag years 31-50 prior to the age of observed cases were 1.47 (95% confidence interval (CI) 0.92, 2.35) for lung cancer and 1.80 (95% CI: 1.14, 2.85) for non-malignant respiratory disease. Rate ratios from the simpler models for the same exposure scenario were 1.15 (95% CI: 0.89-1.48) and 1.23 (95% CI: 1.03-1.46) respectively. Longitudinal cohort studies of prolonged exposures and chronic health outcomes should explore methods allowing for flexibility and non-linearities in the exposure-lag-response. © The Author(s) 2018. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

Incorporating measurement error in n = 1 psychological autoregressive modeling.

PubMed

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

2015-01-01

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

Temporal and micro-spatial heterogeneity in the distribution of Anopheles vectors of malaria along the Kenyan coast

PubMed Central

2013-01-01

Background The distribution of anopheline mosquitoes is determined by temporally dynamic environmental and human-associated variables, operating over a range of spatial scales. Macro-spatial short-term trends are driven predominantly by prior (lagged) seasonal changes in climate, which regulate the abundance of suitable aquatic larval habitats. Micro-spatial distribution is determined by the location of these habitats, proximity and abundance of available human bloodmeals and prevailing micro-climatic conditions. The challenge of analysing—in a single coherent statistical framework—the lagged and distributed effect of seasonal climate changes simultaneously with the effects of an underlying hierarchy of spatial factors has hitherto not been addressed. Methods Data on Anopheles gambiae sensu stricto and A. funestus collected from households in Kilifi district, Kenya, were analysed using polynomial distributed lag generalized linear mixed models (PDL GLMMs). Results Anopheline density was positively and significantly associated with amount of rainfall between 4 to 47 days, negatively and significantly associated with maximum daily temperature between 5 and 35 days, and positively and significantly associated with maximum daily temperature between 29 and 48 days in the past (depending on Anopheles species). Multiple-occupancy households harboured greater mosquito numbers than single-occupancy households. A significant degree of mosquito clustering within households was identified. Conclusions The PDL GLMMs developed here represent a generalizable framework for analysing hierarchically-structured data in combination with explanatory variables which elicit lagged effects. The framework is a valuable tool for facilitating detailed understanding of determinants of the spatio-temporal distribution of Anopheles. Such understanding facilitates delivery of targeted, cost-effective and, in certain circumstances, preventative antivectorial interventions against malaria. PMID:24330615

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

NASA Astrophysics Data System (ADS)

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

2013-08-01

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

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

PubMed

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

2016-03-01

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

Patterns of Activity in A Global Model of A Solar Active Region

NASA Technical Reports Server (NTRS)

Bradshaw, S. J.; Viall, N. M.

2016-01-01

In this work we investigate the global activity patterns predicted from a model active region heated by distributions of nanoflares that have a range of frequencies. What differs is the average frequency of the distributions. The activity patterns are manifested in time lag maps of narrow-band instrument channel pairs. We combine hydrodynamic and forward modeling codes with a magnetic field extrapolation to create a model active region and apply the time lag method to synthetic observations. Our aim is not to reproduce a particular set of observations in detail, but to recover some typical properties and patterns observed in active regions. Our key findings are the following. (1) Cooling dominates the time lag signature and the time lags between the channel pairs are generally consistent with observed values. (2) Shorter coronal loops in the core cool more quickly than longer loops at the periphery. (3) All channel pairs show zero time lag when the line of sight passes through coronal loop footpoints. (4) There is strong evidence that plasma must be re-energized on a timescale comparable to the cooling timescale to reproduce the observed coronal activity, but it is likely that a relatively broad spectrum of heating frequencies are operating across active regions. (5) Due to their highly dynamic nature, we find nanoflare trains produce zero time lags along entire flux tubes in our model active region that are seen between the same channel pairs in observed active regions.

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Comorbidity effects on cocaine dependence treatment and examination of reciprocal relationships between abstinence and depression.

PubMed

Milby, Jesse B; Conti, Kimberly; Wallace, Dennis; Mennemeyer, Stephen; Mrug, Sylvie; Schumacher, Joseph E

2015-02-01

We examined comorbid disorders' prevalence, their impact on abstinence, and the impact of depressive symptoms on abstinence and of abstinence on depressive symptoms. A randomized controlled trial's data on outcomes from treating cocaine dependence were used. It compared abstinence-contingent housing and work to contingency management plus behavioral day treatment. Regardless of original trial arm assignment, groups of participants with no additional Axis I disorders (n = 87) and 1 or more additional Axis I disorders (n = 113) were compared for abstinence. Changes in depression symptoms, measured by the Beck Depression Inventory, were analyzed as a function of 4 cohorts of increased consecutive weeks abstinent. An autoregressive cross-lagged path model examined reciprocal relationships between depression and abstinence. Most prevalent additional disorders were depressive disorders, followed by anxiety disorders. Additional disorders did not significantly affect abstinence. Cohorts with more abstinence were linearly related to lower depression symptoms. The cross-lagged model showed that longer abstinence predicted decreases in depressive symptoms at 6 months. However, depressive symptoms did not predict changes in abstinence. Our study adds to others that have found an effective treatment targeted at specific problems such as substance abuse, social anxiety disorder, and posttraumatic stress disorder that may have the side benefit of reducing depression. Additionally, we find that depression does not interfere with effective substance abuse treatment for cocaine dependency. This may be the 1st formal analysis comparing the ability of cocaine abstinence to predict future depressive symptoms versus depressive symptoms to predict future cocaine abstinence. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Evaluation Of Statistical Models For Forecast Errors From The HBV-Model

NASA Astrophysics Data System (ADS)

Engeland, K.; Kolberg, S.; Renard, B.; Stensland, I.

2009-04-01

Three statistical models for the forecast errors for inflow to the Langvatn reservoir in Northern Norway have been constructed and tested according to how well the distribution and median values of the forecasts errors fit to the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order autoregressive model was constructed for the forecast errors. The parameters were conditioned on climatic conditions. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order autoregressive model was constructed for the forecast errors. For the last model positive and negative errors were modeled separately. The errors were first NQT-transformed before a model where the mean values were conditioned on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: We wanted a) the median values to be close to the observed values; b) the forecast intervals to be narrow; c) the distribution to be correct. The results showed that it is difficult to obtain a correct model for the forecast errors, and that the main challenge is to account for the auto-correlation in the errors. Model 1 and 2 gave similar results, and the main drawback is that the distributions are not correct. The 95% forecast intervals were well identified, but smaller forecast intervals were over-estimated, and larger intervals were under-estimated. Model 3 gave a distribution that fits better, but the median values do not fit well since the auto-correlation is not properly accounted for. If the 95% forecast interval is of interest, Model 2 is recommended. If the whole distribution is of interest, Model 3 is recommended.

Robust Semi-Active Ride Control under Stochastic Excitation

DTIC Science & Technology

2014-01-01

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

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

NASA Technical Reports Server (NTRS)

Larsen, Curtis E.

1988-01-01

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

Incorporating measurement error in n = 1 psychological autoregressive modeling

PubMed Central

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

2015-01-01

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

Chromosome behaviour in Rhoeo spathacea var. variegata.

PubMed

Lin, Y J

1980-01-01

Rhoeo spathacea var. variegata is unusual in that its twelve chromosomes are arranged in a ring at meiosis. The order of the chromosomes has been established, and each chromosome arm has been designated a letter in accordance with the segmental interchange theory. Chromosomes are often irregularly orientated at metaphase I. Chromosomes at anaphase I are generally distributed equally (6-6, 58.75%) although not necessarily balanced. Due to adjacent distribution, 7-5 distribution at anaphase I was frequently observed (24.17%), and due to lagging, 6-1-5 and 5-2-5 distributions were also observed (10.83% and 3.33% respectively). Three types of abnormal distribution, 8-4, 7-1-4 and 6-2-4 were observed very infrequently (2.92% total), and their possible origins are discussed. Irregularities, such as adjacent distribution and lagging, undoubtedly reduce the fertility of the plant because of the resulting unbalanced gametes.

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

PubMed

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

2017-01-01

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

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud

PubMed Central

Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

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

Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

NASA Astrophysics Data System (ADS)

Sardinha-Lourenço, A.; Andrade-Campos, A.; Antunes, A.; Oliveira, M. S.

2018-03-01

Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24-48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.

Probability distribution of financial returns in a model of multiplicative Brownian motion with stochastic diffusion coefficient

NASA Astrophysics Data System (ADS)

Silva, Antonio

2005-03-01

It is well-known that the mathematical theory of Brownian motion was first developed in the Ph. D. thesis of Louis Bachelier for the French stock market before Einstein [1]. In Ref. [2] we studied the so-called Heston model, where the stock-price dynamics is governed by multiplicative Brownian motion with stochastic diffusion coefficient. We solved the corresponding Fokker-Planck equation exactly and found an analytic formula for the time-dependent probability distribution of stock price changes (returns). The formula interpolates between the exponential (tent-shaped) distribution for short time lags and the Gaussian (parabolic) distribution for long time lags. The theoretical formula agrees very well with the actual stock-market data ranging from the Dow-Jones index [2] to individual companies [3], such as Microsoft, Intel, etc. [] [1] Louis Bachelier, ``Th'eorie de la sp'eculation,'' Annales Scientifiques de l''Ecole Normale Sup'erieure, III-17:21-86 (1900).[] [2] A. A. Dragulescu and V. M. Yakovenko, ``Probability distribution of returns in the Heston model with stochastic volatility,'' Quantitative Finance 2, 443--453 (2002); Erratum 3, C15 (2003). [cond-mat/0203046] [] [3] A. C. Silva, R. E. Prange, and V. M. Yakovenko, ``Exponential distribution of financial returns at mesoscopic time lags: a new stylized fact,'' Physica A 344, 227--235 (2004). [cond-mat/0401225

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

PubMed

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

2013-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Lags in the response of mountain plant communities to climate change.

PubMed

Alexander, Jake M; Chalmandrier, Loïc; Lenoir, Jonathan; Burgess, Treena I; Essl, Franz; Haider, Sylvia; Kueffer, Christoph; McDougall, Keith; Milbau, Ann; Nuñez, Martin A; Pauchard, Aníbal; Rabitsch, Wolfgang; Rew, Lisa J; Sanders, Nathan J; Pellissier, Loïc

2018-02-01

Rapid climatic changes and increasing human influence at high elevations around the world will have profound impacts on mountain biodiversity. However, forecasts from statistical models (e.g. species distribution models) rarely consider that plant community changes could substantially lag behind climatic changes, hindering our ability to make temporally realistic projections for the coming century. Indeed, the magnitudes of lags, and the relative importance of the different factors giving rise to them, remain poorly understood. We review evidence for three types of lag: "dispersal lags" affecting plant species' spread along elevational gradients, "establishment lags" following their arrival in recipient communities, and "extinction lags" of resident species. Variation in lags is explained by variation among species in physiological and demographic responses, by effects of altered biotic interactions, and by aspects of the physical environment. Of these, altered biotic interactions could contribute substantially to establishment and extinction lags, yet impacts of biotic interactions on range dynamics are poorly understood. We develop a mechanistic community model to illustrate how species turnover in future communities might lag behind simple expectations based on species' range shifts with unlimited dispersal. The model shows a combined contribution of altered biotic interactions and dispersal lags to plant community turnover along an elevational gradient following climate warming. Our review and simulation support the view that accounting for disequilibrium range dynamics will be essential for realistic forecasts of patterns of biodiversity under climate change, with implications for the conservation of mountain species and the ecosystem functions they provide. © 2017 John Wiley & Sons Ltd.

Time-dependent Electron Acceleration in Blazar Transients: X-Ray Time Lags and Spectral Formation

NASA Astrophysics Data System (ADS)

Lewis, Tiffany R.; Becker, Peter A.; Finke, Justin D.

2016-06-01

Electromagnetic radiation from blazar jets often displays strong variability, extending from radio to γ-ray frequencies. In a few cases, this variability has been characterized using Fourier time lags, such as those detected in the X-rays from Mrk 421 using BeppoSAX. The lack of a theoretical framework to interpret the data has motivated us to develop a new model for the formation of the X-ray spectrum and the time lags in blazar jets based on a transport equation including terms describing stochastic Fermi acceleration, synchrotron losses, shock acceleration, adiabatic expansion, and spatial diffusion. We derive the exact solution for the Fourier transform of the electron distribution and use it to compute the Fourier transform of the synchrotron radiation spectrum and the associated X-ray time lags. The same theoretical framework is also used to compute the peak flare X-ray spectrum, assuming that a steady-state electron distribution is achieved during the peak of the flare. The model parameters are constrained by comparing the theoretical predictions with the observational data for Mrk 421. The resulting integrated model yields, for the first time, a complete first-principles physical explanation for both the formation of the observed time lags and the shape of the peak flare X-ray spectrum. It also yields direct estimates of the strength of the shock and the stochastic magnetohydrodynamical wave acceleration components in the Mrk 421 jet.

Time dependent neural network models for detecting changes of state in complex processes: applications in earth sciences and astronomy.

PubMed

Valdés, Julio J; Bonham-Carter, Graeme

2006-03-01

A computational intelligence approach is used to explore the problem of detecting internal state changes in time dependent processes; described by heterogeneous, multivariate time series with imprecise data and missing values. Such processes are approximated by collections of time dependent non-linear autoregressive models represented by a special kind of neuro-fuzzy neural network. Grid and high throughput computing model mining procedures based on neuro-fuzzy networks and genetic algorithms, generate: (i) collections of models composed of sets of time lag terms from the time series, and (ii) prediction functions represented by neuro-fuzzy networks. The composition of the models and their prediction capabilities, allows the identification of changes in the internal structure of the process. These changes are associated with the alternation of steady and transient states, zones with abnormal behavior, instability, and other situations. This approach is general, and its sensitivity for detecting subtle changes of state is revealed by simulation experiments. Its potential in the study of complex processes in earth sciences and astrophysics is illustrated with applications using paleoclimate and solar data.

Enabling or Cultivating? The Role of Prostate Cancer Patients' Received Partner Support and Self-Efficacy in the Maintenance of Pelvic Floor Exercise Following Tumor Surgery.

PubMed

Hohl, Diana Hilda; Knoll, Nina; Wiedemann, Amelie; Keller, Jan; Scholz, Urte; Schrader, Mark; Burkert, Silke

2016-04-01

To manage incontinence following tumor surgery, prostate cancer patients are advised to perform pelvic floor exercise (PFE). Patients' self-efficacy and support from partners were shown to facilitate PFE. Whereas support may enhance self-efficacy (enabling function), self-efficacy may also cultivate support (cultivation function). Cross-lagged inter-relationships among self-efficacy, support, and PFE were investigated. Post-surgery patient-reported received support, self-efficacy, PFE, and partner-reported provided support were assessed from 175 couples at four times. Autoregressive models tested interrelations among variables, using either patients' or partners' reports of support. Models using patients' data revealed positive associations between self-efficacy and changes in received support, which predicted increased PFE. Using partners' accounts of support provided, these associations were partially cross-validated. Furthermore, partner-provided support was related with increases in patients' self-efficacy. Patients' self-efficacy may cultivate partners' support provision for patients' PFE, whereas evidence of an enabling function of support as a predictor of self-efficacy was inconsistent.

The Relation of Pro-Sociality to Self-Esteem: The Mediational Role of Quality of Friendships.

PubMed

Zuffianò, Antonio; Eisenberg, Nancy; Alessandri, Guido; Luengo Kanacri, Bernadette Paula; Pastorelli, Concetta; Milioni, Michela; Caprara, Gian Vittorio

2016-02-01

The present longitudinal study examined the role of quality of friendship in mediating the relation of pro-sociality to self-esteem over time. Participants were 424 Italian young adults (56% females) assessed at two waves (M(age) = 21.1 at Time 1; M(age) = 25 at Time 2). An autoregressive cross-lagged panel model was used to test the mediational model. Self- and friend-report measures of pro-sociality, quality of friendship, and self-esteem were included in the analyses. Results were in line with the hypothesized paths, with quality of friendship mediating the relation of pro-sociality to later self-esteem above and beyond its high stability. Self-esteem, in turn, predicted pro-sociality 4 years later. Overall, the present findings support the potential benefits of behaving pro-socially for an actor in terms of increased perceived self-worth and also expand previous work by outlining the specific mediational role of the quality of friendships. The theoretical and practical implications of these results are discussed. © 2014 Wiley Periodicals, Inc.

Statistical, time series, and fractal analysis of full stretch of river Yamuna (India) for water quality management.

PubMed

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2015-01-01

River water is a major resource of drinking water on earth. Management of river water is highly needed for surviving. Yamuna is the main river of India, and monthly variation of water quality of river Yamuna, using statistical methods have been compared at different sites for each water parameters. Regression, correlation coefficient, autoregressive integrated moving average (ARIMA), box-Jenkins, residual autocorrelation function (ACF), residual partial autocorrelation function (PACF), lag, fractal, Hurst exponent, and predictability index have been estimated to analyze trend and prediction of water quality. Predictive model is useful at 95% confidence limits and all water parameters reveal platykurtic curve. Brownian motion (true random walk) behavior exists at different sites for BOD, AMM, and total Kjeldahl nitrogen (TKN). Quality of Yamuna River water at Hathnikund is good, declines at Nizamuddin, Mazawali, Agra D/S, and regains good quality again at Juhikha. For all sites, almost all parameters except potential of hydrogen (pH), water temperature (WT) crosses the prescribed limits of World Health Organization (WHO)/United States Environmental Protection Agency (EPA).

Forecasting incidence of dengue in Rajasthan, using time series analyses.

PubMed

Bhatnagar, Sunil; Lal, Vivek; Gupta, Shiv D; Gupta, Om P

2012-01-01

To develop a prediction model for dengue fever/dengue haemorrhagic fever (DF/DHF) using time series data over the past decade in Rajasthan and to forecast monthly DF/DHF incidence for 2011. Seasonal autoregressive integrated moving average (SARIMA) model was used for statistical modeling. During January 2001 to December 2010, the reported DF/DHF cases showed a cyclical pattern with seasonal variation. SARIMA (0,0,1) (0,1,1) 12 model had the lowest normalized Bayesian information criteria (BIC) of 9.426 and mean absolute percentage error (MAPE) of 263.361 and appeared to be the best model. The proportion of variance explained by the model was 54.3%. Adequacy of the model was established through Ljung-Box test (Q statistic 4.910 and P-value 0.996), which showed no significant correlation between residuals at different lag times. The forecast for the year 2011 showed a seasonal peak in the month of October with an estimated 546 cases. Application of SARIMA model may be useful for forecast of cases and impending outbreaks of DF/DHF and other infectious diseases, which exhibit seasonal pattern.

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

PubMed

Berning, Carl C; Schlueter, Elmar

2016-01-01

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

Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.

PubMed

Hassanzadeh, S; Hosseinibalam, F; Alizadeh, R

2009-08-01

This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.

Detectability of Granger causality for subsampled continuous-time neurophysiological processes.

PubMed

Barnett, Lionel; Seth, Anil K

2017-01-01

Granger causality is well established within the neurosciences for inference of directed functional connectivity from neurophysiological data. These data usually consist of time series which subsample a continuous-time biophysiological process. While it is well known that subsampling can lead to imputation of spurious causal connections where none exist, less is known about the effects of subsampling on the ability to reliably detect causal connections which do exist. We present a theoretical analysis of the effects of subsampling on Granger-causal inference. Neurophysiological processes typically feature signal propagation delays on multiple time scales; accordingly, we base our analysis on a distributed-lag, continuous-time stochastic model, and consider Granger causality in continuous time at finite prediction horizons. Via exact analytical solutions, we identify relationships among sampling frequency, underlying causal time scales and detectability of causalities. We reveal complex interactions between the time scale(s) of neural signal propagation and sampling frequency. We demonstrate that detectability decays exponentially as the sample time interval increases beyond causal delay times, identify detectability "black spots" and "sweet spots", and show that downsampling may potentially improve detectability. We also demonstrate that the invariance of Granger causality under causal, invertible filtering fails at finite prediction horizons, with particular implications for inference of Granger causality from fMRI data. Our analysis emphasises that sampling rates for causal analysis of neurophysiological time series should be informed by domain-specific time scales, and that state-space modelling should be preferred to purely autoregressive modelling. On the basis of a very general model that captures the structure of neurophysiological processes, we are able to help identify confounds, and offer practical insights, for successful detection of causal connectivity from neurophysiological recordings. Copyright © 2016 Elsevier B.V. All rights reserved.

Impact of weather factors on hand, foot and mouth disease, and its role in short-term incidence trend forecast in Huainan City, Anhui Province.

PubMed

Zhao, Desheng; Wang, Lulu; Cheng, Jian; Xu, Jun; Xu, Zhiwei; Xie, Mingyu; Yang, Huihui; Li, Kesheng; Wen, Lingying; Wang, Xu; Zhang, Heng; Wang, Shusi; Su, Hong

2017-03-01

Hand, foot, and mouth disease (HFMD) is one of the most common communicable diseases in China, and current climate change had been recognized as a significant contributor. Nevertheless, no reliable models have been put forward to predict the dynamics of HFMD cases based on short-term weather variations. The present study aimed to examine the association between weather factors and HFMD, and to explore the accuracy of seasonal auto-regressive integrated moving average (SARIMA) model with local weather conditions in forecasting HFMD. Weather and HFMD data from 2009 to 2014 in Huainan, China, were used. Poisson regression model combined with a distributed lag non-linear model (DLNM) was applied to examine the relationship between weather factors and HFMD. The forecasting model for HFMD was performed by using the SARIMA model. The results showed that temperature rise was significantly associated with an elevated risk of HFMD. Yet, no correlations between relative humidity, barometric pressure and rainfall, and HFMD were observed. SARIMA models with temperature variable fitted HFMD data better than the model without it (sR 2 increased, while the BIC decreased), and the SARIMA (0, 1, 1)(0, 1, 0) 52 offered the best fit for HFMD data. In addition, compared with females and nursery children, males and scattered children may be more suitable for using SARIMA model to predict the number of HFMD cases and it has high precision. In conclusion, high temperature could increase the risk of contracting HFMD. SARIMA model with temperature variable can effectively improve its forecast accuracy, which can provide valuable information for the policy makers and public health to construct a best-fitting model and optimize HFMD prevention.

Food Security, Institutional Framework and Technology: Examining the Nexus in Nigeria Using ARDL Approach

PubMed Central

Osabohien, Romanus; Osabuohien, Evans; Urhie, Ese

2018-01-01

Background: Growth in agricultural science and technology is deemed essential for in-creasing agricultural output; reduce the vulnerability of rural poverty and in turn, food security. Food security and growth in agricultural output depends on technological usages, which enhances the pro-ductive capacity of the agricultural sector. The indicators of food security utilised in this study in-clude: dietary energy supply, average value of food production, prevalence of food inadequacy, among others. Objective: In this paper, we examined the level of technology and how investment in the agriculture and technology can improve technical know-how in Nigeria with a view to achieving food security. Method: We carried out the analysis on how investment in technology and institutional framework can improve the level of food availability (a key component of food security) in Nigeria using econ-ometric technique based on Autoregressive Distribution Lag (ARDL) framework. Results: The results showed, inter alia, that in Nigeria, there is a high level of food insecurity as a result of low attention on food production occasioned by the pervasive influence of oil that become the major export product. Conclusion: It was noted that the availability of arable land was one of the major factors to increase food production to solve the challenge of food insecurity. Thus, the efforts of reducing the rate of food insecurity are essential in this regards. This can also be achieved, among others, by active interactions between government and farmers, to make contribution to important planning issues that relate to food production in the country and above all, social protection policies should be geared or channelled to agricultural sector to protect farmers who are vulnerable to shocks and avert risks associated with agriculture. PMID:29853816

Impact of weather factors on hand, foot and mouth disease, and its role in short-term incidence trend forecast in Huainan City, Anhui Province

NASA Astrophysics Data System (ADS)

Zhao, Desheng; Wang, Lulu; Cheng, Jian; Xu, Jun; Xu, Zhiwei; Xie, Mingyu; Yang, Huihui; Li, Kesheng; Wen, Lingying; Wang, Xu; Zhang, Heng; Wang, Shusi; Su, Hong

2017-03-01

Hand, foot, and mouth disease (HFMD) is one of the most common communicable diseases in China, and current climate change had been recognized as a significant contributor. Nevertheless, no reliable models have been put forward to predict the dynamics of HFMD cases based on short-term weather variations. The present study aimed to examine the association between weather factors and HFMD, and to explore the accuracy of seasonal auto-regressive integrated moving average (SARIMA) model with local weather conditions in forecasting HFMD. Weather and HFMD data from 2009 to 2014 in Huainan, China, were used. Poisson regression model combined with a distributed lag non-linear model (DLNM) was applied to examine the relationship between weather factors and HFMD. The forecasting model for HFMD was performed by using the SARIMA model. The results showed that temperature rise was significantly associated with an elevated risk of HFMD. Yet, no correlations between relative humidity, barometric pressure and rainfall, and HFMD were observed. SARIMA models with temperature variable fitted HFMD data better than the model without it (s R 2 increased, while the BIC decreased), and the SARIMA (0, 1, 1)(0, 1, 0)52 offered the best fit for HFMD data. In addition, compared with females and nursery children, males and scattered children may be more suitable for using SARIMA model to predict the number of HFMD cases and it has high precision. In conclusion, high temperature could increase the risk of contracting HFMD. SARIMA model with temperature variable can effectively improve its forecast accuracy, which can provide valuable information for the policy makers and public health to construct a best-fitting model and optimize HFMD prevention.

Food Security, Institutional Framework and Technology: Examining the Nexus in Nigeria Using ARDL Approach.

PubMed

Osabohien, Romanus; Osabuohien, Evans; Urhie, Ese

2018-04-01

Growth in agricultural science and technology is deemed essential for in-creasing agricultural output; reduce the vulnerability of rural poverty and in turn, food security. Food security and growth in agricultural output depends on technological usages, which enhances the pro-ductive capacity of the agricultural sector. The indicators of food security utilised in this study in-clude: dietary energy supply, average value of food production, prevalence of food inadequacy, among others. In this paper, we examined the level of technology and how investment in the agriculture and technology can improve technical know-how in Nigeria with a view to achieving food security. We carried out the analysis on how investment in technology and institutional framework can improve the level of food availability (a key component of food security) in Nigeria using econ-ometric technique based on Autoregressive Distribution Lag (ARDL) framework. The results showed, inter alia, that in Nigeria, there is a high level of food insecurity as a result of low attention on food production occasioned by the pervasive influence of oil that become the major export product. It was noted that the availability of arable land was one of the major factors to increase food production to solve the challenge of food insecurity. Thus, the efforts of reducing the rate of food insecurity are essential in this regards. This can also be achieved, among others, by active interactions between government and farmers, to make contribution to important planning issues that relate to food production in the country and above all, social protection policies should be geared or channelled to agricultural sector to protect farmers who are vulnerable to shocks and avert risks associated with agriculture.

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

NASA Astrophysics Data System (ADS)

Borhan, Nurbaizura; Arsad, Zainudin

2016-10-01

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

On the causal links between health indicator, output, combustible renewables and waste consumption, rail transport, and CO2 emissions: the case of Tunisia.

PubMed

Ben Jebli, Mehdi

2016-08-01

This study employs the autoregressive distributed lag (ARDL) approach and Granger causality test to investigate the short- and long-run relationships between health indicator, real GDP, combustible renewables and waste consumption, rail transport, and carbon dioxide (CO2) emissions for the case of Tunisia, spanning the period of 1990-2011. The empirical findings suggest that the Fisher statistic of the Wald test confirm the existence of a long-run relationship between the variables. Moreover, the long-run estimated elasticities of the ARDL model provide that output and combustible renewables and waste consumption have a positive and statistically significant impact on health situation, while CO2 emissions and rail transport both contribute to the decrease of health indicator. Granger causality results affirm that, in the short-run, there is a unidirectional causality running from real GDP to health, a unidirectional causality from health to combustible renewables and waste consumption, and a unidirectional causality from all variables to CO2 emissions. In the long-run, all the computed error correction terms are significant and confirm the existence of long-run association among the variables. Our recommendations for the Tunisian policymakers are as follows: (i) exploiting wastes and renewable fuels can be a good strategy to eliminate pollution caused by emissions and subsequently improve health quality, (ii) the use of renewable energy as a main source for national rail transport is an effective strategy for public health, (iii) renewable energy investment projects are beneficial plans for the country as this contributes to the growth of its own economy and reduce energy dependence, and (iii) more renewable energy consumption leads not only to decrease pollution but also to stimulate health situation because of the increase of doctors and nurses numbers.

Health-Related Coping Behaviors and Mental Health in Military Personnel.

