Modeling and forecasting of KLCI weekly return using WT-ANN integrated model

NASA Astrophysics Data System (ADS)

Liew, Wei-Thong; Liong, Choong-Yeun; Hussain, Saiful Izzuan; Isa, Zaidi

2013-04-01

The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction.

[Application of wavelet neural networks model to forecast incidence of syphilis].

PubMed

Zhou, Xian-Feng; Feng, Zi-Jian; Yang, Wei-Zhong; Li, Xiao-Song

2011-07-01

To apply Wavelet Neural Networks (WNN) model to forecast incidence of Syphilis. Back Propagation Neural Network (BPNN) and WNN were developed based on the monthly incidence of Syphilis in Sichuan province from 2004 to 2008. The accuracy of forecast was compared between the two models. In the training approximation, the mean absolute error (MAE), rooted mean square error (RMSE) and mean absolute percentage error (MAPE) were 0.0719, 0.0862 and 11.52% respectively for WNN, and 0.0892, 0.1183 and 14.87% respectively for BPNN. The three indexes for generalization of models were 0.0497, 0.0513 and 4.60% for WNN, and 0.0816, 0.1119 and 7.25% for BPNN. WNN is a better model for short-term forecasting of Syphilis.

Measuring the Accuracy of Simple Evolving Connectionist System with Varying Distance Formulas

NASA Astrophysics Data System (ADS)

Al-Khowarizmi; Sitompul, O. S.; Suherman; Nababan, E. B.

2017-12-01

Simple Evolving Connectionist System (SECoS) is a minimal implementation of Evolving Connectionist Systems (ECoS) in artificial neural networks. The three-layer network architecture of the SECoS could be built based on the given input. In this study, the activation value for the SECoS learning process, which is commonly calculated using normalized Hamming distance, is also calculated using normalized Manhattan distance and normalized Euclidean distance in order to compare the smallest error value and best learning rate obtained. The accuracy of measurement resulted by the three distance formulas are calculated using mean absolute percentage error. In the training phase with several parameters, such as sensitivity threshold, error threshold, first learning rate, and second learning rate, it was found that normalized Euclidean distance is more accurate than both normalized Hamming distance and normalized Manhattan distance. In the case of beta fibrinogen gene -455 G/A polymorphism patients used as training data, the highest mean absolute percentage error value is obtained with normalized Manhattan distance compared to normalized Euclidean distance and normalized Hamming distance. However, the differences are very small that it can be concluded that the three distance formulas used in SECoS do not have a significant effect on the accuracy of the training results.

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

75 FR 29972 - Certain Seamless Carbon and Alloy Steel Standard, Line, and Pressure Pipe from the People's...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-28

... errors, (1) would result in a change of at least five absolute percentage points in, but not less than 25... determination; or (2) would result in a difference between a weighted-average dumping margin of zero or de...

[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.

Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models

PubMed Central

de Jesus, Karla; Ayala, Helon V. H.; de Jesus, Kelly; Coelho, Leandro dos S.; Medeiros, Alexandre I.A.; Abraldes, José A.; Vaz, Mário A.P.; Fernandes, Ricardo J.; Vilas-Boas, João Paulo

2018-01-01

Abstract Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances. PMID:29599857

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.

A nonlinear model of gold production in Malaysia

NASA Astrophysics Data System (ADS)

Ramli, Norashikin; Muda, Nora; Umor, Mohd Rozi

2014-06-01

Malaysia is a country which is rich in natural resources and one of it is a gold. Gold has already become an important national commodity. This study is conducted to determine a model that can be well fitted with the gold production in Malaysia from the year 1995-2010. Five nonlinear models are presented in this study which are Logistic model, Gompertz, Richard, Weibull and Chapman-Richard model. These model are used to fit the cumulative gold production in Malaysia. The best model is then selected based on the model performance. The performance of the fitted model is measured by sum squares error, root mean squares error, coefficient of determination, mean relative error, mean absolute error and mean absolute percentage error. This study has found that a Weibull model is shown to have significantly outperform compare to the other models. To confirm that Weibull is the best model, the latest data are fitted to the model. Once again, Weibull model gives the lowest readings at all types of measurement error. We can concluded that the future gold production in Malaysia can be predicted according to the Weibull model and this could be important findings for Malaysia to plan their economic activities.

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

Artificial neural network modelling of a large-scale wastewater treatment plant operation.

PubMed

Güçlü, Dünyamin; Dursun, Sükrü

2010-11-01

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

Comparison of INAR(1)-Poisson model and Markov prediction model in forecasting the number of DHF patients in west java Indonesia

NASA Astrophysics Data System (ADS)

Ahdika, Atina; Lusiyana, Novyan

2017-02-01

World Health Organization (WHO) noted Indonesia as the country with the highest dengue (DHF) cases in Southeast Asia. There are no vaccine and specific treatment for DHF. One of the efforts which can be done by both government and resident is doing a prevention action. In statistics, there are some methods to predict the number of DHF cases to be used as the reference to prevent the DHF cases. In this paper, a discrete time series model, INAR(1)-Poisson model in specific, and Markov prediction model are used to predict the number of DHF patients in West Java Indonesia. The result shows that MPM is the best model since it has the smallest value of MAE (mean absolute error) and MAPE (mean absolute percentage error).

Holt-Winters Forecasting: A Study of Practical Applications for Healthcare Managers

DTIC Science & Technology

2006-05-25

Winters Forecasting 5 List of Tables Table 1. Holt-Winters smoothing parameters and Mean Absolute Percentage Errors: Pseudoephedrine prescriptions Table 2...confidence intervals Holt-Winters Forecasting 6 List of Figures Figure 1. Line Plot of Pseudoephedrine Prescriptions forecast using smoothing parameters...The first represents monthly prescriptions of pseudoephedrine . Pseudoephedrine is a drug commonly prescribed to relieve nasal congestion and other

Achievable accuracy of hip screw holding power estimation by insertion torque measurement.

PubMed

Erani, Paolo; Baleani, Massimiliano

2018-02-01

To ensure stability of proximal femoral fractures, the hip screw must firmly engage into the femoral head. Some studies suggested that screw holding power into trabecular bone could be evaluated, intraoperatively, through measurement of screw insertion torque. However, those studies used synthetic bone, instead of trabecular bone, as host material or they did not evaluate accuracy of predictions. We determined prediction accuracy, also assessing the impact of screw design and host material. We measured, under highly-repeatable experimental conditions, disregarding clinical procedure complexities, insertion torque and pullout strength of four screw designs, both in 120 synthetic and 80 trabecular bone specimens of variable density. For both host materials, we calculated the root-mean-square error and the mean-absolute-percentage error of predictions based on the best fitting model of torque-pullout data, in both single-screw and merged dataset. Predictions based on screw-specific regression models were the most accurate. Host material impacts on prediction accuracy: the replacement of synthetic with trabecular bone decreased both root-mean-square errors, from 0.54 ÷ 0.76 kN to 0.21 ÷ 0.40 kN, and mean-absolute-percentage errors, from 14 ÷ 21% to 10 ÷ 12%. However, holding power predicted on low insertion torque remained inaccurate, with errors up to 40% for torques below 1 Nm. In poor-quality trabecular bone, tissue inhomogeneities likely affect pullout strength and insertion torque to different extents, limiting the predictive power of the latter. This bias decreases when the screw engages good-quality bone. Under this condition, predictions become more accurate although this result must be confirmed by close in-vitro simulation of the clinical procedure. Copyright © 2018 Elsevier Ltd. All rights reserved.

Influence of different rotation angles in assessment of lung volumes by 3-dimensional sonography in comparison to magnetic resonance imaging in healthy fetuses.

PubMed

Kehl, Sven; Eckert, Sven; Sütterlin, Marc; Neff, K Wolfgang; Siemer, Jörn

2011-06-01

Three-dimensional (3D) sonographic volumetry is established in gynecology and obstetrics. Assessment of the fetal lung volume by magnetic resonance imaging (MRI) in congenital diaphragmatic hernias has become a routine examination. In vitro studies have shown a good correlation between 3D sonographic measurements and MRI. The aim of this study was to compare the lung volumes of healthy fetuses assessed by 3D sonography to MRI measurements and to investigate the impact of different rotation angles. A total of 126 fetuses between 20 and 40 weeks' gestation were measured by 3D sonography, and 27 of them were also assessed by MRI. The sonographic volumes were calculated by the rotational technique (virtual organ computer-aided analysis) with rotation angles of 6° and 30°. To evaluate the accuracy of 3D sonographic volumetry, percentage error and absolute percentage error values were calculated using MRI volumes as reference points. Formulas to calculate total, right, and left fetal lung volumes according to gestational age and biometric parameters were derived by stepwise regression analysis. Three-dimensional sonographic volumetry showed a high correlation compared to MRI (6° angle, R(2) = 0.971; 30° angle, R(2) = 0.917) with no systematic error for the 6° angle. Moreover, using the 6° rotation angle, the median absolute percentage error was significantly lower compared to the 30° angle (P < .001). The new formulas to calculate total lung volume in healthy fetuses only included gestational age and no biometric parameters (R(2) = 0.853). Three-dimensional sonographic volumetry of lung volumes in healthy fetuses showed a good correlation with MRI. We recommend using an angle of 6° because it assessed the lung volume more accurately. The specifically designed equations help estimate lung volumes in healthy fetuses.

Forecasting in foodservice: model development, testing, and evaluation.

PubMed

Miller, J L; Thompson, P A; Orabella, M M

1991-05-01

This study was designed to develop, test, and evaluate mathematical models appropriate for forecasting menu-item production demand in foodservice. Data were collected from residence and dining hall foodservices at Ohio State University. Objectives of the study were to collect, code, and analyze the data; develop and test models using actual operation data; and compare forecasting results with current methods in use. Customer count was forecast using deseasonalized simple exponential smoothing. Menu-item demand was forecast by multiplying the count forecast by a predicted preference statistic. Forecasting models were evaluated using mean squared error, mean absolute deviation, and mean absolute percentage error techniques. All models were more accurate than current methods. A broad spectrum of forecasting techniques could be used by foodservice managers with access to a personal computer and spread-sheet and database-management software. The findings indicate that mathematical forecasting techniques may be effective in foodservice operations to control costs, increase productivity, and maximize profits.

Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling.

PubMed

Soni, Kirti; Parmar, Kulwinder Singh; Kapoor, Sangeeta; Kumar, Nishant

2016-05-15

A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend. Copyright © 2016 Elsevier B.V. All rights reserved.

Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans.

