Integrating WEPP into the WEPS infrastructure

USDA-ARS?s Scientific Manuscript database

The Wind Erosion Prediction System (WEPS) and the Water Erosion Prediction Project (WEPP) share a common modeling philosophy, that of moving away from primarily empirically based models based on indices or "average conditions", and toward a more process based approach which can be evaluated using ac...

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

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

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.

2014-09-12

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressivemore » Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.« less

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

PubMed Central

Yu, Ying; Wang, Yirui; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient. PMID:28246527

The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China.

PubMed

Wang, Kewei; Song, Wentao; Li, Jinping; Lu, Wu; Yu, Jiangang; Han, Xiaofeng

2016-05-01

The aim of this study is to forecast the incidence of bacillary dysentery with a prediction model. We collected the annual and monthly laboratory data of confirmed cases from January 2004 to December 2014. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast bacillary dysentery incidence in Jiangsu, China. The ARIMA (1, 1, 1) × (1, 1, 2)12 model fitted exactly with the number of cases during January 2004 to December 2014. The fitted model was then used to predict bacillary dysentery incidence during the period January to August 2015, and the number of cases fell within the model's CI for the predicted number of cases during January-August 2015. This study shows that the ARIMA model fits the fluctuations in bacillary dysentery frequency, and it can be used for future forecasting when applied to bacillary dysentery prevention and control. © 2016 APJPH.

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

NASA Astrophysics Data System (ADS)

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan

2014-09-01

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network.

PubMed

Yu, Ying; Wang, Yirui; Gao, Shangce; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

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

NASA Astrophysics Data System (ADS)

Moshkbar-Bakhshayesh, Khalil; Ghofrani, Mohammad B.

2014-08-01

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

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

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

ERIC Educational Resources Information Center

Braden, Jeffery P.; And Others

1990-01-01

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

A Computer Program for the Generation of ARIMA Data

ERIC Educational Resources Information Center

Green, Samuel B.; Noles, Keith O.

1977-01-01

The autoregressive integrated moving averages model (ARIMA) has been applied to time series data in psychological and educational research. A program is described that generates ARIMA data of a known order. The program enables researchers to explore statistical properties of ARIMA data and simulate systems producing time dependent observations.…

The Mathematical Analysis of Style: A Correlation-Based Approach.

ERIC Educational Resources Information Center

Oppenheim, Rosa

1988-01-01

Examines mathematical models of style analysis, focusing on the pattern in which literary characteristics occur. Describes an autoregressive integrated moving average model (ARIMA) for predicting sentence length in different works by the same author and comparable works by different authors. This technique is valuable in characterizing stylistic…

An Optimization of Inventory Demand Forecasting in University Healthcare Centre

NASA Astrophysics Data System (ADS)

Bon, A. T.; Ng, T. K.

2017-01-01

Healthcare industry becomes an important field for human beings nowadays as it concerns about one’s health. With that, forecasting demand for health services is an important step in managerial decision making for all healthcare organizations. Hence, a case study was conducted in University Health Centre to collect historical demand data of Panadol 650mg for 68 months from January 2009 until August 2014. The aim of the research is to optimize the overall inventory demand through forecasting techniques. Quantitative forecasting or time series forecasting model was used in the case study to forecast future data as a function of past data. Furthermore, the data pattern needs to be identified first before applying the forecasting techniques. Trend is the data pattern and then ten forecasting techniques are applied using Risk Simulator Software. Lastly, the best forecasting techniques will be find out with the least forecasting error. Among the ten forecasting techniques include single moving average, single exponential smoothing, double moving average, double exponential smoothing, regression, Holt-Winter’s additive, Seasonal additive, Holt-Winter’s multiplicative, seasonal multiplicative and Autoregressive Integrated Moving Average (ARIMA). According to the forecasting accuracy measurement, the best forecasting technique is regression analysis.

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

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.

Effects of Forecasts on the Revisions of Concurrent Seasonally Adjusted Data Using the X-11 Seasonal Adjustment Procedure.

ERIC Educational Resources Information Center

Bobbitt, Larry; Otto, Mark

Three Autoregressive Integrated Moving Averages (ARIMA) forecast procedures for Census Bureau X-11 concurrent seasonal adjustment were empirically tested. Forty time series from three Census Bureau economic divisions (business, construction, and industry) were analyzed. Forecasts were obtained from fitted seasonal ARIMA models augmented with…

Regional ITS architecture development : a case study : New York-New Jersey-Connecticut region : building a framework for regional ITS integration

DOT National Transportation Integrated Search

1999-01-01

The three-quarter moving composite price index is the weighted average of the indices for three consecutive quarters. The Composite Bid Price Index is composed of six indicator items: common excavation, to indicate the price trend for all roadway exc...

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

PubMed

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

2010-01-01

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

Alternatives to the Moving Average

Treesearch

Paul C. van Deusen

2001-01-01

There are many possible estimators that could be used with annual inventory data. The 5-year moving average has been selected as a default estimator to provide initial results for states having available annual inventory data. User objectives for these estimates are discussed. The characteristics of a moving average are outlined. It is shown that moving average...

A High Precision Prediction Model Using Hybrid Grey Dynamic Model

ERIC Educational Resources Information Center

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

2008-01-01

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

An Intelligent Decision Support System for Workforce Forecast

DTIC Science & Technology

2011-01-01

ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models

A Comparison of Alternative Approaches to the Analysis of Interrupted Time-Series.

ERIC Educational Resources Information Center

Harrop, John W.; Velicer, Wayne F.

1985-01-01

Computer generated data representative of 16 Auto Regressive Integrated Moving Averages (ARIMA) models were used to compare the results of interrupted time-series analysis using: (1) the known model identification, (2) an assumed (l,0,0) model, and (3) an assumed (3,0,0) model as an approximation to the General Transformation approach. (Author/BW)

Impact of the Illinois Seat Belt Use Law on Accidents, Deaths, and Injuries.

ERIC Educational Resources Information Center

Rock, Steven M.

1992-01-01

The impact of the 1985 Illinois seat belt law is explored using Box-Jenkins Auto-Regressive, Integrated Moving Averages (ARIMA) techniques and monthly accident statistical data from the state department of transportation for January-July 1990. A conservative estimate is that the law provides benefits of $15 million per month in Illinois. (SLD)

Models for short term malaria prediction in Sri Lanka

PubMed Central

Briët, Olivier JT; Vounatsou, Penelope; Gunawardena, Dissanayake M; Galappaththy, Gawrie NL; Amerasinghe, Priyanie H

2008-01-01

Background Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control. Methods Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models. Results The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons. Conclusion Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed. PMID:18460204

Quantified moving average strategy of crude oil futures market based on fuzzy logic rules and genetic algorithms

NASA Astrophysics Data System (ADS)

Liu, Xiaojia; An, Haizhong; Wang, Lijun; Guan, Qing

2017-09-01

The moving average strategy is a technical indicator that can generate trading signals to assist investment. While the trading signals tell the traders timing to buy or sell, the moving average cannot tell the trading volume, which is a crucial factor for investment. This paper proposes a fuzzy moving average strategy, in which the fuzzy logic rule is used to determine the strength of trading signals, i.e., the trading volume. To compose one fuzzy logic rule, we use four types of moving averages, the length of the moving average period, the fuzzy extent, and the recommend value. Ten fuzzy logic rules form a fuzzy set, which generates a rating level that decides the trading volume. In this process, we apply genetic algorithms to identify an optimal fuzzy logic rule set and utilize crude oil futures prices from the New York Mercantile Exchange (NYMEX) as the experiment data. Each experiment is repeated for 20 times. The results show that firstly the fuzzy moving average strategy can obtain a more stable rate of return than the moving average strategies. Secondly, holding amounts series is highly sensitive to price series. Thirdly, simple moving average methods are more efficient. Lastly, the fuzzy extents of extremely low, high, and very high are more popular. These results are helpful in investment decisions.

Measurement of greenhouse gas emissions from agricultural sites using open-path optical remote sensing method.

PubMed

Ro, Kyoung S; Johnson, Melvin H; Varma, Ravi M; Hashmonay, Ram A; Hunt, Patrick

2009-08-01

Improved characterization of distributed emission sources of greenhouse gases such as methane from concentrated animal feeding operations require more accurate methods. One promising method is recently used by the USEPA. It employs a vertical radial plume mapping (VRPM) algorithm using optical remote sensing techniques. We evaluated this method to estimate emission rates from simulated distributed methane sources. A scanning open-path tunable diode laser was used to collect path-integrated concentrations (PICs) along different optical paths on a vertical plane downwind of controlled methane releases. Each cycle consists of 3 ground-level PICs and 2 above ground PICs. Three- to 10-cycle moving averages were used to reconstruct mass equivalent concentration plum maps on the vertical plane. The VRPM algorithm estimated emission rates of methane along with meteorological and PIC data collected concomitantly under different atmospheric stability conditions. The derived emission rates compared well with actual released rates irrespective of atmospheric stability conditions. The maximum error was 22 percent when 3-cycle moving average PICs were used; however, it decreased to 11% when 10-cycle moving average PICs were used. Our validation results suggest that this new VRPM method may be used for improved estimations of greenhouse gas emission from a variety of agricultural sources.

A 12-Year Analysis of Nonbattle Injury Among US Service Members Deployed to Iraq and Afghanistan.

PubMed

Le, Tuan D; Gurney, Jennifer M; Nnamani, Nina S; Gross, Kirby R; Chung, Kevin K; Stockinger, Zsolt T; Nessen, Shawn C; Pusateri, Anthony E; Akers, Kevin S

2018-05-30

Nonbattle injury (NBI) among deployed US service members increases the burden on medical systems and results in high rates of attrition, affecting the available force. The possible causes and trends of NBI in the Iraq and Afghanistan wars have, to date, not been comprehensively described. To describe NBI among service members deployed to Iraq and Afghanistan, quantify absolute numbers of NBIs and proportion of NBIs within the Department of Defense Trauma Registry, and document the characteristics of this injury category. In this retrospective cohort study, data from the Department of Defense Trauma Registry on 29 958 service members injured in Iraq and Afghanistan from January 1, 2003, through December 31, 2014, were obtained. Injury incidence, patterns, and severity were characterized by battle injury and NBI. Trends in NBI were modeled using time series analysis with autoregressive integrated moving average and the weighted moving average method. Statistical analysis was performed from January 1, 2003, to December 31, 2014. Primary outcomes were proportion of NBIs and the changes in NBI over time. Among 29 958 casualties (battle injury and NBI) analyzed, 29 003 were in men and 955 were in women; the median age at injury was 24 years (interquartile range, 21-29 years). Nonbattle injury caused 34.1% of total casualties (n = 10 203) and 11.5% of all deaths (206 of 1788). Rates of NBI were higher among women than among men (63.2% [604 of 955] vs 33.1% [9599 of 29 003]; P < .001) and in Operation New Dawn (71.0% [298 of 420]) and Operation Iraqi Freedom (36.3% [6655 of 18 334]) compared with Operation Enduring Freedom (29.0% [3250 of 11 204]) (P < .001). A higher proportion of NBIs occurred in members of the Air Force (66.3% [539 of 810]) and Navy (48.3% [394 of 815]) than in members of the Army (34.7% [7680 of 22 154]) and Marine Corps (25.7% [1584 of 6169]) (P < .001). Leading mechanisms of NBI included falls (2178 [21.3%]), motor vehicle crashes (1921 [18.8%]), machinery or equipment accidents (1283 [12.6%]), blunt objects (1107 [10.8%]), gunshot wounds (728 [7.1%]), and sports (697 [6.8%]), causing predominantly blunt trauma (7080 [69.4%]). The trend in proportion of NBIs did not decrease over time, remaining at approximately 35% (by weighted moving average) after 2006 and approximately 39% by autoregressive integrated moving average. Assuming stable battlefield conditions, the autoregressive integrated moving average model estimated that the proportion of NBIs from 2015 to 2022 would be approximately 41.0% (95% CI, 37.8%-44.3%). In this study, approximately one-third of injuries during the Iraq and Afghanistan wars resulted from NBI, and the proportion of NBIs was steady for 12 years. Understanding the possible causes of NBI during military operations may be useful to target protective measures and safety interventions, thereby conserving fighting strength on the battlefield.

No Evidence of Suicide Increase Following Terrorist Attacks in the United States: An Interrupted Time-Series Analysis of September 11 and Oklahoma City

ERIC Educational Resources Information Center

Pridemore, William Alex; Trahan, Adam; Chamlin, Mitchell B.

2009-01-01

There is substantial evidence of detrimental psychological sequelae following disasters, including terrorist attacks. The effect of these events on extreme responses such as suicide, however, is unclear. We tested competing hypotheses about such effects by employing autoregressive integrated moving average techniques to model the impact of…

The Press Relations of a Local School District: An Analysis of the Emergence of School Issues.

ERIC Educational Resources Information Center

Morris, Jon R.; Guenter, Cornelius

Press coverage of a suburban midwest school district is analyzed as a set of time series of observations including the amount and quality of coverage. Possible shifts in these series because of the emergence of controversial issues are analyzed statistically using the Integrated Moving Average Time Series Model. Evidence of significant shifts in…

Predicting Rehabilitation Success Rate Trends among Ethnic Minorities Served by State Vocational Rehabilitation Agencies: A National Time Series Forecast Model Demonstration Study

ERIC Educational Resources Information Center

Moore, Corey L.; Wang, Ningning; Washington, Janique Tynez

2017-01-01

Purpose: This study assessed and demonstrated the efficacy of two select empirical forecast models (i.e., autoregressive integrated moving average [ARIMA] model vs. grey model [GM]) in accurately predicting state vocational rehabilitation agency (SVRA) rehabilitation success rate trends across six different racial and ethnic population cohorts…

Forecasting Daily Volume and Acuity of Patients in the Emergency Department.

PubMed

Calegari, Rafael; Fogliatto, Flavio S; Lucini, Filipe R; Neyeloff, Jeruza; Kuchenbecker, Ricardo S; Schaan, Beatriz D

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department

PubMed Central

Fogliatto, Flavio S.; Neyeloff, Jeruza; Kuchenbecker, Ricardo S.; Schaan, Beatriz D.

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification. PMID:27725842

Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data

PubMed Central

Tran, Truyen; Luo, Wei; Phung, Dinh; Venkatesh, Svetha

2016-01-01

Background: Modeling patient flow is crucial in understanding resource demand and prioritization. We study patient outflow from an open ward in an Australian hospital, where currently bed allocation is carried out by a manager relying on past experiences and looking at demand. Automatic methods that provide a reasonable estimate of total next-day discharges can aid in efficient bed management. The challenges in building such methods lie in dealing with large amounts of discharge noise introduced by the nonlinear nature of hospital procedures, and the nonavailability of real-time clinical information in wards. Objective Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. Methods We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. Results Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. Conclusions In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments. PMID:27444059

Space trajectory calculation based on G-sensor

NASA Astrophysics Data System (ADS)

Xu, Biya; Zhan, Yinwei; Shao, Yang

2017-08-01

At present, without full use of the mobile phone around us, most of the research in human body posture recognition field is use camera or portable acceleration sensor to collect data. In this paper, G-sensor built-in mobile phone is use to collect data. After processing data with the way of moving average filter and acceleration integral, joint point's space three-dimensional coordinates can be abtained accurately.

Integration of social information by human groups

PubMed Central

Granovskiy, Boris; Gold, Jason M.; Sumpter, David; Goldstone, Robert L.

2015-01-01

We consider a situation in which individuals search for accurate decisions without direct feedback on their accuracy but with information about the decisions made by peers in their group. The “wisdom of crowds” hypothesis states that the average judgment of many individuals can give a good estimate of, for example, the outcomes of sporting events and the answers to trivia questions. Two conditions for the application of wisdom of crowds are that estimates should be independent and unbiased. Here, we study how individuals integrate social information when answering trivia questions with answers that range between 0 and 100% (e.g., ‘What percentage of Americans are left-handed?’). We find that, consistent with the wisdom of crowds hypothesis, average performance improves with group size. However, individuals show a consistent bias to produce estimates that are insufficiently extreme. We find that social information provides significant, albeit small, improvement to group performance. Outliers with answers far from the correct answer move towards the position of the group mean. Given that these outliers also tend to be nearer to 50% than do the answers of other group members, this move creates group polarization away from 50%. By looking at individual performance over different questions we find that some people are more likely to be affected by social influence than others. There is also evidence that people differ in their competence in answering questions, but lack of competence is not significantly correlated with willingness to change guesses. We develop a mathematical model based on these results that postulates a cognitive process in which people first decide whether to take into account peer guesses, and if so, to move in the direction of these guesses. The size of the move is proportional to the distance between their own guess and the average guess of the group. This model closely approximates the distribution of guess movements and shows how outlying incorrect opinions can be systematically removed from a group resulting, in some situations, in improved group performance. However, improvement is only predicted for cases in which the initial guesses of individuals in the group are biased. PMID:26189568

Integration of Social Information by Human Groups.

PubMed

Granovskiy, Boris; Gold, Jason M; Sumpter, David J T; Goldstone, Robert L

2015-07-01

We consider a situation in which individuals search for accurate decisions without direct feedback on their accuracy, but with information about the decisions made by peers in their group. The "wisdom of crowds" hypothesis states that the average judgment of many individuals can give a good estimate of, for example, the outcomes of sporting events and the answers to trivia questions. Two conditions for the application of wisdom of crowds are that estimates should be independent and unbiased. Here, we study how individuals integrate social information when answering trivia questions with answers that range between 0% and 100% (e.g., "What percentage of Americans are left-handed?"). We find that, consistent with the wisdom of crowds hypothesis, average performance improves with group size. However, individuals show a consistent bias to produce estimates that are insufficiently extreme. We find that social information provides significant, albeit small, improvement to group performance. Outliers with answers far from the correct answer move toward the position of the group mean. Given that these outliers also tend to be nearer to 50% than do the answers of other group members, this move creates group polarization away from 50%. By looking at individual performance over different questions we find that some people are more likely to be affected by social influence than others. There is also evidence that people differ in their competence in answering questions, but lack of competence is not significantly correlated with willingness to change guesses. We develop a mathematical model based on these results that postulates a cognitive process in which people first decide whether to take into account peer guesses, and if so, to move in the direction of these guesses. The size of the move is proportional to the distance between their own guess and the average guess of the group. This model closely approximates the distribution of guess movements and shows how outlying incorrect opinions can be systematically removed from a group resulting, in some situations, in improved group performance. However, improvement is only predicted for cases in which the initial guesses of individuals in the group are biased. Copyright © 2015 Cognitive Science Society, Inc.

A Case Study to Improve Emergency Room Patient Flow at Womack Army Medical Center

DTIC Science & Technology

2009-06-01

use just the previous month, moving average 2-month period ( MA2 ) uses the average from the previous two months, moving average 3-month period (MA3...ED prior to discharge by provider) MA2 /MA3/MA4 - moving averages of 2-4 months in length MAD - mean absolute deviation (measure of accuracy for

Acoustic power of a moving point source in a moving medium

NASA Technical Reports Server (NTRS)

Cole, J. E., III; Sarris, I. I.

1976-01-01

The acoustic power output of a moving point-mass source in an acoustic medium which is in uniform motion and infinite in extent is examined. The acoustic medium is considered to be a homogeneous fluid having both zero viscosity and zero thermal conductivity. Two expressions for the acoustic power output are obtained based on a different definition cited in the literature for the average energy-flux vector in an acoustic medium in uniform motion. The acoustic power output of the source is found by integrating the component of acoustic intensity vector in the radial direction over the surface of an infinitely long cylinder which is within the medium and encloses the line of motion of the source. One of the power expressions is found to give unreasonable results even though the flow is uniform.

Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model.

PubMed

Mao, Qiang; Zhang, Kai; Yan, Wu; Cheng, Chaonan

2018-05-02

The aims of this study were to develop a forecasting model for the incidence of tuberculosis (TB) and analyze the seasonality of infections in China; and to provide a useful tool for formulating intervention programs and allocating medical resources. Data for the monthly incidence of TB from January 2004 to December 2015 were obtained from the National Scientific Data Sharing Platform for Population and Health (China). The Box-Jenkins method was applied to fit a seasonal auto-regressive integrated moving average (SARIMA) model to forecast the incidence of TB over the subsequent six months. During the study period of 144 months, 12,321,559 TB cases were reported in China, with an average monthly incidence of 6.4426 per 100,000 of the population. The monthly incidence of TB showed a clear 12-month cycle, and a seasonality with two peaks occurring in January and March and a trough in December. The best-fit model was SARIMA (1,0,0)(0,1,1) 12 , which demonstrated adequate information extraction (white noise test, p>0.05). Based on the analysis, the incidence of TB from January to June 2016 were 6.6335, 4.7208, 5.8193, 5.5474, 5.2202 and 4.9156 per 100,000 of the population, respectively. According to the seasonal pattern of TB incidence in China, the SARIMA model was proposed as a useful tool for monitoring epidemics. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Fast Algorithms for Mining Co-evolving Time Series

DTIC Science & Technology

2011-09-01

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

Central Procurement Workload Projection Model

DTIC Science & Technology

1981-02-01

generated by the P&P Directorates such as procurement actions (PA’s) are pursued. Specifi- cally, Box-Jenkins Autoregressive Integrated Moving Average...Breakout of PA’s to over and under $10,000 23 IV. FINDINGS AND RECOMMENDATIONS 24 A. General 24 B. Findings 24 C. Recommendations 25...the model will predict the actual values and hence the error will be zero . Therefore, after forecasting 3 quarters into the future no error

Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

PubMed Central

Alwee, Razana; Hj Shamsuddin, Siti Mariyam; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012

PubMed Central

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

Background The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. Methods In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). Results The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Conclusion Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis. PMID:26901682

Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria.

PubMed

Ihueze, Chukwutoo C; Onwurah, Uchendu O

2018-03-01

One of the major problems in the world today is the rate of road traffic crashes and deaths on our roads. Majority of these deaths occur in low-and-middle income countries including Nigeria. This study analyzed road traffic crashes in Anambra State, Nigeria with the intention of developing accurate predictive models for forecasting crash frequency in the State using autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with explanatory variables (ARIMAX) modelling techniques. The result showed that ARIMAX model outperformed the ARIMA (1,1,1) model generated when their performances were compared using the lower Bayesian information criterion, mean absolute percentage error, root mean square error; and higher coefficient of determination (R-Squared) as accuracy measures. The findings of this study reveal that incorporating human, vehicle and environmental related factors in time series analysis of crash dataset produces a more robust predictive model than solely using aggregated crash count. This study contributes to the body of knowledge on road traffic safety and provides an approach to forecasting using many human, vehicle and environmental factors. The recommendations made in this study if applied will help in reducing the number of road traffic crashes in Nigeria. Copyright © 2017 Elsevier Ltd. All rights reserved.

Long-Term Prediction of Emergency Department Revenue and Visitor Volume Using Autoregressive Integrated Moving Average Model

PubMed Central

Chen, Chieh-Fan; Ho, Wen-Hsien; Chou, Huei-Yin; Yang, Shu-Mei; Chen, I-Te; Shi, Hon-Yi

2011-01-01

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume. PMID:22203886

Long-term prediction of emergency department revenue and visitor volume using autoregressive integrated moving average model.

PubMed

Chen, Chieh-Fan; Ho, Wen-Hsien; Chou, Huei-Yin; Yang, Shu-Mei; Chen, I-Te; Shi, Hon-Yi

2011-01-01

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.

Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

PubMed

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012.

PubMed

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis.

[The trial of business data analysis at the Department of Radiology by constructing the auto-regressive integrated moving-average (ARIMA) model].

PubMed

Tani, Yuji; Ogasawara, Katsuhiko

2012-01-01

This study aimed to contribute to the management of a healthcare organization by providing management information using time-series analysis of business data accumulated in the hospital information system, which has not been utilized thus far. In this study, we examined the performance of the prediction method using the auto-regressive integrated moving-average (ARIMA) model, using the business data obtained at the Radiology Department. We made the model using the data used for analysis, which was the number of radiological examinations in the past 9 years, and we predicted the number of radiological examinations in the last 1 year. Then, we compared the actual value with the forecast value. We were able to establish that the performance prediction method was simple and cost-effective by using free software. In addition, we were able to build the simple model by pre-processing the removal of trend components using the data. The difference between predicted values and actual values was 10%; however, it was more important to understand the chronological change rather than the individual time-series values. Furthermore, our method was highly versatile and adaptable compared to the general time-series data. Therefore, different healthcare organizations can use our method for the analysis and forecasting of their business data.

High-Resolution Coarse-Grained Modeling Using Oriented Coarse-Grained Sites.

PubMed

Haxton, Thomas K

2015-03-10

We introduce a method to bring nearly atomistic resolution to coarse-grained models, and we apply the method to proteins. Using a small number of coarse-grained sites (about one per eight atoms) but assigning an independent three-dimensional orientation to each site, we preferentially integrate out stiff degrees of freedom (bond lengths and angles, as well as dihedral angles in rings) that are accurately approximated by their average values, while retaining soft degrees of freedom (unconstrained dihedral angles) mostly responsible for conformational variability. We demonstrate that our scheme retains nearly atomistic resolution by mapping all experimental protein configurations in the Protein Data Bank onto coarse-grained configurations and then analytically backmapping those configurations back to all-atom configurations. This roundtrip mapping throws away all information associated with the eliminated (stiff) degrees of freedom except for their average values, which we use to construct optimal backmapping functions. Despite the 4:1 reduction in the number of degrees of freedom, we find that heavy atoms move only 0.051 Å on average during the roundtrip mapping, while hydrogens move 0.179 Å on average, an unprecedented combination of efficiency and accuracy among coarse-grained protein models. We discuss the advantages of such a high-resolution model for parametrizing effective interactions and accurately calculating observables through direct or multiscale simulations.

Experimental investigation of a moving averaging algorithm for motion perpendicular to the leaf travel direction in dynamic MLC target tracking.

PubMed

Yoon, Jai-Woong; Sawant, Amit; Suh, Yelin; Cho, Byung-Chul; Suh, Tae-Suk; Keall, Paul

2011-07-01

In dynamic multileaf collimator (MLC) motion tracking with complex intensity-modulated radiation therapy (IMRT) fields, target motion perpendicular to the MLC leaf travel direction can cause beam holds, which increase beam delivery time by up to a factor of 4. As a means to balance delivery efficiency and accuracy, a moving average algorithm was incorporated into a dynamic MLC motion tracking system (i.e., moving average tracking) to account for target motion perpendicular to the MLC leaf travel direction. The experimental investigation of the moving average algorithm compared with real-time tracking and no compensation beam delivery is described. The properties of the moving average algorithm were measured and compared with those of real-time tracking (dynamic MLC motion tracking accounting for both target motion parallel and perpendicular to the leaf travel direction) and no compensation beam delivery. The algorithm was investigated using a synthetic motion trace with a baseline drift and four patient-measured 3D tumor motion traces representing regular and irregular motions with varying baseline drifts. Each motion trace was reproduced by a moving platform. The delivery efficiency, geometric accuracy, and dosimetric accuracy were evaluated for conformal, step-and-shoot IMRT, and dynamic sliding window IMRT treatment plans using the synthetic and patient motion traces. The dosimetric accuracy was quantified via a tgamma-test with a 3%/3 mm criterion. The delivery efficiency ranged from 89 to 100% for moving average tracking, 26%-100% for real-time tracking, and 100% (by definition) for no compensation. The root-mean-square geometric error ranged from 3.2 to 4.0 mm for moving average tracking, 0.7-1.1 mm for real-time tracking, and 3.7-7.2 mm for no compensation. The percentage of dosimetric points failing the gamma-test ranged from 4 to 30% for moving average tracking, 0%-23% for real-time tracking, and 10%-47% for no compensation. The delivery efficiency of moving average tracking was up to four times higher than that of real-time tracking and approached the efficiency of no compensation for all cases. The geometric accuracy and dosimetric accuracy of the moving average algorithm was between real-time tracking and no compensation, approximately half the percentage of dosimetric points failing the gamma-test compared with no compensation.

A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

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

Aziz, H. M. Abdul; Ukkusuri, Satish V.

We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed thismore » issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10.« less

A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

DOE PAGES

Aziz, H. M. Abdul; Ukkusuri, Satish V.

2017-06-29

We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed thismore » issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10.« less

Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway.

PubMed

Abou-Senna, Hatem; Radwan, Essam; Westerlund, Kurt; Cooper, C David

2013-07-01

The Intergovernmental Panel on Climate Change (IPCC) estimates that baseline global GHG emissions may increase 25-90% from 2000 to 2030, with carbon dioxide (CO2 emissions growing 40-110% over the same period. On-road vehicles are a major source of CO2 emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, for example, carbon monoxide (CO), nitrogen oxides (NO(x)), and particulate matter (PM). Therefore, the need to accurately quantify transportation-related emissions from vehicles is essential. The new US. Environmental Protection Agency (EPA) mobile source emissions model, MOVES2010a (MOVES), can estimate vehicle emissions on a second-by-second basis, creating the opportunity to combine a microscopic traffic simulation model (such as VISSIM) with MOVES to obtain accurate results. This paper presents an examination of four different approaches to capture the environmental impacts of vehicular operations on a 10-mile stretch of Interstate 4 (I-4), an urban limited-access highway in Orlando, FL. First (at the most basic level), emissions were estimated for the entire 10-mile section "by hand" using one average traffic volume and average speed. Then three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-by-second link drive schedules (LDS), and second-by-second operating mode distributions (OPMODE). This paper analyzes how the various approaches affect predicted emissions of CO, NO(x), PM2.5, PM10, and CO2. The results demonstrate that obtaining precise and comprehensive operating mode distributions on a second-by-second basis provides more accurate emission estimates. Specifically, emission rates are highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach. Transportation agencies and researchers in the past have estimated emissions using one average speed and volume on a long stretch of roadway. With MOVES, there is an opportunity for higher precision and accuracy. Integrating a microscopic traffic simulation model (such as VISSIM) with MOVES allows one to obtain precise and accurate emissions estimates. The proposed emission rate estimation process also can be extended to gridded emissions for ozone modeling, or to localized air quality dispersion modeling, where temporal and spatial resolution of emissions is essential to predict the concentration of pollutants near roadways.

Perceptions and Efficacy of Flight Operational Quality Assurance (FOQA) Programs Among Small-scale Operators

DTIC Science & Technology

2012-01-01

regressive Integrated Moving Average ( ARIMA ) model for the data, eliminating the need to identify an appropriate model through trial and error alone...06 .11 13.67 16 .62 16 .14 .11 8.06 16 .95 * Based on the asymptotic chi-square approximation. 8 In general, ARIMA models address three...performance standards and measurement processes and a prevailing climate of organizational trust were important factors. Unfortunately, uneven

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

DTIC Science & Technology

2006-03-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

[Model of multiple seasonal autoregressive integrated moving average model and its application in prediction of the hand-foot-mouth disease incidence in Changsha].

PubMed

Tan, Ting; Chen, Lizhang; Liu, Fuqiang

2014-11-01

To establish multiple seasonal autoregressive integrated moving average model (ARIMA) according to the hand-foot-mouth disease incidence in Changsha, and to explore the feasibility of the multiple seasonal ARIMA in predicting the hand-foot-mouth disease incidence. EVIEWS 6.0 was used to establish multiple seasonal ARIMA according to the hand-foot- mouth disease incidence from May 2008 to August 2013 in Changsha, and the data of the hand- foot-mouth disease incidence from September 2013 to February 2014 were served as the examined samples of the multiple seasonal ARIMA, then the errors were compared between the forecasted incidence and the real value. Finally, the incidence of hand-foot-mouth disease from March 2014 to August 2014 was predicted by the model. After the data sequence was handled by smooth sequence, model identification and model diagnosis, the multiple seasonal ARIMA (1, 0, 1)×(0, 1, 1)12 was established. The R2 value of the model fitting degree was 0.81, the root mean square prediction error was 8.29 and the mean absolute error was 5.83. The multiple seasonal ARIMA is a good prediction model, and the fitting degree is good. It can provide reference for the prevention and control work in hand-foot-mouth disease.

$1.8 Million and counting: how volatile agent education has decreased our spending $1000 per day.

PubMed

Miller, Scott A; Aschenbrenner, Carol A; Traunero, Justin R; Bauman, Loren A; Lobell, Samuel S; Kelly, Jeffrey S; Reynolds, John E

2016-12-01

Volatile anesthetic agents comprise a substantial portion of every hospital's pharmacy budget. Challenged with an initiative to lower anesthetic drug expenditures, we developed an education-based intervention focused on reducing volatile anesthetic costs while preserving access to all available volatile anesthetics. When postintervention evaluation demonstrated a dramatic year-over-year reduction in volatile agent acquisition costs, we undertook a retrospective analysis of volatile anesthetic purchasing data using time series analysis to determine the impact of our educational initiative. We obtained detailed volatile anesthetic purchasing data from the Central Supply of Wake Forest Baptist Health from 2007 to 2014 and integrated these data with the time course of our educational intervention. Aggregate volatile anesthetic purchasing data were analyzed for 7 consecutive fiscal years. The educational initiative emphasized tissue partition coefficients of volatile anesthetics in adipose tissue and muscle and their impact on case management. We used an interrupted time series analysis of monthly cost per unit data using autoregressive integrated moving average modeling, with the monthly cost per unit being the amount spent per bottle of anesthetic agent per month. The cost per unit decreased significantly after the intervention (t=-6.73, P<.001). The autoregressive integrated moving average model predicted that the average cost per unit decreased $48 after the intervention, with 95% confidence interval of $34 to $62. As evident from the data, the purchasing of desflurane and sevoflurane decreased, whereas that of isoflurane increased. An educational initiative focused solely on the selection of volatile anesthetic agent per case significantly reduced volatile anesthetic expense at a tertiary medical center. This approach appears promising for application in other hospitals in the rapidly evolving, value-added health care environment. We were able to accomplish this with instruction on tissue partition coefficients and each agent's individual cost per MAC-hour delivered. Copyright © 2016 Elsevier Inc. All rights reserved.

Smoothing strategies combined with ARIMA and neural networks to improve the forecasting of traffic accidents.

PubMed

Barba, Lida; Rodríguez, Nibaldo; Montt, Cecilia

2014-01-01

Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD). In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used. The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm. The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012. The best result is given by the combination HSVD-ARIMA, with a MAPE of 0:26%, followed by MA-ARIMA with a MAPE of 1:12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15:51%.

25 CFR 700.173 - Average net earnings of business or farm.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 25 Indians 2 2011-04-01 2011-04-01 false Average net earnings of business or farm. 700.173 Section... PROCEDURES Moving and Related Expenses, Temporary Emergency Moves § 700.173 Average net earnings of business or farm. (a) Computing net earnings. For purposes of this subpart, the average annual net earnings of...

25 CFR 700.173 - Average net earnings of business or farm.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 2 2010-04-01 2010-04-01 false Average net earnings of business or farm. 700.173 Section... PROCEDURES Moving and Related Expenses, Temporary Emergency Moves § 700.173 Average net earnings of business or farm. (a) Computing net earnings. For purposes of this subpart, the average annual net earnings of...

Identification of moving vehicle forces on bridge structures via moving average Tikhonov regularization

NASA Astrophysics Data System (ADS)

Pan, Chu-Dong; Yu, Ling; Liu, Huan-Lin

2017-08-01

Traffic-induced moving force identification (MFI) is a typical inverse problem in the field of bridge structural health monitoring. Lots of regularization-based methods have been proposed for MFI. However, the MFI accuracy obtained from the existing methods is low when the moving forces enter into and exit a bridge deck due to low sensitivity of structural responses to the forces at these zones. To overcome this shortcoming, a novel moving average Tikhonov regularization method is proposed for MFI by combining with the moving average concepts. Firstly, the bridge-vehicle interaction moving force is assumed as a discrete finite signal with stable average value (DFS-SAV). Secondly, the reasonable signal feature of DFS-SAV is quantified and introduced for improving the penalty function (∣∣x∣∣2 2) defined in the classical Tikhonov regularization. Then, a feasible two-step strategy is proposed for selecting regularization parameter and balance coefficient defined in the improved penalty function. Finally, both numerical simulations on a simply-supported beam and laboratory experiments on a hollow tube beam are performed for assessing the accuracy and the feasibility of the proposed method. The illustrated results show that the moving forces can be accurately identified with a strong robustness. Some related issues, such as selection of moving window length, effect of different penalty functions, and effect of different car speeds, are discussed as well.

Concept for an off-line gain stabilisation method.

PubMed

Pommé, S; Sibbens, G

2004-01-01

Conceptual ideas are presented for an off-line gain stabilisation method for spectrometry, in particular for alpha-particle spectrometry at low count rate. The method involves list mode storage of individual energy and time stamp data pairs. The 'Stieltjes integral' of measured spectra with respect to a reference spectrum is proposed as an indicator for gain instability. 'Exponentially moving averages' of the latter show the gain shift as a function of time. With this information, the data are relocated stochastically on a point-by-point basis.

Genetically Engineered, Live Attenuated Vaccines Protect Nonhuman Primates Against Aerosol Challenge with a Virulent IE Strain of Venezuelan Equine Encephalitis Virus

DTIC Science & Technology

2005-01-21

integrated moving average ( ARIMA ) model [15,19]. Fore- casted values for the postexposure time periods were based on the training model extrapolated...Smith JF. Genetically engineered, live attenuated vaccines or Venezuelan equine encephalitis: testing in animal models . Vaccine 2003;21(25–26):3854–62...encephalitis: testing in animal models . Vaccine 2003;21(25-26):3854-62] and IE strains of VEE viruses. 15. SUBJECT TERMS Venezuelan equine

Ozone and its projection in regard to climate change

NASA Astrophysics Data System (ADS)

Melkonyan, Ani; Wagner, Patrick

2013-03-01

In this paper, the dependence of ozone-forming potential on temperature was analysed based on data from two stations (with an industrial and rural background, respectively) in North Rhine-Westphalia, Germany, for the period of 1983-2007. After examining the interrelations between ozone, NOx and temperature, a projection of the days with ozone exceedance (over a limit value of a daily maximum 8-h average ≥ 120 μg m-3 for 25 days per year averaged for 3 years) in terms of global climate change was made using probability theory and an autoregression integrated moving average (ARIMA) model. The results show that with a temperature increase of 3 K, the frequency of days when ozone exceeds its limit value will increase by 135% at the industrial station and by 87% at the rural background station.

Assessing the Efficacy of Adjustable Moving Averages Using ASEAN-5 Currencies.

PubMed

Chan Phooi M'ng, Jacinta; Zainudin, Rozaimah

2016-01-01

The objective of this research is to examine the trends in the exchange rate markets of the ASEAN-5 countries (Indonesia (IDR), Malaysia (MYR), the Philippines (PHP), Singapore (SGD), and Thailand (THB)) through the application of dynamic moving average trading systems. This research offers evidence of the usefulness of the time-varying volatility technical analysis indicator, Adjustable Moving Average (AMA') in deciphering trends in these ASEAN-5 exchange rate markets. This time-varying volatility factor, referred to as the Efficacy Ratio in this paper, is embedded in AMA'. The Efficacy Ratio adjusts the AMA' to the prevailing market conditions by avoiding whipsaws (losses due, in part, to acting on wrong trading signals, which generally occur when there is no general direction in the market) in range trading and by entering early into new trends in trend trading. The efficacy of AMA' is assessed against other popular moving-average rules. Based on the January 2005 to December 2014 dataset, our findings show that the moving averages and AMA' are superior to the passive buy-and-hold strategy. Specifically, AMA' outperforms the other models for the United States Dollar against PHP (USD/PHP) and USD/THB currency pairs. The results show that different length moving averages perform better in different periods for the five currencies. This is consistent with our hypothesis that a dynamic adjustable technical indicator is needed to cater for different periods in different markets.

Effect of parameters in moving average method for event detection enhancement using phase sensitive OTDR

NASA Astrophysics Data System (ADS)

Kwon, Yong-Seok; Naeem, Khurram; Jeon, Min Yong; Kwon, Il-bum

2017-04-01

We analyze the relations of parameters in moving average method to enhance the event detectability of phase sensitive optical time domain reflectometer (OTDR). If the external events have unique frequency of vibration, then the control parameters of moving average method should be optimized in order to detect these events efficiently. A phase sensitive OTDR was implemented by a pulsed light source, which is composed of a laser diode, a semiconductor optical amplifier, an erbium-doped fiber amplifier, a fiber Bragg grating filter, and a light receiving part, which has a photo-detector and high speed data acquisition system. The moving average method is operated with the control parameters: total number of raw traces, M, number of averaged traces, N, and step size of moving, n. The raw traces are obtained by the phase sensitive OTDR with sound signals generated by a speaker. Using these trace data, the relation of the control parameters is analyzed. In the result, if the event signal has one frequency, then the optimal values of N, n are existed to detect the event efficiently.

Integrated coherent matter wave circuits

DOE PAGES

Ryu, C.; Boshier, M. G.

2015-09-21

An integrated coherent matter wave circuit is a single device, analogous to an integrated optical circuit, in which coherent de Broglie waves are created and then launched into waveguides where they can be switched, divided, recombined, and detected as they propagate. Applications of such circuits include guided atom interferometers, atomtronic circuits, and precisely controlled delivery of atoms. We report experiments demonstrating integrated circuits for guided coherent matter waves. The circuit elements are created with the painted potential technique, a form of time-averaged optical dipole potential in which a rapidly moving, tightly focused laser beam exerts forces on atoms through theirmore » electric polarizability. Moreover, the source of coherent matter waves is a Bose–Einstein condensate (BEC). Finally, we launch BECs into painted waveguides that guide them around bends and form switches, phase coherent beamsplitters, and closed circuits. These are the basic elements that are needed to engineer arbitrarily complex matter wave circuitry.« less

Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia.

PubMed

Ansari, Mozafar; Othman, Faridah; Abunama, Taher; El-Shafie, Ahmed

2018-04-01

The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this research, the influent flow rate of a sewage treatment plant (STP) was modelled and predicted by autoregressive integrated moving average (ARIMA), nonlinear autoregressive network (NAR) and support vector machine (SVM) regression time series algorithms. To evaluate the models' accuracy, the root mean square error (RMSE) and coefficient of determination (R 2 ) were calculated as initial assessment measures, while relative error (RE), peak flow criterion (PFC) and low flow criterion (LFC) were calculated as final evaluation measures to demonstrate the detailed accuracy of the selected models. An integrated model was developed based on the individual models' prediction ability for low, average and peak flow. An initial assessment of the results showed that the ARIMA model was the least accurate and the NAR model was the most accurate. The RE results also prove that the SVM model's frequency of errors above 10% or below - 10% was greater than the NAR model's. The influent was also forecasted up to 44 weeks ahead by both models. The graphical results indicate that the NAR model made better predictions than the SVM model. The final evaluation of NAR and SVM demonstrated that SVM made better predictions at peak flow and NAR fit well for low and average inflow ranges. The integrated model developed includes the NAR model for low and average influent and the SVM model for peak inflow.

Assessing the Efficacy of Adjustable Moving Averages Using ASEAN-5 Currencies

PubMed Central

2016-01-01

The objective of this research is to examine the trends in the exchange rate markets of the ASEAN-5 countries (Indonesia (IDR), Malaysia (MYR), the Philippines (PHP), Singapore (SGD), and Thailand (THB)) through the application of dynamic moving average trading systems. This research offers evidence of the usefulness of the time-varying volatility technical analysis indicator, Adjustable Moving Average (AMA′) in deciphering trends in these ASEAN-5 exchange rate markets. This time-varying volatility factor, referred to as the Efficacy Ratio in this paper, is embedded in AMA′. The Efficacy Ratio adjusts the AMA′ to the prevailing market conditions by avoiding whipsaws (losses due, in part, to acting on wrong trading signals, which generally occur when there is no general direction in the market) in range trading and by entering early into new trends in trend trading. The efficacy of AMA′ is assessed against other popular moving-average rules. Based on the January 2005 to December 2014 dataset, our findings show that the moving averages and AMA′ are superior to the passive buy-and-hold strategy. Specifically, AMA′ outperforms the other models for the United States Dollar against PHP (USD/PHP) and USD/THB currency pairs. The results show that different length moving averages perform better in different periods for the five currencies. This is consistent with our hypothesis that a dynamic adjustable technical indicator is needed to cater for different periods in different markets. PMID:27574972

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.

Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

PubMed Central

Ebner, Marc; Hameroff, Stuart

2011-01-01

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot”) suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain. PMID:22046178

Lateral information processing by spiking neurons: a theoretical model of the neural correlate of consciousness.

PubMed

Ebner, Marc; Hameroff, Stuart

2011-01-01

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on "autopilot"). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the "conscious pilot") suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious "auto-pilot" cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways "gap junctions" in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain.

Development of an Integrated Chip for Automatic Tracking and Positioning Manipulation for Single Cell Lysis

PubMed Central

Young, Chao-Wang; Hsieh, Jia-Ling; Ay, Chyung

2012-01-01

This study adopted a microelectromechanical fabrication process to design a chip integrated with electroosmotic flow and dielectrophoresis force for single cell lysis. Human histiocytic lymphoma U937 cells were driven rapidly by electroosmotic flow and precisely moved to a specific area for cell lysis. By varying the frequency of AC power, 15 V AC at 1 MHz of frequency configuration achieved 100% cell lysing at the specific area. The integrated chip could successfully manipulate single cells to a specific position and lysis. The overall successful rate of cell tracking, positioning, and cell lysis is 80%. The average speed of cell driving was 17.74 μm/s. This technique will be developed for DNA extraction in biomolecular detection. It can simplify pre-treatment procedures for biotechnological analysis of samples. PMID:22736957

Development of an integrated chip for automatic tracking and positioning manipulation for single cell lysis.

PubMed

Young, Chao-Wang; Hsieh, Jia-Ling; Ay, Chyung

2012-01-01

This study adopted a microelectromechanical fabrication process to design a chip integrated with electroosmotic flow and dielectrophoresis force for single cell lysis. Human histiocytic lymphoma U937 cells were driven rapidly by electroosmotic flow and precisely moved to a specific area for cell lysis. By varying the frequency of AC power, 15 V AC at 1 MHz of frequency configuration achieved 100% cell lysing at the specific area. The integrated chip could successfully manipulate single cells to a specific position and lysis. The overall successful rate of cell tracking, positioning, and cell lysis is 80%. The average speed of cell driving was 17.74 μm/s. This technique will be developed for DNA extraction in biomolecular detection. It can simplify pre-treatment procedures for biotechnological analysis of samples.

Timescale Halo: Average-Speed Targets Elicit More Positive and Less Negative Attributions than Slow or Fast Targets

PubMed Central

Hernandez, Ivan; Preston, Jesse Lee; Hepler, Justin

2014-01-01

Research on the timescale bias has found that observers perceive more capacity for mind in targets moving at an average speed, relative to slow or fast moving targets. The present research revisited the timescale bias as a type of halo effect, where normal-speed people elicit positive evaluations and abnormal-speed (slow and fast) people elicit negative evaluations. In two studies, participants viewed videos of people walking at a slow, average, or fast speed. We find evidence for a timescale halo effect: people walking at an average-speed were attributed more positive mental traits, but fewer negative mental traits, relative to slow or fast moving people. These effects held across both cognitive and emotional dimensions of mind and were mediated by overall positive/negative ratings of the person. These results suggest that, rather than eliciting greater perceptions of general mind, the timescale bias may reflect a generalized positivity toward average speed people relative to slow or fast moving people. PMID:24421882

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

On nonstationarity and antipersistency in global temperature series

NASA Astrophysics Data System (ADS)

KäRner, O.

2002-10-01

Statistical analysis is carried out for satellite-based global daily tropospheric and stratospheric temperature anomaly and solar irradiance data sets. Behavior of the series appears to be nonstationary with stationary daily increments. Estimating long-range dependence between the increments reveals a remarkable difference between the two temperature series. Global average tropospheric temperature anomaly behaves similarly to the solar irradiance anomaly. Their daily increments show antipersistency for scales longer than 2 months. The property points at a cumulative negative feedback in the Earth climate system governing the tropospheric variability during the last 22 years. The result emphasizes a dominating role of the solar irradiance variability in variations of the tropospheric temperature and gives no support to the theory of anthropogenic climate change. The global average stratospheric temperature anomaly proceeds like a 1-dim random walk at least up to 11 years, allowing good presentation by means of the autoregressive integrated moving average (ARIMA) models for monthly series.

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.

Evaluating and improving count-based population inference: A case study from 31 years of monitoring Sandhill Cranes

USGS Publications Warehouse

Gerber, Brian D.; Kendall, William L.

2017-01-01

Monitoring animal populations can be difficult. Limited resources often force monitoring programs to rely on unadjusted or smoothed counts as an index of abundance. Smoothing counts is commonly done using a moving-average estimator to dampen sampling variation. These indices are commonly used to inform management decisions, although their reliability is often unknown. We outline a process to evaluate the biological plausibility of annual changes in population counts and indices from a typical monitoring scenario and compare results with a hierarchical Bayesian time series (HBTS) model. We evaluated spring and fall counts, fall indices, and model-based predictions for the Rocky Mountain population (RMP) of Sandhill Cranes (Antigone canadensis) by integrating juvenile recruitment, harvest, and survival into a stochastic stage-based population model. We used simulation to evaluate population indices from the HBTS model and the commonly used 3-yr moving average estimator. We found counts of the RMP to exhibit biologically unrealistic annual change, while the fall population index was largely biologically realistic. HBTS model predictions suggested that the RMP changed little over 31 yr of monitoring, but the pattern depended on assumptions about the observational process. The HBTS model fall population predictions were biologically plausible if observed crane harvest mortality was compensatory up to natural mortality, as empirical evidence suggests. Simulations indicated that the predicted mean of the HBTS model was generally a more reliable estimate of the true population than population indices derived using a moving 3-yr average estimator. Practitioners could gain considerable advantages from modeling population counts using a hierarchical Bayesian autoregressive approach. Advantages would include: (1) obtaining measures of uncertainty; (2) incorporating direct knowledge of the observational and population processes; (3) accommodating missing years of data; and (4) forecasting population size.

Examination of the Armagh Observatory Annual Mean Temperature Record, 1844-2004

NASA Technical Reports Server (NTRS)

Wilson, Robert M.; Hathaway, David H.