PubMed

Morgan, Jessica Kelley; Hourani, Laurel; Tueller, Stephen

2017-03-01

Our previous research has highlighted the important link between coping behaviors and mental health symptoms in military personnel. This study seeks to extend these findings by examining each coping behavior and mental health issue individually. This study has four specific aims: (1) test cross-sectional relationships between coping and mental health at baseline and follow-up, (2) examine stability of each variable over time, (3) determine the predictive nature of baseline mental health and coping on subsequent mental health and coping, (4) assess the magnitude of each effect to evaluate the differential predictive value of coping behaviors and mental health symptoms. A convenience sample of U.S. Army platoons of the 82nd Airborne was surveyed. We used a two-wave, cross-lagged autoregression design with structural equation modeling to disentangle elements of temporality and to examine the predictive value of mental health status vis-à-vis coping behaviors and vice versa. Separate analyses were performed with each coping strategy and each set of mental health symptoms. This design allowed for the analysis of two synchronous associations (i.e., cross-sectional correlations between the coping strategy and mental health symptoms at each time point), two autoregressive effects (i.e., baseline mental health predicting mental health at follow-up and baseline coping predicting coping at follow-up), and two cross-lagged effects (i.e., baseline coping strategy predicting mental health at follow-up and baseline mental health predicting follow-up coping). Results of descriptive statistics revealed that the most frequently reported coping behavior was thinking of a plan to solve the problem, followed by talking to a friend, engaging in a hobby, and exercising or playing sports. The least often endorsed coping behaviors were smoking marijuana or using illicit drugs and thinking about hurting or killing oneself, followed by having a drink or lighting up a cigarette. We verified many cross-sectional relationships between coping behaviors and mental health symptoms. Specifically, talking to a friend, exercising or playing sports, engaging in a hobby, and thinking of a plan were associated with fewer anxiety, perceived stress, and depression symptoms, whereas smoking a cigarette, having a drink, and thinking about hurting or killing oneself were associated with more anxiety, perceived stress, and depressive symptoms. Marijuana and illicit drug use was also associated with higher depressive symptoms. Saying a prayer was not significantly related to mental health. Only four cross-lagged effects were significant. Those who reported more depressive symptoms at Time 1 reported talking to friends and family less and exercising or playing sports less as coping behaviors at Time 2. Baseline perceived stress predicted less likelihood of engaging in a hobby at follow-up, whereas exercising or playing sports as a coping behavior at baseline predicted lower perceived stress at follow-up. This study expands the evidence for the associations between coping behaviors and psychological health or distress to specific mental health symptoms, particularly in military service members, and provides comparisons of magnitude of each association. Clinically, this knowledge is critical to more efficiently target behaviors with the greatest associations to mental health in military personnel. Reprint & Copyright © 2017 Association of Military Surgeons of the U.S.

Vector Autoregressive (VAR) Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example

PubMed Central

Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M.

2016-01-01

Background Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display inter-related vital sign changes during situations of physiologic stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. Purpose The purpose of this article is to illustrate development of patient-specific VAR models using vital sign time series (VSTS) data in a sample of acutely ill, monitored, step-down unit (SDU) patients, and determine their Granger causal dynamics prior to onset of an incident CRI. Approach CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40–140/minute, RR = 8–36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity; (b) appropriate lag was determined using a lag-length selection criteria; (c) the VAR model was constructed; (d) residual autocorrelation was assessed with the Lagrange Multiplier test; (e) stability of the VAR system was checked; and (f) Granger causality was evaluated in the final stable model. Results The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%) (i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Discussion Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes tend to occur before changes in HR and SpO2. These findings suggest that contextual assessment of RR changes as the earliest sign of CRI is warranted. Use of VAR modeling may be helpful in other nursing research applications based on time series data. PMID:27977564

On the continuity of the stationary state distribution of DPCM

NASA Astrophysics Data System (ADS)

Naraghi-Pour, Morteza; Neuhoff, David L.

1990-03-01

Continuity and singularity properties of the stationary state distribution of differential pulse code modulation (DPCM) are explored. Two-level DPCM (i.e., delta modulation) operating on a first-order autoregressive source is considered, and it is shown that, when the magnitude of the DPCM prediciton coefficient is between zero and one-half, the stationary state distribution is singularly continuous; i.e., it is not discrete but concentrates on an uncountable set with a Lebesgue measure of zero. Consequently, it cannot be represented with a probability density function. For prediction coefficients with magnitude greater than or equal to one-half, the distribution is pure, i.e., either absolutely continuous and representable with a density function, or singular. This problem is compared to the well-known and still substantially unsolved problem of symmetric Bernoulli convolutions.

A numerical study on dual-phase-lag model of bio-heat transfer during hyperthermia treatment.

PubMed

Kumar, P; Kumar, Dinesh; Rai, K N

2015-01-01

The success of hyperthermia in the treatment of cancer depends on the precise prediction and control of temperature. It was absolutely a necessity for hyperthermia treatment planning to understand the temperature distribution within living biological tissues. In this paper, dual-phase-lag model of bio-heat transfer has been studied using Gaussian distribution source term under most generalized boundary condition during hyperthermia treatment. An approximate analytical solution of the present problem has been done by Finite element wavelet Galerkin method which uses Legendre wavelet as a basis function. Multi-resolution analysis of Legendre wavelet in the present case localizes small scale variations of solution and fast switching of functional bases. The whole analysis is presented in dimensionless form. The dual-phase-lag model of bio-heat transfer has compared with Pennes and Thermal wave model of bio-heat transfer and it has been found that large differences in the temperature at the hyperthermia position and time to achieve the hyperthermia temperature exist, when we increase the value of τT. Particular cases when surface subjected to boundary condition of 1st, 2nd and 3rd kind are discussed in detail. The use of dual-phase-lag model of bio-heat transfer and finite element wavelet Galerkin method as a solution method helps in precise prediction of temperature. Gaussian distribution source term helps in control of temperature during hyperthermia treatment. So, it makes this study more useful for clinical applications. Copyright © 2015 Elsevier Ltd. All rights reserved.

Tracking lags in historical plant species' shifts in relation to regional climate change.

PubMed

Ash, Jeremy D; Givnish, Thomas J; Waller, Donald M

2017-03-01

Can species shift their distributions fast enough to track changes in climate? We used abundance data from the 1950s and the 2000s in Wisconsin to measure shifts in the distribution and abundance of 78 forest-understory plant species over the last half-century and compare these shifts to changes in climate. We estimated temporal shifts in the geographic distribution of each species using vectors to connect abundance-weighted centroids from the 1950s and 2000s. These shifts in distribution reflect colonization, extirpation, and changes in abundance within sites, separately quantified here. We then applied climate analog analyses to compute vectors representing the climate change that each species experienced. Species shifted mostly to the northwest (mean: 49 ± 29 km) primarily reflecting processes of colonization and changes in local abundance. Analog climates for these species shifted even further to the northwest, however, exceeding species' shifts by an average of 90 ± 40 km. Most species thus failed to match recent rates of climate change. These lags decline in species that have colonized more sites and those with broader site occupancy, larger seed mass, and higher habitat fidelity. Thus, species' traits appear to affect their responses to climate change, but relationships are weak. As climate change accelerates, these lags will likely increase, potentially threatening the persistence of species lacking the capacity to disperse to new sites or locally adapt. However, species with greater lags have not yet declined more in abundance. The extent of these threats will likely depend on how other drivers of ecological change and interactions among species affect their responses to climate change. © 2016 John Wiley & Sons Ltd.

How treatment improvement in ADHD and cocaine dependence are related to one another: A secondary analysis.

PubMed

Levin, Frances R; Choi, C Jean; Pavlicova, Martina; Mariani, John J; Mahony, Amy; Brooks, Daniel J; Nunes, Edward V; Grabowski, John

2018-05-01

Attention-deficit hyperactivity disorder (ADHD) is overrepresented among individuals seeking treatment for substance use disorders. We previously reported that treatment with extended release mixed amphetamine salts (MAS-XR) increased abstinence, compared to placebo, among patients with co-occurring ADHD and cocaine dependence. This secondary analysis investigates the temporal relationship between ADHD improvement and cocaine abstinence in the first six weeks of the trial. The study was a three-arm, randomized, double-blinded, placebo-controlled, 14-week trial comparing MAS-XR (60 mg or 80 mg daily) versus placebo among 126 participants with ADHD and cocaine dependence. An autoregressive cross-lagged structural equation model was fit and evaluated weekly ADHD improvement (defined as ≥30% reduction in the Adult ADHD Investigator Symptom Rating Scale) and urine-confirmed abstinence over the first six weeks. The proportion of patients with each of the possible overall patterns of response was: ADHD improves before cocaine abstinence: 24%; Cocaine abstinence occurs before ADHD improvement: 12%; ADHD improvement and abstinence occur during the same week: 6%; ADHD improves but abstinence never achieved: 34%; Abstinence achieved but ADHD never improves: 6%; Neither ADHD improvement nor abstinence: 18%. A significant cross-lagged association was found; subjects with ADHD improvement at week 2 had significantly higher odds of cocaine abstinence at week 3 (p = .014). When treating co-occurring ADHD and cocaine dependence with stimulant medication, abstinence is most likely preceded by improvement in ADHD, which tends to occur early with medication treatment. Other observed temporal patterns suggest the potential complexity of the relationship between ADHD and cocaine dependence. Copyright © 2018. Published by Elsevier B.V.

Effect of heat waves on morbidity and mortality due to Parkinson's disease in Madrid: A time-series analysis.

PubMed

Linares, Cristina; Martinez-Martin, Pablo; Rodríguez-Blázquez, Carmen; Forjaz, Maria João; Carmona, Rocío; Díaz, Julio

2016-01-01

Parkinson's disease (PD) is one of the factors which are associated with a higher risk of mortality during heat waves. The use of certain neuroleptic medications to control some of this disease's complications would appear to be related to an increase in heat-related mortality. To analyse the relationship and quantify the short-term effect of high temperatures during heat wave episodes in Madrid on daily mortality and PD-related hospital admissions. We used an ecological time-series study and fit Poisson regression models. We analysed the daily number of deaths due to PD and the number of daily PD-related emergency hospital admissions in the city of Madrid, using maximum daily temperature (°C) as the main environmental variable and chemical air pollution as covariates. We controlled for trend, seasonalities, and the autoregressive nature of the series. There was a maximum daily temperature of 30°C at which PD-related admissions were at a minimum. Similarly, a temperature of 34°C coincides with an increase in the number of admissions. For PD-related admissions, the Relative Risk (RR) for every increase of 1°C above the threshold temperature was 1.13 IC95%:(1.03-1.23) at lags 1 and 5; and for daily PD-related mortality, the RR was 1.14 IC95%:(1.01-1.28) at lag 3. Our results indicate that suffering from PD is a risk factor that contributes to the excess morbidity and mortality associated with high temperatures, and is relevant from the standpoint of public health prevention plans. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

PubMed

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

2017-10-26

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

Use DNA to learn from the past: how modern and ancient DNA studies may help reveal the past and predict the future distribution of species

NASA Astrophysics Data System (ADS)

Edwards, M. E.; Alsos, I. G.; Sjögren, P.; Coissac, E.; Gielly, L.; Yoccoz, N.; Føreid, M. K.; Taberlet, P.

2015-12-01

Knowledge of how climate change affected species distribution in the past may help us predict the effect of ongoing environmental changes. We explore how the use of modern (AFLP fingerprinting techniques) and ancient DNA (metabarcoding P6 loop of chloroplast DNA) help to reveal past distribution of vascular plant species, dispersal processes, and effect of species traits. Based on studies of modern DNA combined with species distribution models, we show the dispersal routes and barriers to dispersal throughout the circumarctic/circumboreal region, likely dispersal vectors, the cost of dispersal in term of loss of genetic diversity, and how these relates to species traits, dispersal distance, and size of colonized region. We also estimate the expected future distribution and loss of genetic diversity and show how this relates to life form and adaptations to dispersal. To gain more knowledge on time lags in past range change events, we rely on palaeorecords. Current data on past distribution are limited by the taxonomic and time resolution of macrofossil and pollen records. We show how this may be improved by studying ancient DNA of lake sediments. DNA of lake sediments recorded about half of the flora surrounding the lake. Compared to macrofossil, the taxonomic resolution is similar but the detection rate is considerable improved. By taking into account main determinants of founder effect, dispersal vectors, and dispersal lags, we may improve our ability to forecast effects of climate change, whereas more studies on ancient DNA may provide us with knowledge on distribution time lags.

TIME-DEPENDENT ELECTRON ACCELERATION IN BLAZAR TRANSIENTS: X-RAY TIME LAGS AND SPECTRAL FORMATION

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

Lewis, Tiffany R.; Becker, Peter A.; Finke, Justin D., E-mail: pbecker@gmu.edu, E-mail: tlewis13@gmu.edu, E-mail: justin.finke@nrl.navy.mil

2016-06-20

Electromagnetic radiation from blazar jets often displays strong variability, extending from radio to γ -ray frequencies. In a few cases, this variability has been characterized using Fourier time lags, such as those detected in the X-rays from Mrk 421 using Beppo SAX. The lack of a theoretical framework to interpret the data has motivated us to develop a new model for the formation of the X-ray spectrum and the time lags in blazar jets based on a transport equation including terms describing stochastic Fermi acceleration, synchrotron losses, shock acceleration, adiabatic expansion, and spatial diffusion. We derive the exact solution formore » the Fourier transform of the electron distribution and use it to compute the Fourier transform of the synchrotron radiation spectrum and the associated X-ray time lags. The same theoretical framework is also used to compute the peak flare X-ray spectrum, assuming that a steady-state electron distribution is achieved during the peak of the flare. The model parameters are constrained by comparing the theoretical predictions with the observational data for Mrk 421. The resulting integrated model yields, for the first time, a complete first-principles physical explanation for both the formation of the observed time lags and the shape of the peak flare X-ray spectrum. It also yields direct estimates of the strength of the shock and the stochastic magnetohydrodynamical wave acceleration components in the Mrk 421 jet.« less

A FOURIER-TRANSFORMED BREMSSTRAHLUNG FLASH MODEL FOR THE PRODUCTION OF X-RAY TIME LAGS IN ACCRETING BLACK HOLE SOURCES

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

Kroon, John J.; Becker, Peter A., E-mail: jkroon@gmu.edu, E-mail: pbecker@gmu.edu

Accreting black hole sources show a wide variety of rapid time variability, including the manifestation of time lags during X-ray transients, in which a delay (phase shift) is observed between the Fourier components of the hard and soft spectra. Despite a large body of observational evidence for time lags, no fundamental physical explanation for the origin of this phenomenon has been presented. We develop a new theoretical model for the production of X-ray time lags based on an exact analytical solution for the Fourier transform describing the diffusion and Comptonization of seed photons propagating through a spherical corona. The resultingmore » Green's function can be convolved with any source distribution to compute the associated Fourier transform and time lags, hence allowing us to explore a wide variety of injection scenarios. We show that thermal Comptonization is able to self-consistently explain both the X-ray time lags and the steady-state (quiescent) X-ray spectrum observed in the low-hard state of Cyg X-1. The reprocessing of bremsstrahlung seed photons produces X-ray time lags that diminish with increasing Fourier frequency, in agreement with the observations for a wide range of sources.« less

Distributed parameter system coupled ARMA expansion identification and adaptive parallel IIR filtering - A unified problem statement. [Auto Regressive Moving-Average

NASA Technical Reports Server (NTRS)

Johnson, C. R., Jr.; Balas, M. J.

1980-01-01

A novel interconnection of distributed parameter system (DPS) identification and adaptive filtering is presented, which culminates in a common statement of coupled autoregressive, moving-average expansion or parallel infinite impulse response configuration adaptive parameterization. The common restricted complexity filter objectives are seen as similar to the reduced-order requirements of the DPS expansion description. The interconnection presents the possibility of an exchange of problem formulations and solution approaches not yet easily addressed in the common finite dimensional lumped-parameter system context. It is concluded that the shared problems raised are nevertheless many and difficult.

Time-Frequency Analysis And Pattern Recognition Using Singular Value Decomposition Of The Wigner-Ville Distribution

NASA Astrophysics Data System (ADS)

Boashash, Boualem; Lovell, Brian; White, Langford

1988-01-01

Time-Frequency analysis based on the Wigner-Ville Distribution (WVD) is shown to be optimal for a class of signals where the variation of instantaneous frequency is the dominant characteristic. Spectral resolution and instantaneous frequency tracking is substantially improved by using a Modified WVD (MWVD) based on an Autoregressive spectral estimator. Enhanced signal-to-noise ratio may be achieved by using 2D windowing in the Time-Frequency domain. The WVD provides a tool for deriving descriptors of signals which highlight their FM characteristics. These descriptors may be used for pattern recognition and data clustering using the methods presented in this paper.

Characterization of autoregressive processes using entropic quantifiers

NASA Astrophysics Data System (ADS)

Traversaro, Francisco; Redelico, Francisco O.

2018-01-01

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

Generation of Some First-Order Autoregressive Markovian Sequences of Positive Random Variables with Given Marginal Distributions,

DTIC Science & Technology

1981-03-01

Again E( XnX 1 Xn) Xn + (l-aB)/X PlXn-1 + (l-Pl)/x 2.11) and X0 E0 gives a stationary sequence. Thus the correla- tions and regressions are the...sequence, although the sample paths will tend to have runs-up. A similar analysis given in Lawrance and Lewis [5] shows that 1 1 + i a + au (3.7) E( XnX

Predictability in community dynamics.

PubMed

Blonder, Benjamin; Moulton, Derek E; Blois, Jessica; Enquist, Brian J; Graae, Bente J; Macias-Fauria, Marc; McGill, Brian; Nogué, Sandra; Ordonez, Alejandro; Sandel, Brody; Svenning, Jens-Christian

2017-03-01

The coupling between community composition and climate change spans a gradient from no lags to strong lags. The no-lag hypothesis is the foundation of many ecophysiological models, correlative species distribution modelling and climate reconstruction approaches. Simple lag hypotheses have become prominent in disequilibrium ecology, proposing that communities track climate change following a fixed function or with a time delay. However, more complex dynamics are possible and may lead to memory effects and alternate unstable states. We develop graphical and analytic methods for assessing these scenarios and show that these dynamics can appear in even simple models. The overall implications are that (1) complex community dynamics may be common and (2) detailed knowledge of past climate change and community states will often be necessary yet sometimes insufficient to make predictions of a community's future state. © 2017 John Wiley & Sons Ltd/CNRS.

Flap-Lag-Torsion Stability in Forward Flight

NASA Technical Reports Server (NTRS)

Panda, B.; Chopra, I.

1985-01-01

An aeroelastic stability of three-degree flap-lag-torsion blade in forward flight is examined. Quasisteady aerodynamics with a dynamic inflow model is used. The nonlinear time dependent periodic blade response is calculated using an iterative procedure based on Floquet theory. The periodic perturbation equations are solved for stability using Floquet transition matrix theory as well as constant coefficient approximation in the fixed reference frame. Results are presented for both stiff-inplane and soft-inplane blade configurations. The effects of several parameters on blade stability are examined, including structural coupling, pitch-flap and pitch-lag coupling, torsion stiffness, steady inflow distribution, dynamic inflow, blade response solution and constant coefficient approximation.

A penalized framework for distributed lag non-linear models.

PubMed

Gasparrini, Antonio; Scheipl, Fabian; Armstrong, Ben; Kenward, Michael G

2017-09-01

Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs). Alternative versions of penalized DLNMs are compared with each other and with the standard unpenalized version in a simulation study. Results show that this penalized extension to the DLNM class provides greater flexibility and improved inferential properties. The framework exploits recent theoretical developments of GAMs and is implemented using efficient routines within freely available software. Real-data applications are illustrated through two reproducible examples in time series and survival analysis. © 2017 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

Evaluation of statistical models for forecast errors from the HBV model

NASA Astrophysics Data System (ADS)

Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur

2010-04-01

SummaryThree statistical models for the forecast errors for inflow into the Langvatn reservoir in Northern Norway have been constructed and tested according to the agreement between (i) the forecast distribution and the observations and (ii) median values of the forecast distribution and the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order auto-regressive model was constructed for the forecast errors. The parameters were conditioned on weather classes. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order auto-regressive model was constructed for the forecast errors. For the third model positive and negative errors were modeled separately. The errors were first NQT-transformed before conditioning the mean error values on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: we wanted (a) the forecast distribution to be reliable; (b) the forecast intervals to be narrow; (c) the median values of the forecast distribution to be close to the observed values. Models 1 and 2 gave almost identical results. The median values improved the forecast with Nash-Sutcliffe R eff increasing from 0.77 for the original forecast to 0.87 for the corrected forecasts. Models 1 and 2 over-estimated the forecast intervals but gave the narrowest intervals. Their main drawback was that the distributions are less reliable than Model 3. For Model 3 the median values did not fit well since the auto-correlation was not accounted for. Since Model 3 did not benefit from the potential variance reduction that lies in bias estimation and removal it gave on average wider forecasts intervals than the two other models. At the same time Model 3 on average slightly under-estimated the forecast intervals, probably explained by the use of average measures to evaluate the fit.

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

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

PubMed

Dettmer, Jan; Dosso, Stan E

2012-10-01

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

Short-term forecasts gain in accuracy. [Regression technique using ''Box-Jenkins'' analysis

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

Not Available

Box-Jenkins time-series models offer accuracy for short-term forecasts that compare with large-scale macroeconomic forecasts. Utilities need to be able to forecast peak demand in order to plan their generating, transmitting, and distribution systems. This new method differs from conventional models by not assuming specific data patterns, but by fitting available data into a tentative pattern on the basis of auto-correlations. Three types of models (autoregressive, moving average, or mixed autoregressive/moving average) can be used according to which provides the most appropriate combination of autocorrelations and related derivatives. Major steps in choosing a model are identifying potential models, estimating the parametersmore » of the problem, and running a diagnostic check to see if the model fits the parameters. The Box-Jenkins technique is well suited for seasonal patterns, which makes it possible to have as short as hourly forecasts of load demand. With accuracy up to two years, the method will allow electricity price-elasticity forecasting that can be applied to facility planning and rate design. (DCK)« less

Lags in the response of mountain plant communities to climate change

PubMed Central

Alexander, Jake M.; Chalmandrier, Loïc; Lenoir, Jonathan; Burgess, Treena I.; Essl, Franz; Haider, Sylvia; Kueffer, Christoph; McDougall, Keith; Milbau, Ann; Nuñez, Martin A.; Pauchard, Aníbal; Rabitsch, Wolfgang; Rew, Lisa J.; Sanders, Nathan J.; Pellissier, Loïc

2018-01-01

Rapid climatic changes and increasing human influence at high elevations around the world will have profound impacts on mountain biodiversity. However, forecasts from statistical models (e.g. species distribution models) rarely consider that plant community changes could substantially lag behind climatic changes, hindering our ability to make temporally realistic projections for the coming century. Indeed, the magnitudes of lags, and the relative importance of the different factors giving rise to them, remain poorly understood. We review evidence for three types of lag: “dispersal lags” affecting plant species’ spread along elevational gradients, “establishment lags” following their arrival in recipient communities, and “extinction lags” of resident species. Variation in lags is explained by variation among species in physiological and demographic responses, by effects of altered biotic interactions, and by aspects of the physical environment. Of these, altered biotic interactions could contribute substantially to establishment and extinction lags, yet impacts of biotic interactions on range dynamics are poorly understood. We develop a mechanistic community model to illustrate how species turnover in future communities might lag behind simple expectations based on species’ range shifts with unlimited dispersal. The model shows a combined contribution of altered biotic interactions and dispersal lags to plant community turnover along an elevational gradient following climate warming. Our review and simulation support the view that accounting for disequilibrium range dynamics will be essential for realistic forecasts of patterns of biodiversity under climate change, with implications for the conservation of mountain species and the ecosystem functions they provide. PMID:29112781

AN INTEGRATED MODEL FOR THE PRODUCTION OF X-RAY TIME LAGS AND QUIESCENT SPECTRA FROM HOMOGENEOUS AND INHOMOGENEOUS BLACK HOLE ACCRETION CORONAE

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

Kroon, John J.; Becker, Peter A., E-mail: jkroon@gmu.edu, E-mail: pbecker@gmu.edu

2016-04-20

Many accreting black holes manifest time lags during outbursts, in which the hard Fourier component typically lags behind the soft component. Despite decades of observations of this phenomenon, the underlying physical explanation for the time lags has remained elusive, although there are suggestions that Compton reverberation plays an important role. However, the lack of analytical solutions has hindered the interpretation of the available data. In this paper, we investigate the generation of X-ray time lags in Compton scattering coronae using a new mathematical approach based on analysis of the Fourier-transformed transport equation. By solving this equation, we obtain the Fouriermore » transform of the radiation Green’s function, which allows us to calculate the exact dependence of the time lags on the Fourier frequency, for both homogeneous and inhomogeneous coronal clouds. We use the new formalism to explore a variety of injection scenarios, including both monochromatic and broadband (bremsstrahlung) seed photon injection. We show that our model can successfully reproduce both the observed time lags and the time-averaged (quiescent) X-ray spectra for Cyg X-1 and GX 339-04, using a single set of coronal parameters for each source. The time lags are the result of impulsive bremsstrahlung injection occurring near the outer edge of the corona, while the time-averaged spectra are the result of continual distributed injection of soft photons throughout the cloud.« less

An objective method to determine the probability distribution of the minimum apparent age of a sample of radio-isotopic dates

NASA Astrophysics Data System (ADS)

Ickert, R. B.; Mundil, R.

2012-12-01

Dateable minerals (especially zircon U-Pb) that crystallized at high temperatures but have been redeposited, pose both unique opportunities and challenges for geochronology. Although they have the potential to provide useful information on the depositional age of their host rocks, their relationship to the host is not always well constrained. For example, primary volcanic deposits will often have a lag time (time between eruption and deposition) that is smaller than can be resolved using radiometric techniques, and the age of eruption and of deposition will be coincident within uncertainty. Alternatively, ordinary clastic sedimentary rocks will usually have a long and variable lag time, even for the youngest minerals. Intermediate cases, for example moderately reworked volcanogenic material, will have a short, but unknown lag time. A compounding problem with U-Pb zircon is that the residence time of crystals in their host magma chamber (time between crystallization and eruption) can be high and is variable, even within the products of a single eruption. In cases where the lag and/or residence time suspected to be large relative to the precision of the date, a common objective is to determine the minimum age of a sample of dates, in order to constrain the maximum age of the deposition of the host rock. However, both the extraction of that age as well as assignment of a meaningful uncertainty is not straightforward. A number of ad hoc techniques have been employed in the literature, which may be appropriate for particular data sets or specific problems, but may yield biased or misleading results. Ludwig (2012) has developed an objective, statistically justified method for the determination of the distribution of the minimum age, but it has not been widely adopted. Here we extend this algorithm with a bootstrap (which can show the effect - if any - of the sampling distribution itself). This method has a number of desirable characteristics: It can incorporate all data points while being resistant to outliers, it utilizes the measurement uncertainties, and it does not require the assumption that any given cluster of data represents a single geological event. In brief, the technique generates a synthetic distribution from the input data by resampling with replacement (a bootstrap). Each resample is a random selection from a Gaussian distribution defined by the mean and uncertainty of the data point. For this distribution, the minimum value is calculated. This procedure is repeated many times (>1000) and a distribution of minimum values is generated, from which a confidence interval can be constructed. We demonstrate the application of this technique using natural and synthetic datasets, show the advantages and limitations, and relate it to other methods. We emphasize that this estimate remains strictly a minimum age - as with any other estimate that does not explicitly incorporate lag or residence time, it will not reflect a depositional age if the lag/residence time is larger than the uncertainty of the estimate. We recommend that this or similar techniques be considered by geochronologists. Ludwig, K.R., 2012. Isoplot 3.75, A geochronological toolkit for Microsoft Excel; Berkeley Geochronology Center Special Publication no. 5

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

SDSS IV MaNGA—Rotation Velocity Lags in the Extraplanar Ionized Gas from MaNGA Observations of Edge-on Galaxies

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

Bizyaev, D.; Pan, K.; Brinkmann, J.

2017-04-20

We present a study of the kinematics of the extraplanar ionized gas around several dozen galaxies observed by the Mapping of Nearby Galaxies at the Apache Point Observatory (MaNGA) survey. We considered a sample of 67 edge-on galaxies out of more than 1400 extragalactic targets observed by MaNGA, in which we found 25 galaxies (or 37%) with regular lagging of the rotation curve at large distances from the galactic midplane. We model the observed H α emission velocity fields in the galaxies, taking projection effects and a simple model for the dust extinction into account. We show that the verticalmore » lag of the rotation curve is necessary in the modeling, and estimate the lag amplitude in the galaxies. We find no correlation between the lag and the star formation rate in the galaxies. At the same time, we report a correlation between the lag and the galactic stellar mass, central stellar velocity dispersion, and axial ratio of the light distribution. These correlations suggest a possible higher ratio of infalling-to-local gas in early-type disk galaxies or a connection between lags and the possible presence of hot gaseous halos, which may be more prevalent in more massive galaxies. These results again demonstrate that observations of extraplanar gas can serve as a potential probe for accretion of gas.« less

SDSS IV MaNGA—Rotation Velocity Lags in the Extraplanar Ionized Gas from MaNGA Observations of Edge-on Galaxies

NASA Astrophysics Data System (ADS)

Bizyaev, D.; Walterbos, R. A. M.; Yoachim, P.; Riffel, R. A.; Fernández-Trincado, J. G.; Pan, K.; Diamond-Stanic, A. M.; Jones, A.; Thomas, D.; Cleary, J.; Brinkmann, J.