PubMed

Zhou, Lingling; Xia, Jing; Yu, Lijing; Wang, Ying; Shi, Yun; Cai, Shunxiang; Nie, Shaofa

2016-03-23

We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model. We used the ARIMA, NARNN and ARIMA-NARNN models to fit and forecast the annual prevalence of schistosomiasis. The modeling time range included was the annual prevalence from 1956 to 2008 while the testing time range included was from 2009 to 2012. The mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the model performance. We reconstructed the hybrid model to forecast the annual prevalence from 2013 to 2016. The modeling and testing errors generated by the ARIMA-NARNN model were lower than those obtained from either the single ARIMA or NARNN models. The predicted annual prevalence from 2013 to 2016 demonstrated an initial decreasing trend, followed by an increase. The ARIMA-NARNN model can be well applied to analyze surveillance data for early warning systems for the control and elimination of schistosomiasis.

The Use of Neural Networks in Identifying Error Sources in Satellite-Derived Tropical SST Estimates

PubMed Central

Lee, Yung-Hsiang; Ho, Chung-Ru; Su, Feng-Chun; Kuo, Nan-Jung; Cheng, Yu-Hsin

2011-01-01

An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE) for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE) is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%. PMID:22164030

Automated body weight prediction of dairy cows using 3-dimensional vision.

PubMed

Song, X; Bokkers, E A M; van der Tol, P P J; Groot Koerkamp, P W G; van Mourik, S

2018-05-01

The objectives of this study were to quantify the error of body weight prediction using automatically measured morphological traits in a 3-dimensional (3-D) vision system and to assess the influence of various sources of uncertainty on body weight prediction. In this case study, an image acquisition setup was created in a cow selection box equipped with a top-view 3-D camera. Morphological traits of hip height, hip width, and rump length were automatically extracted from the raw 3-D images taken of the rump area of dairy cows (n = 30). These traits combined with days in milk, age, and parity were used in multiple linear regression models to predict body weight. To find the best prediction model, an exhaustive feature selection algorithm was used to build intermediate models (n = 63). Each model was validated by leave-one-out cross-validation, giving the root mean square error and mean absolute percentage error. The model consisting of hip width (measurement variability of 0.006 m), days in milk, and parity was the best model, with the lowest errors of 41.2 kg of root mean square error and 5.2% mean absolute percentage error. Our integrated system, including the image acquisition setup, image analysis, and the best prediction model, predicted the body weights with a performance similar to that achieved using semi-automated or manual methods. Moreover, the variability of our simplified morphological trait measurement showed a negligible contribution to the uncertainty of body weight prediction. We suggest that dairy cow body weight prediction can be improved by incorporating more predictive morphological traits and by improving the prediction model structure. The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Comparative study of four time series methods in forecasting typhoid fever incidence in China.

PubMed

Zhang, Xingyu; Liu, Yuanyuan; Yang, Min; Zhang, Tao; Young, Alistair A; Li, Xiaosong

2013-01-01

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention 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 results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.

Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China

PubMed Central

Zhang, Xingyu; Liu, Yuanyuan; Yang, Min; Zhang, Tao; Young, Alistair A.; Li, Xiaosong

2013-01-01

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention 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 results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model. PMID:23650546

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

2017-08-01

The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

Estimation of Fetal Weight during Labor: Still a Challenge.

PubMed

Barros, Joana Goulão; Reis, Inês; Pereira, Isabel; Clode, Nuno; Graça, Luís M

2016-01-01

To evaluate the accuracy of fetal weight prediction by ultrasonography labor employing a formula including the linear measurements of femur length (FL) and mid-thigh soft-tissue thickness (STT). We conducted a prospective study involving singleton uncomplicated term pregnancies within 48 hours of delivery. Only pregnancies with a cephalic fetus admitted in the labor ward for elective cesarean section, induction of labor or spontaneous labor were included. We excluded all non-Caucasian women, the ones previously diagnosed with gestational diabetes and the ones with evidence of ruptured membranes. Fetal weight estimates were calculated using a previously proposed formula [estimated fetal weight = 1687.47 + (54.1 x FL) + (76.68 x STT). The relationship between actual birth weight and estimated fetal weight was analyzed using Pearson's correlation. The formula's performance was assessed by calculating the signed and absolute errors. Mean weight difference and signed percentage error were calculated for birth weight divided into three subgroups: < 3000 g; 3000-4000 g; and > 4000 g. We included for analysis 145 cases and found a significant, yet low, linear relationship between birth weight and estimated fetal weight (p < 0.001; R2 = 0.197) with an absolute mean error of 10.6%. The lowest mean percentage error (0.3%) corresponded to the subgroup with birth weight between 3000 g and 4000 g. This study demonstrates a poor correlation between actual birth weight and the estimated fetal weight using a formula based on femur length and mid-thigh soft-tissue thickness, both linear parameters. Although avoidance of circumferential ultrasound measurements might prove to be beneficial, it is still yet to be found a fetal estimation formula that can be both accurate and simple to perform.

[Value of the tritium test for determining the fat content in the body of rats].

PubMed

Pisarchuk, K L

1990-01-01

An indirect method for estimation of the fat percentage in the animal organism, a tritium test, was studied on laboratory male rats aged 4 and 12 months. Results obtained from the tritium test and direct chemical analysis were compared. With age a mean absolute error of the tritium test increased (from 1 to 8%) as against actual values of the water and fat percentage in the organism obtained by a direct chemical analysis. The data obtained testify to the relative insolvency of the tritium test, as well as the necessity to carry additional investigations in order to obtain adequate data.

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.

Improving the Glucose Meter Error Grid With the Taguchi Loss Function.

PubMed

Krouwer, Jan S

2016-07-01

Glucose meters often have similar performance when compared by error grid analysis. This is one reason that other statistics such as mean absolute relative deviation (MARD) are used to further differentiate performance. The problem with MARD is that too much information is lost. But additional information is available within the A zone of an error grid by using the Taguchi loss function. Applying the Taguchi loss function gives each glucose meter difference from reference a value ranging from 0 (no error) to 1 (error reaches the A zone limit). Values are averaged over all data which provides an indication of risk of an incorrect medical decision. This allows one to differentiate glucose meter performance for the common case where meters have a high percentage of values in the A zone and no values beyond the B zone. Examples are provided using simulated data. © 2015 Diabetes Technology Society.

Perception of musical and lexical tones by Taiwanese-speaking musicians.

PubMed

Lee, Chao-Yang; Lee, Yuh-Fang; Shr, Chia-Lin

2011-07-01

This study explored the relationship between music and speech by examining absolute pitch and lexical tone perception. Taiwanese-speaking musicians were asked to identify musical tones without a reference pitch and multispeaker Taiwanese level tones without acoustic cues typically present for speaker normalization. The results showed that a high percentage of the participants (65% with an exact match required and 81% with one-semitone errors allowed) possessed absolute pitch, as measured by the musical tone identification task. A negative correlation was found between occurrence of absolute pitch and age of onset of musical training, suggesting that the acquisition of absolute pitch resembles the acquisition of speech. The participants were able to identify multispeaker Taiwanese level tones with above-chance accuracy, even though the acoustic cues typically present for speaker normalization were not available in the stimuli. No correlations were found between the performance in musical tone identification and the performance in Taiwanese tone identification. Potential reasons for the lack of association between the two tasks are discussed. © 2011 Acoustical Society of America

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

NASA Astrophysics Data System (ADS)

Strandgren, Johan; Bugliaro, Luca; Sehnke, Frank; Schröder, Leon

2017-09-01

Cirrus clouds play an important role in climate as they tend to warm the Earth-atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SEVIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data. CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m-2. Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness. By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.

Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis.

PubMed

Bergs, Jochen; Heerinckx, Philipe; Verelst, Sandra

2014-04-01

To evaluate an automatic forecasting algorithm in order to predict the number of monthly emergency department (ED) visits one year ahead. We collected retrospective data of the number of monthly visiting patients for a 6-year period (2005-2011) from 4 Belgian Hospitals. We used an automated exponential smoothing approach to predict monthly visits during the year 2011 based on the first 5 years of the dataset. Several in- and post-sample forecasting accuracy measures were calculated. The automatic forecasting algorithm was able to predict monthly visits with a mean absolute percentage error ranging from 2.64% to 4.8%, indicating an accurate prediction. The mean absolute scaled error ranged from 0.53 to 0.68 indicating that, on average, the forecast was better compared with in-sample one-step forecast from the naïve method. The applied automated exponential smoothing approach provided useful predictions of the number of monthly visits a year in advance. Copyright © 2013 Elsevier Ltd. All rights reserved.

Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans

PubMed Central

Zhou, Lingling; Xia, Jing; Yu, Lijing; Wang, Ying; Shi, Yun; Cai, Shunxiang; Nie, Shaofa

2016-01-01

Background: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model. Methods: We used the ARIMA, NARNN and ARIMA-NARNN models to fit and forecast the annual prevalence of schistosomiasis. The modeling time range included was the annual prevalence from 1956 to 2008 while the testing time range included was from 2009 to 2012. The mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the model performance. We reconstructed the hybrid model to forecast the annual prevalence from 2013 to 2016. Results: The modeling and testing errors generated by the ARIMA-NARNN model were lower than those obtained from either the single ARIMA or NARNN models. The predicted annual prevalence from 2013 to 2016 demonstrated an initial decreasing trend, followed by an increase. Conclusions: The ARIMA-NARNN model can be well applied to analyze surveillance data for early warning systems for the control and elimination of schistosomiasis. PMID:27023573

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability.

PubMed

Hossain, Monowar; Mekhilef, Saad; Afifi, Firdaus; Halabi, Laith M; Olatomiwa, Lanre; Seyedmahmoudian, Mehdi; Horan, Ben; Stojcevski, Alex

2018-01-01

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.

PubMed

Wang, K W; Deng, C; Li, J P; Zhang, Y Y; Li, X Y; Wu, M C

2017-04-01

Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

Evaluating the performance of the Lee-Carter method and its variants in modelling and forecasting Malaysian mortality

NASA Astrophysics Data System (ADS)

Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.

2014-12-01

This study investigated the performance of the Lee-Carter (LC) method and it variants in modeling and forecasting Malaysia mortality. These include the original LC, the Lee-Miller (LM) variant and the Booth-Maindonald-Smith (BMS) variant. These methods were evaluated using Malaysia's mortality data which was measured based on age specific death rates (ASDR) for 1971 to 2009 for overall population while those for 1980-2009 were used in separate models for male and female population. The performance of the variants has been examined in term of the goodness of fit of the models and forecasting accuracy. Comparison was made based on several criteria namely, mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE). The results indicate that BMS method was outperformed in in-sample fitting for overall population and when the models were fitted separately for male and female population. However, in the case of out-sample forecast accuracy, BMS method only best when the data were fitted to overall population. When the data were fitted separately for male and female, LCnone performed better for male population and LM method is good for female population.