2006-01-01

The long-term annual mean temperature record (1844-2004) of the Armagh Observatory (Armagh, Northern Ireland, United Kingdom) is examined for evidence of systematic variation, in particular, as related to solar/geomagnetic forcing and secular variation. Indeed, both are apparent in the temperature record. Moving averages for 10 years of temperature are found to highly correlate against both 10-year moving averages of the aa-geomagnetic index and sunspot number, having correlation coefficients of approx. 0.7, inferring that nearly half the variance in the 10-year moving average of temperature can be explained by solar/geomagnetic forcing. The residuals appear episodic in nature, with cooling seen in the 1880s and again near 1980. Seven of the last 10 years of the temperature record has exceeded 10 C, unprecedented in the overall record. Variation of sunspot cyclic averages and 2-cycle moving averages of temperature strongly associate with similar averages for the solar/geomagnetic cycle, with the residuals displaying an apparent 9-cycle variation and a steep rise in temperature associated with cycle 23. Hale cycle averages of temperature for even-odd pairs of sunspot cycles correlate against similar averages for the solar/geomagnetic cycle and, especially, against the length of the Hale cycle. Indications are that annual mean temperature will likely exceed 10 C over the next decade.

Motion coherence affects human perception and pursuit similarly.

PubMed

Beutter, B R; Stone, L S

2000-01-01

Pursuit and perception both require accurate information about the motion of objects. Recovering the motion of objects by integrating the motion of their components is a difficult visual task. Successful integration produces coherent global object motion, while a failure to integrate leaves the incoherent local motions of the components unlinked. We compared the ability of perception and pursuit to perform motion integration by measuring direction judgments and the concomitant eye-movement responses to line-figure parallelograms moving behind stationary rectangular apertures. The apertures were constructed such that only the line segments corresponding to the parallelogram's sides were visible; thus, recovering global motion required the integration of the local segment motion. We investigated several potential motion-integration rules by using stimuli with different object, vector-average, and line-segment terminator-motion directions. We used an oculometric decision rule to directly compare direction discrimination for pursuit and perception. For visible apertures, the percept was a coherent object, and both the pursuit and perceptual performance were close to the object-motion prediction. For invisible apertures, the percept was incoherently moving segments, and both the pursuit and perceptual performance were close to the terminator-motion prediction. Furthermore, both psychometric and oculometric direction thresholds were much higher for invisible apertures than for visible apertures. We constructed a model in which both perception and pursuit are driven by a shared motion-processing stage, with perception having an additional input from an independent static-processing stage. Model simulations were consistent with our perceptual and oculomotor data. Based on these results, we propose the use of pursuit as an objective and continuous measure of perceptual coherence. Our results support the view that pursuit and perception share a common motion-integration stage, perhaps within areas MT or MST.

Motion coherence affects human perception and pursuit similarly

NASA Technical Reports Server (NTRS)

Beutter, B. R.; Stone, L. S.

2000-01-01

Pursuit and perception both require accurate information about the motion of objects. Recovering the motion of objects by integrating the motion of their components is a difficult visual task. Successful integration produces coherent global object motion, while a failure to integrate leaves the incoherent local motions of the components unlinked. We compared the ability of perception and pursuit to perform motion integration by measuring direction judgments and the concomitant eye-movement responses to line-figure parallelograms moving behind stationary rectangular apertures. The apertures were constructed such that only the line segments corresponding to the parallelogram's sides were visible; thus, recovering global motion required the integration of the local segment motion. We investigated several potential motion-integration rules by using stimuli with different object, vector-average, and line-segment terminator-motion directions. We used an oculometric decision rule to directly compare direction discrimination for pursuit and perception. For visible apertures, the percept was a coherent object, and both the pursuit and perceptual performance were close to the object-motion prediction. For invisible apertures, the percept was incoherently moving segments, and both the pursuit and perceptual performance were close to the terminator-motion prediction. Furthermore, both psychometric and oculometric direction thresholds were much higher for invisible apertures than for visible apertures. We constructed a model in which both perception and pursuit are driven by a shared motion-processing stage, with perception having an additional input from an independent static-processing stage. Model simulations were consistent with our perceptual and oculomotor data. Based on these results, we propose the use of pursuit as an objective and continuous measure of perceptual coherence. Our results support the view that pursuit and perception share a common motion-integration stage, perhaps within areas MT or MST.

A Fiber Interferometer for the Magnetized Shock Experiment

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

Yoo, Christian

2012-08-30

The Magnetized Shock Experiment (MSX) at Los Alamos National Laboratory requires remote diagnostics of plasma density. Laser interferometry can be used to determine the line-integrated density of the plasma. A multi-chord heterodyne fiber optic Mach-Zehnder interferometer is being assembled and integrated into the experiment. The advantage of the fiber coupling is that many different view chords can be easily obtained by simply moving transmit and receive fiber couplers. Several such fiber sets will be implemented to provide a time history of line-averaged density for several chords at once. The multiple chord data can then be Abel inverted to provide radiallymore » resolved spatial profiles of density. We describe the design and execution of this multiple fiber interferometer.« less

Thermoelectric integrated membrane evaporation water recovery technology

NASA Technical Reports Server (NTRS)

Roebelen, G. J., Jr.; Winkler, H. E.; Dehner, G. F.

1982-01-01

The recently developed Thermoelectric Integrated Membrane Evaporation Subsystem (TIMES) offers a highly competitive approach to water recovery from waste fluids for future on-orbit stations such as the Space Operations Center. Low power, compactness and gravity insensitive operation are featured in this vacuum distillation subsystem that combines a hollow fiber membrane evaporator with a thermoelectric heat pump. The hollow fiber elements provide positive liquid/gas phase control with no moving parts other than pumps and an accumulator, thus solving problems inherent in other reclamation subsystem designs. In an extensive test program, over 850 hours of operation were accumulated during which time high quality product water was recovered from both urine and wash water at an average steady state production rate of 2.2 pounds per hour.

A Fiber Interferometer for the Magnetized Shock Experiment

NASA Astrophysics Data System (ADS)

Yoo, C. B.; Gao, K. W.; Weber, T. E.; Intrator, T. P.

2012-10-01

The Magnetized Shock Experiment (MSX) at Los Alamos National Laboratory requires remote diagnostics of plasma density. Laser interferometry can be used to determine the line-integrated density of the plasma. A multi-chord heterodyne fiber optic Mach-Zehnder interferometer is being assembled and integrated into the experiment. The advantage of the fiber coupling is that many different view chords can be easily obtained by simply moving transmit and receive fiber couplers. Several such fiber sets will be implemented to provide a time history of line-averaged density for several chords at once. The multiple chord data can then be Abel inverted to provide radially resolved spatial profiles of density. We describe the design and execution of this multiple fiber interferometer.

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.

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

NASA Astrophysics Data System (ADS)

Massah, Mozhdeh; Kantz, Holger

2016-04-01

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

Comparing Gravimetric and Real-Time Sampling of PM2.5 Concentrations Inside Truck Cabins

PubMed Central

Zhu, Ying; Smith, Thomas J.; Davis, Mary E.; Levy, Jonathan I.; Herrick, Robert; Jiang, Hongyu

2012-01-01

As part of a study on truck drivers’ exposure and health risk, pickup and delivery (P&D) truck drivers’ on-road exposure patterns to PM2.5 were assessed in five weeklong sampling trips in metropolitan areas of five U.S. cities from April to August of 2006. Drivers were sampled with real-time (DustTrak) and gravimetric samplers to measure average in-cabin PM2.5 concentrations and to compare their correspondence in moving trucks. In addition, GPS measurements of truck locations, meteorological data, and driver behavioral data were collected throughout the day to determine which factors influence the relationship between real-time and gravimetric samplers. Results indicate that the association between average real-time and gravimetric PM2.5 measurements on moving trucks was fairly consistent (Spearman rank correlation of 0.63), with DustTrak measurements exceeding gravimetric measurements by approximately a factor of 2. This ratio differed significantly only between the industrial Midwest cities and the other three sampled cities scattered in the South and West. There was also limited evidence of an effect of truck age. Filter samples collected concurrently with DustTrak measurements can be used to calibrate average mass concentration responses for the DustTrak, allowing for real-time measurements to be integrated into longer-term studies of inter-city and intra-urban exposure patterns for truck drivers. PMID:21991940

Comparing gravimetric and real-time sampling of PM(2.5) concentrations inside truck cabins.

PubMed

Zhu, Ying; Smith, Thomas J; Davis, Mary E; Levy, Jonathan I; Herrick, Robert; Jiang, Hongyu

2011-11-01

As part of a study on truck drivers' exposure and health risk, pickup and delivery (P&D) truck drivers' on-road exposure patterns to PM(2.5) were assessed in five, weeklong sampling trips in metropolitan areas of five U.S. cities from April to August of 2006. Drivers were sampled with real-time (DustTrak) and gravimetric samplers to measure average in-cabin PM(2.5) concentrations and to compare their correspondence in moving trucks. In addition, GPS measurements of truck locations, meteorological data, and driver behavioral data were collected throughout the day to determine which factors influence the relationship between real-time and gravimetric samplers. Results indicate that the association between average real-time and gravimetric PM(2.5) measurements on moving trucks was fairly consistent (Spearman rank correlation of 0.63), with DustTrak measurements exceeding gravimetric measurements by approximately a factor of 2. This ratio differed significantly only between the industrial Midwest cities and the other three sampled cities scattered in the South and West. There was also limited evidence of an effect of truck age. Filter samples collected concurrently with DustTrak measurements can be used to calibrate average mass concentration responses for the DustTrak, allowing for real-time measurements to be integrated into longer-term studies of inter-city and intra-urban exposure patterns for truck drivers.

Maps of averaged spectral deviations from soil lines and their comparison with traditional soil maps

NASA Astrophysics Data System (ADS)

Rukhovich, D. I.; Rukhovich, A. D.; Rukhovich, D. D.; Simakova, M. S.; Kulyanitsa, A. L.; Bryzzhev, A. V.; Koroleva, P. V.

2016-07-01

The analysis of 34 cloudless fragments of Landsat 5, 7, and 8 images (1985-2014) on the territory of Plavsk, Arsen'evsk, and Chern districts of Tula oblast has been performed. It is shown that bare soil surface on the RED-NIR plots derived from the images cannot be described in the form of a sector of spectral plane as it can be done for the NDVI values. The notion of spectral neighborhood of soil line (SNSL) is suggested. It is defined as the sum of points of the RED-NIR spectral space, which are characterized by spectral characteristics of the bare soil applied for constructing soil lines. The way of the SNSL separation along the line of the lowest concentration density of points on the RED-NIR spectral space is suggested. This line separates bare soil surface from vegetating plants. The SNSL has been applied to construct soil line (SL) for each of the 34 images and to delineate bare soil surface on them. Distances from the points with averaged RED-NIR coordinates to the SL have been calculated using the method of moving window. These distances can be referred to as averaged spectral deviations (ASDs). The calculations have been performed strictly for the SNSL areas. As a result, 34 maps of ASDs have been created. These maps contain ASD values for 6036 points of a grid used in the study. Then, the integral map of normalized ASD values has been built with due account for the number of points participating in the calculation (i.e., lying in the SNSL) within the moving window. The integral map of ASD values has been compared with four traditional soil maps on the studied territory. It is shown that this integral map can be interpreted in terms of soil taxa: the areas of seven soil subtypes (soddy moderately podzolic, soddy slightly podzolic, light gray forest. gray forest, dark gray forest, podzolized chernozems, and leached chernozems) belonging to three soil types (soddy-podzolic, gray forest, and chernozemic soils) can be delineated on it.

Modeling Geodetic Processes with Levy α-Stable Distribution and FARIMA

NASA Astrophysics Data System (ADS)

Montillet, Jean-Philippe; Yu, Kegen

2015-04-01

Over the last years the scientific community has been using the auto regressive moving average (ARMA) model in the modeling of the noise in global positioning system (GPS) time series (daily solution). This work starts with the investigation of the limit of the ARMA model which is widely used in signal processing when the measurement noise is white. Since a typical GPS time series consists of geophysical signals (e.g., seasonal signal) and stochastic processes (e.g., coloured and white noise), the ARMA model may be inappropriate. Therefore, the application of the fractional auto-regressive integrated moving average (FARIMA) model is investigated. The simulation results using simulated time series as well as real GPS time series from a few selected stations around Australia show that the FARIMA model fits the time series better than other models when the coloured noise is larger than the white noise. The second fold of this work focuses on fitting the GPS time series with the family of Levy α-stable distributions. Using this distribution, a hypothesis test is developed to eliminate effectively coarse outliers from GPS time series, achieving better performance than using the rule of thumb of n standard deviations (with n chosen empirically).

Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents

PubMed Central

Rodríguez, Nibaldo

2014-01-01

Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD). In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used. The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm. The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012. The best result is given by the combination HSVD-ARIMA, with a MAPE of 0 : 26%, followed by MA-ARIMA with a MAPE of 1 : 12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15 : 51%. PMID:25243200

Investigation of the moving structures in a coronal bright point

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

Ning, Zongjun; Guo, Yang, E-mail: ningzongjun@pmo.ac.cn

2014-10-10

We have explored the moving structures in a coronal bright point (CBP) observed by the Solar Dynamic Observatory Atmospheric Imaging Assembly (AIA) on 2011 March 5. This CBP event has a lifetime of ∼20 minutes and is bright with a curved shape along a magnetic loop connecting a pair of negative and positive fields. AIA imaging observations show that a lot of bright structures are moving intermittently along the loop legs toward the two footpoints from the CBP brightness core. Such moving bright structures are clearly seen at AIA 304 Å. In order to analyze their features, the CBP ismore » cut along the motion direction with a curved slit which is wide enough to cover the bulk of the CBP. After integrating the flux along the slit width, we get the spacetime slices at nine AIA wavelengths. The oblique streaks starting from the edge of the CBP brightness core are identified as moving bright structures, especially on the derivative images of the brightness spacetime slices. They seem to originate from the same position near the loop top. We find that these oblique streaks are bi-directional, simultaneous, symmetrical, and periodic. The average speed is about 380 km s{sup –1}, and the period is typically between 80 and 100 s. Nonlinear force-free field extrapolation shows the possibility that magnetic reconnection takes place during the CBP, and our findings indicate that these moving bright structures could be the observational outflows after magnetic reconnection in the CBP.« less

THE VELOCITY DISTRIBUTION OF NEARBY STARS FROM HIPPARCOS DATA. II. THE NATURE OF THE LOW-VELOCITY MOVING GROUPS

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

Bovy, Jo; Hogg, David W., E-mail: jo.bovy@nyu.ed

2010-07-10

The velocity distribution of nearby stars ({approx}<100 pc) contains many overdensities or 'moving groups', clumps of comoving stars, that are inconsistent with the standard assumption of an axisymmetric, time-independent, and steady-state Galaxy. We study the age and metallicity properties of the low-velocity moving groups based on the reconstruction of the local velocity distribution in Paper I of this series. We perform stringent, conservative hypothesis testing to establish for each of these moving groups whether it could conceivably consist of a coeval population of stars. We conclude that they do not: the moving groups are neither trivially associated with their eponymousmore » open clusters nor with any other inhomogeneous star formation event. Concerning a possible dynamical origin of the moving groups, we test whether any of the moving groups has a higher or lower metallicity than the background population of thin disk stars, as would generically be the case if the moving groups are associated with resonances of the bar or spiral structure. We find clear evidence that the Hyades moving group has higher than average metallicity and weak evidence that the Sirius moving group has lower than average metallicity, which could indicate that these two groups are related to the inner Lindblad resonance of the spiral structure. Further, we find weak evidence that the Hercules moving group has higher than average metallicity, as would be the case if it is associated with the bar's outer Lindblad resonance. The Pleiades moving group shows no clear metallicity anomaly, arguing against a common dynamical origin for the Hyades and Pleiades groups. Overall, however, the moving groups are barely distinguishable from the background population of stars, raising the likelihood that the moving groups are associated with transient perturbations.« less

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

Ryu, C.; Boshier, M. G.

An integrated coherent matter wave circuit is a single device, analogous to an integrated optical circuit, in which coherent de Broglie waves are created and then launched into waveguides where they can be switched, divided, recombined, and detected as they propagate. Applications of such circuits include guided atom interferometers, atomtronic circuits, and precisely controlled delivery of atoms. We report experiments demonstrating integrated circuits for guided coherent matter waves. The circuit elements are created with the painted potential technique, a form of time-averaged optical dipole potential in which a rapidly moving, tightly focused laser beam exerts forces on atoms through theirmore » electric polarizability. Moreover, the source of coherent matter waves is a Bose–Einstein condensate (BEC). Finally, we launch BECs into painted waveguides that guide them around bends and form switches, phase coherent beamsplitters, and closed circuits. These are the basic elements that are needed to engineer arbitrarily complex matter wave circuitry.« less

Waste tyre pyrolysis: modelling of a moving bed reactor.

PubMed

Aylón, E; Fernández-Colino, A; Murillo, R; Grasa, G; Navarro, M V; García, T; Mastral, A M

2010-12-01

This paper describes the development of a new model for waste tyre pyrolysis in a moving bed reactor. This model comprises three different sub-models: a kinetic sub-model that predicts solid conversion in terms of reaction time and temperature, a heat transfer sub-model that calculates the temperature profile inside the particle and the energy flux from the surroundings to the tyre particles and, finally, a hydrodynamic model that predicts the solid flow pattern inside the reactor. These three sub-models have been integrated in order to develop a comprehensive reactor model. Experimental results were obtained in a continuous moving bed reactor and used to validate model predictions, with good approximation achieved between the experimental and simulated results. In addition, a parametric study of the model was carried out, which showed that tyre particle heating is clearly faster than average particle residence time inside the reactor. Therefore, this fast particle heating together with fast reaction kinetics enables total solid conversion to be achieved in this system in accordance with the predictive model. Copyright © 2010 Elsevier Ltd. All rights reserved.

National evaluation of obesity screening and treatment among veterans with and without mental health disorders.

PubMed

Littman, Alyson J; Damschroder, Laura J; Verchinina, Lilia; Lai, Zongshan; Kim, Hyungjin Myra; Hoerster, Katherine D; Klingaman, Elizabeth A; Goldberg, Richard W; Owen, Richard R; Goodrich, David E

2015-01-01

The objective was to determine whether obesity screening and weight management program participation and outcomes are equitable for individuals with serious mental illness (SMI) and depressive disorder (DD) compared to those without SMI/DD in Veterans Health Administration (VHA), the largest integrated US health system, which requires obesity screening and offers weight management to all in need. We used chart-reviewed, clinical and administrative VHA data from fiscal years 2010-2012 to estimate obesity screening and participation in the VHA's weight management program (MOVE!) across groups. Six- and 12-month weight changes in MOVE! participants were estimated using linear mixed models adjusted for confounders. Compared to individuals without SMI/DD, individuals with SMI or DD were less frequently screened for obesity (94%-94.7% vs. 95.7%) but had greater participation in MOVE! (10.1%-10.4% vs. 7.4%). MOVE! participants with SMI or DD lost approximately 1 lb less at 6 months. At 12 months, average weight loss for individuals with SMI or neither SMI/DD was comparable (-3.5 and -3.3 lb, respectively), but individuals with DD lost less weight (mean=-2.7 lb). Disparities in obesity screening and treatment outcomes across mental health diagnosis groups were modest. However, participation in MOVE! was low for every group, which limits population impact. Published by Elsevier Inc.

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.

On the Relationship between Solar Wind Speed, Earthward-Directed Coronal Mass Ejections, Geomagnetic Activity, and the Sunspot Cycle Using 12-Month Moving Averages

NASA Technical Reports Server (NTRS)

Wilson, Robert M.; Hathaway, David H.

2008-01-01

For 1996 .2006 (cycle 23), 12-month moving averages of the aa geomagnetic index strongly correlate (r = 0.92) with 12-month moving averages of solar wind speed, and 12-month moving averages of the number of coronal mass ejections (CMEs) (halo and partial halo events) strongly correlate (r = 0.87) with 12-month moving averages of sunspot number. In particular, the minimum (15.8, September/October 1997) and maximum (38.0, August 2003) values of the aa geomagnetic index occur simultaneously with the minimum (376 km/s) and maximum (547 km/s) solar wind speeds, both being strongly correlated with the following recurrent component (due to high-speed streams). The large peak of aa geomagnetic activity in cycle 23, the largest on record, spans the interval late 2002 to mid 2004 and is associated with a decreased number of halo and partial halo CMEs, whereas the smaller secondary peak of early 2005 seems to be associated with a slight rebound in the number of halo and partial halo CMEs. Based on the observed aaM during the declining portion of cycle 23, RM for cycle 24 is predicted to be larger than average, being about 168+/-60 (the 90% prediction interval), whereas based on the expected aam for cycle 24 (greater than or equal to 14.6), RM for cycle 24 should measure greater than or equal to 118+/-30, yielding an overlap of about 128+/-20.

Time-resolved distance determination by tryptophan fluorescence quenching: probing intermediates in membrane protein folding.

PubMed

Kleinschmidt, J H; Tamm, L K

1999-04-20

The mechanism of insertion and folding of an integral membrane protein has been investigated with the beta-barrel forming outer membrane protein A (OmpA) of Escherichia coli. This work describes a new approach to this problem by combining structural information obtained from tryptophan fluorescence quenching at different depths in the lipid bilayer with the kinetics of the refolding process. Experiments carried out over a temperature range between 2 and 40 degrees C allowed us to detect, trap, and characterize previously unidentified folding intermediates on the pathway of OmpA insertion and folding into lipid bilayers. Three membrane-bound intermediates were found in which the average distances of the Trps were 14-16, 10-11, and 0-5 A, respectively, from the bilayer center. The first folding intermediate is stable at 2 degrees C for at least 1 h. A second intermediate has been isolated at temperatures between 7 and 20 degrees C. The Trps move 4-5 A closer to the center of the bilayer at this stage. Subsequently, in an intermediate that is observable at 26-28 degrees C, the Trps move another 5-10 A closer to the center of the bilayer. The final (native) structure is observed at higher temperatures of refolding. In this structure, the Trps are located on average about 9-10 A from the bilayer center. Monitoring the evolution of Trp fluorescence quenching by a set of brominated lipids during refolding at various temperatures therefore allowed us to identify and characterize intermediate states in the folding process of an integral membrane protein.

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

PubMed

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

2014-01-01

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

Forecasting and prediction of scorpion sting cases in Biskra province, Algeria, using a seasonal autoregressive integrated moving average model.

PubMed

Selmane, Schehrazad; L'Hadj, Mohamed

2016-01-01

The aims of this study were to highlight some epidemiological aspects of scorpion envenomations, to analyse and interpret the available data for Biskra province, Algeria, and to develop a forecasting model for scorpion sting cases in Biskra province, which records the highest number of scorpion stings in Algeria. In addition to analysing the epidemiological profile of scorpion stings that occurred throughout the year 2013, we used the Box-Jenkins approach to fit a seasonal autoregressive integrated moving average (SARIMA) model to the monthly recorded scorpion sting cases in Biskra from 2000 to 2012. The epidemiological analysis revealed that scorpion stings were reported continuously throughout the year, with peaks in the summer months. The most affected age group was 15 to 49 years old, with a male predominance. The most prone human body areas were the upper and lower limbs. The majority of cases (95.9%) were classified as mild envenomations. The time series analysis showed that a (5,1,0)×(0,1,1) 12 SARIMA model offered the best fit to the scorpion sting surveillance data. This model was used to predict scorpion sting cases for the year 2013, and the fitted data showed considerable agreement with the actual data. SARIMA models are useful for monitoring scorpion sting cases, and provide an estimate of the variability to be expected in future scorpion sting cases. This knowledge is helpful in predicting whether an unusual situation is developing or not, and could therefore assist decision-makers in strengthening the province's prevention and control measures and in initiating rapid response measures.

Integrating Research and Practice: Distractions, Controversies, and Options for Moving Forward

ERIC Educational Resources Information Center

Gambrill, Eileen

2015-01-01

Integrating practice and research is vital in all helping professions in order to offer the most ethical, evidence-informed interventions to clients. This article describes some avoidable distractions that hinder integration, discusses controversies related to integration, and describes options for moving forward, including making wasted resources…

Edge Preserved Speckle Noise Reduction Using Integrated Fuzzy Filters

PubMed Central

Dewal, M. L.; Rohit, Manoj Kumar

2014-01-01

Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are studied and analyzed along with 26 denoising techniques. It is observed that geometric filter retains noise and, to address this issue, wiener filter is embedded into the geometric filter during iteration process. The performance of geometric-wiener filter is further enhanced using fuzzy filters and the proposed despeckling techniques are called integrated fuzzy filters. Fuzzy filters based on moving average and median value are employed in the integrated fuzzy filters. The performances of integrated fuzzy filters are tested on echocardiographic images and synthetic images in terms of image quality metrics. It is observed that the performance parameters are highest in case of integrated fuzzy filters in comparison to fuzzy and geometric-fuzzy filters. The clinical validation reveals that the output images obtained using geometric-wiener, integrated fuzzy, nonlocal means, and details preserving anisotropic diffusion filters are acceptable. The necessary finer details are retained in the denoised echocardiographic images. PMID:27437499

Temporal patterns and a disease forecasting model of dengue hemorrhagic fever in Jakarta based on 10 years of surveillance data.

PubMed

Sitepu, Monika S; Kaewkungwal, Jaranit; Luplerdlop, Nathanej; Soonthornworasiri, Ngamphol; Silawan, Tassanee; Poungsombat, Supawadee; Lawpoolsri, Saranath

2013-03-01

This study aimed to describe the temporal patterns of dengue transmission in Jakarta from 2001 to 2010, using data from the national surveillance system. The Box-Jenkins forecasting technique was used to develop a seasonal autoregressive integrated moving average (SARIMA) model for the study period and subsequently applied to forecast DHF incidence in 2011 in Jakarta Utara, Jakarta Pusat, Jakarta Barat, and the municipalities of Jakarta Province. Dengue incidence in 2011, based on the forecasting model was predicted to increase from the previous year.

Passenger Flow Forecasting Research for Airport Terminal Based on SARIMA Time Series Model

NASA Astrophysics Data System (ADS)

Li, Ziyu; Bi, Jun; Li, Zhiyin

2017-12-01

Based on the data of practical operating of Kunming Changshui International Airport during2016, this paper proposes Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict the passenger flow. This article not only considers the non-stationary and autocorrelation of the sequence, but also considers the daily periodicity of the sequence. The prediction results can accurately describe the change trend of airport passenger flow and provide scientific decision support for the optimal allocation of airport resources and optimization of departure process. The result shows that this model is applicable to the short-term prediction of airport terminal departure passenger traffic and the average error ranges from 1% to 3%. The difference between the predicted and the true values of passenger traffic flow is quite small, which indicates that the model has fairly good passenger traffic flow prediction ability.

Annual forest inventory estimates based on the moving average

Treesearch

Francis A. Roesch; James R. Steinman; Michael T. Thompson

2002-01-01

Three interpretations of the simple moving average estimator, as applied to the USDA Forest Service's annual forest inventory design, are presented. A corresponding approach to composite estimation over arbitrarily defined land areas and time intervals is given for each interpretation, under the assumption that the investigator is armed with only the spatial/...

78 FR 26879 - Medicare Program; Inpatient Rehabilitation Facility Prospective Payment System for Federal Fiscal...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-08

...: Centers for Medicare & Medicaid Services (CMS), HHS. ACTION: Proposed rule. SUMMARY: This proposed rule..., especially the teaching status adjustment factor. Therefore, we implemented a 3-year moving average approach... moving average to calculate the facility-level adjustment factors. For FY 2011, we issued a notice to...

The Choice of Spatial Interpolation Method Affects Research Conclusions

NASA Astrophysics Data System (ADS)

Eludoyin, A. O.; Ijisesan, O. S.; Eludoyin, O. M.

2017-12-01

Studies from developing countries using spatial interpolations in geographical information systems (GIS) are few and recent. Many of the studies have adopted interpolation procedures including kriging, moving average or Inverse Weighted Average (IDW) and nearest point without the necessary recourse to their uncertainties. This study compared the results of modelled representations of popular interpolation procedures from two commonly used GIS software (ILWIS and ArcGIS) at the Obafemi Awolowo University, Ile-Ife, Nigeria. Data used were concentrations of selected biochemical variables (BOD5, COD, SO4, NO3, pH, suspended and dissolved solids) in Ere stream at Ayepe-Olode, in the southwest Nigeria. Water samples were collected using a depth-integrated grab sampling approach at three locations (upstream, downstream and along a palm oil effluent discharge point in the stream); four stations were sited along each location (Figure 1). Data were first subjected to examination of their spatial distributions and associated variogram variables (nugget, sill and range), using the PAleontological STatistics (PAST3), before the mean values were interpolated in selected GIS software for the variables using each of kriging (simple), moving average and nearest point approaches. Further, the determined variogram variables were substituted with the default values in the selected software, and their results were compared. The study showed that the different point interpolation methods did not produce similar results. For example, whereas the values of conductivity was interpolated to vary as 120.1 - 219.5 µScm-1 with kriging interpolation, it varied as 105.6 - 220.0 µScm-1 and 135.0 - 173.9µScm-1 with nearest point and moving average interpolations, respectively (Figure 2). It also showed that whereas the computed variogram model produced the best fit lines (with least associated error value, Sserror) with Gaussian model, the Spherical model was assumed default for all the distributions in the software, such that the value of nugget was assumed as 0.00, when it was rarely so (Figure 3). The study concluded that interpolation procedures may affect decisions and conclusions on modelling inferences.

Watershed Regressions for Pesticides (WARP) for Predicting Annual Maximum and Annual Maximum Moving-Average Concentrations of Atrazine in Streams

USGS Publications Warehouse

Stone, Wesley W.; Gilliom, Robert J.; Crawford, Charles G.

2008-01-01

Regression models were developed for predicting annual maximum and selected annual maximum moving-average concentrations of atrazine in streams using the Watershed Regressions for Pesticides (WARP) methodology developed by the National Water-Quality Assessment Program (NAWQA) of the U.S. Geological Survey (USGS). The current effort builds on the original WARP models, which were based on the annual mean and selected percentiles of the annual frequency distribution of atrazine concentrations. Estimates of annual maximum and annual maximum moving-average concentrations for selected durations are needed to characterize the levels of atrazine and other pesticides for comparison to specific water-quality benchmarks for evaluation of potential concerns regarding human health or aquatic life. Separate regression models were derived for the annual maximum and annual maximum 21-day, 60-day, and 90-day moving-average concentrations. Development of the regression models used the same explanatory variables, transformations, model development data, model validation data, and regression methods as those used in the original development of WARP. The models accounted for 72 to 75 percent of the variability in the concentration statistics among the 112 sampling sites used for model development. Predicted concentration statistics from the four models were within a factor of 10 of the observed concentration statistics for most of the model development and validation sites. Overall, performance of the models for the development and validation sites supports the application of the WARP models for predicting annual maximum and selected annual maximum moving-average atrazine concentration in streams and provides a framework to interpret the predictions in terms of uncertainty. For streams with inadequate direct measurements of atrazine concentrations, the WARP model predictions for the annual maximum and the annual maximum moving-average atrazine concentrations can be used to characterize the probable levels of atrazine for comparison to specific water-quality benchmarks. Sites with a high probability of exceeding a benchmark for human health or aquatic life can be prioritized for monitoring.

Weather variability, tides, and Barmah Forest virus disease in the Gladstone region, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Nicholls, Neville; Mackenzie, John S; McMichael, Anthony J; Dale, Pat; Tong, Shilu

2006-05-01

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (b=0.15, p-value<0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (b=-1.03, p-value=0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats.

PubMed

Buckingham-Jeffery, Elizabeth; Morbey, Roger; House, Thomas; Elliot, Alex J; Harcourt, Sally; Smith, Gillian E

2017-05-19

As service provision and patient behaviour varies by day, healthcare data used for public health surveillance can exhibit large day of the week effects. These regular effects are further complicated by the impact of public holidays. Real-time syndromic surveillance requires the daily analysis of a range of healthcare data sources, including family doctor consultations (called general practitioners, or GPs, in the UK). Failure to adjust for such reporting biases during analysis of syndromic GP surveillance data could lead to misinterpretations including false alarms or delays in the detection of outbreaks. The simplest smoothing method to remove a day of the week effect from daily time series data is a 7-day moving average. Public Health England developed the working day moving average in an attempt also to remove public holiday effects from daily GP data. However, neither of these methods adequately account for the combination of day of the week and public holiday effects. The extended working day moving average was developed. This is a further data-driven method for adding a smooth trend curve to a time series graph of daily healthcare data, that aims to take both public holiday and day of the week effects into account. It is based on the assumption that the number of people seeking healthcare services is a combination of illness levels/severity and the ability or desire of patients to seek healthcare each day. The extended working day moving average was compared to the seven-day and working day moving averages through application to data from two syndromic indicators from the GP in-hours syndromic surveillance system managed by Public Health England. The extended working day moving average successfully smoothed the syndromic healthcare data by taking into account the combined day of the week and public holiday effects. In comparison, the seven-day and working day moving averages were unable to account for all these effects, which led to misleading smoothing curves. The results from this study make it possible to identify trends and unusual activity in syndromic surveillance data from GP services in real-time independently of the effects caused by day of the week and public holidays, thereby improving the public health action resulting from the analysis of these data.

Moving in the Right Direction: Helping Children Cope with a Relocation

ERIC Educational Resources Information Center

Kruse, Tricia

2012-01-01

According to national figures, 37.1 million people moved in 2009 (U.S. Census Bureau, 2010). In fact, the average American will move 11.7 times in their lifetime. Why are Americans moving so much? There are a variety of reasons. Regardless of the reason, moving is a common experience for children. If one looks at the developmental characteristics…

Geophysical Factor Resolving of Rainfall Mechanism for Super Typhoons by Using Multiple Spatiotemporal Components Analysis

NASA Astrophysics Data System (ADS)

Huang, Chien-Lin; Hsu, Nien-Sheng

2016-04-01

This study develops a novel methodology to resolve the geophysical cause of typhoon-induced rainfall considering diverse dynamic co-evolution at multiple spatiotemporal components. The multi-order hidden patterns of complex hydrological process in chaos are detected to understand the fundamental laws of rainfall mechanism. The discovered spatiotemporal features are utilized to develop a state-of-the-art descriptive statistical model for mechanism validation, modeling and further prediction during typhoons. The time series of hourly typhoon precipitation from different types of moving track, atmospheric field and landforms are respectively precede the signal analytical process to qualify each type of rainfall cause and to quantify the corresponding affected degree based on the measured geophysical atmospheric-hydrological variables. This study applies the developed methodology in Taiwan Island which is constituted by complex diverse landform formation. The identified driving-causes include: (1) cloud height to ground surface; (2) co-movement effect induced by typhoon wind field with monsoon; (3) stem capacity; (4) interaction between typhoon rain band and terrain; (5) structural intensity variance of typhoon; and (6) integrated cloudy density of rain band. Results show that: (1) for the central maximum wind speed exceeding 51 m/sec, Causes (1) and (3) are the primary ones to generate rainfall; (2) for the typhoon moving toward the direction of 155° to 175°, Cause (2) is the primary one; (3) for the direction of 90° to 155°, Cause (4) is the primary one; (4) for the typhoon passing through mountain chain which above 3500 m, Cause (5) is the primary one; and (5) for the moving speed lower than 18 km/hr, Cause (6) is the primary one. Besides, the multiple geophysical component-based precipitation modeling can achieve 81% of average accuracy and 0.732 of average correlation coefficient (CC) within average 46 hours of duration, that improve their predictability.

Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach

NASA Astrophysics Data System (ADS)

Moeeni, Hamid; Bonakdari, Hossein; Ebtehaj, Isa

2017-03-01

Forecasting reservoir inflow is one of the most important components of water resources and hydroelectric systems operation management. Seasonal autoregressive integrated moving average (SARIMA) models have been frequently used for predicting river flow. SARIMA models are linear and do not consider the random component of statistical data. To overcome this shortcoming, monthly inflow is predicted in this study based on a combination of seasonal autoregressive integrated moving average (SARIMA) and gene expression programming (GEP) models, which is a new hybrid method (SARIMA-GEP). To this end, a four-step process is employed. First, the monthly inflow datasets are pre-processed. Second, the datasets are modelled linearly with SARIMA and in the third stage, the non-linearity of residual series caused by linear modelling is evaluated. After confirming the non-linearity, the residuals are modelled in the fourth step using a gene expression programming (GEP) method. The proposed hybrid model is employed to predict the monthly inflow to the Jamishan Dam in west Iran. Thirty years' worth of site measurements of monthly reservoir dam inflow with extreme seasonal variations are used. The results of this hybrid model (SARIMA-GEP) are compared with SARIMA, GEP, artificial neural network (ANN) and SARIMA-ANN models. The results indicate that the SARIMA-GEP model ( R 2=78.8, VAF =78.8, RMSE =0.89, MAPE =43.4, CRM =0.053) outperforms SARIMA and GEP and SARIMA-ANN ( R 2=68.3, VAF =66.4, RMSE =1.12, MAPE =56.6, CRM =0.032) displays better performance than the SARIMA and ANN models. A comparison of the two hybrid models indicates the superiority of SARIMA-GEP over the SARIMA-ANN model.

Effect of environmental factors on Internet searches related to sinusitis.

PubMed

Willson, Thomas J; Lospinoso, Joshua; Weitzel, Erik K; McMains, Kevin C

2015-11-01

Sinusitis significantly affects the population of the United States, exacting direct cost and lost productivity. Patients are likely to search the Internet for information related to their health before seeking care by a healthcare professional. Utilizing data generated from these searches may serve as an epidemiologic surrogate. A retrospective time series analysis was performed. Google search trend data from the Dallas-Fort Worth metro region for the years 2012 and 2013 were collected from www.google.com/trends for terms related to sinusitis based on literature outlining the most important symptoms for diagnosis. Additional terms were selected based on common English language terms used to describe the disease. Twelve months of data from the same time period and location for common pollutants (nitrogen dioxide, ozone, sulfur dioxide, and particulates), pollen and mold counts, and influenza-like illness were also collected. Statistical analysis was performed using Pearson correlation coefficients, and potential search activity predictors were assessed using autoregressive integrated moving average. Pearson correlation was strongest between the terms congestion and influenza-like illness (r=0.615), and sinus and influenza-like illness (r=0.534) and nitrogen dioxide (r=0.487). Autoregressive integrated moving average analysis revealed ozone, influenza-like illness, and nitrogen dioxide levels to be potential predictors for sinus pressure searches, with estimates of 0.118, 0.349, and 0.438, respectively. Nitrogen dioxide was also a potential predictor for the terms congestion and sinus, with estimates of 0.191 and 0.272, respectively. Google search activity for related terms follow the pattern of seasonal influenza-like illness and nitrogen dioxide. These data highlight the epidemiologic potential of this novel surveillance method. NA. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

Forecasting and prediction of scorpion sting cases in Biskra province, Algeria, using a seasonal autoregressive integrated moving average model

PubMed Central

2016-01-01

OBJECTIVES The aims of this study were to highlight some epidemiological aspects of scorpion envenomations, to analyse and interpret the available data for Biskra province, Algeria, and to develop a forecasting model for scorpion sting cases in Biskra province, which records the highest number of scorpion stings in Algeria. METHODS In addition to analysing the epidemiological profile of scorpion stings that occurred throughout the year 2013, we used the Box-Jenkins approach to fit a seasonal autoregressive integrated moving average (SARIMA) model to the monthly recorded scorpion sting cases in Biskra from 2000 to 2012. RESULTS The epidemiological analysis revealed that scorpion stings were reported continuously throughout the year, with peaks in the summer months. The most affected age group was 15 to 49 years old, with a male predominance. The most prone human body areas were the upper and lower limbs. The majority of cases (95.9%) were classified as mild envenomations. The time series analysis showed that a (5,1,0)×(0,1,1)12 SARIMA model offered the best fit to the scorpion sting surveillance data. This model was used to predict scorpion sting cases for the year 2013, and the fitted data showed considerable agreement with the actual data. CONCLUSIONS SARIMA models are useful for monitoring scorpion sting cases, and provide an estimate of the variability to be expected in future scorpion sting cases. This knowledge is helpful in predicting whether an unusual situation is developing or not, and could therefore assist decision-makers in strengthening the province’s prevention and control measures and in initiating rapid response measures. PMID:27866407

An autoregressive integrated moving average model for short-term prediction of hepatitis C virus seropositivity among male volunteer blood donors in Karachi, Pakistan

PubMed Central

Akhtar, Saeed; Rozi, Shafquat

2009-01-01

AIM: To identify the stochastic autoregressive integrated moving average (ARIMA) model for short term forecasting of hepatitis C virus (HCV) seropositivity among volunteer blood donors in Karachi, Pakistan. METHODS: Ninety-six months (1998-2005) data on HCV seropositive cases (1000-1 × month-1) among male volunteer blood donors tested at four major blood banks in Karachi, Pakistan were subjected to ARIMA modeling. Subsequently, a fitted ARIMA model was used to forecast HCV seropositive donors for 91-96 mo to contrast with observed series of the same months. To assess the forecast accuracy, the mean absolute error rate (%) between the observed and predicted HCV seroprevalence was calculated. Finally, a fitted ARIMA model was used for short-term forecasts beyond the observed series. RESULTS: The goodness-of-fit test of the optimum ARIMA (2,1,7) model showed non-significant autocorrelations in the residuals of the model. The forecasts by ARIMA for 91-96 mo closely followed the pattern of observed series for the same months, with mean monthly absolute forecast errors (%) over 6 mo of 6.5%. The short-term forecasts beyond the observed series adequately captured the pattern in the data and showed increasing tendency of HCV seropositivity with a mean ± SD HCV seroprevalence (1000-1 × month-1) of 24.3 ± 1.4 over the forecast interval. CONCLUSION: To curtail HCV spread, public health authorities need to educate communities and health care providers about HCV transmission routes based on known HCV epidemiology in Pakistan and its neighboring countries. Future research may focus on factors associated with hyperendemic levels of HCV infection. PMID:19340903

Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China

PubMed Central

Liang, Hao; Gao, Lian; Liang, Bingyu; Huang, Jiegang; Zang, Ning; Liao, Yanyan; Yu, Jun; Lai, Jingzhen; Qin, Fengxiang; Su, Jinming; Ye, Li; Chen, Hui

2016-01-01

Background Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease. Methods The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models. Results The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models. Conclusions The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County. PMID:27258555

Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model.

PubMed

Zhang, Xujun; Pang, Yuanyuan; Cui, Mengjing; Stallones, Lorann; Xiang, Huiyun

2015-02-01

Road traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China. A seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung-Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012. The seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were -483.679, -475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung-Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012. This study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China. Copyright © 2015 Elsevier Inc. All rights reserved.

Application of seasonal auto-regressive integrated moving average model in forecasting the incidence of hand-foot-mouth disease in Wuhan, China.

PubMed

Peng, Ying; Yu, Bin; Wang, Peng; Kong, De-Guang; Chen, Bang-Hua; Yang, Xiao-Bing

2017-12-01

Outbreaks of hand-foot-mouth disease (HFMD) have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average (ARIMA) model for time series analysis was designed in this study. Eighty-four-month (from January 2009 to December 2015) retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination (R 2 ), normalized Bayesian Information Criterion (BIC) and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as (1,0,1)(0,1,1) 12 , with the largest coefficient of determination (R 2 =0.743) and lowest normalized BIC (BIC=3.645) value. The residuals of the model also showed non-significant autocorrelations (P Box-Ljung (Q) =0.299). The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.

A comparison of several techniques for imputing tree level data

Treesearch

David Gartner

2002-01-01

As Forest Inventory and Analysis (FIA) changes from periodic surveys to the multipanel annual survey, new analytical methods become available. The current official statistic is the moving average. One alternative is an updated moving average. Several methods of updating plot per acre volume have been discussed previously. However, these methods may not be appropriate...

New Students' Peer Integration and Exposure to Deviant Peers: Spurious Effects of School Moves?

ERIC Educational Resources Information Center

Siennick, Sonja E.; Widdowson, Alex O.; Ragan, Daniel T.

2017-01-01

School moves during adolescence predict lower peer integration and higher exposure to delinquent peers. Yet mobility and peer problems have several common correlates, so differences in movers' and non-movers' social adjustment may be due to selection rather than causal effects of school moves. Drawing on survey and social network data from a…

Computational aeroelasticity using a pressure-based solver

NASA Astrophysics Data System (ADS)

Kamakoti, Ramji

A computational methodology for performing fluid-structure interaction computations for three-dimensional elastic wing geometries is presented. The flow solver used is based on an unsteady Reynolds-Averaged Navier-Stokes (RANS) model. A well validated k-ε turbulence model with wall function treatment for near wall region was used to perform turbulent flow calculations. Relative merits of alternative flow solvers were investigated. The predictor-corrector-based Pressure Implicit Splitting of Operators (PISO) algorithm was found to be computationally economic for unsteady flow computations. Wing structure was modeled using Bernoulli-Euler beam theory. A fully implicit time-marching scheme (using the Newmark integration method) was used to integrate the equations of motion for structure. Bilinear interpolation and linear extrapolation techniques were used to transfer necessary information between fluid and structure solvers. Geometry deformation was accounted for by using a moving boundary module. The moving grid capability was based on a master/slave concept and transfinite interpolation techniques. Since computations were performed on a moving mesh system, the geometric conservation law must be preserved. This is achieved by appropriately evaluating the Jacobian values associated with each cell. Accurate computation of contravariant velocities for unsteady flows using the momentum interpolation method on collocated, curvilinear grids was also addressed. Flutter computations were performed for the AGARD 445.6 wing at subsonic, transonic and supersonic Mach numbers. Unsteady computations were performed at various dynamic pressures to predict the flutter boundary. Results showed favorable agreement of experiment and previous numerical results. The computational methodology exhibited capabilities to predict both qualitative and quantitative features of aeroelasticity.

Steady-State Pursuit Is Driven by Object Motion Rather Than the Vector Average of Local Motions

NASA Technical Reports Server (NTRS)

Stone, Leland S.; Beutter, B. R.; Lorenceau, J. D.; Ahumada, Al (Technical Monitor)

1997-01-01

We have previously shown that humans can pursue the motion of objects whose trajectories can be recovered only by spatio-temporal integration of local motion signals. We now explore the integration rule used to derive the target-motion signal driving pursuit. We measured the pursuit response of 4 observers (2 naive) to the motion of a line-figure diamond viewed through two vertical bar apertures (0.2 cd/square m). The comers were always occluded so that only four line segments (93 cd/square m) were visible behind the occluding foreground (38 cd/square m). The diamond was flattened (40 & 140 degree vertex angles) such that vector averaging of the local normal motions and vertical integration (e.g. IOC) yield very I or different predictions, analogous to using a Type II plaid. The diamond moved along Lissajous-figure trajectories (Ax = Ay = 2 degrees; TFx = 0.8 Hz; TFy = 0.4 Hz). We presented only 1.25 cycles and used 6 different randomly interleaved initial relative phases to minimize the role of predictive strategies. Observers were instructed to track the diamond and reported that its motion was always coherent (unlike type II plaids). Saccade-free portions of the horizontal and vertical eye-position traces sampled at 240 Hz were fit by separate sinusoids. Pursuit gain with respect to the diamond averaged 0.7 across subjects and directions. The ratio of the mean vertical to horizontal amplitude of the pursuit response was 1.7 +/- 0.7 averaged across subjects (1SD). This is close to the prediction of 1.0 from vertical motion-integration rules, but far from 7.7 predicted by vector averaging and infinity predicted by segment- or terminator-tracking strategies. Because there is no retinal motion which directly corresponds to the diamond's motion, steady-state pursuit of our "virtual" diamond is not closed-loop in the traditional sense. Thus, accurate pursuit is unlikely to result simply from local retinal negative feedback. We conclude that the signal driving steady-state pursuit is not the vector average of local motion signals, but rather a more vertical estimate of object motion, derived in extrastriate cortical areas beyond V1, perhaps NIT or MST.

Quantifying rapid changes in cardiovascular state with a moving ensemble average.

PubMed

Cieslak, Matthew; Ryan, William S; Babenko, Viktoriya; Erro, Hannah; Rathbun, Zoe M; Meiring, Wendy; Kelsey, Robert M; Blascovich, Jim; Grafton, Scott T

2018-04-01

MEAP, the moving ensemble analysis pipeline, is a new open-source tool designed to perform multisubject preprocessing and analysis of cardiovascular data, including electrocardiogram (ECG), impedance cardiogram (ICG), and continuous blood pressure (BP). In addition to traditional ensemble averaging, MEAP implements a moving ensemble averaging method that allows for the continuous estimation of indices related to cardiovascular state, including cardiac output, preejection period, heart rate variability, and total peripheral resistance, among others. Here, we define the moving ensemble technique mathematically, highlighting its differences from fixed-window ensemble averaging. We describe MEAP's interface and features for signal processing, artifact correction, and cardiovascular-based fMRI analysis. We demonstrate the accuracy of MEAP's novel B point detection algorithm on a large collection of hand-labeled ICG waveforms. As a proof of concept, two subjects completed a series of four physical and cognitive tasks (cold pressor, Valsalva maneuver, video game, random dot kinetogram) on 3 separate days while ECG, ICG, and BP were recorded. Critically, the moving ensemble method reliably captures the rapid cyclical cardiovascular changes related to the baroreflex during the Valsalva maneuver and the classic cold pressor response. Cardiovascular measures were seen to vary considerably within repetitions of the same cognitive task for each individual, suggesting that a carefully designed paradigm could be used to capture fast-acting event-related changes in cardiovascular state. © 2017 Society for Psychophysiological Research.

Time-Series Analysis: Assessing the Effects of Multiple Educational Interventions in a Small-Enrollment Course

NASA Astrophysics Data System (ADS)

Warren, Aaron R.

2009-11-01

Time-series designs are an alternative to pretest-posttest methods that are able to identify and measure the impacts of multiple educational interventions, even for small student populations. Here, we use an instrument employing standard multiple-choice conceptual questions to collect data from students at regular intervals. The questions are modified by asking students to distribute 100 Confidence Points among the options in order to indicate the perceived likelihood of each answer option being the correct one. Tracking the class-averaged ratings for each option produces a set of time-series. ARIMA (autoregressive integrated moving average) analysis is then used to test for, and measure, changes in each series. In particular, it is possible to discern which educational interventions produce significant changes in class performance. Cluster analysis can also identify groups of students whose ratings evolve in similar ways. A brief overview of our methods and an example are presented.

Numerical Investigation of a Model Scramjet Combustor Using DDES

NASA Astrophysics Data System (ADS)

Shin, Junsu; Sung, Hong-Gye

2017-04-01

Non-reactive flows moving through a model scramjet were investigated using a delayed detached eddy simulation (DDES), which is a hybrid scheme combining Reynolds averaged Navier-Stokes scheme and a large eddy simulation. The three dimensional Navier-Stokes equations were solved numerically on a structural grid using finite volume methods. An in-house was developed. This code used a monotonic upstream-centered scheme for conservation laws (MUSCL) with an advection upstream splitting method by pressure weight function (AUSMPW+) for space. In addition, a 4th order Runge-Kutta scheme was used with preconditioning for time integration. The geometries and boundary conditions of a scramjet combustor operated by DLR, a German aerospace center, were considered. The profiles of the lower wall pressure and axial velocity obtained from a time-averaged solution were compared with experimental results. Also, the mixing efficiency and total pressure recovery factor were provided in order to inspect the performance of the combustor.