2017-04-01

We present a study of the kinematics of the extraplanar ionized gas around several dozen galaxies observed by the Mapping of Nearby Galaxies at the Apache Point Observatory (MaNGA) survey. We considered a sample of 67 edge-on galaxies out of more than 1400 extragalactic targets observed by MaNGA, in which we found 25 galaxies (or 37%) with regular lagging of the rotation curve at large distances from the galactic midplane. We model the observed Hα emission velocity fields in the galaxies, taking projection effects and a simple model for the dust extinction into account. We show that the vertical lag of the rotation curve is necessary in the modeling, and estimate the lag amplitude in the galaxies. We find no correlation between the lag and the star formation rate in the galaxies. At the same time, we report a correlation between the lag and the galactic stellar mass, central stellar velocity dispersion, and axial ratio of the light distribution. These correlations suggest a possible higher ratio of infalling-to-local gas in early-type disk galaxies or a connection between lags and the possible presence of hot gaseous halos, which may be more prevalent in more massive galaxies. These results again demonstrate that observations of extraplanar gas can serve as a potential probe for accretion of gas.

[Influence of humidex on incidence of bacillary dysentery in Hefei: a time-series study].

PubMed

Zhang, H; Zhao, K F; He, R X; Zhao, D S; Xie, M Y; Wang, S S; Bai, L J; Cheng, Q; Zhang, Y W; Su, H

2017-11-10

Objective: To investigate the effect of humidex combined with mean temperature and relative humidity on the incidence of bacillary dysentery in Hefei. Methods: Daily counts of bacillary dysentery cases and weather data in Hefei were collected from January 1, 2006 to December 31, 2013. Then, the humidex was calculated from temperature and relative humidity. A Poisson generalized linear regression combined with distributed lag non-linear model was applied to analyze the relationship between humidex and the incidence of bacillary dysentery, after adjusting for long-term and seasonal trends, day of week and other weather confounders. Stratified analyses by gender, age and address were also conducted. Results: The risk of bacillary dysentery increased with the rise of humidex. The adverse effect of high humidex (90 percentile of humidex) appeared in 2-days lag and it was the largest at 4-days lag ( RR =1.063, 95 %CI : 1.037-1.090). Subgroup analyses indicated that all groups were affected by high humidex at lag 2-5 days. Conclusion: High humidex could significantly increase the risk of bacillary dysentery, and the lagged effects were observed.

Association between flood and the morbidity of bacillary dysentery in Zibo City, China: a symmetric bidirectional case-crossover study

NASA Astrophysics Data System (ADS)

Zhang, Feifei; Ding, Guoyong; Liu, Zhidong; Zhang, Caixia; Jiang, Baofa

2016-12-01

This study examined the relationship between daily morbidity of bacillary dysentery and flood in 2007 in Zibo City, China, using a symmetric bidirectional case-crossover study. Odds ratios (ORs) and 95 % confidence intervals (CIs) on the basis of multivariate model and stratified analysis at different lagged days were calculated to estimate the risk of flood on bacillary dysentery. A total of 902 notified bacillary dysentery cases were identified during the study period. The median of case distribution was 7-year-old and biased to children. Multivariable analysis showed that flood was associated with an increased risk of bacillary dysentery, with the largest OR of 1.849 (95 % CI 1.229-2.780) at 2-day lag. Gender-specific analysis showed that there was a significant association between flood and bacillary dysentery among males only (ORs >1 from lag 1 to lag 5), with the strongest lagged effect at 2-day lag (OR = 2.820, 95 % CI 1.629-4.881), and the result of age-specific indicated that youngsters had a slightly larger risk to develop flood-related bacillary dysentery than older people at one shorter lagged day (OR = 2.000, 95 % CI 1.128-3.546 in youngsters at lag 2; OR = 1.879, 95 % CI 1.069-3.305 in older people at lag 3). Our study has confirmed that there is a positive association between flood and the risk of bacillary dysentery in selected study area. Males and youngsters may be the vulnerable and high-risk populations to develop the flood-related bacillary dysentery. Results from this study will provide recommendations to make available strategies for government to deal with negative health outcomes due to floods.

Association between flood and the morbidity of bacillary dysentery in Zibo City, China: a symmetric bidirectional case-crossover study.

PubMed

Zhang, Feifei; Ding, Guoyong; Liu, Zhidong; Zhang, Caixia; Jiang, Baofa

2016-12-01

This study examined the relationship between daily morbidity of bacillary dysentery and flood in 2007 in Zibo City, China, using a symmetric bidirectional case-crossover study. Odds ratios (ORs) and 95 % confidence intervals (CIs) on the basis of multivariate model and stratified analysis at different lagged days were calculated to estimate the risk of flood on bacillary dysentery. A total of 902 notified bacillary dysentery cases were identified during the study period. The median of case distribution was 7-year-old and biased to children. Multivariable analysis showed that flood was associated with an increased risk of bacillary dysentery, with the largest OR of 1.849 (95 % CI 1.229-2.780) at 2-day lag. Gender-specific analysis showed that there was a significant association between flood and bacillary dysentery among males only (ORs >1 from lag 1 to lag 5), with the strongest lagged effect at 2-day lag (OR = 2.820, 95 % CI 1.629-4.881), and the result of age-specific indicated that youngsters had a slightly larger risk to develop flood-related bacillary dysentery than older people at one shorter lagged day (OR = 2.000, 95 % CI 1.128-3.546 in youngsters at lag 2; OR = 1.879, 95 % CI 1.069-3.305 in older people at lag 3). Our study has confirmed that there is a positive association between flood and the risk of bacillary dysentery in selected study area. Males and youngsters may be the vulnerable and high-risk populations to develop the flood-related bacillary dysentery. Results from this study will provide recommendations to make available strategies for government to deal with negative health outcomes due to floods.

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

NASA Astrophysics Data System (ADS)

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

2013-06-01

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

Forecasting coconut production in the Philippines with ARIMA model

NASA Astrophysics Data System (ADS)

Lim, Cristina Teresa

2015-02-01

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

Association between Daily Hydrogen Sulfide Exposure and Incidence of Emergency Hospital Visits: A Population-Based Study

PubMed Central

Finnbjornsdottir, Ragnhildur Gudrun; Carlsen, Hanne Krage; Thorsteinsson, Throstur; Oudin, Anna; Lund, Sigrun Helga; Gislason, Thorarinn; Rafnsson, Vilhjalmur

2016-01-01

Background The adverse health effects of high concentrations of hydrogen sulfide (H2S) exposure are well known, though the possible effects of low concentrations have not been thoroughly studied. The aim was to study short-term associations between modelled ambient low-level concentrations of intermittent hydrogen sulfide (H2S) and emergency hospital visits with heart diseases (HD), respiratory diseases, and stroke as primary diagnosis. Methods The study is population-based, using data from patient-, and population-registers from the only acute care institution in the Reykjavik capital area, between 1 January, 2007 and 30 June, 2014. The study population was individuals (≥18yr) living in the Reykjavik capital area. The H2S emission originates from a geothermal power plant in the vicinity. A model was used to estimate H2S exposure in different sections of the area. A generalized linear model assuming Poisson distribution was used to investigate the association between emergency hospital visits and H2S exposure. Distributed lag models were adjusted for seasonality, gender, age, traffic zones, and other relevant factors. Lag days from 0 to 4 were considered. Results The total number of emergency hospital visits was 32961 with a mean age of 70 years. In fully adjusted un-stratified models, H2S concentrations exceeding 7.00μg/m3 were associated with increases in emergency hospital visits with HD as primary diagnosis at lag 0 risk ratio (RR): 1.067; 95% confidence interval (CI): 1.024–1.111, lag 2 RR: 1.049; 95%CI: 1.005–1.095, and lag 4 RR: 1.046; 95%CI: 1.004–1.089. Among males an association was found between H2S concentrations exceeding 7.00μg/m3, and HD at lag 0 RR: 1.087; 95%CI: 1.032–1.146 and lag 4 RR: 1080; 95%CI: 1.025–1.138; and among those 73 years and older at lag 0 RR: 1.075; 95%CI: 1.014–1.140 and lag 3 RR: 1.072; 95%CI: 1.009–1.139. No associations were found with other diseases. Conclusions The study showed an association between emergency hospital visits with HD as primary diagnosis and same day H2S concentrations exceeding 7.00μg/m3, more pronounced among males and those 73 years and older than among females and younger individuals. PMID:27218467

Forecasting typhoid fever incidence in the Cordillera administrative region in the Philippines using seasonal ARIMA models

NASA Astrophysics Data System (ADS)

Cawiding, Olive R.; Natividad, Gina May R.; Bato, Crisostomo V.; Addawe, Rizavel C.

2017-11-01

The prevalence of typhoid fever in developing countries such as the Philippines calls for a need for accurate forecasting of the disease. This will be of great assistance in strategic disease prevention. This paper presents a development of useful models that predict the behavior of typhoid fever incidence based on the monthly incidence in the provinces of the Cordillera Administrative Region from 2010 to 2015 using univariate time series analysis. The data used was obtained from the Cordillera Office of the Department of Health (DOH-CAR). Seasonal autoregressive moving average (SARIMA) models were used to incorporate the seasonality of the data. A comparison of the results of the obtained models revealed that the SARIMA (1,1,7)(0,0,1)12 with a fixed coefficient at the seventh lag produces the smallest root mean square error (RMSE), mean absolute error (MAE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The model suggested that for the year 2016, the number of cases would increase from the months of July to September and have a drop in December. This was then validated using the data collected from January 2016 to December 2016.

Sexual Hookups and Adverse Health Outcomes: A Longitudinal Study of First-Year College Women

PubMed Central

Fielder, Robyn L.; Walsh, Jennifer L.; Carey, Kate B.; Carey, Michael P.

2013-01-01

“Hookups” are sexual encounters between partners who are not in a romantic relationship and do not expect commitment. We examined the associations between sexual hookup behavior and depression, sexual victimization (SV), and sexually transmitted infections (STIs) among first-year college women. In this longitudinal study, 483 women completed 13 monthly surveys assessing oral and vaginal sex with hookup and romantic partners, depression, SV, and self-reported STIs. Participants also provided biological specimens that were tested for STIs. During the study, 50% of participants reported hookup sex, and 62% reported romantic sex. Covariates included previous levels of the outcome, alcohol use, impulsivity, sensation-seeking, and romantic sex. Autoregressive cross-lagged models showed that controlling for covariates, hookup behavior during college was correlated with depression, Bs = .21, ps < .05, and SV, Bs = .19, ps < .05. Additionally, pre-college hookup behavior predicted SV early in college, B = .62, p < .05. Hookup sex, OR 1.32, p < .05, and romantic sex, OR 1.19, p < .05, were associated with STIs. Overall, sexual hookup behavior among college women was positively correlated with experiencing depression, SV, and STIs, but the nature of these associations remains unclear, and hooking up did not predict future depression. PMID:24350600

WOMEN'S AGE AT FIRST MARRIAGE AND LONG-TERM ECONOMIC EMPOWERMENT IN EGYPT.

PubMed

Yount, Kathryn M; Crandall, AliceAnn; Cheong, Yuk Fai

2018-02-01

Sustainable Development Goal (SDG) 5 calls on nations to promote gender equality and to empower women and girls. SDG5 also recognizes the value of women's economic empowerment, entailing equal rights to economic resources and full participation at all levels in economic decisions. Also according to SDG5, eliminating harmful practices-such as child marriage before age 18-is a prerequisite for women's economic empowerment. Using national data for 4,129 married women 15-43 years who took part in the Egypt Labor Market Panel Survey (ELMPS 1998-2012), we performed autoregressive, cross-lagged panel analyses to assess whether women's first marriage in adulthood (at 18 years or older, as reported in 2006), was positively associated with their long-term post-marital economic empowerment, measured as their engagement in market work and latent family economic agency in 2012. Women's first marriage in adulthood had positive unadjusted associations with their market work and family economic agency in 2012. These associations persisted after accounting for market work and family economic agency in 2006, pre-marital resources for empowerment, and cumulative fertility. Policies to discourage child marriage may show promise to enhance women's long-term post-marital economic empowerment.

Social Modulation or Hormonal Causation? Linkages of Testosterone with Sexual Activity and Relationship Quality in a Nationally Representative Longitudinal Sample of Older Adults.

PubMed

Das, Aniruddha; Sawin, Nicole

2016-11-01

This study used population-representative longitudinal data from the 2005-2006 and 2010-2011 waves of the National Social Life, Health and Aging Project-a probability sample of US adults aged 57-85 at baseline (N = 650 women and 620 men)-to examine the causal direction in linkages of endogenous testosterone (T) with sexual activity and relationship quality. For both genders, our autoregressive effects indicated a large amount of temporal stability, not just in individual-level attributes (T, masturbation) but also dyadic ones (partnered sex, relationship quality)-indicating that a need for more nuanced theories of relational processes. Cross-lagged results suggested gender-specific effects-generally more consistent with sexual or relational modulation of T than with hormonal causation. Specifically, men's findings indicated their T might be elevated by their sexual (masturbatory) activity but not vice versa, although androgen levels did lower men's subsequent relationship quality. Women's T, in contrast, was negatively influenced not just by their higher relationship quality but also by their more frequent partnered sex-perhaps reflecting a changing function of sexual activity in late life.

An empirical evaluation of two theoretically-based hypotheses on the directional association between self-worth and hope.

PubMed

McDavid, Lindley; McDonough, Meghan H; Smith, Alan L

2015-06-01

Fostering self-worth and hope are important goals of positive youth development (PYD) efforts, yet intervention design is complicated by contrasting theoretical hypotheses regarding the directional association between these constructs. Therefore, within a longitudinal design we tested: (1) that self-worth predicts changes in hope (self theory; Harter, 1999), and (2) that hope predicts changes in self-worth (hope theory; Snyder, 2002) over time. Youth (N = 321; Mage = 10.33 years) in a physical activity-based PYD program completed surveys 37-45 days prior to and on the second day and third-to-last day of the program. A latent variable panel model that included autoregressive and cross-lagged paths indicated that self-worth was a significant predictor of change in hope, but hope did not predict change in self-worth. Therefore, the directional association between self-worth and hope is better explained by self-theory and PYD programs should aim to enhance perceptions of self-worth to build perceptions of hope. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Effect of Victimization on Impulse Control and Binge Drinking among Serious Juvenile Offenders from Adolescence to Young Adulthood.

PubMed

Davis, Jordan P; Dumas, Tara M; Berey, Benjamin L; Merrin, Gabriel J; Cimpian, Joseph R; Roberts, Brent W

2017-07-01

A vast literature has found longitudinal effects of early life stress on substance use and self-regulatory processes. These associations may vary by period-specific development among youth involved in the juvenile justice system. The current study used an accelerated longitudinal design and auto-regressive latent trajectory with structure residuals (ALT-SR) model to examine the within-person cross-lagged associations between binge drinking, impulse control, and victimization from 15 to 25 years of age. A large sample (N = 1100) of justice-involved youth were followed longitudinally for 7 years (M age baseline  = 15.8, M age conclusion  = 22.8). In general, the sample was ethnically diverse (41% Black, 34% Hispanic, 21% White, 4.3% Other) and primarily male (87.2%). Participants reported on their frequency of binge drinking, impulse control, and frequency of victimization at each time point. The results indicated that, during adolescence, victimization and binge drinking attenuated impulse control, which resulted in more binge drinking and victimization during young adulthood. The current study highlights the importance of assessing developmental processes and period-specific transitions among at risk youth, especially for youth experiencing early life stress.

The progression of the entropy of a five dimensional psychotherapeutic system

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

Badalamenti, A.F.; Langs, R.J.

This paper presents a study of the deterministic and stochastic behavior of the entropy of a 5-dimensional, 2400 state, system across each of six psychotherapeutic sessions. The growth of entropy was found to be logarithmic in each session. The stochastic behavior of a moving 600 second estimator of entropy revealed a Box-Jenkins model of type (1,1,0) - that is, the first difference of the entropy series was first order autoregressive or prior state sensitive. In addition, the patient and therapist entropy series exhibited no significant cross correlation across lags of -300 to +300 seconds. Yet all such pairs of seriesmore » exhibited high coherency past the frequency of .06 (on a full range of 0 to .5). Furthermore, all the patients and therapists were attracted to a geometric center of mass in 5-dimensional space which was different from the geometric center of the region where the system lived. The process significance of the findings and the relationship between the deterministic and stochastic results are discussed. The paper is then placed in the broader context of our efforts to provide new and meaningful quantitative dimensions and mathematical models to psychotherapy research. 59 refs.« less

Disentangling the Temporal Relationship Between Parental Depressive Symptoms and Early Child Behavior Problems: A Transactional Framework

PubMed Central

Bagner, Daniel M.; Pettit, Jeremy W.; Lewinsohn, Peter M.; Seeley, John R.; Jaccard, James

2015-01-01

Despite the considerable amount of research demonstrating the relationship between parental depressive symptoms and child behavior problems, few studies have examined the direction of the relationship between these variables. Therefore, the purpose of this study was to examine transactional effects between parental depressive symptoms and child behavior problems. Participants were 209 parent-child dyads drawn from the Oregon Adolescent Depression Project who completed at least 2 of 4 annual questionnaire assessments between the child’s age of 4 and 7 years. Structural equation modeling was used to examine the autoregressive paths from one year to the next year within each construct, as well as cross-lagged paths from one year to the next year between constructs. Findings indicated that parental depressive symptoms at each year predicted child behavior problems at the subsequent year and vice versa. No support was found for differential gender effects. These findings highlight the reciprocal relationship between parental depressive symptoms and child behavior problems and suggest intervention programs for young children should assess for and target parental depression when appropriate. Future research should examine these relationships across a broader developmental spectrum and in more diverse, heterogeneous samples. PMID:22963145

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

PubMed

Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

2010-12-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies conservation planning. Journal compilation © 2010 Society for Conservation Biology. No claim to original US government works.

Malaria incidence trends and their association with climatic variables in rural Gwanda, Zimbabwe, 2005-2015.

PubMed

Gunda, Resign; Chimbari, Moses John; Shamu, Shepherd; Sartorius, Benn; Mukaratirwa, Samson

2017-09-30

Malaria is a public health problem in Zimbabwe. Although many studies have indicated that climate change may influence the distribution of malaria, there is paucity of information on its trends and association with climatic variables in Zimbabwe. To address this shortfall, the trends of malaria incidence and its interaction with climatic variables in rural Gwanda, Zimbabwe for the period January 2005 to April 2015 was assessed. Retrospective data analysis of reported cases of malaria in three selected Gwanda district rural wards (Buvuma, Ntalale and Selonga) was carried out. Data on malaria cases was collected from the district health information system and ward clinics while data on precipitation and temperature were obtained from the climate hazards group infrared precipitation with station data (CHIRPS) database and the moderate resolution imaging spectro-radiometer (MODIS) satellite data, respectively. Distributed lag non-linear models (DLNLM) were used to determine the temporal lagged association between monthly malaria incidence and monthly climatic variables. There were 246 confirmed malaria cases in the three wards with a mean incidence of 0.16/1000 population/month. The majority of malaria cases (95%) occurred in the > 5 years age category. The results showed no correlation between trends of clinical malaria (unconfirmed) and confirmed malaria cases in all the three study wards. There was a significant association between malaria incidence and the climatic variables in Buvuma and Selonga wards at specific lag periods. In Ntalale ward, only precipitation (1- and 3-month lag) and mean temperature (1- and 2-month lag) were significantly associated with incidence at specific lag periods (p < 0.05). DLNM results suggest a key risk period in current month, based on key climatic conditions in the 1-4 month period prior. As the period of high malaria risk is associated with precipitation and temperature at 1-4 month prior in a seasonal cycle, intensifying malaria control activities over this period will likely contribute to lowering the seasonal malaria incidence.

Evaluation of Airborne Remote Sensing Techniques for Predicting the Distribution of Energetic Compounds on Impact Areas

DTIC Science & Technology

2007-08-01

GPS) antennas. A fluxgate magnetometer is mounted in the forward assembly to compensate for the magnetic signature of the aircraft. A laser...recorded digitally on the ORAGS™ console (Figure 5) inside the helicopter in a binary format. The magnetometers are sampled at a 1200-Hz sample rate and...GPS. Accurate positioning requires a correction for this lag. Time lags among the magnetometers , fluxgate , and GPS signals were measured by a

Extending the Distributed Lag Model framework to handle chemical mixtures.

PubMed

Bello, Ghalib A; Arora, Manish; Austin, Christine; Horton, Megan K; Wright, Robert O; Gennings, Chris

2017-07-01

Distributed Lag Models (DLMs) are used in environmental health studies to analyze the time-delayed effect of an exposure on an outcome of interest. Given the increasing need for analytical tools for evaluation of the effects of exposure to multi-pollutant mixtures, this study attempts to extend the classical DLM framework to accommodate and evaluate multiple longitudinally observed exposures. We introduce 2 techniques for quantifying the time-varying mixture effect of multiple exposures on an outcome of interest. Lagged WQS, the first technique, is based on Weighted Quantile Sum (WQS) regression, a penalized regression method that estimates mixture effects using a weighted index. We also introduce Tree-based DLMs, a nonparametric alternative for assessment of lagged mixture effects. This technique is based on the Random Forest (RF) algorithm, a nonparametric, tree-based estimation technique that has shown excellent performance in a wide variety of domains. In a simulation study, we tested the feasibility of these techniques and evaluated their performance in comparison to standard methodology. Both methods exhibited relatively robust performance, accurately capturing pre-defined non-linear functional relationships in different simulation settings. Further, we applied these techniques to data on perinatal exposure to environmental metal toxicants, with the goal of evaluating the effects of exposure on neurodevelopment. Our methods identified critical neurodevelopmental windows showing significant sensitivity to metal mixtures. Copyright © 2017 Elsevier Inc. All rights reserved.

Multiscale statistics of trajectories with applications to fluid particles in turbulence and football players

NASA Astrophysics Data System (ADS)

Schneider, Kai; Kadoch, Benjamin; Bos, Wouter

2017-11-01

The angle between two subsequent particle displacement increments is evaluated as a function of the time lag. The directional change of particles can thus be quantified at different scales and multiscale statistics can be performed. Flow dependent and geometry dependent features can be distinguished. The mean angle satisfies scaling behaviors for short time lags based on the smoothness of the trajectories. For intermediate time lags a power law behavior can be observed for some turbulent flows, which can be related to Kolmogorov scaling. The long time behavior depends on the confinement geometry of the flow. We show that the shape of the probability distribution function of the directional change can be well described by a Fischer distribution. Results for two-dimensional (direct and inverse cascade) and three-dimensional turbulence with and without confinement, illustrate the properties of the proposed multiscale statistics. The presented Monte-Carlo simulations allow disentangling geometry dependent and flow independent features. Finally, we also analyze trajectories of football players, which are, in general, not randomly spaced on a field.

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

NASA Astrophysics Data System (ADS)

Ouyang, Huei-Tau

2017-07-01

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

Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen-Grossberg BAM neural networks with impulses.

PubMed

Yang, Wengui; Yu, Wenwu; Cao, Jinde; Alsaadi, Fuad E; Hayat, Tasawar

2018-02-01

This paper investigates the stability and lag synchronization for memristor-based fuzzy Cohen-Grossberg bidirectional associative memory (BAM) neural networks with mixed delays (asynchronous time delays and continuously distributed delays) and impulses. By applying the inequality analysis technique, homeomorphism theory and some suitable Lyapunov-Krasovskii functionals, some new sufficient conditions for the uniqueness and global exponential stability of equilibrium point are established. Furthermore, we obtain several sufficient criteria concerning globally exponential lag synchronization for the proposed system based on the framework of Filippov solution, differential inclusion theory and control theory. In addition, some examples with numerical simulations are given to illustrate the feasibility and validity of obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prestressing force monitoring method for a box girder through distributed long-gauge FBG sensors

NASA Astrophysics Data System (ADS)

Chen, Shi-Zhi; Wu, Gang; Xing, Tuo; Feng, De-Cheng

2018-01-01

Monitoring prestressing forces is essential for prestressed concrete box girder bridges. However, the current monitoring methods used for prestressing force were not applicable for a box girder neither because of the sensor’s setup being constrained or shear lag effect not being properly considered. Through combining with the previous analysis model of shear lag effect in the box girder, this paper proposed an indirect monitoring method for on-site determination of prestressing force in a concrete box girder utilizing the distributed long-gauge fiber Bragg grating sensor. The performance of this method was initially verified using numerical simulation for three different distribution forms of prestressing tendons. Then, an experiment involving two concrete box girders was conducted to study the feasibility of this method under different prestressing levels preliminarily. The results of both numerical simulation and lab experiment validated this method’s practicability in a box girder.

Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: a case study in endemic districts of Bhutan.

PubMed

Wangdi, Kinley; Singhasivanon, Pratap; Silawan, Tassanee; Lawpoolsri, Saranath; White, Nicholas J; Kaewkungwal, Jaranit

2010-09-03

Malaria still remains a public health problem in some districts of Bhutan despite marked reduction of cases in last few years. To strengthen the country's prevention and control measures, this study was carried out to develop forecasting and prediction models of malaria incidence in the endemic districts of Bhutan using time series and ARIMAX. This study was carried out retrospectively using the monthly reported malaria cases from the health centres to Vector-borne Disease Control Programme (VDCP) and the meteorological data from Meteorological Unit, Department of Energy, Ministry of Economic Affairs. Time series analysis was performed on monthly malaria cases, from 1994 to 2008, in seven malaria endemic districts. The time series models derived from a multiplicative seasonal autoregressive integrated moving average (ARIMA) was deployed to identify the best model using data from 1994 to 2006. The best-fit model was selected for each individual district and for the overall endemic area was developed and the monthly cases from January to December 2009 and 2010 were forecasted. In developing the prediction model, the monthly reported malaria cases and the meteorological factors from 1996 to 2008 of the seven districts were analysed. The method of ARIMAX modelling was employed to determine predictors of malaria of the subsequent month. It was found that the ARIMA (p, d, q) (P, D, Q)s model (p and P representing the auto regressive and seasonal autoregressive; d and D representing the non-seasonal differences and seasonal differencing; and q and Q the moving average parameters and seasonal moving average parameters, respectively and s representing the length of the seasonal period) for the overall endemic districts was (2,1,1)(0,1,1)12; the modelling data from each district revealed two most common ARIMA models including (2,1,1)(0,1,1)12 and (1,1,1)(0,1,1)12. The forecasted monthly malaria cases from January to December 2009 and 2010 varied from 15 to 82 cases in 2009 and 67 to 149 cases in 2010, where population in 2009 was 285,375 and the expected population of 2010 to be 289,085. The ARIMAX model of monthly cases and climatic factors showed considerable variations among the different districts. In general, the mean maximum temperature lagged at one month was a strong positive predictor of an increased malaria cases for four districts. The monthly number of cases of the previous month was also a significant predictor in one district, whereas no variable could predict malaria cases for two districts. The ARIMA models of time-series analysis were useful in forecasting the number of cases in the endemic areas of Bhutan. There was no consistency in the predictors of malaria cases when using ARIMAX model with selected lag times and climatic predictors. The ARIMA forecasting models could be employed for planning and managing malaria prevention and control programme in Bhutan.

Gamma-Ray Burst Intensity Distributions

NASA Technical Reports Server (NTRS)

Band, David L.; Norris, Jay P.; Bonnell, Jerry T.

2004-01-01

We use the lag-luminosity relation to calculate self-consistently the redshifts, apparent peak bolometric luminosities L(sub B1), and isotropic energies E(sub iso) for a large sample of BATSE bursts. We consider two different forms of the lag-luminosity relation; for both forms the median redshift, for our burst database is 1.6. We model the resulting sample of burst energies with power law and Gaussian dis- tributions, both of which are reasonable models. The power law model has an index of a = 1.76 plus or minus 0.05 (95% confidence) as opposed to the index of a = 2 predicted by the simple universal jet profile model; however, reasonable refinements to this model permit much greater flexibility in reconciling predicted and observed energy distributions.

Stochastic generators of multi-site daily temperature: comparison of performances in various applications

NASA Astrophysics Data System (ADS)

Evin, Guillaume; Favre, Anne-Catherine; Hingray, Benoit

2018-02-01

We present a multi-site stochastic model for the generation of average daily temperature, which includes a flexible parametric distribution and a multivariate autoregressive process. Different versions of this model are applied to a set of 26 stations located in Switzerland. The importance of specific statistical characteristics of the model (seasonality, marginal distributions of standardized temperature, spatial and temporal dependence) is discussed. In particular, the proposed marginal distribution is shown to improve the reproduction of extreme temperatures (minima and maxima). We also demonstrate that the frequency and duration of cold spells and heat waves are dramatically underestimated when the autocorrelation of temperature is not taken into account in the model. An adequate representation of these characteristics can be crucial depending on the field of application, and we discuss potential implications in different contexts (agriculture, forestry, hydrology, human health).