Model assessment using a multi-metric ranking technique

NASA Astrophysics Data System (ADS)

Fitzpatrick, P. J.; Lau, Y.; Alaka, G.; Marks, F.

2017-12-01

Validation comparisons of multiple models presents challenges when skill levels are similar, especially in regimes dominated by the climatological mean. Assessing skill separation will require advanced validation metrics and identifying adeptness in extreme events, but maintain simplicity for management decisions. Flexibility for operations is also an asset. This work postulates a weighted tally and consolidation technique which ranks results by multiple types of metrics. Variables include absolute error, bias, acceptable absolute error percentages, outlier metrics, model efficiency, Pearson correlation, Kendall's Tau, reliability Index, multiplicative gross error, and root mean squared differences. Other metrics, such as root mean square difference and rank correlation were also explored, but removed when the information was discovered to be generally duplicative to other metrics. While equal weights are applied, weights could be altered depending for preferred metrics. Two examples are shown comparing ocean models' currents and tropical cyclone products, including experimental products. The importance of using magnitude and direction for tropical cyclone track forecasts instead of distance, along-track, and cross-track are discussed. Tropical cyclone intensity and structure prediction are also assessed. Vector correlations are not included in the ranking process, but found useful in an independent context, and will be briefly reported.

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability

PubMed Central

Mekhilef, Saad; Afifi, Firdaus; Halabi, Laith M.; Olatomiwa, Lanre; Seyedmahmoudian, Mehdi; Stojcevski, Alex

2018-01-01

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations. PMID:29702645

Retrieval of the aerosol optical thickness from UV global irradiance measurements

NASA Astrophysics Data System (ADS)

Costa, M. J.; Salgueiro, V.; Bortoli, D.; Obregón, M. A.; Antón, M.; Silva, A. M.

2015-12-01

The UV irradiance is measured at Évora since several years, where a CIMEL sunphotometer integrated in AERONET is also installed. In the present work, measurements of UVA (315 - 400 nm) irradiances taken with Kipp&Zonen radiometers, as well as satellite data of ozone total column values, are used in combination with radiative transfer calculations, to estimate the aerosol optical thickness (AOT) in the UV. The retrieved UV AOT in Évora is compared with AERONET AOT (at 340 and 380 nm) and a fairly good agreement is found with a root mean square error of 0.05 (normalized root mean square error of 8.3%) and a mean absolute error of 0.04 (mean percentage error of 2.9%). The methodology is then used to estimate the UV AOT in Sines, an industrialized site on the Atlantic western coast, where the UV irradiance is monitored since 2013 but no aerosol information is available.

Water Level Prediction of Lake Cascade Mahakam Using Adaptive Neural Network Backpropagation (ANNBP)

NASA Astrophysics Data System (ADS)

Mislan; Gaffar, A. F. O.; Haviluddin; Puspitasari, N.

2018-04-01

A natural hazard information and flood events are indispensable as a form of prevention and improvement. One of the causes is flooding in the areas around the lake. Therefore, forecasting the surface of Lake water level to anticipate flooding is required. The purpose of this paper is implemented computational intelligence method namely Adaptive Neural Network Backpropagation (ANNBP) to forecasting the Lake Cascade Mahakam. Based on experiment, performance of ANNBP indicated that Lake water level prediction have been accurate by using mean square error (MSE) and mean absolute percentage error (MAPE). In other words, computational intelligence method can produce good accuracy. A hybrid and optimization of computational intelligence are focus in the future work.

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.

PubMed

Azeez, Adeboye; Obaromi, Davies; Odeyemi, Akinwumi; Ndege, James; Muntabayi, Ruffin

2016-07-26

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.

Ecological footprint model using the support vector machine technique.

PubMed

Ma, Haibo; Chang, Wenjuan; Cui, Guangbai

2012-01-01

The per capita ecological footprint (EF) is one of the most widely recognized measures of environmental sustainability. It aims to quantify the Earth's biological resources required to support human activity. In this paper, we summarize relevant previous literature, and present five factors that influence per capita EF. These factors are: National gross domestic product (GDP), urbanization (independent of economic development), distribution of income (measured by the Gini coefficient), export dependence (measured by the percentage of exports to total GDP), and service intensity (measured by the percentage of service to total GDP). A new ecological footprint model based on a support vector machine (SVM), which is a machine-learning method based on the structural risk minimization principle from statistical learning theory was conducted to calculate the per capita EF of 24 nations using data from 123 nations. The calculation accuracy was measured by average absolute error and average relative error. They were 0.004883 and 0.351078% respectively. Our results demonstrate that the EF model based on SVM has good calculation performance.

12 CFR 1229.9 - Discretionary actions applicable to significantly undercapitalized Banks.

Code of Federal Regulations, 2011 CFR

2011-01-01

... absolute dollar amount, as a percentage of current obligations or in any other form chosen by the Director...-balance sheet obligations. Such reduction may be stated in an absolute dollar amount, as a percentage of... absolute dollar amount, as a percentage of current assets or in any other form chosen by the Director; (4...

12 CFR 1229.9 - Discretionary actions applicable to significantly undercapitalized Banks.

Code of Federal Regulations, 2010 CFR

2010-01-01

... absolute dollar amount, as a percentage of current obligations or in any other form chosen by the Director...-balance sheet obligations. Such reduction may be stated in an absolute dollar amount, as a percentage of... absolute dollar amount, as a percentage of current assets or in any other form chosen by the Director; (4...

Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction

PubMed Central

Chen, Yasheng; Juttukonda, Meher; Su, Yi; Benzinger, Tammie; Rubin, Brian G.; Lee, Yueh Z.; Lin, Weili; Shen, Dinggang; Lalush, David

2015-01-01

Purpose To develop a positron emission tomography (PET) attenuation correction method for brain PET/magnetic resonance (MR) imaging by estimating pseudo computed tomographic (CT) images from T1-weighted MR and atlas CT images. Materials and Methods In this institutional review board–approved and HIPAA-compliant study, PET/MR/CT images were acquired in 20 subjects after obtaining written consent. A probabilistic air segmentation and sparse regression (PASSR) method was developed for pseudo CT estimation. Air segmentation was performed with assistance from a probabilistic air map. For nonair regions, the pseudo CT numbers were estimated via sparse regression by using atlas MR patches. The mean absolute percentage error (MAPE) on PET images was computed as the normalized mean absolute difference in PET signal intensity between a method and the reference standard continuous CT attenuation correction method. Friedman analysis of variance and Wilcoxon matched-pairs tests were performed for statistical comparison of MAPE between the PASSR method and Dixon segmentation, CT segmentation, and population averaged CT atlas (mean atlas) methods. Results The PASSR method yielded a mean MAPE ± standard deviation of 2.42% ± 1.0, 3.28% ± 0.93, and 2.16% ± 1.75, respectively, in the whole brain, gray matter, and white matter, which were significantly lower than the Dixon, CT segmentation, and mean atlas values (P < .01). Moreover, 68.0% ± 16.5, 85.8% ± 12.9, and 96.0% ± 2.5 of whole-brain volume had within ±2%, ±5%, and ±10% percentage error by using PASSR, respectively, which was significantly higher than other methods (P < .01). Conclusion PASSR outperformed the Dixon, CT segmentation, and mean atlas methods by reducing PET error owing to attenuation correction. © RSNA, 2014 PMID:25521778

Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems.

PubMed

Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

2013-01-01

An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. THE ANFIS AND ANN MODELS WERE COMPARED IN TERMS OF SIX STATISTICAL INDICES CALCULATED BY COMPARING THEIR PREDICTION RESULTS WITH ACTUAL DATA: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R (2)). Graphical plots were also used for model comparison. The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions.

Leuconostoc Mesenteroides Growth in Food Products: Prediction and Sensitivity Analysis by Adaptive-Network-Based Fuzzy Inference Systems

PubMed Central

Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

2013-01-01

Background An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. Methods The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R 2). Graphical plots were also used for model comparison. Conclusions The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. PMID:23705023

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

An empirical model for estimating solar radiation in the Algerian Sahara

NASA Astrophysics Data System (ADS)

Benatiallah, Djelloul; Benatiallah, Ali; Bouchouicha, Kada; Hamouda, Messaoud; Nasri, Bahous

2018-05-01

The present work aims to determine the empirical model R.sun that will allow us to evaluate the solar radiation flues on a horizontal plane and in clear-sky on the located Adrar city (27°18 N and 0°11 W) of Algeria and compare with the results measured at the localized site. The expected results of this comparison are of importance for the investment study of solar systems (solar power plants for electricity production, CSP) and also for the design and performance analysis of any system using the solar energy. Statistical indicators used to evaluate the accuracy of the model where the mean bias error (MBE), root mean square error (RMSE) and coefficient of determination. The results show that for global radiation, the daily correlation coefficient is 0.9984. The mean absolute percentage error is 9.44 %. The daily mean bias error is -7.94 %. The daily root mean square error is 12.31 %.

Design and validation of a portable, inexpensive and multi-beam timing light system using the Nintendo Wii hand controllers.