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…

Using Baidu Search Index to Predict Dengue Outbreak in China

NASA Astrophysics Data System (ADS)

Liu, Kangkang; Wang, Tao; Yang, Zhicong; Huang, Xiaodong; Milinovich, Gabriel J.; Lu, Yi; Jing, Qinlong; Xia, Yao; Zhao, Zhengyang; Yang, Yang; Tong, Shilu; Hu, Wenbiao; Lu, Jiahai

2016-12-01

This study identified the possible threshold to predict dengue fever (DF) outbreaks using Baidu Search Index (BSI). Time-series classification and regression tree models based on BSI were used to develop a predictive model for DF outbreak in Guangzhou and Zhongshan, China. In the regression tree models, the mean autochthonous DF incidence rate increased approximately 30-fold in Guangzhou when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 382. When the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 91.8, there was approximately 9-fold increase of the mean autochthonous DF incidence rate in Zhongshan. In the classification tree models, the results showed that when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 99.3, there was 89.28% chance of DF outbreak in Guangzhou, while, in Zhongshan, when the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 68.1, the chance of DF outbreak rose up to 100%. The study indicated that less cost internet-based surveillance systems can be the valuable complement to traditional DF surveillance in China.

Comparison between wavelet transform and moving average as filter method of MODIS imagery to recognize paddy cropping pattern in West Java

NASA Astrophysics Data System (ADS)

Dwi Nugroho, Kreshna; Pebrianto, Singgih; Arif Fatoni, Muhammad; Fatikhunnada, Alvin; Liyantono; Setiawan, Yudi

2017-01-01

Information on the area and spatial distribution of paddy field are needed to support sustainable agricultural and food security program. Mapping or distribution of cropping pattern paddy field is important to obtain sustainability paddy field area. It can be done by direct observation and remote sensing method. This paper discusses remote sensing for paddy field monitoring based on MODIS time series data. In time series MODIS data, difficult to direct classified of data, because of temporal noise. Therefore wavelet transform and moving average are needed as filter methods. The Objective of this study is to recognize paddy cropping pattern with wavelet transform and moving average in West Java using MODIS imagery (MOD13Q1) from 2001 to 2015 then compared between both of methods. The result showed the spatial distribution almost have the same cropping pattern. The accuracy of wavelet transform (75.5%) is higher than moving average (70.5%). Both methods showed that the majority of the cropping pattern in West Java have pattern paddy-fallow-paddy-fallow with various time planting. The difference of the planting schedule was occurs caused by the availability of irrigation water.

Assessing air quality in Aksaray with time series analysis

NASA Astrophysics Data System (ADS)

Kadilar, Gamze Özel; Kadilar, Cem

2017-04-01

Sulphur dioxide (SO2) is a major air pollutant caused by the dominant usage of diesel, petrol and fuels by vehicles and industries. One of the most air-polluted city in Turkey is Aksaray. Hence, in this study, the level of SO2 is analyzed in Aksaray based on the database monitored at air quality monitoring station of Turkey. Seasonal Autoregressive Integrated Moving Average (SARIMA) approach is used to forecast the level of SO2 air quality parameter. The results indicate that the seasonal ARIMA model provides reliable and satisfactory predictions for the air quality parameters and expected to be an alternative tool for practical assessment and justification.

Capillary Electrophoresis Sensitivity Enhancement Based on Adaptive Moving Average Method.

PubMed

Drevinskas, Tomas; Telksnys, Laimutis; Maruška, Audrius; Gorbatsova, Jelena; Kaljurand, Mihkel

2018-06-05

In the present work, we demonstrate a novel approach to improve the sensitivity of the "out of lab" portable capillary electrophoretic measurements. Nowadays, many signal enhancement methods are (i) underused (nonoptimal), (ii) overused (distorts the data), or (iii) inapplicable in field-portable instrumentation because of a lack of computational power. The described innovative migration velocity-adaptive moving average method uses an optimal averaging window size and can be easily implemented with a microcontroller. The contactless conductivity detection was used as a model for the development of a signal processing method and the demonstration of its impact on the sensitivity. The frequency characteristics of the recorded electropherograms and peaks were clarified. Higher electrophoretic mobility analytes exhibit higher-frequency peaks, whereas lower electrophoretic mobility analytes exhibit lower-frequency peaks. On the basis of the obtained data, a migration velocity-adaptive moving average algorithm was created, adapted, and programmed into capillary electrophoresis data-processing software. Employing the developed algorithm, each data point is processed depending on a certain migration time of the analyte. Because of the implemented migration velocity-adaptive moving average method, the signal-to-noise ratio improved up to 11 times for sampling frequency of 4.6 Hz and up to 22 times for sampling frequency of 25 Hz. This paper could potentially be used as a methodological guideline for the development of new smoothing algorithms that require adaptive conditions in capillary electrophoresis and other separation methods.

An improved moving average technical trading rule

NASA Astrophysics Data System (ADS)

Papailias, Fotis; Thomakos, Dimitrios D.

2015-06-01

This paper proposes a modified version of the widely used price and moving average cross-over trading strategies. The suggested approach (presented in its 'long only' version) is a combination of cross-over 'buy' signals and a dynamic threshold value which acts as a dynamic trailing stop. The trading behaviour and performance from this modified strategy are different from the standard approach with results showing that, on average, the proposed modification increases the cumulative return and the Sharpe ratio of the investor while exhibiting smaller maximum drawdown and smaller drawdown duration than the standard strategy.

Human factors considerations for integrating traffic information on airport moving maps.

DOT National Transportation Integrated Search

2011-05-01

The purpose of this research effort was to identify human factors considerations in the integration of traffic information and surface indications and alerts for runway status on airport moving maps for flight deck displays. The information is primar...

Ultrafast optical ranging using microresonator soliton frequency combs

NASA Astrophysics Data System (ADS)

Trocha, P.; Karpov, M.; Ganin, D.; Pfeiffer, M. H. P.; Kordts, A.; Wolf, S.; Krockenberger, J.; Marin-Palomo, P.; Weimann, C.; Randel, S.; Freude, W.; Kippenberg, T. J.; Koos, C.

2018-02-01

Light detection and ranging is widely used in science and industry. Over the past decade, optical frequency combs were shown to offer advantages in optical ranging, enabling fast distance acquisition with high accuracy. Driven by emerging high-volume applications such as industrial sensing, drone navigation, or autonomous driving, there is now a growing demand for compact ranging systems. Here, we show that soliton Kerr comb generation in integrated silicon nitride microresonators provides a route to high-performance chip-scale ranging systems. We demonstrate dual-comb distance measurements with Allan deviations down to 12 nanometers at averaging times of 13 microseconds along with ultrafast ranging at acquisition rates of 100 megahertz, allowing for in-flight sampling of gun projectiles moving at 150 meters per second. Combining integrated soliton-comb ranging systems with chip-scale nanophotonic phased arrays could enable compact ultrafast ranging systems for emerging mass applications.

Mobility in hospital work: towards a pervasive computing hospital environment.

PubMed

Morán, Elisa B; Tentori, Monica; González, Víctor M; Favela, Jesus; Martínez-Garcia, Ana I

2007-01-01

Handheld computers are increasingly being used by hospital workers. With the integration of wireless networks into hospital information systems, handheld computers can provide the basis for a pervasive computing hospital environment; to develop this designers need empirical information to understand how hospital workers interact with information while moving around. To characterise the medical phenomena we report the results of a workplace study conducted in a hospital. We found that individuals spend about half of their time at their base location, where most of their interactions occur. On average, our informants spent 23% of their time performing information management tasks, followed by coordination (17.08%), clinical case assessment (15.35%) and direct patient care (12.6%). We discuss how our results offer insights for the design of pervasive computing technology, and directions for further research and development in this field such as transferring information between heterogeneous devices and integration of the physical and digital domains.

Ambient temperature and biomarkers of heart failure: a repeated measures analysis.

PubMed

Wilker, Elissa H; Yeh, Gloria; Wellenius, Gregory A; Davis, Roger B; Phillips, Russell S; Mittleman, Murray A

2012-08-01

Extreme temperatures have been associated with hospitalization and death among individuals with heart failure, but few studies have explored the underlying mechanisms. We hypothesized that outdoor temperature in the Boston, Massachusetts, area (1- to 4-day moving averages) would be associated with higher levels of biomarkers of inflammation and myocyte injury in a repeated-measures study of individuals with stable heart failure. We analyzed data from a completed clinical trial that randomized 100 patients to 12 weeks of tai chi classes or to time-matched education control. B-type natriuretic peptide (BNP), C-reactive protein (CRP), and tumor necrosis factor (TNF) were measured at baseline, 6 weeks, and 12 weeks. Endothelin-1 was measured at baseline and 12 weeks. We used fixed effects models to evaluate associations with measures of temperature that were adjusted for time-varying covariates. Higher apparent temperature was associated with higher levels of BNP beginning with 2-day moving averages and reached statistical significance for 3- and 4-day moving averages. CRP results followed a similar pattern but were delayed by 1 day. A 5°C change in 3- and 4-day moving averages of apparent temperature was associated with 11.3% [95% confidence interval (CI): 1.1, 22.5; p = 0.03) and 11.4% (95% CI: 1.2, 22.5; p = 0.03) higher BNP. A 5°C change in the 4-day moving average of apparent temperature was associated with 21.6% (95% CI: 2.5, 44.2; p = 0.03) higher CRP. No clear associations with TNF or endothelin-1 were observed. Among patients undergoing treatment for heart failure, we observed positive associations between temperature and both BNP and CRP-predictors of heart failure prognosis and severity.

SM91: Observations of interchange between acceleration and thermalization processes in auroral electrons

NASA Technical Reports Server (NTRS)

Pongratz, M.

1972-01-01

Results from a Nike-Tomahawk sounding rocket flight launched from Fort Churchill are presented. The rocket was launched into a breakup aurora at magnetic local midnight on 21 March 1968. The rocket was instrumented to measure electrons with an electrostatic analyzer electron spectrometer which made 29 measurements in the energy interval 0.5 KeV to 30 KeV. Complete energy spectra were obtained at a rate of 10/sec. Pitch angle information is presented via 3 computed average per rocket spin. The dumped electron average corresponds to averages over electrons moving nearly parallel to the B vector. The mirroring electron average corresponds to averages over electrons moving nearly perpendicular to the B vector. The average was also computed over the entire downward hemisphere (the precipitated electron average). The observations were obtained in an altitude range of 10 km at 230 km altitude.

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.

Noise is the new signal: Moving beyond zeroth-order geomorphology (Invited)

NASA Astrophysics Data System (ADS)

Jerolmack, D. J.

2010-12-01

The last several decades have witnessed a rapid growth in our understanding of landscape evolution, led by the development of geomorphic transport laws - time- and space-averaged equations relating mass flux to some physical process(es). In statistical mechanics this approach is called mean field theory (MFT), in which complex many-body interactions are replaced with an external field that represents the average effect of those interactions. Because MFT neglects all fluctuations around the mean, it has been described as a zeroth-order fluctuation model. The mean field approach to geomorphology has enabled the development of landscape evolution models, and led to a fundamental understanding of many landform patterns. Recent research, however, has highlighted two limitations of MFT: (1) The integral (averaging) time and space scales in geomorphic systems are sometimes poorly defined and often quite large, placing the mean field approximation on uncertain footing, and; (2) In systems exhibiting fractal behavior, an integral scale does not exist - e.g., properties like mass flux are scale-dependent. In both cases, fluctuations in sediment transport are non-negligible over the scales of interest. In this talk I will synthesize recent experimental and theoretical work that confronts these limitations. Discrete element models of fluid and grain interactions show promise for elucidating transport mechanics and pattern-forming instabilities, but require detailed knowledge of micro-scale processes and are computationally expensive. An alternative approach is to begin with a reasonable MFT, and then add higher-order terms that capture the statistical dynamics of fluctuations. In either case, moving beyond zeroth-order geomorphology requires a careful examination of the origins and structure of transport “noise”. I will attempt to show how studying the signal in noise can both reveal interesting new physics, and also help to formalize the applicability of geomorphic transport laws. Flooding on an experimental alluvial fan. Intensity is related to the cumulative amount of time flow has visited an area of the fan over the experiment. Dark areas represent an emergent channel network resulting from stochastic migration of river channels.

Weather Variability, Tides, and Barmah Forest Virus Disease in the Gladstone Region, Australia

PubMed Central

Naish, Suchithra; Hu, Wenbiao; Nicholls, Neville; Mackenzie, John S.; McMichael, Anthony J.; Dale, Pat; Tong, Shilu

2006-01-01

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention. PMID:16675420

Neural net forecasting for geomagnetic activity

NASA Technical Reports Server (NTRS)

Hernandez, J. V.; Tajima, T.; Horton, W.

1993-01-01

We use neural nets to construct nonlinear models to forecast the AL index given solar wind and interplanetary magnetic field (IMF) data. We follow two approaches: (1) the state space reconstruction approach, which is a nonlinear generalization of autoregressive-moving average models (ARMA) and (2) the nonlinear filter approach, which reduces to a moving average model (MA) in the linear limit. The database used here is that of Bargatze et al. (1985).

Queues with Choice via Delay Differential Equations

NASA Astrophysics Data System (ADS)

Pender, Jamol; Rand, Richard H.; Wesson, Elizabeth

Delay or queue length information has the potential to influence the decision of a customer to join a queue. Thus, it is imperative for managers of queueing systems to understand how the information that they provide will affect the performance of the system. To this end, we construct and analyze two two-dimensional deterministic fluid models that incorporate customer choice behavior based on delayed queue length information. In the first fluid model, customers join each queue according to a Multinomial Logit Model, however, the queue length information the customer receives is delayed by a constant Δ. We show that the delay can cause oscillations or asynchronous behavior in the model based on the value of Δ. In the second model, customers receive information about the queue length through a moving average of the queue length. Although it has been shown empirically that giving patients moving average information causes oscillations and asynchronous behavior to occur in U.S. hospitals, we analytically and mathematically show for the first time that the moving average fluid model can exhibit oscillations and determine their dependence on the moving average window. Thus, our analysis provides new insight on how operators of service systems should report queue length information to customers and how delayed information can produce unwanted system dynamics.

Let’s move our health! The experience of 40 physical activity motivational workshops

PubMed

Bouté, Catherine; Cailliez, Elisabeth; D Hour, Alain; Goxe, Didier; Gusto, Gaëlle; Copin, Nane; Lantieri, Olivier

2016-10-19

Aims: To set up physical activity promotion workshops in health centres to help people with a sedentary lifestyle achieve an adequate level of physical activity. Methods: This health programme, called ‘Bougeons Notre Santé’ (Let’s move our health) has been implemented since 2006 by four health centres in the Pays de la Loire region, in France. This article describes implementation of the programme, its feasibility, how it can be integrated into a global preventive approach and its outcomes on promoting more physical activity. The “Let’s move our health!” programme comprises four group meetings with participants over a period of several months. At these meetings, participants discuss, exchange and monitor their qualitative and quantitative level of physical activity. Realistic and achievable goals are set in consultation with each participant in relation to their personal circumstances and are monitored with a pedometer and a follow-up diary. Support on healthy eating is also provided. This programme is an opportunity to promote health and refer participants to existing local resources. Results: Forty groups, comprising a total of 275 people, have participated in the programme since 2006. After the four meetings, participants had increased their physical activity level by an average of 723 steps per day and 85% reported that they had changed their eating habits. Conclusion: This health promotion programme is feasible and effective: an increase in the physical activity of participants was observed, together with a favourable impact on perceived health, well-being and social links. These workshops are integrated into a network of associations and institutional partners and could be implemented by similar social or health organisations.

MARD—A moving average rose diagram application for the geosciences

NASA Astrophysics Data System (ADS)

Munro, Mark A.; Blenkinsop, Thomas G.

2012-12-01

MARD 1.0 is a computer program for generating smoothed rose diagrams by using a moving average, which is designed for use across the wide range of disciplines encompassed within the Earth Sciences. Available in MATLAB®, Microsoft® Excel and GNU Octave formats, the program is fully compatible with both Microsoft® Windows and Macintosh operating systems. Each version has been implemented in a user-friendly way that requires no prior experience in programming with the software. MARD conducts a moving average smoothing, a form of signal processing low-pass filter, upon the raw circular data according to a set of pre-defined conditions selected by the user. This form of signal processing filter smoothes the angular dataset, emphasising significant circular trends whilst reducing background noise. Customisable parameters include whether the data is uni- or bi-directional, the angular range (or aperture) over which the data is averaged, and whether an unweighted or weighted moving average is to be applied. In addition to the uni- and bi-directional options, the MATLAB® and Octave versions also possess a function for plotting 2-dimensional dips/pitches in a single, lower, hemisphere. The rose diagrams from each version are exportable as one of a selection of common graphical formats. Frequently employed statistical measures that determine the vector mean, mean resultant (or length), circular standard deviation and circular variance are also included. MARD's scope is demonstrated via its application to a variety of datasets within the Earth Sciences.

Graph-based structural change detection for rotating machinery monitoring

NASA Astrophysics Data System (ADS)

Lu, Guoliang; Liu, Jie; Yan, Peng

2018-01-01

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

Forecasting of Water Consumptions Expenditure Using Holt-Winter’s and ARIMA

NASA Astrophysics Data System (ADS)

Razali, S. N. A. M.; Rusiman, M. S.; Zawawi, N. I.; Arbin, N.

2018-04-01

This study is carried out to forecast water consumption expenditure of Malaysian university specifically at University Tun Hussein Onn Malaysia (UTHM). The proposed Holt-Winter’s and Auto-Regressive Integrated Moving Average (ARIMA) models were applied to forecast the water consumption expenditure in Ringgit Malaysia from year 2006 until year 2014. The two models were compared and performance measurement of the Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) were used. It is found that ARIMA model showed better results regarding the accuracy of forecast with lower values of MAPE and MAD. Analysis showed that ARIMA (2,1,4) model provided a reasonable forecasting tool for university campus water usage.

Hybrid empirical mode decomposition- ARIMA for forecasting exchange rates

NASA Astrophysics Data System (ADS)

Abadan, Siti Sarah; Shabri, Ani; Ismail, Shuhaida

2015-02-01

This paper studied the forecasting of monthly Malaysian Ringgit (MYR)/ United State Dollar (USD) exchange rates using the hybrid of two methods which are the empirical model decomposition (EMD) and the autoregressive integrated moving average (ARIMA). MYR is pegged to USD during the Asian financial crisis causing the exchange rates are fixed to 3.800 from 2nd of September 1998 until 21st of July 2005. Thus, the chosen data in this paper is the post-July 2005 data, starting from August 2005 to July 2010. The comparative study using root mean square error (RMSE) and mean absolute error (MAE) showed that the EMD-ARIMA outperformed the single-ARIMA and the random walk benchmark model.

Segregation in Post-Civil Rights America: Stalled Integration or End of the Segregated Century?

PubMed Central

Massey, Douglas S.; Rugh, Jacob S.

2016-01-01

In this paper we adjudicate between competing claims of persisting segregation and rapid integration by analyzing trends in residential dissimilarity and spatial isolation for African Americans, Hispanics, and Asians living in 287 consistently defined metropolitan areas from 1970 to 2010. On average, black segregation and isolation have fallen steadily but still remain very high in many areas, particularly those areas historically characterized by hypersegregation. In contrast, Hispanic segregation has increased slightly but Hispanic isolation has risen substantially owing to rapid population growth. Asian segregation has changed little and remains moderate, and although Asian isolation has increased it remains at low levels compared with other groups. Multivariate analyses reveal that segregation and isolation are being actively produced in some areas by restrictive density zoning regimes, large and/or rising minority percentages, lagging minority socioeconomic status, and active expressions of anti-black and anti-Latino sentiment, especially in large metropolitan areas. Areas displaying these characteristics are either integrating very slowly (in the case of blacks) or becoming more segregated (in the case of Hispanics), whereas those lacking these attributes are clearly moving toward integration, often quite rapidly. PMID:26966459

Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models.

PubMed

Hu, Wenbiao; Tong, Shilu; Mengersen, Kerrie; Connell, Des

2007-09-01

Few studies have examined the relationship between weather variables and cryptosporidiosis in Australia. This paper examines the potential impact of weather variability on the transmission of cryptosporidiosis and explores the possibility of developing an empirical forecast system. Data on weather variables, notified cryptosporidiosis cases, and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics for the period of January 1, 1996-December 31, 2004, respectively. Time series Poisson regression and seasonal auto-regression integrated moving average (SARIMA) models were performed to examine the potential impact of weather variability on the transmission of cryptosporidiosis. Both the time series Poisson regression and SARIMA models show that seasonal and monthly maximum temperature at a prior moving average of 1 and 3 months were significantly associated with cryptosporidiosis disease. It suggests that there may be 50 more cases a year for an increase of 1 degrees C maximum temperature on average in Brisbane. Model assessments indicated that the SARIMA model had better predictive ability than the Poisson regression model (SARIMA: root mean square error (RMSE): 0.40, Akaike information criterion (AIC): -12.53; Poisson regression: RMSE: 0.54, AIC: -2.84). Furthermore, the analysis of residuals shows that the time series Poisson regression appeared to violate a modeling assumption, in that residual autocorrelation persisted. The results of this study suggest that weather variability (particularly maximum temperature) may have played a significant role in the transmission of cryptosporidiosis. A SARIMA model may be a better predictive model than a Poisson regression model in the assessment of the relationship between weather variability and the incidence of cryptosporidiosis.

The compressible aerodynamics of rotating blades based on an acoustic formulation

NASA Technical Reports Server (NTRS)

Long, L. N.

1983-01-01

An acoustic formula derived for the calculation of the noise of moving bodies is applied to aerodynamic problems. The acoustic formulation is a time domain result suitable for slender wings and bodies moving at subsonic speeds. A singular integral equation is derived in terms of the surface pressure which must then be solved numerically for aerodynamic purposes. However, as the 'observer' is moved onto the body surface, the divergent integrals in the acoustic formulation are semiconvergent. The procedure for regularization (or taking principal values of divergent integrals) is explained, and some numerical examples for ellipsoids, wings, and lifting rotors are presented. The numerical results show good agreement with available measured surface pressure data.

Iterative Procedures for Exact Maximum Likelihood Estimation in the First-Order Gaussian Moving Average Model

DTIC Science & Technology

1990-11-01

1 = Q- 1 - 1 QlaaQ- 1.1 + a’Q-1a This is a simple case of a general formula called Woodbury’s formula by some authors; see, for example, Phadke and...1 2. The First-Order Moving Average Model ..... .................. 3. Some Approaches to the Iterative...the approximate likelihood function in some time series models. Useful suggestions have been the Cholesky decomposition of the covariance matrix and

Decadal Trends of Atlantic Basin Tropical Cyclones (1950-1999)

NASA Technical Reports Server (NTRS)

Wilson, Robert M.

2001-01-01

Ten-year moving averages of the seasonal rates for 'named storms,' tropical storms, hurricanes, and major (or intense) hurricanes in the Atlantic basin suggest that the present epoch is one of enhanced activity, marked by seasonal rates typically equal to or above respective long-term median rates. As an example, the 10-year moving average of the seasonal rates for named storms is now higher than for any previous year over the past 50 years, measuring 10.65 in 1994, or 2.65 units higher than its median rate of 8. Also, the 10-year moving average for tropical storms has more than doubled, from 2.15 in 1955 to 4.60 in 1992, with 16 of the past 20 years having a seasonal rate of three or more (the median rate). For hurricanes and major hurricanes, their respective 10-year moving averages turned upward, rising above long-term median rates (5.5 and 2, respectively) in 1992, a response to the abrupt increase in seasonal rates that occurred in 1995. Taken together, the outlook for future hurricane seasons is for all categories of Atlantic basin tropical cyclones to have seasonal rates at levels equal to or above long-term median rates, especially during non-El Nino-related seasons. Only during El Nino-related seasons does it appear likely that seasonal rates might be slightly diminished.

Motile and non-motile sperm diagnostic manipulation using optoelectronic tweezers.

PubMed

Ohta, Aaron T; Garcia, Maurice; Valley, Justin K; Banie, Lia; Hsu, Hsan-Yin; Jamshidi, Arash; Neale, Steven L; Lue, Tom; Wu, Ming C

2010-12-07

Optoelectronic tweezers was used to manipulate human spermatozoa to determine whether their response to OET predicts sperm viability among non-motile sperm. We review the electro-physical basis for how live and dead human spermatozoa respond to OET. The maximal velocity that non-motile spermatozoa could be induced to move by attraction or repulsion to a moving OET field was measured. Viable sperm are attracted to OET fields and can be induced to move at an average maximal velocity of 8.8 ± 4.2 µm s(-1), while non-viable sperm are repelled to OET, and are induced to move at an average maximal velocity of -0.8 ± 1.0 µm s(-1). Manipulation of the sperm using OET does not appear to result in increased DNA fragmentation, making this a potential method by which to identify viable non-motile sperm for assisted reproductive technologies.

Transport of the moving barrier driven by chiral active particles

NASA Astrophysics Data System (ADS)

Liao, Jing-jing; Huang, Xiao-qun; Ai, Bao-quan

2018-03-01

Transport of a moving V-shaped barrier exposed to a bath of chiral active particles is investigated in a two-dimensional channel. Due to the chirality of active particles and the transversal asymmetry of the barrier position, active particles can power and steer the directed transport of the barrier in the longitudinal direction. The transport of the barrier is determined by the chirality of active particles. The moving barrier and active particles move in the opposite directions. The average velocity of the barrier is much larger than that of active particles. There exist optimal parameters (the chirality, the self-propulsion speed, the packing fraction, and the channel width) at which the average velocity of the barrier takes its maximal value. In particular, tailoring the geometry of the barrier and the active concentration provides novel strategies to control the transport properties of micro-objects or cargoes in an active medium.

Volatility-constrained multifractal detrended cross-correlation analysis: Cross-correlation among Mainland China, US, and Hong Kong stock markets

NASA Astrophysics Data System (ADS)

Cao, Guangxi; Zhang, Minjia; Li, Qingchen

2017-04-01

This study focuses on multifractal detrended cross-correlation analysis of the different volatility intervals of Mainland China, US, and Hong Kong stock markets. A volatility-constrained multifractal detrended cross-correlation analysis (VC-MF-DCCA) method is proposed to study the volatility conductivity of Mainland China, US, and Hong Kong stock markets. Empirical results indicate that fluctuation may be related to important activities in real markets. The Hang Seng Index (HSI) stock market is more influential than the Shanghai Composite Index (SCI) stock market. Furthermore, the SCI stock market is more influential than the Dow Jones Industrial Average stock market. The conductivity between the HSI and SCI stock markets is the strongest. HSI was the most influential market in the large fluctuation interval of 1991 to 2014. The autoregressive fractionally integrated moving average method is used to verify the validity of VC-MF-DCCA. Results show that VC-MF-DCCA is effective.

Prediction of South China sea level using seasonal ARIMA models

NASA Astrophysics Data System (ADS)

Fernandez, Flerida Regine; Po, Rodolfo; Montero, Neil; Addawe, Rizavel

2017-11-01

Accelerating sea level rise is an indicator of global warming and poses a threat to low-lying places and coastal countries. This study aims to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to the time series obtained from the TOPEX and Jason series of satellite radar altimetries of the South China Sea from the year 2008 to 2015. With altimetric measurements taken in a 10-day repeat cycle, monthly averages of the satellite altimetry measurements were taken to compose the data set used in the study. SARIMA models were then tried and fitted to the time series in order to find the best-fit model. Results show that the SARIMA(1,0,0)(0,1,1)12 model best fits the time series and was used to forecast the values for January 2016 to December 2016. The 12-month forecast using SARIMA(1,0,0)(0,1,1)12 shows that the sea level gradually increases from January to September 2016, and decreases until December 2016.

First results of a study on turbulent boundary layers in oscillating flow with a mean adverse pressure gradient

NASA Technical Reports Server (NTRS)

Houdeville, R.; Cousteix, J.

1979-01-01

The development of a turbulent unsteady boundary layer with a mean pressure gradient strong enough to induce separation, in order to complete the extend results obtained for the flat plate configuration is presented. The longitudinal component of the velocity is measured using constant temperature hot wire anemometer. The region where negative velocities exist is investigated with a laser Doppler velocimeter system with BRAGG cells. The boundary layer responds by forced pulsation to the perturbation of potential flow. The unsteady effects observed are very important. The average location of the zero skin friction point moves periodically at the perturbation frequency. Average velocity profiles from different instants in the cycle are compared. The existence of a logarithmic region enables a simple calculation of the maximum phase shift of the velocity in the boundary layer. An attempt of calculation by an integral method of boundary layer development is presented, up to the point where reverse flow starts appearing.

Solar corona electron density distribution

NASA Astrophysics Data System (ADS)

Esposito, P. B.; Edenhofer, P.; Lueneburg, E.

1980-07-01

The paper discusses the three and one-half months of single-frequency time delay data which were acquired from the Helios 2 spacecraft around the time of its solar occultation. The excess time delay due to integrated effect of free electrons along the signal's ray path could be separated and modeled following the determination of the spacecraft trajectory. An average solar corona and equatorial electron density profile during solar minimum were deduced from the time delay measurements acquired within 5-60 solar radii of the sun. As a point of reference at 10 solar radii from the sun, an average electron density was 4500 el/cu cm. However, an asymmetry was found in the electron density as the ray path moved from the west to east solar limb. This may be related to the fact that during entry into occultation the heliographic latitude of the ray path was about 6 deg, while during exit it was 7 deg. The Helios density model is compared with similar models deduced from different experimental techniques.

Stochastic approaches for time series forecasting of boron: a case study of Western Turkey.

PubMed

Durdu, Omer Faruk

2010-10-01

In the present study, a seasonal and non-seasonal prediction of boron concentrations time series data for the period of 1996-2004 from Büyük Menderes river in western Turkey are addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict boron content in the Büyük Menderes catchment. Initially, the Box-Whisker plots and Kendall's tau test are used to identify the trends during the study period. The measurements locations do not show significant overall trend in boron concentrations, though marginal increasing and decreasing trends are observed for certain periods at some locations. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, and diagnostic checking. In the model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of boron data series, different ARIMA models are identified. The model gives the minimum Akaike information criterion (AIC) is selected as the best-fit model. The parameter estimation step indicates that the estimated model parameters are significantly different from zero. The diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicate that the residuals are independent, normally distributed, and homoscadastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The comparison of the mean and variance of 3-year (2002-2004) observed data vs predicted data from the selected best models show that the boron model from ARIMA modeling approaches could be used in a safe manner since the predicted values from these models preserve the basic statistics of observed data in terms of mean. The ARIMA modeling approach is recommended for predicting boron concentration series of a river.

[A new kinematics method of determing elbow rotation axis and evaluation of its feasibility].

PubMed

Han, W; Song, J; Wang, G Z; Ding, H; Li, G S; Gong, M Q; Jiang, X Y; Wang, M Y

2016-04-18

To study a new positioning method of elbow external fixation rotation axis, and to evaluate its feasibility. Four normal adult volunteers and six Sawbone elbow models were brought into this experiment. The kinematic data of five elbow flexion were collected respectively by optical positioning system. The rotation axes of the elbow joints were fitted by the least square method. The kinematic data and fitting results were visually displayed. According to the fitting results, the average moving planes and rotation axes were calculated. Thus, the rotation axes of new kinematic methods were obtained. By using standard clinical methods, the entrance and exit points of rotation axes of six Sawbone elbow models were located under X-ray. And The kirschner wires were placed as the representatives of rotation axes using traditional positioning methods. Then, the entrance point deviation, the exit point deviation and the angle deviation of two kinds of located rotation axes were compared. As to the four volunteers, the indicators represented circular degree and coplanarity of elbow flexion movement trajectory of each volunteer were both about 1 mm. All the distance deviations of the moving axes to the average moving rotation axes of the five volunteers were less than 3 mm. All the angle deviations of the moving axes to the average moving rotation axes of the five volunteers were less than 5°. As to the six Sawbone models, the average entrance point deviations, the average exit point deviations and the average angle deviations of two different rotation axes determined by two kinds of located methods were respectively 1.697 2 mm, 1.838 3 mm and 1.321 7°. All the deviations were very small. They were all in an acceptable range of clinical practice. The values that represent circular degree and coplanarity of volunteer's elbow single curvature movement trajectory are very small. The result shows that the elbow single curvature movement can be regarded as the approximate fixed axis movement. The new method can replace the traditional method in accuracy. It can make up the deficiency of the traditional fixed axis method.

A spline-based non-linear diffeomorphism for multimodal prostate registration.

PubMed

Mitra, Jhimli; Kato, Zoltan; Martí, Robert; Oliver, Arnau; Lladó, Xavier; Sidibé, Désiré; Ghose, Soumya; Vilanova, Joan C; Comet, Josep; Meriaudeau, Fabrice

2012-08-01

This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape representations is used to find the point correspondences between the 2D fixed and moving images. The registration method involves parametric estimation of the non-linear diffeomorphism between the multimodal images and has its basis in solving a set of non-linear equations of thin-plate splines. The solution is obtained as the least-squares solution of an over-determined system of non-linear equations constructed by integrating a set of non-linear functions over the fixed and moving images. However, this may not result in clinically acceptable transformations of the anatomical targets. Therefore, the regularized bending energy of the thin-plate splines along with the localization error of established correspondences should be included in the system of equations. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate mid-gland ultrasound and magnetic resonance images. The results obtained in terms of Dice similarity coefficient show an average of 0.980±0.004, average 95% Hausdorff distance of 1.63±0.48 mm and mean target registration and target localization errors of 1.60±1.17 mm and 0.15±0.12 mm respectively. Copyright © 2012 Elsevier B.V. All rights reserved.

Focus on Teacher Salaries: An Update on Average Salaries and Recent Legislative Actions in the SREB States.

ERIC Educational Resources Information Center

Gaines, Gale F.

Focused state efforts have helped teacher salaries in Southern Regional Education Board (SREB) states move toward the national average. Preliminary 2000-01 estimates put SREB's average teacher salary at its highest point in 22 years compared to the national average. The SREB average teacher salary is approximately 90 percent of the national…

Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

PubMed

Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

2017-09-01

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to combine the advantages brought forward by the proposed EWMA-GLRT fault detection chart with the KPCA model. Thus, it is used to enhance fault detection of the Cad System in E. coli model through monitoring some of the key variables involved in this model such as enzymes, transport proteins, regulatory proteins, lysine, and cadaverine. The results demonstrate the effectiveness of the proposed KPCA-based EWMA-GLRT method over Q , GLRT, EWMA, Shewhart, and moving window-GLRT methods. The detection performance is assessed and evaluated in terms of FAR, missed detection rates, and average run length (ARL 1 ) values.

Integration of forward-looking infrared (FLIR) and traffic information for moving obstacle detection with integrity

NASA Astrophysics Data System (ADS)

Zhu, Zhen; Vana, Sudha; Bhattacharya, Sumit; Uijt de Haag, Maarten

2009-05-01

This paper discusses the integration of Forward-looking Infrared (FLIR) and traffic information from, for example, the Automatic Dependent Surveillance - Broadcast (ADS-B) or the Traffic Information Service-Broadcast (TIS-B). The goal of this integration method is to obtain an improved state estimate of a moving obstacle within the Field-of-View of the FLIR with added integrity. The focus of the paper will be on the approach phase of the flight. The paper will address methods to extract moving objects from the FLIR imagery and geo-reference these objects using outputs of both the onboard Global Positioning System (GPS) and the Inertial Navigation System (INS). The proposed extraction method uses a priori airport information and terrain databases. Furthermore, state information from the traffic information sources will be extracted and integrated with the state estimates from the FLIR. Finally, a method will be addressed that performs a consistency check between both sources of traffic information. The methods discussed in this paper will be evaluated using flight test data collected with a Gulfstream V in Reno, NV (GVSITE) and simulated ADS-B.

Distributed Sensor Fusion for Scalar Field Mapping Using Mobile Sensor Networks.

PubMed

La, Hung Manh; Sheng, Weihua

2013-04-01

In this paper, autonomous mobile sensor networks are deployed to measure a scalar field and build its map. We develop a novel method for multiple mobile sensor nodes to build this map using noisy sensor measurements. Our method consists of two parts. First, we develop a distributed sensor fusion algorithm by integrating two different distributed consensus filters to achieve cooperative sensing among sensor nodes. This fusion algorithm has two phases. In the first phase, the weighted average consensus filter is developed, which allows each sensor node to find an estimate of the value of the scalar field at each time step. In the second phase, the average consensus filter is used to allow each sensor node to find a confidence of the estimate at each time step. The final estimate of the value of the scalar field is iteratively updated during the movement of the mobile sensors via weighted average. Second, we develop the distributed flocking-control algorithm to drive the mobile sensors to form a network and track the virtual leader moving along the field when only a small subset of the mobile sensors know the information of the leader. Experimental results are provided to demonstrate our proposed algorithms.

Mechanistic approach to generalized technical analysis of share prices and stock market indices

NASA Astrophysics Data System (ADS)

Ausloos, M.; Ivanova, K.

2002-05-01

Classical technical analysis methods of stock evolution are recalled, i.e. the notion of moving averages and momentum indicators. The moving averages lead to define death and gold crosses, resistance and support lines. Momentum indicators lead the price trend, thus give signals before the price trend turns over. The classical technical analysis investment strategy is thereby sketched. Next, we present a generalization of these tricks drawing on physical principles, i.e. taking into account not only the price of a stock but also the volume of transactions. The latter becomes a time dependent generalized mass. The notion of pressure, acceleration and force are deduced. A generalized (kinetic) energy is easily defined. It is understood that the momentum indicators take into account the sign of the fluctuations, while the energy is geared toward the absolute value of the fluctuations. They have different patterns which are checked by searching for the crossing points of their respective moving averages. The case of IBM evolution over 1990-2000 is used for illustrations.

An impact analysis of forecasting methods and forecasting parameters on bullwhip effect

NASA Astrophysics Data System (ADS)

Silitonga, R. Y. H.; Jelly, N.

2018-04-01

Bullwhip effect is an increase of variance of demand fluctuation from downstream to upstream of supply chain. Forecasting methods and forecasting parameters were recognized as some factors that affect bullwhip phenomena. To study these factors, we can develop simulations. There are several ways to simulate bullwhip effect in previous studies, such as mathematical equation modelling, information control modelling, computer program, and many more. In this study a spreadsheet program named Bullwhip Explorer was used to simulate bullwhip effect. Several scenarios were developed to show the change in bullwhip effect ratio because of the difference in forecasting methods and forecasting parameters. Forecasting methods used were mean demand, moving average, exponential smoothing, demand signalling, and minimum expected mean squared error. Forecasting parameters were moving average period, smoothing parameter, signalling factor, and safety stock factor. It showed that decreasing moving average period, increasing smoothing parameter, increasing signalling factor can create bigger bullwhip effect ratio. Meanwhile, safety stock factor had no impact to bullwhip effect.

[Application of multiple seasonal autoregressive integrated moving average model in predicting the mumps incidence].

PubMed

Hui, Shisheng; Chen, Lizhang; Liu, Fuqiang; Ouyang, Yanhao

2015-12-01

To establish multiple seasonal autoregressive integrated moving average model(ARIMA) according to mumps disease incidence in Hunan province, and to predict the mumps incidence from May 2015 to April 2016 in Hunan province by the model. The data were downloaded from "Disease Surveillance Information Reporting Management System" in China Information System for Disease Control and Prevention. The monthly incidence of mumps in Hunan province was collected from January 2004 to April 2015 according to the onset date, including clinical diagnosis and laboratory confirmed cases. The predictive analysis method was the ARIMA model in SPSS 18.0 software, the ARIMA model was established on the monthly incidence of mumps from January 2004 to April 2014, and the date from May 2014 to April 2015 was used as the testing sample, Box-Ljung Q test was used to test the residual of the selected model. Finally, the monthly incidence of mumps from May 2015 to April 2016 was predicted by the model. The peak months of the mumps incidence were May to July every year, and the secondary peak months were November to January of the following year, during January 2004 to April 2014 in Hunan province. After the data sequence was handled by smooth sequence, model identification, establishment and diagnosis, the ARIMA(2,1,1) × (0,1,1)(12) was established, Box-Ljung Q test found, Q=8.40, P=0.868, the residual sequence was white noise, the established model to the data information extraction was complete, the model was reasonable. The R(2) value of the model fitting degree was 0.871, and the value of BIC was -1.646, while the average absolute error of the predicted value and the actual value was 0.025/100 000, the average relative error was 13.004%. The relative error of the model for the prediction of the mumps incidence in Hunan province was small, and the predicting results were reliable. Using the ARIMA(2,1,1) ×(0,1,1)(12) model to predict the mumps incidence from April 2016 to May 2015 in Hunan province, the peak months of the mumps incidence were May to July, and the secondary peak months were November to January of the following year, the incidence of the peak month was close to the same period. The ARIMA(2,1,1)×(0,1,1)(12) model is well fitted the trend of the mumps disease incidence in Hunan province, it has some practical value for the prevention and control of the disease.

The Acceleration of Charged Particles at a Spherical Shock Moving through an Irregular Magnetic Field

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

Giacalone, J.

We investigate the physics of charged-particle acceleration at spherical shocks moving into a uniform plasma containing a turbulent magnetic field with a uniform mean. This has applications to particle acceleration at astrophysical shocks, most notably, to supernovae blast waves. We numerically integrate the equations of motion of a large number of test protons moving under the influence of electric and magnetic fields determined from a kinematically defined plasma flow associated with a radially propagating blast wave. Distribution functions are determined from the positions and velocities of the protons. The unshocked plasma contains a magnetic field with a uniform mean andmore » an irregular component having a Kolmogorov-like power spectrum. The field inside the blast wave is determined from Maxwell’s equations. The angle between the average magnetic field and unit normal to the shock varies with position along its surface. It is quasi-perpendicular to the unit normal near the sphere’s equator, and quasi-parallel to it near the poles. We find that the highest intensities of particles, accelerated by the shock, are at the poles of the blast wave. The particles “collect” at the poles as they approximately adhere to magnetic field lines that move poleward from their initial encounter with the shock at the equator, as the shock expands. The field lines at the poles have been connected to the shock the longest. We also find that the highest-energy protons are initially accelerated near the equator or near the quasi-perpendicular portion of the shock, where the acceleration is more rapid.« less

On the Milankovitch orbital elements for perturbed Keplerian motion

NASA Astrophysics Data System (ADS)

Rosengren, Aaron J.; Scheeres, Daniel J.

2014-03-01

We consider sets of natural vectorial orbital elements of the Milankovitch type for perturbed Keplerian motion. These elements are closely related to the two vectorial first integrals of the unperturbed two-body problem; namely, the angular momentum vector and the Laplace-Runge-Lenz vector. After a detailed historical discussion of the origin and development of such elements, nonsingular equations for the time variations of these sets of elements under perturbations are established, both in Lagrangian and Gaussian form. After averaging, a compact, elegant, and symmetrical form of secular Milankovitch-like equations is obtained, which reminds of the structure of canonical systems of equations in Hamiltonian mechanics. As an application of this vectorial formulation, we analyze the motion of an object orbiting about a planet (idealized as a point mass moving in a heliocentric elliptical orbit) and subject to solar radiation pressure acceleration (obeying an inverse-square law). We show that the corresponding secular problem is integrable and we give an explicit closed-form solution.

Non-moving Hadamard matrix diffusers for speckle reduction in laser pico-projectors

NASA Astrophysics Data System (ADS)

Thomas, Weston; Middlebrook, Christopher

2014-12-01

Personal electronic devices such as cell phones and tablets continue to decrease in size while the number of features and add-ons keep increasing. One particular feature of great interest is an integrated projector system. Laser pico-projectors have been considered, but the technology has not been developed enough to warrant integration. With new advancements in diode technology and MEMS devices, laser-based projection is currently being advanced for pico-projectors. A primary problem encountered when using a pico-projector is coherent interference known as speckle. Laser speckle can lead to eye irritation and headaches after prolonged viewing. Diffractive optical elements known as diffusers have been examined as a means to lower speckle contrast. This paper presents a binary diffuser known as a Hadamard matrix diffuser. Using two static in-line Hadamard diffusers eliminates the need for rotation or vibration of the diffuser for temporal averaging. Two Hadamard diffusers were fabricated and contrast values measured showing good agreement with theory and simulated values.

Neural network versus classical time series forecasting models

NASA Astrophysics Data System (ADS)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

ARIMA representation for daily solar irradiance and surface air temperature time series

NASA Astrophysics Data System (ADS)

Kärner, Olavi

2009-06-01

Autoregressive integrated moving average (ARIMA) models are used to compare long-range temporal variability of the total solar irradiance (TSI) at the top of the atmosphere (TOA) and surface air temperature series. The comparison shows that one and the same type of the model is applicable to represent the TSI and air temperature series. In terms of the model type surface air temperature imitates closely that for the TSI. This may mean that currently no other forcing to the climate system is capable to change the random walk type variability established by the varying activity of the rotating Sun. The result should inspire more detailed examination of the dependence of various climate series on short-range fluctuations of TSI.

The impact of using weight estimated from mammographic images vs. self-reported weight on breast cancer risk calculation

NASA Astrophysics Data System (ADS)

Nair, Kalyani P.; Harkness, Elaine F.; Gadde, Soujanye; Lim, Yit Y.; Maxwell, Anthony J.; Moschidis, Emmanouil; Foden, Philip; Cuzick, Jack; Brentnall, Adam; Evans, D. Gareth; Howell, Anthony; Astley, Susan M.

2017-03-01

Personalised breast screening requires assessment of individual risk of breast cancer, of which one contributory factor is weight. Self-reported weight has been used for this purpose, but may be unreliable. We explore the use of volume of fat in the breast, measured from digital mammograms. Volumetric breast density measurements were used to determine the volume of fat in the breasts of 40,431 women taking part in the Predicting Risk Of Cancer At Screening (PROCAS) study. Tyrer-Cuzick risk using self-reported weight was calculated for each woman. Weight was also estimated from the relationship between self-reported weight and breast fat volume in the cohort, and used to re-calculate Tyrer-Cuzick risk. Women were assigned to risk categories according to 10 year risk (below average <2%, average 2-3.49%, above average 3.5-4.99%, moderate 5-7.99%, high >=8%) and the original and re-calculated Tyrer-Cuzick risks were compared. Of the 716 women diagnosed with breast cancer during the study, 15 (2.1%) moved into a lower risk category, and 37 (5.2%) moved into a higher category when using weight estimated from breast fat volume. Of the 39,715 women without a cancer diagnosis, 1009 (2.5%) moved into a lower risk category, and 1721 (4.3%) into a higher risk category. The majority of changes were between below average and average risk categories (38.5% of those with a cancer diagnosis, and 34.6% of those without). No individual moved more than one risk group. Automated breast fat measures may provide a suitable alternative to self-reported weight for risk assessment in personalized screening.

S3/S4 Integrated Truss being moved into the Space Shuttle Payloa

NASA Image and Video Library

2007-02-07

In the Space Station Processing Facility, an overhead crane moves the S3/S4 integrated truss to a payload canister. After it is stowed in the canister, the S3/S4 truss will be transported to the launch pad. The truss is the payload on mission STS-117, targeted for launch on March 15.

Dynamics of actin-based movement by Rickettsia rickettsii in vero cells.

PubMed

Heinzen, R A; Grieshaber, S S; Van Kirk, L S; Devin, C J

1999-08-01

Actin-based motility (ABM) is a virulence mechanism exploited by invasive bacterial pathogens in the genera Listeria, Shigella, and Rickettsia. Due to experimental constraints imposed by the lack of genetic tools and their obligate intracellular nature, little is known about rickettsial ABM relative to Listeria and Shigella ABM systems. In this study, we directly compared the dynamics and behavior of ABM of Rickettsia rickettsii and Listeria monocytogenes. A time-lapse video of moving intracellular bacteria was obtained by laser-scanning confocal microscopy of infected Vero cells synthesizing beta-actin coupled to green fluorescent protein (GFP). Analysis of time-lapse images demonstrated that R. rickettsii organisms move through the cell cytoplasm at an average rate of 4.8 +/- 0.6 micrometer/min (mean +/- standard deviation). This speed was 2.5 times slower than that of L. monocytogenes, which moved at an average rate of 12.0 +/- 3.1 micrometers/min. Although rickettsiae moved more slowly, the actin filaments comprising the actin comet tail were significantly more stable, with an average half-life approximately three times that of L. monocytogenes (100.6 +/- 19.2 s versus 33.0 +/- 7.6 s, respectively). The actin tail associated with intracytoplasmic rickettsiae remained stationary in the cytoplasm as the organism moved forward. In contrast, actin tails of rickettsiae trapped within the nucleus displayed dramatic movements. The observed phenotypic differences between the ABM of Listeria and Rickettsia may indicate fundamental differences in the mechanisms of actin recruitment and polymerization.

Books average previous decade of economic misery.

PubMed

Bentley, R Alexander; Acerbi, Alberto; Ormerod, Paul; Lampos, Vasileios

2014-01-01

For the 20(th) century since the Depression, we find a strong correlation between a 'literary misery index' derived from English language books and a moving average of the previous decade of the annual U.S. economic misery index, which is the sum of inflation and unemployment rates. We find a peak in the goodness of fit at 11 years for the moving average. The fit between the two misery indices holds when using different techniques to measure the literary misery index, and this fit is significantly better than other possible correlations with different emotion indices. To check the robustness of the results, we also analysed books written in German language and obtained very similar correlations with the German economic misery index. The results suggest that millions of books published every year average the authors' shared economic experiences over the past decade.

Up-down Asymmetries in Speed Perception

NASA Technical Reports Server (NTRS)

Thompson, Peter; Stone, Leland S.