Association with meteo-climatological factors and daily emergency visits for renal colic and urinary calculi in Cuneo, Italy. A retrospective observational study, 2007-2010

NASA Astrophysics Data System (ADS)

Condemi, Vincenzo; Gestro, Massimo; Dozio, Elena; Tartaglino, Bruno; Corsi Romanelli, Massimiliano Marco; Solimene, Umberto; Meco, Roberto

2015-03-01

The incidence of nephrolithiasis is rising worldwide, especially in women and with increasing age. Incidence and prevalence of kidney stones are affected by genetic, nutritional, and environmental factors. The aim of this study is to investigate the link between various meteorological factors (independent variables) and the daily number of visits to the Emergency Department (ED of the S. Croce and Carle Hospital of Cuneo for renal colic (RC) and urinary stones (UC) as the dependent variable over the years 2007-2010. The Poisson generalized regression models (PGAMs) have been used in different progressive ways. The results of PGAMs (stage 1) adjusted for seasonal and calendar factors confirmed a significant correlation ( p < 0.03) with the thermal parameter. Evaluation of the dose-response effect [PGAMs combined with distributed lags nonlinear models (DLNMs)—stage 2], expressed in terms of relative risk (RR) and cumulative relative risk (RRC), indicated a relative significant effect up to 15 lag days of lag (RR > 1), with a first peak after 5 days (lag ranges 0-1, 0-3, and 0-5) and a second weak peak observed along the 5-15 lag range days. The estimated RR for females was significant, mainly in the second and fourth age group considered (19-44 and >65 years): RR for total ED visits 1.27, confidence interval (CI) 1.11-1.46 (lag 0-5 days); RR 1.42, CI 1.01-2.01 (lag 0-10 days); and RR 1.35, CI 1.09-1.68 (lag 0-15 days). The research also indicated a moderate involvement of the thermal factor in the onset of RC caused by UC, exclusively in the female sex. Further studies will be necessary to confirm these results.

Remotely Sensed Environmental Conditions and Malaria Mortality in Three Malaria Endemic Regions in Western Kenya.

PubMed

Sewe, Maquins Odhiambo; Ahlm, Clas; Rocklöv, Joacim

2016-01-01

Malaria is an important cause of morbidity and mortality in malaria endemic countries. The malaria mosquito vectors depend on environmental conditions, such as temperature and rainfall, for reproduction and survival. To investigate the potential for weather driven early warning systems to prevent disease occurrence, the disease relationship to weather conditions need to be carefully investigated. Where meteorological observations are scarce, satellite derived products provide new opportunities to study the disease patterns depending on remotely sensed variables. In this study, we explored the lagged association of Normalized Difference Vegetation Index (NVDI), day Land Surface Temperature (LST) and precipitation on malaria mortality in three areas in Western Kenya. The lagged effect of each environmental variable on weekly malaria mortality was modeled using a Distributed Lag Non Linear Modeling approach. For each variable we constructed a natural spline basis with 3 degrees of freedom for both the lag dimension and the variable. Lag periods up to 12 weeks were considered. The effect of day LST varied between the areas with longer lags. In all the three areas, malaria mortality was associated with precipitation. The risk increased with increasing weekly total precipitation above 20 mm and peaking at 80 mm. The NDVI threshold for increased mortality risk was between 0.3 and 0.4 at shorter lags. This study identified lag patterns and association of remote- sensing environmental factors and malaria mortality in three malaria endemic regions in Western Kenya. Our results show that rainfall has the most consistent predictive pattern to malaria transmission in the endemic study area. Results highlight a potential for development of locally based early warning forecasts that could potentially reduce the disease burden by enabling timely control actions.

Ambient ozone concentration and emergency department visits for panic attacks.

PubMed

Cho, Jaelim; Choi, Yoon Jung; Sohn, Jungwoo; Suh, Mina; Cho, Seong-Kyung; Ha, Kyoung Hwa; Kim, Changsoo; Shin, Dong Chun

2015-03-01

The effect of ambient air pollution on panic disorder in the general population has not yet been thoroughly elucidated, although the occurrence of panic disorder in workers exposed to organic solvents has been reported previously. We investigated the association of ambient air pollution with the risk of panic attack-related emergency department visits. Using health insurance claims, we collected data from emergency department visits for panic attacks in Seoul, Republic of Korea (2005-2009). Daily air pollutant concentrations were obtained using automatic monitoring system data. We conducted a time-series study using a generalized additive model with Poisson distribution, which included spline variables (date of visit, daily mean temperature, and relative humidity) and parametric variables (daily mean air pollutant concentration, national holiday, and day of the week). In addition to single lag models (lag1 to lag3), cumulative lag models (lag0-1 to lag0-3) were constructed using moving-average concentrations on the days leading up to the visit. The risk was expressed as relative risk (RR) per one standard deviation of each air pollutant and its 95% confidence interval (95% CI). A total of 2320 emergency department visits for panic attacks were observed during the study period. The adjusted RR of panic attack-related emergency department visits was 1.051 (95% CI, 1.014-1.090) for same-day exposure to ozone. In cumulative models, adjusted RRs were 1.068 (1.029-1.107) in lag0-2 and 1.074 (1.035-1.114) in lag0-3. The ambient ozone concentration was significantly associated with emergency department visits for panic attacks. Copyright © 2015 Elsevier Ltd. All rights reserved.

Sample selection and spatial models of housing price indexes, and, A disequilibrium analysis of the U.S. gasoline market using panel data

NASA Astrophysics Data System (ADS)

Hu, Haixin

This dissertation consists of two parts. The first part studies the sample selection and spatial models of housing price index using transaction data on detached single-family houses of two California metropolitan areas from 1990 through 2008. House prices are often spatially correlated due to shared amenities, or when the properties are viewed as close substitutes in a housing submarket. There have been many studies that address spatial correlation in the context of housing markets. However, none has used spatial models to construct housing price indexes at zip code level for the entire time period analyzed in this dissertation to the best of my knowledge. In this paper, I study a first-order autoregressive spatial model with four different weighing matrix schemes. Four sets of housing price indexes are constructed accordingly. Gatzlaff and Haurin (1997, 1998) study the sample selection problem in housing index by using Heckman's two-step method. This method, however, is generally inefficient and can cause multicollinearity problem. Also, it requires data on unsold houses in order to carry out the first-step probit regression. Maximum likelihood (ML) method can be used to estimate a truncated incidental model which allows one to correct for sample selection based on transaction data only. However, convergence problem is very prevalent in practice. In this paper I adopt Lewbel's (2007) sample selection correction method which does not require one to model or estimate the selection model, except for some very general assumptions. I then extend this method to correct for spatial correlation. In the second part, I analyze the U.S. gasoline market with a disequilibrium model that allows lagged-latent variables, endogenous prices, and panel data with fixed effects. Most existing studies (see the survey of Espey, 1998, Energy Economics) of the gasoline market assume equilibrium. In practice, however, prices do not always adjust fast enough to clear the market. Equilibrium assumptions greatly simplify statistical inference, but are very restrictive and can produce conflicting estimates. For example, econometric models of markets that assume equilibrium often produce more elastic demand price elasticity than their disequilibrium counterparts (Holt and Johnson, 1989, Review of Economics and Statistics, Oczkowski, 1998, Economics Letters). The few studies that allow disequilibrium, however, have been limited to macroeconomic time-series data without lagged-latent variables. While time series data allows one to investigate national trends, it cannot be used to identify and analyze regional differences and the role of local markets. Exclusion of the lagged-latent variables is also undesirable because such variables capture adjustment costs and inter-temporal spillovers. Simulation methods offer tractable solutions to dynamic and panel data disequilibrium models (Lee, 1997, Journal of Econometrics), but assume normally distributed errors. This paper compares estimates of price/income elasticity and excess supply/demand across time periods, regions, and model specifications, using both equilibrium and disequilibrium methods. In the equilibrium model, I compare the within group estimator with Anderson and Hsiao's first-difference 2SLS estimator. In the disequilibrium model, I extend Amemiya's 2SLS by using Newey's efficient estimator with optimal instruments.

Spatial Autocorrelation And Autoregressive Models In Ecology

Treesearch

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

2003-01-01

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

Statistical description of turbulent transport for flux driven toroidal plasmas

NASA Astrophysics Data System (ADS)

Anderson, J.; Imadera, K.; Kishimoto, Y.; Li, J. Q.; Nordman, H.

2017-06-01

A novel methodology to analyze non-Gaussian probability distribution functions (PDFs) of intermittent turbulent transport in global full-f gyrokinetic simulations is presented. In this work, the auto-regressive integrated moving average (ARIMA) model is applied to time series data of intermittent turbulent heat transport to separate noise and oscillatory trends, allowing for the extraction of non-Gaussian features of the PDFs. It was shown that non-Gaussian tails of the PDFs from first principles based gyrokinetic simulations agree with an analytical estimation based on a two fluid model.

Understanding User Behavioral Patterns in Open Knowledge Communities

ERIC Educational Resources Information Center

Yang, Xianmin; Song, Shuqiang; Zhao, Xinshuo; Yu, Shengquan

2018-01-01

Open knowledge communities (OKCs) have become popular in the era of knowledge economy. This study aimed to explore how users collaboratively create and share knowledge in OKCs. In particular, this research identified the behavior distribution and behavioral patterns of users by conducting frequency distribution and lag sequential analyses. Some…

Population trends influence species ability to track climate change

Treesearch

Joel Ralston; William V. DeLuca; Richard E. Feldman; David I. King

2016-01-01

Shifts of distributions have been attributed to species tracking their fundamental climate niches through space. However, several studies have now demonstrated that niche tracking is imperfect, that species' climate niches may vary with population trends, and that geographic distributions may lag behind rapid climate change. These reports of imperfect niche...

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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

Mathematical model with autoregressive process for electrocardiogram signals

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Application of multivariate autoregressive spectrum estimation to ULF waves

NASA Technical Reports Server (NTRS)

Ioannidis, G. A.

1975-01-01

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

Investigation of Spectral Lag and Epeak as Joint Luminosity Indicators in GRBs

NASA Technical Reports Server (NTRS)

White, Nicholas E. (Technical Monitor); Norris, Jay P.

2003-01-01

Models for gamma-ray bursts which invoke jetted, colliding shells would appear to have at least two determinants for luminosity, e.g., observer viewing angle and Lorentz factor, or possibly shell mass. The latter two internal physical parameters may vary from pulse to pulse within a burst, and such variation might be reflected in evolution of observables such as spectral lag and peak in the spectral energy distribution. We analyze bright BATSE bursts using the 16-channel medium energy resolution (MER) data, with time resolutions of 16 and 64 ms, measuring spectral lags and peak energies for significant pulse structures within a burst, identified using a Bayesian block algorithm. We then explore correlations between the measured parameters and total flux for the individual pulse structures.

Single-cell analysis of S. cerevisiae growth recovery after a sublethal heat-stress applied during an alcoholic fermentation.

PubMed

Tibayrenc, Pierre; Preziosi-Belloy, Laurence; Ghommidh, Charles

2011-06-01

Interest in bioethanol production has experienced a resurgence in the last few years. Poor temperature control in industrial fermentation tanks exposes the yeast cells used for this production to intermittent heat stress which impairs fermentation efficiency. Therefore, there is a need for yeast strains with improved tolerance, able to recover from such temperature variations. Accordingly, this paper reports the development of methods for the characterization of Saccharomyces cerevisiae growth recovery after a sublethal heat stress. Single-cell measurements were carried out in order to detect cell-to-cell variability. Alcoholic batch fermentations were performed on a defined medium in a 2 l instrumented bioreactor. A rapid temperature shift from 33 to 43 °C was applied when ethanol concentration reached 50 g l⁻¹. Samples were collected at different times after the temperature shift. Single cell growth capability, lag-time and initial growth rate were determined by monitoring the growth of a statistically significant number of cells after agar medium plating. The rapid temperature shift resulted in an immediate arrest of growth and triggered a progressive loss of cultivability from 100 to 0.0001% within 8 h. Heat-injured cells were able to recover their growth capability on agar medium after a lag phase. Lag-time was longer and more widely distributed as the time of heat exposure increased. Thus, lag-time distribution gives an insight into strain sensitivity to heat-stress, and could be helpful for the selection of yeast strains of technological interest.

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

Treesearch

Consuelo Brandeis; Dayton M. Lambert

2014-01-01

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

Volatility in GARCH Models of Business Tendency Index

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Functional MRI and Multivariate Autoregressive Models

PubMed Central

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

2010-01-01

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

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

PubMed

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

2016-09-26

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

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

PubMed Central

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

2016-01-01

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

Kepler AutoRegressive Planet Search: Motivation & Methodology

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

Modelling the incidence of Plasmodium vivax and Plasmodium falciparum malaria in Afghanistan 2006-2009.

PubMed

Alegana, Victor A; Wright, Jim A; Nahzat, Sami M; Butt, Waqar; Sediqi, Amad W; Habib, Naeem; Snow, Robert W; Atkinson, Peter M; Noor, Abdisalan M

2014-01-01

Identifying areas that support high malaria risks and where populations lack access to health care is central to reducing the burden in Afghanistan. This study investigated the incidence of Plasmodium vivax and Plasmodium falciparum using routine data to help focus malaria interventions. To estimate incidence, the study modelled utilisation of the public health sector using fever treatment data from the 2012 national Malaria Indicator Survey. A probabilistic measure of attendance was applied to population density metrics to define the proportion of the population within catchment of a public health facility. Malaria data were used in a Bayesian spatio-temporal conditional-autoregressive model with ecological or environmental covariates, to examine the spatial and temporal variation of incidence. From the analysis of healthcare utilisation, over 80% of the population was within 2 hours' travel of the nearest public health facility, while 64.4% were within 30 minutes' travel. The mean incidence of P. vivax in 2009 was 5.4 (95% Crl 3.2-9.2) cases per 1000 population compared to 1.2 (95% Crl 0.4-2.9) cases per 1000 population for P. falciparum. P. vivax peaked in August while P. falciparum peaked in November. 32% of the estimated 30.5 million people lived in regions where annual incidence was at least 1 case per 1,000 population of P. vivax; 23.7% of the population lived in areas where annual P. falciparum case incidence was at least 1 per 1000. This study showed how routine data can be combined with household survey data to model malaria incidence. The incidence of both P. vivax and P. falciparum in Afghanistan remain low but the co-distribution of both parasites and the lag in their peak season provides challenges to malaria control in Afghanistan. Future improved case definition to determine levels of imported risks may be useful for the elimination ambitions in Afghanistan.

Modelling the Incidence of Plasmodium vivax and Plasmodium falciparum Malaria in Afghanistan 2006–2009

PubMed Central

Alegana, Victor A.; Wright, Jim A.; Nahzat, Sami M.; Butt, Waqar; Sediqi, Amad W.; Habib, Naeem; Snow, Robert W.; Atkinson, Peter M.; Noor, Abdisalan M.

2014-01-01

Background Identifying areas that support high malaria risks and where populations lack access to health care is central to reducing the burden in Afghanistan. This study investigated the incidence of Plasmodium vivax and Plasmodium falciparum using routine data to help focus malaria interventions. Methods To estimate incidence, the study modelled utilisation of the public health sector using fever treatment data from the 2012 national Malaria Indicator Survey. A probabilistic measure of attendance was applied to population density metrics to define the proportion of the population within catchment of a public health facility. Malaria data were used in a Bayesian spatio-temporal conditional-autoregressive model with ecological or environmental covariates, to examine the spatial and temporal variation of incidence. Findings From the analysis of healthcare utilisation, over 80% of the population was within 2 hours’ travel of the nearest public health facility, while 64.4% were within 30 minutes’ travel. The mean incidence of P. vivax in 2009 was 5.4 (95% Crl 3.2–9.2) cases per 1000 population compared to 1.2 (95% Crl 0.4–2.9) cases per 1000 population for P. falciparum. P. vivax peaked in August while P. falciparum peaked in November. 32% of the estimated 30.5 million people lived in regions where annual incidence was at least 1 case per 1,000 population of P. vivax; 23.7% of the population lived in areas where annual P. falciparum case incidence was at least 1 per 1000. Conclusion This study showed how routine data can be combined with household survey data to model malaria incidence. The incidence of both P. vivax and P. falciparum in Afghanistan remain low but the co-distribution of both parasites and the lag in their peak season provides challenges to malaria control in Afghanistan. Future improved case definition to determine levels of imported risks may be useful for the elimination ambitions in Afghanistan. PMID:25033452

Climate change and associated spatial heterogeneity of Pakistan: Empirical evidence using multidisciplinary approach.

PubMed

Ali, Ghaffar

2018-09-01

Climate change is a multidimensional phenomenon, which has various implications for the environment and socio-economic conditions of the people. Its effects are deeper in an agrarian economy which is susceptible to the vagaries of nature. Therefore, climate change directly impacts the society in different ways, and society must pay the cost. Focusing on this truth, the main objective of this research was to investigate the empirical changes and spatial heterogeneity in the climate of Pakistan in real terms using time series data. Climate change and variability in Pakistan, over time, were estimated from 1961 to 2014 using all the climate variables for the very first time. Several studies were available on climate change impacts, mitigation, and adaptation; however, it was difficult to observe exactly how much change occurred in which province and when. A multidisciplinary approach was utilized to estimate the absolute change through a combination of environmental, econometric, and remote sensing methods. Moreover, the Autoregressive Distributed Lag (ARDL) model was used to ascertain the extent of variability in climate change and information was digitalized through ground truthing. Results showed that the average temperature of Pakistan increased by 2°C between 1960 and 1987 and 4°C between 1988 and 2014, and R 2 was 0.978. The rate of temperature increased 0.09°C between 1960 and 2014. The mean annual precipitation of Pakistan increased by 478mm, and its R 2 were 0.34-0.64. The mean annual humidity of Pakistan increased by 2.94%, and the rate of humidity has been increased by 0.97% from 1988 to 2014. Notably, Sindh and Balochistan provinces have shown a significant spatial heterogeneity regarding the increase in precipitation. Statistically all variables are significant. This would serve as a baseline information for climate change-related studies in Pakistan and its application in different sectors. This would also serve the plant breeders and policymakers of the country. Copyright © 2018 Elsevier B.V. All rights reserved.

Allostatic Load and Health in the Older Population of England: A Crossed-Lagged Analysis

PubMed Central

Read, Sanna; Grundy, Emily

2014-01-01

Objective Allostatic load, a composite measure of accumulated physical wear and tear, has been proposed as an early sign of physiological dysregulation predictive of health problems, functional limitation, and disability. However, much previous research has been cross sectional and few studies consider repeated measures. We investigate the directionality of associations between allostatic load, self-rated health, and a measure of physical function (walking speed). Methods The sample included men and women 60 and older who participated in Wave 2 (2004) and Wave 4 (2008) of the English Longitudinal Study of Ageing (n = 6132 in Wave 2). Allostatic load was measured with nine biomarkers using a multisystem summary approach. Self-rated health was measured using a global 5 point summary indicator. Time to walk 8 ft was used as a measure of function. We fitted and tested autoregressive cross-lagged models between the allostatic load measure, self-rated health, and walking speed in Waves 2 and 4. Models were adjusted for age, sex, educational level, and smoking status at Wave 2 and for time-varying indicators of marital status, wealth, physical activity, and social support. Results Allostatic load predicted slower walking speed (standardized estimate = −0.08, 95% confidence interval [CI] = −0.10 to −0.05). Better self-rated health predicted faster walking speed (standardized estimate = 0.11, 95% CI = 0.08-0.13) as well as lower allostatic load (standardized estimate = −0.15, 95% CI = −0.22 to −0.09), whereas paths from allostatic load and walking speed to self-rated health were weaker (standardized estimates = −0.05 [95% CI = −0.07 to −0.02] and 0.06 [95% CI = 0.04–0.08]). Conclusions Allostatic load can be a useful risk indicator of subsequent poor health or function. PMID:25153937

Modeling exposure–lag–response associations with distributed lag non-linear models

PubMed Central

Gasparrini, Antonio

2014-01-01

In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure–lag–response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24027094

The cold effect of ambient temperature on ischemic and hemorrhagic stroke hospital admissions: A large database study in Beijing, China between years 2013 and 2014-Utilizing a distributed lag non-linear analysis.

PubMed

Luo, Yanxia; Li, Haibin; Huang, Fangfang; Van Halm-Lutterodt, Nicholas; Qin Xu; Wang, Anxin; Guo, Jin; Tao, Lixin; Li, Xia; Liu, Mengyang; Zheng, Deqiang; Chen, Sipeng; Zhang, Feng; Yang, Xinghua; Tan, Peng; Wang, Wei; Xie, Xueqin; Guo, Xiuhua

2018-01-01

The effects of ambient temperature on stroke death in China have been well addressed. However, few studies are focused on the attributable burden for the incident of different types of stroke due to ambient temperature, especially in Beijing, China. We purpose to assess the influence of ambient temperature on hospital stroke admissions in Beijing, China. Data on daily temperature, air pollution, and relative humidity measurements and stroke admissions in Beijing were obtained between 2013 and 2014. Distributed lag non-linear model was employed to determine the association between daily ambient temperature and stroke admissions. Relative risk (RR) with 95% confidence interval (CI) and Attribution fraction (AF) with 95% CI were calculated based on stroke subtype, gender and age group. A total number of 147, 624 stroke admitted cases (including hemorrhagic and ischemic types of stroke) were documented. A non-linear acute effect of cold temperature on ischemic and hemorrhagic stroke hospital admissions was evaluated. Compared with the 25th percentile of temperature (1.2 °C), the cumulative RR of extreme cold temperature (first percentile of temperature, -9.6 °C) was 1.51 (95% CI: 1.08-2.10) over lag 0-14 days for ischemic type and 1.28 (95% CI: 1.03-1.59) for hemorrhagic stroke over lag 0-3 days. Overall, 1.57% (95% CI: 0.06%-2.88%) of ischemic stroke and 1.90% (95% CI: 0.40%-3.41%) of hemorrhagic stroke was attributed to the extreme cold temperature over lag 0-7 days and lag 0-3 days, respectively. The cold temperature's impact on stroke admissions was found to be more obvious in male gender and the youth compared to female gender and the elderly. Exposure to extreme cold temperature is associated with increasing both ischemic and hemorrhagic stroke admissions in Beijing, China. Copyright © 2017 Elsevier Ltd. All rights reserved.

The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes.

PubMed

Bao, Junzhe; Wang, Zhenkun; Yu, Chuanhua; Li, Xudong

2016-05-04

Global climate change is one of the most serious environmental issues faced by humanity, and the resultant change in frequency and intensity of heat waves and cold spells could increase mortality. The influence of temperature on human health could be immediate or delayed. Latitude, relative humidity, and air pollution may influence the temperature-mortality relationship. We studied the influence of temperature on mortality and its lag effect in four Chinese cities with a range of latitudes over 2008-2011, adjusting for relative humidity and air pollution. We recorded the city-specific distributions of temperature and mortality by month and adopted a Poisson regression model combined with a distributed lag nonlinear model to investigate the lag effect of temperature on mortality. We found that the coldest months in the study area are December through March and the hottest months are June through September. The ratios of deaths during cold months to hot months were 1.43, 1.54, 1.37 and 1.12 for the cities of Wuhan, Changsha, Guilin and Haikou, respectively. The effects of extremely high temperatures generally persisted for 3 days, whereas the risk of extremely low temperatures could persist for 21 days. Compared with the optimum temperature of each city, at a lag of 21 days, the relative risks (95 % confidence interval) of extreme cold temperatures were 4.78 (3.63, 6.29), 2.38 (1.35, 4.19), 2.62 (1.15, 5.95) and 2.62 (1.44, 4.79) for Wuhan, Changsha, Guilin and Haikou, respectively. The respective risks were 1.35 (1.18, 1.55), 1.19 (0.96, 1.48), 1.22 (0.82, 1.82) and 2.47 (1.61, 3.78) for extreme hot temperatures, at a lag of 3 days. Temperature-mortality relationships vary among cities at different latitudes. Local governments should establish regional prevention and protection measures to more effectively confront and adapt to local climate change. The effects of hot temperatures predominantly occur over the short term, whereas those of cold temperatures can persist for an extended number of days.

An energy-balance model with multiply-periodic and quasi-chaotic free oscillations. [for climate forecasting

NASA Technical Reports Server (NTRS)

Bhattacharya, K.; Ghil, M.

1979-01-01

A slightly modified version of the one-dimensional time-dependent energy-balance climate model of Ghil and Bhattacharya (1978) is presented. The albedo-temperature parameterization has been reformulated and the smoothing of the temperature distribution in the tropics has been eliminated. The model albedo depends on time-lagged temperature in order to account for finite growth and decay time of continental ice sheets. Two distinct regimes of oscillatory behavior which depend on the value of the albedo-temperature time lag are considered.

Scaling of Ln(Permeability) in Sediments and Velocity Distributions in Turbulence: The Possibility of an Analogy.

NASA Astrophysics Data System (ADS)

Molz, F. J.; Kozubowski, T. J.; Miller, R. S.; Podgorski, K.

2005-12-01

The theory of non-stationary stochastic processes with stationary increments gives rise to stochastic fractals. When such fractals are used to represent measurements of (assumed stationary) physical properties, such as ln(k) increments in sediments or velocity increments "delta(v)" in turbulent flows, the resulting measurements exhibit scaling, either spatial, temporal or both. (In the present context, such scaling refers to systematic changes in the statistical properties of the increment distributions, such as variance, with the lag size over which the increments are determined.) Depending on the class of probability density functions (PDFs) that describe the increment distributions, the resulting stochastic fractals will display different properties. Until recently, the stationary increment process was represented using mainly Gaussian, Gamma or Levy PDFs. However, measurements in both sediments and fluid turbulence indicate that these PDFs are not commonly observed. Based on recent data and previous studies referenced and discussed in Meerschaert et al. (2004) and Molz et al. (2005), the measured increment PDFs display an approximate double exponential (Laplace) shape at smaller lags, and this shape evolves towards Gaussian at larger lags. A model for this behavior based on the Generalized Laplace PDF family called fractional Laplace motion, in analogy with its Gaussian counterpart - fractional Brownian motion, has been suggested (Meerschaert et al., 2004) and the necessary mathematics elaborated (Kozubowski et al., 2005). The resulting stochastic fractal is not a typical self-affine monofractal, but it does exhibit monofractal-like scaling in certain lag size ranges. To date, it has been shown that the Generalized Laplace family fits ln(k) increment distributions and reproduces the original 1941 theory of Kolmogorov when applied to Eulerian turbulent velocity increments. However, to make a physically self-consistent application to turbulence, one must adopt a Lagrangian viewpoint, and the details of this approach are still being developed. The potential analogy between turbulent delta(v) and sediment delta[ln(k)] is intriguing, and perhaps offers insight into the underlying chaotic processes that constitute turbulence and may result also in the pervasive heterogeneity observed in most natural sediments. Properties of the new Laplace fractal are presented, and potential applications to both sediments and fluid turbulence are discussed.

Time-lag of the earthquake energy release between three seismic regions

NASA Astrophysics Data System (ADS)

Tsapanos, Theodoros M.; Liritzis, Ioannis

1992-06-01

Three complete data sets of strong earthquakes ( M≥5.5), which occurred in the seismic regions of Chile, Mexico and Kamchatka during the time period 1899 1985, have been used to test the existence of a time-lag in the seismic energy release between these regions. These data sets were cross-correlated in order to determine whether any pair of the sets are correlated. For this purpose statistical tests, such as the T-test, the Fisher's transformation and probability distribution have been applied to determine the significance of the obtained correlation coefficients. The results show that the time-lag between Chile and Kamchatka is -2, which means that Kamchatka precedes Chile by 2 years, with a correlation coefficient significant at 99.80% level, a weak correlation between Kamchatka-Mexico and noncorrelation for Mexico-Chile.

Supersonic Pitch Damping Predictions of Blunt Entry Vehicles from Static CFD Solutions

NASA Technical Reports Server (NTRS)

Schoenenberger, Mark

2013-01-01

A technique for predicting supersonic pitch damping of blunt axisymmetric bodies from static CFD data is presented. The contributions to static pitching moment due to forebody and aftbody pressure distributions are broken out and considered separately. The one-dimension moment equation is cast to model the separate contributions from forebody and aftbody pressures with no traditional damping term included. The aftbody contribution to pitching moment is lagged by a phase angle of the natural oscillation period. This lag represents the time for aftbody wake structures to equilibrate while the body is oscillation. The characteristic equation of this formulation indicates that the lagged backshell moment adds a damping moment equivalent in form to a constant pitch damping term. CFD calculations of the backshell's contribution to the static pitching moment for a range of angles-of-attack is used to predict pitch damping coefficients. These predictions are compared with ballistic range data taken of the Mars Exploration Rover (MER) capsule and forced oscillation data of the Mars Viking capsule. The lag model appears to capture dynamic stability variation due to backshell geometry as well as Mach number.

Single molecular biology: coming of age in DNA replication.