PubMed

Clark, Ross A; Paterson, Kade; Ritchie, Callan; Blundell, Simon; Bryant, Adam L

2011-03-01

Commercial timing light systems (CTLS) provide precise measurement of athletes running velocity, however they are often expensive and difficult to transport. In this study an inexpensive, wireless and portable timing light system was created using the infrared camera in Nintendo Wii hand controllers (NWHC). System creation with gold-standard validation. A Windows-based software program using NWHC to replicate a dual-beam timing gate was created. Firstly, data collected during 2m walking and running trials were validated against a 3D kinematic system. Secondly, data recorded during 5m running trials at various intensities from standing or flying starts were compared to a single beam CTLS and the independent and average scores of three handheld stopwatch (HS) operators. Intraclass correlation coefficient and Bland-Altman plots were used to assess validity. Absolute error quartiles and percentage of trials in absolute error threshold ranges were used to determine accuracy. The NWHC system was valid when compared against the 3D kinematic system (ICC=0.99, median absolute error (MAR)=2.95%). For the flying 5m trials the NWHC system possessed excellent validity and precision (ICC=0.97, MAR<3%) when compared with the CTLS. In contrast, the NWHC system and the HS values during standing start trials possessed only modest validity (ICC<0.75) and accuracy (MAR>8%). A NWHC timing light system is inexpensive, portable and valid for assessing running velocity. Errors in the 5m standing start trials may have been due to erroneous event detection by either the commercial or NWHC-based timing light systems. Copyright © 2010 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Modeling the relationship between photosynthetically active radiation and global horizontal irradiance using singular spectrum analysis

NASA Astrophysics Data System (ADS)

Zempila, Melina-Maria; Taylor, Michael; Bais, Alkiviadis; Kazadzis, Stelios

2016-10-01

We report on the construction of generic models to calculate photosynthetically active radiation (PAR) from global horizontal irradiance (GHI), and vice versa. Our study took place at stations of the Greek UV network (UVNET) and the Hellenic solar energy network (HNSE) with measurements from NILU-UV multi-filter radiometers and CM pyranometers, chosen due to their long (≈1 M record/site) high temporal resolution (≈1 min) record that captures a broad range of atmospheric environments and cloudiness conditions. The uncertainty of the PAR measurements is quantified to be ±6.5% while the uncertainty involved in GHI measurements is up to ≈±7% according to the manufacturer. We show how multi-linear regression and nonlinear neural network (NN) models, trained at a calibration site (Thessaloniki) can be made generic provided that the input-output time series are processed with multi-channel singular spectrum analysis (M-SSA). Without M-SSA, both linear and nonlinear models perform well only locally. M-SSA with 50 time-lags is found to be sufficient for identification of trend, periodic and noise components in aerosol, cloud parameters and irradiance, and to construct regularized noise models of PAR from GHI irradiances. Reconstructed PAR and GHI time series capture ≈95% of the variance of the cross-validated target measurements and have median absolute percentage errors <2%. The intra-site median absolute error of M-SSA processed models were ≈8.2±1.7 W/m2 for PAR and ≈9.2±4.2 W/m2 for GHI. When applying the models trained at Thessaloniki to other stations, the average absolute mean bias between the model estimates and measured values was found to be ≈1.2 W/m2 for PAR and ≈0.8 W/m2 for GHI. For the models, percentage errors are well within the uncertainty of the measurements at all sites. Generic NN models were found to perform marginally better than their linear counterparts.

Modeling and Predicting the Electrical Conductivity of Composite Cathode for Solid Oxide Fuel Cell by Using Support Vector Regression

NASA Astrophysics Data System (ADS)

Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J.

2012-07-01

The electrical conductivity of solid oxide fuel cell (SOFC) cathode is one of the most important indices affecting the efficiency of SOFC. In order to improve the performance of fuel cell system, it is advantageous to have accurate model with which one can predict the electrical conductivity. In this paper, a model utilizing support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter optimization was established to modeling and predicting the electrical conductivity of Ba0.5Sr0.5Co0.8Fe0.2 O3-δ-xSm0.5Sr0.5CoO3-δ (BSCF-xSSC) composite cathode under two influence factors, including operating temperature (T) and SSC content (x) in BSCF-xSSC composite cathode. The leave-one-out cross validation (LOOCV) test result by SVR strongly supports that the generalization ability of SVR model is high enough. The absolute percentage error (APE) of 27 samples does not exceed 0.05%. The mean absolute percentage error (MAPE) of all 30 samples is only 0.09% and the correlation coefficient (R2) as high as 0.999. This investigation suggests that the hybrid PSO-SVR approach may be not only a promising and practical methodology to simulate the properties of fuel cell system, but also a powerful tool to be used for optimal designing or controlling the operating process of a SOFC system.

Depicting mass flow rate of R134a /LPG refrigerant through straight and helical coiled adiabatic capillary tubes of vapor compression refrigeration system using artificial neural network approach

NASA Astrophysics Data System (ADS)

Gill, Jatinder; Singh, Jagdev

2018-07-01

In this work, an experimental investigation is carried out with R134a and LPG refrigerant mixture for depicting mass flow rate through straight and helical coil adiabatic capillary tubes in a vapor compression refrigeration system. Various experiments were conducted under steady-state conditions, by changing capillary tube length, inner diameter, coil diameter and degree of subcooling. The results showed that mass flow rate through helical coil capillary tube was found lower than straight capillary tube by about 5-16%. Dimensionless correlation and Artificial Neural Network (ANN) models were developed to predict mass flow rate. It was found that dimensionless correlation and ANN model predictions agreed well with experimental results and brought out an absolute fraction of variance of 0.961 and 0.988, root mean square error of 0.489 and 0.275 and mean absolute percentage error of 4.75% and 2.31% respectively. The results suggested that ANN model shows better statistical prediction than dimensionless correlation model.

Development of Bio-impedance Analyzer (BIA) for Body Fat Calculation

NASA Astrophysics Data System (ADS)

Riyadi, Munawar A.; Nugraha, A.; Santoso, M. B.; Septaditya, D.; Prakoso, T.

2017-04-01

Common weight scales cannot assess body composition or determine fat mass and fat-fress mass that make up the body weight. This research propose bio-impedance analysis (BIA) tool capable to body composition assessment. This tool uses four electrodes, two of which are used for 50 kHz sine wave current flow to the body and the rest are used to measure the voltage produced by the body for impedance analysis. Parameters such as height, weight, age, and gender are provided individually. These parameters together with impedance measurements are then in the process to produce a body fat percentage. The experimental result shows impressive repeatability for successive measurements (stdev ≤ 0.25% fat mass). Moreover, result on the hand to hand node scheme reveals average absolute difference of total subjects between two analyzer tools of 0.48% (fat mass) with maximum absolute discrepancy of 1.22% (fat mass). On the other hand, the relative error normalized to Omron’s HBF-306 as comparison tool reveals less than 2% relative error. As a result, the system performance offers good evaluation tool for fat mass in the body.

Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks.

PubMed

Darajeh, Negisa; Idris, Azni; Fard Masoumi, Hamid Reza; Nourani, Abolfazl; Truong, Paul; Rezania, Shahabaldin

2017-05-04

Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied for modeling of Chemical Oxygen Demand (COD) and biodegradable organic matter (BOD) removal from palm oil mill secondary effluent (POMSE) by vetiver system. The independent variable, including POMSE concentration, vetiver slips density, and removal time, has been considered as input parameters to optimize the network, while the removal percentage of COD and BOD were selected as output. To determine the number of hidden layer nodes, the root mean squared error of testing set was minimized, and the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the quick propagation (QP) algorithm had minimum root mean squared error and absolute average deviation, and maximum coefficient of determination. The importance values of the variables was included vetiver slips density with 42.41%, time with 29.8%, and the POMSE concentration with 27.79%, which showed none of them, is negligible. Results show that the ANN has great potential ability in prediction of COD and BOD removal from POMSE with residual standard error (RSE) of less than 0.45%.

Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

NASA Astrophysics Data System (ADS)

Manikumari, N.; Murugappan, A.; Vinodhini, G.

2017-07-01

Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

Arima model and exponential smoothing method: A comparison

NASA Astrophysics Data System (ADS)

Wan Ahmad, Wan Kamarul Ariffin; Ahmad, Sabri

2013-04-01

This study shows the comparison between Autoregressive Moving Average (ARIMA) model and Exponential Smoothing Method in making a prediction. The comparison is focused on the ability of both methods in making the forecasts with the different number of data sources and the different length of forecasting period. For this purpose, the data from The Price of Crude Palm Oil (RM/tonne), Exchange Rates of Ringgit Malaysia (RM) in comparison to Great Britain Pound (GBP) and also The Price of SMR 20 Rubber Type (cents/kg) with three different time series are used in the comparison process. Then, forecasting accuracy of each model is measured by examinethe prediction error that producedby using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute deviation (MAD). The study shows that the ARIMA model can produce a better prediction for the long-term forecasting with limited data sources, butcannot produce a better prediction for time series with a narrow range of one point to another as in the time series for Exchange Rates. On the contrary, Exponential Smoothing Method can produce a better forecasting for Exchange Rates that has a narrow range of one point to another for its time series, while itcannot produce a better prediction for a longer forecasting period.

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

PubMed

Preti, Antonio; Lentini, Gianluca

2016-01-01

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

Tourism forecasting using modified empirical mode decomposition and group method of data handling

NASA Astrophysics Data System (ADS)

Yahya, N. A.; Samsudin, R.; Shabri, A.

2017-09-01

In this study, a hybrid model using modified Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) model is proposed for tourism forecasting. This approach reconstructs intrinsic mode functions (IMFs) produced by EMD using trial and error method. The new component and the remaining IMFs is then predicted respectively using GMDH model. Finally, the forecasted results for each component are aggregated to construct an ensemble forecast. The data used in this experiment are monthly time series data of tourist arrivals from China, Thailand and India to Malaysia from year 2000 to 2016. The performance of the model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) where conventional GMDH model and EMD-GMDH model are used as benchmark models. Empirical results proved that the proposed model performed better forecasts than the benchmarked models.

Prediction of the compression ratio for municipal solid waste using decision tree.

PubMed

Heshmati R, Ali Akbar; Mokhtari, Maryam; Shakiba Rad, Saeed

2014-01-01

The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

NASA Astrophysics Data System (ADS)

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

Quantitative EEG correlations with brain glucose metabolic rate during anesthesia in volunteers.

PubMed

Alkire, M T

1998-08-01

To help elucidate the relationship between anesthetic-induced changes in the electroencephalogram (EEG) and the concurrent cerebral metabolic changes caused by anesthesia, positron emission tomography data of cerebral metabolism obtained in volunteers during anesthesia were correlated retrospectively with various concurrently measured EEG descriptors. Volunteers underwent functional brain imaging using the 18fluorodeoxyglucose technique; one scan always assessed awake-baseline cerebral metabolism (n = 7), and the other scans assessed metabolism during propofol sedation (n = 4), propofol anesthesia (n = 4), or isoflurane anesthesia (n = 5). The EEG was recorded continuously during metabolism assessment using a frontal-mastoid montage. Power spectrum variables, median frequency, 95% spectral edge, and bispectral index (BIS) values subsequently were correlated with the percentage of absolute cerebral metabolic reduction (PACMR) of glucose utilization caused by anesthesia. The percentage of absolute cerebral metabolic reduction, evident during anesthesia, trended median frequency (r = -0.46, P = 0.11), and the spectral edge (r = -0.52, P = 0.07), and correlated with anesthetic type (r = -0.70, P < 0.05), relative beta power (r = -0.60, P < 0.05), total power (r = 0.71,P < 0.01), and bispectral index (r = -0.81,P < 0.001). After controlling for anesthetic type, only bispectral index (r = 0.40, P = 0.08) and alpha power (r = 0.37, P = 0.10) approached significance for explaining residual percentage of absolute cerebral metabolic reduction prediction error. Some EEG descriptors correlated linearly with the magnitude of the cerebral metabolic reduction caused by propofol and isoflurane anesthesia. These data suggest that a physiologic link exists between the EEG and cerebral metabolism during anesthesia that is mathematically quantifiable.