1997-01-01

We compared speed matches for pairs of stimuli that moved in opposite directions (upward and downward). Stimuli were elliptical patches (2 deg horizontally by 1 deg vertically) of horizontal sinusoidal gratings of spatial. frequency 2 cycles/deg. Two sequential 380 msec reveal presentations were compared. One of each pair of gratings (the standard) moved at 4 Hz (2 deg/sec), the other (the test) moved at a rate determined by a simple up-down staircase. The point of subjectively equal speed was calculated from the average of the last eight reversals. The task was to fixate a central point and to determine which one of the pair appeared to move faster. Eight of 10 observers perceived the upward drifting grating as moving faster than a grating moving downward but otherwise identical. on average (N = 10), when the standard moved downward, it was matched by a test moving upward at 94.7+/-1.7(SE)% of the standard speed, and when the standard moved upward it was matched by a test moving downward at 105.1+/-2.3(SE)% of the standard speed. Extending this paradigm over a range of spatial (1.5 to 13.5 c/d) and temporal (1.5 to 13.5 Hz) frequencies, preliminary results (N = 4) suggest that, under the conditions of our experiment, upward matter is seen as faster than downward for speeds greater than approx.1 deg/sec, but the effect appears to reverse at speeds below approx.1 deg/sec with downward motion perceived as faster. Given that an up-down asymmetry has been observed for the optokinetic response, both perceptual and oculomotor contributions to this phenomenon deserve exploration.

Putting a Twist on Inquiry

ERIC Educational Resources Information Center

Kemp, Andrew

2005-01-01

Everything moves. Even apparently stationary objects such as houses, roads, or mountains are moving because they sit on a spinning planet orbiting the Sun. Not surprisingly, the concepts of motion and the forces that affect moving objects are an integral part of the middle school science curriculum. However, middle school students are often taught…

Tropical Cyclone Activity in the North Atlantic Basin During the Weather Satellite Era, 1960-2014

NASA Technical Reports Server (NTRS)

Wilson, Robert M.

2016-01-01

This Technical Publication (TP) represents an extension of previous work concerning the tropical cyclone activity in the North Atlantic basin during the weather satellite era, 1960-2014, in particular, that of an article published in The Journal of the Alabama Academy of Science. With the launch of the TIROS-1 polar-orbiting satellite in April 1960, a new era of global weather observation and monitoring began. Prior to this, the conditions of the North Atlantic basin were determined only from ship reports, island reports, and long-range aircraft reconnaissance. Consequently, storms that formed far from land, away from shipping lanes, and beyond the reach of aircraft possibly could be missed altogether, thereby leading to an underestimate of the true number of tropical cyclones forming in the basin. Additionally, new analysis techniques have come into use which sometimes has led to the inclusion of one or more storms at the end of a nominal hurricane season that otherwise would not have been included. In this TP, examined are the yearly (or seasonal) and 10-year moving average (10-year moving average) values of the (1) first storm day (FSD), last storm day (LSD), and length of season (LOS); (2) frequencies of tropical cyclones (by class); (3) average peak 1-minute sustained wind speed () and average lowest pressure (); (4) average genesis location in terms of north latitudinal () and west longitudinal () positions; (5) sum and average power dissipation index (); (6) sum and average accumulated cyclone energy (); (7) sum and average number of storm days (); (8) sum of the number of hurricane days (NHD) and number of major hurricane days (NMHD); (9) net tropical cyclone activity index (NTCA); (10) largest individual storm (LIS) PWS, LP, PDI, ACE, NSD, NHD, NMHD; and (11) number of category 4 and 5 hurricanes (N4/5). Also examined are the December-May (D-M) and June-November (J-N) averages and 10-year moving average values of several climatic factors, including the (1) oceanic Nino index (); (2) Atlantic multi-decadal oscillation () index; (3) Atlantic meridional mode () index; (4) global land-ocean temperature index (); and (5) quasi-biennial oscillation () index. Lastly, the associational aspects (using both linear and nonparametric statistical tests) between selected tropical cyclone parameters and the climatic factors are examined based on their 10-year moving average trend values.

An integrated collision prediction and avoidance scheme for mobile robots in non-stationary environments

NASA Technical Reports Server (NTRS)

Kyriakopoulos, K. J.; Saridis, G. N.

1993-01-01

A formulation that makes possible the integration of collision prediction and avoidance stages for mobile robots moving in general terrains containing moving obstacles is presented. A dynamic model of the mobile robot and the dynamic constraints are derived. Collision avoidance is guaranteed if the distance between the robot and a moving obstacle is nonzero. A nominal trajectory is assumed to be known from off-line planning. The main idea is to change the velocity along the nominal trajectory so that collisions are avoided. A feedback control is developed and local asymptotic stability is proved if the velocity of the moving obstacle is bounded. Furthermore, a solution to the problem of inverse dynamics for the mobile robot is given. Simulation results verify the value of the proposed strategy.

Negative energy seen by accelerated observers

NASA Astrophysics Data System (ADS)

Ford, L. H.; Roman, Thomas A.

2013-04-01

The sampled negative energy density seen by inertial observers, in arbitrary quantum states is limited by quantum inequalities, which take the form of an inverse relation between the magnitude and duration of the negative energy. The quantum inequalities severely limit the utilization of negative energy to produce gross macroscopic effects, such as violations of the second law of thermodynamics. The restrictions on the sampled energy density along the worldlines of accelerated observers are much weaker than for inertial observers. Here we will illustrate this with several explicit examples. We consider the worldline of a particle undergoing sinusoidal motion in space in the presence of a single mode squeezed vacuum state of the electromagnetic field. We show that it is possible for the integrated energy density along such a worldline to become arbitrarily negative at a constant average rate. Thus the averaged weak energy condition is violated in these examples. This can be the case even when the particle moves at nonrelativistic speeds. We use the Raychaudhuri equation to show that there can be net defocusing of a congruence of these accelerated worldlines. This defocusing is an operational signature of the negative integrated energy density. These results in no way invalidate nor undermine either the validity or utility of the quantum inequalities for inertial observers. In particular, they do not change previous constraints on the production of macroscopic effects with negative energy, e.g., the maintenance of traversable wormholes.

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.

Kinesin-microtubule interactions during gliding assays under magnetic force

NASA Astrophysics Data System (ADS)

Fallesen, Todd L.

Conventional kinesin is a motor protein capable of converting the chemical energy of ATP into mechanical work. In the cell, this is used to actively transport vesicles through the intracellular matrix. The relationship between the velocity of a single kinesin, as it works against an increasing opposing load, has been well studied. The relationship between the velocity of a cargo being moved by multiple kinesin motors against an opposing load has not been established. A major difficulty in determining the force-velocity relationship for multiple motors is determining the number of motors that are moving a cargo against an opposing load. Here I report on a novel method for detaching microtubules bound to a superparamagnetic bead from kinesin anchor points in an upside down gliding assay using a uniform magnetic field perpendicular to the direction of microtubule travel. The anchor points are presumably kinesin motors bound to the surface which microtubules are gliding over. Determining the distance between anchor points, d, allows the calculation of the average number of kinesins, n, that are moving a microtubule. It is possible to calculate the fraction of motors able to move microtubules as well, which is determined to be ˜ 5%. Using a uniform magnetic field parallel to the direction of microtubule travel, it is possible to impart a uniform magnetic field on a microtubule bound to a superparamagnetic bead. We are able to decrease the average velocity of microtubules driven by multiple kinesin motors moving against an opposing force. Using the average number of kinesins on a microtubule, we estimate that there are an average 2-7 kinesins acting against the opposing force. By fitting Gaussians to the smoothed distributions of microtubule velocities acting against an opposing force, multiple velocities are seen, presumably for n, n-1, n-2, etc motors acting together. When these velocities are scaled for the average number of motors on a microtubule, the force-velocity relationship for multiple motors follows the same trend as for one motor, supporting the hypothesis that multiple motors share the load.

Stochastic modelling of the monthly average maximum and minimum temperature patterns in India 1981-2015

NASA Astrophysics Data System (ADS)

Narasimha Murthy, K. V.; Saravana, R.; Vijaya Kumar, K.

2018-04-01

The paper investigates the stochastic modelling and forecasting of monthly average maximum and minimum temperature patterns through suitable seasonal auto regressive integrated moving average (SARIMA) model for the period 1981-2015 in India. The variations and distributions of monthly maximum and minimum temperatures are analyzed through Box plots and cumulative distribution functions. The time series plot indicates that the maximum temperature series contain sharp peaks in almost all the years, while it is not true for the minimum temperature series, so both the series are modelled separately. The possible SARIMA model has been chosen based on observing autocorrelation function (ACF), partial autocorrelation function (PACF), and inverse autocorrelation function (IACF) of the logarithmic transformed temperature series. The SARIMA (1, 0, 0) × (0, 1, 1)12 model is selected for monthly average maximum and minimum temperature series based on minimum Bayesian information criteria. The model parameters are obtained using maximum-likelihood method with the help of standard error of residuals. The adequacy of the selected model is determined using correlation diagnostic checking through ACF, PACF, IACF, and p values of Ljung-Box test statistic of residuals and using normal diagnostic checking through the kernel and normal density curves of histogram and Q-Q plot. Finally, the forecasting of monthly maximum and minimum temperature patterns of India for the next 3 years has been noticed with the help of selected model.

Class III correction using an inter-arch spring-loaded module

PubMed Central

2014-01-01

Background A retrospective study was conducted to determine the cephalometric changes in a group of Class III patients treated with the inter-arch spring-loaded module (CS2000®, Dynaflex, St. Ann, MO, USA). Methods Thirty Caucasian patients (15 males, 15 females) with an average pre-treatment age of 9.6 years were treated consecutively with this appliance and compared with a control group of subjects from the Bolton-Brush Study who were matched in age, gender, and craniofacial morphology to the treatment group. Lateral cephalograms were taken before treatment and after removal of the CS2000® appliance. The treatment effects of the CS2000® appliance were calculated by subtracting the changes due to growth (control group) from the treatment changes. Results All patients were improved to a Class I dental arch relationship with a positive overjet. Significant sagittal, vertical, and angular changes were found between the pre- and post-treatment radiographs. With an average treatment time of 1.3 years, the maxillary base moved forward by 0.8 mm, while the mandibular base moved backward by 2.8 mm together with improvements in the ANB and Wits measurements. The maxillary incisor moved forward by 1.3 mm and the mandibular incisor moved forward by 1.0 mm. The maxillary molar moved forward by 1.0 mm while the mandibular molar moved backward by 0.6 mm. The average overjet correction was 3.9 mm and 92% of the correction was due to skeletal contribution and 8% was due to dental contribution. The average molar correction was 5.2 mm and 69% of the correction was due to skeletal contribution and 31% was due to dental contribution. Conclusions Mild to moderate Class III malocclusion can be corrected using the inter-arch spring-loaded appliance with minimal patient compliance. The overjet correction was contributed by forward movement of the maxilla, backward and downward movement of the mandible, and proclination of the maxillary incisors. The molar relationship was corrected by mesialization of the maxillary molars, distalization of the mandibular molars together with a rotation of the occlusal plane. PMID:24934153

Designing components using smartMOVE electroactive polymer technology

NASA Astrophysics Data System (ADS)

Rosenthal, Marcus; Weaber, Chris; Polyakov, Ilya; Zarrabi, Al; Gise, Peter

2008-03-01

Designing components using SmartMOVE TM electroactive polymer technology requires an understanding of the basic operation principles and the necessary design tools for integration into actuator, sensor and energy generation applications. Artificial Muscle, Inc. is collaborating with OEMs to develop customized solutions for their applications using smartMOVE. SmartMOVE is an advanced and elegant way to obtain almost any kind of movement using dielectric elastomer electroactive polymers. Integration of this technology offers the unique capability to create highly precise and customized motion for devices and systems that require actuation. Applications of SmartMOVE include linear actuators for medical, consumer and industrial applications, such as pumps, valves, optical or haptic devices. This paper will present design guidelines for selecting a smartMOVE actuator design to match the stroke, force, power, size, speed, environmental and reliability requirements for a range of applications. Power supply and controller design and selection will also be introduced. An overview of some of the most versatile configuration options will be presented with performance comparisons. A case example will include the selection, optimization, and performance overview of a smartMOVE actuator for the cell phone camera auto-focus and proportional valve applications.

Separation of variables in the special diagonal Hamilton-Jacobi equation: Application to the dynamical problem of a particle constrained on a moving surface

NASA Technical Reports Server (NTRS)

Blanchard, D. L.; Chan, F. K.

1973-01-01

For a time-dependent, n-dimensional, special diagonal Hamilton-Jacobi equation a necessary and sufficient condition for the separation of variables to yield a complete integral of the form was established by specifying the admissible forms in terms of arbitrary functions. A complete integral was then expressed in terms of these arbitrary functions and also the n irreducible constants. As an application of the results obtained for the two-dimensional Hamilton-Jacobi equation, analysis was made for a comparatively wide class of dynamical problems involving a particle moving in Euclidean three-dimensional space under the action of external forces but constrained on a moving surface. All the possible cases in which this equation had a complete integral of the form were obtained and these are tubulated for reference.

Books Average Previous Decade of Economic Misery

PubMed Central

Bentley, R. Alexander; Acerbi, Alberto; Ormerod, Paul; Lampos, Vasileios

2014-01-01

For the 20th century since the Depression, we find a strong correlation between a ‘literary misery index’ derived from English language books and a moving average of the previous decade of the annual U.S. economic misery index, which is the sum of inflation and unemployment rates. We find a peak in the goodness of fit at 11 years for the moving average. The fit between the two misery indices holds when using different techniques to measure the literary misery index, and this fit is significantly better than other possible correlations with different emotion indices. To check the robustness of the results, we also analysed books written in German language and obtained very similar correlations with the German economic misery index. The results suggest that millions of books published every year average the authors' shared economic experiences over the past decade. PMID:24416159

Studies on the dynamic stability of an axially moving nanobeam based on the nonlocal strain gradient theory

NASA Astrophysics Data System (ADS)

Wang, Jing; Shen, Huoming; Zhang, Bo; Liu, Juan

2018-06-01

In this paper, we studied the parametric resonance issue of an axially moving viscoelastic nanobeam with varying velocity. Based on the nonlocal strain gradient theory, we established the transversal vibration equation of the axially moving nanobeam and the corresponding boundary condition. By applying the average method, we obtained a set of self-governing ordinary differential equations when the excitation frequency of the moving parameters is twice the intrinsic frequency or near the sum of certain second-order intrinsic frequencies. On the plane of parametric excitation frequency and excitation amplitude, we can obtain the instability region generated by the resonance, and through numerical simulation, we analyze the influence of the scale effect and system parameters on the instability region. The results indicate that the viscoelastic damping decreases the resonance instability region, and the average velocity and stiffness make the instability region move to the left- and right-hand sides. Meanwhile, the scale effect of the system is obvious. The nonlocal parameter exhibits not only the stiffness softening effect but also the damping weakening effect, while the material characteristic length parameter exhibits the stiffness hardening effect and damping reinforcement effect.

Dynamical Evolution of Meteoroid Streams, Developments Over the Last 30 Years

NASA Technical Reports Server (NTRS)

Williams, I. P.

2011-01-01

As soon as reliable methods for observationally determining the heliocentric orbits of meteoroids and hence the mean orbit of a meteoroid stream in the 1950s and 60s, astronomers strived to investigate the evolution of the orbit under the effects of gravitational perturbations from the planets. At first, the limitations in the capabilities of computers, both in terms of speed and memory, placed severe restrictions on what was possible to do. As a consequence, secular perturbation methods, where the perturbations are averaged over one orbit became the norm. The most popular of these is the Halphen- Goryachev method which was used extensively until the early 1980s. The main disadvantage of these methods lies in the fact that close encounter can be missed, however they remain useful for performing very long-term integrations. Direct integration methods determine the effects of the perturbing forces at many points on an orbit. This give a better picture of the orbital evolution of an individual meteoroid, but many meteoroids have to be integrated in order to obtain a realistic picture of the evolution of a meteoroid stream. The notion of generating a family of hypothetical meteoroids to represent a stream and directly integrate the motion of each was probably first used by Williams Murray & Hughes (1979), to investigate the Quadrantids. Because of computing limitations, only 10 test meteoroids were used. Only two years later, Hughes et. al. (1981) had increased the number of particles 20-fold to 200 while after a further year, Fox Williams and Hughes used 500 000 test meteoroids to model the Geminid stream. With such a number of meteoroids it was possible for the first time to produce a realistic cross-section of the stream on the ecliptic. From that point on there has been a continued increase in the number of meteoroids, the length of time over which integration is carried out and the frequency with which results can be plotted so that it is now possible to produce moving images of the stream. As a consequence, over recent years, emphasis has moved to considering stream formation and the role fragmentation plays in this.

Application of Integral Optical Flow for Determining Crowd Movement from Video Images Obtained Using Video Surveillance Systems

NASA Astrophysics Data System (ADS)

Chen, H.; Ye, Sh.; Nedzvedz, O. V.; Ablameyko, S. V.

2018-03-01

Study of crowd movement is an important practical problem, and its solution is used in video surveillance systems for preventing various emergency situations. In the general case, a group of fast-moving people is of more interest than a group of stationary or slow-moving people. We propose a new method for crowd movement analysis using a video sequence, based on integral optical flow. We have determined several characteristics of a moving crowd such as density, speed, direction of motion, symmetry, and in/out index. These characteristics are used for further analysis of a video scene.

S3/S4 Integrated Truss being moved into the Space Shuttle Payloa

NASA Image and Video Library

2007-02-07

In the Space Station Processing Facility, workers attach an overhead crane to the S3/S4 integrated truss in order to move it to the payload canister. After it is stowed in the canister, the S3/S4 truss will be transported to the launch pad. The truss is the payload on mission STS-117, targeted for launch on March 15.

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach.

PubMed

Elgendi, Mohamed

2016-11-02

Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages ("TERMA") involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages ( W 1 and W 2 ) have to follow the inequality ( 8 × W 1 ) ≥ W 2 ≥ ( 2 × W 1 ) . Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions.

A strategy to decide whether to move the last case of the day in an operating room to another empty operating room to decrease overtime labor costs.

PubMed

Dexter, F

2000-10-01

We examined how to program an operating room (OR) information system to assist the OR manager in deciding whether to move the last case of the day in one OR to another OR that is empty to decrease overtime labor costs. We first developed a statistical strategy to predict whether moving the case would decrease overtime labor costs for first shift nurses and anesthesia providers. The strategy was based on using historical case duration data stored in a surgical services information system. Second, we estimated the incremental overtime labor costs achieved if our strategy was used for moving cases versus movement of cases by an OR manager who knew in advance exactly how long each case would last. We found that if our strategy was used to decide whether to move cases, then depending on parameter values, only 2.0 to 4.3 more min of overtime would be required per case than if the OR manager had perfect retrospective knowledge of case durations. The use of other information technologies to assist in the decision of whether to move a case, such as real-time patient tracking information systems, closed-circuit cameras, or graphical airport-style displays can, on average, reduce overtime by no more than only 2 to 4 min per case that can be moved. The use of other information technologies to assist in the decision of whether to move a case, such as real-time patient tracking information systems, closed-circuit cameras, or graphical airport-style displays, can, on average, reduce overtime by no more than only 2 to 4 min per case that can be moved.

Peak Running Intensity of International Rugby: Implications for Training Prescription.

PubMed

Delaney, Jace A; Thornton, Heidi R; Pryor, John F; Stewart, Andrew M; Dascombe, Ben J; Duthie, Grant M

2017-09-01

To quantify the duration and position-specific peak running intensities of international rugby union for the prescription and monitoring of specific training methodologies. Global positioning systems (GPS) were used to assess the activity profile of 67 elite-level rugby union players from 2 nations across 33 international matches. A moving-average approach was used to identify the peak relative distance (m/min), average acceleration/deceleration (AveAcc; m/s 2 ), and average metabolic power (P met ) for a range of durations (1-10 min). Differences between positions and durations were described using a magnitude-based network. Peak running intensity increased as the length of the moving average decreased. There were likely small to moderate increases in relative distance and AveAcc for outside backs, halfbacks, and loose forwards compared with the tight 5 group across all moving-average durations (effect size [ES] = 0.27-1.00). P met demands were at least likely greater for outside backs and halfbacks than for the tight 5 (ES = 0.86-0.99). Halfbacks demonstrated the greatest relative distance and P met outputs but were similar to outside backs and loose forwards in AveAcc demands. The current study has presented a framework to describe the peak running intensities achieved during international rugby competition by position, which are considerably higher than previously reported whole-period averages. These data provide further knowledge of the peak activity profiles of international rugby competition, and this information can be used to assist coaches and practitioners in adequately preparing athletes for the most demanding periods of play.

Aging and the Visual Perception of Motion Direction: Solving the Aperture Problem.

PubMed

Shain, Lindsey M; Norman, J Farley

2018-07-01

An experiment required younger and older adults to estimate coherent visual motion direction from multiple motion signals, where each motion signal was locally ambiguous with respect to the true direction of pattern motion. Thus, accurate performance required the successful integration of motion signals across space (i.e., accurate performance required solution of the aperture problem) . The observers viewed arrays of either 64 or 9 moving line segments; because these lines moved behind apertures, their individual local motions were ambiguous with respect to direction (i.e., were subject to the aperture problem). Following 2.4 seconds of pattern motion on each trial (true motion directions ranged over the entire range of 360° in the fronto-parallel plane), the observers estimated the coherent direction of motion. There was an effect of direction, such that cardinal directions of pattern motion were judged with less error than oblique directions. In addition, a large effect of aging occurred-The average absolute errors of the older observers were 46% and 30.4% higher in magnitude than those exhibited by the younger observers for the 64 and 9 aperture conditions, respectively. Finally, the observers' precision markedly deteriorated as the number of apertures was reduced from 64 to 9.

[Application of ARIMA model on prediction of malaria incidence].

PubMed

Jing, Xia; Hua-Xun, Zhang; Wen, Lin; Su-Jian, Pei; Ling-Cong, Sun; Xiao-Rong, Dong; Mu-Min, Cao; Dong-Ni, Wu; Shunxiang, Cai

2016-01-29

To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA). SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation. The model of ARIMA (1, 1, 1) (1, 1, 0) 12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable. The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.

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

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

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

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

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

NASA Astrophysics Data System (ADS)

Loch-Olszewska, Hanna; Szwabiński, Janusz

2018-05-01

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

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

PubMed

Loch-Olszewska, Hanna; Szwabiński, Janusz

2018-05-28

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

GPC-Based Stable Reconfigurable Control

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

Forecasting seeing and parameters of long-exposure images by means of ARIMA

NASA Astrophysics Data System (ADS)

Kornilov, Matwey V.

2016-02-01

Atmospheric turbulence is the one of the major limiting factors for ground-based astronomical observations. In this paper, the problem of short-term forecasting seeing is discussed. The real data that were obtained by atmospheric optical turbulence (OT) measurements above Mount Shatdzhatmaz in 2007-2013 have been analysed. Linear auto-regressive integrated moving average (ARIMA) models are used for the forecasting. A new procedure for forecasting the image characteristics of direct astronomical observations (central image intensity, full width at half maximum, radius encircling 80 % of the energy) has been proposed. Probability density functions of the forecast of these quantities are 1.5-2 times thinner than the respective unconditional probability density functions. Overall, this study found that the described technique could adequately describe temporal stochastic variations of the OT power.

Computational problems in autoregressive moving average (ARMA) models

NASA Technical Reports Server (NTRS)

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

1981-01-01

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

PERIODIC AUTOREGRESSIVE-MOVING AVERAGE (PARMA) MODELING WITH APPLICATIONS TO WATER RESOURCES.

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

Results involving correlation properties and parameter estimation for autogressive-moving average models with periodic parameters are presented. A multivariate representation of the PARMA model is used to derive parameter space restrictions and difference equations for the periodic autocorrelations. Close approximation to the likelihood function for Gaussian PARMA processes results in efficient maximum-likelihood estimation procedures. Terms in the Fourier expansion of the parameters are sequentially included, and a selection criterion is given for determining the optimal number of harmonics to be included. Application of the techniques is demonstrated through analysis of a monthly streamflow time series.

Estimating Perturbation and Meta-Stability in the Daily Attendance Rates of Six Small High Schools

NASA Astrophysics Data System (ADS)

Koopmans, Matthijs

This paper discusses the daily attendance rates in six small high schools over a ten-year period and evaluates how stable those rates are. “Stability” is approached from two vantage points: pulse models are fitted to estimate the impact of sudden perturbations and their reverberation through the series, and Autoregressive Fractionally Integrated Moving Average (ARFIMA) techniques are used to detect dependencies over the long range of the series. The analyses are meant to (1) exemplify the utility of time series approaches in educational research, which lacks a time series tradition, (2) discuss some time series features that seem to be particular to daily attendance rate trajectories such as the distinct downward pull coming from extreme observations, and (3) present an analytical approach to handle the important yet distinct patterns of variability that can be found in these data. The analysis also illustrates why the assumption of stability that underlies the habitual reporting of weekly, monthly and yearly averages in the educational literature is questionable, as it reveals dynamical processes (perturbation, meta-stability) that remain hidden in such summaries.

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.

An emission processing system for air quality modelling in the Mexico City metropolitan area: Evaluation and comparison of the MOBILE6.2-Mexico and MOVES-Mexico traffic emissions.

PubMed

Guevara, M; Tena, C; Soret, A; Serradell, K; Guzmán, D; Retama, A; Camacho, P; Jaimes-Palomera, M; Mediavilla, A

2017-04-15

This article describes the High-Elective Resolution Modelling Emission System for Mexico (HERMES-Mex) model, an emission processing tool developed to transform the official Mexico City Metropolitan Area (MCMA) emission inventory into hourly, gridded (up to 1km 2 ) and speciated emissions used to drive mesoscale air quality simulations with the Community Multi-scale Air Quality (CMAQ) model. The methods and ancillary information used for the spatial and temporal disaggregation and speciation of the emissions are presented and discussed. The resulting emission system is evaluated, and a case study on CO, NO 2 , O 3 , VOC and PM 2.5 concentrations is conducted to demonstrate its applicability. Moreover, resulting traffic emissions from the Mobile Source Emission Factor Model for Mexico (MOBILE6.2-Mexico) and the MOtor Vehicle Emission Simulator for Mexico (MOVES-Mexico) models are integrated in the tool to assess and compare their performance. NO x and VOC total emissions modelled are reduced by 37% and 26% in the MCMA when replacing MOBILE6.2-Mexico for MOVES-Mexico traffic emissions. In terms of air quality, the system composed by the Weather Research and Forecasting model (WRF) coupled with the HERMES-Mex and CMAQ models properly reproduces the pollutant levels and patterns measured in the MCMA. The system's performance clearly improves in urban stations with a strong influence of traffic sources when applying MOVES-Mexico emissions. Despite reducing estimations of modelled precursor emissions, O 3 peak averages are increased in the MCMA core urban area (up to 30ppb) when using MOVES-Mexico mobile emissions due to its VOC-limited regime, while concentrations in the surrounding suburban/rural areas decrease or increase depending on the meteorological conditions of the day. The results obtained suggest that the HERMES-Mex model can be used to provide model-ready emissions for air quality modelling in the MCMA. Copyright © 2017 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.

Detection of Fast Moving and Accelerating Targets Compensating Range and Doppler Migration

DTIC Science & Technology

2014-06-01

Radon -Fourier transform has been introduced to realize long- term coherent integration of the moving targets with range migration [8, 9]. Radon ...2010) Long-time coherent integration for radar target detection base on Radon -Fourier transform, in Proceedings of the IEEE Radar Conference, pp...432–436. 9. Xu, J., Yu, J., Peng, Y. & Xia, X. (2011) Radon -Fourier transform for radar target detection, I: Generalized Doppler filter bank, IEEE

Integration across Time Determines Path Deviation Discrimination for Moving Objects

PubMed Central

Whitaker, David; Levi, Dennis M.; Kennedy, Graeme J.

2008-01-01

Background Human vision is vital in determining our interaction with the outside world. In this study we characterize our ability to judge changes in the direction of motion of objects–a common task which can allow us either to intercept moving objects, or else avoid them if they pose a threat. Methodology/Principal Findings Observers were presented with objects which moved across a computer monitor on a linear path until the midline, at which point they changed their direction of motion, and observers were required to judge the direction of change. In keeping with the variety of objects we encounter in the real world, we varied characteristics of the moving stimuli such as velocity, extent of motion path and the object size. Furthermore, we compared performance for moving objects with the ability of observers to detect a deviation in a line which formed the static trace of the motion path, since it has been suggested that a form of static memory trace may form the basis for these types of judgment. The static line judgments were well described by a ‘scale invariant’ model in which any two stimuli which possess the same two-dimensional geometry (length/width) result in the same level of performance. Performance for the moving objects was entirely different. Irrespective of the path length, object size or velocity of motion, path deviation thresholds depended simply upon the duration of the motion path in seconds. Conclusions/Significance Human vision has long been known to integrate information across space in order to solve spatial tasks such as judgment of orientation or position. Here we demonstrate an intriguing mechanism which integrates direction information across time in order to optimize the judgment of path deviation for moving objects. PMID:18414653

47 CFR 64.1900 - Nondominant interexchange carrier certifications regarding geographic rate averaging and rate...

Code of Federal Regulations, 2012 CFR

2012-10-01

... certifications regarding geographic rate averaging and rate integration requirements. 64.1900 Section 64.1900... Rate Averaging and Rate Integration Requirements § 64.1900 Nondominant interexchange carrier certifications regarding geographic rate averaging and rate integration requirements. (a) A nondominant provider...

47 CFR 64.1900 - Nondominant interexchange carrier certifications regarding geographic rate averaging and rate...

Code of Federal Regulations, 2013 CFR

2013-10-01

... certifications regarding geographic rate averaging and rate integration requirements. 64.1900 Section 64.1900... Rate Averaging and Rate Integration Requirements § 64.1900 Nondominant interexchange carrier certifications regarding geographic rate averaging and rate integration requirements. (a) A nondominant provider...

47 CFR 64.1900 - Nondominant interexchange carrier certifications regarding geographic rate averaging and rate...

Code of Federal Regulations, 2014 CFR

2014-10-01

... certifications regarding geographic rate averaging and rate integration requirements. 64.1900 Section 64.1900... Rate Averaging and Rate Integration Requirements § 64.1900 Nondominant interexchange carrier certifications regarding geographic rate averaging and rate integration requirements. (a) A nondominant provider...

47 CFR 64.1900 - Nondominant interexchange carrier certifications regarding geographic rate averaging and rate...

Code of Federal Regulations, 2011 CFR

2011-10-01

... certifications regarding geographic rate averaging and rate integration requirements. 64.1900 Section 64.1900... Rate Averaging and Rate Integration Requirements § 64.1900 Nondominant interexchange carrier certifications regarding geographic rate averaging and rate integration requirements. (a) A nondominant provider...

47 CFR 64.1900 - Nondominant interexchange carrier certifications regarding geographic rate averaging and rate...

Code of Federal Regulations, 2010 CFR

2010-10-01

... certifications regarding geographic rate averaging and rate integration requirements. 64.1900 Section 64.1900... Rate Averaging and Rate Integration Requirements § 64.1900 Nondominant interexchange carrier certifications regarding geographic rate averaging and rate integration requirements. (a) A nondominant provider...

Distractor Interference during Smooth Pursuit Eye Movements

ERIC Educational Resources Information Center

Spering, Miriam; Gegenfurtner, Karl R.; Kerzel, Dirk

2006-01-01

When 2 targets for pursuit eye movements move in different directions, the eye velocity follows the vector average (S. G. Lisberger & V. P. Ferrera, 1997). The present study investigates the mechanisms of target selection when observers are instructed to follow a predefined horizontal target and to ignore a moving distractor stimulus. Results show…

Sound source identification and sound radiation modeling in a moving medium using the time-domain equivalent source method.

PubMed

Zhang, Xiao-Zheng; Bi, Chuan-Xing; Zhang, Yong-Bin; Xu, Liang

2015-05-01

Planar near-field acoustic holography has been successfully extended to reconstruct the sound field in a moving medium, however, the reconstructed field still contains the convection effect that might lead to the wrong identification of sound sources. In order to accurately identify sound sources in a moving medium, a time-domain equivalent source method is developed. In the method, the real source is replaced by a series of time-domain equivalent sources whose strengths are solved iteratively by utilizing the measured pressure and the known convective time-domain Green's function, and time averaging is used to reduce the instability in the iterative solving process. Since these solved equivalent source strengths are independent of the convection effect, they can be used not only to identify sound sources but also to model sound radiations in both moving and static media. Numerical simulations are performed to investigate the influence of noise on the solved equivalent source strengths and the effect of time averaging on reducing the instability, and to demonstrate the advantages of the proposed method on the source identification and sound radiation modeling.

In-use activity, fuel use, and emissions of heavy-duty diesel roll-off refuse trucks.

PubMed

Sandhu, Gurdas S; Frey, H Christopher; Bartelt-Hunt, Shannon; Jones, Elizabeth

2015-03-01

The objectives of this study were to quantify real-world activity, fuel use, and emissions for heavy duty diesel roll-off refuse trucks; evaluate the contribution of duty cycles and emissions controls to variability in cycle average fuel use and emission rates; quantify the effect of vehicle weight on fuel use and emission rates; and compare empirical cycle average emission rates with the U.S. Environmental Protection Agency's MOVES emission factor model predictions. Measurements were made at 1 Hz on six trucks of model years 2005 to 2012, using onboard systems. The trucks traveled 870 miles, had an average speed of 16 mph, and collected 165 tons of trash. The average fuel economy was 4.4 mpg, which is approximately twice previously reported values for residential trash collection trucks. On average, 50% of time is spent idling and about 58% of emissions occur in urban areas. Newer trucks with selective catalytic reduction and diesel particulate filter had NOx and PM cycle average emission rates that were 80% lower and 95% lower, respectively, compared to older trucks without. On average, the combined can and trash weight was about 55% of chassis weight. The marginal effect of vehicle weight on fuel use and emissions is highest at low loads and decreases as load increases. Among 36 cycle average rates (6 trucks×6 cycles), MOVES-predicted values and estimates based on real-world data have similar relative trends. MOVES-predicted CO2 emissions are similar to those of the real world, while NOx and PM emissions are, on average, 43% lower and 300% higher, respectively. The real-world data presented here can be used to estimate benefits of replacing old trucks with new trucks. Further, the data can be used to improve emission inventories and model predictions. In-use measurements of the real-world activity, fuel use, and emissions of heavy-duty diesel roll-off refuse trucks can be used to improve the accuracy of predictive models, such as MOVES, and emissions inventories. Further, the activity data from this study can be used to generate more representative duty cycles for more accurate chassis dynamometer testing. Comparisons of old and new model year diesel trucks are useful in analyzing the effect of fleet turnover. The analysis of effect of haul weight on fuel use can be used by fleet managers to optimize operations to reduce fuel cost.

47 CFR 64.1801 - Geographic rate averaging and rate integration.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 3 2010-10-01 2010-10-01 false Geographic rate averaging and rate integration. 64.1801 Section 64.1801 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) COMMON... Rate Integration § 64.1801 Geographic rate averaging and rate integration. (a) The rates charged by...

Long-Term PM2.5 Exposure and Respiratory, Cancer, and Cardiovascular Mortality in Older US Adults.

PubMed

Pun, Vivian C; Kazemiparkouhi, Fatemeh; Manjourides, Justin; Suh, Helen H

2017-10-15

The impact of chronic exposure to fine particulate matter (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5)) on respiratory disease and lung cancer mortality is poorly understood. In a cohort of 18.9 million Medicare beneficiaries (4.2 million deaths) living across the conterminous United States between 2000 and 2008, we examined the association between chronic PM2.5 exposure and cause-specific mortality. We evaluated confounding through adjustment for neighborhood behavioral covariates and decomposition of PM2.5 into 2 spatiotemporal scales. We found significantly positive associations of 12-month moving average PM2.5 exposures (per 10-μg/m3 increase) with respiratory, chronic obstructive pulmonary disease, and pneumonia mortality, with risk ratios ranging from 1.10 to 1.24. We also found significant PM2.5-associated elevated risks for cardiovascular and lung cancer mortality. Risk ratios generally increased with longer moving averages; for example, an elevation in 60-month moving average PM2.5 exposures was linked to 1.33 times the lung cancer mortality risk (95% confidence interval: 1.24, 1.40), as compared with 1.13 (95% confidence interval: 1.11, 1.15) for 12-month moving average exposures. Observed associations were robust in multivariable models, although evidence of unmeasured confounding remained. In this large cohort of US elderly, we provide important new evidence that long-term PM2.5 exposure is significantly related to increased mortality from respiratory disease, lung cancer, and cardiovascular disease. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Effect of air pollution on pediatric respiratory emergency room visits and hospital admissions.

PubMed

Farhat, S C L; Paulo, R L P; Shimoda, T M; Conceição, G M S; Lin, C A; Braga, A L F; Warth, M P N; Saldiva, P H N

2005-02-01

In order to assess the effect of air pollution on pediatric respiratory morbidity, we carried out a time series study using daily levels of PM10, SO2, NO2, ozone, and CO and daily numbers of pediatric respiratory emergency room visits and hospital admissions at the Children's Institute of the University of Sao Paulo Medical School, from August 1996 to August 1997. In this period there were 43,635 hospital emergency room visits, 4534 of which were due to lower respiratory tract disease. The total number of hospital admissions was 6785, 1021 of which were due to lower respiratory tract infectious and/or obstructive diseases. The three health end-points under investigation were the daily number of emergency room visits due to lower respiratory tract diseases, hospital admissions due to pneumonia, and hospital admissions due to asthma or bronchiolitis. Generalized additive Poisson regression models were fitted, controlling for smooth functions of time, temperature and humidity, and an indicator of weekdays. NO2 was positively associated with all outcomes. Interquartile range increases (65.04 microg/m3) in NO2 moving averages were associated with an 18.4% increase (95% confidence interval, 95% CI = 12.5-24.3) in emergency room visits due to lower respiratory tract diseases (4-day moving average), a 17.6% increase (95% CI = 3.3-32.7) in hospital admissions due to pneumonia or bronchopneumonia (3-day moving average), and a 31.4% increase (95% CI = 7.2-55.7) in hospital admissions due to asthma or bronchiolitis (2-day moving average). The study showed that air pollution considerably affects children's respiratory morbidity, deserving attention from the health authorities.

Dog days of summer: Influences on decision of wolves to move pups

USGS Publications Warehouse

Ausband, David E.; Mitchell, Michael S.; Bassing, Sarah B.; Nordhagen, Matthew; Smith, Douglas W.; Stahler, Daniel R.

2016-01-01

For animals that forage widely, protecting young from predation can span relatively long time periods due to the inability of young to travel with and be protected by their parents. Moving relatively immobile young to improve access to important resources, limit detection of concentrated scent by predators, and decrease infestations by ectoparasites can be advantageous. Moving young, however, can also expose them to increased mortality risks (e.g., accidents, getting lost, predation). For group-living animals that live in variable environments and care for young over extended time periods, the influence of biotic factors (e.g., group size, predation risk) and abiotic factors (e.g., temperature and precipitation) on the decision to move young is unknown. We used data from 25 satellite-collared wolves ( Canis lupus ) in Idaho, Montana, and Yellowstone National Park to evaluate how these factors could influence the decision to move pups during the pup-rearing season. We hypothesized that litter size, the number of adults in a group, and perceived predation risk would positively affect the number of times gray wolves moved pups. We further hypothesized that wolves would move their pups more often when it was hot and dry to ensure sufficient access to water. Contrary to our hypothesis, monthly temperature above the 30-year average was negatively related to the number of times wolves moved their pups. Monthly precipitation above the 30-year average, however, was positively related to the amount of time wolves spent at pup-rearing sites after leaving the natal den. We found little relationship between risk of predation (by grizzly bears, humans, or conspecifics) or group and litter sizes and number of times wolves moved their pups. Our findings suggest that abiotic factors most strongly influence the decision of wolves to move pups, although responses to unpredictable biotic events (e.g., a predator encountering pups) cannot be ruled out.

Integration of altitude and airspeed information into a primary flight display via moving-tape formats: Evaluation during random tracking task

NASA Technical Reports Server (NTRS)

Abbott, Terence S.; Nataupsky, Mark; Steinmetz, George G.

1987-01-01

A ground-based aircraft simulation study was conducted to determine the effects on pilot preference and performance of integrating airspeed and altitude information into an advanced electronic primary flight display via moving-tape (linear moving scale) formats. Several key issues relating to the implementation of moving-tape formats were examined in this study: tape centering, tape orientation, and trend information. The factor of centering refers to whether the tape was centered about the actual airspeed or altitude or about some other defined reference value. Tape orientation refers to whether the represented values are arranged in descending or ascending order. Two pilots participated in this study, with each performing 32 runs along seemingly random, previously unknown flight profiles. The data taken, analyzed, and presented consisted of path performance parameters, pilot-control inputs, and electrical brain response measurements.

Assessment and prediction of road accident injuries trend using time-series models in Kurdistan.

PubMed

Parvareh, Maryam; Karimi, Asrin; Rezaei, Satar; Woldemichael, Abraha; Nili, Sairan; Nouri, Bijan; Nasab, Nader Esmail

2018-01-01

Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants' accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0) 12 , and SARIMA (1, 1, 1) (0, 0, 1) 12 , respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the accidents during the high-risk periods in order to control and decrease the rate of the injuries.

Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore.

PubMed

Earnest, Arul; Chen, Mark I; Ng, Donald; Sin, Leo Yee

2005-05-11

The main objective of this study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases) and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. We found that the ARIMA (1,0,3) model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE) for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious diseases as well.

Exact nonstationary responses of rectangular thin plate on Pasternak foundation excited by stochastic moving loads

NASA Astrophysics Data System (ADS)

Chen, Guohai; Meng, Zeng; Yang, Dixiong

2018-01-01

This paper develops an efficient method termed as PE-PIM to address the exact nonstationary responses of pavement structure, which is modeled as a rectangular thin plate resting on bi-parametric Pasternak elastic foundation subjected to stochastic moving loads with constant acceleration. Firstly, analytical power spectral density (PSD) functions of random responses for thin plate are derived by integrating pseudo excitation method (PEM) with Duhamel's integral. Based on PEM, the new equivalent von Mises stress (NEVMS) is proposed, whose PSD function contains all cross-PSD functions between stress components. Then, the PE-PIM that combines the PEM with precise integration method (PIM) is presented to achieve efficiently stochastic responses of the plate by replacing Duhamel's integral with the PIM. Moreover, the semi-analytical Monte Carlo simulation is employed to verify the computational results of the developed PE-PIM. Finally, numerical examples demonstrate the high accuracy and efficiency of PE-PIM for nonstationary random vibration analysis. The effects of velocity and acceleration of moving load, boundary conditions of the plate and foundation stiffness on the deflection and NEVMS responses are scrutinized.

Biomass characteristics and simultaneous nitrification-denitrification under long sludge retention time in an integrated reactor treating rural domestic sewage.

PubMed

Gong, Lingxiao; Jun, Li; Yang, Qing; Wang, Shuying; Ma, Bin; Peng, Yongzhen

2012-09-01

In this work, a novel integrated reactor incorporating anoxic fixed bed biofilm reactor (FBBR), oxic moving bed biofilm reactor (MBBR) and settler sequentially was proposed for nitrogen removal from rural domestic sewage. For purposes of achieving high efficiency, low costs and easy maintenance, biomass characteristics and simultaneous nitrification-denitrification (SND) were investigated under long sludge retention time during a 149-day period. The results showed that enhanced SND with proportions of 37.7-42.2% tapped the reactor potentials of efficiency and economy both, despite of C/N ratio of 2.5-4.0 in influent. TN was removed averagely by 69.3% at least, even under internal recycling ratio of 200% and less proportions of biomass assimilation (<3%). Consequently, lower internal recycle and intermittent wasted sludge discharge were feasible to save costs, together with cancellations of sludge return and anoxic stir. Furthermore, biomass with low observed heterotrophic yields (0.053 ± 0.035 g VSS/g COD) and VSS/TSS ratio (<0.55) in MBBR, simplified wasted sludge disposal. Copyright © 2012 Elsevier Ltd. All rights reserved.

A comparative study of shallow groundwater level simulation with three time series models in a coastal aquifer of South China

NASA Astrophysics Data System (ADS)

Yang, Q.; Wang, Y.; Zhang, J.; Delgado, J.

2017-05-01

Accurate and reliable groundwater level forecasting models can help ensure the sustainable use of a watershed's aquifers for urban and rural water supply. In this paper, three time series analysis methods, Holt-Winters (HW), integrated time series (ITS), and seasonal autoregressive integrated moving average (SARIMA), are explored to simulate the groundwater level in a coastal aquifer, China. The monthly groundwater table depth data collected in a long time series from 2000 to 2011 are simulated and compared with those three time series models. The error criteria are estimated using coefficient of determination ( R 2), Nash-Sutcliffe model efficiency coefficient ( E), and root-mean-squared error. The results indicate that three models are all accurate in reproducing the historical time series of groundwater levels. The comparisons of three models show that HW model is more accurate in predicting the groundwater levels than SARIMA and ITS models. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies.

Work-related accidents among the Iranian population: a time series analysis, 2000–2011

PubMed Central

Karimlou, Masoud; Imani, Mehdi; Hosseini, Agha-Fatemeh; Dehnad, Afsaneh; Vahabi, Nasim; Bakhtiyari, Mahmood

2015-01-01

Background Work-related accidents result in human suffering and economic losses and are considered as a major health problem worldwide, especially in the economically developing world. Objectives To introduce seasonal autoregressive moving average (ARIMA) models for time series analysis of work-related accident data for workers insured by the Iranian Social Security Organization (ISSO) between 2000 and 2011. Methods In this retrospective study, all insured people experiencing at least one work-related accident during a 10-year period were included in the analyses. We used Box–Jenkins modeling to develop a time series model of the total number of accidents. Results There was an average of 1476 accidents per month (1476·05±458·77, mean±SD). The final ARIMA (p,d,q) (P,D,Q)s model for fitting to data was: ARIMA(1,1,1)×(0,1,1)12 consisting of the first ordering of the autoregressive, moving average and seasonal moving average parameters with 20·942 mean absolute percentage error (MAPE). Conclusions The final model showed that time series analysis of ARIMA models was useful for forecasting the number of work-related accidents in Iran. In addition, the forecasted number of work-related accidents for 2011 explained the stability of occurrence of these accidents in recent years, indicating a need for preventive occupational health and safety policies such as safety inspection. PMID:26119774

Work-related accidents among the Iranian population: a time series analysis, 2000-2011.

PubMed

Karimlou, Masoud; Salehi, Masoud; Imani, Mehdi; Hosseini, Agha-Fatemeh; Dehnad, Afsaneh; Vahabi, Nasim; Bakhtiyari, Mahmood

2015-01-01

Work-related accidents result in human suffering and economic losses and are considered as a major health problem worldwide, especially in the economically developing world. To introduce seasonal autoregressive moving average (ARIMA) models for time series analysis of work-related accident data for workers insured by the Iranian Social Security Organization (ISSO) between 2000 and 2011. In this retrospective study, all insured people experiencing at least one work-related accident during a 10-year period were included in the analyses. We used Box-Jenkins modeling to develop a time series model of the total number of accidents. There was an average of 1476 accidents per month (1476·05±458·77, mean±SD). The final ARIMA (p,d,q) (P,D,Q)s model for fitting to data was: ARIMA(1,1,1)×(0,1,1)12 consisting of the first ordering of the autoregressive, moving average and seasonal moving average parameters with 20·942 mean absolute percentage error (MAPE). The final model showed that time series analysis of ARIMA models was useful for forecasting the number of work-related accidents in Iran. In addition, the forecasted number of work-related accidents for 2011 explained the stability of occurrence of these accidents in recent years, indicating a need for preventive occupational health and safety policies such as safety inspection.

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.

Maximum likelihood estimation for periodic autoregressive moving average models

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

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

A fixed-memory moving, expanding window for obtaining scatter corrections in X-ray CT and other stochastic averages

NASA Astrophysics Data System (ADS)

Levine, Zachary H.; Pintar, Adam L.

2015-11-01

A simple algorithm for averaging a stochastic sequence of 1D arrays in a moving, expanding window is provided. The samples are grouped in bins which increase exponentially in size so that a constant fraction of the samples is retained at any point in the sequence. The algorithm is shown to have particular relevance for a class of Monte Carlo sampling problems which includes one characteristic of iterative reconstruction in computed tomography. The code is available in the CPC program library in both Fortran 95 and C and is also available in R through CRAN.

Detrending moving average algorithm for multifractals

NASA Astrophysics Data System (ADS)

Gu, Gao-Feng; Zhou, Wei-Xing

2010-07-01

The detrending moving average (DMA) algorithm is a widely used technique to quantify the long-term correlations of nonstationary time series and the long-range correlations of fractal surfaces, which contains a parameter θ determining the position of the detrending window. We develop multifractal detrending moving average (MFDMA) algorithms for the analysis of one-dimensional multifractal measures and higher-dimensional multifractals, which is a generalization of the DMA method. The performance of the one-dimensional and two-dimensional MFDMA methods is investigated using synthetic multifractal measures with analytical solutions for backward (θ=0) , centered (θ=0.5) , and forward (θ=1) detrending windows. We find that the estimated multifractal scaling exponent τ(q) and the singularity spectrum f(α) are in good agreement with the theoretical values. In addition, the backward MFDMA method has the best performance, which provides the most accurate estimates of the scaling exponents with lowest error bars, while the centered MFDMA method has the worse performance. It is found that the backward MFDMA algorithm also outperforms the multifractal detrended fluctuation analysis. The one-dimensional backward MFDMA method is applied to analyzing the time series of Shanghai Stock Exchange Composite Index and its multifractal nature is confirmed.

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

PubMed Central

Elgendi, Mohamed

2016-01-01

Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages (“TERMA”) involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages (W1 and W2) have to follow the inequality (8×W1)≥W2≥(2×W1). Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions. PMID:27827852

Heterogeneous CPU-GPU moving targets detection for UAV video

NASA Astrophysics Data System (ADS)

Li, Maowen; Tang, Linbo; Han, Yuqi; Yu, Chunlei; Zhang, Chao; Fu, Huiquan

2017-07-01

Moving targets detection is gaining popularity in civilian and military applications. On some monitoring platform of motion detection, some low-resolution stationary cameras are replaced by moving HD camera based on UAVs. The pixels of moving targets in the HD Video taken by UAV are always in a minority, and the background of the frame is usually moving because of the motion of UAVs. The high computational cost of the algorithm prevents running it at higher resolutions the pixels of frame. Hence, to solve the problem of moving targets detection based UAVs video, we propose a heterogeneous CPU-GPU moving target detection algorithm for UAV video. More specifically, we use background registration to eliminate the impact of the moving background and frame difference to detect small moving targets. In order to achieve the effect of real-time processing, we design the solution of heterogeneous CPU-GPU framework for our method. The experimental results show that our method can detect the main moving targets from the HD video taken by UAV, and the average process time is 52.16ms per frame which is fast enough to solve the problem.