PubMed

Liu, Xiao-Jing; Lou, Hui-Qiang

2017-09-20

DNA replication is an essential process of the living organisms. To achieve precise and reliable replication, DNA polymerases play a central role in DNA synthesis. Previous investigations have shown that the average rates of DNA synthesis on the leading and lagging strands in a replisome must be similar to avoid the formation of significant gaps in the nascent strands. The underlying mechanism has been assumed to be coordination between leading- and lagging-strand polymerases. However, Kowalczykowski's lab members recently performed single molecule techniques in E. coli and showed the real-time behavior of a replisome. The leading- and lagging-strand polymerases function stochastically and independently. Furthermore, when a DNA polymerase is paused, the helicase slows down in a self-regulating fail-safe mechanism, akin to a ''dead-man's switch''. Based on the real-time single-molecular observation, the authors propose that leading- and lagging-strand polymerases synthesize DNA stochastically within a Gaussian distribution. Along with the development and application of single-molecule techniques, we will witness a new age of DNA replication and other biological researches.

Stress in nonregular work arrangements: A longitudinal study of task- and employment-related aspects of stress.

PubMed

Vahle-Hinz, Tim

2016-10-01

In nonregular forms of employment, such as fixed-term or temporary agency work, 2 sources of stress must be distinguished: task-related stress components (e.g., time pressure) and employment-related stress components (e.g., effort to maintain employment). The present study investigated the relationship between task- and employment-related demands and resources and indicators of strain, well-being, work engagement, and self-rated performance in a sample of nonregular employed workers. Using a 2-wave longitudinal design, the results of autoregressive cross-lagged structural equation models demonstrated that time pressure, as a task-related demand, is positively related to strain and negatively related to well-being and self-rated performance. Autonomy, as a task-related resource, exhibited no significant relationships in the current study. Employment-related demands exhibited negative relationships with well-being and work engagement as well as negative and positive relationships with self-rated performance over time. Employment-related resources were primarily positive predictors of well-being and self-rated performance. Fit indices of comparative models indicated that reciprocal effect models (which enable causal and reverse effects) best fit the data. Accordingly, demands and resources predicted strain, well-being, work engagement, and self-rated performance over time and vice versa. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Are Video Games a Gateway to Gambling? A Longitudinal Study Based on a Representative Norwegian Sample.

PubMed

Molde, Helge; Holmøy, Bjørn; Merkesdal, Aleksander Garvik; Torsheim, Torbjørn; Mentzoni, Rune Aune; Hanns, Daniel; Sagoe, Dominic; Pallesen, Ståle

2018-06-05

The scope and variety of video games and monetary gambling opportunities are expanding rapidly. In many ways, these forms of entertainment are converging on digital and online video games and gambling sites. However, little is known about the relationship between video gaming and gambling. The present study explored the possibility of a directional relationship between measures of problem gaming and problem gambling, while also controlling for the influence of sex and age. In contrast to most previous investigations which are based on cross-sectional designs and non-representative samples, the present study utilized a longitudinal design conducted over 2 years (2013, 2015) and comprising 4601 participants (males 47.2%, age range 16-74) drawn from a random sample from the general population. Video gaming and gambling were assessed using the Gaming Addiction Scale for Adolescents and the Canadian Problem Gambling Index, respectively. Using an autoregressive cross-lagged structural equation model, we found a positive relationship between scores on problematic gaming and later scores on problematic gambling, whereas we found no evidence of the reverse relationship. Hence, video gaming problems appear to be a gateway behavior to problematic gambling behavior. In future research, one should continue to monitor the possible reciprocal behavioral influences between gambling and video gaming.

The Role of Family for Youth Friendships: Examining a Social Anxiety Mechanism.

PubMed

Mak, Hio Wa; Fosco, Gregory M; Feinberg, Mark E

2018-02-01

The quality of family relationships and youth friendships are intricately linked. Previous studies have examined different mechanisms of family-peer linkage, but few have examined social anxiety. The present study examined whether parental rejection and family climate predicted changes in youth social anxiety, which in turn predicted changes in friendship quality and loneliness. Possible bidirectional associations also were examined. Data for mothers, fathers, and youth (M age at Time 1 = 11.27; 52.3% were female) from 687 two-parent households over three time points are presented. Results from autoregressive, cross-lagged analyses revealed that father rejection (not mother rejection or family climate) at Time 1 (Fall of 6th Grade) predicted increased youth social anxiety at Time 2 (Spring of 7th Grade), which in turn, predicted increased loneliness at Time 3 (Spring of 8th Grade). The indirect effect of father rejection on loneliness was statistically significant. Mother rejection, father rejection, and a poor family climate were associated with decreased friendship quality and increased loneliness over time. Finally, there was some evidence of transactional associations between father rejection and youth social anxiety as well as between social anxiety and loneliness. This study's findings underscore the important role of fathers in youth social anxiety and subsequent social adjustment.

Multiscale Granger causality

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Effect of climate variables on cocoa black pod incidence in Sabah using ARIMAX model

NASA Astrophysics Data System (ADS)

Ling Sheng Chang, Albert; Ramba, Haya; Mohd. Jaaffar, Ahmad Kamil; Kim Phin, Chong; Chong Mun, Ho

2016-06-01

Cocoa black pod disease is one of the major diseases affecting the cocoa production in Malaysia and also around the world. Studies have shown that the climate variables have influenced the cocoa black pod disease incidence and it is important to quantify the black pod disease variation due to the effect of climate variables. Application of time series analysis especially auto-regressive moving average (ARIMA) model has been widely used in economics study and can be used to quantify the effect of climate variables on black pod incidence to forecast the right time to control the incidence. However, ARIMA model does not capture some turning points in cocoa black pod incidence. In order to improve forecasting performance, other explanatory variables such as climate variables should be included into ARIMA model as ARIMAX model. Therefore, this paper is to study the effect of climate variables on the cocoa black pod disease incidence using ARIMAX model. The findings of the study showed ARIMAX model using MA(1) and relative humidity at lag 7 days, RHt - 7 gave better R square value compared to ARIMA model using MA(1) which could be used to forecast the black pod incidence to assist the farmers determine timely application of fungicide spraying and culture practices to control the black pod incidence.

Principal sequence pattern analysis of episodes of excess mortality due to heat in the Barcelona metropolitan area.

PubMed

Peña, Juan Carlos; Aran, Montserrat; Raso, José Miguel; Pérez-Zanón, Nuria

2015-04-01

The aim of the study is to classify the synoptic sequences associated with excess mortality during the warm season in the Barcelona metropolitan area. To achieve this purpose, we undertook a principal sequence pattern analysis that incorporates different atmospheric levels, in an attempt at identifying the main features that account for dynamic and thermodynamic atmospheric processes. The sequence length was determined by the short-term displacement between temperature and mortality. To detect this lag, we applied the cross-correlation function to the residuals obtained from the modelling of the daily temperature and mortality series of summer. These residuals were estimated by means of an autoregressive integrated moving average (ARIMA) model. A 7-day sequence emerged as the basic temporal unit for evaluating the synoptic background that triggers the temperature related to excess mortality in the Barcelona metropolitan area. The principal sequence pattern analysis distinguished three main synoptic patterns: two dynamic configurations produced by southern fluxes related to an Atlantic low, which can be associated with heat waves recorded in southern Europe, and a third pattern identified by a stagnation situation associated with the persistence of a blocking anticyclone over Europe, related to heat waves recorded in northern and central western Europe.

PTSD symptoms lead to modification in the memory of the trauma: a prospective study of former prisoners of war.

PubMed

Dekel, Sharon; Solomon, Zahava; Ein-Dor, Tsachi

2016-03-01

With the growing interest in the role of trauma memory in posttraumatic stress disorder (PTSD), this prospective study examined long-term changes in memory and the bidirectional relationship between symptoms of PTSD and trauma memory. A sample of Israeli former prisoners of the 1973 Yom Kippur War (N = 103) was assessed in 1991 and in 2008. Participants' PTSD symptom clusters, measured by the PTSD Inventory, and recollections of subjective and objective exposure during captivity, measured by a self-report questionnaire, were assessed at both times. Data on prewar and postwar negative life events and psychotherapy were also collected. Repeated-measures analysis revealed that participants' recollections were increasingly negative over time (P < .001). Applying an autoregressive cross-lagged modeling strategy showed that the PTSD symptoms of hyperarousal facilitated subsequent amplifications in their recollections (P < .01). These findings challenge the accuracy of reports of traumatic experiences and show that PTSD symptoms, in part, lead to the formation of more negative recollections over time. The findings suggest that the original memory is repeatedly updated under the influence of the individual's emotional state. The findings are discussed in the context of the reconsolidation theory of memory. © Copyright 2016 Physicians Postgraduate Press, Inc.

The relationship between automatic thoughts and depression in a cognitive-behavioral treatment for people living with HIV/AIDS: Exploring temporality and causality.

PubMed

Riley, Kristen E; Lee, Jasper S; Safren, Steven A

2017-10-01

Depression in people living with HIV/AIDS (PLWHA) is highly prevalent and related to worse adherence to antiretroviral therapy, but is amenable to change via CBT. Cognitive-behavioral therapy for adherence and depression (CBT-AD) specifically addresses negative automatic thoughts (ATs) as one component of the treatment. There is little research on the temporal nature of the relation between ATs and depression. HIV-positive adults with depression (N=240) were randomized to CBT-AD, information/supportive psychotherapy for adherence and depression (ISP-AD), or one session of adherence counseling alone (ETAU). ATs were self-reported (Automatic Thoughts Questionnaire; ATQ) and depression was assessed by blinded interview (Montgomery-Asberg Depression Rating Scale; MADRS) at baseline, and 4-, 8-, and 12-months. We performed autoregressive cross-lagged panel models. Broadly, decreases in ATs were followed by decreases in depression, but decreases in depression were not followed by decreases in ATs. In CBT-AD, decreases in ATs were followed by decreases in depression, and vice versa. However, in the ISP group, while depression and ATs both significantly influenced each other, not all relations were in the direction expected. This study adds to the evidence base for cognitive interventions to decrease depression in individuals with a chronic medical condition, HIV/AIDS.

The Progression of Severe Behavior Disorder in Young Children with Intellectual and Developmental Disabilities

PubMed Central

Medeiros, Kristen; Curby, Timothy W.; Bernstein, Alec; Rojahn, Johannes; Schroeder, Stephen R.

2015-01-01

Behavior disorders, such as self-injurious, stereotypic, and aggressive behavior are common among individuals with intellectual or developmental disabilities. While we have learned much about those behaviors over the past few decades, longitudinal research that looks at developmental trajectory has been rare. This study was designed to examine the trajectory of these three forms of severe behavior disorders over a one year time period. The behaviors were measured on two dimensions: frequency of occurrence and severity. Participants were 160 infants and toddlers at risk for developmental delays in Lima, Peru. Using structural equation modeling, we found that the frequency of self-injury and stereotypic behavior and the severity of aggressive behavior remained stable over the 12 month period. Uni-directional structural models fit the data best for self-injurious and aggressive behavior (with frequency being a leading indicator of future severity of self-injury and severity being a leading indicator of future frequency for aggression). For stereotypic behavior, a cross-lagged autoregressive model fit the data best, with both dimensions of frequency and severity involved as leading indicators of each other. These models did not vary significantly across diagnostic groups, suggesting that toddlers exhibiting behavior disorders may be assisted with interventions that target the specific frequencies or severities of behaviors, regardless of diagnostic category. PMID:24012587

"I didn't know it was possible to feel that tired": exploring the complex bidirectional associations between maternal depressive symptoms and fatigue in a prospective pregnancy cohort study.

PubMed

Giallo, Rebecca; Gartland, Deirdre; Woolhouse, Hannah; Brown, Stephanie

2016-02-01

Depressive and fatigue symptoms are common health concerns for women in the postnatal period. Few studies have sought to investigate the role of fatigue in the development and maintenance of depressive symptoms. The aim of this paper was to examine the relationship between depressive symptoms and fatigue over the course of the first 4 years postpartum, in particular focusing on the extent to which fatigue at earlier time points predicted later depressive symptoms and vice versa. Data from over 1000 women participating in a longitudinal study of Australian women's physical and psychological health and recovery after childbirth were used. An autoregressive cross-lagged panel model was tested to assess the mutual influences of fatigue and depressive symptoms across five time points at 3, 6, 12 and 18 months postpartum, and at 4 years postpartum. A complex bidirectional relationship between fatigue and depressive symptoms from 3 months to 4 years postpartum was observed, where fatigue at earlier time points predicted depressive symptoms at later time points, and vice versa. The findings of this study suggest interventions targeting the prevention and management of fatigue may also confer some benefit in improving or preventing the development of depression symptoms in the early parenting period.

Predictors of growth and decline in leisure time physical activity from adolescence to adulthood.

PubMed

Wichstrøm, Lars; von Soest, Tilmann; Kvalem, Ingela Lundin

2013-07-01

To study the predictors of change in leisure time physical activity (LTPA) from adolescence to young adulthood. A nationally representative sample of 3,251 Norwegian students between 12 and 19 years of age were initially surveyed, and follow-up surveys were conducted three times over a 13-year period. The initial response rate was 97%, and retention rates for the three follow-up sessions were 92%, 84%, and 82%, respectively. Four groups of predictors were assessed: sociodemographics, such as gender, age, parental socioeconomic status, pubertal status, and grades; previous LTPA, such as the amount of LTPA and sports club membership; athletic self-concept and depressive symptoms; and other health behaviors, such as smoking, dieting, and body mass. Autoregressive cross-lagged analyses were supplemented with latent growth-curve analyses. Membership in a sports club and a positive athletic self-concept in adolescence predicted a high level of LTPA in adulthood, whereas smoking tobacco, high BMI, and depressive symptoms in adolescence predicted low levels of LTPA. Engaging adolescents in organized sports and enhancing adolescents' athletic self-concept may increase the number of adults who are physically active. Preventive efforts to reduce tobacco consumption, obesity, and depression in adolescence may also contribute to an increase in adult LTPA. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Children's perceptions of the classroom environment and social and academic performance: a longitudinal analysis of the contribution of the Responsive Classroom approach.

PubMed

Brock, Laura L; Nishida, Tracy K; Chiong, Cynthia; Grimm, Kevin J; Rimm-Kaufman, Sara E

2008-04-01

This study examines the contribution of the Responsive Classroom (RC) Approach, a set of teaching practices that integrate social and academic learning, to children's perceptions of their classroom, and children's academic and social performance over time. Three questions emerge: (a) What is the concurrent and cumulative relation between children's perceptions of the classroom and social and academic outcomes over time? (b) What is the contribution of teacher's use of RC practices to children's perceptions and social and academic outcomes? (c) Do children's perceptions of the classroom mediate the relation between RC teacher practices and child outcomes? Cross-lagged autoregressive structural equation models were used to analyze teacher and child-report questionnaire data, along with standardized test scores collected over 3 years from a sample of 520 children in grades 3-5. Results indicate a significant positive relation between RC teacher practices and child perceptions and outcomes over time. Further, children's perceptions partially mediated the relation between RC teacher practices and social competence. However, the models did not demonstrate that child perceptions mediated the relation between RC practices and achievement outcomes. Results are explained in terms of the contribution of teacher practices to children's perceptions and student performance.

How Socio-Environmental Factors Are Associated with Japanese Encephalitis in Shaanxi, China—A Bayesian Spatial Analysis

PubMed Central

Zhang, Shaobai; Hu, Wenbiao; Zhuang, Guihua

2018-01-01

Evidence indicated that socio-environmental factors were associated with occurrence of Japanese encephalitis (JE). This study explored the association of climate and socioeconomic factors with JE (2006–2014) in Shaanxi, China. JE data at the county level in Shaanxi were supplied by Shaanxi Center for Disease Control and Prevention. Population and socioeconomic data were obtained from the China Population Census in 2010 and statistical yearbooks. Meteorological data were acquired from the China Meteorological Administration. A Bayesian conditional autoregressive model was used to examine the association of meteorological and socioeconomic factors with JE. A total of 1197 JE cases were included in this study. Urbanization rate was inversely associated with JE incidence during the whole study period. Meteorological variables were significantly associated with JE incidence between 2012 and 2014. The excessive precipitation at lag of 1–2 months in the north of Shaanxi in June 2013 had an impact on the increase of local JE incidence. The spatial residual variations indicated that the whole study area had more stable risk (0.80–1.19 across all the counties) between 2012 and 2014 than earlier years. Public health interventions need to be implemented to reduce JE incidence, especially in rural areas and after extreme weather. PMID:29584661

Characterizing the relationship between temperature and mortality in tropical and subtropical cities: a distributed lag non-linear model analysis in Hue, Viet Nam, 2009-2013.

PubMed

Dang, Tran Ngoc; Seposo, Xerxes T; Duc, Nguyen Huu Chau; Thang, Tran Binh; An, Do Dang; Hang, Lai Thi Minh; Long, Tran Thanh; Loan, Bui Thi Hong; Honda, Yasushi

2016-01-01

The relationship between temperature and mortality has been found to be U-, V-, or J-shaped in developed temperate countries; however, in developing tropical/subtropical cities, it remains unclear. Our goal was to investigate the relationship between temperature and mortality in Hue, a subtropical city in Viet Nam. We collected daily mortality data from the Vietnamese A6 mortality reporting system for 6,214 deceased persons between 2009 and 2013. A distributed lag non-linear model was used to examine the temperature effects on all-cause and cause-specific mortality by assuming negative binomial distribution for count data. We developed an objective-oriented model selection with four steps following the Akaike information criterion (AIC) rule (i.e. a smaller AIC value indicates a better model). High temperature-related mortality was more strongly associated with short lags, whereas low temperature-related mortality was more strongly associated with long lags. The low temperatures increased risk in all-category mortality compared to high temperatures. We observed elevated temperature-mortality risk in vulnerable groups: elderly people (high temperature effect, relative risk [RR]=1.42, 95% confidence interval [CI]=1.11-1.83; low temperature effect, RR=2.0, 95% CI=1.13-3.52), females (low temperature effect, RR=2.19, 95% CI=1.14-4.21), people with respiratory disease (high temperature effect, RR=2.45, 95% CI=0.91-6.63), and those with cardiovascular disease (high temperature effect, RR=1.6, 95% CI=1.15-2.22; low temperature effect, RR=1.99, 95% CI=0.92-4.28). In Hue, the temperature significantly increased the risk of mortality, especially in vulnerable groups (i.e. elderly, female, people with respiratory and cardiovascular diseases). These findings may provide a foundation for developing adequate policies to address the effects of temperature on health in Hue City.

Characterizing the relationship between temperature and mortality in tropical and subtropical cities: a distributed lag non-linear model analysis in Hue, Viet Nam, 2009–2013

PubMed Central

Dang, Tran Ngoc; Seposo, Xerxes T.; Duc, Nguyen Huu Chau; Thang, Tran Binh; An, Do Dang; Hang, Lai Thi Minh; Long, Tran Thanh; Loan, Bui Thi Hong; Honda, Yasushi

2016-01-01

Background The relationship between temperature and mortality has been found to be U-, V-, or J-shaped in developed temperate countries; however, in developing tropical/subtropical cities, it remains unclear. Objectives Our goal was to investigate the relationship between temperature and mortality in Hue, a subtropical city in Viet Nam. Design We collected daily mortality data from the Vietnamese A6 mortality reporting system for 6,214 deceased persons between 2009 and 2013. A distributed lag non-linear model was used to examine the temperature effects on all-cause and cause-specific mortality by assuming negative binomial distribution for count data. We developed an objective-oriented model selection with four steps following the Akaike information criterion (AIC) rule (i.e. a smaller AIC value indicates a better model). Results High temperature-related mortality was more strongly associated with short lags, whereas low temperature-related mortality was more strongly associated with long lags. The low temperatures increased risk in all-category mortality compared to high temperatures. We observed elevated temperature-mortality risk in vulnerable groups: elderly people (high temperature effect, relative risk [RR]=1.42, 95% confidence interval [CI]=1.11–1.83; low temperature effect, RR=2.0, 95% CI=1.13–3.52), females (low temperature effect, RR=2.19, 95% CI=1.14–4.21), people with respiratory disease (high temperature effect, RR=2.45, 95% CI=0.91–6.63), and those with cardiovascular disease (high temperature effect, RR=1.6, 95% CI=1.15–2.22; low temperature effect, RR=1.99, 95% CI=0.92–4.28). Conclusions In Hue, the temperature significantly increased the risk of mortality, especially in vulnerable groups (i.e. elderly, female, people with respiratory and cardiovascular diseases). These findings may provide a foundation for developing adequate policies to address the effects of temperature on health in Hue City. PMID:26781954

Accounting for inherent variability of growth in microbial risk assessment.

PubMed

Marks, H M; Coleman, M E

2005-04-15

Risk assessments of pathogens need to account for the growth of small number of cells under varying conditions. In order to determine the possible risks that occur when there are small numbers of cells, stochastic models of growth are needed that would capture the distribution of the number of cells over replicate trials of the same scenario or environmental conditions. This paper provides a simple stochastic growth model, accounting only for inherent cell-growth variability, assuming constant growth kinetic parameters, for an initial, small, numbers of cells assumed to be transforming from a stationary to an exponential phase. Two, basic, microbial sets of assumptions are considered: serial, where it is assume that cells transform through a lag phase before entering the exponential phase of growth; and parallel, where it is assumed that lag and exponential phases develop in parallel. The model is based on, first determining the distribution of the time when growth commences, and then modelling the conditional distribution of the number of cells. For the latter distribution, it is found that a Weibull distribution provides a simple approximation to the conditional distribution of the relative growth, so that the model developed in this paper can be easily implemented in risk assessments using commercial software packages.

Association with meteo-climatological factors and daily emergency visits for renal colic and urinary calculi in Cuneo, Italy. A retrospective observational study, 2007-2010.

PubMed

Condemi, Vincenzo; Gestro, Massimo; Dozio, Elena; Tartaglino, Bruno; Corsi Romanelli, Massimiliano Marco; Solimene, Umberto; Meco, Roberto

2015-03-01

The incidence of nephrolithiasis is rising worldwide, especially in women and with increasing age. Incidence and prevalence of kidney stones are affected by genetic, nutritional, and environmental factors. The aim of this study is to investigate the link between various meteorological factors (independent variables) and the daily number of visits to the Emergency Department (ED of the S. Croce and Carle Hospital of Cuneo for renal colic (RC) and urinary stones (UC) as the dependent variable over the years 2007-2010.The Poisson generalized regression models (PGAMs) have been used in different progressive ways. The results of PGAMs (stage 1) adjusted for seasonal and calendar factors confirmed a significant correlation (p < 0.03) with the thermal parameter. Evaluation of the dose-response effect [PGAMs combined with distributed lags nonlinear models (DLNMs)-stage 2], expressed in terms of relative risk (RR) and cumulative relative risk (RRC), indicated a relative significant effect up to 15 lag days of lag (RR > 1), with a first peak after 5 days (lag ranges 0-1, 0-3, and 0-5) and a second weak peak observed along the 5-15 lag range days. The estimated RR for females was significant, mainly in the second and fourth age group considered (19-44 and >65 years): RR for total ED visits 1.27, confidence interval (CI) 1.11-1.46 (lag 0-5 days); RR 1.42, CI 1.01-2.01 (lag 0-10 days); and RR 1.35, CI 1.09-1.68 (lag 0-15 days). The research also indicated a moderate involvement of the thermal factor in the onset of RC caused by UC, exclusively in the female sex. Further studies will be necessary to confirm these results.

Transport and time lag of chlorofluorocarbon gases in the unsaturated zone, Rabis Creek, Denmark

USGS Publications Warehouse

Engesgaard, Peter; Højberg, Anker L.; Hinsby, Klaus; Jensen, Karsten H.; Laier, Troels; Larsen, Flemming; Busenberg, Eurybiades; Plummer, Niel

2004-01-01

Transport of chlorofluorocarbon (CFC) gases through the unsaturated zone to the water table is affected by gas diffusion, air–water exchange (solubility), sorption to the soil matrix, advective–dispersive transport in the water phase, and, in some cases, anaerobic degradation. In deep unsaturated zones, this may lead to a time lag between entry of gases at the land surface and recharge to groundwater. Data from a Danish field site were used to investigate how time lag is affected by variations in water content and to explore the use of simple analytical solutions to calculate time lag. Numerical simulations demonstrate that either degradation or sorption of CFC-11 takes place, whereas CFC-12 and CFC-113 are nonreactive. Water flow did not appreciably affect transport. An analytical solution for the period with a linear increase in atmospheric CFC concentrations (approximately early 1970s to early 1990s) was used to calculate CFC profiles and time lags. We compared the analytical results with numerical simulations. The time lags in the 15-m-deep unsaturated zone increase from 4.2 to between 5.2 and 6.1 yr and from 3.4 to 3.9 yr for CFC-11 and CFC-12, respectively, when simulations change from use of an exponential to a linear increase in atmospheric concentrations. The CFC concentrations at the water table before the early 1990s can be estimated by displacing the atmospheric input function by these fixed time lags. A sensitivity study demonstrates conditions under which a time lag in the unsaturated zone becomes important. The most critical parameter is the tortuosity coefficient. The analytical approach is valid for the low range of tortuosity coefficients (τ = 0.1–0.4) and unsaturated zones greater than approximately 20 m in thickness. In these cases the CFC distribution may still be from either the exponential or linear phase. In other cases, the use of numerical models, as described in our work and elsewhere, is an option.

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

ERIC Educational Resources Information Center

Murphy, Daniel L.; Pituch, Keenan A.

2009-01-01

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

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

Treesearch

Jeffrey P. Prestemon; David T. Butry

2005-01-01

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

Theoretical results on fractionally integrated exponential generalized autoregressive conditional heteroskedastic processes

NASA Astrophysics Data System (ADS)

Lopes, Sílvia R. C.; Prass, Taiane S.

2014-05-01

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

An out of phase coupling between the atmosphere and the ocean over the North Atlantic Ocean

NASA Astrophysics Data System (ADS)

Ribera, Pedro; Ordoñez, Paulina; Gallego, David; Peña-Ortiz, Cristina

2017-04-01

An oscillation band, with a period ranging between 40 and 60 years, has been identified as the most intense signal over the North Atlantic Ocean using several oceanic and atmospheric reanalyses between 1856 and the present. This signal represents the Atlantic Multidecadal Oscillation, an oscillation between warmer and colder than normal conditions in SST. Simultaneously, those changes in SST are accompanied by changes in atmospheric conditions represented by surface pressure, temperature and circulation. In fact, the evolution of the surface pressure pattern along this oscillation shows a North Atlantic Oscillation-like pattern, suggesting the existence of an out of phase coupling between atmospheric and oceanic conditions. Further analysis shows that the evolution of the oceanic SST distribution modifies atmospheric baroclinic conditions in the mid to high latitudes of the North Atlantic and leads the atmospheric variability by 6-7 years. If AMO represents the oceanic conditons and NAO represents the atmospheric variability then it could be said that AMO of one sign leads NAO of the opposite sign with a lag of 6-7 years. On the other hand, the evolution of atmospheric conditions, represented by pressure distribution patterns, favors atmospheric circulation anomalies and induces a heat advection which tends to change the sign of the existing SST distribution and oceanic conditions with a lag of 16-17 years. In this case, NAO of one sign leads AMO of the same sign with a lag of 16-17 years.

Per capita alcohol consumption and suicide mortality in a panel of US states from 1950 to 2002

PubMed Central

Kerr, William C.; Subbaraman, Meenakshi; Ye, Yu

2011-01-01

Introduction and Aims The relationship between per capita alcohol consumption and suicide rates has been found to vary in significance and magnitude across countries. This study utilizes a panel of time-series measures from the US states to estimate the effects of changes in current and lagged alcohol sales on suicide mortality risk. Design and Methods Generalized least squares estimation utilized 53 years of data from 48 US states or state groups to estimate relationships between total and beverage-specific alcohol consumption measures and age-standardized suicide mortality rates in first-differenced semi-logged models. Results An additional liter of ethanol from total alcohol sales was estimated to increase suicide rates by 2.3% in models utilizing a distributed lag specification while no effect was found in models including only current alcohol consumption. A similar result is found for men, while for women both current and distributed lag measures were found to be significantly related to suicide rates with an effect of about 3.2% per liter from current and 5.8% per liter from the lagged measure. Beverage-specific models indicate that spirits is most closely linked with suicide risk for women while beer and wine are for men. Unemployment rates are consistently positively related to suicide rates. Discussion and Conclusions Results suggest that chronic effects, potentially related to alcohol abuse and dependence, are the main source of alcohol’s impact on suicide rates in the US for men and are responsible for about half of the effect for women. PMID:21896069

Statistical properties of Fourier-based time-lag estimates

NASA Astrophysics Data System (ADS)

Epitropakis, A.; Papadakis, I. E.