Reliable absolute analog code retrieval approach for 3D measurement

NASA Astrophysics Data System (ADS)

Yu, Shuang; Zhang, Jing; Yu, Xiaoyang; Sun, Xiaoming; Wu, Haibin; Chen, Deyun

2017-11-01

The wrapped phase of phase-shifting approach can be unwrapped by using Gray code, but both the wrapped phase error and Gray code decoding error can result in period jump error, which will lead to gross measurement error. Therefore, this paper presents a reliable absolute analog code retrieval approach. The combination of unequal-period Gray code and phase shifting patterns at high frequencies are used to obtain high-frequency absolute analog code, and at low frequencies, the same unequal-period combination patterns are used to obtain the low-frequency absolute analog code. Next, the difference between the two absolute analog codes was employed to eliminate period jump errors, and a reliable unwrapped result can be obtained. Error analysis was used to determine the applicable conditions, and this approach was verified through theoretical analysis. The proposed approach was further verified experimentally. Theoretical analysis and experimental results demonstrate that the proposed approach can perform reliable analog code unwrapping.

High-resolution spatial databases of monthly climate variables (1961-2010) over a complex terrain region in southwestern China

NASA Astrophysics Data System (ADS)

Wu, Wei; Xu, An-Ding; Liu, Hong-Bin

2015-01-01

Climate data in gridded format are critical for understanding climate change and its impact on eco-environment. The aim of the current study is to develop spatial databases for three climate variables (maximum, minimum temperatures, and relative humidity) over a large region with complex topography in southwestern China. Five widely used approaches including inverse distance weighting, ordinary kriging, universal kriging, co-kriging, and thin-plate smoothing spline were tested. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) showed that thin-plate smoothing spline with latitude, longitude, and elevation outperformed other models. Average RMSE, MAE, and MAPE of the best models were 1.16 °C, 0.74 °C, and 7.38 % for maximum temperature; 0.826 °C, 0.58 °C, and 6.41 % for minimum temperature; and 3.44, 2.28, and 3.21 % for relative humidity, respectively. Spatial datasets of annual and monthly climate variables with 1-km resolution covering the period 1961-2010 were then obtained using the best performance methods. Comparative study showed that the current outcomes were in well agreement with public datasets. Based on the gridded datasets, changes in temperature variables were investigated across the study area. Future study might be needed to capture the uncertainty induced by environmental conditions through remote sensing and knowledge-based methods.

Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city-China

NASA Astrophysics Data System (ADS)

Tang, Jinjun; Zhang, Shen; Chen, Xinqiang; Liu, Fang; Zou, Yajie

2018-03-01

Understanding Origin-Destination distribution of taxi trips is very important for improving effects of transportation planning and enhancing quality of taxi services. This study proposes a new method based on Entropy-Maximizing theory to model OD distribution in Harbin city using large-scale taxi GPS trajectories. Firstly, a K-means clustering method is utilized to partition raw pick-up and drop-off location into different zones, and trips are assumed to start from and end at zone centers. A generalized cost function is further defined by considering travel distance, time and fee between each OD pair. GPS data collected from more than 1000 taxis at an interval of 30 s during one month are divided into two parts: data from first twenty days is treated as training dataset and last ten days is taken as testing dataset. The training dataset is used to calibrate model while testing dataset is used to validate model. Furthermore, three indicators, mean absolute error (MAE), root mean square error (RMSE) and mean percentage absolute error (MPAE), are applied to evaluate training and testing performance of Entropy-Maximizing model versus Gravity model. The results demonstrate Entropy-Maximizing model is superior to Gravity model. Findings of the study are used to validate the feasibility of OD distribution from taxi GPS data in urban system.

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.

PubMed

Wu, Wei; Guo, Junqiao; An, Shuyi; Guan, Peng; Ren, Yangwu; Xia, Linzi; Zhou, Baosen

2015-01-01

Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.

Forecasting Container Throughput at the Doraleh Port in Djibouti through Time Series Analysis

NASA Astrophysics Data System (ADS)

Mohamed Ismael, Hawa; Vandyck, George Kobina

The Doraleh Container Terminal (DCT) located in Djibouti has been noted as the most technologically advanced container terminal on the African continent. DCT's strategic location at the crossroads of the main shipping lanes connecting Asia, Africa and Europe put it in a unique position to provide important shipping services to vessels plying that route. This paper aims to forecast container throughput through the Doraleh Container Port in Djibouti by Time Series Analysis. A selection of univariate forecasting models has been used, namely Triple Exponential Smoothing Model, Grey Model and Linear Regression Model. By utilizing the above three models and their combination, the forecast of container throughput through the Doraleh port was realized. A comparison of the different forecasting results of the three models, in addition to the combination forecast is then undertaken, based on commonly used evaluation criteria Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The study found that the Linear Regression forecasting Model was the best prediction method for forecasting the container throughput, since its forecast error was the least. Based on the regression model, a ten (10) year forecast for container throughput at DCT has been made.

New Model for Estimating Glomerular Filtration Rate in Patients With Cancer

PubMed Central

Janowitz, Tobias; Williams, Edward H.; Marshall, Andrea; Ainsworth, Nicola; Thomas, Peter B.; Sammut, Stephen J.; Shepherd, Scott; White, Jeff; Mark, Patrick B.; Lynch, Andy G.; Jodrell, Duncan I.; Tavaré, Simon; Earl, Helena

2017-01-01

Purpose The glomerular filtration rate (GFR) is essential for carboplatin chemotherapy dosing; however, the best method to estimate GFR in patients with cancer is unknown. We identify the most accurate and least biased method. Methods We obtained data on age, sex, height, weight, serum creatinine concentrations, and results for GFR from chromium-51 (51Cr) EDTA excretion measurements (51Cr-EDTA GFR) from white patients ≥ 18 years of age with histologically confirmed cancer diagnoses at the Cambridge University Hospital NHS Trust, United Kingdom. We developed a new multivariable linear model for GFR using statistical regression analysis. 51Cr-EDTA GFR was compared with the estimated GFR (eGFR) from seven published models and our new model, using the statistics root-mean-squared-error (RMSE) and median residual and on an internal and external validation data set. We performed a comparison of carboplatin dosing accuracy on the basis of an absolute percentage error > 20%. Results Between August 2006 and January 2013, data from 2,471 patients were obtained. The new model improved the eGFR accuracy (RMSE, 15.00 mL/min; 95% CI, 14.12 to 16.00 mL/min) compared with all published models. Body surface area (BSA)–adjusted chronic kidney disease epidemiology (CKD-EPI) was the most accurate published model for eGFR (RMSE, 16.30 mL/min; 95% CI, 15.34 to 17.38 mL/min) for the internal validation set. Importantly, the new model reduced the fraction of patients with a carboplatin dose absolute percentage error > 20% to 14.17% in contrast to 18.62% for the BSA-adjusted CKD-EPI and 25.51% for the Cockcroft-Gault formula. The results were externally validated. Conclusion In a large data set from patients with cancer, BSA-adjusted CKD-EPI is the most accurate published model to predict GFR. The new model improves this estimation and may present a new standard of care. PMID:28686534

New Model for Estimating Glomerular Filtration Rate in Patients With Cancer.

PubMed

Janowitz, Tobias; Williams, Edward H; Marshall, Andrea; Ainsworth, Nicola; Thomas, Peter B; Sammut, Stephen J; Shepherd, Scott; White, Jeff; Mark, Patrick B; Lynch, Andy G; Jodrell, Duncan I; Tavaré, Simon; Earl, Helena

2017-08-20

Purpose The glomerular filtration rate (GFR) is essential for carboplatin chemotherapy dosing; however, the best method to estimate GFR in patients with cancer is unknown. We identify the most accurate and least biased method. Methods We obtained data on age, sex, height, weight, serum creatinine concentrations, and results for GFR from chromium-51 ( 51 Cr) EDTA excretion measurements ( 51 Cr-EDTA GFR) from white patients ≥ 18 years of age with histologically confirmed cancer diagnoses at the Cambridge University Hospital NHS Trust, United Kingdom. We developed a new multivariable linear model for GFR using statistical regression analysis. 51 Cr-EDTA GFR was compared with the estimated GFR (eGFR) from seven published models and our new model, using the statistics root-mean-squared-error (RMSE) and median residual and on an internal and external validation data set. We performed a comparison of carboplatin dosing accuracy on the basis of an absolute percentage error > 20%. Results Between August 2006 and January 2013, data from 2,471 patients were obtained. The new model improved the eGFR accuracy (RMSE, 15.00 mL/min; 95% CI, 14.12 to 16.00 mL/min) compared with all published models. Body surface area (BSA)-adjusted chronic kidney disease epidemiology (CKD-EPI) was the most accurate published model for eGFR (RMSE, 16.30 mL/min; 95% CI, 15.34 to 17.38 mL/min) for the internal validation set. Importantly, the new model reduced the fraction of patients with a carboplatin dose absolute percentage error > 20% to 14.17% in contrast to 18.62% for the BSA-adjusted CKD-EPI and 25.51% for the Cockcroft-Gault formula. The results were externally validated. Conclusion In a large data set from patients with cancer, BSA-adjusted CKD-EPI is the most accurate published model to predict GFR. The new model improves this estimation and may present a new standard of care.

Peripheral Quantitative Computed Tomography: Measurement Sensitivity in Persons With and Without Spinal Cord Injury

PubMed Central

Shields, Richard K.; Dudley-Javoroski, Shauna; Boaldin, Kathryn M.; Corey, Trent A.; Fog, Daniel B.; Ruen, Jacquelyn M.