Industrial Based Migration in India. A Case Study of Dumdum "Dunlop Industrial Zone"

NASA Astrophysics Data System (ADS)

Das, Biplab; Bandyopadhyay, Aditya; Sen, Jayashree

2012-10-01

Migration is a very important part in our present society. Basically Millions of people moved during the industrial revolution. Some simply moved from a village to a town in the hope of finding work whilst others moved from one country to another in search of a better way of life. The main reason for moving home during the 19th century was to find work. On one hand this involved migration from the countryside to the growing industrial cities, on the other it involved rates of migration, emigration, and the social changes that were drastically affecting factors such as marriage,birth and death rates. These social changes taking place as a result of capitalism had far ranging affects, such as lowering the average age of marriage and increasing the size of the average family.Migration was not just people moving out of the country, it also invloved a lot of people moving into Britain. In the 1840's Ireland suffered a terrible famine. Faced with a massive cost of feeding the starving population many local landowners paid for labourers to emigrate.There was a shift away from agriculturally based rural dwelling towards urban habitation to meet the mass demand for labour that new industry required. There became great regional differences in population levels and in the structure of their demography. This was due to rates of migration, emigration, and the social changes that were drastically affecting factors such as marriage, birth and death rates. These social changes taking place as a result of capitalism had far ranging affects, such as lowering the average age of marriage and increasing the size of the average family. There is n serious disagreement as to the extent of the population changes that occurred but one key question that always arouses debate is that of whether an expanding population resulted in economic growth or vice versa, i.e. was industrialization a catalyst for population growth? A clear answer is difficult to decipher as the two variables are so closely and fundamentally interlinked, but it seems that both factors provided impetus for each otherís take off. If anything, population and economic growth were complimentary towards one another rather than simply being causative factors.

SU-F-T-497: Spatiotemporally Optimal, Personalized Prescription Scheme for Glioblastoma Patients Using the Proliferation and Invasion Glioma Model

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

Kim, M; Rockhill, J; Phillips, M

Purpose: To investigate a spatiotemporally optimal radiotherapy prescription scheme and its potential benefit for glioblastoma (GBM) patients using the proliferation and invasion (PI) glioma model. Methods: Standard prescription for GBM was assumed to deliver 46Gy in 23 fractions to GTV1+2cm margin and additional 14Gy in 7 fractions to GTV2+2cm margin. We simulated the tumor proliferation and invasion in 2D according to the PI glioma model with a moving velocity of 0.029(slow-move), 0.079(average-move), and 0.13(fast-move) mm/day for GTV2 with a radius of 1 and 2cm. For each tumor, the margin around GTV1 and GTV2 was varied to 0–6 cm and 1–3more » cm respectively. Total dose to GTV1 was constrained such that the equivalent uniform dose (EUD) to normal brain equals EUD with the standard prescription. A non-stationary dose policy, where the fractional dose varies, was investigated to estimate the temporal effect of the radiation dose. The efficacy of an optimal prescription scheme was evaluated by tumor cell-surviving fraction (SF), EUD, and the expected survival time. Results: Optimal prescription for the slow-move tumors was to use 3.0(small)-3.5(large) cm margins to GTV1, and 1.5cm margin to GTV2. For the average- and fast-move tumors, it was optimal to use 6.0cm margin for GTV1 suggesting that whole brain therapy is optimal, and then 1.5cm (average-move) and 1.5–3.0cm (fast-move, small-large) margins for GTV2. It was optimal to deliver the boost sequentially using a linearly decreasing fractional dose for all tumors. Optimal prescription led to 0.001–0.465% of the tumor SF resulted from using the standard prescription, and increased tumor EUD by 25.3–49.3% and the estimated survival time by 7.6–22.2 months. Conclusion: It is feasible to optimize a prescription scheme depending on the individual tumor characteristics. A personalized prescription scheme could potentially increase tumor EUD and the expected survival time significantly without increasing EUD to normal brain.« less

Market and organizational factors associated with hospital vertical integration into sub-acute care.

PubMed

Hogan, Tory H; Lemak, Christy Harris; Hearld, Larry R; Sen, Bisakha P; Wheeler, Jack R C; Menachemi, Nir

2018-04-11

Changes in payment models incentivize hospitals to vertically integrate into sub-acute care (SAC) services. Through vertical integration into SAC, hospitals have the potential to reduce the transaction costs associated with moving patients throughout the care continuum and reduce the likelihood that patients will be readmitted. The purpose of this study is to examine the correlates of hospital vertical integration into SAC. Using panel data of U.S. acute care hospitals (2008-2012), we conducted logit regression models to examine environmental and organizational factors associated with hospital vertical integration. Results are reported as average marginal effects. Among 3,775 unique hospitals (16,269 hospital-year observations), 25.7% vertically integrated into skilled nursing facilities during at least 1 year of the study period. One measure of complexity, the availability of skilled nursing facilities in a county (ME = -1.780, p < .001), was negatively associated with hospital vertical integration into SAC. Measures of munificence, percentage of the county population eligible for Medicare (ME = 0.018, p < .001) and rural geographic location (ME = 0.069, p < .001), were positively associated with hospital vertical integration into SAC. Dynamism, when measured as the change county population between 2008 and 2011 (ME = 1.19e-06, p < .001), was positively associated with hospital vertical integration into SAC. Organizational resources, when measured as swing beds (ME = 0.069, p < .001), were positively associated with hospital vertical integration into SAC. Organizational resources, when measured as investor owned (ME = -0.052, p < .1) and system affiliation (ME = -0.041, p < .1), were negatively associated with hospital vertical integration into SAC. Hospital adaption to the changing health care landscape through vertical integration varies across market and organizational conditions. Current Centers for Medicare and Medicaid reimbursement programs do not take these factors into consideration. Vertical integration strategy into SAC may be more appropriate under certain market conditions. Hospital leaders may consider how to best align their organization's SAC strategy with their operating environment.

Real time detection of farm-level swine mycobacteriosis outbreak using time series modeling of the number of condemned intestines in abattoirs.

PubMed

Adachi, Yasumoto; Makita, Kohei

2015-09-01

Mycobacteriosis in swine is a common zoonosis found in abattoirs during meat inspections, and the veterinary authority is expected to inform the producer for corrective actions when an outbreak is detected. The expected value of the number of condemned carcasses due to mycobacteriosis therefore would be a useful threshold to detect an outbreak, and the present study aims to develop such an expected value through time series modeling. The model was developed using eight years of inspection data (2003 to 2010) obtained at 2 abattoirs of the Higashi-Mokoto Meat Inspection Center, Japan. The resulting model was validated by comparing the predicted time-dependent values for the subsequent 2 years with the actual data for 2 years between 2011 and 2012. For the modeling, at first, periodicities were checked using Fast Fourier Transformation, and the ensemble average profiles for weekly periodicities were calculated. An Auto-Regressive Integrated Moving Average (ARIMA) model was fitted to the residual of the ensemble average on the basis of minimum Akaike's information criterion (AIC). The sum of the ARIMA model and the weekly ensemble average was regarded as the time-dependent expected value. During 2011 and 2012, the number of whole or partial condemned carcasses exceeded the 95% confidence interval of the predicted values 20 times. All of these events were associated with the slaughtering of pigs from three producers with the highest rate of condemnation due to mycobacteriosis.

Do alcohol excise taxes affect traffic accidents? Evidence from Estonia.

PubMed

Saar, Indrek

2015-01-01

This article examines the association between alcohol excise tax rates and alcohol-related traffic accidents in Estonia. Monthly time series of traffic accidents involving drunken motor vehicle drivers from 1998 through 2013 were regressed on real average alcohol excise tax rates while controlling for changes in economic conditions and the traffic environment. Specifically, regression models with autoregressive integrated moving average (ARIMA) errors were estimated in order to deal with serial correlation in residuals. Counterfactual models were also estimated in order to check the robustness of the results, using the level of non-alcohol-related traffic accidents as a dependent variable. A statistically significant (P <.01) strong negative relationship between the real average alcohol excise tax rate and alcohol-related traffic accidents was disclosed under alternative model specifications. For instance, the regression model with ARIMA (0, 1, 1)(0, 1, 1) errors revealed that a 1-unit increase in the tax rate is associated with a 1.6% decrease in the level of accidents per 100,000 population involving drunk motor vehicle drivers. No similar association was found in the cases of counterfactual models for non-alcohol-related traffic accidents. This article indicates that the level of alcohol-related traffic accidents in Estonia has been affected by changes in real average alcohol excise taxes during the period 1998-2013. Therefore, in addition to other measures, the use of alcohol taxation is warranted as a policy instrument in tackling alcohol-related traffic accidents.

The Micromechanics of the Moving Contact Line

NASA Technical Reports Server (NTRS)

Han, Minsub; Lichter, Seth; Lin, Chih-Yu; Perng, Yeong-Yan

1996-01-01

The proposed research is divided into three components concerned with molecular structure, molecular orientation, and continuum averages of discrete systems. In the experimental program, we propose exploring how changes in interfacial molecular structure generate contact line motion. Rather than rely on the electrostatic and electrokinetic fields arising from the molecules themselves, we augment their interactions by an imposed field at the solid/liquid interface. By controling the field, we can manipulate the molecular structure at the solid/liquid interface. In response to controlled changes in molecular structure, we observe the resultant contact line motion. In the analytical portion of the proposed research we seek to formulate a system of equations governing fluid motion which accounts for the orientation of fluid molecules. In preliminary work, we have focused on describing how molecular orientation affects the forces generated at the moving contact line. Ideally, as assumed above, the discrete behavior of molecules can be averaged into a continuum theory. In the numerical portion of the proposed research, we inquire whether the contact line region is, in fact, large enough to possess a well-defined average. Additionally, we ask what types of behavior distinguish discrete systems from continuum systems. Might the smallness of the contact line region, in itself, lead to behavior different from that in the bulk? Taken together, our proposed research seeks to identify and accurately account for some of the molecular dynamics of the moving contact line, and attempts to formulate a description from which one can compute the forces at the moving contact line.

The Usage of the U.S. Environmental Protection Agency’s (USEPA) Motor Vehicle Emission Simulator (MOVES) with the Federal Aviation Administration’s (FAA) Emissions and Dispersion Modeling System (EDMS)

DOT National Transportation Integrated Search

2014-03-01

As of March 2013, USEPA requires the usage of MOVES as a replacement for MOBILE. This means that EDMS analysts must use MOVES with EDMS instead of MOBILE. The plan is not to modify EDMS which continues to be integrated with MOBILE6; but instead, FAA ...

Anatomy as the Backbone of an Integrated First Year Medical Curriculum: Design and Implementation

PubMed Central

Klement, Brenda J.; Paulsen, Douglas F.; Wineski, Lawrence E

2011-01-01

Morehouse School of Medicine chose to restructure its first year medical curriculum in 2005. The anatomy faculty had prior experience in integrating courses, stemming from the successful integration of individual anatomical sciences courses into a single course called Human Morphology. The integration process was expanded to include the other first year basic science courses (Biochemistry, Physiology, and Neurobiology) as we progressed toward an integrated curriculum. A team, consisting of the course directors, a curriculum coordinator and the Associate Dean for Educational and Faculty Affairs, was assembled to build the new curriculum. For the initial phase, the original course titles were retained but the lecture order was reorganized around the Human Morphology topic sequence. The material from all four courses was organized into four sequential units. Other curricular changes included placing laboratories and lectures more consistently in the daily routine, reducing lecture time from 120 to 90 minute blocks, eliminating unnecessary duplication of content, and increasing the amount of independent study time. Examinations were constructed to include questions from all courses on a single test, reducing the number of examination days in each block from three to one. The entire restructuring process took two years to complete, and the revised curriculum was implemented for the students entering in 2007. The outcomes of the restructured curriculum include a reduction in the number of contact hours by 28%, higher or equivalent subject examination average scores, enhanced student satisfaction, and a first year curriculum team better prepared to move forward with future integration. PMID:21538939

Flash trajectory imaging of target 3D motion

NASA Astrophysics Data System (ADS)

Wang, Xinwei; Zhou, Yan; Fan, Songtao; He, Jun; Liu, Yuliang

2011-03-01

We present a flash trajectory imaging technique which can directly obtain target trajectory and realize non-contact measurement of motion parameters by range-gated imaging and time delay integration. Range-gated imaging gives the range of targets and realizes silhouette detection which can directly extract targets from complex background and decrease the complexity of moving target image processing. Time delay integration increases information of one single frame of image so that one can directly gain the moving trajectory. In this paper, we have studied the algorithm about flash trajectory imaging and performed initial experiments which successfully obtained the trajectory of a falling badminton. Our research demonstrates that flash trajectory imaging is an effective approach to imaging target trajectory and can give motion parameters of moving targets.

Depth extraction method with high accuracy in integral imaging based on moving array lenslet technique

NASA Astrophysics Data System (ADS)

Wang, Yao-yao; Zhang, Juan; Zhao, Xue-wei; Song, Li-pei; Zhang, Bo; Zhao, Xing

2018-03-01

In order to improve depth extraction accuracy, a method using moving array lenslet technique (MALT) in pickup stage is proposed, which can decrease the depth interval caused by pixelation. In this method, the lenslet array is moved along the horizontal and vertical directions simultaneously for N times in a pitch to get N sets of elemental images. Computational integral imaging reconstruction method for MALT is taken to obtain the slice images of the 3D scene, and the sum modulus (SMD) blur metric is taken on these slice images to achieve the depth information of the 3D scene. Simulation and optical experiments are carried out to verify the feasibility of this method.

Career on the move: geography, stratification, and scientific impact.

PubMed

Deville, Pierre; Wang, Dashun; Sinatra, Roberta; Song, Chaoming; Blondel, Vincent D; Barabási, Albert-László

2014-04-24

Changing institutions is an integral part of an academic life. Yet little is known about the mobility patterns of scientists at an institutional level and how these career choices affect scientific outcomes. Here, we examine over 420,000 papers, to track the affiliation information of individual scientists, allowing us to reconstruct their career trajectories over decades. We find that career movements are not only temporally and spatially localized, but also characterized by a high degree of stratification in institutional ranking. When cross-group movement occurs, we find that while going from elite to lower-rank institutions on average associates with modest decrease in scientific performance, transitioning into elite institutions does not result in subsequent performance gain. These results offer empirical evidence on institutional level career choices and movements and have potential implications for science policy.

No evidence of suicide increase following terrorist attacks in the United States: an interrupted time-series analysis of September 11 and Oklahoma City.

PubMed

Pridemore, William Alex; Trahan, Adam; Chamlin, Mitchell B

2009-12-01

There is substantial evidence of detrimental psychological sequelae following disasters, including terrorist attacks. The effect of these events on extreme responses such as suicide, however, is unclear. We tested competing hypotheses about such effects by employing autoregressive integrated moving average techniques to model the impact of September 11 and the Oklahoma City bombing on monthly suicide counts at the local, state, and national level. Unlike prior studies that provided conflicting evidence, rigorous time series techniques revealed no support for an increase or decrease in suicides following these events. We conclude that while terrorist attacks produce subsequent psychological morbidity and may affect self and collective efficacy well beyond their immediate impact, these effects are not strong enough to influence levels of suicide mortality.

Lethal firearm-related violence against Canadian women: did tightening gun laws have an impact on women's health and safety?

PubMed

McPhedran, Samara; Mauser, Gary

2013-01-01

Domestic violence remains a significant public health issue around the world, and policy makers continually strive to implement effective legislative frameworks to reduce lethal violence against women. This article examines whether the 1995 Firearms Act (Bill C-68) had a significant impact on female firearm homicide victimization rates in Canada. Time series of gender-disaggregated data from 1974 to 2009 were examined. Two different analytic approaches were used: the autoregressive integrated moving average (ARIMA) modelling and the Zivot-Andrews (ZA) structural breakpoint tests. There was little evidence to suggest that increased firearms legislation in Canada had a significant impact on preexisting trends in lethal firearm violence against women. These results do not support the view that increasing firearms legislation is associated with a reduced incidence of firearm-related female domestic homicide victimization.

Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

NASA Astrophysics Data System (ADS)

Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.

2016-10-01

Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.

[Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].

PubMed

Ke-Wei, Wang; Yu, Wu; Jin-Ping, Li; Yu-Yu, Jiang

2016-07-12

To explore the effect of the autoregressive integrated moving average model-nonlinear auto-regressive neural network (ARIMA-NARNN) model on predicting schistosomiasis infection rates of population. The ARIMA model, NARNN model and ARIMA-NARNN model were established based on monthly schistosomiasis infection rates from January 2005 to February 2015 in Jiangsu Province, China. The fitting and prediction performances of the three models were compared. Compared to the ARIMA model and NARNN model, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4, respectively. The ARIMA-NARNN model could effectively fit and predict schistosomiasis infection rates of population, which might have a great application value for the prevention and control of schistosomiasis.

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

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

Symbiosis of Steel, Energy, and CO2 Evolution in Korea

NASA Astrophysics Data System (ADS)

Lee, Hyunjoung; Matsuura, Hiroyuki; Sohn, Il

2016-09-01

This study looks at the energy intensity of the steel industry and the greenhouse gas intensity involved with the production of steel. Using several sources of steel production data and the corresponding energy sources used provides a time-series analysis of the greenhouse gas (GHG) and energy intensity from 1990 to 2014. The impact of the steel economy with the gross domestic product (GDP) provides indirect importance of the general manufacturing sector within Korea and in particular the steel industry. Beyond 2008, the shift in excess materials production and significant increase in total imports have led to an imbalance in the Korean steel market and continue to inhibit the growth of the domestic steel market. The forecast of the GHG and energy intensity along with the steel production up to 2030 is provided using the auto regressive integrated moving average analysis.

Career on the Move: Geography, Stratification, and Scientific Impact

PubMed Central

Deville, Pierre; Wang, Dashun; Sinatra, Roberta; Song, Chaoming; Blondel, Vincent D.; Barabási, Albert-László

2014-01-01

Changing institutions is an integral part of an academic life. Yet little is known about the mobility patterns of scientists at an institutional level and how these career choices affect scientific outcomes. Here, we examine over 420,000 papers, to track the affiliation information of individual scientists, allowing us to reconstruct their career trajectories over decades. We find that career movements are not only temporally and spatially localized, but also characterized by a high degree of stratification in institutional ranking. When cross-group movement occurs, we find that while going from elite to lower-rank institutions on average associates with modest decrease in scientific performance, transitioning into elite institutions does not result in subsequent performance gain. These results offer empirical evidence on institutional level career choices and movements and have potential implications for science policy. PMID:24759743

Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

PubMed Central

Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

2014-01-01

Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586

Alteration of Box-Jenkins methodology by implementing genetic algorithm method

NASA Astrophysics Data System (ADS)

Ismail, Zuhaimy; Maarof, Mohd Zulariffin Md; Fadzli, Mohammad

2015-02-01

A time series is a set of values sequentially observed through time. The Box-Jenkins methodology is a systematic method of identifying, fitting, checking and using integrated autoregressive moving average time series model for forecasting. Box-Jenkins method is an appropriate for a medium to a long length (at least 50) time series data observation. When modeling a medium to a long length (at least 50), the difficulty arose in choosing the accurate order of model identification level and to discover the right parameter estimation. This presents the development of Genetic Algorithm heuristic method in solving the identification and estimation models problems in Box-Jenkins. Data on International Tourist arrivals to Malaysia were used to illustrate the effectiveness of this proposed method. The forecast results that generated from this proposed model outperformed single traditional Box-Jenkins model.

Career on the Move: Geography, Stratification, and Scientific Impact

NASA Astrophysics Data System (ADS)

Deville, Pierre; Wang, Dashun; Sinatra, Roberta; Song, Chaoming; Blondel, Vincent D.; Barabási, Albert-László

2014-04-01

Changing institutions is an integral part of an academic life. Yet little is known about the mobility patterns of scientists at an institutional level and how these career choices affect scientific outcomes. Here, we examine over 420,000 papers, to track the affiliation information of individual scientists, allowing us to reconstruct their career trajectories over decades. We find that career movements are not only temporally and spatially localized, but also characterized by a high degree of stratification in institutional ranking. When cross-group movement occurs, we find that while going from elite to lower-rank institutions on average associates with modest decrease in scientific performance, transitioning into elite institutions does not result in subsequent performance gain. These results offer empirical evidence on institutional level career choices and movements and have potential implications for science policy.

Accelerating Harmonization in Digital Health.

PubMed

Moore, Carolyn; Werner, Laurie; BenDor, Amanda Puckett; Bailey, Mike; Khan, Nighat

2017-01-01

Digital tools play an important role in supporting front-line health workers who deliver primary care. This paper explores the current state of efforts undertaken to move away from single-purpose applications of digital health towards integrated systems and solutions that align with national strategies. Through examples from health information systems, data and health worker training, this paper demonstrates how governments and stakeholders are working to integrate digital health services. We emphasize three factors as crucial for this integration: development and implementation of national digital health strategies; technical interoperability and collaborative approaches to ensure that digital health has an impact on the primary care level. Consolidation of technologies will enable an integrated, scaleable approach to the use of digital health to support health workers. As this edition explores a paradigm shift towards harmonization in primary healthcare systems, this paper explores complementary efforts undertaken to move away from single-purpose applications of digital health towards integrated systems and solutions that align with national strategies. It describes a paradigm shift towards integrated and interoperable systems that respond to health workers' needs in training, data and health information; and calls for the consolidation and integration of digital health tools and approaches across health areas, functions and levels of the health system. It then considers the critical factors that must be in place to support this paradigm shift. This paper aims not only to describe steps taken to move from fractured pilots to effective systems, but to propose a new perspective focused on consolidation and collaboration guided by national digital health strategies.

Use of spatiotemporal characteristics of ambient PM2.5 in rural South India to infer local versus regional contributions.

PubMed

Kumar, M Kishore; Sreekanth, V; Salmon, Maëlle; Tonne, Cathryn; Marshall, Julian D

2018-08-01

This study uses spatiotemporal patterns in ambient concentrations to infer the contribution of regional versus local sources. We collected 12 months of monitoring data for outdoor fine particulate matter (PM 2.5 ) in rural southern India. Rural India includes more than one-tenth of the global population and annually accounts for around half a million air pollution deaths, yet little is known about the relative contribution of local sources to outdoor air pollution. We measured 1-min averaged outdoor PM 2.5 concentrations during June 2015-May 2016 in three villages, which varied in population size, socioeconomic status, and type and usage of domestic fuel. The daily geometric-mean PM 2.5 concentration was ∼30 μg m -3 (geometric standard deviation: ∼1.5). Concentrations exceeded the Indian National Ambient Air Quality standards (60 μg m -3 ) during 2-5% of observation days. Average concentrations were ∼25 μg m -3 higher during winter than during monsoon and ∼8 μg m -3 higher during morning hours than the diurnal average. A moving average subtraction method based on 1-min average PM 2.5 concentrations indicated that local contributions (e.g., nearby biomass combustion, brick kilns) were greater in the most populated village, and that overall the majority of ambient PM 2.5 in our study was regional, implying that local air pollution control strategies alone may have limited influence on local ambient concentrations. We compared the relatively new moving average subtraction method against a more established approach. Both methods broadly agree on the relative contribution of local sources across the three sites. The moving average subtraction method has broad applicability across locations. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Determining the feasibility of robotic courier medication delivery in a hospital setting.

PubMed

Kirschling, Thomas E; Rough, Steve S; Ludwig, Brad C

2009-10-01

The feasibility of a robotic courier medication delivery system in a hospital setting was evaluated. Robotic couriers are self-guiding, self-propelling robots that navigate hallways and elevators to pull an attached or integrated cart to a desired destination. A robotic courier medication delivery system was pilot tested in two patient care units at a 471-bed tertiary care academic medical center. Average transit for the existing manual medication delivery system hourly hospitalwide deliveries was 32.6 minutes. Of this, 32.3% was spent at the patient care unit and 67.7% was spent pushing the cart or waiting at an elevator. The robotic courier medication delivery system traveled as fast as 1.65 ft/sec (52% speed of the manual system) in the absence of barriers but moved at an average rate of 0.84 ft/sec (26% speed of the manual system) during the study, primarily due to hallway obstacles. The robotic courier was utilized for 50% of the possible 1750 runs during the 125-day pilot due to technical or situational difficulties. Of the runs that were sent, a total of 79 runs failed, yielding an overall 91% success rate. During the final month of the pilot, the success rate reached 95.6%. Customer satisfaction with the traditional manual delivery system was high. Customer satisfaction with deliveries declined after implementation of the robotic courier medication distribution system. A robotic courier medication delivery system was implemented but was not expanded beyond the two pilot units. Challenges of implementation included ongoing education on how to properly move the robotic courier and keeping the hallway clear of obstacles.

Performance evaluation of ionospheric time delay forecasting models using GPS observations at a low-latitude station

NASA Astrophysics Data System (ADS)

Sivavaraprasad, G.; Venkata Ratnam, D.

2017-07-01

Ionospheric delay is one of the major atmospheric effects on the performance of satellite-based radio navigation systems. It limits the accuracy and availability of Global Positioning System (GPS) measurements, related to critical societal and safety applications. The temporal and spatial gradients of ionospheric total electron content (TEC) are driven by several unknown priori geophysical conditions and solar-terrestrial phenomena. Thereby, the prediction of ionospheric delay is challenging especially over Indian sub-continent. Therefore, an appropriate short/long-term ionospheric delay forecasting model is necessary. Hence, the intent of this paper is to forecast ionospheric delays by considering day to day, monthly and seasonal ionospheric TEC variations. GPS-TEC data (January 2013-December 2013) is extracted from a multi frequency GPS receiver established at K L University, Vaddeswaram, Guntur station (geographic: 16.37°N, 80.37°E; geomagnetic: 7.44°N, 153.75°E), India. An evaluation, in terms of forecasting capabilities, of three ionospheric time delay models - an Auto Regressive Moving Average (ARMA) model, Auto Regressive Integrated Moving Average (ARIMA) model, and a Holt-Winter's model is presented. The performances of these models are evaluated through error measurement analysis during both geomagnetic quiet and disturbed days. It is found that, ARMA model is effectively forecasting the ionospheric delay with an accuracy of 82-94%, which is 10% more superior to ARIMA and Holt-Winter's models. Moreover, the modeled VTEC derived from International Reference Ionosphere, IRI (IRI-2012) model and new global TEC model, Neustrelitz TEC Model (NTCM-GL) have compared with forecasted VTEC values of ARMA, ARIMA and Holt-Winter's models during geomagnetic quiet days. The forecast results are indicating that ARMA model would be useful to set up an early warning system for ionospheric disturbances at low latitude regions.

The economic impact of a smoke-free bylaw on restaurant and bar sales in Ottawa, Canada.

PubMed

Luk, Rita; Ferrence, Roberta; Gmel, Gerhard

2006-05-01

On 1 August 2001, the City of Ottawa (Canada's Capital) implemented a smoke-free bylaw that completely prohibited smoking in work-places and public places, including restaurants and bars, with no exemption for separately ventilated smoking rooms. This paper evaluates the effects of this bylaw on restaurant and bar sales. DATA AND MEASURES: We used retail sales tax data from March 1998 to June 2002 to construct two outcome measures: the ratio of licensed restaurant and bar sales to total retail sales and the ratio of unlicensed restaurant sales to total retail sales. Restaurant and bar sales were subtracted from total retail sales in the denominator of these measures. We employed an interrupted time-series design. Autoregressive integrated moving average (ARIMA) intervention analysis was used to test for three possible impacts that the bylaw might have on the sales of restaurants and bars. We repeated the analysis using regression with autoregressive moving average (ARMA) errors method to triangulate our results. Outcome measures showed declining trends at baseline before the bylaw went into effect. Results from ARIMA intervention and regression analyses did not support the hypotheses that the smoke-free bylaw had an impact that resulted in (1) abrupt permanent, (2) gradual permanent or (3) abrupt temporary changes in restaurant and bar sales. While a large body of research has found no significant adverse impact of smoke-free legislation on restaurant and bar sales in the United States, Australia and elsewhere, our study confirms these results in a northern region with a bilingual population, which has important implications for impending policy in Europe and other areas.

Cost and efficacy comparison of integrated pest management strategies with monthly spray insecticide applications for German cockroach (Dictyoptera: Blattellidae) control in public housing.

PubMed

Miller, D M; Meek, F

2004-04-01

The long-term costs and efficacy of two treatment methodologies for German cockroach, Blattella germanica (L.), control were compared in the public housing environment. The "traditional" treatment for German cockroaches consisted of monthly baseboard and crack and crevice treatment (TBCC) by using spray and dust formulation insecticides. The integrated pest management treatment (IPM) involved initial vacuuming of apartments followed by monthly or quarterly applications of baits and insect growth regulator (IGR) devices. Cockroach populations in the IPM treatment were also monitored with sticky traps. Technician time and the amount of product applied were used to measure cost in both treatments. Twenty-four hour sticky trap catch was used as an indicator of treatment efficacy. The cost of the IPM treatment was found to be significantly greater than the traditional treatment, particularly at the initiation of the test. In the first month (clean-out), the average cost per apartment unit was dollar 14.60, whereas the average cost of a TBCC unit was dollar 2.75. In the second month of treatment, the average cost of IPM was still significantly greater than the TBCC cost. However, after month 4 the cost of the two treatments was no longer significantly different because many of the IPM apartments were moved to a quarterly treatment schedule. To evaluate the long-term costs of the two treatments over the entire year, technician time and product quantities were averaged over all units treated within the 12-mo test period (total 600 U per treatment). The average per unit cost of the IPM treatment was (dollar 4.06). The average IPM cost was significantly greater than that of the TBCC treatment at dollar 1.50 per unit. Although the TBCC was significantly less expensive than the IPM treatment, it was also less effective. Trap catch data indicated that the TBCC treatment had little, if any, effect on the cockroach populations over the course of the year. Cockroach populations in the TBCC treatment remained steady for the first 5 mo of the test and then had a threefold increase during the summer. Cockroach populations in the IPM treatment were significantly reduced from an average of 24.7 cockroaches per unit before treatment to an average 3.9 cockroaches per unit in month 4. The suppressed cockroach populations (< 5 per unit) in the IPM treatment remained constant for the remaining 8 mo of the test.

Higher Education Quality and Work-Based Learning: Two Concepts Not yet Fully Integrated

ERIC Educational Resources Information Center

Gibbs, Paul; Armsby, Pauline

2010-01-01

This short paper recognises the growth in emphasis in work-based learning as Europe moves forward on economically driven life-long learning. We support such a move and point to issues which still need to be resolved.

Coulomb's Law in a Moving Medium--A Review Exercise in Advanced Undergraduate Electromagnetism

ERIC Educational Resources Information Center

Sastry, G. P.

1978-01-01

The electromagnetic field of a static charge in a moving medium is evaluated using elements of special relativity, residue calculus, and Fourier integration. Some of the concepts in electrodynamics that are of current research value are discussed. (BB)

Model-based analysis of pattern motion processing in mouse primary visual cortex

PubMed Central

Muir, Dylan R.; Roth, Morgane M.; Helmchen, Fritjof; Kampa, Björn M.

2015-01-01

Neurons in sensory areas of neocortex exhibit responses tuned to specific features of the environment. In visual cortex, information about features such as edges or textures with particular orientations must be integrated to recognize a visual scene or object. Connectivity studies in rodent cortex have revealed that neurons make specific connections within sub-networks sharing common input tuning. In principle, this sub-network architecture enables local cortical circuits to integrate sensory information. However, whether feature integration indeed occurs locally in rodent primary sensory areas has not been examined directly. We studied local integration of sensory features in primary visual cortex (V1) of the mouse by presenting drifting grating and plaid stimuli, while recording the activity of neuronal populations with two-photon calcium imaging. Using a Bayesian model-based analysis framework, we classified single-cell responses as being selective for either individual grating components or for moving plaid patterns. Rather than relying on trial-averaged responses, our model-based framework takes into account single-trial responses and can easily be extended to consider any number of arbitrary predictive models. Our analysis method was able to successfully classify significantly more responses than traditional partial correlation (PC) analysis, and provides a rigorous statistical framework to rank any number of models and reject poorly performing models. We also found a large proportion of cells that respond strongly to only one stimulus class. In addition, a quarter of selectively responding neurons had more complex responses that could not be explained by any simple integration model. Our results show that a broad range of pattern integration processes already take place at the level of V1. This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features. PMID:26300738

Execution of saccadic eye movements affects speed perception

PubMed Central

Goettker, Alexander; Braun, Doris I.; Schütz, Alexander C.; Gegenfurtner, Karl R.

2018-01-01

Due to the foveal organization of our visual system we have to constantly move our eyes to gain precise information about our environment. Doing so massively alters the retinal input. This is problematic for the perception of moving objects, because physical motion and retinal motion become decoupled and the brain has to discount the eye movements to recover the speed of moving objects. Two different types of eye movements, pursuit and saccades, are combined for tracking. We investigated how the way we track moving targets can affect the perceived target speed. We found that the execution of corrective saccades during pursuit initiation modifies how fast the target is perceived compared with pure pursuit. When participants executed a forward (catch-up) saccade they perceived the target to be moving faster. When they executed a backward saccade they perceived the target to be moving more slowly. Variations in pursuit velocity without corrective saccades did not affect perceptual judgments. We present a model for these effects, assuming that the eye velocity signal for small corrective saccades gets integrated with the retinal velocity signal during pursuit. In our model, the execution of corrective saccades modulates the integration of these two signals by giving less weight to the retinal information around the time of corrective saccades. PMID:29440494

Evidence of redshifts in the average solar line profiles of C IV and Si IV from OSO-8 observations

NASA Technical Reports Server (NTRS)

Roussel-Dupre, D.; Shine, R. A.

1982-01-01

Line profiles of C IV and Si V obtained by the Colorado spectrometer on OSO-8 are presented. It is shown that the mean profiles are redshifted with a magnitude varying from 6-20 km/s, and with a mean of 12 km/s. An apparent average downflow of material in the 50,000-100,000 K temperature range is measured. The redshifts are observed in the line center positions of spatially and temporally averaged profiles and are measured either relative to chromospheric Si I lines or from a comparison of sun center and limb profiles. The observations of 6-20 km/s redshifts place constraints on the mechanisms that dominate EUV line emission since it requires a strong weighting of the emission in regions of downward moving material, and since there is little evidence for corresponding upward moving materials in these lines.

CPV for the rooftop market: novel approaches to tracking integration in photovoltaic modules

NASA Astrophysics Data System (ADS)

Apostoleris, Harry; Stefancich, Marco; Alexander-Katz, Alfredo; Chiesa, Matteo

2016-03-01

Concentrated photovoltaics (CPV) has long been recognized as an effective approach to enabling the use of high cost, high-efficiency solar cells for enhanced solar energy conversion, but is excluded from the domestic rooftop market due to the requirement that solar concentrators track the sun. This market may be opened up by integrating of the tracking mechanism into the module itself. Tracking integration may take the form of a miniaturization of a conventional tracking apparatus, or optical tracking, in which tracking is achieved through variation of optical properties such as refractive index or transparency rather than mechanical movement of the receiver. We have demonstrated a simple system using a heat-responsive transparency switching material to create a moving aperture that tracks the position of a moving light spot. We use this behavior to create a concentrating light trap with a moving aperture that reactively tracks the sun. Taking the other approach, we have fabricated 3D-printed parabolic mini-concentrators which can track the sun using small motors in a low-profile geometry. We characterize the performance of the concentrators and consider the impact of tracking integration on the broader PV market.

A New Trend-Following Indicator: Using SSA to Design Trading Rules

NASA Astrophysics Data System (ADS)

Leles, Michel Carlo Rodrigues; Mozelli, Leonardo Amaral; Guimarães, Homero Nogueira

Singular Spectrum Analysis (SSA) is a non-parametric approach that can be used to decompose a time-series as trends, oscillations and noise. Trend-following strategies rely on the principle that financial markets move in trends for an extended period of time. Moving Averages (MAs) are the standard indicator to design such strategies. In this study, SSA is used as an alternative method to enhance trend resolution in comparison with the traditional MA. New trading rules using SSA as indicator are proposed. This paper shows that for the Down Jones Industrial Average (DJIA) and Shangai Securities Composite Index (SSCI) time-series the SSA trading rules provided, in general, better results in comparison to MA trading rules.

Moving towards Universal Health Coverage through the Development of Integrated Service Delivery Packages for Primary Health Care in the Solomon Islands

PubMed Central

Whiting, Stephen; Postma, Sjoerd; Jamshaid de Lorenzo, Ayesha; Aumua, Audrey

2016-01-01

The Solomon Islands Government is pursuing integrated care with the goal of improving the quality of health service delivery to rural populations. Under the auspices of Universal Health Coverage, integrated service delivery packages were developed which defined the clinical and public health services that should be provided at different levels of the health system. The process of developing integrated service delivery packages helped to identify key policy decisions the government needed to make in order to improve service quality and efficiency. The integrated service delivery packages have instigated the revision of job descriptions and are feeding into the development of a human resource plan for health. They are also being used to guide infrastructure development and health system planning and should lead to better management of resources. The integrated service delivery packages have become a key tool to operationalise the government’s policy to move towards a more efficient, equitable, quality and sustainable health system. PMID:28321177

Estimation of underground river water availability based on rainfall in the Maros karst region, South Sulawesi

NASA Astrophysics Data System (ADS)

Arsyad, Muhammad; Ihsan, Nasrul; Tiwow, Vistarani Arini

2016-02-01

Maros karst region, covering an area of 43.750 hectares, has water resources that determine the life around it. Water resources in Maros karst are in the rock layers or river underground in the cave. The data used in this study are primary and secondary data. Primary data includes characteristics of the medium. Secondary data is rainfall data from BMKG, water discharge data from the PSDA, South Sulawesi province in 1990-2010, and the other characteristics data Maros karst, namely cave, flora and fauna of the Bantimurung Bulusaraung National Park. Data analysis was conducted using laboratory test for medium characteristics Maros karst, rainfall and water discharge were analyzed using Minitab Program 1.5 to determine their profile. The average rainfall above 200 mm per year occurs in the range of 1999 to 2005. The availability of the water discharge at over 50 m3/s was happened in 1993 and 1995. Prediction was done by modeling Autoregressive Integrated Moving Average (ARIMA), with the rainfall data shows that the average precipitation for four years (2011-2014) will sharply fluctuate. The prediction of water discharge in Maros karst region was done for the period from January to August in 2011, including the type of 0. In 2012, the addition of the water discharge started up in early 2014.

Frequency modulation at a moving material interface and a conservation law for wave number. [acoustic wave reflection and transmission

NASA Technical Reports Server (NTRS)

Kleinstein, G. G.; Gunzburger, M. D.

1976-01-01

An integral conservation law for wave numbers is considered. In order to test the validity of the proposed conservation law, a complete solution for the reflection and transmission of an acoustic wave impinging normally on a material interface moving at a constant speed is derived. The agreement between the frequency condition thus deduced from the dynamic equations of motion and the frequency condition derived from the jump condition associated with the integral equation supports the proposed law as a true conservation law. Additional comparisons such as amplitude discontinuities and Snells' law in a moving media further confirm the stated proposition. Results are stated concerning frequency and wave number relations across a shock front as predicted by the proposed conservation law.

Distractor interference during smooth pursuit eye movements.

PubMed

Spering, Miriam; Gegenfurtner, Karl R; Kerzel, Dirk

2006-10-01

When 2 targets for pursuit eye movements move in different directions, the eye velocity follows the vector average (S. G. Lisberger & V. P. Ferrera, 1997). The present study investigates the mechanisms of target selection when observers are instructed to follow a predefined horizontal target and to ignore a moving distractor stimulus. Results show that at 140 ms after distractor onset, horizontal eye velocity is decreased by about 25%. Vertical eye velocity increases or decreases by 1 degrees /s in the direction opposite from the distractor. This deviation varies in size with distractor direction, velocity, and contrast. The effect was present during the initiation and steady-state tracking phase of pursuit but only when the observer had prior information about target motion. Neither vector averaging nor winner-take-all models could predict the response to a moving to-be-ignored distractor during steady-state tracking of a predefined target. The contributions of perceptual mislocalization and spatial attention to the vertical deviation in pursuit are discussed. Copyright 2006 APA.

Changes in healthcare use among individuals who move into public housing: a population-based investigation.

PubMed

Hinds, Aynslie M; Bechtel, Brian; Distasio, Jino; Roos, Leslie L; Lix, Lisa M

2018-06-05

Residence in public housing, a subsidized and managed government program, may affect health and healthcare utilization. We compared healthcare use in the year before individuals moved into public housing with usage during their first year of tenancy. We also described trends in use. We used linked population-based administrative data housed in the Population Research Data Repository at the Manitoba Centre for Health Policy. The cohort consisted of individuals who moved into public housing in 2009 and 2010. We counted the number of hospitalizations, general practitioner (GP) visits, specialist visits, emergency department visits, and prescriptions drugs dispensed in the twelve 30-day intervals (i.e., months) immediately preceding and following the public housing move-in date. Generalized linear models with generalized estimating equations tested for a period (pre/post-move-in) by month interaction. Odds ratios (ORs), incident rate ratios (IRRs), and means are reported along with 95% confidence intervals (95% CIs). The cohort included 1942 individuals; the majority were female (73.4%) who lived in low income areas and received government assistance (68.1%). On average, the cohort had more than four health conditions. Over the 24 30-day intervals, the percentage of the cohort that visited a GP, specialist, and an emergency department ranged between 37.0% and 43.0%, 10.0% and 14.0%, and 6.0% and 10.0%, respectively, while the percentage of the cohort hospitalized ranged from 1.0% to 5.0%. Generally, these percentages were highest in the few months before the move-in date and lowest in the few months after the move-in date. The period by month interaction was statistically significant for hospitalizations, GP visits, and prescription drug use. The average change in the odds, rate, or mean was smaller in the post-move-in period than in the pre-move-in period. Use of some healthcare services declined after people moved into public housing; however, the decrease was only observed in the first few months and utilization rebounded. Knowledge of healthcare trends before individuals move in are informative for ensuring the appropriate supports are available to new public housing residents. Further study is needed to determine if decreased healthcare utilization following a move is attributable to decreased access.

Response of MDOF strongly nonlinear systems to fractional Gaussian noises.

PubMed

Deng, Mao-Lin; Zhu, Wei-Qiu

2016-08-01

In the present paper, multi-degree-of-freedom strongly nonlinear systems are modeled as quasi-Hamiltonian systems and the stochastic averaging method for quasi-Hamiltonian systems (including quasi-non-integrable, completely integrable and non-resonant, completely integrable and resonant, partially integrable and non-resonant, and partially integrable and resonant Hamiltonian systems) driven by fractional Gaussian noise is introduced. The averaged fractional stochastic differential equations (SDEs) are derived. The simulation results for some examples show that the averaged SDEs can be used to predict the response of the original systems and the simulation time for the averaged SDEs is less than that for the original systems.

Response of MDOF strongly nonlinear systems to fractional Gaussian noises

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

Deng, Mao-Lin; Zhu, Wei-Qiu, E-mail: wqzhu@zju.edu.cn

2016-08-15

In the present paper, multi-degree-of-freedom strongly nonlinear systems are modeled as quasi-Hamiltonian systems and the stochastic averaging method for quasi-Hamiltonian systems (including quasi-non-integrable, completely integrable and non-resonant, completely integrable and resonant, partially integrable and non-resonant, and partially integrable and resonant Hamiltonian systems) driven by fractional Gaussian noise is introduced. The averaged fractional stochastic differential equations (SDEs) are derived. The simulation results for some examples show that the averaged SDEs can be used to predict the response of the original systems and the simulation time for the averaged SDEs is less than that for the original systems.

The change of sleeping and lying posture of Japanese black cows after moving into new environment.

PubMed

Fukasawa, Michiru; Komatsu, Tokushi; Higashiyama, Yumi

2018-04-25

The environmental change is one of the stressful events in livestock production. Change in environment disturbed cow behavior and cows needed several days to reach stable behavioral pattern, especially sleeping posture (SP) and lying posture (LP) have been used as an indicator for relax and well-acclimated to its environment. The aim of this study examines how long does Japanese black cow required for stabilization of SP and LP after moving into new environment. Seven pregnant Japanese black cows were used. Cows were moved into new tie-stall shed and measured sleeping and lying posture 17 times during 35 experimental days. Both SP and LP were detected by accelerometer fixed on middle occipital and hip-cross, respectively. Daily total time, frequency, and average bout of both SP and LP were calculated. Daily SP time was the shortest on day 1, and increased to the highest on day3. It decreased until day 9, after that stabilized about 65 min /day till the end of experiment. The longest average SP bout was shown on day 1, and it decreased to stabilize till day 7. Daily LP time was changed as same manner as daily SP time. The average SP bout showed the longest on day 1, and it decreased to stable level till day 7. On the other hand, the average LP bout showed the shortest on day1, and it was increased to stable level till on day 7. These results showed that pregnant Japanese black cows needed 1 week to stabilize their SP. However, there were different change pattern between the average SP and LP bout, even though the change pattern of daily SP and LP time were similar.

An Operator-Integration-Factor Splitting (OIFS) method for Incompressible Flows in Moving Domains

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

Patel, Saumil S.; Fischer, Paul F.; Min, Misun

In this paper, we present a characteristic-based numerical procedure for simulating incompressible flows in domains with moving boundaries. Our approach utilizes an operator-integration-factor splitting technique to help produce an effcient and stable numerical scheme. Using the spectral element method and an arbitrary Lagrangian-Eulerian formulation, we investigate flows where the convective acceleration effects are non-negligible. Several examples, ranging from laminar to turbulent flows, are considered. Comparisons with a standard, semi-implicit time-stepping procedure illustrate the improved performance of the scheme.

Time-series modeling and prediction of global monthly absolute temperature for environmental decision making

NASA Astrophysics Data System (ADS)

Ye, Liming; Yang, Guixia; Van Ranst, Eric; Tang, Huajun

2013-03-01

A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined (DSC) approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average (SARIMA) models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term (˜10-year) environmental planning and decision making.

The Preemptive Stocker Dispatching Rule of Automatic Material Handling System in 300 mm Semiconductor Manufacturing Factories

NASA Astrophysics Data System (ADS)

Wang, C. N.; Lin, H. S.; Hsu, H. P.; Wang, Yen-Hui; Chang, Y. P.

2016-04-01

The integrated circuit (IC) manufacturing industry is one of the biggest output industries in this century. The 300mm wafer fabs is the major fab size of this industry. The automatic material handling system (AMHS) has become one of the most concerned issues among semiconductor manufacturers. The major lot delivery of 300mm fabs is used overhead hoist transport (OHT). The traffic jams are happened frequently due to the wide variety of products and big amount of OHTs moving in the fabs. The purpose of this study is to enhance the delivery performance of automatic material handling and reduce the delay and waiting time of product transportation for both hot lots and normal lots. Therefore, this study proposes an effective OHT dispatching rule: preemptive stocker dispatching (PSD). Simulation experiments are conducted and one of the best differentiated preemptive rule, differentiated preemptive dispatching (DPD), is used for comparison. Compared with DPD, The results indicated that PSD rule can reduce average variable delivery time of normal lots by 13.15%, decreasing average variable delivery time of hot lots by 17.67%. Thus, the PSD rule can effectively reduce the delivery time and enhance productivity in 300 mm wafer fabs.

Move-by-move dynamics of the advantage in chess matches reveals population-level learning of the game.

PubMed

Ribeiro, Haroldo V; Mendes, Renio S; Lenzi, Ervin K; del Castillo-Mussot, Marcelo; Amaral, Luís A N

2013-01-01

The complexity of chess matches has attracted broad interest since its invention. This complexity and the availability of large number of recorded matches make chess an ideal model systems for the study of population-level learning of a complex system. We systematically investigate the move-by-move dynamics of the white player's advantage from over seventy thousand high level chess matches spanning over 150 years. We find that the average advantage of the white player is positive and that it has been increasing over time. Currently, the average advantage of the white player is 0.17 pawns but it is exponentially approaching a value of 0.23 pawns with a characteristic time scale of 67 years. We also study the diffusion of the move dependence of the white player's advantage and find that it is non-Gaussian, has long-ranged anti-correlations and that after an initial period with no diffusion it becomes super-diffusive. We find that the duration of the non-diffusive period, corresponding to the opening stage of a match, is increasing in length and exponentially approaching a value of 15.6 moves with a characteristic time scale of 130 years. We interpret these two trends as a resulting from learning of the features of the game. Additionally, we find that the exponent [Formula: see text] characterizing the super-diffusive regime is increasing toward a value of 1.9, close to the ballistic regime. We suggest that this trend is due to the increased broadening of the range of abilities of chess players participating in major tournaments.

Move-by-Move Dynamics of the Advantage in Chess Matches Reveals Population-Level Learning of the Game

PubMed Central

Ribeiro, Haroldo V.; Mendes, Renio S.; Lenzi, Ervin K.; del Castillo-Mussot, Marcelo; Amaral, Luís A. N.

2013-01-01

The complexity of chess matches has attracted broad interest since its invention. This complexity and the availability of large number of recorded matches make chess an ideal model systems for the study of population-level learning of a complex system. We systematically investigate the move-by-move dynamics of the white player’s advantage from over seventy thousand high level chess matches spanning over 150 years. We find that the average advantage of the white player is positive and that it has been increasing over time. Currently, the average advantage of the white player is 0.17 pawns but it is exponentially approaching a value of 0.23 pawns with a characteristic time scale of 67 years. We also study the diffusion of the move dependence of the white player’s advantage and find that it is non-Gaussian, has long-ranged anti-correlations and that after an initial period with no diffusion it becomes super-diffusive. We find that the duration of the non-diffusive period, corresponding to the opening stage of a match, is increasing in length and exponentially approaching a value of 15.6 moves with a characteristic time scale of 130 years. We interpret these two trends as a resulting from learning of the features of the game. Additionally, we find that the exponent characterizing the super-diffusive regime is increasing toward a value of 1.9, close to the ballistic regime. We suggest that this trend is due to the increased broadening of the range of abilities of chess players participating in major tournaments. PMID:23382876

Use of the temporal median and trimmed mean mitigates effects of respiratory motion in multiple-acquisition abdominal diffusion imaging

NASA Astrophysics Data System (ADS)

Jerome, N. P.; Orton, M. R.; d'Arcy, J. A.; Feiweier, T.; Tunariu, N.; Koh, D.-M.; Leach, M. O.; Collins, D. J.