2016-06-01

Context. The study of X-ray time-lag spectra in active galactic nuclei (AGN) is currently an active research area, since it has the potential to illuminate the physics and geometry of the innermost region (I.e. close to the putative super-massive black hole) in these objects. To obtain reliable information from these studies, the statistical properties of time-lags estimated from data must be known as accurately as possible. Aims: We investigated the statistical properties of Fourier-based time-lag estimates (I.e. based on the cross-periodogram), using evenly sampled time series with no missing points. Our aim is to provide practical "guidelines" on estimating time-lags that are minimally biased (I.e. whose mean is close to their intrinsic value) and have known errors. Methods: Our investigation is based on both analytical work and extensive numerical simulations. The latter consisted of generating artificial time series with various signal-to-noise ratios and sampling patterns/durations similar to those offered by AGN observations with present and past X-ray satellites. We also considered a range of different model time-lag spectra commonly assumed in X-ray analyses of compact accreting systems. Results: Discrete sampling, binning and finite light curve duration cause the mean of the time-lag estimates to have a smaller magnitude than their intrinsic values. Smoothing (I.e. binning over consecutive frequencies) of the cross-periodogram can add extra bias at low frequencies. The use of light curves with low signal-to-noise ratio reduces the intrinsic coherence, and can introduce a bias to the sample coherence, time-lag estimates, and their predicted error. Conclusions: Our results have direct implications for X-ray time-lag studies in AGN, but can also be applied to similar studies in other research fields. We find that: a) time-lags should be estimated at frequencies lower than ≈ 1/2 the Nyquist frequency to minimise the effects of discrete binning of the observed time series; b) smoothing of the cross-periodogram should be avoided, as this may introduce significant bias to the time-lag estimates, which can be taken into account by assuming a model cross-spectrum (and not just a model time-lag spectrum); c) time-lags should be estimated by dividing observed time series into a number, say m, of shorter data segments and averaging the resulting cross-periodograms; d) if the data segments have a duration ≳ 20 ks, the time-lag bias is ≲15% of its intrinsic value for the model cross-spectra and power-spectra considered in this work. This bias should be estimated in practise (by considering possible intrinsic cross-spectra that may be applicable to the time-lag spectra at hand) to assess the reliability of any time-lag analysis; e) the effects of experimental noise can be minimised by only estimating time-lags in the frequency range where the sample coherence is larger than 1.2/(1 + 0.2m). In this range, the amplitude of noise variations caused by measurement errors is smaller than the amplitude of the signal's intrinsic variations. As long as m ≳ 20, time-lags estimated by averaging over individual data segments have analytical error estimates that are within 95% of the true scatter around their mean, and their distribution is similar, albeit not identical, to a Gaussian.

Association of short-term exposure to ground-level ozone and respiratory outpatient clinic visits in a rural location – Sublette County, Wyoming, 2008–2011

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

Pride, Kerry R., E-mail: hgp3@cdc.gov; Wyoming Department of Health, 6101 Yellowstone Road, Suite 510, Cheyenne, WY 82002; Peel, Jennifer L.

Objective: Short-term exposure to ground-level ozone has been linked to adverse respiratory and other health effects; previous studies typically have focused on summer ground-level ozone in urban areas. During 2008–2011, Sublette County, Wyoming (population: ~10,000 persons), experienced periods of elevated ground-level ozone concentrations during the winter. This study sought to evaluate the association of daily ground-level ozone concentrations and health clinic visits for respiratory disease in this rural county. Methods: Clinic visits for respiratory disease were ascertained from electronic billing records of the two clinics in Sublette County for January 1, 2008–December 31, 2011. A time-stratified case-crossover design, adjusted formore » temperature and humidity, was used to investigate associations between ground-level ozone concentrations measured at one station and clinic visits for a respiratory health concern by using an unconstrained distributed lag of 0–3 days and single-day lags of 0 day, 1 day, 2 days, and 3 days. Results: The data set included 12,742 case-days and 43,285 selected control-days. The mean ground-level ozone observed was 47±8 ppb. The unconstrained distributed lag of 0–3 days was consistent with a null association (adjusted odds ratio [aOR]: 1.001; 95% confidence interval [CI]: 0.990–1.012); results for lags 0, 2, and 3 days were consistent with the null. However, the results for lag 1 were indicative of a positive association; for every 10-ppb increase in the 8-h maximum average ground-level ozone, a 3.0% increase in respiratory clinic visits the following day was observed (aOR: 1.031; 95% CI: 0.994–1.069). Season modified the adverse respiratory effects: ground-level ozone was significantly associated with respiratory clinic visits during the winter months. The patterns of results from all sensitivity analyzes were consistent with the a priori model. Conclusions: The results demonstrate an association of increasing ground-level ozone with an increase in clinic visits for adverse respiratory-related effects in the following day (lag day 1) in Sublette County; the magnitude was strongest during the winter months; this association during the winter months in a rural location warrants further investigation. - Highlights: • We assessed elevated ground-level ozone in frontier Sublette County, Wyoming. • Ground-level ozone concentrations were moderately to highly correlated between stations. • Adverse respiratory-related clinic visits occurred year round at lag 1. • Strongest association of clinic visits was in the coldest months at lag 1.« less

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

NASA Astrophysics Data System (ADS)

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

2015-09-01

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

A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data.

PubMed

Zheng, Yin; Zhang, Yu-Jin; Larochelle, Hugo

2016-06-01

Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.

The comparison study among several data transformations in autoregressive modeling

NASA Astrophysics Data System (ADS)

Setiyowati, Susi; Waluyo, Ramdhani Try

2015-12-01

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

The Association between Dust Storms and Daily Non-Accidental Mortality in the United States, 1993-2005.

PubMed

Crooks, James Lewis; Cascio, Wayne E; Percy, Madelyn S; Reyes, Jeanette; Neas, Lucas M; Hilborn, Elizabeth D

2016-11-01

The impact of dust storms on human health has been studied in the context of Asian, Saharan, Arabian, and Australian storms, but there has been no recent population-level epidemiological research on the dust storms in North America. The relevance of dust storms to public health is likely to increase as extreme weather events are predicted to become more frequent with anticipated changes in climate through the 21st century. We examined the association between dust storms and county-level non-accidental mortality in the United States from 1993 through 2005. Dust storm incidence data, including date and approximate location, are taken from the U.S. National Weather Service storm database. County-level mortality data for the years 1993-2005 were acquired from the National Center for Health Statistics. Distributed lag conditional logistic regression models under a time-stratified case-crossover design were used to study the relationship between dust storms and daily mortality counts over the whole United States and in Arizona and California specifically. End points included total non-accidental mortality and three mortality subgroups (cardiovascular, respiratory, and other non-accidental). We estimated that for the United States as a whole, total non-accidental mortality increased by 7.4% (95% CI: 1.6, 13.5; p = 0.011) and 6.7% (95% CI: 1.1, 12.6; p = 0.018) at 2- and 3-day lags, respectively, and by an average of 2.7% (95% CI: 0.4, 5.1; p = 0.023) over lags 0-5 compared with referent days. Significant associations with non-accidental mortality were estimated for California (lag 2 and 0-5 day) and Arizona (lag 3), for cardiovascular mortality in the United States (lag 2) and Arizona (lag 3), and for other non-accidental mortality in California (lags 1-3 and 0-5). Dust storms are associated with increases in lagged non-accidental and cardiovascular mortality. Citation: Crooks JL, Cascio WE, Percy MS, Reyes J, Neas LM, Hilborn ED. 2016. The association between dust storms and daily non-accidental mortality in the United States, 1993-2005. Environ Health Perspect 124:1735-1743; http://dx.doi.org/10.1289/EHP216.

Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: A case study in endemic districts of Bhutan

PubMed Central

2010-01-01

Background Malaria still remains a public health problem in some districts of Bhutan despite marked reduction of cases in last few years. To strengthen the country's prevention and control measures, this study was carried out to develop forecasting and prediction models of malaria incidence in the endemic districts of Bhutan using time series and ARIMAX. Methods This study was carried out retrospectively using the monthly reported malaria cases from the health centres to Vector-borne Disease Control Programme (VDCP) and the meteorological data from Meteorological Unit, Department of Energy, Ministry of Economic Affairs. Time series analysis was performed on monthly malaria cases, from 1994 to 2008, in seven malaria endemic districts. The time series models derived from a multiplicative seasonal autoregressive integrated moving average (ARIMA) was deployed to identify the best model using data from 1994 to 2006. The best-fit model was selected for each individual district and for the overall endemic area was developed and the monthly cases from January to December 2009 and 2010 were forecasted. In developing the prediction model, the monthly reported malaria cases and the meteorological factors from 1996 to 2008 of the seven districts were analysed. The method of ARIMAX modelling was employed to determine predictors of malaria of the subsequent month. Results It was found that the ARIMA (p, d, q) (P, D, Q)s model (p and P representing the auto regressive and seasonal autoregressive; d and D representing the non-seasonal differences and seasonal differencing; and q and Q the moving average parameters and seasonal moving average parameters, respectively and s representing the length of the seasonal period) for the overall endemic districts was (2,1,1)(0,1,1)12; the modelling data from each district revealed two most common ARIMA models including (2,1,1)(0,1,1)12 and (1,1,1)(0,1,1)12. The forecasted monthly malaria cases from January to December 2009 and 2010 varied from 15 to 82 cases in 2009 and 67 to 149 cases in 2010, where population in 2009 was 285,375 and the expected population of 2010 to be 289,085. The ARIMAX model of monthly cases and climatic factors showed considerable variations among the different districts. In general, the mean maximum temperature lagged at one month was a strong positive predictor of an increased malaria cases for four districts. The monthly number of cases of the previous month was also a significant predictor in one district, whereas no variable could predict malaria cases for two districts. Conclusions The ARIMA models of time-series analysis were useful in forecasting the number of cases in the endemic areas of Bhutan. There was no consistency in the predictors of malaria cases when using ARIMAX model with selected lag times and climatic predictors. The ARIMA forecasting models could be employed for planning and managing malaria prevention and control programme in Bhutan. PMID:20813066

Texture classification using autoregressive filtering

NASA Technical Reports Server (NTRS)

Lawton, W. M.; Lee, M.

1984-01-01

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

Lag threads organize the brain’s intrinsic activity

PubMed Central

Mitra, Anish; Snyder, Abraham Z.; Blazey, Tyler; Raichle, Marcus E.

2015-01-01

It has been widely reported that intrinsic brain activity, in a variety of animals including humans, is spatiotemporally structured. Specifically, propagated slow activity has been repeatedly demonstrated in animals. In human resting-state fMRI, spontaneous activity has been understood predominantly in terms of zero-lag temporal synchrony within widely distributed functional systems (resting-state networks). Here, we use resting-state fMRI from 1,376 normal, young adults to demonstrate that multiple, highly reproducible, temporal sequences of propagated activity, which we term “lag threads,” are present in the brain. Moreover, this propagated activity is largely unidirectional within conventionally understood resting-state networks. Modeling experiments show that resting-state networks naturally emerge as a consequence of shared patterns of propagation. An implication of these results is that common physiologic mechanisms may underlie spontaneous activity as imaged with fMRI in humans and slowly propagated activity as studied in animals. PMID:25825720

Mechanism of asymmetric polymerase assembly at the eukaryotic replication fork

PubMed Central

Georgescu, Roxana E; Langston, Lance; Yao, Nina Y; Yurieva, Olga; Zhang, Dan; Finkelstein, Jeff; Agarwal, Tani; O’Donnell, Mike E

2015-01-01

Eukaryotes use distinct polymerases for leading- and lagging-strand replication, but how they target their respective strands is uncertain. We reconstituted Saccharomyces cerevisiae replication forks and found that CMG helicase selects polymerase (Pol) ε to the exclusion of Pol δ on the leading strand. Even if Pol δ assembles on the leading strand, Pol ε rapidly replaces it. Pol δ–PCNA is distributive with CMG, in contrast to its high stability on primed ssDNA. Hence CMG will not stabilize Pol δ, instead leaving the leading strand accessible for Pol ε and stabilizing Pol ε. Comparison of Pol ε and Pol δ on a lagging-strand model DNA reveals the opposite. Pol δ dominates over excess Pol ε on PCNA-primed ssDNA. Thus, PCNA strongly favors Pol δ over Pol ε on the lagging strand, but CMG over-rides and flips this balance in favor of Pol ε on the leading strand. PMID:24997598

Study on the association between ambient air pollution and daily cardiovascular and respiratory mortality in an urban district of Beijing.

PubMed

Zhang, Fengying; Li, Liping; Krafft, Thomas; Lv, Jinmei; Wang, Wuyi; Pei, Desheng

2011-06-01

The association between daily cardiovascular/respiratory mortality and air pollution in an urban district of Beijing was investigated over a 6-year period (January 2003 to December 2008). The purpose of this study was to evaluate the relative importance of the major air pollutants [particulate matter (PM), SO2, NO2] as predictors of daily cardiovascular/respiratory mortality. The time-series studied comprises years with lower level interventions to control air pollution (2003-2006) and years with high level interventions in preparation for and during the Olympics/Paralympics (2007-2008). Concentrations of PM10, SO2, and NO2, were measured daily during the study period. A generalized additive model was used to evaluate daily numbers of cardiovascular/respiratory deaths in relation to each air pollutant, controlling for time trends and meteorological influences such as temperature and relative humidity. The results show that the daily cardiovascular/respiratory death rates were significantly associated with the concentration air pollutants, especially deaths related to cardiovascular disease. The current day effects of PM10 and NO2 were higher than that of single lags (distributed lags) and moving average lags for respiratory disease mortality. The largest RR of SO2 for respiratory disease mortality was in Lag02. For cardiovascular disease mortality, the largest RR was in Lag01 for PM10, and in current day (Lag0) for SO2 and NO2. NO2 was associated with the largest RRs for deaths from both cardiovascular disease and respiratory disease.

The weekly associations between climatic factors and Plasmodium vivax and Plasmodium falciparum malaria in China, 2005-2014.

PubMed

Hundessa, Samuel; Williams, Gail; Li, Shanshan; Guo, Jinpeng; Zhang, Wenyi; Guo, Yuming

2017-05-01

Meteorological factors play a crucial role in malaria transmission, but limited evidence is available from China. This study aimed to estimate the weekly associations between meteorological factors and Plasmodium vivax and Plasmodium falciparum malaria in China. The Distributed Lag Non-Linear Model was used to examine non-linearity and delayed effects of average temperature, rainfall, relative humidity, sunshine hours, wind speed and atmospheric pressure on malaria. Average temperature was associated with P. vivax and P. falciparum cases over long ranges of lags. The effect was more immediate on P. vivax (0-6 weeks) than on P. falciparum (1-9 weeks). Relative humidity was associated with P. vivax and P. falciparum over 8-10 weeks and 5-8 weeks lag, respectively. A significant effect of wind speed on P. vivax was observed at 0-2 weeks lag, but no association was found with P. falciparum. Rainfall had a decreasing effect on P. vivax, but no association was found with P. falciparum. Sunshine hours were negatively associated with P. falciparum, but the association was unclear for P. vixax. However, the effects of atmospheric pressure on both malaria types were not significant at any lag. Our study highlights a substantial effect of weekly climatic factors on P. vivax and P. falciparum malaria transmission in China, with different lags. This provides an evidence base for health authorities in developing a malaria early-warning system. © The Author 2017. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Using Remote Sensing, Weather, and Demographic Data to Create Risk Maps for Zika, Dengue, and Chikungunya in Brazil

NASA Astrophysics Data System (ADS)

Manore, C.; Conrad, J.; Del Valle, S.; Ziemann, A.; Fairchild, G.; Generous, E. N.

2017-12-01

Mosquito-borne diseases such as Zika, dengue, and chikungunya viruses have dynamics coupled to weather, ecology, human infrastructure, socio-economic demographics, and behavior. We use time-varying remote sensing and weather data, along with demographics and ecozones to predict risk through time for Zika, dengue, and chikungunya outbreaks in Brazil. We use distributed lag methods to quantify the lag between outbreaks and weather. Our statistical model indicates that the relationships between the variables are complex, but that quantifying risk is possible with the right data at appropriate spatio-temporal scales.

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Comparison of the Lag Screw Placements for the Treatment of Stable and Unstable Intertrochanteric Femoral Fractures regarding Trabecular Bone Failure.

PubMed

Celik, Talip; Mutlu, Ibrahim; Ozkan, Arif; Kisioglu, Yasin

2016-01-01

Background. In this study, the cut-out risk of Dynamic Hip Screw (DHS) was investigated in nine different positions of the lag screw for two fracture types by using Finite Element Analysis (FEA). Methods. Two types of fractures (31-A1.1 and A2.1 in AO classification) were generated in the femur model obtained from Computerized Tomography images. The DHS model was placed into the fractured femur model in nine different positions. Tip-Apex Distances were measured using SolidWorks. In FEA, the force applied to the femoral head was determined according to the maximum value being observed during walking. Results. The highest volume percentage exceeding the yield strength of trabecular bone was obtained in posterior-inferior region in both fracture types. The best placement region for the lag screw was found in the middle of both fracture types. There are compatible results between Tip-Apex Distances and the cut-out risk except for posterior-superior and superior region of 31-A2.1 fracture type. Conclusion. The position of the lag screw affects the risk of cut-out significantly. Also, Tip-Apex Distance is a good predictor of the cut-out risk. All in all, we can supposedly say that the density distribution of the trabecular bone is a more efficient factor compared to the positions of lag screw in the cut-out risk.

New scaling model for variables and increments with heavy-tailed distributions

NASA Astrophysics Data System (ADS)

Riva, Monica; Neuman, Shlomo P.; Guadagnini, Alberto

2015-06-01

Many hydrological (as well as diverse earth, environmental, ecological, biological, physical, social, financial and other) variables, Y, exhibit frequency distributions that are difficult to reconcile with those of their spatial or temporal increments, ΔY. Whereas distributions of Y (or its logarithm) are at times slightly asymmetric with relatively mild peaks and tails, those of ΔY tend to be symmetric with peaks that grow sharper, and tails that become heavier, as the separation distance (lag) between pairs of Y values decreases. No statistical model known to us captures these behaviors of Y and ΔY in a unified and consistent manner. We propose a new, generalized sub-Gaussian model that does so. We derive analytical expressions for probability distribution functions (pdfs) of Y and ΔY as well as corresponding lead statistical moments. In our model the peak and tails of the ΔY pdf scale with lag in line with observed behavior. The model allows one to estimate, accurately and efficiently, all relevant parameters by analyzing jointly sample moments of Y and ΔY. We illustrate key features of our new model and method of inference on synthetically generated samples and neutron porosity data from a deep borehole.

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

NASA Astrophysics Data System (ADS)

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

1998-09-01

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

Cross-lagged structural equation models for the relationship between health-related state and behaviours and body bullying in adolescence: findings from longitudinal study ELANA

PubMed Central

Straatmann, Viviane S.; Almquist, Ylva B.; Oliveira, Aldair J.; Rostila, Mikael; Lopes, Claudia S.

2018-01-01

We investigated the stability and the directionality of being body bullied and a set of four variables– 1) Body Mass Index (BMI), 2) moderate and vigorous physical activity (MVPA), 3) television time (TV) and 4) video game/computer time (VG)-, termed in the present study as ‘health-related state and behaviours (HRSB)’–across adolescence. The Adolescent Nutritional Assessment Longitudinal Study (ELANA) is a cohort study conducted among middle school students from two public and four private schools in Rio de Janeiro-Brazil. We analysed data from 2010 (T1) and 2012 (T2) among 810 adolescents (aged 9–15 at T1). Gender-specific structural equation models (SEM) were estimated, including autoregressive paths for the HRSB and being body bullied over time, correlations at T1 and T2, respectively, and cross-lagged effects. The results presented significant stability coefficients for almost all variables over time in both genders (except for MVPA in boys and girls and TV time among girls). There were positive correlations between BMI and being body bullied, as well as between TV and VG for boys (0.32, p<0.001 and 0.24, p<0.001, respectively) and girls (0.30, p<0.001 and 0.30, p<0.001, respectively) at T1. It remained significant at T2 (boys: 0.18, p<0.05 and 0.16, p<0.01; girls: 0.21, p<0.01 and 0.22, p<0.01, respectively). Examining the cross-lagged paths between being body bullied and HRSB, we observed that the reciprocal model provided the best fit for boys, indicating that BMI at T1 had a significant effect in being body bullied at T2 (0.12, p<0.05) and being body bullied at T1 had an effect on VG at T2 (0.14, p<0.01). Among girls the forward causation model showed the best fit, demonstrating a significant effect of being body bullied at T1 on VG at T2 (0.16, p<0.01). Apart from MVPA, both being body bullying and HRSB were largely stable across adolescence. For boys and girls alike, exposure to being body bullied seemed to increase their time spent on VG, while for boys BMI also predicted being body bullied. This study highlighted the complex interplay between being body bullied and HRSB and the importance of acknowledging gender differences in this context. PMID:29342218

Cross-lagged structural equation models for the relationship between health-related state and behaviours and body bullying in adolescence: findings from longitudinal study ELANA.

PubMed

Straatmann, Viviane S; Almquist, Ylva B; Oliveira, Aldair J; Rostila, Mikael; Lopes, Claudia S

2018-01-01

We investigated the stability and the directionality of being body bullied and a set of four variables- 1) Body Mass Index (BMI), 2) moderate and vigorous physical activity (MVPA), 3) television time (TV) and 4) video game/computer time (VG)-, termed in the present study as 'health-related state and behaviours (HRSB)'-across adolescence. The Adolescent Nutritional Assessment Longitudinal Study (ELANA) is a cohort study conducted among middle school students from two public and four private schools in Rio de Janeiro-Brazil. We analysed data from 2010 (T1) and 2012 (T2) among 810 adolescents (aged 9-15 at T1). Gender-specific structural equation models (SEM) were estimated, including autoregressive paths for the HRSB and being body bullied over time, correlations at T1 and T2, respectively, and cross-lagged effects. The results presented significant stability coefficients for almost all variables over time in both genders (except for MVPA in boys and girls and TV time among girls). There were positive correlations between BMI and being body bullied, as well as between TV and VG for boys (0.32, p<0.001 and 0.24, p<0.001, respectively) and girls (0.30, p<0.001 and 0.30, p<0.001, respectively) at T1. It remained significant at T2 (boys: 0.18, p<0.05 and 0.16, p<0.01; girls: 0.21, p<0.01 and 0.22, p<0.01, respectively). Examining the cross-lagged paths between being body bullied and HRSB, we observed that the reciprocal model provided the best fit for boys, indicating that BMI at T1 had a significant effect in being body bullied at T2 (0.12, p<0.05) and being body bullied at T1 had an effect on VG at T2 (0.14, p<0.01). Among girls the forward causation model showed the best fit, demonstrating a significant effect of being body bullied at T1 on VG at T2 (0.16, p<0.01). Apart from MVPA, both being body bullying and HRSB were largely stable across adolescence. For boys and girls alike, exposure to being body bullied seemed to increase their time spent on VG, while for boys BMI also predicted being body bullied. This study highlighted the complex interplay between being body bullied and HRSB and the importance of acknowledging gender differences in this context.

Improvement of Reynolds-Stress and Triple-Product Lag Models

NASA Technical Reports Server (NTRS)

Olsen, Michael E.; Lillard, Randolph P.

2017-01-01

The Reynolds-stress and triple product Lag models were created with a normal stress distribution which was denied by a 4:3:2 distribution of streamwise, spanwise and wall normal stresses, and a ratio of r(sub w) = 0.3k in the log layer region of high Reynolds number flat plate flow, which implies R11(+)= [4/(9/2)*.3] approximately 2.96. More recent measurements show a more complex picture of the log layer region at high Reynolds numbers. The first cut at improving these models along with the direction for future refinements is described. Comparison with recent high Reynolds number data shows areas where further work is needed, but also shows inclusion of the modeled turbulent transport terms improve the prediction where they influence the solution. Additional work is needed to make the model better match experiment, but there is significant improvement in many of the details of the log layer behavior.

Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-01-01

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

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.

Urbanization and Income Inequality in Post-Reform China: A Causal Analysis Based on Time Series Data

PubMed Central

Chen, Guo; Glasmeier, Amy K.; Zhang, Min; Shao, Yang

2016-01-01

This paper investigates the potential causal relationship(s) between China’s urbanization and income inequality since the start of the economic reform. Based on the economic theory of urbanization and income distribution, we analyze the annual time series of China’s urbanization rate and Gini index from 1978 to 2014. The results show that urbanization has an immediate alleviating effect on income inequality, as indicated by the negative relationship between the two time series at the same year (lag = 0). However, urbanization also seems to have a lagged aggravating effect on income inequality, as indicated by positive relationship between urbanization and the Gini index series at lag 1. Although the link between urbanization and income inequality is not surprising, the lagged aggravating effect of urbanization on the Gini index challenges the popular belief that urbanization in post-reform China generally helps reduce income inequality. At deeper levels, our results suggest an urgent need to focus on the social dimension of urbanization as China transitions to the next stage of modernization. Comprehensive social reforms must be prioritized to avoid a long-term economic dichotomy and permanent social segregation. PMID:27433966

Urbanization and Income Inequality in Post-Reform China: A Causal Analysis Based on Time Series Data.

PubMed

Chen, Guo; Glasmeier, Amy K; Zhang, Min; Shao, Yang

2016-01-01

This paper investigates the potential causal relationship(s) between China's urbanization and income inequality since the start of the economic reform. Based on the economic theory of urbanization and income distribution, we analyze the annual time series of China's urbanization rate and Gini index from 1978 to 2014. The results show that urbanization has an immediate alleviating effect on income inequality, as indicated by the negative relationship between the two time series at the same year (lag = 0). However, urbanization also seems to have a lagged aggravating effect on income inequality, as indicated by positive relationship between urbanization and the Gini index series at lag 1. Although the link between urbanization and income inequality is not surprising, the lagged aggravating effect of urbanization on the Gini index challenges the popular belief that urbanization in post-reform China generally helps reduce income inequality. At deeper levels, our results suggest an urgent need to focus on the social dimension of urbanization as China transitions to the next stage of modernization. Comprehensive social reforms must be prioritized to avoid a long-term economic dichotomy and permanent social segregation.

Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia-A generalised linear model with break-point analysis.

PubMed

Alkhaldy, Ibrahim

2017-04-01

The aim of this study was to examine the role of environmental factors in the temporal distribution of dengue fever in Jeddah, Saudi Arabia. The relationship between dengue fever cases and climatic factors such as relative humidity and temperature was investigated during 2006-2009 to determine whether there is any relationship between dengue fever cases and climatic parameters in Jeddah City, Saudi Arabia. A generalised linear model (GLM) with a break-point was used to determine how different levels of temperature and relative humidity affected the distribution of the number of cases of dengue fever. Break-point analysis was performed to modelled the effect before and after a break-point (change point) in the explanatory parameters under various scenarios. Akaike information criterion (AIC) and cross validation (CV) were used to assess the performance of the models. The results showed that maximum temperature and mean relative humidity are most probably the better predictors of the number of dengue fever cases in Jeddah. In this study three scenarios were modelled: no time lag, 1-week lag and 2-weeks lag. Among these scenarios, the 1-week lag model using mean relative humidity as an explanatory variable showed better performance. This study showed a clear relationship between the meteorological variables and the number of dengue fever cases in Jeddah. The results also demonstrated that meteorological variables can be successfully used to estimate the number of dengue fever cases for a given period of time. Break-point analysis provides further insight into the association between meteorological parameters and dengue fever cases by dividing the meteorological parameters into certain break-points. Copyright © 2016 Elsevier B.V. All rights reserved.

Emergence of the interplay between hierarchy and contact splitting in biological adhesion highlighted through a hierarchical shear lag model.

PubMed

Brely, Lucas; Bosia, Federico; Pugno, Nicola M

2018-06-20

Contact unit size reduction is a widely studied mechanism as a means to improve adhesion in natural fibrillar systems, such as those observed in beetles or geckos. However, these animals also display complex structural features in the way the contact is subdivided in a hierarchical manner. Here, we study the influence of hierarchical fibrillar architectures on the load distribution over the contact elements of the adhesive system, and the corresponding delamination behaviour. We present an analytical model to derive the load distribution in a fibrillar system loaded in shear, including hierarchical splitting of contacts, i.e. a "hierarchical shear-lag" model that generalizes the well-known shear-lag model used in mechanics. The influence on the detachment process is investigated introducing a numerical procedure that allows the derivation of the maximum delamination force as a function of the considered geometry, including statistical variability of local adhesive energy. Our study suggests that contact splitting generates improved adhesion only in the ideal case of extremely compliant contacts. In real cases, to produce efficient adhesive performance, contact splitting needs to be coupled with hierarchical architectures to counterbalance high load concentrations resulting from contact unit size reduction, generating multiple delamination fronts and helping to avoid detrimental non-uniform load distributions. We show that these results can be summarized in a generalized adhesion scaling scheme for hierarchical structures, proving the beneficial effect of multiple hierarchical levels. The model can thus be used to predict the adhesive performance of hierarchical adhesive structures, as well as the mechanical behaviour of composite materials with hierarchical reinforcements.

Modified Confidence Intervals for the Mean of an Autoregressive Process.

DTIC Science & Technology

1985-08-01

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

Autoregressive Methods for Spectral Estimation from Interferograms.

DTIC Science & Technology

1986-09-19

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

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

NASA Astrophysics Data System (ADS)

Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman

2017-08-01

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

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

NASA Technical Reports Server (NTRS)

Scargle, J. D.

1981-01-01

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

A generalization of random matrix theory and its application to statistical physics.

PubMed

Wang, Duan; Zhang, Xin; Horvatic, Davor; Podobnik, Boris; Eugene Stanley, H

2017-02-01

To study the statistical structure of crosscorrelations in empirical data, we generalize random matrix theory and propose a new method of cross-correlation analysis, known as autoregressive random matrix theory (ARRMT). ARRMT takes into account the influence of auto-correlations in the study of cross-correlations in multiple time series. We first analytically and numerically determine how auto-correlations affect the eigenvalue distribution of the correlation matrix. Then we introduce ARRMT with a detailed procedure of how to implement the method. Finally, we illustrate the method using two examples taken from inflation rates for air pressure data for 95 US cities.