2012-01-01

Objectives To determine (1) the error attributable to external tibia-length measurements by using peripheral quantitative computed tomography (pQCT) and (2) the effect these errors have on scan location and tibia trabecular bone mineral density (BMD) after spinal cord injury (SCI). Design Blinded comparison and criterion standard in matched cohorts. Setting Primary care university hospital. Participants Eight able-bodied subjects underwent tibia length measurement. A separate cohort of 7 men with SCI and 7 able-bodied age-matched male controls underwent pQCT analysis. Interventions Not applicable. Main Outcome Measures The projected worst-case tibia-length–measurement error translated into a pQCT slice placement error of ±3mm. We collected pQCT slices at the distal 4% tibia site, 3mm proximal and 3mm distal to that site, and then quantified BMD error attributable to slice placement. Results Absolute BMD error was greater for able-bodied than for SCI subjects (5.87mg/cm3 vs 4.5mg/cm3). However, the percentage error in BMD was larger for SCI than able-bodied subjects (4.56% vs 2.23%). Conclusions During cross-sectional studies of various populations, BMD differences up to 5% may be attributable to variation in limb-length–measurement error. PMID:17023249

Empirical ocean color algorithms and bio-optical properties of the western coastal waters of Svalbard, Arctic

NASA Astrophysics Data System (ADS)

Son, Young-Sun; Kim, Hyun-cheol

2018-05-01

Chlorophyll (Chl) concentration is one of the key indicators identifying changes in the Arctic marine ecosystem. However, current Chl algorithms are not accurate in the Arctic Ocean due to different bio-optical properties from those in the lower latitude oceans. In this study, we evaluated the current Chl algorithms and analyzed the cause of the error in the western coastal waters of Svalbard, which are known to be sensitive to climate change. The NASA standard algorithms showed to overestimate the Chl concentration in the region. This was due to the high non-algal particles (NAP) absorption and colored dissolved organic matter (CDOM) variability at the blue wavelength. In addition, at lower Chl concentrations (0.1-0.3 mg m-3), chlorophyll-specific absorption coefficients were ∼2.3 times higher than those of other Arctic oceans. This was another reason for the overestimation of Chl concentration. OC4 algorithm-based regionally tuned-Svalbard Chl (SC4) algorithm for retrieving more accurate Chl estimates reduced the mean absolute percentage difference (APD) error from 215% to 49%, the mean relative percentage difference (RPD) error from 212% to 16%, and the normalized root mean square (RMS) error from 211% to 68%. This region has abundant suspended matter due to the melting of tidal glaciers. We evaluated the performance of total suspended matter (TSM) algorithms. Previous published TSM algorithms generally overestimated the TSM concentration in this region. The Svalbard TSM-single band algorithm for low TSM range (ST-SB-L) decreased the APD and RPD errors by 52% and 14%, respectively, but the RMS error still remained high (105%).

A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China

PubMed Central

Wang, Ying; Lu, Zhouqin; Tian, Lihong; Tan, Li; Shi, Yun; Nie, Shaofa; Liu, Li

2014-01-01

Backgrounds/Objective Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas. Methods A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model. Results The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869×10−4, 0.0029, 0.0419 with a corresponding testing error of 0.9375×10−4, 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend. Conclusion The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases. PMID:25119882

Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones.

PubMed

Anthimopoulos, Marios; Dehais, Joachim; Shevchik, Sergey; Ransford, Botwey H; Duke, David; Diem, Peter; Mougiakakou, Stavroula

2015-05-01

Individuals with type 1 diabetes (T1D) have to count the carbohydrates (CHOs) of their meal to estimate the prandial insulin dose needed to compensate for the meal's effect on blood glucose levels. CHO counting is very challenging but also crucial, since an error of 20 grams can substantially impair postprandial control. The GoCARB system is a smartphone application designed to support T1D patients with CHO counting of nonpacked foods. In a typical scenario, the user places a reference card next to the dish and acquires 2 images with his/her smartphone. From these images, the plate is detected and the different food items on the plate are automatically segmented and recognized, while their 3D shape is reconstructed. Finally, the food volumes are calculated and the CHO content is estimated by combining the previous results and using the USDA nutritional database. To evaluate the proposed system, a set of 24 multi-food dishes was used. For each dish, 3 pairs of images were taken and for each pair, the system was applied 4 times. The mean absolute percentage error in CHO estimation was 10 ± 12%, which led to a mean absolute error of 6 ± 8 CHO grams for normal-sized dishes. The laboratory experiments demonstrated the feasibility of the GoCARB prototype system since the error was below the initial goal of 20 grams. However, further improvements and evaluation are needed prior launching a system able to meet the inter- and intracultural eating habits. © 2015 Diabetes Technology Society.

Robust estimation of adaptive tensors of curvature by tensor voting.

PubMed

Tong, Wai-Shun; Tang, Chi-Keung

2005-03-01

Although curvature estimation from a given mesh or regularly sampled point set is a well-studied problem, it is still challenging when the input consists of a cloud of unstructured points corrupted by misalignment error and outlier noise. Such input is ubiquitous in computer vision. In this paper, we propose a three-pass tensor voting algorithm to robustly estimate curvature tensors, from which accurate principal curvatures and directions can be calculated. Our quantitative estimation is an improvement over the previous two-pass algorithm, where only qualitative curvature estimation (sign of Gaussian curvature) is performed. To overcome misalignment errors, our improved method automatically corrects input point locations at subvoxel precision, which also rejects outliers that are uncorrectable. To adapt to different scales locally, we define the RadiusHit of a curvature tensor to quantify estimation accuracy and applicability. Our curvature estimation algorithm has been proven with detailed quantitative experiments, performing better in a variety of standard error metrics (percentage error in curvature magnitudes, absolute angle difference in curvature direction) in the presence of a large amount of misalignment noise.

Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe.

PubMed

Ebtehaj, Isa; Bonakdari, Hossein

2014-01-01

The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations.

Astigmatism error modification for absolute shape reconstruction using Fourier transform method

NASA Astrophysics Data System (ADS)

He, Yuhang; Li, Qiang; Gao, Bo; Liu, Ang; Xu, Kaiyuan; Wei, Xiaohong; Chai, Liqun

2014-12-01

A method is proposed to modify astigmatism errors in absolute shape reconstruction of optical plane using Fourier transform method. If a transmission and reflection flat are used in an absolute test, two translation measurements lead to obtain the absolute shapes by making use of the characteristic relationship between the differential and original shapes in spatial frequency domain. However, because the translation device cannot guarantee the test and reference flats rigidly parallel to each other after the translations, a tilt error exists in the obtained differential data, which caused power and astigmatism errors in the reconstructed shapes. In order to modify the astigmatism errors, a rotation measurement is added. Based on the rotation invariability of the form of Zernike polynomial in circular domain, the astigmatism terms are calculated by solving polynomial coefficient equations related to the rotation differential data, and subsequently the astigmatism terms including error are modified. Computer simulation proves the validity of the proposed method.

Bayesian dynamic modeling of time series of dengue disease case counts.

PubMed

Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander

2017-07-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

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.

Top-of-Climb Matching Method for Reducing Aircraft Trajectory Prediction Errors.

PubMed

Thipphavong, David P

2016-09-01

The inaccuracies of the aircraft performance models utilized by trajectory predictors with regard to takeoff weight, thrust, climb profile, and other parameters result in altitude errors during the climb phase that often exceed the vertical separation standard of 1000 feet. This study investigates the potential reduction in altitude trajectory prediction errors that could be achieved for climbing flights if just one additional parameter is made available: top-of-climb (TOC) time. The TOC-matching method developed and evaluated in this paper is straightforward: a set of candidate trajectory predictions is generated using different aircraft weight parameters, and the one that most closely matches TOC in terms of time is selected. This algorithm was tested using more than 1000 climbing flights in Fort Worth Center. Compared to the baseline trajectory predictions of a real-time research prototype (Center/TRACON Automation System), the TOC-matching method reduced the altitude root mean square error (RMSE) for a 5-minute prediction time by 38%. It also decreased the percentage of flights with absolute altitude error greater than the vertical separation standard of 1000 ft for the same look-ahead time from 55% to 30%.

Top-of-Climb Matching Method for Reducing Aircraft Trajectory Prediction Errors

PubMed Central

Thipphavong, David P.

2017-01-01

The inaccuracies of the aircraft performance models utilized by trajectory predictors with regard to takeoff weight, thrust, climb profile, and other parameters result in altitude errors during the climb phase that often exceed the vertical separation standard of 1000 feet. This study investigates the potential reduction in altitude trajectory prediction errors that could be achieved for climbing flights if just one additional parameter is made available: top-of-climb (TOC) time. The TOC-matching method developed and evaluated in this paper is straightforward: a set of candidate trajectory predictions is generated using different aircraft weight parameters, and the one that most closely matches TOC in terms of time is selected. This algorithm was tested using more than 1000 climbing flights in Fort Worth Center. Compared to the baseline trajectory predictions of a real-time research prototype (Center/TRACON Automation System), the TOC-matching method reduced the altitude root mean square error (RMSE) for a 5-minute prediction time by 38%. It also decreased the percentage of flights with absolute altitude error greater than the vertical separation standard of 1000 ft for the same look-ahead time from 55% to 30%. PMID:28684883

Top-of-Climb Matching Method for Reducing Aircraft Trajectory Prediction Errors

NASA Technical Reports Server (NTRS)

Thipphavong, David P.

2016-01-01

The inaccuracies of the aircraft performance models utilized by trajectory predictors with regard to takeoff weight, thrust, climb profile, and other parameters result in altitude errors during the climb phase that often exceed the vertical separation standard of 1000 feet. This study investigates the potential reduction in altitude trajectory prediction errors that could be achieved for climbing flights if just one additional parameter is made available: top-of-climb (TOC) time. The TOC-matching method developed and evaluated in this paper is straightforward: a set of candidate trajectory predictions is generated using different aircraft weight parameters, and the one that most closely matches TOC in terms of time is selected. This algorithm was tested using more than 1000 climbing flights in Fort Worth Center. Compared to the baseline trajectory predictions of a real-time research prototype (Center/TRACON Automation System), the TOC-matching method reduced the altitude root mean square error (RMSE) for a 5-minute prediction time by 38%. It also decreased the percentage of flights with absolute altitude error greater than the vertical separation standard of 1000 ft for the same look-ahead time from 55% to 30%.

Evaluation of non-rigid registration parameters for atlas-based segmentation of CT images of human cochlea

NASA Astrophysics Data System (ADS)

Elfarnawany, Mai; Alam, S. Riyahi; Agrawal, Sumit K.; Ladak, Hanif M.