2015-01-01

Respiratory motion commonly confounds abdominal diffusion-weighted magnetic resonance imaging, where averaging of successive samples at different parts of the respiratory cycle, performed in the scanner, manifests the motion as blurring of tissue boundaries and structural features and can introduce bias into calculated diffusion metrics. Storing multiple averages separately allows processing using metrics other than the mean; in this prospective volunteer study, median and trimmed mean values of signal intensity for each voxel over repeated averages and diffusion-weighting directions are shown to give images with sharper tissue boundaries and structural features for moving tissues, while not compromising non-moving structures. Expert visual scoring of derived diffusion maps is significantly higher for the median than for the mean, with modest improvement from the trimmed mean. Diffusion metrics derived from mono- and bi-exponential diffusion models are comparable for non-moving structures, demonstrating a lack of introduced bias from using the median. The use of the median is a simple and computationally inexpensive alternative to complex and expensive registration algorithms, requiring only additional data storage (and no additional scanning time) while returning visually superior images that will facilitate the appropriate placement of regions-of-interest when analysing abdominal diffusion-weighted magnetic resonance images, for assessment of disease characteristics and treatment response.

A novel algorithm for Bluetooth ECG.

PubMed

Pandya, Utpal T; Desai, Uday B

2012-11-01

In wireless transmission of ECG, data latency will be significant when battery power level and data transmission distance are not maintained. In applications like home monitoring or personalized care, to overcome the joint effect of previous issues of wireless transmission and other ECG measurement noises, a novel filtering strategy is required. Here, a novel algorithm, identified as peak rejection adaptive sampling modified moving average (PRASMMA) algorithm for wireless ECG is introduced. This algorithm first removes error in bit pattern of received data if occurred in wireless transmission and then removes baseline drift. Afterward, a modified moving average is implemented except in the region of each QRS complexes. The algorithm also sets its filtering parameters according to different sampling rate selected for acquisition of signals. To demonstrate the work, a prototyped Bluetooth-based ECG module is used to capture ECG with different sampling rate and in different position of patient. This module transmits ECG wirelessly to Bluetooth-enabled devices where the PRASMMA algorithm is applied on captured ECG. The performance of PRASMMA algorithm is compared with moving average and S-Golay algorithms visually as well as numerically. The results show that the PRASMMA algorithm can significantly improve the ECG reconstruction by efficiently removing the noise and its use can be extended to any parameters where peaks are importance for diagnostic purpose.

Use of the temporal median and trimmed mean mitigates effects of respiratory motion in multiple-acquisition abdominal diffusion imaging.

PubMed

Jerome, N P; Orton, M R; d'Arcy, J A; Feiweier, T; Tunariu, N; Koh, D-M; Leach, M O; Collins, D J

2015-01-21

Respiratory motion commonly confounds abdominal diffusion-weighted magnetic resonance imaging, where averaging of successive samples at different parts of the respiratory cycle, performed in the scanner, manifests the motion as blurring of tissue boundaries and structural features and can introduce bias into calculated diffusion metrics. Storing multiple averages separately allows processing using metrics other than the mean; in this prospective volunteer study, median and trimmed mean values of signal intensity for each voxel over repeated averages and diffusion-weighting directions are shown to give images with sharper tissue boundaries and structural features for moving tissues, while not compromising non-moving structures. Expert visual scoring of derived diffusion maps is significantly higher for the median than for the mean, with modest improvement from the trimmed mean. Diffusion metrics derived from mono- and bi-exponential diffusion models are comparable for non-moving structures, demonstrating a lack of introduced bias from using the median. The use of the median is a simple and computationally inexpensive alternative to complex and expensive registration algorithms, requiring only additional data storage (and no additional scanning time) while returning visually superior images that will facilitate the appropriate placement of regions-of-interest when analysing abdominal diffusion-weighted magnetic resonance images, for assessment of disease characteristics and treatment response.

Enhancement of the Comb Filtering Selectivity Using Iterative Moving Average for Periodic Waveform and Harmonic Elimination

PubMed Central

Wu, Yan; Aarts, Ronald M.

2018-01-01

A recurring problem regarding the use of conventional comb filter approaches for elimination of periodic waveforms is the degree of selectivity achieved by the filtering process. Some applications, such as the gradient artefact correction in EEG recordings during coregistered EEG-fMRI, require a highly selective comb filtering that provides effective attenuation in the stopbands and gain close to unity in the pass-bands. In this paper, we present a novel comb filtering implementation whereby the iterative filtering application of FIR moving average-based approaches is exploited in order to enhance the comb filtering selectivity. Our results indicate that the proposed approach can be used to effectively approximate the FIR moving average filter characteristics to those of an ideal filter. A cascaded implementation using the proposed approach shows to further increase the attenuation in the filter stopbands. Moreover, broadening of the bandwidth of the comb filtering stopbands around −3 dB according to the fundamental frequency of the stopband can be achieved by the novel method, which constitutes an important characteristic to account for broadening of the harmonic gradient artefact spectral lines. In parallel, the proposed filtering implementation can also be used to design a novel notch filtering approach with enhanced selectivity as well. PMID:29599955

Leg kinematics and muscle activity during treadmill running in the cockroach, Blaberus discoidalis: I. Slow running.

PubMed

Watson, J T; Ritzmann, R E

1998-01-01

We have combined high-speed video motion analysis of leg movements with electromyogram (EMG) recordings from leg muscles in cockroaches running on a treadmill. The mesothoracic (T2) and metathoracic (T3) legs have different kinematics. While in each leg the coxa-femur (CF) joint moves in unison with the femurtibia (FT) joint, the relative joint excursions differ between T2 and T3 legs. In T3 legs, the two joints move through approximately the same excursion. In T2 legs, the FT joint moves through a narrower range of angles than the CF joint. In spite of these differences in motion, no differences between the T2 and T3 legs were seen in timing or qualitative patterns of depressor coxa and extensor tibia activity. The average firing frequencies of slow depressor coxa (Ds) and slow extensor tibia (SETi) motor neurons are directly proportional to the average angular velocity of their joints during stance. The average Ds and SETi firing frequency appears to be modulated on a cycle-by-cycle basis to control running speed and orientation. In contrast, while the frequency variations within Ds and SETi bursts were consistent across cycles, the variations within each burst did not parallel variations in the velocity of the relevant joints.

2009 Combat Vehicles Conference (BRIEFING CHARTS)

DTIC Science & Technology

2009-10-14

Strategy to Field 531 Systems Targeting Under Armor and FS3 Integration on A3 BFIST on the Move Our #1 Priority is to Support Units Engaged in O C i...Armored Knight Program • Targeting Under Armor /On the Move effort underway to • The M1200 Armored Knight provides increase survivability of...increased survivability Sustainment Survivability 32 10/13/2009 BFIST Program Overview • Targeting Under Armor /On the Move effort underway to

Orion EM-1 Interim Cryogenic Propulsion Stage (ICPS) move from HIF to DOC

NASA Image and Video Library

2017-04-12

The Orion EM-1 Interim Cryogenic Propulsion Stage is moved from the Horizontal Integration Facility (HIF) to the Delta Operations Center (DOC) at Cape Canaveral Air Force Station to continue processing for it's future mission on the Space Launch System rocket.

Notes sur les mouvements recursifs (Notes on Regressive Moves).

ERIC Educational Resources Information Center

Auchlin, Antoine; And Others

1981-01-01

Examines the phenomenon of regressive moves (retro-interpretation) in the light of a hypothesis according to which the formation of complex and hierarchically organized conversation units is subordinated to the linearity of discourse. Analyzes a transactional exchange, describing the interplay of integration, anticipation, and retro-interpretation…

The Joy of Moving, Singing, and Being Silly.

ERIC Educational Resources Information Center

Liebler, Scott

1997-01-01

Explains how moving, singing, and having fun build character, confidence, and coordination while helping children grow up feeling good about themselves. These activities provide an integrated, multisensory approach to learning. Employing a variety of fun and effective activities also helps children release stress and tension. (TJQ)

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

PubMed

Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

2018-01-01

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

FARMWORKERS, A REPRINT FROM THE 1966 MANPOWER REPORT.

ERIC Educational Resources Information Center

Manpower Administration (DOL), Washington, DC.

ALTHOUGH THE AVERAGE STANDARD OF LIVING OF FARM PEOPLE HAS BEEN RISING STEADILY, THEY CONTINUE TO FACE SEVERE PROBLEMS OF UNDEREMPLOYMENT AND POVERTY. THE AVERAGE PER CAPITA INCOME OF FARM RESIDENTS IS LESS THAN TWO-THIRDS THAT OF THE NONFARM POPULATION. MILLIONS HAVE MOVED TO CITIES, LEAVING STAGNATING RURAL COMMUNITIES, AND INCREASING THE CITY…

Severe Weather Guide - Mediterranean Ports. 7. Marseille

DTIC Science & Technology

1988-03-01

the afternoon. Upper—level westerlies and the associated storm track is moved northward during summer, so extratropical cyclones and associated...autumn as the extratropical storm track moves southward. Precipitation amount is the highest of the year, with an average of 3 inches (76 mm) for the...18 SUBJECT TERMS (Continue on reverse if necessary and identify by block number) Storm haven Mediterranean meteorology Marseille port

Polymer Coatings Degradation Properties

DTIC Science & Technology

1985-02-01

undertaken 124). The Box-Jenkins approach first evaluates the partial auto -correlation function and determines the order of the moving average memory function...78 - Tables 15 and 16 show the resalit- f- a, the partial auto correlation plots. Second order moving .-. "ra ;;th -he appropriate lags were...coated films. Kaempf, Guenter; Papenroth, Wolfgang; Kunststoffe Date: 1982 Volume: 72 Number:7 Pages: 424-429 Parameters influencing the accelerated

Simulation of Unsteady Flows Using an Unstructured Navier-Stokes Solver on Moving and Stationary Grids

NASA Technical Reports Server (NTRS)

Biedron, Robert T.; Vatsa, Veer N.; Atkins, Harold L.

2005-01-01

We apply an unsteady Reynolds-averaged Navier-Stokes (URANS) solver for unstructured grids to unsteady flows on moving and stationary grids. Example problems considered are relevant to active flow control and stability and control. Computational results are presented using the Spalart-Allmaras turbulence model and are compared to experimental data. The effect of grid and time-step refinement are examined.

Traffic-Related Air Pollution, Blood Pressure, and Adaptive Response of Mitochondrial Abundance.

PubMed

Zhong, Jia; Cayir, Akin; Trevisi, Letizia; Sanchez-Guerra, Marco; Lin, Xinyi; Peng, Cheng; Bind, Marie-Abèle; Prada, Diddier; Laue, Hannah; Brennan, Kasey J M; Dereix, Alexandra; Sparrow, David; Vokonas, Pantel; Schwartz, Joel; Baccarelli, Andrea A

2016-01-26

Exposure to black carbon (BC), a tracer of vehicular-traffic pollution, is associated with increased blood pressure (BP). Identifying biological factors that attenuate BC effects on BP can inform prevention. We evaluated the role of mitochondrial abundance, an adaptive mechanism compensating for cellular-redox imbalance, in the BC-BP relationship. At ≥ 1 visits among 675 older men from the Normative Aging Study (observations=1252), we assessed daily BP and ambient BC levels from a stationary monitor. To determine blood mitochondrial abundance, we used whole blood to analyze mitochondrial-to-nuclear DNA ratio (mtDNA/nDNA) using quantitative polymerase chain reaction. Every standard deviation increase in the 28-day BC moving average was associated with 1.97 mm Hg (95% confidence interval [CI], 1.23-2.72; P<0.0001) and 3.46 mm Hg (95% CI, 2.06-4.87; P<0.0001) higher diastolic and systolic BP, respectively. Positive BC-BP associations existed throughout all time windows. BC moving averages (5-day to 28-day) were associated with increased mtDNA/nDNA; every standard deviation increase in 28-day BC moving average was associated with 0.12 standard deviation (95% CI, 0.03-0.20; P=0.007) higher mtDNA/nDNA. High mtDNA/nDNA significantly attenuated the BC-systolic BP association throughout all time windows. The estimated effect of 28-day BC moving average on systolic BP was 1.95-fold larger for individuals at the lowest mtDNA/nDNA quartile midpoint (4.68 mm Hg; 95% CI, 3.03-6.33; P<0.0001), in comparison with the top quartile midpoint (2.40 mm Hg; 95% CI, 0.81-3.99; P=0.003). In older adults, short-term to moderate-term ambient BC levels were associated with increased BP and blood mitochondrial abundance. Our findings indicate that increased blood mitochondrial abundance is a compensatory response and attenuates the cardiac effects of BC. © 2015 American Heart Association, Inc.

Associations between Changes in City and Address Specific Temperature and QT Interval - The VA Normative Aging Study

PubMed Central

Mehta, Amar J.; Kloog, Itai; Zanobetti, Antonella; Coull, Brent A.; Sparrow, David; Vokonas, Pantel; Schwartz, Joel

2014-01-01

Background The underlying mechanisms of the association between ambient temperature and cardiovascular morbidity and mortality are not well understood, particularly for daily temperature variability. We evaluated if daily mean temperature and standard deviation of temperature was associated with heart rate-corrected QT interval (QTc) duration, a marker of ventricular repolarization in a prospective cohort of older men. Methods This longitudinal analysis included 487 older men participating in the VA Normative Aging Study with up to three visits between 2000–2008 (n = 743). We analyzed associations between QTc and moving averages (1–7, 14, 21, and 28 days) of the 24-hour mean and standard deviation of temperature as measured from a local weather monitor, and the 24-hour mean temperature estimated from a spatiotemporal prediction model, in time-varying linear mixed-effect regression. Effect modification by season, diabetes, coronary heart disease, obesity, and age was also evaluated. Results Higher mean temperature as measured from the local monitor, and estimated from the prediction model, was associated with longer QTc at moving averages of 21 and 28 days. Increased 24-hr standard deviation of temperature was associated with longer QTc at moving averages from 4 and up to 28 days; a 1.9°C interquartile range increase in 4-day moving average standard deviation of temperature was associated with a 2.8 msec (95%CI: 0.4, 5.2) longer QTc. Associations between 24-hr standard deviation of temperature and QTc were stronger in colder months, and in participants with diabetes and coronary heart disease. Conclusion/Significance In this sample of older men, elevated mean temperature was associated with longer QTc, and increased variability of temperature was associated with longer QTc, particularly during colder months and among individuals with diabetes and coronary heart disease. These findings may offer insight of an important underlying mechanism of temperature-related cardiovascular morbidity and mortality in an older population. PMID:25238150

Pedagogical alternatives for triple integrals: moving towards more inclusive and personalized learning

NASA Astrophysics Data System (ADS)

Tisdell, Christopher C.

2018-07-01

This paper is based on the presumption that teaching multiple ways to solve the same problem has academic and social value. In particular, we argue that such a multifaceted approach to pedagogy moves towards an environment of more inclusive and personalized learning. From a mathematics education perspective, our discussion is framed around pedagogical approaches to triple integrals seen in a standard multivariable calculus curriculum. We present some critical perspectives regarding the dominant and long-standing approach to the teaching of triple integrals currently seen in hegemonic calculus textbooks; and we illustrate the need for more diverse pedagogical methods. Finally, we take a constructive position by introducing a new and alternate pedagogical approach to solve some of the classical problems involving triple integrals from the literature through a simple application of integration by parts. This pedagogical alternative for triple integrals is designed to question the dominant one-size-fits-all approach of rearranging the order of integration and the privileging of graphical methods; and to enable a shift towards a more inclusive, enhanced and personalized learning experience.

Home Visiting: Looking Back and Moving Forward

ERIC Educational Resources Information Center

Boller, Kimberly; Strong, Debra A.; Daro, Deborah

2010-01-01

Recent large federal investments in services for pregnant women and young children will fuel the expansion of home visiting services across the U.S. The authors summarize the history of home visiting and describe trends toward evidence-based and national program models. Moving to an integrated system requires supports for implementation with…

Moving to Inclusion: A Socio-Cultural Analysis of Practice

ERIC Educational Resources Information Center

Grenier, Michelle

2010-01-01

Difference, like nature, calls forth possibilities for developing transformative relationships. According to Keller in 1985, "Difference thus invites a form of engagement and understanding that allows for the preservation of the individual. Self and other survive in a structural integrity?" Moving towards inclusion requires that we consider…

An Integrated Enrollment Forecast Model. IR Applications, Volume 15, January 18, 2008

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2008-01-01

Enrollment forecasting is the central component of effective budget and program planning. The integrated enrollment forecast model is developed to achieve a better understanding of the variables affecting student enrollment and, ultimately, to perform accurate forecasts. The transfer function model of the autoregressive integrated moving average…

The Integration of Extrarational and Rational Learning Processes: Moving Towards the Whole Learner.

ERIC Educational Resources Information Center

Puk, Tom

1996-01-01

Discusses the dichotomy between rational and nonrational learning processes, arguing for an integration of both. Reviews information processing theory and related learning strategies. Presents a model instructional strategy that fully integrates rational and nonrational processes. Describes implications for teaching and learning of the learning…

Development of S-ARIMA Model for Forecasting Demand in a Beverage Supply Chain

NASA Astrophysics Data System (ADS)

Mircetic, Dejan; Nikolicic, Svetlana; Maslaric, Marinko; Ralevic, Nebojsa; Debelic, Borna

2016-11-01

Demand forecasting is one of the key activities in planning the freight flows in supply chains, and accordingly it is essential for planning and scheduling of logistic activities within observed supply chain. Accurate demand forecasting models directly influence the decrease of logistics costs, since they provide an assessment of customer demand. Customer demand is a key component for planning all logistic processes in supply chain, and therefore determining levels of customer demand is of great interest for supply chain managers. In this paper we deal with exactly this kind of problem, and we develop the seasonal Autoregressive IntegratedMoving Average (SARIMA) model for forecasting demand patterns of a major product of an observed beverage company. The model is easy to understand, flexible to use and appropriate for assisting the expert in decision making process about consumer demand in particular periods.

The rotational motion of an earth orbiting gyroscope according to the Einstein theory of general relativity

NASA Technical Reports Server (NTRS)

Hoots, F. R.; Fitzpatrick, P. M.

1979-01-01

The classical Poisson equations of rotational motion are used to study the attitude motions of an earth orbiting, rapidly spinning gyroscope perturbed by the effects of general relativity (Einstein theory). The center of mass of the gyroscope is assumed to move about a rotating oblate earth in an evolving elliptic orbit which includes all first-order oblateness effects produced by the earth. A method of averaging is used to obtain a transformation of variables, for the nonresonance case, which significantly simplifies the Poisson differential equations of motion of the gyroscope. Long-term solutions are obtained by an exact analytical integration of the simplified transformed equations. These solutions may be used to predict both the orientation of the gyroscope and the motion of its rotational angular momentum vector as viewed from its center of mass. The results are valid for all eccentricities and all inclinations not near the critical inclination.

A novel hybrid ensemble learning paradigm for tourism forecasting

NASA Astrophysics Data System (ADS)

Shabri, Ani

2015-02-01

In this paper, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) is proposed to forecast tourism demand. This methodology first decomposes the original visitor arrival series into several Intrinsic Model Function (IMFs) components and one residual component by EMD technique. Then, IMFs components and the residual components is forecasted respectively using GMDH model whose input variables are selected by using Partial Autocorrelation Function (PACF). The final forecasted result for tourism series is produced by aggregating all the forecasted results. For evaluating the performance of the proposed EMD-GMDH methodologies, the monthly data of tourist arrivals from Singapore to Malaysia are used as an illustrative example. Empirical results show that the proposed EMD-GMDH model outperforms the EMD-ARIMA as well as the GMDH and ARIMA (Autoregressive Integrated Moving Average) models without time series decomposition.

Spectral analysis based on fast Fourier transformation (FFT) of surveillance data: the case of scarlet fever in China.

PubMed

Zhang, T; Yang, M; Xiao, X; Feng, Z; Li, C; Zhou, Z; Ren, Q; Li, X

2014-03-01

Many infectious diseases exhibit repetitive or regular behaviour over time. Time-domain approaches, such as the seasonal autoregressive integrated moving average model, are often utilized to examine the cyclical behaviour of such diseases. The limitations for time-domain approaches include over-differencing and over-fitting; furthermore, the use of these approaches is inappropriate when the assumption of linearity may not hold. In this study, we implemented a simple and efficient procedure based on the fast Fourier transformation (FFT) approach to evaluate the epidemic dynamic of scarlet fever incidence (2004-2010) in China. This method demonstrated good internal and external validities and overcame some shortcomings of time-domain approaches. The procedure also elucidated the cycling behaviour in terms of environmental factors. We concluded that, under appropriate circumstances of data structure, spectral analysis based on the FFT approach may be applicable for the study of oscillating diseases.

A travel time forecasting model based on change-point detection method

NASA Astrophysics Data System (ADS)

LI, Shupeng; GUANG, Xiaoping; QIAN, Yongsheng; ZENG, Junwei

2017-06-01

Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

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

Changing incidence of psychotic disorders among the young in Zurich.

PubMed

Ajdacic-Gross, Vladeta; Lauber, Christoph; Warnke, Inge; Haker, Helene; Murray, Robin M; Rössler, Wulf

2007-09-01

There is controversy over whether the incidence rates of schizophrenia and psychotic disorders have changed in recent decades. To detect deviations from trends in incidence, we analysed admission data of patients with an ICD-8/9/10 diagnosis of psychotic disorders in the Canton Zurich / Switzerland, for the period 1977-2005. The data was derived from the central psychiatric register of the Canton Zurich. Ex-post forecasting with ARIMA (Autoregressive Integrated Moving Average) models was used to assess departures from existing trends. In addition, age-period-cohort analysis was applied to determine hidden birth cohort effects. First admission rates of patients with psychotic disorders were constant in men and showed a downward trend in women. However, the rates in the youngest age groups showed a strong increase in the second half of the 1990's. The trend reversal among the youngest age groups coincides with the increased use of cannabis among young Swiss in the 1990's.

Mid-IR super-continuum generation

NASA Astrophysics Data System (ADS)

Islam, Mohammed N.; Xia, Chenan; Freeman, Mike J.; Mauricio, Jeremiah; Zakel, Andy; Ke, Kevin; Xu, Zhao; Terry, Fred L., Jr.

2009-02-01

A Mid-InfraRed FIber Laser (MIRFIL) has been developed that generates super-continuum covering the spectral range from 0.8 to 4.5 microns with a time-averaged power as high as 10.5W. The MIRFIL is an all-fiber integrated laser with no moving parts and no mode-locked lasers that uses commercial off-the-shelf parts and leverages the mature telecom/fiber optics platform. The MIRFIL power can be easily scaled by changing the repetition rate and modifying the erbium-doped fiber amplifier. Some of the applications using the super-continuum laser will be described in defense, homeland security and healthcare. For example, the MIRFIL is being applied to a catheter-based medical diagnostic system to detect vulnerable plaque, which is responsible for most heart attacks resulting from hardening-of-the-arteries or atherosclerosis. More generally, the MIRFIL can be a platform for selective ablation of lipids without damaging normal protein or smooth muscle tissue.

Impact of economic fluctuations on suicide mortality in Canada (1926-2008): Testing the Durkheim, Ginsberg, and Henry and Short theories.

PubMed

Thibodeau, Lise; Lachaud, James

2016-01-01

Three theories have been proposed to explain the relationship between suicide and economic fluctuations, including the Durkheim (nonlinear), Ginsberg (procyclical), and Henry and Short (countercyclical) theories. This study tested the effect of economic fluctuations, measured by unemployment rate, on suicide rates in Canada from 1926 to 2008. Autoregressive integrated moving average time-series models were used. The results showed a significant relationship between suicide and economic fluctuation; this association was positive during the contraction period (1926-1950) and negative in the period of economic expansion (1951-1973). Males and females showed differential effects in the period of moderate unemployment (1974-2008). In addition, the suicide rate of mid-adults (45-64) was most impacted by economic fluctuations. Our study tends to support Durkheim's theory and suggests the need for public health responses in times of economic contraction and expansion.

Developing a predictive tropospheric ozone model for Tabriz

NASA Astrophysics Data System (ADS)

Khatibi, Rahman; Naghipour, Leila; Ghorbani, Mohammad A.; Smith, Michael S.; Karimi, Vahid; Farhoudi, Reza; Delafrouz, Hadi; Arvanaghi, Hadi

2013-04-01

Predictive ozone models are becoming indispensable tools by providing a capability for pollution alerts to serve people who are vulnerable to the risks. We have developed a tropospheric ozone prediction capability for Tabriz, Iran, by using the following five modeling strategies: three regression-type methods: Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Gene Expression Programming (GEP); and two auto-regression-type models: Nonlinear Local Prediction (NLP) to implement chaos theory and Auto-Regressive Integrated Moving Average (ARIMA) models. The regression-type modeling strategies explain the data in terms of: temperature, solar radiation, dew point temperature, and wind speed, by regressing present ozone values to their past values. The ozone time series are available at various time intervals, including hourly intervals, from August 2010 to March 2011. The results for MLR, ANN and GEP models are not overly good but those produced by NLP and ARIMA are promising for the establishing a forecasting capability.

Seasonal trend analysis and ARIMA modeling of relative humidity and wind speed time series around Yamula Dam

NASA Astrophysics Data System (ADS)

Eymen, Abdurrahman; Köylü, Ümran

2018-02-01

Local climate change is determined by analysis of long-term recorded meteorological data. In the statistical analysis of the meteorological data, the Mann-Kendall rank test, which is one of the non-parametrical tests, has been used; on the other hand, for determining the power of the trend, Theil-Sen method has been used on the data obtained from 16 meteorological stations. The stations cover the provinces of Kayseri, Sivas, Yozgat, and Nevşehir in the Central Anatolia region of Turkey. Changes in land-use affect local climate. Dams are structures that cause major changes on the land. Yamula Dam is located 25 km northwest of Kayseri. The dam has huge water body which is approximately 85 km2. The mentioned tests have been used for detecting the presence of any positive or negative trend in meteorological data. The meteorological data in relation to the seasonal average, maximum, and minimum values of the relative humidity and seasonal average wind speed have been organized as time series and the tests have been conducted accordingly. As a result of these tests, the following have been identified: increase was observed in minimum relative humidity values in the spring, summer, and autumn seasons. As for the seasonal average wind speed, decrease was detected for nine stations in all seasons, whereas increase was observed in four stations. After the trend analysis, pre-dam mean relative humidity time series were modeled with Autoregressive Integrated Moving Averages (ARIMA) model which is statistical modeling tool. Post-dam relative humidity values were predicted by ARIMA models.

JPRS Report, East Asia, Southeast Asia, Vietnam: TAP CHI CONG SAN, No. 5, May 1989

DTIC Science & Technology

1989-10-10

has shown that moving social systems from a preclass state to a class state with class pressures and finally to abolishing all classes is an... moved far ahead of the socialist countries with respect to many economic norms. The problem now is not to return to capitalism to develop the economy...This has brought good results. From a one-crop system, forest production has begun moving toward integrated commercial activities. A forcstry

A moving control volume approach to computing hydrodynamic forces and torques on immersed bodies

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

Nangia, Nishant; Johansen, Hans; Patankar, Neelesh A.

Here, we present a moving control volume (CV) approach to computing hydrodynamic forces and torques on complex geometries. The method requires surface and volumetric integrals over a simple and regular Cartesian box that moves with an arbitrary velocity to enclose the body at all times. The moving box is aligned with Cartesian grid faces, which makes the integral evaluation straightforward in an immersed boundary (IB) framework. Discontinuous and noisy derivatives of velocity and pressure at the fluid–structure interface are avoided and far-field (smooth) velo city and pressure information is used. We re-visit the approach to compute hydrodynamic forces and torquesmore » through force/torque balance equations in a Lagrangian frame that some of us took in a prior work (Bhalla et al., 2013 [13]). We prove the equivalence of the two approaches for IB methods, thanks to the use of Peskin's delta functions. Both approaches are able to suppress spurious force oscillations and are in excellent agreement, as expected theoretically. Test cases ranging from Stokes to high Reynolds number regimes are considered. We discuss regridding issues for the moving CV method in an adaptive mesh refinement (AMR) context. The proposed moving CV method is not limited to a specific IB method and can also be used, for example, with embedded boundary methods.« less

A moving control volume approach to computing hydrodynamic forces and torques on immersed bodies

DOE PAGES

Nangia, Nishant; Johansen, Hans; Patankar, Neelesh A.; ...

2017-10-01

Here, we present a moving control volume (CV) approach to computing hydrodynamic forces and torques on complex geometries. The method requires surface and volumetric integrals over a simple and regular Cartesian box that moves with an arbitrary velocity to enclose the body at all times. The moving box is aligned with Cartesian grid faces, which makes the integral evaluation straightforward in an immersed boundary (IB) framework. Discontinuous and noisy derivatives of velocity and pressure at the fluid–structure interface are avoided and far-field (smooth) velo city and pressure information is used. We re-visit the approach to compute hydrodynamic forces and torquesmore » through force/torque balance equations in a Lagrangian frame that some of us took in a prior work (Bhalla et al., 2013 [13]). We prove the equivalence of the two approaches for IB methods, thanks to the use of Peskin's delta functions. Both approaches are able to suppress spurious force oscillations and are in excellent agreement, as expected theoretically. Test cases ranging from Stokes to high Reynolds number regimes are considered. We discuss regridding issues for the moving CV method in an adaptive mesh refinement (AMR) context. The proposed moving CV method is not limited to a specific IB method and can also be used, for example, with embedded boundary methods.« less

Precise positioning of an ion in an integrated Paul trap-cavity system using radiofrequency signals

NASA Astrophysics Data System (ADS)

Kassa, Ezra; Takahashi, Hiroki; Christoforou, Costas; Keller, Matthias

2018-03-01

We report a novel miniature Paul ion trap design with an integrated optical fibre cavity which can serve as a building block for a fibre-linked quantum network. In such cavity quantum electrodynamic set-ups, the optimal coupling of the ions to the cavity mode is of vital importance and this is achieved by moving the ion relative to the cavity mode. The trap presented herein features an endcap-style design complemented with extra electrodes on which additional radiofrequency voltages are applied to fully control the pseudopotential minimum in three dimensions. This method lifts the need to use three-dimensional translation stages for moving the fibre cavity with respect to the ion and achieves high integrability, mechanical rigidity and scalability. Not based on modifying the capacitive load of the trap, this method leads to precise control of the pseudopotential minimum allowing the ion to be moved with precisions limited only by the ion's position spread. We demonstrate this by coupling the ion to the fibre cavity and probing the cavity mode profile.

Reinventing the Solar Power Satellite

NASA Technical Reports Server (NTRS)

Landis, Geoffrey A.

2004-01-01

The selling price of electrical power varies with time. The economic viability of space solar power is maximum if the power can be sold at peak power rates, instead of baseline rate. Price and demand of electricity was examined from spot-market data from four example markets: New England, New York City, suburban New York, and California. The data was averaged to show the average price and demand for power as a function of time of day and time of year. Demand varies roughly by a factor of two between the early-morning minimum demand, and the afternoon maximum; both the amount of peak power, and the location of the peak, depends significantly on the location and the weather. The demand curves were compared to the availability curves for solar energy and for tracking and non-tracking satellite solar power systems in order to compare the market value of terrestrial and solar electrical power. In part 2, new designs for a space solar power (SSP) system were analyzed to provide electrical power to Earth for economically competitive rates. The approach was to look at innovative power architectures to more practical approaches to space solar power. A significant barrier is the initial investment required before the first power is returned. Three new concepts for solar power satellites were invented and analyzed: a solar power satellite in the Earth-Sun L2 point, a geosynchronous no-moving parts solar power satellite, and a nontracking geosynchronous solar power satellite with integral phased array. The integral-array satellite had several advantages, including an initial investment cost approximately eight times lower than the conventional design.

Integrating Retraction Modeling Into an Atlas-Based Framework for Brain Shift Prediction

PubMed Central

Chen, Ishita; Ong, Rowena E.; Simpson, Amber L.; Sun, Kay; Thompson, Reid C.

2015-01-01

In recent work, an atlas-based statistical model for brain shift prediction, which accounts for uncertainty in the intraoperative environment, has been proposed. Previous work reported in the literature using this technique did not account for local deformation caused by surgical retraction. It is challenging to precisely localize the retractor location prior to surgery and the retractor is often moved in the course of the procedure. This paper proposes a technique that involves computing the retractor-induced brain deformation in the operating room through an active model solve and linearly superposing the solution with the precomputed deformation atlas. As a result, the new method takes advantage of the atlas-based framework’s accounting for uncertainties while also incorporating the effects of retraction with minimal intraoperative computing. This new approach was tested using simulation and phantom experiments. The results showed an improvement in average shift correction from 50% (ranging from 14 to 81%) for gravity atlas alone to 80% using the active solve retraction component (ranging from 73 to 85%). This paper presents a novel yet simple way to integrate retraction into the atlas-based brain shift computation framework. PMID:23864146

Efficient Bayesian inference for natural time series using ARFIMA processes

NASA Astrophysics Data System (ADS)

Graves, T.; Gramacy, R. B.; Franzke, C. L. E.; Watkins, N. W.

2015-11-01

Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long memory (LM). LM implies that these quantities experience non-trivial temporal memory, which potentially not only enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LM. In this paper we present a modern and systematic approach to the inference of LM. We use the flexible autoregressive fractional integrated moving average (ARFIMA) model, which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LM, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g., short-memory effects) can be integrated over in order to focus on long-memory parameters and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data and the central England temperature (CET) time series, with favorable comparison to the standard estimators. For CET we also extend our method to seasonal long memory.

An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

PubMed

Kuo, R J; Wu, P; Wang, C P

2002-09-01

Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

Tracking Movements of Individual Anoplophora glabripennis (Coleoptera: Cerambycidae) Adults: Application of Harmonic Radar

Treesearch

David W. Williams; Guohong Li; Ruitong Gao

2004-01-01

Movements of 55 Anoplophora glabripennis (Motschulsky) adults were monitored on 200 willow trees, Salix babylonica L., at a site appx. 80 km southeast of Beijing, China, for 9-14 d in an individual mark-recapture study using harmonic radar. The average movement distance was appx. 14 m, with many beetles not moving at all and others moving >90 m. The rate of movement...

Cab technology integration laboratory demonstration with moving map technology

DOT National Transportation Integrated Search

2013-03-31

A human performance study was conducted at the John A. Volpe National Transportation Systems Center (Volpe Center) using a locomotive research simulatorthe Cab Technology Integration Laboratory (CTIL)that was acquired by the Federal Railroad Ad...

Beyond Horse Race Comparisons of National Performance Averages: Math Performance Variation within and between Classrooms in 38 Countries

ERIC Educational Resources Information Center

Huang, Min-Hsiung

2009-01-01

Reports of international studies of student achievement often receive public attention worldwide. However, this attention overly focuses on the national rankings of average student performance. To move beyond the simplistic comparison of national mean scores, this study investigates (a) country differences in the measures of variability as well as…

Shadowfax: Moving mesh hydrodynamical integration code

NASA Astrophysics Data System (ADS)

Vandenbroucke, Bert

2016-05-01

Shadowfax simulates galaxy evolution. Written in object-oriented modular C++, it evolves a mixture of gas, subject to the laws of hydrodynamics and gravity, and any collisionless fluid only subject to gravity, such as cold dark matter or stars. For the hydrodynamical integration, it makes use of a (co-) moving Lagrangian mesh. The code has a 2D and 3D version, contains utility programs to generate initial conditions and visualize simulation snapshots, and its input/output is compatible with a number of other simulation codes, e.g. Gadget2 (ascl:0003.001) and GIZMO (ascl:1410.003).

Tracking integration in concentrating photovoltaics using laterally moving optics.

PubMed

Duerr, Fabian; Meuret, Youri; Thienpont, Hugo

2011-05-09

In this work the concept of tracking-integrated concentrating photovoltaics is studied and its capabilities are quantitatively analyzed. The design strategy desists from ideal concentration performance to reduce the external mechanical solar tracking effort in favor of a compact installation, possibly resulting in lower overall cost. The proposed optical design is based on an extended Simultaneous Multiple Surface (SMS) algorithm and uses two laterally moving plano-convex lenses to achieve high concentration over a wide angular range of ±24°. It achieves 500× concentration, outperforming its conventional concentrating photovoltaic counterparts on a polar aligned single axis tracker.

Integrated Personal Health Records: Transformative Tools for Consumer-Centric Care

PubMed Central

Detmer, Don; Bloomrosen, Meryl; Raymond, Brian; Tang, Paul

2008-01-01

Background Integrated personal health records (PHRs) offer significant potential to stimulate transformational changes in health care delivery and self-care by patients. In 2006, an invitational roundtable sponsored by Kaiser Permanente Institute, the American Medical Informatics Association, and the Agency for Healthcare Research and Quality was held to identify the transformative potential of PHRs, as well as barriers to realizing this potential and a framework for action to move them closer to the health care mainstream. This paper highlights and builds on the insights shared during the roundtable. Discussion While there is a spectrum of dominant PHR models, (standalone, tethered, integrated), the authors state that only the integrated model has true transformative potential to strengthen consumers' ability to manage their own health care. Integrated PHRs improve the quality, completeness, depth, and accessibility of health information provided by patients; enable facile communication between patients and providers; provide access to health knowledge for patients; ensure portability of medical records and other personal health information; and incorporate auto-population of content. Numerous factors impede widespread adoption of integrated PHRs: obstacles in the health care system/culture; issues of consumer confidence and trust; lack of technical standards for interoperability; lack of HIT infrastructure; the digital divide; uncertain value realization/ROI; and uncertain market demand. Recent efforts have led to progress on standards for integrated PHRs, and government agencies and private companies are offering different models to consumers, but substantial obstacles remain to be addressed. Immediate steps to advance integrated PHRs should include sharing existing knowledge and expanding knowledge about them, building on existing efforts, and continuing dialogue among public and private sector stakeholders. Summary Integrated PHRs promote active, ongoing patient collaboration in care delivery and decision making. With some exceptions, however, the integrated PHR model is still a theoretical framework for consumer-centric health care. The authors pose questions that need to be answered so that the field can move forward to realize the potential of integrated PHRs. How can integrated PHRs be moved from concept to practical application? Would a coordinating body expedite this progress? How can existing initiatives and policy levers serve as catalysts to advance integrated PHRs? PMID:18837999

Quality user support: Supporting quality users

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

Woolley, T.C.

1994-12-31

During the past decade, fundamental changes have occurred in technical computing in the oil industry. Technical computing systems have moved from local, fragmented quantity, to global, integrated, quality. The compute power available to the average geoscientist at his desktop has grown exponentially. Technical computing applications have increased in integration and complexity. At the same time, there has been a significant change in the work force due to the pressures of restructuring, and the increased focus on international opportunities. The profile of the user of technical computing resources has changed. Users are generally more mature, knowledgeable, and team oriented than theirmore » predecessors. In the 1990s, computer literacy is a requirement. This paper describes the steps taken by Oryx Energy Company to address the problems and opportunities created by the explosive growth in computing power and needs, coupled with the contraction of the business. A successful user support strategy will be described. Characteristics of the program include: (1) Client driven support; (2) Empowerment of highly skilled professionals to fill the support role; (3) Routine and ongoing modification to the support plan; (4) Utilization of the support assignment to create highly trained advocates on the line; (5) Integration of the support role to the reservoir management team. Results of the plan include a highly trained work force, stakeholder teams that include support personnel, and global support from a centralized support organization.« less

Simulations of moving effect of coastal vegetation on tsunami damping

NASA Astrophysics Data System (ADS)

Tsai, Ching-Piao; Chen, Ying-Chi; Octaviani Sihombing, Tri; Lin, Chang

2017-05-01

A coupled wave-vegetation simulation is presented for the moving effect of the coastal vegetation on tsunami wave height damping. The problem is idealized by solitary wave propagation on a group of emergent cylinders. The numerical model is based on general Reynolds-averaged Navier-Stokes equations with renormalization group turbulent closure model by using volume of fluid technique. The general moving object (GMO) model developed in computational fluid dynamics (CFD) code Flow-3D is applied to simulate the coupled motion of vegetation with wave dynamically. The damping of wave height and the turbulent kinetic energy along moving and stationary cylinders are discussed. The simulated results show that the damping of wave height and the turbulent kinetic energy by the moving cylinders are clearly less than by the stationary cylinders. The result implies that the wave decay by the coastal vegetation may be overestimated if the vegetation was represented as stationary state.

Exploring "What" to Learn in Physical Education

ERIC Educational Resources Information Center

Nyberg, Gunn; Larsson, Håkan

2014-01-01

Purpose: The aim of this article is to show a need for explicating "what" there is to learn in physical education (PE) with a particular focus on learning to move with the meaning potential seen as integral to moving. Further, the aim is to provide an example of exploring "bodily knowing" from the perspective of practical…

Incorporating Children's Literature into the Content Reading Classroom.

ERIC Educational Resources Information Center

Goerss, Betty L.

The trend in many schools is to move away from using the textbook exclusively in content area classrooms and move toward the integration of various pieces of children's literature, in many instances as a thematic unit. Using a thematic approach and incorporating trade books provides students with opportunities for cumulative learning and the…

Collaborative e-Learning: e-Portfolios for Assessment, Teaching and Learning

ERIC Educational Resources Information Center

Luchoomun, Dharmadeo; McLuckie, Joe; van Wesel, Maarten

2010-01-01

This paper presents an innovative approach to e-learning by exploring a number of initiatives where there is a move towards collaborative use of Personal Development Plans (PDPs) integrated with e-portfolios as mechanisms for delivering such plans. It considers whether such a move towards more product orientated assessment might enhance student…

Vision sensor and dual MEMS gyroscope integrated system for attitude determination on moving base

NASA Astrophysics Data System (ADS)

Guo, Xiaoting; Sun, Changku; Wang, Peng; Huang, Lu

2018-01-01

To determine the relative attitude between the objects on a moving base and the base reference system by a MEMS (Micro-Electro-Mechanical Systems) gyroscope, the motion information of the base is redundant, which must be removed from the gyroscope. Our strategy is to add an auxiliary gyroscope attached to the reference system. The master gyroscope is to sense the total motion, and the auxiliary gyroscope is to sense the motion of the moving base. By a generalized difference method, relative attitude in a non-inertial frame can be determined by dual gyroscopes. With the vision sensor suppressing accumulative drift of the MEMS gyroscope, the vision and dual MEMS gyroscope integration system is formed. Coordinate system definitions and spatial transform are executed in order to fuse inertial and visual data from different coordinate systems together. And a nonlinear filter algorithm, Cubature Kalman filter, is used to fuse slow visual data and fast inertial data together. A practical experimental setup is built up and used to validate feasibility and effectiveness of our proposed attitude determination system in the non-inertial frame on the moving base.

Migration, Adjustment, and Integration of the Indian Into the Urban Environment.

ERIC Educational Resources Information Center

McCaskill, Donald N.

The migration, adjustment, and integration patterns of Canadian Indian and Metis families in an urban setting were studied. Data were collected in 1968 via a 64-item interview schedule administered to a sample of 71 families moving into the city of Winnepeg, Canada. Addressing the problems of migration, adjustment, and integration, analysis…

Information Technology Integration in Teacher Education: Supporting the Paradigm Shift in Hong Kong.

ERIC Educational Resources Information Center

Lee, Kar Tin

2001-01-01

Examines the integration of information technology (IT) at the Hong Kong Institute of Education, presenting the rationale for this move, characteristics of IT integration, and program development issues for making IT application a critical component of contemporary teacher education. The paper presents a framework for program development and…

Orion EM-1 Crew Module Move from Clean Room to Work Station

NASA Image and Video Library

2017-05-11

Workers have moved the Orion crew module pressure vessel for NASA’s Exploration Mission 1 (EM-1) out of a clean room inside the Neil Armstrong Operations and Checkout Building high bay at NASA’s Kennedy Space Center in Florida. The crew module will be moved to a work station where it will undergo additional processing to prepare it for launch in 2019. The spacecraft is being prepared for its first integrated flight atop the Space Launch System rocket on Exploration Mission-1.

Twin imaging phenomenon of integral imaging.

PubMed

Hu, Juanmei; Lou, Yimin; Wu, Fengmin; Chen, Aixi

2018-05-14

The imaging principles and phenomena of integral imaging technique have been studied in detail using geometrical optics, wave optics, or light filed theory. However, most of the conclusions are only suit for the integral imaging systems using diffused illumination. In this work, a kind of twin imaging phenomenon and mechanism has been observed in a non-diffused illumination reflective integral imaging system. Interactive twin images including a real and a virtual 3D image of one object can be activated in the system. The imaging phenomenon is similar to the conjugate imaging effect of hologram, but it base on the refraction and reflection instead of diffraction. The imaging characteristics and mechanisms different from traditional integral imaging are deduced analytically. Thin film integral imaging systems with 80μm thickness have also been made to verify the imaging phenomenon. Vivid lighting interactive twin 3D images have been realized using a light-emitting diode (LED) light source. When the LED is moving, the twin 3D images are moving synchronously. This interesting phenomenon shows a good application prospect in interactive 3D display, argument reality, and security authentication.

A time-domain Kirchhoff formula for the convective acoustic wave equation

NASA Astrophysics Data System (ADS)

Ghorbaniasl, Ghader; Siozos-Rousoulis, Leonidas; Lacor, Chris

2016-03-01

Kirchhoff's integral method allows propagated sound to be predicted, based on the pressure and its derivatives in time and space obtained on a data surface located in the linear flow region. Kirchhoff's formula for noise prediction from high-speed rotors and propellers suffers from the limitation of the observer located in uniform flow, thus requiring an extension to arbitrarily moving media. This paper presents a Kirchhoff formulation for moving surfaces in a uniform moving medium of arbitrary configuration. First, the convective wave equation is derived in a moving frame, based on the generalized functions theory. The Kirchhoff formula is then obtained for moving surfaces in the time domain. The formula has a similar form to the Kirchhoff formulation for moving surfaces of Farassat and Myers, with the presence of additional terms owing to the moving medium effect. The equation explicitly accounts for the influence of mean flow and angle of attack on the radiated noise. The formula is verified by analytical cases of a monopole source located in a moving medium.

Forecasting daily patient volumes in the emergency department.

PubMed

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

2008-02-01

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

Turbulent boundary layers over nonstationary plane boundaries

NASA Technical Reports Server (NTRS)

Roper, A. T.; Gentry, G. L., Jr.

1978-01-01

Methods of predicting integral parameters and skin friction coefficients of turbulent boundary layers developing over moving ground planes were evaluated. The three methods evaluated were: relative integral parameter method; relative power law method; and modified law of the wall method.

Integration of altitude and airspeed information into a primary flight display via moving-tape formats

NASA Technical Reports Server (NTRS)

Abbott, Terence S.; Steinmetz, George G.

1987-01-01

A ground-based aircraft simulation study was conducted to determine the effect on pilot performance of replacing the electromechanical altimeter and airspeed indicators with electronically generated representations integrated into the primary flight display via moving-tape (linear moving scale) formats. Several key factors relating to moving-tape formats were examined during the study: tape centering, secondary (trend) information, and tape orientation. The factor of centering refers to whether the tape was centered about the actual airspeed or altitude or about some defined reference value. Tape orientation refers to whether the values represented are arranged in either descending or ascending order. Six pilots participated in this study, with each subject performing 18 runs along a single, known flight profile. Subjective results indicated that the moving-tape formats were generally better than that of the conventional instruments. They also indicated that an actual-centered fixed pointer was preferred to a reference-centered pointer. Performance data for a visual secondary task showed that formats not containing trend information produced better performance; however, no difference was noted in airspeed tracking or altitude tracking performance. Regarding tape orientation, subjective comments indicated that there was lower work load and better performance when the airspeed tape had the high numbers at the top.

Three Least-Squares Minimization Approaches to Interpret Gravity Data Due to Dipping Faults

NASA Astrophysics Data System (ADS)

Abdelrahman, E. M.; Essa, K. S.

2015-02-01

We have developed three different least-squares minimization approaches to determine, successively, the depth, dip angle, and amplitude coefficient related to the thickness and density contrast of a buried dipping fault from first moving average residual gravity anomalies. By defining the zero-anomaly distance and the anomaly value at the origin of the moving average residual profile, the problem of depth determination is transformed into a constrained nonlinear gravity inversion. After estimating the depth of the fault, the dip angle is estimated by solving a nonlinear inverse problem. Finally, after estimating the depth and dip angle, the amplitude coefficient is determined using a linear equation. This method can be applied to residuals as well as to measured gravity data because it uses the moving average residual gravity anomalies to estimate the model parameters of the faulted structure. The proposed method was tested on noise-corrupted synthetic and real gravity data. In the case of the synthetic data, good results are obtained when errors are given in the zero-anomaly distance and the anomaly value at the origin, and even when the origin is determined approximately. In the case of practical data (Bouguer anomaly over Gazal fault, south Aswan, Egypt), the fault parameters obtained are in good agreement with the actual ones and with those given in the published literature.

A monitoring tool for performance improvement in plastic surgery at the individual level.

PubMed

Maruthappu, Mahiben; Duclos, Antoine; Orgill, Dennis; Carty, Matthew J

2013-05-01

The assessment of performance in surgery is expanding significantly. Application of relevant frameworks to plastic surgery, however, has been limited. In this article, the authors present two robust graphic tools commonly used in other industries that may serve to monitor individual surgeon operative time while factoring in patient- and surgeon-specific elements. The authors reviewed performance data from all bilateral reduction mammaplasties performed at their institution by eight surgeons between 1995 and 2010. Operative time was used as a proxy for performance. Cumulative sum charts and exponentially weighted moving average charts were generated using a train-test analytic approach, and used to monitor surgical performance. Charts mapped crude, patient case-mix-adjusted, and case-mix and surgical-experience-adjusted performance. Operative time was found to decline from 182 minutes to 118 minutes with surgical experience (p < 0.001). Cumulative sum and exponentially weighted moving average charts were generated using 1995 to 2007 data (1053 procedures) and tested on 2008 to 2010 data (246 procedures). The sensitivity and accuracy of these charts were significantly improved by adjustment for case mix and surgeon experience. The consideration of patient- and surgeon-specific factors is essential for correct interpretation of performance in plastic surgery at the individual surgeon level. Cumulative sum and exponentially weighted moving average charts represent accurate methods of monitoring operative time to control and potentially improve surgeon performance over the course of a career.

Optimization and validation of moving average quality control procedures using bias detection curves and moving average validation charts.