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

PubMed

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

2017-10-01

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

Evaluation of a Game-Based Simulation During Distributed Exercises

DTIC Science & Technology

2010-09-01

the management team guiding development of the software. The questionnaires have not been used enough to collect data sufficient for factor...capable of internationally distributed exercises without excessive time lags or technical problems, given that commercial games seem to manage while...established by RDECOM-STTC military liaison and managers . Engineering constraints combined to limit the number of participants and the possible roles that

Catchment Legacies and Time Lags: A Parsimonious Watershed Model to Predict the Effects of Legacy Storage on Nitrogen Export

PubMed Central

Van Meter, Kimberly J.; Basu, Nandita B.

2015-01-01

Nutrient legacies in anthropogenic landscapes, accumulated over decades of fertilizer application, lead to time lags between implementation of conservation measures and improvements in water quality. Quantification of such time lags has remained difficult, however, due to an incomplete understanding of controls on nutrient depletion trajectories after changes in land-use or management practices. In this study, we have developed a parsimonious watershed model for quantifying catchment-scale time lags based on both soil nutrient accumulations (biogeochemical legacy) and groundwater travel time distributions (hydrologic legacy). The model accurately predicted the time lags observed in an Iowa watershed that had undergone a 41% conversion of area from row crop to native prairie. We explored the time scales of change for stream nutrient concentrations as a function of both natural and anthropogenic controls, from topography to spatial patterns of land-use change. Our results demonstrate that the existence of biogeochemical nutrient legacies increases time lags beyond those due to hydrologic legacy alone. In addition, we show that the maximum concentration reduction benefits vary according to the spatial pattern of intervention, with preferential conversion of land parcels having the shortest catchment-scale travel times providing proportionally greater concentration reductions as well as faster response times. In contrast, a random pattern of conversion results in a 1:1 relationship between percent land conversion and percent concentration reduction, irrespective of denitrification rates within the landscape. Our modeling framework allows for the quantification of tradeoffs between costs associated with implementation of conservation measures and the time needed to see the desired concentration reductions, making it of great value to decision makers regarding optimal implementation of watershed conservation measures. PMID:25985290

A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control.

PubMed

Phung, Dung; Talukder, Mohammad Radwanur Rahman; Rutherford, Shannon; Chu, Cordia

2016-10-01

To develop a prediction score scheme useful for prevention practitioners and authorities to implement dengue preparedness and controls in the Mekong Delta region (MDR). We applied a spatial scan statistic to identify high-risk dengue clusters in the MDR and used generalised linear-distributed lag models to examine climate-dengue associations using dengue case records and meteorological data from 2003 to 2013. The significant predictors were collapsed into categorical scales, and the β-coefficients of predictors were converted to prediction scores. The score scheme was validated for predicting dengue outbreaks using ROC analysis. The north-eastern MDR was identified as the high-risk cluster. A 1 °C increase in temperature at lag 1-4 and 5-8 weeks increased the dengue risk 11% (95% CI, 9-13) and 7% (95% CI, 6-8), respectively. A 1% rise in humidity increased dengue risk 0.9% (95% CI, 0.2-1.4) at lag 1-4 and 0.8% (95% CI, 0.2-1.4) at lag 5-8 weeks. Similarly, a 1-mm increase in rainfall increased dengue risk 0.1% (95% CI, 0.05-0.16) at lag 1-4 and 0.11% (95% CI, 0.07-0.16) at lag 5-8 weeks. The predicted scores performed with high accuracy in diagnosing the dengue outbreaks (96.3%). This study demonstrates the potential usefulness of a dengue prediction score scheme derived from complex statistical models for high-risk dengue clusters. We recommend a further study to examine the possibility of incorporating such a score scheme into the dengue early warning system in similar climate settings. © 2016 John Wiley & Sons Ltd.

Catchment legacies and time lags: a parsimonious watershed model to predict the effects of legacy storage on nitrogen export.

PubMed

Van Meter, Kimberly J; Basu, Nandita B

2015-01-01

Nutrient legacies in anthropogenic landscapes, accumulated over decades of fertilizer application, lead to time lags between implementation of conservation measures and improvements in water quality. Quantification of such time lags has remained difficult, however, due to an incomplete understanding of controls on nutrient depletion trajectories after changes in land-use or management practices. In this study, we have developed a parsimonious watershed model for quantifying catchment-scale time lags based on both soil nutrient accumulations (biogeochemical legacy) and groundwater travel time distributions (hydrologic legacy). The model accurately predicted the time lags observed in an Iowa watershed that had undergone a 41% conversion of area from row crop to native prairie. We explored the time scales of change for stream nutrient concentrations as a function of both natural and anthropogenic controls, from topography to spatial patterns of land-use change. Our results demonstrate that the existence of biogeochemical nutrient legacies increases time lags beyond those due to hydrologic legacy alone. In addition, we show that the maximum concentration reduction benefits vary according to the spatial pattern of intervention, with preferential conversion of land parcels having the shortest catchment-scale travel times providing proportionally greater concentration reductions as well as faster response times. In contrast, a random pattern of conversion results in a 1:1 relationship between percent land conversion and percent concentration reduction, irrespective of denitrification rates within the landscape. Our modeling framework allows for the quantification of tradeoffs between costs associated with implementation of conservation measures and the time needed to see the desired concentration reductions, making it of great value to decision makers regarding optimal implementation of watershed conservation measures.

Theory for Transitions Between Exponential and Stationary Phases: Universal Laws for Lag Time

NASA Astrophysics Data System (ADS)

Himeoka, Yusuke; Kaneko, Kunihiko

2017-04-01

The quantitative characterization of bacterial growth has attracted substantial attention since Monod's pioneering study. Theoretical and experimental works have uncovered several laws for describing the exponential growth phase, in which the number of cells grows exponentially. However, microorganism growth also exhibits lag, stationary, and death phases under starvation conditions, in which cell growth is highly suppressed, for which quantitative laws or theories are markedly underdeveloped. In fact, the models commonly adopted for the exponential phase that consist of autocatalytic chemical components, including ribosomes, can only show exponential growth or decay in a population; thus, phases that halt growth are not realized. Here, we propose a simple, coarse-grained cell model that includes an extra class of macromolecular components in addition to the autocatalytic active components that facilitate cellular growth. These extra components form a complex with the active components to inhibit the catalytic process. Depending on the nutrient condition, the model exhibits typical transitions among the lag, exponential, stationary, and death phases. Furthermore, the lag time needed for growth recovery after starvation follows the square root of the starvation time and is inversely related to the maximal growth rate. This is in agreement with experimental observations, in which the length of time of cell starvation is memorized in the slow accumulation of molecules. Moreover, the lag time distributed among cells is skewed with a long time tail. If the starvation time is longer, an exponential tail appears, which is also consistent with experimental data. Our theory further predicts a strong dependence of lag time on the speed of substrate depletion, which can be tested experimentally. The present model and theoretical analysis provide universal growth laws beyond the exponential phase, offering insight into how cells halt growth without entering the death phase.

The Association between Dust Storms and Daily Non-Accidental Mortality in the United States, 1993–2005

PubMed Central

Crooks, James Lewis; Cascio, Wayne E.; Percy, Madelyn S.; Reyes, Jeanette; Neas, Lucas M.; Hilborn, Elizabeth D.

2016-01-01

Background: The impact of dust storms on human health has been studied in the context of Asian, Saharan, Arabian, and Australian storms, but there has been no recent population-level epidemiological research on the dust storms in North America. The relevance of dust storms to public health is likely to increase as extreme weather events are predicted to become more frequent with anticipated changes in climate through the 21st century. Objectives: We examined the association between dust storms and county-level non-accidental mortality in the United States from 1993 through 2005. Methods: Dust storm incidence data, including date and approximate location, are taken from the U.S. National Weather Service storm database. County-level mortality data for the years 1993–2005 were acquired from the National Center for Health Statistics. Distributed lag conditional logistic regression models under a time-stratified case-crossover design were used to study the relationship between dust storms and daily mortality counts over the whole United States and in Arizona and California specifically. End points included total non-accidental mortality and three mortality subgroups (cardiovascular, respiratory, and other non-accidental). Results: We estimated that for the United States as a whole, total non-accidental mortality increased by 7.4% (95% CI: 1.6, 13.5; p = 0.011) and 6.7% (95% CI: 1.1, 12.6; p = 0.018) at 2- and 3-day lags, respectively, and by an average of 2.7% (95% CI: 0.4, 5.1; p = 0.023) over lags 0–5 compared with referent days. Significant associations with non-accidental mortality were estimated for California (lag 2 and 0–5 day) and Arizona (lag 3), for cardiovascular mortality in the United States (lag 2) and Arizona (lag 3), and for other non-accidental mortality in California (lags 1–3 and 0–5). Conclusions: Dust storms are associated with increases in lagged non-accidental and cardiovascular mortality. Citation: Crooks JL, Cascio WE, Percy MS, Reyes J, Neas LM, Hilborn ED. 2016. The association between dust storms and daily non-accidental mortality in the United States, 1993–2005. Environ Health Perspect 124:1735–1743; http://dx.doi.org/10.1289/EHP216 PMID:27128449

INVASIVE SPECIES: PREDICTING GEOGRAPHIC DISTRIBUTIONS USING ECOLOGICAL NICHE MODELING

EPA Science Inventory

Present approaches to species invasions are reactive in nature. This scenario results in management that perpetually lags behind the most recent invasion and makes control much more difficult. In contrast, spatially explicit ecological niche modeling provides an effective solut...

The geographical co-distribution and socio-ecological drivers of childhood pneumonia and diarrhoea in Queensland, Australia.

PubMed

Xu, Z; Hu, W; Tong, S

2015-04-01

SUMMARY This study aimed to explore the spatio-temporal patterns, geographical co-distribution, and socio-ecological drivers of childhood pneumonia and diarrhoea in Queensland. A Bayesian conditional autoregressive model was used to quantify the impacts of socio-ecological factors on both childhood pneumonia and diarrhoea at a postal area level. A distinct seasonality of childhood pneumonia and diarrhoea was found. Childhood pneumonia and diarrhoea were mainly distributed in the northwest of Queensland. Mount Isa city was the high-risk cluster where childhood pneumonia and diarrhoea co-distributed. Emergency department visits (EDVs) for pneumonia increased by 3% per 10-mm increase in monthly average rainfall in wet seasons. By comparison, a 10-mm increase in monthly average rainfall may cause an increase of 4% in EDVs for diarrhoea. Monthly average temperature was negatively associated with EDVs for childhood diarrhoea in wet seasons. Low socioeconomic index for areas (SEIFA) was associated with high EDVs for childhood pneumonia. Future pneumonia and diarrhoea prevention and control measures in Queensland should focus more on Mount Isa.

Mean air temperature as a risk factor for stroke mortality in São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Ikefuti, Priscilla V.; Barrozo, Ligia V.; Braga, Alfésio L. F.

2018-05-01

In Brazil, chronic diseases account for the largest percentage of all deaths among men and women. Among the cardiovascular diseases, stroke is the leading cause of death, accounting for 10% of all deaths. We evaluated associations between stroke and mean air temperature using recorded mortality data and meteorological station data from 2002 to 2011. A time series analysis was applied to 55,633 mortality cases. Ischemic and hemorrhagic strokes (IS and HS, respectively) were divided to test different impact on which subgroup. Poisson regression with distributed lag non-linear model was used and adjusted for seasonality, pollutants, humidity, and days of the week. HS mortality was associated with low mean temperatures for men relative risk (RR) = 2.43 (95% CI, 1.12-5.28) and women RR = 1.39 (95% CI, 1.03-1.86). RR of IS mortality was not significant using a 21-day lag window. Analyzing the lag response separately, we observed that the effect of temperature is acute in stroke mortality (higher risk among lags 0-5). However, for IS, higher mean temperatures were significant for this subtype with more than 15-day lag. Our findings showed that mean air temperature is associated with stroke mortality in the city of São Paulo for men and women and IS and HS may have different triggers. Further studies are needed to evaluate physiologic differences between these two subtypes of stroke.

Comparison of the Lag Screw Placements for the Treatment of Stable and Unstable Intertrochanteric Femoral Fractures regarding Trabecular Bone Failure

PubMed Central

Mutlu, Ibrahim; Ozkan, Arif; Kisioglu, Yasin

2016-01-01

Background. In this study, the cut-out risk of Dynamic Hip Screw (DHS) was investigated in nine different positions of the lag screw for two fracture types by using Finite Element Analysis (FEA). Methods. Two types of fractures (31-A1.1 and A2.1 in AO classification) were generated in the femur model obtained from Computerized Tomography images. The DHS model was placed into the fractured femur model in nine different positions. Tip-Apex Distances were measured using SolidWorks. In FEA, the force applied to the femoral head was determined according to the maximum value being observed during walking. Results. The highest volume percentage exceeding the yield strength of trabecular bone was obtained in posterior-inferior region in both fracture types. The best placement region for the lag screw was found in the middle of both fracture types. There are compatible results between Tip-Apex Distances and the cut-out risk except for posterior-superior and superior region of 31-A2.1 fracture type. Conclusion. The position of the lag screw affects the risk of cut-out significantly. Also, Tip-Apex Distance is a good predictor of the cut-out risk. All in all, we can supposedly say that the density distribution of the trabecular bone is a more efficient factor compared to the positions of lag screw in the cut-out risk. PMID:27995133

Fine Particulate air Pollution is Associated with Higher Vulnerability to Atrial Fibrillation—The APACR Study

PubMed Central

Liao, Duanping; Shaffer, Michele L.; He, Fan; Rodriguez-Colon, Sol; Wu, Rongling; Whitsel, Eric A.; Bixler, Edward O.; Cascio, Wayne E.

2011-01-01

The acute effects and the time course of fine particulate pollution (PM2.5) on atrial fibrillation/flutter (AF) predictors, including P-wave duration, PR interval duration, and P-wave complexity, were investigated in a community-dwelling sample of 106 nonsmokers. Individual-level 24-h beat-to-beat electrocardiogram (ECG) data were visually examined. After identifying and removing artifacts and arrhythmic beats, the 30-min averages of the AF predictors were calculated. A personal PM2.5 monitor was used to measure individual-level, real-time PM2.5 exposures during the same 24-h period, and corresponding 30-min average PM2.5 concentration were calculated. Under a linear mixed-effects modeling framework, distributed lag models were used to estimate regression coefficients (βs) associating PM2.5 with AF predictors. Most of the adverse effects on AF predictors occurred within 1.5–2 h after PM2.5 exposure. The multivariable adjusted βs per 10-µg/m3 rise in PM2.5 at lag 1 and lag 2 were significantly associated with P-wave complexity. PM2.5 exposure was also significantly associated with prolonged PR duration at lag 3 and lag 4. Higher PM2.5 was found to be associated with increases in P-wave complexity and PR duration. Maximal effects were observed within 2 h. These findings suggest that PM2.5 adversely affects AF predictors; thus, PM2.5 may be indicative of greater susceptibility to AF. PMID:21480044

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

PubMed Central

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

2011-01-01

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

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

PubMed Central

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

2014-01-01

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

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

PubMed

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

2014-01-01

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

Habitat Use and Selection by Giant Pandas.

PubMed

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

2016-01-01

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

Habitat Use and Selection by Giant Pandas

PubMed Central

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

2016-01-01

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

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.

PubMed

Wu, Wei; Guo, Junqiao; An, Shuyi; Guan, Peng; Ren, Yangwu; Xia, Linzi; Zhou, Baosen

2015-01-01

Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.

Anomalous Fluctuations in Autoregressive Models with Long-Term Memory

NASA Astrophysics Data System (ADS)

Sakaguchi, Hidetsugu; Honjo, Haruo

2015-10-01

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

On the Stationarity of Multiple Autoregressive Approximants: Theory and Algorithms

DTIC Science & Technology

1976-08-01

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

Forecasting Instability Indicators in the Horn of Africa

DTIC Science & Technology

2008-03-01

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

Shear-lag effect and its effect on the design of high-rise buildings

NASA Astrophysics Data System (ADS)

Thanh Dat, Bui; Traykov, Alexander; Traykova, Marina

2018-03-01

For super high-rise buildings, the analysis and selection of suitable structural solutions are very important. The structure has not only to carry the gravity loads (self-weight, live load, etc.), but also to resist lateral loads (wind and earthquake loads). As the buildings become taller, the demand on different structural systems dramatically increases. The article considers the division of the structural systems of tall buildings into two main categories - interior structures for which the major part of the lateral load resisting system is located within the interior of the building, and exterior structures for which the major part of the lateral load resisting system is located at the building perimeter. The basic types of each of the main structural categories are described. In particular, the framed tube structures, which belong to the second main category of exterior structures, seem to be very efficient. That type of structure system allows tall buildings resist the lateral loads. However, those tube systems are affected by shear lag effect - a nonlinear distribution of stresses across the sides of the section, which is commonly found in box girders under lateral loads. Based on a numerical example, some general conclusions for the influence of the shear-lag effect on frequencies, periods, distribution and variation of the magnitude of the internal forces in the structure are presented.

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

PubMed

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

1981-06-01

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

Prediction of global ionospheric VTEC maps using an adaptive autoregressive model

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

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

PubMed

MacNab, Ying C

2016-08-01

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

Applying Bayesian hierarchical models to examine motorcycle crashes at signalized intersections.

PubMed

Haque, Md Mazharul; Chin, Hoong Chor; Huang, Helai

2010-01-01

Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and T signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag-1 dependence specification in the error term is the most suitable. Results show that the number of lanes at the four-legged signalized intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median and an uncontrolled left-turn lane at major roadways of four-legged intersections exacerbate this potential hazard. For T signalized intersections, the presence of exclusive right-turn lane at both major and minor roadways and an uncontrolled left-turn lane at major roadways increases motorcycle crashes. Motorcycle crashes increase on high-speed roadways because they are more vulnerable and less likely to react in time during conflicts. The presence of red light cameras reduces motorcycle crashes significantly for both four-legged and T intersections. With the red light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.

The social ties that bind: social anxiety and academic achievement across the university years.

PubMed

Brook, Christina A; Willoughby, Teena

2015-05-01

Given that engagement and integration in university/college are considered key to successful academic achievement, the identifying features of social anxiety, including fear of negative evaluation and distress and avoidance of new or all social situations, may be particularly disadvantageous in the social and evaluative contexts that are integral to university/college life. Thus, the purpose of this study was to examine the direct effects of social anxiety on academic achievement, as well as investigate an indirect mechanism through which social anxiety might impact on academic achievement, namely, the formation of new social ties in university. The participants were 942 (71.7 % female; M = 19 years at Time 1) students enrolled in a mid-sized university in Southern Ontario, Canada. Students completed annual assessments of social anxiety, social ties, and academic achievement for three consecutive years. The results from an autoregressive cross-lag path analysis indicated that social anxiety had a significant and negative direct relationship with academic achievement. Moreover, the negative indirect effect of social anxiety on academic achievement through social ties was significant, as was the opposing direction of effects (i.e., the indirect effect of academic achievement on social anxiety through social ties). These findings highlight the critical role that social ties appear to play in successful academic outcomes and in alleviating the effects of social anxiety during university/college.

A longitudinal investigation of the relation between nonsuicidal self-injury and spirituality/religiosity.

PubMed

Good, Marie; Hamza, Chloe; Willoughby, Teena

2017-04-01

Despite increased research on factors that predict engagement in nonsuicidal self-injury (NSSI), one factor that has been neglected is spirituality/religiosity. While some researchers suggest that spiritual/religious beliefs and practice may protect against aversive mental health outcomes, it also is possible that certain aspects of spirituality/religiosity - specifically doubt and questioning - may be distressing. In this study, we examined whether multiple dimensions of spirituality/religiosity, including the often-overlooked experience of doubt/questioning, were associated with engagement in NSSI among university students over time. Participants included 1,132 (70.5% female) first-year undergraduate students (Mean age=19.06, SD=1.05) from a Canadian university who were surveyed first in their freshman year, and again one year later. Auto-regressive cross-lagged analyses revealed a bidirectional relation between doubt/questioning and NSSI, where higher doubt/questioning predicted increased NSSI over time (after controlling for baseline depressive symptoms), and vice versa. There were no longitudinal associations between general spirituality/religiosity (i.e., general beliefs/practice) and NSSI. Our findings suggest questioning and doubt may be distressing for some individuals, and predict increased risk for NSSI as a form of coping. Further, higher NSSI may predict increases in questioning/doubt over time. However, the hypothesis that general spirituality/religiosity may protect against NSSI, was not supported. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Reciprocal within-day associations between incidental affect and exercise: An EMA study.

PubMed

Emerson, Jessica A; Dunsiger, Shira; Williams, David M

2018-01-01

Previous research suggests that how people feel throughout the course of a day (i.e. incidental affect) is predictive of exercise behaviour. A mostly separate literature suggests that exercise can lead to more positive incidental affect. This study examines the potential reciprocal effects of incidental affect and exercise behaviour within the same day. Fifty-nine low-active (exercise <60 min/week), overweight (BMI: 25.0-39.9) adults (ages 18-65) participated in a six-month print-based exercise promotion programme. Ecological momentary assessment was used to record self-reported exercise sessions in real time and incidental affective valence (feeling good/bad) as assessed by the 11-point Feeling Scale at random times throughout the day. Use of a within-subjects cross-lagged, autoregressive model showed that participants were more likely to exercise on days when they experienced more positive incidental affect earlier in the day (b = .58, SE = .10, p < .01), and participants were more likely to experience more positive incidental affect on days when they had exercised (b = .26, SE = .03, p < .01), with the former association significantly stronger than the latter (t = 23.54, p < .01). The findings suggest a positive feedback loop whereby feeling good and exercising are reciprocally influential within the course of a day.

Temporal Associations Among Chronic PTSD Symptoms in U.S. Combat Veterans.

PubMed

Doron-LaMarca, Susan; Niles, Barbara L; King, Daniel W; King, Lynda A; Pless Kaiser, Anica; Lyons, Michael J

2015-10-01

The present study examined fluctuation over time in symptoms of posttraumatic stress disorder (PTSD) among 34 combat veterans (28 with diagnosed PTSD, 6 with subclinical symptoms) assessed every 2 weeks for up to 2 years (range of assessments = 13-52). Temporal relationships were examined among four PTSD symptom clusters (reexperiencing, avoidance, emotional numbing, and hyperarousal) with particular attention to the influence of hyperarousal. Multilevel cross-lagged random coefficients autoregression for intensive time series data analyses were used to model symptom fluctuation decades after combat experiences. As anticipated, hyperarousal predicted subsequent fluctuations in the 3 other PTSD symptom clusters (reexperiencing, avoidance, emotional numbing) at subsequent 2-week intervals (rs = .45, .36, and .40, respectively). Additionally, emotional numbing influenced later reexperiencing and avoidance, and reexperiencing influenced later hyperarousal (rs = .44, .40, and .34, respectively). These findings underscore the important influence of hyperarousal. Furthermore, results indicate a bidirectional relationship between hyperarousal and reexperiencing as well as a possible chaining of symptoms (hyperarousal → emotional numbing → reexperiencing → hyperarousal) and establish potential internal, intrapersonal mechanisms for the maintenance of persistent PTSD symptoms. Results suggested that clinical interventions targeting hyperarousal and emotional numbing symptoms may hold promise for PTSD of long duration. Published 2015. This article is a US Government work and is in the public domain in the USA. View this article online at wileyonlinelibrary.com.

Synchronous neural interactions assessed by magnetoencephalography: a functional biomarker for brain disorders

NASA Astrophysics Data System (ADS)

Georgopoulos, Apostolos P.; Karageorgiou, Elissaios; Leuthold, Arthur C.; Lewis, Scott M.; Lynch, Joshua K.; Alonso, Aurelio A.; Aslam, Zaheer; Carpenter, Adam F.; Georgopoulos, Angeliki; Hemmy, Laura S.; Koutlas, Ioannis G.; Langheim, Frederick J. P.; Riley McCarten, J.; McPherson, Susan E.; Pardo, José V.; Pardo, Patricia J.; Parry, Gareth J.; Rottunda, Susan J.; Segal, Barbara M.; Sponheim, Scott R.; Stanwyck, John J.; Stephane, Massoud; Westermeyer, Joseph J.

2007-12-01

We report on a test to assess the dynamic brain function at high temporal resolution using magnetoencephalography (MEG). The essence of the test is the measurement of the dynamic synchronous neural interactions, an essential aspect of the brain function. MEG signals were recorded from 248 axial gradiometers while 142 human subjects fixated a spot of light for 45-60 s. After fitting an autoregressive integrative moving average (ARIMA) model and taking the stationary residuals, all pairwise, zero-lag, partial cross-correlations (PCCij0) and their z-transforms (zij0) between i and j sensors were calculated, providing estimates of the strength and sign (positive, negative) of direct synchronous coupling at 1 ms temporal resolution. We found that subsets of zij0 successfully classified individual subjects to their respective groups (multiple sclerosis, Alzheimer's disease, schizophrenia, Sjögren's syndrome, chronic alcoholism, facial pain, healthy controls) and gave excellent external cross-validation results. Contribution by the authors: Designed research (APG); acquired data (AAA, IGK, FJPL, ACL, SML, JJS); analyzed data (APG, EK, ACL, JKL); wrote the paper (APG, EK, ACL, SML); contributed subjects (AAA, ZA, AFC, AG, LSH, IGK, FJPL, SML, JRM, SEM, JVP, PJP, GJP, SJR, BMS, SRS, MS, JJS, JJW); discussed results (All); contributed equally (ZA, AFC, AG, LSH, FJPL, JRM, SEM, JVP, PJP, GJP, SJR, BMS, SRS, MS, JJS, JJW).

Predicting Malaria occurrence in Southwest and North central Nigeria using Meteorological parameters

NASA Astrophysics Data System (ADS)

Akinbobola, A.; Omotosho, J. Bayo

2013-09-01

Malaria is a major public health problem especially in the tropics with the potential to significantly increase in response to changing weather and climate. This study explored the impact of weather and climate and its variability on the occurrence and transmission of malaria in Akure, the tropical rain forest area of southwest and Kaduna, in the savanna area of Nigeria. We investigate this supposition by looking at the relationship between rainfall, relative humidity, minimum and maximum temperature, and malaria at the two stations. This study uses monthly data of 7 years (2001-2007) for both meteorological data and record of reported cases of malaria infection. Autoregressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Of all the models tested, the ARIMA (1, 0, 1) model fits the malaria incidence data best for Akure and Kaduna according to normalized Bayesian information criterion (BIC) and goodness-of-fit criteria. Humidity and rainfall have almost the same trend of association in all the stations while maximum temperature share the same negative association at southwestern stations and positive in the northern station. Rainfall and humidity have a positive association with malaria incidence at lag of 1 month. In all, we found that minimum temperature is not a limiting factor for malaria transmission in Akure but otherwise in the other stations.

Pathways between self-esteem and depression in couples.

PubMed

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

2017-04-01

Guided by concepts from a relational developmental perspective, this study examined intra- and interpersonal associations between self-esteem and depressive symptoms in a sample of 1,407 couples surveyed annually across 6 years in the Panel Analysis of Intimate Relations and Family Dynamics (pairfam) study. Autoregressive cross-lagged model results demonstrated that self-esteem predicted future depressive symptoms for male partners at all times, replicating the vulnerability model for men (low self-esteem is a risk factor for future depression). Additionally, a cross-partner association emerged between symptoms of depression: Higher depressive symptoms in one partner were associated with higher levels of depression in the other partner one year later. Finally, supportive dyadic coping, the support that partners reported providing to one another in times of stress, was tested as a potential interpersonal mediator of pathways between self-esteem and depression. Female partners' higher initial levels of self-esteem predicted male partners' subsequent reports of increased supportive dyadic coping, which, in turn, predicted higher self-esteem and fewer symptoms of depression among female partners in the future. Male partners' initially higher symptoms of depression predicted less frequent supportive dyadic coping subsequently reported by female partners, which was associated with increased feelings of depression in the future. Couple relations represent an important contextual factor that may be implicated in the developmental pathways connecting self-esteem and symptoms of depression. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Time-Integral Correlations of Multiple Variables With the Relativistic-Electron Flux at Geosynchronous Orbit: The Strong Roles of Substorm-Injected Electrons and the Ion Plasma Sheet

NASA Astrophysics Data System (ADS)

Borovsky, Joseph E.

2017-12-01

Time-integral correlations are examined between the geosynchronous relativistic electron flux index Fe1.2 and 31 variables of the solar wind and magnetosphere. An "evolutionary algorithm" is used to maximize correlations. Time integrations (into the past) of the variables are found to be superior to time-lagged variables for maximizing correlations with the radiation belt. Physical arguments are given as to why. Dominant correlations are found for the substorm-injected electron flux at geosynchronous orbit and for the pressure of the ion plasma sheet. Different sets of variables are constructed and correlated with Fe1.2: some sets maximize the correlations, and some sets are based on purely solar wind variables. Examining known physical mechanisms that act on the radiation belt, sets of correlations are constructed (1) using magnetospheric variables that control those physical mechanisms and (2) using the solar wind variables that control those magnetospheric variables. Fe1.2-increasing intervals are correlated separately from Fe1.2-decreasing intervals, and the introduction of autoregression into the time-integral correlations is explored. A great impediment to discerning physical cause and effect from the correlations is the fact that all solar wind variables are intercorrelated and carry much of the same information about the time sequence of the solar wind that drives the time sequence of the magnetosphere.