2017-02-01

Cochlear implant surgery is a hearing restoration procedure for patients with profound hearing loss. In this surgery, an electrode is inserted into the cochlea to stimulate the auditory nerve and restore the patient's hearing. Clinical computed tomography (CT) images are used for planning and evaluation of electrode placement, but their low resolution limits the visualization of internal cochlear structures. Therefore, high resolution micro-CT images are used to develop atlas-based segmentation methods to extract these nonvisible anatomical features in clinical CT images. Accurate registration of the high and low resolution CT images is a prerequisite for reliable atlas-based segmentation. In this study, we evaluate and compare different non-rigid B-spline registration parameters using micro-CT and clinical CT images of five cadaveric human cochleae. The varying registration parameters are cost function (normalized correlation (NC), mutual information and mean square error), interpolation method (linear, windowed-sinc and B-spline) and sampling percentage (1%, 10% and 100%). We compare the registration results visually and quantitatively using the Dice similarity coefficient (DSC), Hausdorff distance (HD) and absolute percentage error in cochlear volume. Using MI or MSE cost functions and linear or windowed-sinc interpolation resulted in visually undesirable deformation of internal cochlear structures. Quantitatively, the transforms using 100% sampling percentage yielded the highest DSC and smallest HD (0.828+/-0.021 and 0.25+/-0.09mm respectively). Therefore, B-spline registration with cost function: NC, interpolation: B-spline and sampling percentage: moments 100% can be the foundation of developing an optimized atlas-based segmentation algorithm of intracochlear structures in clinical CT images.

Spatial and temporal variability of the overall error of National Atmospheric Deposition Program measurements determined by the USGS collocated-sampler program, water years 1989-2001

USGS Publications Warehouse

Wetherbee, G.A.; Latysh, N.E.; Gordon, J.D.

2005-01-01

Data from the U.S. Geological Survey (USGS) collocated-sampler program for the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) are used to estimate the overall error of NADP/NTN measurements. Absolute errors are estimated by comparison of paired measurements from collocated instruments. Spatial and temporal differences in absolute error were identified and are consistent with longitudinal distributions of NADP/NTN measurements and spatial differences in precipitation characteristics. The magnitude of error for calcium, magnesium, ammonium, nitrate, and sulfate concentrations, specific conductance, and sample volume is of minor environmental significance to data users. Data collected after a 1994 sample-handling protocol change are prone to less absolute error than data collected prior to 1994. Absolute errors are smaller during non-winter months than during winter months for selected constituents at sites where frozen precipitation is common. Minimum resolvable differences are estimated for different regions of the USA to aid spatial and temporal watershed analyses.

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China

PubMed Central

Wu, Wei; Guo, Junqiao; An, Shuyi; Guan, Peng; Ren, Yangwu; Xia, Linzi; Zhou, Baosen

2015-01-01

Background Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Methods Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. Results The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Conclusion Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS. PMID:26270814

Prediction of BP reactivity to talking using hybrid soft computing approaches.

PubMed

Kaur, Gurmanik; Arora, Ajat Shatru; Jain, Vijender Kumar

2014-01-01

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R (2)), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China.

PubMed

Zhang, Liping; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian

2014-06-01

In this paper, by using a particle swarm optimization algorithm to solve the optimal parameter estimation problem, an improved Nash nonlinear grey Bernoulli model termed PSO-NNGBM(1,1) is proposed. To test the forecasting performance, the optimized model is applied for forecasting the incidence of hepatitis B in Xinjiang, China. Four models, traditional GM(1,1), grey Verhulst model (GVM), original nonlinear grey Bernoulli model (NGBM(1,1)) and Holt-Winters exponential smoothing method, are also established for comparison with the proposed model under the criteria of mean absolute percentage error and root mean square percent error. The prediction results show that the optimized NNGBM(1,1) model is more accurate and performs better than the traditional GM(1,1), GVM, NGBM(1,1) and Holt-Winters exponential smoothing method. Copyright © 2014. Published by Elsevier Ltd.

Gene expression programming approach for the estimation of moisture ratio in herbal plants drying with vacuum heat pump dryer

NASA Astrophysics Data System (ADS)

Dikmen, Erkan; Ayaz, Mahir; Gül, Doğan; Şahin, Arzu Şencan

2017-07-01

The determination of drying behavior of herbal plants is a complex process. In this study, gene expression programming (GEP) model was used to determine drying behavior of herbal plants as fresh sweet basil, parsley and dill leaves. Time and drying temperatures are input parameters for the estimation of moisture ratio of herbal plants. The results of the GEP model are compared with experimental drying data. The statistical values as mean absolute percentage error, root-mean-squared error and R-square are used to calculate the difference between values predicted by the GEP model and the values actually observed from the experimental study. It was found that the results of the GEP model and experimental study are in moderately well agreement. The results have shown that the GEP model can be considered as an efficient modelling technique for the prediction of moisture ratio of herbal plants.

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

Absolute and Relative Reliability of Percentage of Syllables Stuttered and Severity Rating Scales

ERIC Educational Resources Information Center

Karimi, Hamid; O'Brian, Sue; Onslow, Mark; Jones, Mark

2014-01-01

Purpose: Percentage of syllables stuttered (%SS) and severity rating (SR) scales are measures in common use to quantify stuttering severity and its changes during basic and clinical research conditions. However, their reliability has not been assessed with indices measuring both relative and absolute reliability. This study was designed to provide…

Validation of the ADAMO Care Watch for step counting in older adults.

PubMed

Magistro, Daniele; Brustio, Paolo Riccardo; Ivaldi, Marco; Esliger, Dale Winfield; Zecca, Massimiliano; Rainoldi, Alberto; Boccia, Gennaro

2018-01-01

Accurate measurement devices are required to objectively quantify physical activity. Wearable activity monitors, such as pedometers, may serve as affordable and feasible instruments for measuring physical activity levels in older adults during their normal activities of daily living. Currently few available accelerometer-based steps counting devices have been shown to be accurate at slow walking speeds, therefore there is still lacking appropriate devices tailored for slow speed ambulation, typical of older adults. This study aimed to assess the validity of step counting using the pedometer function of the ADAMO Care Watch, containing an embedded algorithm for measuring physical activity in older adults. Twenty older adults aged ≥ 65 years (mean ± SD, 75±7 years; range, 68-91) and 20 young adults (25±5 years, range 20-40), wore a care watch on each wrist and performed a number of randomly ordered tasks: walking at slow, normal and fast self-paced speeds; a Timed Up and Go test (TUG); a step test and ascending/descending stairs. The criterion measure was the actual number of steps observed, counted with a manual tally counter. Absolute percentage error scores, Intraclass Correlation Coefficients (ICC), and Bland-Altman plots were used to assess validity. ADAMO Care Watch demonstrated high validity during slow and normal speeds (range 0.5-1.5 m/s) showing an absolute error from 1.3% to 1.9% in the older adult group and from 0.7% to 2.7% in the young adult group. The percentage error for the 30-metre walking tasks increased with faster pace in both young adult (17%) and older adult groups (6%). In the TUG test, there was less error in the steps recorded for older adults (1.3% to 2.2%) than the young adults (6.6% to 7.2%). For the total sample, the ICCs for the ADAMO Care Watch for the 30-metre walking tasks at each speed and for the TUG test were ranged between 0.931 to 0.985. These findings provide evidence that the ADAMO Care Watch demonstrated highly accurate measurements of the steps count in all activities, particularly walking at normal and slow speeds. Therefore, these data support the inclusion of the ADAMO Care Watch in clinical applications for measuring the number of steps taken by older adults at normal, slow walking speeds.

Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system

NASA Astrophysics Data System (ADS)

Salehi, Mohammad Reza; Noori, Leila; Abiri, Ebrahim

2016-11-01

In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm2), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (-40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.

Reproducibility of forearm vasodilator response to intra-arterial infusion of calcitonin gene-related peptide assessed by venous occlusion plethysmography

PubMed Central

Vanmolkot, Floris H M; de Hoon, Jan N J M

2005-01-01

Aims To assess the reproducibility of the forearm blood flow (FBF) response to intra-arterial infusion of calcitonin-gene related peptide (CGRP), measured by venous occlusion plethysmography. In addition, to compare different ways of expressing the FBF response and perform sample size calculations. Methods On two separate visits, CGRP (10 ng min−1 dl−1 forearm) was infused for 45 min into the brachial artery of six healthy subjects. Reproducibility was assessed by calculating mean difference, repeatability coefficient, within-subject coefficient of variation (WCV) and intraclass correlation coefficient. Results CGRP increased FBF from 2.8 ± 0.4 and 3.2 ± 0.7 (at baseline) to 15.4 ± 1.4 and 15.2 ± 1.5 ml min−1 dl−1 forearm (at 45 min) on visits 1 and 2, respectively (P < 0.0001 for both visits). Mean difference in FBF at 45 min between both visits was 0.3 ml min−1 dl−1 forearm (repeatability coefficient: 4.1 ml min−1 dl−1 forearm). This FBF response appeared to be more reproducible when expressed as absolute FBF in the infused arm (WCV 11%) compared with absolute FBF-ratio between both arms (WCV 37%), percentage change from baseline in FBF in the infused arm (WCV 29%) and percentage change from baseline in FBF-ratio (WCV 40%). When expressed as absolute FBF, a sample size of five (95% confidence interval: 2–12) subjects gives 90% power at a type I error probability of 0.05 to detect a 25% shift in FBF response. Conclusions Intra-arterial infusion of CGRP results in a forearm vasodilator response which is reproducible between days. This response is most reproducible when expressed as absolute FBF. The presented methodology provides a suitable pharmacodynamic model to assess the in vivo activity of CGRP-receptor antagonists in a small number of subjects. PMID:15801933

Determination and error analysis of emittance and spectral emittance measurements by remote sensing

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Kumar, R.

1977-01-01

The author has identified the following significant results. From the theory of remote sensing of surface temperatures, an equation of the upper bound of absolute error of emittance was determined. It showed that the absolute error decreased with an increase in contact temperature, whereas, it increased with an increase in environmental integrated radiant flux density. Change in emittance had little effect on the absolute error. A plot of the difference between temperature and band radiance temperature vs. emittance was provided for the wavelength intervals: 4.5 to 5.5 microns, 8 to 13.5 microns, and 10.2 to 12.5 microns.

Motion-induced error reduction by combining Fourier transform profilometry with phase-shifting profilometry.

PubMed

Li, Beiwen; Liu, Ziping; Zhang, Song

2016-10-03

We propose a hybrid computational framework to reduce motion-induced measurement error by combining the Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP). The proposed method is composed of three major steps: Step 1 is to extract continuous relative phase maps for each isolated object with single-shot FTP method and spatial phase unwrapping; Step 2 is to obtain an absolute phase map of the entire scene using PSP method, albeit motion-induced errors exist on the extracted absolute phase map; and Step 3 is to shift the continuous relative phase maps from Step 1 to generate final absolute phase maps for each isolated object by referring to the absolute phase map with error from Step 2. Experiments demonstrate the success of the proposed computational framework for measuring multiple isolated rapidly moving objects.

The input ambiguity hypothesis and case blindness: an account of cross-linguistic and intra-linguistic differences in case errors.