PubMed

van Rossum, Huub H; Kemperman, Hans

2017-02-01

To date, no practical tools are available to obtain optimal settings for moving average (MA) as a continuous analytical quality control instrument. Also, there is no knowledge of the true bias detection properties of applied MA. We describe the use of bias detection curves for MA optimization and MA validation charts for validation of MA. MA optimization was performed on a data set of previously obtained consecutive assay results. Bias introduction and MA bias detection were simulated for multiple MA procedures (combination of truncation limits, calculation algorithms and control limits) and performed for various biases. Bias detection curves were generated by plotting the median number of test results needed for bias detection against the simulated introduced bias. In MA validation charts the minimum, median, and maximum numbers of assay results required for MA bias detection are shown for various bias. Their use was demonstrated for sodium, potassium, and albumin. Bias detection curves allowed optimization of MA settings by graphical comparison of bias detection properties of multiple MA. The optimal MA was selected based on the bias detection characteristics obtained. MA validation charts were generated for selected optimal MA and provided insight into the range of results required for MA bias detection. Bias detection curves and MA validation charts are useful tools for optimization and validation of MA procedures.

An Estimation of the Likelihood of Significant Eruptions During 2000-2009 Using Poisson Statistics on Two-Point Moving Averages of the Volcanic Time Series

NASA Technical Reports Server (NTRS)

Wilson, Robert M.

2001-01-01

Since 1750, the number of cataclysmic volcanic eruptions (volcanic explosivity index (VEI)>=4) per decade spans 2-11, with 96 percent located in the tropics and extra-tropical Northern Hemisphere. A two-point moving average of the volcanic time series has higher values since the 1860's than before, being 8.00 in the 1910's (the highest value) and 6.50 in the 1980's, the highest since the 1910's peak. Because of the usual behavior of the first difference of the two-point moving averages, one infers that its value for the 1990's will measure approximately 6.50 +/- 1, implying that approximately 7 +/- 4 cataclysmic volcanic eruptions should be expected during the present decade (2000-2009). Because cataclysmic volcanic eruptions (especially those having VEI>=5) nearly always have been associated with short-term episodes of global cooling, the occurrence of even one might confuse our ability to assess the effects of global warming. Poisson probability distributions reveal that the probability of one or more events with a VEI>=4 within the next ten years is >99 percent. It is approximately 49 percent for an event with a VEI>=5, and 18 percent for an event with a VEI>=6. Hence, the likelihood that a climatically significant volcanic eruption will occur within the next ten years appears reasonably high.

Development of an Instrument to Assess and Deliberate on the Integration of Mathematics into Student-Centered Science Learning

ERIC Educational Resources Information Center

Judson, Eugene

2013-01-01

It has long been noted in research literature that there does not exist a shared definition of integration of science and mathematics. Classifying a particular set of integration practices by one of many labels has limited value. Moving past the definition debate, this study describes the development and testing of the Mathematics Integrated into…

The Hurst exponent in energy futures prices

NASA Astrophysics Data System (ADS)

Serletis, Apostolos; Rosenberg, Aryeh Adam

2007-07-01

This paper extends the work in Elder and Serletis [Long memory in energy futures prices, Rev. Financial Econ., forthcoming, 2007] and Serletis et al. [Detrended fluctuation analysis of the US stock market, Int. J. Bifurcation Chaos, forthcoming, 2007] by re-examining the empirical evidence for random walk type behavior in energy futures prices. In doing so, it uses daily data on energy futures traded on the New York Mercantile Exchange, over the period from July 2, 1990 to November 1, 2006, and a statistical physics approach-the ‘detrending moving average’ technique-providing a reliable framework for testing the information efficiency in financial markets as shown by Alessio et al. [Second-order moving average and scaling of stochastic time series, Eur. Phys. J. B 27 (2002) 197-200] and Carbone et al. [Time-dependent hurst exponent in financial time series. Physica A 344 (2004) 267-271; Analysis of clusters formed by the moving average of a long-range correlated time series. Phys. Rev. E 69 (2004) 026105]. The results show that energy futures returns display long memory and that the particular form of long memory is anti-persistence.

Behavior and Frequency Analysis of Aurelia aurita by Using in situ Target Strength at a Port in Southwestern Korea

NASA Astrophysics Data System (ADS)

Yoon, Eun-A.; Hwang, Doo-Jin; Chae, Jinho; Yoon, Won Duk; Lee, Kyounghoon

2018-03-01

This study was carried out to determine the in situ target strength and behavioral characteristics of moon jellyfish ( Aurelia aurita) using two frequencies (38 and 120 kHz) that present a 2- frequency-difference method for distinguishing A. aurita from other marine planktonic organisms. The average TS was shown as -71.9 -67.9 dB at 38 kHz and -75.5 -66.0 dB at 120 kHz and the average ΔMVBS120-38 kHz was similar at -1.5 3.5 dB. The TS values varied in a range of about 14 dB from -83.3 and -69.0 dB depending on the pulsation of A. aurita. The species moved in a range of -0.1 1.0 m and they mostly moved horizontally with moving speeds of 0.3 0.6 m·s-1. The TS and behavioral characteristics of A. aurita can distinguish the species from others. The acoustic technology can also contribute to understanding the distribution and abundance of the species.

Environmental Assessment: Installation Development at Sheppard Air Force Base, Texas

DTIC Science & Technology

2007-05-01

column, or in topographic depressions. Water is then utilized by plants and is respired, or it moves slowly into groundwater and/or eventually to surface...water bodies where it slowly moves through the hydrologic cycle. Removal of vegetation decreases infiltration into the soil column and thereby...School District JP-4 jet propulsion fuel 4 kts knots Ldn Day- Night Average Sound Level Leq equivalent noise level Lmax maximum sound level lb pound

Integrating toxicogenomics data into cancer adverse outcome pathways

EPA Science Inventory

Integrating toxicogenomics data into adverse outcome pathways for cancer.J. Christopher CortonNHEERL/ORD, EPA, Research Triangle Park, NCAs the toxicology field continues to move towards a new paradigm in toxicity testing and safety assessment, there is the expectation that model...

Multisensory Integration of Visual and Vestibular Signals Improves Heading Discrimination in the Presence of a Moving Object

PubMed Central

Dokka, Kalpana; DeAngelis, Gregory C.

2015-01-01

Humans and animals are fairly accurate in judging their direction of self-motion (i.e., heading) from optic flow when moving through a stationary environment. However, an object moving independently in the world alters the optic flow field and may bias heading perception if the visual system cannot dissociate object motion from self-motion. We investigated whether adding vestibular self-motion signals to optic flow enhances the accuracy of heading judgments in the presence of a moving object. Macaque monkeys were trained to report their heading (leftward or rightward relative to straight-forward) when self-motion was specified by vestibular, visual, or combined visual-vestibular signals, while viewing a display in which an object moved independently in the (virtual) world. The moving object induced significant biases in perceived heading when self-motion was signaled by either visual or vestibular cues alone. However, this bias was greatly reduced when visual and vestibular cues together signaled self-motion. In addition, multisensory heading discrimination thresholds measured in the presence of a moving object were largely consistent with the predictions of an optimal cue integration strategy. These findings demonstrate that multisensory cues facilitate the perceptual dissociation of self-motion and object motion, consistent with computational work that suggests that an appropriate decoding of multisensory visual-vestibular neurons can estimate heading while discounting the effects of object motion. SIGNIFICANCE STATEMENT Objects that move independently in the world alter the optic flow field and can induce errors in perceiving the direction of self-motion (heading). We show that adding vestibular (inertial) self-motion signals to optic flow almost completely eliminates the errors in perceived heading induced by an independently moving object. Furthermore, this increased accuracy occurs without a substantial loss in the precision. Our results thus demonstrate that vestibular signals play a critical role in dissociating self-motion from object motion. PMID:26446214

MOVES-Matrix and distributed computing for microscale line source dispersion analysis.

PubMed

Liu, Haobing; Xu, Xiaodan; Rodgers, Michael O; Xu, Yanzhi Ann; Guensler, Randall L

2017-07-01

MOVES and AERMOD are the U.S. Environmental Protection Agency's recommended models for use in project-level transportation conformity and hot-spot analysis. However, the structure and algorithms involved in running MOVES make analyses cumbersome and time-consuming. Likewise, the modeling setup process, including extensive data requirements and required input formats, in AERMOD lead to a high potential for analysis error in dispersion modeling. This study presents a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix, a high-performance emission modeling tool, with the microscale dispersion models CALINE4 and AERMOD. MOVES-Matrix was prepared by iteratively running MOVES across all possible iterations of vehicle source-type, fuel, operating conditions, and environmental parameters to create a huge multi-dimensional emission rate lookup matrix. AERMOD and CALINE4 are connected with MOVES-Matrix in a distributed computing cluster using a series of Python scripts. This streamlined system built on MOVES-Matrix generates exactly the same emission rates and concentration results as using MOVES with AERMOD and CALINE4, but the approach is more than 200 times faster than using the MOVES graphical user interface. Because AERMOD requires detailed meteorological input, which is difficult to obtain, this study also recommends using CALINE4 as a screening tool for identifying the potential area that may exceed air quality standards before using AERMOD (and identifying areas that are exceedingly unlikely to exceed air quality standards). CALINE4 worst case method yields consistently higher concentration results than AERMOD for all comparisons in this paper, as expected given the nature of the meteorological data employed. The paper demonstrates a distributed computing method for line source dispersion modeling that integrates MOVES-Matrix with the CALINE4 and AERMOD. This streamlined system generates exactly the same emission rates and concentration results as traditional way to use MOVES with AERMOD and CALINE4, which are regulatory models approved by the U.S. EPA for conformity analysis, but the approach is more than 200 times faster than implementing the MOVES model. We highlighted the potentially significant benefit of using CALINE4 as screening tool for identifying potential area that may exceeds air quality standards before using AERMOD, which requires much more meteorology input than CALINE4.

Efficiency and multifractality analysis of CSI 300 based on multifractal detrending moving average algorithm

NASA Astrophysics Data System (ADS)

Zhou, Weijie; Dang, Yaoguo; Gu, Rongbao

2013-03-01

We apply the multifractal detrending moving average (MFDMA) to investigate and compare the efficiency and multifractality of 5-min high-frequency China Securities Index 300 (CSI 300). The results show that the CSI 300 market becomes closer to weak-form efficiency after the introduction of CSI 300 future. We find that the CSI 300 is featured by multifractality and there are less complexity and risk after the CSI 300 index future was introduced. With the shuffling, surrogating and removing extreme values procedures, we unveil that extreme events and fat-distribution are the main origin of multifractality. Besides, we discuss the knotting phenomena in multifractality, and find that the scaling range and the irregular fluctuations for large scales in the Fq(s) vs s plot can cause a knot.

Gauging the Nearness and Size of Cycle Maximum

NASA Technical Reports Server (NTRS)

Wilson, Robert M.; Hathaway, David H.

2003-01-01

A simple method for monitoring the nearness and size of conventional cycle maximum for an ongoing sunspot cycle is examined. The method uses the observed maximum daily value and the maximum monthly mean value of international sunspot number and the maximum value of the 2-mo moving average of monthly mean sunspot number to effect the estimation. For cycle 23, a maximum daily value of 246, a maximum monthly mean of 170.1, and a maximum 2-mo moving average of 148.9 were each observed in July 2000. Taken together, these values strongly suggest that conventional maximum amplitude for cycle 23 would be approx. 124.5, occurring near July 2002 +/-5 mo, very close to the now well-established conventional maximum amplitude and occurrence date for cycle 23-120.8 in April 2000.

An algorithm for testing the efficient market hypothesis.

PubMed

Boboc, Ioana-Andreea; Dinică, Mihai-Cristian

2013-01-01

The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH).

An Algorithm for Testing the Efficient Market Hypothesis

PubMed Central

Boboc, Ioana-Andreea; Dinică, Mihai-Cristian

2013-01-01

The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH). PMID:24205148

Air quality at night markets in Taiwan.

PubMed

Zhao, Ping; Lin, Chi-Chi

2010-03-01

In Taiwan, there are more than 300 night markets and they have attracted more and more visitors in recent years. Air quality in night markets has become a public concern. To characterize the current air quality in night markets, four major night markets in Kaohsiung were selected for this study. The results of this study showed that the mean carbon dioxide (CO2) concentrations at fixed and moving sites in night markets ranged from 326 to 427 parts per million (ppm) during non-open hours and from 433 to 916 ppm during open hours. The average carbon monoxide (CO) concentrations at fixed and moving sites in night markets ranged from 0.2 to 2.8 ppm during non-open hours and from 2.1 to 14.1 ppm during open hours. The average 1-hr levels of particulate matter with aerodynamic diameters less than 10 microm (PM10) and less than 2.5 microm (PM2.5) at fixed and moving sites in night markets were high, ranging from 186 to 451 microg/m3 and from 175 to 418 microg/m3, respectively. The levels of PM2.5 accounted for 80-97% of their respective PM10 concentrations. The average formaldehyde (HCHO) concentrations at fixed and moving sites in night markets ranged from 0 to 0.05 ppm during non-open hours and from 0.02 to 0.27 ppm during open hours. The average concentration of individual polycyclic aromatic hydrocarbons (PAHs) was found in the range of 0.09 x 10(4) to 1.8 x 10(4) ng/m3. The total identified PAHs (TIPs) ranged from 7.8 x 10(1) to 20 x 10(1) ng/m3 during non-open hours and from 1.5 x 10(4) to 4.0 x 10(4) ng/m3 during open hours. Of the total analyzed PAHs, the low-molecular-weight PAHs (two to three rings) were the dominant species, corresponding to an average of 97% during non-open hours and 88% during open hours, whereas high-molecular-weight PAHs (four to six rings) represented 3 and 12% of the total detected PAHs in the gas phase during non-open and open hours, respectively.

Evaluation of Supported Placements in Integrated Community Environments Project (SPICE). Final Report.

ERIC Educational Resources Information Center

Wilson, Leslie; And Others

This evaluation project was designed to assess 37 persons (ages 21-72) who had moved from intermediate care facilities or skilled nursing facilities into innovative one-person or two-person community integrated living arrangements as a result of the Supported Placements in Integrated Community Environments project. The 37 persons had severe or…

Moving out of Conflict: The Contribution of Integrated Schools in Northern Ireland to Identity, Attitudes, Forgiveness and Reconciliation

ERIC Educational Resources Information Center

McGlynn, Claire; Niens, Ulrike; Cairns, Ed; Hewstone, Miles

2004-01-01

As the integrated education movement in Northern Ireland passes its twenty-first anniversary, it is pertinent to explore the legacy of mixed Catholic and Protestant schooling. This paper summarises the findings of different studies regarding the impact of integrated education in Northern Ireland on social identity, intergroup attitudes and…

Moving from Outsider to Insider: Making Meaning while Making Music

ERIC Educational Resources Information Center

Beegle, Amy

2011-01-01

In addition to examining musical practices of students' cultural heritages, general music educators can teach the value and meaning of music in their own lives by finding ways to integrate a particular "foreign" music into their own experience. Moving from a cultural outsider to more of an insider is illustrated through tales of the…

How Research Helped Us to Move from Awareness to Action and Then to Systems Development

ERIC Educational Resources Information Center

Armstrong, Patricia; Grant, Jim

2004-01-01

How can an organisation move from awareness raising, in the form of natural history poster production, to the development of systems that change organisations? Through close integration of research and practice, the Gould League has achieved this transformation. It began with extensive research into best practice environmental education, going…

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

PubMed Central

Bridwell, David A.; Cavanagh, James F.; Collins, Anne G. E.; Nunez, Michael D.; Srinivasan, Ramesh; Stober, Sebastian; Calhoun, Vince D.

2018-01-01

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function. PMID:29632480

Model studies of surface noise interference in ground-probing radar

NASA Astrophysics Data System (ADS)

Arcone, S. A.; Delaney, A. J.

1985-11-01

Ground-probing radar can be an effective tool for exploring the top 10 to 20 m of ground, especially in cold regions where the freezing of water decreases signal absorption. However, the large electrical variability of the surface, combined with the short wavelengths used, can often cause severe ground clutter that can mask a desired, deeper return. In this study a model facility was constructed consisting of a metallic reflector covered by sand. Troughs of saturated sand were emplaced at the surface to carry surface electrical properties and to act as a noise source to interfere with the bottom reflections. Antenna polarization and height, and signal stacking in both static (antennas stationary) and dynamic (antennas moving) modes were then investigated as methods for reducing the surface clutter. Polarization parallel to the profile direction (perpendicular to the troughs' axes) gave profiles superior to the perpendicular case because of the dimensional sensitivity of the antenna radiation. Dynamic stacking greatly improved the signal-to-noise ratio because noise sources were averaged as the antennas moved, while the desired reflector, buried at constant depth, was enhanced. Raising the antennas above the surface also reduced noise because the surface area over which reflections were integrated increased. All three noise reduction techniques could be effective in surveys for reflectors at nearly constant depth such as groundwater tables or ice/water interfaces.

Evaluation of Decision Rules in a Tiered Assessment of Inhalation Exposure to Nanomaterials.

PubMed

Brouwer, Derk; Boessen, Ruud; van Duuren-Stuurman, Birgit; Bard, Delphine; Moehlmann, Carsten; Bekker, Cindy; Fransman, Wouter; Klein Entink, Rinke

2016-10-01

Tiered or stepwise approaches to assess occupational exposure to nano-objects, and their agglomerates and aggregates have been proposed, which require decision rules (DRs) to move to a next tier, or terminate the assessment. In a desk study the performance of a number of DRs based on the evaluation of results from direct reading instruments was investigated by both statistical simulations and the application of the DRs to real workplace data sets. A statistical model that accounts for autocorrelation patterns in time-series, i.e. autoregressive integrated moving average (ARIMA), was used as 'gold' standard. The simulations showed that none of the proposed DRs covered the entire range of simulated scenarios with respect to the ARIMA model parameters, however, a combined DR showed a slightly better agreement. Application of the DRs to real workplace datasets (n = 117) revealed sensitivity up to 0.72, whereas the lowest observed specificity was 0.95. The selection of the most appropriate DR is very much dependent on the consequences of the decision, i.e. ruling in or ruling out of scenarios for further evaluation. Since a basic assessment may also comprise of other type of measurements and information, an evaluation logic was proposed which embeds the DRs, but furthermore supports decision making in view of a tiered-approach exposure assessment. © The Author 2016. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

A Note on Feynman Path Integral for Electromagnetic External Fields

NASA Astrophysics Data System (ADS)

Botelho, Luiz C. L.

2017-08-01

We propose a Fresnel stochastic white noise framework to analyze the nature of the Feynman paths entering on the Feynman Path Integral expression for the Feynman Propagator of a particle quantum mechanically moving under an external electromagnetic time-independent potential.

Moving singularity creep crack growth analysis with the /Delta T/c and C/asterisk/ integrals. [path-independent vector and energy rate line integrals

NASA Technical Reports Server (NTRS)

Stonesifer, R. B.; Atluri, S. N.

1982-01-01

The physical meaning of (Delta T)c and its applicability to creep crack growth are reviewed. Numerical evaluation of (Delta T)c and C(asterisk) is discussed with results being given for compact specimen and strip geometries. A moving crack-tip singularity, creep crack growth simulation procedure is described and demonstrated. The results of several crack growth simulation analyses indicate that creep crack growth in 304 stainless steel occurs under essentially steady-state conditions. Based on this result, a simple methodology for predicting creep crack growth behavior is summarized.

Modeling integrated fixed-film activated sludge and moving-bed biofilm reactor systems II: evaluation.

PubMed

Boltz, Joshua P; Johnson, Bruce R; Daigger, Glen T; Sandino, Julian; Elenter, Deborah

2009-06-01

A steady-state model presented by Boltz, Johnson, Daigger, and Sandino (2009) describing integrated fixed-film activated sludge (IFAS) and moving-bed biofilm reactor (MBBR) systems has been demonstrated to simulate, with reasonable accuracy, four wastewater treatment configurations with published operational data. Conditions simulated include combined carbon oxidation and nitrification (both IFAS and MBBR), tertiary nitrification MBBR, and post denitrification IFAS with methanol addition as the external carbon source. Simulation results illustrate that the IFAS/MBBR model is sufficiently accurate for describing ammonia-nitrogen reduction, nitrate/nitrite-nitrogen reduction and production, biofilm and suspended biomass distribution, and sludge production.

Analysis of a turbulent boundary layer over a moving ground plane

NASA Technical Reports Server (NTRS)

Roper, A. T.; Gentry, G. L., Jr.

1972-01-01

Four methods of predicting the integral and friction parameters for a turbulent boundary layer over a moving ground plane were evaluated by using test information obtained in 76.2- by 50.8-centimeter tunnel. The tunnel was operated in the open sidewall configuration. These methods are (1) relative integral parameter method, (2) modified power law method, (3) relative power law method, and (4) modified law of the wall method. The modified law of the wall method predicts a more rapid decrease in skin friction with an increase in the ratio of belt velocity to free steam velocity than do methods (1) and (3).

Seminar Proceedings Implementation of Nonstructural Measures Held at Ft. Belvoir, Virginia on 15, 16 and 17 November 1983

DTIC Science & Technology

1983-11-01

S-Approximate Household inventory item average chance of being moved (%) High Electric toaster Vacuum cleaner 80 Colour television Medium Record...most rtadily moved are small items of electrical. I equipment and valuable items such as colour televisions. However, many respondents reported that...WESSEX WATER AUTHORITY, "Somerset Land Drainage District, land drainage sur ey report", Wessex Water Authority, Bridgwater, England, 1979. .34 "* • I.U

Responding to traveling patients' seasonal demand for health care services.

PubMed

Al-Haque, Shahed; Ceyhan, Mehmet Erkan; Chan, Stephanie H; Nightingale, Deborah J

2015-01-01

The Veterans Health Administration (VHA) provides care to over 8 million Veterans and operates over 1,700 sites of care across 21 regional networks in the United States. Health care providers within VHA report large seasonal variation in the demand for services, especially in the southern United States because of arrival of "snowbirds" during the winter. Because resource allocation activities are primarily carried out through an annual budgeting process, the seasonal load imposed by "traveling Veterans"-Veterans that seek care at VHA sites outside of their home network-make providing high-quality services more challenging. This work constitutes the first major effort within VHA to understand the impact of traveling Veterans. We discovered strong seasonal fluctuations in demand at a clinic located in the southeastern United States and developed a seasonal autoregressive integrated moving average model to help the clinic forecast demand for its services with significantly less error than historical averaging. Monte Carlo simulation of the clinic revealed that physicians are overutilized, suggesting the need to re-evaluate how the clinic is currently staffed. More broadly, this study demonstrates how operations management methods can assist operational decision making at other clinics and medical centers both within and outside VHA. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

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.

Parapharyngeal space surgery via a transoral approach using a robotic surgical system: transoral robotic surgery.

PubMed

Park, Young Min; De Virgilio, Armando; Kim, Won Shik; Chung, Hyun Pil; Kim, Se-Heon

2013-03-01

In transoral robotic surgery (TORS), if an endoscopic arm equipped with two integrated cameras is placed close to a lesion, a three-dimensionally magnified view of the operative field can be obtained. More important is that the operation can be performed precisely and bimanually using two instrument arms that can move freely within a limited working space. We performed TORS to treat several diseases that occur in the parapharyngeal space (PPS) and subsequently analyzed the treatment outcomes to confirm the validity of this procedure. Between February 2009 and February 2012, 11 patients who required surgical treatment for the removal of a parapharyngeal lesion were enrolled in this prospective study. Nine patients received TORS for parapharyngeal tumor resection, and 2 patients with stylohyoid syndrome underwent TORS for resection of an elongated styloid process. The average age of the patients included in this study was 42 years. Five patients were male, and 6 patients were female. TORS was successfully performed in all 11 patients. The average robotic system docking and operation times were 9.9 minutes (range, 5-24 minutes) and 54.2 minutes (range, 26-150 minutes), respectively. Patients were able to swallow normally the day after the operation. The average blood loss during the robotic operation was minimal (11.8 mL). The average hospital stay was 2.6 days. There were no significant complications in the perioperative or postoperative period. All patients were extremely satisfied with their cosmetic outcomes. PPS surgery via a transoral approach using a robotic surgical system is technically feasible and secures a better cosmetic outcome than the transcervical, transparotid, or transmandibular approach. This new surgical method is safe and effective for benign diseases of the PPS.

Real-time, continuous, fluorescence sensing in a freely-moving subject with an implanted hybrid VCSEL/CMOS biosensor

PubMed Central

O’Sullivan, Thomas D.; Heitz, Roxana T.; Parashurama, Natesh; Barkin, David B.; Wooley, Bruce A.; Gambhir, Sanjiv S.; Harris, James S.; Levi, Ofer

2013-01-01

Performance improvements in instrumentation for optical imaging have contributed greatly to molecular imaging in living subjects. In order to advance molecular imaging in freely moving, untethered subjects, we designed a miniature vertical-cavity surface-emitting laser (VCSEL)-based biosensor measuring 1cm3 and weighing 0.7g that accurately detects both fluorophore and tumor-targeted molecular probes in small animals. We integrated a critical enabling component, a complementary metal-oxide semiconductor (CMOS) read-out integrated circuit, which digitized the fluorescence signal to achieve autofluorescence-limited sensitivity. After surgical implantation of the lightweight sensor for two weeks, we obtained continuous and dynamic fluorophore measurements while the subject was un-anesthetized and mobile. The technology demonstrated here represents a critical step in the path toward untethered optical sensing using an integrated optoelectronic implant. PMID:24009996

Plans, Patterns, and Move Categories Guiding a Highly Selective Search

NASA Astrophysics Data System (ADS)

Trippen, Gerhard

In this paper we present our ideas for an Arimaa-playing program (also called a bot) that uses plans and pattern matching to guide a highly selective search. We restrict move generation to moves in certain move categories to reduce the number of moves considered by the bot significantly. Arimaa is a modern board game that can be played with a standard Chess set. However, the rules of the game are not at all like those of Chess. Furthermore, Arimaa was designed to be as simple and intuitive as possible for humans, yet challenging for computers. While all established Arimaa bots use alpha-beta search with a variety of pruning techniques and other heuristics ending in an extensive positional leaf node evaluation, our new bot, Rat, starts with a positional evaluation of the current position. Based on features found in the current position - supported by pattern matching using a directed position graph - our bot Rat decides which of a given set of plans to follow. The plan then dictates what types of moves can be chosen. This is another major difference from bots that generate "all" possible moves for a particular position. Rat is only allowed to generate moves that belong to certain categories. Leaf nodes are evaluated only by a straightforward material evaluation to help avoid moves that lose material. This highly selective search looks, on average, at only 5 moves out of 5,000 to over 40,000 possible moves in a middle game position.

A time-domain Kirchhoff formula for the convective acoustic wave equation

PubMed Central

Ghorbaniasl, Ghader; Siozos-Rousoulis, Leonidas; Lacor, Chris

2016-01-01

Kirchhoff’s integral method allows propagated sound to be predicted, based on the pressure and its derivatives in time and space obtained on a data surface located in the linear flow region. Kirchhoff’s formula for noise prediction from high-speed rotors and propellers suffers from the limitation of the observer located in uniform flow, thus requiring an extension to arbitrarily moving media. This paper presents a Kirchhoff formulation for moving surfaces in a uniform moving medium of arbitrary configuration. First, the convective wave equation is derived in a moving frame, based on the generalized functions theory. The Kirchhoff formula is then obtained for moving surfaces in the time domain. The formula has a similar form to the Kirchhoff formulation for moving surfaces of Farassat and Myers, with the presence of additional terms owing to the moving medium effect. The equation explicitly accounts for the influence of mean flow and angle of attack on the radiated noise. The formula is verified by analytical cases of a monopole source located in a moving medium. PMID:27118912

A Generation at Risk: When the Baby Boomers Reach Golden Pond.

ERIC Educational Resources Information Center

Butler, Robert N.

The 20th century has seen average life expectancy in the United States move from under 50 years to over 70 years. Coupled with this increase in average life expectancy is the aging of the 76.4 million persons born between 1946 and 1964. As they approach retirement, these baby-boomers will have to balance their own needs with those of living…

Comparison of 3-D Multi-Lag Cross-Correlation and Speckle Brightness Aberration Correction Algorithms on Static and Moving Targets

PubMed Central

Ivancevich, Nikolas M.; Dahl, Jeremy J.; Smith, Stephen W.

2010-01-01

Phase correction has the potential to increase the image quality of 3-D ultrasound, especially transcranial ultrasound. We implemented and compared 2 algorithms for aberration correction, multi-lag cross-correlation and speckle brightness, using static and moving targets. We corrected three 75-ns rms electronic aberrators with full-width at half-maximum (FWHM) auto-correlation lengths of 1.35, 2.7, and 5.4 mm. Cross-correlation proved the better algorithm at 2.7 and 5.4 mm correlation lengths (P < 0.05). Static cross-correlation performed better than moving-target cross-correlation at the 2.7 mm correlation length (P < 0.05). Finally, we compared the static and moving-target cross-correlation on a flow phantom with a skull casting aberrator. Using signal from static targets, the correction resulted in an average contrast increase of 22.2%, compared with 13.2% using signal from moving targets. The contrast-to-noise ratio (CNR) increased by 20.5% and 12.8% using static and moving targets, respectively. Doppler signal strength increased by 5.6% and 4.9% for the static and moving-targets methods, respectively. PMID:19942503

Comparison of 3-D multi-lag cross- correlation and speckle brightness aberration correction algorithms on static and moving targets.

PubMed

Ivancevich, Nikolas M; Dahl, Jeremy J; Smith, Stephen W

2009-10-01

Phase correction has the potential to increase the image quality of 3-D ultrasound, especially transcranial ultrasound. We implemented and compared 2 algorithms for aberration correction, multi-lag cross-correlation and speckle brightness, using static and moving targets. We corrected three 75-ns rms electronic aberrators with full-width at half-maximum (FWHM) auto-correlation lengths of 1.35, 2.7, and 5.4 mm. Cross-correlation proved the better algorithm at 2.7 and 5.4 mm correlation lengths (P < 0.05). Static cross-correlation performed better than moving-target cross-correlation at the 2.7 mm correlation length (P < 0.05). Finally, we compared the static and moving-target cross-correlation on a flow phantom with a skull casting aberrator. Using signal from static targets, the correction resulted in an average contrast increase of 22.2%, compared with 13.2% using signal from moving targets. The contrast-to-noise ratio (CNR) increased by 20.5% and 12.8% using static and moving targets, respectively. Doppler signal strength increased by 5.6% and 4.9% for the static and moving-targets methods, respectively.

Recent Enhancements To The FUN3D Flow Solver For Moving-Mesh Applications

NASA Technical Reports Server (NTRS)

Biedron, Robert T,; Thomas, James L.

2009-01-01

An unsteady Reynolds-averaged Navier-Stokes solver for unstructured grids has been extended to handle general mesh movement involving rigid, deforming, and overset meshes. Mesh deformation is achieved through analogy to elastic media by solving the linear elasticity equations. A general method for specifying the motion of moving bodies within the mesh has been implemented that allows for inherited motion through parent-child relationships, enabling simulations involving multiple moving bodies. Several example calculations are shown to illustrate the range of potential applications. For problems in which an isolated body is rotating with a fixed rate, a noninertial reference-frame formulation is available. An example calculation for a tilt-wing rotor is used to demonstrate that the time-dependent moving grid and noninertial formulations produce the same results in the limit of zero time-step size.

Humane Letters: Notes on the Concept of Integrity and the Meanings of Humanism

ERIC Educational Resources Information Center

Higgins, Chris

2009-01-01

In this article, the author calls for an analysis of integrity and contends that attempting to describe wholeness precisely and incisively is not necessarily a contradiction in terms. The author makes some distinctions about integrity using two moves, one inspired by Plato, and one by Aristotle. The author uses the phrase "humane letters" to name…

Vocabulary on the Move: Investigating an Intelligent Mobile Phone-Based Vocabulary Tutor

ERIC Educational Resources Information Center

Stockwell, Glenn

2007-01-01

Mobile learning has long been identified as one of the natural directions in which CALL is expected to move, and as smaller portable technologies become less expensive, lighter and more powerful, they have the potential to become a more integral part of language learning courses as opposed to the more supplemental role often assigned to computer…

"Move-In" Violence: White Resistance to Neighborhood Integration in the 1980's. Special Report.

ERIC Educational Resources Information Center

Southern Poverty Law Center, Montgomery, AL.

Racist violence has followed the migration of minority families to the suburbs as intransigent whites resort to arson and other violence to preserve racially segregated neighborhoods. A study of this phenomenon of "move-in violence" for the years 1985-86 found it to be a serious, under-reported social problem nationwide. In cases where arrests…

The Bicycle Illusion: Sidewalk Science Informs the Integration of Motion and Shape Perception

ERIC Educational Resources Information Center

Masson, Michael E. J.; Dodd, Michael D.; Enns, James T.

2009-01-01

The authors describe a new visual illusion first discovered in a natural setting. A cyclist riding beside a pair of sagging chains that connect fence posts appears to move up and down with the chains. In this illusion, a static shape (the chains) affects the perception of a moving shape (the bicycle), and this influence involves assimilation…

Integral transforms of the quantum mechanical path integral: Hit function and path-averaged potential.

PubMed

Edwards, James P; Gerber, Urs; Schubert, Christian; Trejo, Maria Anabel; Weber, Axel

2018-04-01

We introduce two integral transforms of the quantum mechanical transition kernel that represent physical information about the path integral. These transforms can be interpreted as probability distributions on particle trajectories measuring respectively the relative contribution to the path integral from paths crossing a given spatial point (the hit function) and the likelihood of values of the line integral of the potential along a path in the ensemble (the path-averaged potential).

Integral transforms of the quantum mechanical path integral: Hit function and path-averaged potential

NASA Astrophysics Data System (ADS)

Edwards, James P.; Gerber, Urs; Schubert, Christian; Trejo, Maria Anabel; Weber, Axel

2018-04-01

We introduce two integral transforms of the quantum mechanical transition kernel that represent physical information about the path integral. These transforms can be interpreted as probability distributions on particle trajectories measuring respectively the relative contribution to the path integral from paths crossing a given spatial point (the hit function) and the likelihood of values of the line integral of the potential along a path in the ensemble (the path-averaged potential).

Quantifying streamflow change caused by forest disturbance at a large spatial scale: A single watershed study

NASA Astrophysics Data System (ADS)

Wei, Xiaohua; Zhang, Mingfang

2010-12-01

Climatic variability and forest disturbance are commonly recognized as two major drivers influencing streamflow change in large-scale forested watersheds. The greatest challenge in evaluating quantitative hydrological effects of forest disturbance is the removal of climatic effect on hydrology. In this paper, a method was designed to quantify respective contributions of large-scale forest disturbance and climatic variability on streamflow using the Willow River watershed (2860 km2) located in the central part of British Columbia, Canada. Long-term (>50 years) data on hydrology, climate, and timber harvesting history represented by equivalent clear-cutting area (ECA) were available to discern climatic and forestry influences on streamflow by three steps. First, effective precipitation, an integrated climatic index, was generated by subtracting evapotranspiration from precipitation. Second, modified double mass curves were developed by plotting accumulated annual streamflow against annual effective precipitation, which presented a much clearer picture of the cumulative effects of forest disturbance on streamflow following removal of climatic influence. The average annual streamflow changes that were attributed to forest disturbances and climatic variability were then estimated to be +58.7 and -72.4 mm, respectively. The positive (increasing) and negative (decreasing) values in streamflow change indicated opposite change directions, which suggest an offsetting effect between forest disturbance and climatic variability in the study watershed. Finally, a multivariate Autoregressive Integrated Moving Average (ARIMA) model was generated to establish quantitative relationships between accumulated annual streamflow deviation attributed to forest disturbances and annual ECA. The model was then used to project streamflow change under various timber harvesting scenarios. The methodology can be effectively applied to any large-scale single watershed where long-term data (>50 years) are available.

Improvements in mode-based waveform modeling and application to Eurasian velocity structure

NASA Astrophysics Data System (ADS)

Panning, M. P.; Marone, F.; Kim, A.; Capdeville, Y.; Cupillard, P.; Gung, Y.; Romanowicz, B.

2006-12-01

We introduce several recent improvements to mode-based 3D and asymptotic waveform modeling and examine how to integrate them with numerical approaches for an improved model of upper-mantle structure under eastern Eurasia. The first step in our approach is to create a large-scale starting model including shear anisotropy using Nonlinear Asymptotic Coupling Theory (NACT; Li and Romanowicz, 1995), which models the 2D sensitivity of the waveform to the great-circle path between source and receiver. We have recently improved this approach by implementing new crustal corrections which include a non-linear correction for the difference between the average structure of several large regions from the global model with further linear corrections to account for the local structure along the path between source and receiver (Marone and Romanowicz, 2006; Panning and Romanowicz, 2006). This model is further refined using a 3D implementation of Born scattering (Capdeville, 2005). We have made several recent improvements to this method, in particular introducing the ability to represent perturbations to discontinuities. While the approach treats all sensitivity as linear perturbations to the waveform, we have also experimented with a non-linear modification analogous to that used in the development of NACT. This allows us to treat large accumulated phase delays determined from a path-average approximation non-linearly, while still using the full 3D sensitivity of the Born approximation. Further refinement of shallow regions of the model is obtained using broadband forward finite-difference waveform modeling. We are also integrating a regional Spectral Element Method code into our tomographic modeling, allowing us to move beyond many assumptions inherent in the analytic mode-based approaches, while still taking advantage of their computational efficiency. Illustrations of the effects of these increasingly sophisticated steps will be presented.

A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China.

PubMed

Xu, Lilai; Gao, Peiqing; Cui, Shenghui; Liu, Chun

2013-06-01

Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the planning, operation and optimization of any MSW management system. Comprehensive information on waste generation for month-scale, medium-term and long-term time scales is especially needed, considering the necessity of MSW management upgrade facing many developing countries. Several existing models are available but of little use in forecasting MSW generation at multiple time scales. The goal of this study is to propose a hybrid model that combines the seasonal autoregressive integrated moving average (SARIMA) model and grey system theory to forecast MSW generation at multiple time scales without needing to consider other variables such as demographics and socioeconomic factors. To demonstrate its applicability, a case study of Xiamen City, China was performed. Results show that the model is robust enough to fit and forecast seasonal and annual dynamics of MSW generation at month-scale, medium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xiamen City will peak at 132.2 thousand tonnes in July 2015 - 1.5 times the volume in July 2010. In the medium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an average growth rate of 10%. In the long term, a large volume of MSW will be output annually and will increase to 2486.3 thousand tonnes by 2020 - 2.5 times the value for 2010. The hybrid model proposed in this paper can enable decision makers to develop integrated policies and measures for waste management over the long term. Copyright © 2013 Elsevier Ltd. All rights reserved.

Forecasting influenza in Hong Kong with Google search queries and statistical model fusion.

PubMed

Xu, Qinneng; Gel, Yulia R; Ramirez Ramirez, L Leticia; Nezafati, Kusha; Zhang, Qingpeng; Tsui, Kwok-Leung

2017-01-01

The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources. Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates. DL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks. The proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient.

Effectiveness of an Integrated Approach to HIV and Hypertension Care in Rural South Africa: Controlled Interrupted Time-Series Analysis.

PubMed

Ameh, Soter; Klipstein-Grobusch, Kerstin; Musenge, Eustasius; Kahn, Kathleen; Tollman, Stephen; Gómez-Olivé, Francesc Xavier

2017-08-01

South Africa faces a dual burden of HIV/AIDS and noncommunicable diseases. In 2011, a pilot integrated chronic disease management (ICDM) model was introduced by the National Health Department into selected primary health care (PHC) facilities. The objective of this study was to assess the effectiveness of the ICDM model in controlling patients' CD4 counts (>350 cells/mm) and blood pressure [BP (<140/90 mm Hg)] in PHC facilities in the Bushbuckridge municipality, South Africa. A controlled interrupted time-series study was conducted using the data from patients' clinical records collected multiple times before and after the ICDM model was initiated in PHC facilities in Bushbuckridge. Patients ≥18 years were recruited by proportionate sampling from the pilot (n = 435) and comparing (n = 443) PHC facilities from 2011 to 2013. Health outcomes for patients were retrieved from facility records for 30 months. We performed controlled segmented regression to model the monthly averages of individuals' propensity scores using autoregressive moving average model at 5% significance level. The pilot facilities had 6% greater likelihood of controlling patients' CD4 counts than the comparison facilities (coefficient = 0.057; 95% confidence interval: 0.056 to 0.058; P < 0.001). Compared with the comparison facilities, the pilot facilities had 1.0% greater likelihood of controlling patients' BP (coefficient = 0.010; 95% confidence interval: 0.003 to 0.016; P = 0.002). Application of the model had a small effect in controlling patients' CD4 counts and BP, but showed no overall clinical benefit for the patients; hence, the need to more extensively leverage the HIV program for hypertension treatment.

A suite of standard post-tagging evaluation metrics can help assess tag retention for field-based fish telemetry research

USGS Publications Warehouse

Gerber, Kayla M.; Mather, Martha E.; Smith, Joseph M.

2017-01-01

Telemetry can inform many scientific and research questions if a context exists for integrating individual studies into the larger body of literature. Creating cumulative distributions of post-tagging evaluation metrics would allow individual researchers to relate their telemetry data to other studies. Widespread reporting of standard metrics is a precursor to the calculation of benchmarks for these distributions (e.g., mean, SD, 95% CI). Here we illustrate five types of standard post-tagging evaluation metrics using acoustically tagged Blue Catfish (Ictalurus furcatus) released into a Kansas reservoir. These metrics included: (1) percent of tagged fish detected overall, (2) percent of tagged fish detected daily using abacus plot data, (3) average number of (and percent of available) receiver sites visited, (4) date of last movement between receiver sites (and percent of tagged fish moving during that time period), and (5) number (and percent) of fish that egressed through exit gates. These metrics were calculated for one to three time periods: early (<10 d), during (weekly), and at the end of the study (5 months). Over three-quarters of our tagged fish were detected early (85%) and at the end (85%) of the study. Using abacus plot data, all tagged fish (100%) were detected at least one day and 96% were detected for > 5 days early in the study. On average, tagged Blue Catfish visited 9 (50%) and 13 (72%) of 18 within-reservoir receivers early and at the end of the study, respectively. At the end of the study, 73% of all tagged fish were detected moving between receivers. Creating statistical benchmarks for individual metrics can provide useful reference points. In addition, combining multiple metrics can inform ecology and research design. Consequently, individual researchers and the field of telemetry research can benefit from widespread, detailed, and standard reporting of post-tagging detection metrics.

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Effect of Rolling Massage on Particle Moving Behaviour in Blood Vessels

NASA Astrophysics Data System (ADS)

Yi, Hou-Hui; Fan, Li-Juan; Yang, Xiao-Feng; Chen, Yan-Yan

2008-09-01

The rolling massage manipulation is a classic Chinese massage, which is expected to eliminate many diseases. Here the effect of the rolling massage on the particle moving property in the blood vessels under the rolling massage manipulation is studied by the lattice Boltzmann simulation. The simulation results show that the particle moving behaviour depends on the rolling velocity, the distance between particle position and rolling position. The average values, including particle translational velocity and angular velocity, increase as the rolling velocity increases almost linearly. The result is helpful to understand the mechanism of the massage and develop the rolling techniques.

Experimental comparisons of hypothesis test and moving average based combustion phase controllers.

PubMed

Gao, Jinwu; Wu, Yuhu; Shen, Tielong

2016-11-01

For engine control, combustion phase is the most effective and direct parameter to improve fuel efficiency. In this paper, the statistical control strategy based on hypothesis test criterion is discussed. Taking location of peak pressure (LPP) as combustion phase indicator, the statistical model of LPP is first proposed, and then the controller design method is discussed on the basis of both Z and T tests. For comparison, moving average based control strategy is also presented and implemented in this study. The experiments on a spark ignition gasoline engine at various operating conditions show that the hypothesis test based controller is able to regulate LPP close to set point while maintaining the rapid transient response, and the variance of LPP is also well constrained. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Neonatal heart rate prediction.

PubMed

Abdel-Rahman, Yumna; Jeremic, Aleksander; Tan, Kenneth

2009-01-01

Technological advances have caused a decrease in the number of infant deaths. Pre-term infants now have a substantially increased chance of survival. One of the mechanisms that is vital to saving the lives of these infants is continuous monitoring and early diagnosis. With continuous monitoring huge amounts of data are collected with so much information embedded in them. By using statistical analysis this information can be extracted and used to aid diagnosis and to understand development. In this study we have a large dataset containing over 180 pre-term infants whose heart rates were recorded over the length of their stay in the Neonatal Intensive Care Unit (NICU). We test two types of models, empirical bayesian and autoregressive moving average. We then attempt to predict future values. The autoregressive moving average model showed better results but required more computation.

Structural equation modeling of the inflammatory response to traffic air pollution

PubMed Central

Baja, Emmanuel S.; Schwartz, Joel D.; Coull, Brent A.; Wellenius, Gregory A.; Vokonas, Pantel S.; Suh, Helen H.

2015-01-01

Several epidemiological studies have reported conflicting results on the effect of traffic-related pollutants on markers of inflammation. In a Bayesian framework, we examined the effect of traffic pollution on inflammation using structural equation models (SEMs). We studied measurements of C-reactive protein (CRP), soluble vascular cell adhesion molecule-1 (sVCAM-1), and soluble intracellular adhesion molecule-1 (sICAM-1) for 749 elderly men from the Normative Aging Study. Using repeated measures SEMs, we fit a latent variable for traffic pollution that is reflected by levels of black carbon, carbon monoxide, nitrogen monoxide and nitrogen dioxide to estimate its effect on a latent variable for inflammation that included sICAM-1, sVCAM-1 and CRP. Exposure periods were assessed using 1-, 2-, 3-, 7-, 14- and 30-day moving averages previsit. We compared our findings using SEMs with those obtained using linear mixed models. Traffic pollution was related to increased inflammation for 3-, 7-, 14- and 30-day exposure periods. An inter-quartile range increase in traffic pollution was associated with a 2.3% (95% posterior interval (PI): 0.0–4.7%) increase in inflammation for the 3-day moving average, with the most significant association observed for the 30-day moving average (23.9%; 95% PI: 13.9–36.7%). Traffic pollution adversely impacts inflammation in the elderly. SEMs in a Bayesian framework can comprehensively incorporate multiple pollutants and health outcomes simultaneously in air pollution–cardiovascular epidemiological studies. PMID:23232970

Learning curves for single incision and conventional laparoscopic right hemicolectomy: a multidimensional analysis.

PubMed

Park, Yoonah; Yong, Yuen Geng; Yun, Seong Hyeon; Jung, Kyung Uk; Huh, Jung Wook; Cho, Yong Beom; Kim, Hee Cheol; Lee, Woo Yong; Chun, Ho-Kyung

2015-05-01

This study aimed to compare the learning curves and early postoperative outcomes for conventional laparoscopic (CL) and single incision laparoscopic (SIL) right hemicolectomy (RHC). This retrospective study included the initial 35 cases in each group. Learning curves were evaluated by the moving average of operative time, mean operative time of every five consecutive cases, and cumulative sum (CUSUM) analysis. The learning phase was considered overcome when the moving average of operative times reached a plateau, and when the mean operative time of every five consecutive cases reached a low point and subsequently did not vary by more than 30 minutes. Six patients with missing data in the CL RHC group were excluded from the analyses. According to the mean operative time of every five consecutive cases, learning phase of SIL and CL RHC was completed between 26 and 30 cases, and 16 and 20 cases, respectively. Moving average analysis revealed that approximately 31 (SIL) and 25 (CL) cases were needed to complete the learning phase, respectively. CUSUM analysis demonstrated that 10 (SIL) and two (CL) cases were required to reach a steady state of complication-free performance, respectively. Postoperative complications rate was higher in SIL than in CL group, but the difference was not statistically significant (17.1% vs. 3.4%). The learning phase of SIL RHC is longer than that of CL RHC. Early oncological outcomes of both techniques were comparable. However, SIL RHC had a statistically insignificant higher complication rate than CL RHC during the learning phase.

An Estimate of the Likelihood for a Climatically Significant Volcanic Eruption Within the Present Decade (2000-2009)

NASA Technical Reports Server (NTRS)

Wilson, Robert M.; Franklin, M. Rose (Technical Monitor)

2000-01-01

Since 1750, the number of cataclysmic volcanic eruptions (i.e., those having a volcanic explosivity index, or VEI, equal to 4 or larger) per decade is found to span 2-11, with 96% located in the tropics and extra-tropical Northern Hemisphere, A two-point moving average of the time series has higher values since the 1860s than before, measuring 8.00 in the 1910s (the highest value) and measuring 6.50 in the 1980s, the highest since the 18 1 0s' peak. On the basis of the usual behavior of the first difference of the two-point moving averages, one infers that the two-point moving average for the 1990s will measure about 6.50 +/- 1.00, implying that about 7 +/- 4 cataclysmic volcanic eruptions should be expected during the present decade (2000-2009). Because cataclysmic volcanic eruptions (especially, those having VEI equal to 5 or larger) nearly always have been associated with episodes of short-term global cooling, the occurrence of even one could ameliorate the effects of global warming. Poisson probability distributions reveal that the probability of one or more VEI equal to 4 or larger events occurring within the next ten years is >99%, while it is about 49% for VEI equal to 5 or larger events and 18% for VEI equal to 6 or larger events. Hence, the likelihood that a, climatically significant volcanic eruption will occur within the next 10 years appears reasonably high.

Influence of foundation mass and surface roughness on dynamic response of beam on dynamic foundation subjected to the moving load

NASA Astrophysics Data System (ADS)

Tran Quoc, Tinh; Khong Trong, Toan; Luong Van, Hai

2018-04-01

In this paper, Improved Moving Element Method (IMEM) is used to analyze the dynamic response of Euler-Bernoulli beam structures on the dynamic foundation model subjected to the moving load. The effects of characteristic foundation model parameters such as Winkler stiffness, shear layer based on the Pasternak model, viscoelastic dashpot and characteristic parameter of mass on foundation. Beams are modeled by moving elements while the load is fixed. Based on the principle of the publicly virtual balancing and the theory of moving element method, the motion differential equation of the system is established and solved by means of the numerical integration based on the Newmark algorithm. The influence of mass on foundation and the roughness of the beam surface on the dynamic response of beam are examined in details.

Substorm-related plasma sheet motions as determined from differential timing of plasma changes at the ISEE satellites

NASA Technical Reports Server (NTRS)

Forbes, T. G.; Hones, E. W., Jr.; Bame, S. J.; Asbridge, J. R.; Paschmann, G.; Sckopke, N.; Russell, C. T.