The Comorbidity Between Internet Gaming Disorder and Depression: Interrelationship and Neural Mechanisms

PubMed Central

Liu, Lu; Yao, Yuan-Wei; Li, Chiang-shan R.; Zhang, Jin-Tao; Xia, Cui-Cui; Lan, Jing; Ma, Shan-Shan; Zhou, Nan; Fang, Xiao-Yi

2018-01-01

Internet gaming disorder (IGD) is characterized by cognitive and emotional deficits. Previous studies have reported the co-occurrence of IGD and depression. However, extant brain imaging research has largely focused on cognitive deficits in IGD. Few studies have addressed the comorbidity between IGD and depression symptoms and underlying neural mechanisms. Here, we systematically investigated this issue by combining a longitudinal survey study, a cross-sectional resting-state functional connectivity (rsFC) study and an intervention study. Autoregressive cross-lagged modeling on a longitudinal dataset of college students showed that IGD severity and depression are reciprocally predictive. At the neural level, individuals with IGD exhibited enhanced rsFC between the left amygdala and right dorsolateral prefrontal cortex (DLPFC), inferior frontal and precentral gyrus, compared with control participants, and the amygdala-frontoparietal connectivity at the baseline negatively predicted reduction in depression symptoms following a psychotherapy intervention. Further, following the intervention, individuals with IGD showed decreased connectivity between the left amygdala and left middle frontal and precentral gyrus, as compared with the non-intervention group. These findings together suggest that IGD may be closely associated with depression; aberrant rsFC between emotion and executive control networks may underlie depression and represent a therapeutic target in individuals with IGD. Registry name: The behavioral and brain mechanism of IGD; URL: https://www.clinicaltrials.gov/ct2/show/NCT02550405; Registration number: NCT02550405. PMID:29740358

Family cohesion and posttraumatic intrusion and avoidance among war veterans: a 20-year longitudinal study.

PubMed

Zerach, Gadi; Solomon, Zahava; Horesh, Danny; Ein-Dor, Tsachi

2013-02-01

The bi-directional relationships between combat-induced posttraumatic symptoms and family relations are yet to be understood. The present study assesses the longitudinal interrelationship of posttraumatic intrusion and avoidance and family cohesion among 208 Israeli combat veterans from the 1982 Lebanon War. Two groups of veterans were assessed with self-report questionnaires 1, 3 and 20 years after the war: a combat stress reaction (CSR) group and a matched non-CSR control group. Latent Trajectories Modeling showed that veterans of the CSR group reported higher intrusion and avoidance than non-CSR veterans at all three points of time. With time, there was a decline in these symptoms in both groups, but the decline was more salient among the CSR group. The latter also reported lower levels of family cohesion. Furthermore, an incline in family cohesion levels was found in both groups over the years. Most importantly, Autoregressive Cross-Lagged Modeling among CSR and non-CSR veterans revealed that CSR veterans' posttraumatic symptoms in 1983 predicted lower family cohesion in 1985, and lower family cohesion, in turn, predicted posttraumatic symptoms in 2002. The findings suggest that psychological breakdown on the battlefield is a marker for future family cohesion difficulties. Our results lend further support for the bi-directional mutual effects of posttraumatic symptoms and family cohesion over time.

Fluctuations of the experience of togetherness within the team over time: task-cohesion and shared understanding throughout a sporting regular season.

PubMed

Bourbousson, Jérôme; Fortes-Bourbousson, Marina

2017-06-01

Based on a diagnosis action research design, the present study assessed the fluctuations of the team experience of togetherness. Reported experiences of 12 basketball team members playing in the under-18 years old national championship were studied during a four-month training and competitive period. Time series analysis (Auto-Regressive Integrated Moving Average procedures) served to describe temporal properties of the way in which the fluctuations of task-cohesion and shared understanding were step-by-step experienced over time, respectively. Correlations, running-correlations and cross-lagged correlations were used to describe the temporal links that governed the relationships between both phenomena. The results indicated that the task-cohesion dimensions differed mainly for shared understanding dynamics in that their time fluctuations were not embedded in external events, and that the variations in shared understanding tend to precede 'individual attractions to the task' variations with seven team practical sessions. This study argues for further investigation of how 'togetherness' is experienced alternatively as a feeling of cohesion or shared understanding. Practitioner Summary: The present action research study investigated the experience that the team members have to share information during practice, and the subsequent benefices on team cohesion. Results call for specific interventions that make team members accept the fluctuating nature of team phenomena, to help them maintaining their daily efforts.

The progression of severe behavior disorder in young children with intellectual and developmental disabilities.

PubMed

Medeiros, Kristen; Curby, Timothy W; Bernstein, Alec; Rojahn, Johannes; Schroeder, Stephen R

2013-11-01

Behavior disorders, such as self-injurious, stereotypic, and aggressive behavior are common among individuals with intellectual or developmental disabilities. While we have learned much about those behaviors over the past few decades, longitudinal research that looks at developmental trajectory has been rare. This study was designed to examine the trajectory of these three forms of severe behavior disorders over a one year time period. The behaviors were measured on two dimensions: frequency of occurrence and severity. Participants were 160 infants and toddlers at risk for developmental delays in Lima, Peru. Using structural equation modeling, we found that the frequency of self-injury and stereotypic behavior and the severity of aggressive behavior remained stable over the 12-month period. Uni-directional structural models fit the data best for self-injurious and aggressive behavior (with frequency being a leading indicator of future severity of self-injury and severity being a leading indicator of future frequency for aggression). For stereotypic behavior, a cross-lagged autoregressive model fit the data best, with both dimensions of frequency and severity involved as leading indicators of each other. These models did not vary significantly across diagnostic groups, suggesting that toddlers exhibiting behavior disorders may be assisted with interventions that target the specific frequencies or severities of behaviors, regardless of diagnostic category. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

PubMed

Giebel, S; Rainer, M

2010-01-01

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

Improving models of democracy: the example of lagged effects of economic development, education, and gender equality.

PubMed

Balaev, Mikhail

2014-07-01

The author examines how time delayed effects of economic development, education, and gender equality influence political democracy. Literature review shows inadequate understanding of lagged effects, which raises methodological and theoretical issues with the current quantitative studies of democracy. Using country-years as a unit of analysis, the author estimates a series of OLS PCSE models for each predictor with a systematic analysis of the distributions of the lagged effects. The second set of multiple OLS PCSE regressions are estimated including all three independent variables. The results show that economic development, education, and gender have three unique trajectories of the time-delayed effects: Economic development has long-term effects, education produces continuous effects regardless of the timing, and gender equality has the most prominent immediate and short term effects. The results call for the reassessment of model specifications and theoretical setups in the quantitative studies of democracy. Copyright © 2014 Elsevier Inc. All rights reserved.

Oscillatory shear rheology measurements and Newtonian modeling of insoluble monolayers

NASA Astrophysics Data System (ADS)

Rasheed, Fayaz; Raghunandan, Aditya; Hirsa, Amir H.; Lopez, Juan M.

2017-04-01

Circular systems are advantageous for interfacial studies since they do not suffer from end effects, but their hydrodynamics is more complicated because their flows are not unidirectional. Here, we analyze the shear rheology of a harmonically driven knife-edge viscometer through experiments and computations based on the Navier-Stokes equations with a Newtonian interface. The measured distribution of phase lag in the surface velocity relative to the knife-edge speed is found to have a good signal-to-noise ratio and provides robust comparisons to the computations. For monomolecular films of stearic acid, the surface shear viscosity deduced from the model was found to be the same whether the film is driven steady or oscillatory, for an order of magnitude range in driving frequencies and amplitudes. Results show that increasing either the amplitude or forcing frequency steepens the phase lag next to the knife edge. In all cases, the phase lag is linearly proportional to the radial distance from the knife edge and scales with surface shear viscosity to the power -1 /2 .

Can the Air Pollution Index be used to communicate the health risks of air pollution?

PubMed

Li, Li; Lin, Guo-Zhen; Liu, Hua-Zhang; Guo, Yuming; Ou, Chun-Quan; Chen, Ping-Yan

2015-10-01

The validity of using the Air Pollution Index (API) to assess health impacts of air pollution and potential modification by individual characteristics on air pollution effects remain uncertain. We applied distributed lag non-linear models (DLNMs) to assess associations of daily API, specific pollution indices for PM10, SO2, NO2 and the weighted combined API (APIw) with mortality during 2003-2011 in Guangzhou, China. An increase of 10 in API was associated with a 0.88% (95% confidence interval (CI): 0.50, 1.27%) increase of non-accidental mortality at lag 0-2 days. Harvesting effects appeared after 2 days' exposure. The effect estimate of API over lag 0-15 days was statistically significant and similar with those of pollutant-specific indices and APIw. Stronger associations between API and mortality were observed in the elderly, females and residents with low educational attainment. In conclusion, the API can be used to communicate health risks of air pollution. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatio-temporal distribution of dengue fever under scenarios of climate change in the southern Taiwan

NASA Astrophysics Data System (ADS)

Lee, Chieh-Han; Yu, Hwa-Lung

2014-05-01

Dengue fever has been recognized as the most important widespread vector-borne infectious disease in recent decades. Over 40% of the world's population is risk from dengue and about 50-100 million people are infected world wide annually. Previous studies have found that dengue fever is highly correlated with climate covariates. Thus, the potential effects of global climate change on dengue fever are crucial to epidemic concern, in particular, the transmission of the disease. This present study investigated the nonlinearity of time-delayed impact of climate on spatio-temporal variations of dengue fever in the southern Taiwan during 1998 to 2011. A distributed lag nonlinear model (DLNM) is used to assess the nonlinear lagged effects of meteorology. The statistically significant meteorological factors are considered, including weekly minimum temperature and maximum 24-hour rainfall. The relative risk and the distribution of dengue fever then predict under various climate change scenarios. The result shows that the relative risk is similar for different scenarios. In addition, the impact of rainfall on the incidence risk is higher than temperature. Moreover, the incidence risk is associated to spatially population distribution. The results can be served as practical reference for environmental regulators for the epidemic prevention under climate change scenarios.

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

PubMed Central

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

2009-01-01

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

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

PubMed

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

2018-02-01

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

Nonlinear GARCH model and 1 / f noise

NASA Astrophysics Data System (ADS)

Kononovicius, A.; Ruseckas, J.

2015-06-01

Auto-regressive conditionally heteroskedastic (ARCH) family models are still used, by practitioners in business and economic policy making, as a conditional volatility forecasting models. Furthermore ARCH models still are attracting an interest of the researchers. In this contribution we consider the well known GARCH(1,1) process and its nonlinear modifications, reminiscent of NGARCH model. We investigate the possibility to reproduce power law statistics, probability density function and power spectral density, using ARCH family models. For this purpose we derive stochastic differential equations from the GARCH processes in consideration. We find the obtained equations to be similar to a general class of stochastic differential equations known to reproduce power law statistics. We show that linear GARCH(1,1) process has power law distribution, but its power spectral density is Brownian noise-like. However, the nonlinear modifications exhibit both power law distribution and power spectral density of the 1 /fβ form, including 1 / f noise.

Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models.

PubMed

Malloy, Elizabeth J; Morris, Jeffrey S; Adar, Sara D; Suh, Helen; Gold, Diane R; Coull, Brent A

2010-07-01

Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1-7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants.

Quantifying the influence of temperature on hand, foot and mouth disease incidence in Wuhan, Central China.

PubMed

Huang, Jiao; Chen, Shi; Wu, Yang; Tong, Yeqing; Wang, Lei; Zhu, Min; Hu, Shuhua; Guan, Xuhua; Wei, Sheng

2018-01-31

Hand, foot and mouth disease (HFMD) is a substantial burden throughout Asia, but the effects of temperature pattern on HFMD risk are inconsistent. To quantify the effect of temperature on HFMD incidence, Wuhan was chosen as the study site because of its high temperature variability and high HFMD incidence. Daily series of HFMD counts and meteorological variables during 2010-2015 were obtained. Distributed lag non-linear models were applied to characterize the temperature-HFMD relationship and to assess its variability across different ages, genders, and types of child care. Totally, 80,219 patients of 0-5 years experienced HFMD in 2010-2015 in Wuhan. The cumulative relative risk of HFMD increased linearly with temperature over 7 days (lag0-7), while it presented as an approximately inverted V-shape over 14 days (lag0-14). The cumulative relative risk at lag0-14 peaked at 26.4 °C with value of 2.78 (95%CI: 2.08-3.72) compared with the 5 th percentile temperature (1.7 °C). Subgroup analyses revealed that children attended daycare were more vulnerable to temperature variation than those cared for at home. This study suggests that public health actions should take into consideration local weather conditions and demographic characteristics.

Effect of heat shock and recovery temperature on variability of single cell lag time of Cronobacter turicensis.

PubMed

Xu, Y Zh; Métris, A; Stasinopoulos, D M; Forsythe, S J; Sutherland, J P

2015-02-01

The effect of heat stress and subsequent recovery temperature on the individual cellular lag of Cronobacter turicensis was analysed using optical density measurements. Low numbers of cells were obtained through serial dilution and the time to reach an optical density of 0.035 was determined. Assuming the lag of a single cell follows a shifted Gamma distribution with a fixed shape parameter, the effect of recovery temperature on the individual lag of untreated and sublethally heat treated cells of Cr. turicensis were modelled. It was found that the shift parameter (Tshift) increased asymptotically as the temperature decreased while the logarithm of the scale parameter (θ) decreased linearly with recovery temperature. To test the validity of the model in food, growth of low numbers of untreated and heat treated Cr. turicensis in artificially contaminated infant first milk was measured experimentally and compared with predictions obtained by Monte Carlo simulations. Although the model for untreated cells slightly underestimated the actual growth in first milk at low temperatures, the model for heat treated cells was in agreement with the data derived from the challenge tests and provides a basis for reliable quantitative microbiological risk assessments for Cronobacter spp. in infant milk. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed

Mocan, N H

1990-01-01

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

Processing on weak electric signals by the autoregressive model

NASA Astrophysics Data System (ADS)

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

2008-10-01

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

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

DTIC Science & Technology

1987-02-04

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

Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

PubMed

Carvajal, Thaddeus M; Viacrusis, Katherine M; Hernandez, Lara Fides T; Ho, Howell T; Amalin, Divina M; Watanabe, Kozo

2018-04-17

Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting. Dengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 - December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated. Among the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model. The study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern.

Bed conduction impact on fiber optic distributed temperature sensing water temperature measurements

NASA Astrophysics Data System (ADS)

O'Donnell Meininger, T.; Selker, J. S.

2015-02-01

Error in distributed temperature sensing (DTS) water temperature measurements may be introduced by contact of the fiber optic cable sensor with bed materials (e.g., seafloor, lakebed, streambed). Heat conduction from the bed materials can affect cable temperature and the resulting DTS measurements. In the Middle Fork John Day River, apparent water temperature measurements were influenced by cable sensor contact with aquatic vegetation and fine sediment bed materials. Affected cable segments measured a diurnal temperature range reduced by 10% and lagged by 20-40 min relative to that of ambient stream temperature. The diurnal temperature range deeper within the vegetation-sediment bed material was reduced 70% and lagged 240 min relative to ambient stream temperature. These site-specific results illustrate the potential magnitude of bed-conduction impacts with buried DTS measurements. Researchers who deploy DTS for water temperature monitoring should understand the importance of the environment into which the cable is placed on the range and phase of temperature measurements.

A Protocol for Aging Anurans Using Skeletochronology

USGS Publications Warehouse

McCreary, Brome; Pearl, Christopher A.; Adams, Michael J.

2008-01-01

Age distribution information can be an important part of understanding the biology of any population. Age estimates collected from the annual growth rings found in tooth and bone cross sections, often referred to as Lines of Arrested Growth (LAGs), have been used in the study of various animals. In this manual, we describe in detail all necessary steps required to obtain estimates of age from anuran bone cross sections via skeletochronological assessment. We include comprehensive descriptions of how to fix and decalcify toe specimens (phalanges), process a phalange prior to embedding, embed the phalange in paraffin, section the phalange using a microtome, stain and mount the cross sections of the phalange and read the LAGs to obtain age estimates.

The Effect of Clearance Distribution on the Performance of a Compression-ignition Engine with a Precombustion Chamber

NASA Technical Reports Server (NTRS)

Moore, C. S.; Collins, J. H. Jr

1932-01-01

The clearance distribution in a precombustion chamber cylinder head was varied so that for a constant compression ratio of 13.5 the spherical auxiliary chambers contained 20, 35, 50, and 70 per cent of the total clearance volume. Each chamber was connected to the cylinder by a single circular passage, flared at both ends, and of a cross-sectional area proportional to the chamber volume, thereby giving the same calculated air-flow velocity through each passage. Results of engine-performance tests are presented with variations of power, fuel consumption, explosion pressure, rate of pressure rise, ignition lag, heat loss to the cooling water, and motoring characteristics. For good performance the minimum auxiliary chamber volume, with the cylinder head design used, was 35 per cent of the total clearance volume; for larger volumes the performance improves but slightly. With the auxiliary chamber that contained 35 percent of the clearance volume there were obtained the lowest explosion pressures, medium rates of pressure rise, and slightly less than the maximum power. For all clearance distributions an increase in engine speed decreased the ignition lag in seconds and increased the rate of pressure rise.

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

NASA Astrophysics Data System (ADS)

Wang, Qijie

2015-08-01

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

Acceleration and Velocity Sensing from Measured Strain

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi; Truax, Roger

2015-01-01

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

Sleep analysis for wearable devices applying autoregressive parametric models.

PubMed

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

2005-01-01

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

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

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Information theoretical approach to discovering solar wind drivers of the outer radiation belt

DOE PAGES

Wing, Simon; Johnson, Jay R.; Camporeale, Enrico; ...

2016-07-29

The solar wind-magnetosphere system is nonlinear. The solar wind drivers of geosynchronous electrons with energy range of 1.8–3.5 MeV are investigated using mutual information, conditional mutual information (CMI), and transfer entropy (TE). These information theoretical tools can establish linear and nonlinear relationships as well as information transfer. The information transfer from solar wind velocity ( Vsw) to geosynchronous MeV electron flux ( Je) peaks with a lag time of 2 days. As previously reported, Je is anticorrelated with solar wind density ( nsw) with a lag of 1 day. However, this lag time and anticorrelation can be attributed at leastmore » partly to the Je( t + 2 days) correlation with Vsw( t) and nsw( t + 1 day) anticorrelation with Vsw( t). Analyses of solar wind driving of the magnetosphere need to consider the large lag times, up to 3 days, in the ( Vsw, nsw) anticorrelation. Using CMI to remove the effects of Vsw, the response of Je to nsw is 30% smaller and has a lag time < 24 h, suggesting that the MeV electron loss mechanism due to nsw or solar wind dynamic pressure has to start operating in < 24 h. nsw transfers about 36% as much information as Vsw (the primary driver) to Je. Nonstationarity in the system dynamics is investigated using windowed TE. Here, when the data are ordered according to transfer entropy value, it is possible to understand details of the triangle distribution that has been identified between Je( t + 2 days) versus Vsw( t).« less

Information theoretical approach to discovering solar wind drivers of the outer radiation belt

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

Wing, Simon; Johnson, Jay R.; Camporeale, Enrico

The solar wind-magnetosphere system is nonlinear. The solar wind drivers of geosynchronous electrons with energy range of 1.8–3.5 MeV are investigated using mutual information, conditional mutual information (CMI), and transfer entropy (TE). These information theoretical tools can establish linear and nonlinear relationships as well as information transfer. The information transfer from solar wind velocity ( Vsw) to geosynchronous MeV electron flux ( Je) peaks with a lag time of 2 days. As previously reported, Je is anticorrelated with solar wind density ( nsw) with a lag of 1 day. However, this lag time and anticorrelation can be attributed at leastmore » partly to the Je( t + 2 days) correlation with Vsw( t) and nsw( t + 1 day) anticorrelation with Vsw( t). Analyses of solar wind driving of the magnetosphere need to consider the large lag times, up to 3 days, in the ( Vsw, nsw) anticorrelation. Using CMI to remove the effects of Vsw, the response of Je to nsw is 30% smaller and has a lag time < 24 h, suggesting that the MeV electron loss mechanism due to nsw or solar wind dynamic pressure has to start operating in < 24 h. nsw transfers about 36% as much information as Vsw (the primary driver) to Je. Nonstationarity in the system dynamics is investigated using windowed TE. Here, when the data are ordered according to transfer entropy value, it is possible to understand details of the triangle distribution that has been identified between Je( t + 2 days) versus Vsw( t).« less

Haze is an important medium for the spread of rotavirus.

PubMed

Ye, Qing; Fu, Jun-Feng; Mao, Jian-Hua; Shen, Hong-Qiang; Chen, Xue-Jun; Shao, Wen-Xia; Shang, Shi-Qiang; Wu, Yi-Feng

2016-09-01

This study investigated whether the rotavirus infection rate in children is associated with temperature and air pollutants in Hangzhou, China. This study applied a distributed lag non-linear model (DLNM) to assess the effects of daily meteorological data and air pollutants on the rotavirus positive rate among outpatient children. There was a negative correlation between temperature and the rotavirus infection rate. The impact of temperature on the detection rate of rotavirus presented an evident lag effect, the temperature change shows the greatest impact on the detection rate of rotavirus approximate at lag one day, and the maximum relative risk (RR) was approximately 1.3. In 2015, the maximum cumulative RR due to the cumulative effect caused by the temperature drop was 2.5. Particulate matter (PM) 2.5 and PM10 were the primary air pollutants in Hangzhou. The highest RR of rotavirus infection occurred at lag 1-1.5 days after the increase in the concentration of these pollutants, and the RR increased gradually with the increase in concentration. Based on the average concentrations of PM2.5 of 53.9 μg/m(3) and PM10 of 80.6 μg/m(3) in Hangzhou in 2015, the cumulative RR caused by the cumulative effect was 2.5 and 2.2, respectively. The current study suggests that temperature is an important factor impacting the rotavirus infection rate of children in Hangzhou. Air pollutants significantly increased the risk of rotavirus infection, and dosage, lag and cumulative effects were observed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quality control mechanisms exclude incorrect polymerases from the eukaryotic replication fork

PubMed Central

Schauer, Grant D.; O’Donnell, Michael E.

2017-01-01

The eukaryotic genome is primarily replicated by two DNA polymerases, Pol ε and Pol δ, that function on the leading and lagging strands, respectively. Previous studies have established recruitment mechanisms whereby Cdc45-Mcm2-7-GINS (CMG) helicase binds Pol ε and tethers it to the leading strand, and PCNA (proliferating cell nuclear antigen) binds tightly to Pol δ and recruits it to the lagging strand. The current report identifies quality control mechanisms that exclude the improper polymerase from a particular strand. We find that the replication factor C (RFC) clamp loader specifically inhibits Pol ε on the lagging strand, and CMG protects Pol ε against RFC inhibition on the leading strand. Previous studies show that Pol δ is slow and distributive with CMG on the leading strand. However, Saccharomyces cerevisiae Pol δ–PCNA is a rapid and processive enzyme, suggesting that CMG may bind and alter Pol δ activity or position it on the lagging strand. Measurements of polymerase binding to CMG demonstrate Pol ε binds CMG with a Kd value of 12 nM, but Pol δ binding CMG is undetectable. Pol δ, like bacterial replicases, undergoes collision release upon completing replication, and we propose Pol δ–PCNA collides with the slower CMG, and in the absence of a stabilizing Pol δ–CMG interaction, the collision release process is triggered, ejecting Pol δ on the leading strand. Hence, by eviction of incorrect polymerases at the fork, the clamp machinery directs quality control on the lagging strand and CMG enforces quality control on the leading strand. PMID:28069954

Landscape genetics in a changing world: disentangling historical and contemporary influences and inferring change.

PubMed

Epps, Clinton W; Keyghobadi, Nusha

2015-12-01

Landscape genetics seeks to determine the effect of landscape features on gene flow and genetic structure. Often, such analyses are intended to inform conservation and management. However, depending on the many factors that influence the time to reach equilibrium, genetic structure may more strongly represent past rather than contemporary landscapes. This well-known lag between current demographic processes and population genetic structure often makes it challenging to interpret how contemporary landscapes and anthropogenic activity shape gene flow. Here, we review the theoretical framework for factors that influence time lags, summarize approaches to address this temporal disconnect in landscape genetic studies, and evaluate ways to make inferences about landscape change and its effects on species using genetic data alone or in combination with other data. Those approaches include comparing correlation of genetic structure with historical versus contemporary landscapes, using molecular markers with different rates of evolution, contrasting metrics of genetic structure and gene flow that reflect population genetic processes operating at different temporal scales, comparing historical and contemporary samples, combining genetic data with contemporary estimates of species distribution or movement, and controlling for phylogeographic history. We recommend using simulated data sets to explore time lags in genetic structure, and argue that time lags should be explicitly considered both when designing and interpreting landscape genetic studies. We conclude that the time lag problem can be exploited to strengthen inferences about recent landscape changes and to establish conservation baselines, particularly when genetic data are combined with other data. © 2015 John Wiley & Sons Ltd.

Mutual information identifies spurious Hurst phenomena in resting state EEG and fMRI data

NASA Astrophysics Data System (ADS)

von Wegner, Frederic; Laufs, Helmut; Tagliazucchi, Enzo

2018-02-01

Long-range memory in time series is often quantified by the Hurst exponent H , a measure of the signal's variance across several time scales. We analyze neurophysiological time series from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state experiments with two standard Hurst exponent estimators and with the time-lagged mutual information function applied to discretized versions of the signals. A confidence interval for the mutual information function is obtained from surrogate Markov processes with equilibrium distribution and transition matrix identical to the underlying signal. For EEG signals, we construct an additional mutual information confidence interval from a short-range correlated, tenth-order autoregressive model. We reproduce the previously described Hurst phenomenon (H >0.5 ) in the analytical amplitude of alpha frequency band oscillations, in EEG microstate sequences, and in fMRI signals, but we show that the Hurst phenomenon occurs without long-range memory in the information-theoretical sense. We find that the mutual information function of neurophysiological data behaves differently from fractional Gaussian noise (fGn), for which the Hurst phenomenon is a sufficient condition to prove long-range memory. Two other well-characterized, short-range correlated stochastic processes (Ornstein-Uhlenbeck, Cox-Ingersoll-Ross) also yield H >0.5 , whereas their mutual information functions lie within the Markovian confidence intervals, similar to neural signals. In these processes, which do not have long-range memory by construction, a spurious Hurst phenomenon occurs due to slow relaxation times and heteroscedasticity (time-varying conditional variance). In summary, we find that mutual information correctly distinguishes long-range from short-range dependence in the theoretical and experimental cases discussed. Our results also suggest that the stationary fGn process is not sufficient to describe neural data, which seem to belong to a more general class of stochastic processes, in which multiscale variance effects produce Hurst phenomena without long-range dependence. In our experimental data, the Hurst phenomenon and long-range memory appear as different system properties that should be estimated and interpreted independently.

General dependencies and causality analysis of road traffic fatalities in OECD countries.

PubMed

Yaseen, Muhammad Rizwan; Ali, Qamar; Khan, Muhammad Tariq Iqbal

2018-05-07

The road traffic accidents were responsible for material and human loss which was equal to 2.8 to 5% of gross national product (GNP). However, literature does not explore the elasticity coefficients and nexus of road traffic fatalities with foreign direct investment, health expenditures, trade openness, mobile subscriptions, the number of researchers in R&D department, and environmental particulate matter. This study filled this research gap by exploring the nexus between road traffic fatalities, foreign direct investment, health expenditures, trade openness, mobile subscriptions, the number of researchers, and environmental particulate matter in Organization for Economic Cooperation and Development (OECD) countries by using panel data from 1995 to 2015. The panel Autoregressive Distributed Lag (ARDL) bound test was used for the detection of cointegration between the variables after checking the stationarity in selected variables with different panel unit root tests. Panel vector error correction model explored the causality of road traffic fatalities, foreign direct investment, PM2.5 in the environment, and trade openness in the long run. Road traffic fatalities showed short run bi-directional causality with foreign direct investment and health expenditures. The short run bi-directional causality was also observed between trade and foreign direct investment and cellular mobile subscriptions and foreign direct investment. The panel fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOLS) showed the 0.947% reduction in road fatalities for 1% increase in the health expenditures in OECD countries. The significant reduction in road fatalities was also observed due to 1% increase in trade openness and researchers in R&D, which implies the importance of trade and research for road safety. It is required to invest in the health sector for the safety of precious human lives like the hospitals with latest medical equipment and improvement in the emergency services in the country. The research and development activities should be enhanced especially for the health and transportation sectors. The trade of environment-friendly technology should be promoted for the protection of environment.