1981-01-01

From an ISEE survey of substorm dropouts and recoveries during the period February 5 to May 25, 1978, 66 timing events observed by the Los Alamos Scientific Laboratory/Max-Planck-Institut Fast Plasma Experiments were studied in detail. Near substorm onset, both the average timing velocity and the bulk flow velocity at the edge of the plasma sheet are inward, toward the center. Measured normal to the surface of the plasma sheet, the timing velocity is 23 + or - 18 km/s and the proton flow velocity is 20 + or - 8 km/s. During substorm recovery, the plasma sheet reappears moving outward with an average timing velocity of 133 + or - 31 km/s; however, the corresponding proton flow velocity is only 3 + or - 7 km/s in the same direction. It is suggested that the difference between the average timing velocity for the expansion of the plasma sheet and the plasma bulk flow perpendicular to the surface of the sheet during substorm recovery is most likely the result of surface waves moving past the position of the satellites.

Modified Exponential Weighted Moving Average (EWMA) Control Chart on Autocorrelation Data

NASA Astrophysics Data System (ADS)

Herdiani, Erna Tri; Fandrilla, Geysa; Sunusi, Nurtiti

2018-03-01

In general, observations of the statistical process control are assumed to be mutually independence. However, this assumption is often violated in practice. Consequently, statistical process controls were developed for interrelated processes, including Shewhart, Cumulative Sum (CUSUM), and exponentially weighted moving average (EWMA) control charts in the data that were autocorrelation. One researcher stated that this chart is not suitable if the same control limits are used in the case of independent variables. For this reason, it is necessary to apply the time series model in building the control chart. A classical control chart for independent variables is usually applied to residual processes. This procedure is permitted provided that residuals are independent. In 1978, Shewhart modification for the autoregressive process was introduced by using the distance between the sample mean and the target value compared to the standard deviation of the autocorrelation process. In this paper we will examine the mean of EWMA for autocorrelation process derived from Montgomery and Patel. Performance to be investigated was investigated by examining Average Run Length (ARL) based on the Markov Chain Method.

The vacuum friction paradox and related puzzles

NASA Astrophysics Data System (ADS)

Barnett, Stephen M.; Sonnleitner, Matthias

2018-04-01

The frequency of light emitted by a moving source is shifted by a factor proportional to its velocity. We find that this Doppler shift requires the existence of a paradoxical effect: that a moving atom radiating in otherwise empty space feels a net or average force acing against its direction motion and proportional in magnitude to is speed. Yet there is no preferred rest frame, either in relativity or in Newtonian mechanics, so how can there be a vacuum friction force?

26 CFR 1.401(l)-1 - Permitted disparity in employer-provided contributions or benefits.

Code of Federal Regulations, 2014 CFR

2014-04-01

... with respect to an employee's average annual compensation at or below the integration level (expressed... or below the integration level (expressed as a percentage of such plan year compensation). (5... plan with respect to an employee's average annual compensation above the integration level (expressed...

Hydrogeology and leachate movement near two chemical-waste sites in Oswego County, New York

USGS Publications Warehouse

Anderson, H.R.; Miller, Todd S.

1986-01-01

Forty-five observation wells and test holes were installed at two chemical waste disposal sites in Oswego County, New York, to evaluate the hydrogeologic conditions and the rate and direction of leachate migration. At the site near Oswego groundwater moves northward at an average velocity of 0.4 ft/day through unconsolidated glacial deposits and discharges into White Creek and Wine Creek, which border the site and discharge to Lake Ontario. Leaking barrels by chemical wastes have contaminated the groundwater within the site, as evidenced by detection of 10 ' priority pollutant ' organic compounds, and elevated values of specific conductance, chloride, arsenic, lead, and mercury. At the site near Fulton, where 8,000 barrels of chemical wastes are buried, groundwater in the sandy surficial aquifer bordering the landfill on the south and east moves southward and eastward at an average velocity of 2.8 ft/day and discharges to Bell Creek, which discharges to the Oswego River, or moves beneath the landfill. Leachate is migrating eastward, southeastward, and southwestward, as evidenced by elevated values of specific conductance, temperature, and concentrations of several trace metals at wells east, southeast, and southwest of the site. (USGS)

Interim Cryogenic Propulsion Stage (ICPS) for EM-1 Transport fro

NASA Image and Video Library

2017-04-11

The Interim Cryogenic Propulsion Stage (ICPS) for NASA's Space Launch System rocket is moved inside the Delta Operations Center at Cape Canaveral Air Force Station in Florida. The ICPS was moved from the United Launch Alliance (ULA) Horizontal Integration Facility near Space Launch Complex 37 at the Cape. The ICPS is the first integrated piece of flight hardware to arrive for the SLS. It is the in-space stage that is located toward the top of the rocket, between the Launch Vehicle Stage Adapter and the Orion Spacecraft Adapter. It will provide some of the in-space propulsion during Orion's first flight test atop the SLS on Exploration Mission-1.

Variance of discharge estimates sampled using acoustic Doppler current profilers from moving boats

USGS Publications Warehouse

Garcia, Carlos M.; Tarrab, Leticia; Oberg, Kevin; Szupiany, Ricardo; Cantero, Mariano I.

2012-01-01

This paper presents a model for quantifying the random errors (i.e., variance) of acoustic Doppler current profiler (ADCP) discharge measurements from moving boats for different sampling times. The model focuses on the random processes in the sampled flow field and has been developed using statistical methods currently available for uncertainty analysis of velocity time series. Analysis of field data collected using ADCP from moving boats from three natural rivers of varying sizes and flow conditions shows that, even though the estimate of the integral time scale of the actual turbulent flow field is larger than the sampling interval, the integral time scale of the sampled flow field is on the order of the sampling interval. Thus, an equation for computing the variance error in discharge measurements associated with different sampling times, assuming uncorrelated flow fields is appropriate. The approach is used to help define optimal sampling strategies by choosing the exposure time required for ADCPs to accurately measure flow discharge.

The Accuracy of Talking Pedometers when Used during Free-Living: A Comparison of Four Devices

ERIC Educational Resources Information Center

Albright, Carolyn; Jerome, Gerald J.

2011-01-01

The purpose of this study was to determine the accuracy of four commercially available talking pedometers in measuring accumulated daily steps of adult participants while they moved independently. Ten young sighted adults (with an average age of 24.1 [plus or minus] 4.6 years), 10 older sighted adults (with an average age of 73 [plus or minus] 5.5…

Comparison of estimators for rolling samples using Forest Inventory and Analysis data

Treesearch

Devin S. Johnson; Michael S. Williams; Raymond L. Czaplewski

2003-01-01

The performance of three classes of weighted average estimators is studied for an annual inventory design similar to the Forest Inventory and Analysis program of the United States. The first class is based on an ARIMA(0,1,1) time series model. The equal weight, simple moving average is a member of this class. The second class is based on an ARIMA(0,2,2) time series...

Hybrid Wavelet De-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

NASA Astrophysics Data System (ADS)

WANG, D.; Wang, Y.; Zeng, X.

2017-12-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, Wavelet De-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

Implementation of an anonymisation tool for clinical trials using a clinical trial processor integrated with an existing trial patient data information system.

PubMed

Aryanto, Kadek Y E; Broekema, André; Oudkerk, Matthijs; van Ooijen, Peter M A

2012-01-01

To present an adapted Clinical Trial Processor (CTP) test set-up for receiving, anonymising and saving Digital Imaging and Communications in Medicine (DICOM) data using external input from the original database of an existing clinical study information system to guide the anonymisation process. Two methods are presented for an adapted CTP test set-up. In the first method, images are pushed from the Picture Archiving and Communication System (PACS) using the DICOM protocol through a local network. In the second method, images are transferred through the internet using the HTTPS protocol. In total 25,000 images from 50 patients were moved from the PACS, anonymised and stored within roughly 2 h using the first method. In the second method, an average of 10 images per minute were transferred and processed over a residential connection. In both methods, no duplicated images were stored when previous images were retransferred. The anonymised images are stored in appropriate directories. The CTP can transfer and process DICOM images correctly in a very easy set-up providing a fast, secure and stable environment. The adapted CTP allows easy integration into an environment in which patient data are already included in an existing information system.

Real-time fluorescence microscopy monitoring of porphyrin biodistribution

NASA Astrophysics Data System (ADS)

Kimel, Sol; Gottfried, Varda; Kunzi-Rapp, Karin; Akguen, Nermin; Schneckenburger, Herbert

1996-01-01

In vivo uptake of the natural porphyrins, uroporphyrin III (UP), coproporphyrin III (CP) and protoporphyrin IX (PP), was monitored by fluorescence microscopy. Experiments were performed using the chick chorioallantoic membrane (CAM) model, which allowed video documentation of fluorescence both in real time and after integration over a chosen time interval (usually 2 s). Sensitizers at a concentration of 50 (mu) M (100 (mu) L) were injected into a medium-sized vein (diameter approximately 40 micrometer) using an ultra-fine 10 micrometer diameter needle. Fluorescence images were quantitated by subtracting the fluorescence intensity of surrounding CAM tissue (Fmatrix) from the intravascular fluorescence intensity (Fintravascular), after transformation of the video frames into digital form. The differential fluorescence intensity, Fintravascular - Fmatrix, is a measure of the biodistribution. Real time measurements clearly showed that CP and UP fluorescence is associated with moving erythrocytes and not with endothelial cells of the vessel wall. Fluorescence intensity was monitored, up to 60 minutes after injection, by averaging the fluorescence over time intervals of 2 s and recording the integrated images. The fluorescence intensity reached its maximum in about 20 - 30 min after injection, presumably after monomerization inside erythrocyte membranes. The results are interpreted in terms of physical-chemical characteristics (e.g. hydrophilicity) and correlated with the photodynamically induced hemostasis in CAM blood vessels.

A landslide-quake detection algorithm with STA/LTA and diagnostic functions of moving average and scintillation index: A preliminary case study of the 2009 Typhoon Morakot in Taiwan

NASA Astrophysics Data System (ADS)

Wu, Yu-Jie; Lin, Guan-Wei

2017-04-01

Since 1999, Taiwan has experienced a rapid rise in the number of landslides, and the number even reached a peak after the 2009 Typhoon Morakot. Although it is proved that the ground-motion signals induced by slope processes could be recorded by seismograph, it is difficult to be distinguished from continuous seismic records due to the lack of distinct P and S waves. In this study, we combine three common seismic detectors including the short-term average/long-term average (STA/LTA) approach, and two diagnostic functions of moving average and scintillation index. Based on these detectors, we have established an auto-detection algorithm of landslide-quakes and the detection thresholds are defined to distinguish landslide-quake from earthquakes and background noises. To further improve the proposed detection algorithm, we apply it to seismic archives recorded by Broadband Array in Taiwan for Seismology (BATS) during the 2009 Typhoon Morakots and consequently the discrete landslide-quakes detected by the automatic algorithm are located. The detection algorithm show that the landslide-detection results are consistent with that of visual inspection and hence can be used to automatically monitor landslide-quakes.

Using the Theory of Habitus to Move beyond the Study of Barriers to Technology Integration

ERIC Educational Resources Information Center

Belland, Brian R.

2009-01-01

The integration of technology by K-12 teachers was promoted to aid the shift to a more student-centered classroom (e.g., Roblyer, M. D., & Edwards, J. (2000). "Integrating educational technology into teaching" (2nd ed.). Upper Saddle River, NJ: Merrill). However, growth in the power of and access to technology in schools has not been accompanied…

An integrated microfluidic cell for detection, manipulation, and sorting of single micron-sized magnetic beads

NASA Astrophysics Data System (ADS)

Jiang, Z.; Llandro, J.; Mitrelias, T.; Bland, J. A. C.

2006-04-01

A lab-on-a-chip integrated microfluidic cell has been developed for magnetic biosensing, which is comprised of anisotropic magnetoresistance (AMR) sensors optimized for the detection of single magnetic beads and electrodes to manipulate and sort the beads, integrated into a microfluidic channel. The device is designed to read out the real-time signal from 9 μm diameter magnetic beads moving over AMR sensors patterned into 18×4.5 μm rectangles and 10 μm diameter rings and arranged in Wheatstone bridges. The beads are moved over the sensors along a 75×75 μm wide channel patterned in SU8. Beads of different magnetic moments can be sorted through a magnetostatic sorting gate into different branches of the microfluidic channel using a magnetic field gradient applied by lithographically defined 120 nm thick Cu striplines carrying 0.2 A current.

KARMA4

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

Khalil, Mohammad; Salloum, Maher; Lee, Jina

2017-07-10

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

HELIOSHEATH MAGNETIC FIELDS BETWEEN 104 AND 113 AU IN A REGION OF DECLINING SPEEDS AND A STAGNATION REGION

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

Burlaga, L. F.; Ness, N. F., E-mail: lburlagahsp@verizon.net, E-mail: nfnudel@yahoo.com

2012-04-10

We examine the relationships between the magnetic field and the radial velocity component V{sub R} observed in the heliosheath by instruments on Voyager 1 (V1). No increase in the magnetic field strength B was observed in a region where V{sub R} decreased linearly from 70 km s{sup -1} to 0 km s{sup -1} as plasma moved outward past V1. An unusually broad transition from positive to negative polarity was observed during a Almost-Equal-To 26 day interval when the heliospheric current sheet (HCS) moved below the latitude of V1 and the speed of V1 was comparable to the radial speed ofmore » the heliosheath flow. When V1 moved through a region where V{sub R} Almost-Equal-To 0 (the 'stagnation region'), B increased linearly with time by a factor of two, and the average of B was 0.14 nT. Nothing comparable to this was observed previously. The magnetic polarity was negative throughout the stagnation region for Almost-Equal-To 580 days until 2011 DOY 235, indicating that the HCS was below the latitude of V1. The average passage times of the magnetic holes and proton boundary layers were the same during 2009 and 2011, because the plasma moved past V1 during 2009 at the same speed that V1 moved through the stagnation region during 2011. The microscale fluctuations of B in the stagnation region during 2011 are qualitatively the same as those observed in the heliosheath during 2009. These results suggest that the stagnation region is a part of the heliosheath, rather than a 'transition region' associated with the heliopause.« less

Creating an Environment Where Your Family Will Thrive

ERIC Educational Resources Information Center

Hanlon, Kerri

2009-01-01

When a consultant advised the author and her family to move to a new area and make a separate wing for her son, Sean, the author realized that the consultant did not share her vision for Sean and how he integrates into the family. Instead of moving to a new area, the author decided to renovate the house to make it handicapped accessible for her…

Adaptive Counseling and Therapy: An Integrative, Eclectic Model.

ERIC Educational Resources Information Center

Howard, George S.; And Others

1986-01-01

Presents an integrative model, Adaptive Counseling and Therapy (ACT), for selecting a progression of therapist styles as clients move through developmental stages during the course of counseling and psychotherapy. ACT is intended to be useful to practitioners in case conceptualization and in the application of effective treatment planning.…

Moving toward a Mobile Learning Landscape: Presenting a M-Learning Integration Framework

ERIC Educational Resources Information Center

Crompton, Helen

2017-01-01

Purpose: Mobile devices transcend the educational affordances provided by conventional tethered electronic and traditional learning. However, empirical findings show that educators are not integrating technology effectively into the curriculum. This paper aims to discuss these issues. Design/Methodology/Approach: In this study, a thematic…

Human factors considerations for the integration of traffic information and alerts on an airport surface map

DOT National Transportation Integrated Search

2010-12-01

The purpose of this document is to provide human factors considerations in the integration of traffic information and indications and alerts for runway status on an airport surface moving map. The US DOT Volpe Center, in support of the Federal Aviati...

Matters of the Heart: Bringing the Values to Life at Eastman Kodak Company.

ERIC Educational Resources Information Center

Tette, Rick; Murray, Mark

1997-01-01

Describes the rationale and implementation of the Eastman Kodak Company's "Fundamentals for Kodak Renewal" employee program. Using adventure activities, employees move through awareness, agreement, and alignment stages to integrate the company's basic values of respect for the dignity of the individual, uncompromising integrity, trust,…

Testing an Integrated Model of Advice Giving in Supportive Interactions

ERIC Educational Resources Information Center

Feng, Bo

2009-01-01

Viewing supportive communication as a multistage process, the present study proposed and tested an integrated model of advice giving, which specifies three sequential moves in supportive interactions involving advice: emotional support, problem inquiry and analysis, and advice. Seven hundred and fifty-two participants read and responded to a…

Get It Together: Integrating Data with XML.

ERIC Educational Resources Information Center

Miller, Ron

2003-01-01

Discusses the use of XML for data integration to move data across different platforms, including across the Internet, from a variety of sources. Topics include flexibility; standards; organizing databases; unstructured data and the use of meta tags to encode it with XML information; cost effectiveness; and eliminating client software licenses.…

Turn the Wheel: Integral School Counseling for Male Adolescents.

ERIC Educational Resources Information Center

Forbes, David

2003-01-01

This article formulates an overarching, inclusive model of integral counseling that enables school counselors to help male adolescents challenge the norm of conventional masculinity. The model draws from 3 areas: transpersonal counseling, holistic education, and mindful social action. The aim is to move the students' level of self-development and…

Develop Education Systems that Integrate All Levels

ERIC Educational Resources Information Center

Kiker, Jason

2007-01-01

During the last few years, the development of seamless education systems to promote students' postsecondary success has been discussed by policymakers at the local, state and federal levels as well as reform-minded individuals. Florida, Washington, Iowa, Georgia and California either have statewide integrated systems or are moving quickly toward…

How to Move Away from the Silos of Business Management Education?

ERIC Educational Resources Information Center

Nisula, Karoliina; Pekkola, Samuli

2018-01-01

Business management education is criticized for being too theoretical and fractional. Despite the numerous efforts to build integrated and experiential business curricula, learning is still organized in disciplinary silos. The curriculum integration efforts are carried out in separate sections of the curriculum rather than the core. There are…

Research-Based Integrated Reading and Writing Course Development

ERIC Educational Resources Information Center

Pierce, Calisa A.

2017-01-01

With the continuing national emphases on acceleration and completion, an integrated reading and writing course (a combined developmental reading and developmental writing course, with all levels compressed into a single course) is one way to move students more quickly and efficiently through the developmental sequence while still maintaining…

The impact of household cooking and heating with solid fuels on ambient PM2.5 in peri-urban Beijing

NASA Astrophysics Data System (ADS)

Liao, Jiawen; Zimmermann Jin, Anna; Chafe, Zoë A.; Pillarisetti, Ajay; Yu, Tao; Shan, Ming; Yang, Xudong; Li, Haixi; Liu, Guangqing; Smith, Kirk R.

2017-09-01

Household cooking and space heating with biomass and coal have adverse impacts on both indoor and outdoor air quality and are associated with a significant health burden. Though household heating with biomass and coal is common in northern China, the contribution of space heating to ambient air pollution is not well studied. We investigated the impact of space heating on ambient air pollution in a village 40 km southwest of central Beijing during the winter heating season, from January to March 2013. Ambient PM2.5 concentrations and meteorological conditions were measured continuously at rooftop sites in the village during two winter months in 2013. The use of coal- and biomass-burning cookstoves and space heating devices was measured over time with Stove Use Monitors (SUMs) in 33 households and was coupled with fuel consumption data from household surveys to estimate hourly household PM2.5 emissions from cooking and space heating over the same period. We developed a multivariate linear regression model to assess the relationship between household PM2.5 emissions and the hourly average ambient PM2.5 concentration, and a time series autoregressive integrated moving average (ARIMA) regression model to account for autocorrelation. During the heating season, the average hourly ambient PM2.5 concentration was 139 ± 107 μg/m3 (mean ± SD) with strong autocorrelation in hourly concentration. The average primary PM2.5 emission per hour from village household space heating was 0.736 ± 0.138 kg/hour. The linear multivariate regression model indicated that during the heating season - after adjusting for meteorological effects - 39% (95% CI: 26%, 54%) of hourly averaged ambient PM2.5 was associated with household space heating emissions from the previous hour. Our study suggests that a comprehensive pollution control strategy for northern China, including Beijing, should address uncontrolled emissions from household solid fuel combustion in surrounding areas, particularly during the winter heating season.

Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

NASA Astrophysics Data System (ADS)

Ni, X. Y.; Huang, H.; Du, W. P.

2017-02-01

The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words 'Beijing; Air pollution' show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods.

STS-98 U.S. Lab Destiny is moved out of Atlantis' payload bay

NASA Technical Reports Server (NTRS)

2001-01-01

KENNEDY SPACE CENTER, Fla. -- The U.S. Lab Destiny is ready to be moved from Atlantis''' payload bay into the Payload Changeout Room. After the move, Atlantis will roll back to the Vehicle Assembly Building to allow workers to conduct inspections, continuity checks and X-ray analysis on the 36 solid rocket booster cables located inside each booster'''s system tunnel. An extensive evaluation of NASA'''s SRB cable inventory revealed conductor damage in four (of about 200) cables on the shelf. Shuttle managers decided to prove the integrity of the system tunnel cables already on Atlantis.

SEEDS Moving Group Status Update

NASA Technical Reports Server (NTRS)

McElwain, Michael

2011-01-01

I will summarize the current status of the SEEDS Moving Group category and describe the importance of this sub-sample for the entire SEEDS survey. This presentation will include analysis of the sensitivity for the Moving Groups with general a comparison to other the other sub-categories. I will discuss the future impact of the Subaru SCExAO system for these targets and the advantage of using a specialized integral field spectrograph. Finally, I will present the impact of a pupil grid mask in order to produce fiducial spots in the focal plane that can be used for both photometry and astrometry.

Testing a simplified method for measuring velocity integration in saccades using a manipulation of target contrast.

PubMed

Etchells, Peter J; Benton, Christopher P; Ludwig, Casimir J H; Gilchrist, Iain D

2011-01-01

A growing number of studies in vision research employ analyses of how perturbations in visual stimuli influence behavior on single trials. Recently, we have developed a method along such lines to assess the time course over which object velocity information is extracted on a trial-by-trial basis in order to produce an accurate intercepting saccade to a moving target. Here, we present a simplified version of this methodology, and use it to investigate how changes in stimulus contrast affect the temporal velocity integration window used when generating saccades to moving targets. Observers generated saccades to one of two moving targets which were presented at high (80%) or low (7.5%) contrast. In 50% of trials, target velocity stepped up or down after a variable interval after the saccadic go signal. The extent to which the saccade endpoint can be accounted for as a weighted combination of the pre- or post-step velocities allows for identification of the temporal velocity integration window. Our results show that the temporal integration window takes longer to peak in the low when compared to high contrast condition. By enabling the assessment of how information such as changes in velocity can be used in the programming of a saccadic eye movement on single trials, this study describes and tests a novel methodology with which to look at the internal processing mechanisms that transform sensory visual inputs into oculomotor outputs.

A hybrid least squares support vector machines and GMDH approach for river flow forecasting

NASA Astrophysics Data System (ADS)

Samsudin, R.; Saad, P.; Shabri, A.

2010-06-01

This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.

Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks

PubMed Central

2015-01-01

Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency. PMID:26539722

Forecasting conditional climate-change using a hybrid approach

USGS Publications Warehouse

Esfahani, Akbar Akbari; Friedel, Michael J.

2014-01-01

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

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

Effect of a powered drive on pushing and pulling forces when transporting bariatric hospital beds.

PubMed

Wiggermann, Neal

2017-01-01

Powered drives designed to assist with moving hospital beds are commercially available but no studies have evaluated whether they reduce the push and pull forces likely contributing to injury in caregivers. This study measured hand forces of 10 caregivers maneuvering a manual and powered bariatric bed through simulated hospital environments (hallway, elevator, and ramp). Peak push and pull forces exceeded previously established psychophysical limits for all activities with the manual bed. For the powered bed, peak forces were significantly (p < 0.05) lower for all tasks, and below psychophysical limits. Powered drive reduced peak forces between 38% (maneuvering into elevator) and 94% (descending ramp). Powered drive also reduced stopping distance by 55%. When maneuvering, the integral of hand force was 34% lower with powered drive, but average forces during straight-line pushing did not differ between beds. Powered drive may reduce the risk of injury or the number of caregivers needed for transport. Copyright © 2016 Elsevier Ltd. All rights reserved.

Volatility Behaviors of Financial Time Series by Percolation System on Sierpinski Carpet Lattice

NASA Astrophysics Data System (ADS)

Pei, Anqi; Wang, Jun

2015-01-01

The financial time series is simulated and investigated by the percolation system on the Sierpinski carpet lattice, where percolation is usually employed to describe the behavior of connected clusters in a random graph, and the Sierpinski carpet lattice is a graph which corresponds the fractal — Sierpinski carpet. To study the fluctuation behavior of returns for the financial model and the Shanghai Composite Index, we establish a daily volatility measure — multifractal volatility (MFV) measure to obtain MFV series, which have long-range cross-correlations with squared daily return series. The autoregressive fractionally integrated moving average (ARFIMA) model is used to analyze the MFV series, which performs better when compared to other volatility series. By a comparative study of the multifractality and volatility analysis of the data, the simulation data of the proposed model exhibits very similar behaviors to those of the real stock index, which indicates somewhat rationality of the model to the market application.

Understanding the source of multifractality in financial markets

NASA Astrophysics Data System (ADS)

Barunik, Jozef; Aste, Tomaso; Di Matteo, T.; Liu, Ruipeng

2012-09-01

In this paper, we use the generalized Hurst exponent approach to study the multi-scaling behavior of different financial time series. We show that this approach is robust and powerful in detecting different types of multi-scaling. We observe a puzzling phenomenon where an apparent increase in multifractality is measured in time series generated from shuffled returns, where all time-correlations are destroyed, while the return distributions are conserved. This effect is robust and it is reproduced in several real financial data including stock market indices, exchange rates and interest rates. In order to understand the origin of this effect we investigate different simulated time series by means of the Markov switching multifractal model, autoregressive fractionally integrated moving average processes with stable innovations, fractional Brownian motion and Levy flights. Overall we conclude that the multifractality observed in financial time series is mainly a consequence of the characteristic fat-tailed distribution of the returns and time-correlations have the effect to decrease the measured multifractality.

Freely chosen cadence during a covert manipulation of ambient temperature.

PubMed

Hartley, Geoffrey L; Cheung, Stephen S

2013-01-01

The present study investigated relationships between changes in power output (PO) to torque (TOR) or freely chosen cadence (FCC) during thermal loading. Twenty participants cycled at a constant rating of perceived exertion while ambient temperature (Ta) was covertly manipulated at 20-min intervals of 20 °C, 35 °C, and 20 °C. The magnitude responses of PO, FCC and TOR were analyzed using repeated-measures ANOVA, while the temporal correlations were analyzed using Auto-Regressive Integrated Moving Averages (ARIMA). Increases in Ta caused significant thermal strain (p < .01), and subsequently, a decrease in PO and TOR magnitude (p < .01), whereas FCC remained unchanged (p = .51). ARIMA indicates that changes in PO were highly correlated to TOR (stationary r2 = .954, p = .04), while FCC was moderately correlated (stationary r2 = .717, p = .01) to PO. In conclusion, changes in PO are caused by a modulation in TOR, whereas FCC remains unchanged and therefore, unaffected by thermal stressors.

Statistical analysis of Hasegawa-Wakatani turbulence

NASA Astrophysics Data System (ADS)

Anderson, Johan; Hnat, Bogdan

2017-06-01

Resistive drift wave turbulence is a multipurpose paradigm that can be used to understand transport at the edge of fusion devices. The Hasegawa-Wakatani model captures the essential physics of drift turbulence while retaining the simplicity needed to gain a qualitative understanding of this process. We provide a theoretical interpretation of numerically generated probability density functions (PDFs) of intermittent events in Hasegawa-Wakatani turbulence with enforced equipartition of energy in large scale zonal flows, and small scale drift turbulence. We find that for a wide range of adiabatic index values, the stochastic component representing the small scale turbulent eddies of the flow, obtained from the autoregressive integrated moving average model, exhibits super-diffusive statistics, consistent with intermittent transport. The PDFs of large events (above one standard deviation) are well approximated by the Laplace distribution, while small events often exhibit a Gaussian character. Furthermore, there exists a strong influence of zonal flows, for example, via shearing and then viscous dissipation maintaining a sub-diffusive character of the fluxes.

Short-term forecasting of emergency inpatient flow.

PubMed

Abraham, Gad; Byrnes, Graham B; Bain, Christopher A

2009-05-01

Hospital managers have to manage resources effectively, while maintaining a high quality of care. For hospitals where admissions from the emergency department to the wards represent a large proportion of admissions, the ability to forecast these admissions and the resultant ward occupancy is especially useful for resource planning purposes. Since emergency admissions often compete with planned elective admissions, modeling emergency demand may result in improved elective planning as well. We compare several models for forecasting daily emergency inpatient admissions and occupancy. The models are applied to three years of daily data. By measuring their mean square error in a cross-validation framework, we find that emergency admissions are largely random, and hence, unpredictable, whereas emergency occupancy can be forecasted using a model combining regression and autoregressive integrated moving average (ARIMA) model, or a seasonal ARIMA model, for up to one week ahead. Faced with variable admissions and occupancy, hospitals must prepare a reserve capacity of beds and staff. Our approach allows estimation of the required reserve capacity.

Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

PubMed Central

Young, Alistair A.; Li, Xiaosong

2014-01-01

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases. PMID:24505382

Do smoke-free laws affect revenues in pubs and restaurants?

PubMed

Melberg, Hans Olav; Lund, Karl E

2012-02-01

In the debate about laws regulating smoking in restaurants and pubs, there has been some controversy as to whether smoke-free laws would reduce revenues in the hospitality industry. Norway presents an interesting case for three reasons. First, it was among the first countries to implement smoke-free laws, so it is possible to assess the long-term effects. Second, it has a cold climate so if there is a negative effect on revenue one would expect to find it in Norway. Third, the data from Norway are detailed enough to distinguish between revenue from pubs and restaurants. Autoregressive integrated moving average (ARIMA) intervention analysis of bi-monthly observations of revenues in restaurants and pubs show that the law did not have a statistically significant long-term effect on revenue in restaurants or on restaurant revenue as a share of personal consumption. Similar analysis for pubs shows that there was no significant long-run effect on pub revenue.

Is nonsmoking dangerous to the health of restaurants? The effect of California's indoor smoking ban on restaurant revenues.

PubMed

Stolzenberg, Lisa; D'Alessio, Stewart J

2007-02-01

The state of California passed the Smoke-Free Workplace Act on January 1, 1995. This legislation effectively banned indoor smoking in all public and private workplaces including restaurants. Many restaurant owners, especially owners of restaurants that served alcohol, opposed the ban for fear that their businesses would be affected adversely because of the loss of patrons who smoked. Using an interrupted times-series autoregressive integrative moving average study design, the authors assess the effect of California's indoor smoking ban on revenue rates for all restaurants, for non-alcohol-serving restaurants, and for alcohol-serving restaurants. Results showed that revenues for alcohol-serving restaurants dropped by about 4% immediately following the establishment of the indoor smoking ban. However, this reduction was temporary because revenues for alcohol-serving restaurants quickly returned to normal levels. Findings also revealed that the indoor smoking ban had little observable impact on the revenue rate for restaurants overall and for non-alcohol-serving restaurants.

Role of quality of service metrics in visual target acquisition and tracking in resource constrained environments

NASA Astrophysics Data System (ADS)

Anderson, Monica; David, Phillip

2007-04-01

Implementation of an intelligent, automated target acquisition and tracking systems alleviates the need for operators to monitor video continuously. This system could identify situations that fatigued operators could easily miss. If an automated acquisition and tracking system plans motions to maximize a coverage metric, how does the performance of that system change when the user intervenes and manually moves the camera? How can the operator give input to the system about what is important and understand how that relates to the overall task balance between surveillance and coverage? In this paper, we address these issues by introducing a new formulation of the average linear uncovered length (ALUL) metric, specially designed for use in surveilling urban environments. This metric coordinates the often competing goals of acquiring new targets and tracking existing targets. In addition, it provides current system performance feedback to system users in terms of the system's theoretical maximum and minimum performance. We show the successful integration of the algorithm via simulation.

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

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

PubMed

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

2018-06-01

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

Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997–2003

PubMed Central

Gomez-Elipe, Alberto; Otero, Angel; van Herp, Michel; Aguirre-Jaime, Armando

2007-01-01

Background The objective of this work was to develop a model to predict malaria incidence in an area of unstable transmission by studying the association between environmental variables and disease dynamics. Methods The study was carried out in Karuzi, a province in the Burundi highlands, using time series of monthly notifications of malaria cases from local health facilities, data from rain and temperature records, and the normalized difference vegetation index (NDVI). Using autoregressive integrated moving average (ARIMA) methodology, a model showing the relation between monthly notifications of malaria cases and the environmental variables was developed. Results The best forecasting model (R2adj = 82%, p < 0.0001 and 93% forecasting accuracy in the range ± 4 cases per 100 inhabitants) included the NDVI, mean maximum temperature, rainfall and number of malaria cases in the preceding month. Conclusion This model is a simple and useful tool for producing reasonably reliable forecasts of the malaria incidence rate in the study area. PMID:17892540

Increase in suicides the months after the death of Robin Williams in the US

PubMed Central

Santaella-Tenorio, Julian; Keyes, Katherine M.

2018-01-01

Investigating suicides following the death of Robin Williams, a beloved actor and comedian, on August 11th, 2014, we used time-series analysis to estimate the expected number of suicides during the months following Williams’ death. Monthly suicide count data in the US (1999–2015) were from the Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research (CDC WONDER). Expected suicides were calculated using a seasonal autoregressive integrated moving averages model to account for both the seasonal patterns and autoregression. Time-series models indicated that we would expect 16,849 suicides from August to December 2014; however, we observed 18,690 suicides in that period, suggesting an excess of 1,841 cases (9.85% increase). Although excess suicides were observed across gender and age groups, males and persons aged 30–44 had the greatest increase in excess suicide events. This study documents associations between Robin Williams’ death and suicide deaths in the population thereafter. PMID:29415016

RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

PubMed

Stránský, V; Thinová, L

2017-11-01

In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Forecasting dengue hemorrhagic fever cases using ARIMA model: a case study in Asahan district

NASA Astrophysics Data System (ADS)

Siregar, Fazidah A.; Makmur, Tri; Saprin, S.

2018-01-01

Time series analysis had been increasingly used to forecast the number of dengue hemorrhagic fever in many studies. Since no vaccine exist and poor public health infrastructure, predicting the occurrence of dengue hemorrhagic fever (DHF) is crucial. This study was conducted to determine trend and forecasting the occurrence of DHF in Asahan district, North Sumatera Province. Monthly reported dengue cases for the years 2012-2016 were obtained from the district health offices. A time series analysis was conducted by Autoregressive integrated moving average (ARIMA) modeling to forecast the occurrence of DHF. The results demonstrated that the reported DHF cases showed a seasonal variation. The SARIMA (1,0,0)(0,1,1)12 model was the best model and adequate for the data. The SARIMA model for DHF is necessary and could applied to predict the incidence of DHF in Asahan district and assist with design public health maesures to prevent and control the diseases.

Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

PubMed

Jin, Junghwan; Kim, Jinsoo

2015-01-01

Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

A study on industrial accident rate forecasting and program development of estimated zero accident time in Korea.

PubMed

Kim, Tae-gu; Kang, Young-sig; Lee, Hyung-won

2011-01-01

To begin a zero accident campaign for industry, the first thing is to estimate the industrial accident rate and the zero accident time systematically. This paper considers the social and technical change of the business environment after beginning the zero accident campaign through quantitative time series analysis methods. These methods include sum of squared errors (SSE), regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, and the proposed analytic function method (AFM). The program is developed to estimate the accident rate, zero accident time and achievement probability of an efficient industrial environment. In this paper, MFC (Microsoft Foundation Class) software of Visual Studio 2008 was used to develop a zero accident program. The results of this paper will provide major information for industrial accident prevention and be an important part of stimulating the zero accident campaign within all industrial environments.

Modeling and forecasting rainfall patterns of southwest monsoons in North-East India as a SARIMA process

NASA Astrophysics Data System (ADS)

Narasimha Murthy, K. V.; Saravana, R.; Vijaya Kumar, K.

2018-02-01

Weather forecasting is an important issue in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. India experiences the precious Southwest monsoon season for four months from June to September. The present paper describes an empirical study for modeling and forecasting the time series of Southwest monsoon rainfall patterns in the North-East India. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The study has shown that the SARIMA (0, 1, 1) (1, 0, 1)4 model is appropriate for analyzing and forecasting the future rainfall patterns. The Analysis of Means (ANOM) is a useful alternative to the analysis of variance (ANOVA) for comparing the group of treatments to study the variations and critical comparisons of rainfall patterns in different months of the season.

Long-term synchronized electrophysiological and behavioral wireless monitoring of freely moving animals

PubMed Central

Grand, Laszlo; Ftomov, Sergiu; Timofeev, Igor

2012-01-01

Parallel electrophysiological recording and behavioral monitoring of freely moving animals is essential for a better understanding of the neural mechanisms underlying behavior. In this paper we describe a novel wireless recording technique, which is capable of synchronously recording in vivo multichannel electrophysiological (LFP, MUA, EOG, EMG) and activity data (accelerometer, video) from freely moving cats. The method is based on the integration of commercially available components into a simple monitoring system and is complete with accelerometers and the needed signal processing tools. LFP activities of freely moving group-housed cats were recorded from multiple intracortical areas and from the hippocampus. EMG, EOG, accelerometer and video were simultaneously acquired with LFP activities 24-h a day for 3 months. These recordings confirm the possibility of using our wireless method for 24-h long-term monitoring of neurophysiological and behavioral data of freely moving experimental animals such as cats, ferrets, rabbits and other large animals. PMID:23099345

Switching moving boundary models for two-phase flow evaporators and condensers

NASA Astrophysics Data System (ADS)

Bonilla, Javier; Dormido, Sebastián; Cellier, François E.

2015-03-01

The moving boundary method is an appealing approach for the design, testing and validation of advanced control schemes for evaporators and condensers. When it comes to advanced control strategies, not only accurate but fast dynamic models are required. Moving boundary models are fast low-order dynamic models, and they can describe the dynamic behavior with high accuracy. This paper presents a mathematical formulation based on physical principles for two-phase flow moving boundary evaporator and condenser models which support dynamic switching between all possible flow configurations. The models were implemented in a library using the equation-based object-oriented Modelica language. Several integrity tests in steady-state and transient predictions together with stability tests verified the models. Experimental data from a direct steam generation parabolic-trough solar thermal power plant is used to validate and compare the developed moving boundary models against finite volume models.

Road Traffic Injury Trends in the City of Valledupar, Colombia. A Time Series Study from 2008 to 2012

PubMed Central

Rodríguez, Jorge Martín; Peñaloza, Rolando Enrique; Moreno Montoya, José

2015-01-01

Objective To analyze the behavior temporal of road-traffic injuries (RTI) in Valledupar, Colombia from January 2008 to December 2012. Methodology An observational study was conducted based on records from the Colombian National Legal Medicine and Forensic Sciences Institute regional office in Valledupar. Different variables were analyzed, such as the injured person’s sex, age, education level, and type of road user; the timeframe, place and circumstances of crashes and the vehicles associated with the occurrence. Furthermore, a time series analysis was conducted using an auto-regressive integrated moving average. Results There were 105 events per month on an average, 64.9% of RTI involved men; 82.3% of the persons injured were from 18 to 59 years of age; the average age was 35.4 years of age; the road users most involved in RTI were motorcyclists (69%), followed by pedestrians (12%). 70% had up to upper-secondary education. Sunday was the day with the most RTI occurrences; 93% of the RTI occurred in the urban area. The time series showed a seasonal pattern and a significant trend effect. The modeling process verified the existence of both memory and extrinsic variables related. Conclusions An RTI occurrence pattern was identified, which showed an upward trend during the period analyzed. Motorcyclists were the main road users involved in RTI, which suggests the need to design and implement specific measures for that type of road user, from regulations for graduated licensing for young drivers to monitoring road user behavior for the promotion of road safety. PMID:26657887

Road Traffic Injury Trends in the City of Valledupar, Colombia. A Time Series Study from 2008 to 2012.

PubMed

Rodríguez, Jorge Martín; Peñaloza, Rolando Enrique; Moreno Montoya, José

2015-01-01

To analyze the behavior temporal of road-traffic injuries (RTI) in Valledupar, Colombia from January 2008 to December 2012. An observational study was conducted based on records from the Colombian National Legal Medicine and Forensic Sciences Institute regional office in Valledupar. Different variables were analyzed, such as the injured person's sex, age, education level, and type of road user; the timeframe, place and circumstances of crashes and the vehicles associated with the occurrence. Furthermore, a time series analysis was conducted using an auto-regressive integrated moving average. There were 105 events per month on an average, 64.9% of RTI involved men; 82.3% of the persons injured were from 18 to 59 years of age; the average age was 35.4 years of age; the road users most involved in RTI were motorcyclists (69%), followed by pedestrians (12%). 70% had up to upper-secondary education. Sunday was the day with the most RTI occurrences; 93% of the RTI occurred in the urban area. The time series showed a seasonal pattern and a significant trend effect. The modeling process verified the existence of both memory and extrinsic variables related. An RTI occurrence pattern was identified, which showed an upward trend during the period analyzed. Motorcyclists were the main road users involved in RTI, which suggests the need to design and implement specific measures for that type of road user, from regulations for graduated licensing for young drivers to monitoring road user behavior for the promotion of road safety.

Intelligent transportation systems infrastructure initiative

DOT National Transportation Integrated Search

1997-01-01

The three-quarter moving composite price index is the weighted average of the indices for three consecutive quarters. The Composite Bid Price Index is composed of six indicator items: common excavation, to indicate the price trend for all roadway exc...

Comparing the performance of FA, DFA and DMA using different synthetic long-range correlated time series

PubMed Central

Shao, Ying-Hui; Gu, Gao-Feng; Jiang, Zhi-Qiang; Zhou, Wei-Xing; Sornette, Didier

2012-01-01

Notwithstanding the significant efforts to develop estimators of long-range correlations (LRC) and to compare their performance, no clear consensus exists on what is the best method and under which conditions. In addition, synthetic tests suggest that the performance of LRC estimators varies when using different generators of LRC time series. Here, we compare the performances of four estimators [Fluctuation Analysis (FA), Detrended Fluctuation Analysis (DFA), Backward Detrending Moving Average (BDMA), and Centred Detrending Moving Average (CDMA)]. We use three different generators [Fractional Gaussian Noises, and two ways of generating Fractional Brownian Motions]. We find that CDMA has the best performance and DFA is only slightly worse in some situations, while FA performs the worst. In addition, CDMA and DFA are less sensitive to the scaling range than FA. Hence, CDMA and DFA remain “The Methods of Choice” in determining the Hurst index of time series. PMID:23150785

CLASSICAL AREAS OF PHENOMENOLOGY: Lattice Boltzmann simulation of behaviour of particles moving in blood vessels under the rolling massage

NASA Astrophysics Data System (ADS)

Yi, Hou-Hui; Yang, Xiao-Feng; Wang, Cai-Feng; Li, Hua-Bing

2009-07-01

The rolling massage is one of the most important manipulations in Chinese massage, which is expected to eliminate many diseases. Here, the effect of the rolling massage on a pair of particles moving in blood vessels under rolling massage manipulation is studied by the lattice Boltzmann simulation. The simulated results show that the motion of each particle is considerably modified by the rolling massage, and it depends on the relative rolling velocity, the rolling depth, and the distance between particle position and rolling position. Both particles' translational average velocities increase almost linearly as the rolling velocity increases, and obey the same law. The increment of the average relative angular velocity for the leading particle is smaller than that of the trailing one. The result is helpful for understanding the mechanism of the massage and to further develop the rolling techniques.

Compression of head-related transfer function using autoregressive-moving-average models and Legendre polynomials.

PubMed

Shekarchi, Sayedali; Hallam, John; Christensen-Dalsgaard, Jakob

2013-11-01

Head-related transfer functions (HRTFs) are generally large datasets, which can be an important constraint for embedded real-time applications. A method is proposed here to reduce redundancy and compress the datasets. In this method, HRTFs are first compressed by conversion into autoregressive-moving-average (ARMA) filters whose coefficients are calculated using Prony's method. Such filters are specified by a few coefficients which can generate the full head-related impulse responses (HRIRs). Next, Legendre polynomials (LPs) are used to compress the ARMA filter coefficients. LPs are derived on the sphere and form an orthonormal basis set for spherical functions. Higher-order LPs capture increasingly fine spatial details. The number of LPs needed to represent an HRTF, therefore, is indicative of its spatial complexity. The results indicate that compression ratios can exceed 98% while maintaining a spectral error of less than 4 dB in the recovered HRTFs.

Direct determination approach for the multifractal detrending moving average analysis

NASA Astrophysics Data System (ADS)

Xu, Hai-Chuan; Gu, Gao-Feng; Zhou, Wei-Xing

2017-11-01

In the canonical framework, we propose an alternative approach for the multifractal analysis based on the detrending moving average method (MF-DMA). We define a canonical measure such that the multifractal mass exponent τ (q ) is related to the partition function and the multifractal spectrum f (α ) can be directly determined. The performances of the direct determination approach and the traditional approach of the MF-DMA are compared based on three synthetic multifractal and monofractal measures generated from the one-dimensional p -model, the two-dimensional p -model, and the fractional Brownian motions. We find that both approaches have comparable performances to unveil the fractal and multifractal nature. In other words, without loss of accuracy, the multifractal spectrum f (α ) can be directly determined using the new approach with less computation cost. We also apply the new MF-DMA approach to the volatility time series of stock prices and confirm the presence of multifractality.

[A peak recognition algorithm designed for chromatographic peaks of transformer oil].

PubMed

Ou, Linjun; Cao, Jian

2014-09-01

In the field of the chromatographic peak identification of the transformer oil, the traditional first-order derivative requires slope threshold to achieve peak identification. In terms of its shortcomings of low automation and easy distortion, the first-order derivative method was improved by applying the moving average iterative method and the normalized analysis techniques to identify the peaks. Accurate identification of the chromatographic peaks was realized through using multiple iterations of the moving average of signal curves and square wave curves to determine the optimal value of the normalized peak identification parameters, combined with the absolute peak retention times and peak window. The experimental results show that this algorithm can accurately identify the peaks and is not sensitive to the noise, the chromatographic peak width or the peak shape changes. It has strong adaptability to meet the on-site requirements of online monitoring devices of dissolved gases in transformer oil.

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

PubMed

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

2017-11-01

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

Optimized nested Markov chain Monte Carlo sampling: theory

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

Coe, Joshua D; Shaw, M Sam; Sewell, Thomas D

2009-01-01

Metropolis Monte Carlo sampling of a reference potential is used to build a Markov chain in the isothermal-isobaric ensemble. At the endpoints of the chain, the energy is reevaluated at a different level of approximation (the 'full' energy) and a composite move encompassing all of the intervening steps is accepted on the basis of a modified Metropolis criterion. By manipulating the thermodynamic variables characterizing the reference system we maximize the average acceptance probability of composite moves, lengthening significantly the random walk made between consecutive evaluations of the full energy at a fixed acceptance probability. This provides maximally decorrelated samples ofmore » the full potential, thereby lowering the total number required to build ensemble averages of a given variance. The efficiency of the method is illustrated using model potentials appropriate to molecular fluids at high pressure. Implications for ab initio or density functional theory (DFT) treatment are discussed.« less

Finite-size effect and the components of multifractality in transport economics volatility based on multifractal detrending moving average method

NASA Astrophysics Data System (ADS)

Chen, Feier; Tian, Kang; Ding, Xiaoxu; Miao, Yuqi; Lu, Chunxia

2016-11-01

Analysis of freight rate volatility characteristics attracts more attention after year 2008 due to the effect of credit crunch and slowdown in marine transportation. The multifractal detrended fluctuation analysis technique is employed to analyze the time series of Baltic Dry Bulk Freight Rate Index and the market trend of two bulk ship sizes, namely Capesize and Panamax for the period: March 1st 1999-February 26th 2015. In this paper, the degree of the multifractality with different fluctuation sizes is calculated. Besides, multifractal detrending moving average (MF-DMA) counting technique has been developed to quantify the components of multifractal spectrum with the finite-size effect taken into consideration. Numerical results show that both Capesize and Panamax freight rate index time series are of multifractal nature. The origin of multifractality for the bulk freight rate market series is found mostly due to nonlinear correlation.

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

NASA Astrophysics Data System (ADS)

Uilhoorn, F. E.

2016-10-01

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

The research of PSD location method in micro laser welding fields

NASA Astrophysics Data System (ADS)

Zhang, Qiue; Zhang, Rong; Dong, Hua

2010-11-01

In the field of micro laser welding, besides the special requirement in the parameter of lasers, the locating in welding points accurately is very important. The article adopt position sensitive detector (PSD) as hard core, combine optic system, electric circuits and PC and software processing, confirm the location of welding points. The signal detection circuits adopt the special integrate circuit H-2476 to process weak signal. It is an integrated circuit for high-speed, high-sensitivity optical range finding, which has stronger noiseproof feature, combine digital filter arithmetic, carry out repair the any non-ideal factors, increasing the measure precision. The amplifier adopt programmable amplifier LTC6915. The system adapt two dimension stepping motor drive the workbench, computer and corresponding software processing, make sure the location of spot weld. According to different workpieces to design the clamps. The system on-line detect PSD 's output signal in the moving processing. At the workbench moves in the X direction, the filaments offset is detected dynamic. Analyze the X axes moving sampling signal direction could be estimate the Y axes moving direction, and regulate the Y axes moving values. The workbench driver adopt A3979, it is a stepping motor driver with insert transducer and operate easily. It adapts the requirement of location in micro laser welding fields, real-time control to adjust by computer. It can be content up 20 μm's laser micro welding requirement on the whole. Using laser powder cladding technology achieve inter-penetration welding of high quality and reliability.

[Capacity of the legal framework of public health institutions in Mexico to support their functional integration].

PubMed

Ibarra, Ignacio; Martínez, Gabriel; Aguilera, Nelly; Orozco, Emanuel; Fajardo-Dolci, Germán E; González-Block, Miguel A

2013-01-01

Evaluate the capacity of the federal legal framework to govern financing of health institutions in the public sector through innovative schemes--otherwise known as functional integration--, enabling them to purchase and sell health services to and from other public providers as a strategy to improve their performance. Based on indicators of normative alignment with respect to functional integration across public health provider and governance institutions, content analysis was undertaken of national health programs and relevant laws and guidelines for financial coordination. Significant progress was identified in the implementation of agreements for the coordination of public institutions. While the legal framework provides for a National Health System and a health sector, gaps and contradictions limit their scope. The General Register of Health is also moving forward, yet it lacks the necessary legal foundation to become a comprehensive tool for integration. The medical service exchange agreements are also moving forward based on tariffs and shared guidelines. However, there is a lack of incentives to promote the expansion of these agreements. It is recommended to update the legal framework for the coordination of the National Health System, ensuring a more harmonious and general focus to provide functional integration with the needed impulse.