PubMed

Pelham, Sabra D

2011-03-01

English-acquiring children frequently make pronoun case errors, while German-acquiring children rarely do. Nonetheless, German-acquiring children frequently make article case errors. It is proposed that when child-directed speech contains a high percentage of case-ambiguous forms, case errors are common in child language; when percentages are low, case errors are rare. Input to English and German children was analyzed for percentage of case-ambiguous personal pronouns on adult tiers of corpora from 24 English-acquiring and 24 German-acquiring children. Also analyzed for German was the percentage of case-ambiguous articles. Case-ambiguous pronouns averaged 63·3% in English, compared with 7·6% in German. The percentage of case-ambiguous articles in German was 77·0%. These percentages align with the children's errors reported in the literature. It appears children may be sensitive to levels of ambiguity such that low ambiguity may aid error-free acquisition, while high ambiguity may blind children to case distinctions, resulting in errors.

Performance Study of Earth Networks Total Lightning Network using Rocket-Triggered Lightning Data in 2014

NASA Astrophysics Data System (ADS)

Heckman, S.

2015-12-01

Modern lightning locating systems (LLS) provide real-time monitoring and early warning of lightningactivities. In addition, LLS provide valuable data for statistical analysis in lightning research. It isimportant to know the performance of such LLS. In the present study, the performance of the EarthNetworks Total Lightning Network (ENTLN) is studied using rocket-triggered lightning data acquired atthe International Center for Lightning Research and Testing (ICLRT), Camp Blanding, Florida.In the present study, 18 flashes triggered at ICLRT in 2014 were analyzed and they comprise of 78negative cloud-to-ground return strokes. The geometric mean, median, minimum, and maximum for thepeak currents of the 78 return strokes are 13.4 kA, 13.6 kA, 3.7 kA, and 38.4 kA, respectively. The peakcurrents represent typical subsequent return strokes in natural cloud-to-ground lightning.Earth Networks has developed a new data processor to improve the performance of their network. Inthis study, results are presented for the ENTLN data using the old processor (originally reported in 2014)and the ENTLN data simulated using the new processor. The flash detection efficiency, stroke detectionefficiency, percentage of misclassification, median location error, median peak current estimation error,and median absolute peak current estimation error for the originally reported data from old processorare 100%, 94%, 49%, 271 m, 5%, and 13%, respectively, and those for the simulated data using the newprocessor are 100%, 99%, 9%, 280 m, 11%, and 15%, respectively. The use of new processor resulted inhigher stroke detection efficiency and lower percentage of misclassification. It is worth noting that theslight differences in median location error, median peak current estimation error, and median absolutepeak current estimation error for the two processors are due to the fact that the new processordetected more number of return strokes than the old processor.

Students' Mathematical Work on Absolute Value: Focusing on Conceptions, Errors and Obstacles

ERIC Educational Resources Information Center

Elia, Iliada; Özel, Serkan; Gagatsis, Athanasios; Panaoura, Areti; Özel, Zeynep Ebrar Yetkiner

2016-01-01

This study investigates students' conceptions of absolute value (AV), their performance in various items on AV, their errors in these items and the relationships between students' conceptions and their performance and errors. The Mathematical Working Space (MWS) is used as a framework for studying students' mathematical work on AV and the…

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

Time series forecasting of future claims amount of SOCSO's employment injury scheme (EIS)

NASA Astrophysics Data System (ADS)

Zulkifli, Faiz; Ismail, Isma Liana; Chek, Mohd Zaki Awang; Jamal, Nur Faezah; Ridzwan, Ahmad Nur Azam Ahmad; Jelas, Imran Md; Noor, Syamsul Ikram Mohd; Ahmad, Abu Bakar

2012-09-01

The Employment Injury Scheme (EIS) provides protection to employees who are injured due to accidents whilst working, commuting from home to the work place or during employee takes a break during an authorized recess time or while travelling that is related with his work. The main purpose of this study is to forecast value on claims amount of EIS for the year 2011 until 2015 by using appropriate models. These models were tested on the actual EIS data from year 1972 until year 2010. Three different forecasting models are chosen for comparisons. These are the Naïve with Trend Model, Average Percent Change Model and Double Exponential Smoothing Model. The best model is selected based on the smallest value of error measures using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). From the result, the best model that best fit the forecast for the EIS is the Average Percent Change Model. Furthermore, the result also shows the claims amount of EIS for the year 2011 to year 2015 continue to trend upwards from year 2010.

Accuracy evaluation of Fourier series analysis and singular spectrum analysis for predicting the volume of motorcycle sales in Indonesia

NASA Astrophysics Data System (ADS)

Sasmita, Yoga; Darmawan, Gumgum

2017-08-01

This research aims to evaluate the performance of forecasting by Fourier Series Analysis (FSA) and Singular Spectrum Analysis (SSA) which are more explorative and not requiring parametric assumption. Those methods are applied to predicting the volume of motorcycle sales in Indonesia from January 2005 to December 2016 (monthly). Both models are suitable for seasonal and trend component data. Technically, FSA defines time domain as the result of trend and seasonal component in different frequencies which is difficult to identify in the time domain analysis. With the hidden period is 2,918 ≈ 3 and significant model order is 3, FSA model is used to predict testing data. Meanwhile, SSA has two main processes, decomposition and reconstruction. SSA decomposes the time series data into different components. The reconstruction process starts with grouping the decomposition result based on similarity period of each component in trajectory matrix. With the optimum of window length (L = 53) and grouping effect (r = 4), SSA predicting testing data. Forecasting accuracy evaluation is done based on Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The result shows that in the next 12 month, SSA has MAPE = 13.54 percent, MAE = 61,168.43 and RMSE = 75,244.92 and FSA has MAPE = 28.19 percent, MAE = 119,718.43 and RMSE = 142,511.17. Therefore, to predict volume of motorcycle sales in the next period should use SSA method which has better performance based on its accuracy.

Comparative Time Series Analysis of Aerosol Optical Depth over Sites in United States and China Using ARIMA Modeling

NASA Astrophysics Data System (ADS)

Li, X.; Zhang, C.; Li, W.

2017-12-01

Long-term spatiotemporal analysis and modeling of aerosol optical depth (AOD) distribution is of paramount importance to study radiative forcing, climate change, and human health. This study is focused on the trends and variations of AOD over six stations located in United States and China during 2003 to 2015, using satellite-retrieved Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 retrievals and ground measurements derived from Aerosol Robotic NETwork (AERONET). An autoregressive integrated moving average (ARIMA) model is applied to simulate and predict AOD values. The R2, adjusted R2, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Bayesian Information Criterion (BIC) are used as indices to select the best fitted model. Results show that there is a persistent decreasing trend in AOD for both MODIS data and AERONET data over three stations. Monthly and seasonal AOD variations reveal consistent aerosol patterns over stations along mid-latitudes. Regional differences impacted by climatology and land cover types are observed for the selected stations. Statistical validation of time series models indicates that the non-seasonal ARIMA model performs better for AERONET AOD data than for MODIS AOD data over most stations, suggesting the method works better for data with higher quality. By contrast, the seasonal ARIMA model reproduces the seasonal variations of MODIS AOD data much more precisely. Overall, the reasonably predicted results indicate the applicability and feasibility of the stochastic ARIMA modeling technique to forecast future and missing AOD values.

Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data

NASA Astrophysics Data System (ADS)

Wessel, Birgit; Huber, Martin; Wohlfart, Christian; Marschalk, Ursula; Kosmann, Detlev; Roth, Achim

2018-05-01

The primary goal of the German TanDEM-X mission is the generation of a highly accurate and global Digital Elevation Model (DEM) with global accuracies of at least 10 m absolute height error (linear 90% error). The global TanDEM-X DEM acquired with single-pass SAR interferometry was finished in September 2016. This paper provides a unique accuracy assessment of the final TanDEM-X global DEM using two different GPS point reference data sets, which are distributed across all continents, to fully characterize the absolute height error. Firstly, the absolute vertical accuracy is examined by about three million globally distributed kinematic GPS (KGPS) points derived from 19 KGPS tracks covering a total length of about 66,000 km. Secondly, a comparison is performed with more than 23,000 "GPS on Bench Marks" (GPS-on-BM) points provided by the US National Geodetic Survey (NGS) scattered across 14 different land cover types of the US National Land Cover Data base (NLCD). Both GPS comparisons prove an absolute vertical mean error of TanDEM-X DEM smaller than ±0.20 m, a Root Means Square Error (RMSE) smaller than 1.4 m and an excellent absolute 90% linear height error below 2 m. The RMSE values are sensitive to land cover types. For low vegetation the RMSE is ±1.1 m, whereas it is slightly higher for developed areas (±1.4 m) and for forests (±1.8 m). This validation confirms an outstanding absolute height error at 90% confidence level of the global TanDEM-X DEM outperforming the requirement by a factor of five. Due to its extensive and globally distributed reference data sets, this study is of considerable interests for scientific and commercial applications.

Resection plane-dependent error in computed tomography volumetry of the right hepatic lobe in living liver donors

PubMed Central

Kwon, Heon-Ju; Kim, Bohyun; Kim, So Yeon; Lee, Chul Seung; Lee, Jeongjin; Song, Gi Won; Lee, Sung Gyu

2018-01-01

Background/Aims Computed tomography (CT) hepatic volumetry is currently accepted as the most reliable method for preoperative estimation of graft weight in living donor liver transplantation (LDLT). However, several factors can cause inaccuracies in CT volumetry compared to real graft weight. The purpose of this study was to determine the frequency and degree of resection plane-dependent error in CT volumetry of the right hepatic lobe in LDLT. Methods Forty-six living liver donors underwent CT before donor surgery and on postoperative day 7. Prospective CT volumetry (VP) was measured via the assumptive hepatectomy plane. Retrospective liver volume (VR) was measured using the actual plane by comparing preoperative and postoperative CT. Compared with intraoperatively measured weight (W), errors in percentage (%) VP and VR were evaluated. Plane-dependent error in VP was defined as the absolute difference between VP and VR. % plane-dependent error was defined as follows: |VP–VR|/W∙100. Results Mean VP, VR, and W were 761.9 mL, 755.0 mL, and 696.9 g. Mean and % errors in VP were 73.3 mL and 10.7%. Mean error and % error in VR were 64.4 mL and 9.3%. Mean plane-dependent error in VP was 32.4 mL. Mean % plane-dependent error was 4.7%. Plane-dependent error in VP exceeded 10% of W in approximately 10% of the subjects in our study. Conclusions There was approximately 5% plane-dependent error in liver VP on CT volumetry. Plane-dependent error in VP exceeded 10% of W in approximately 10% of LDLT donors in our study. This error should be considered, especially when CT volumetry is performed by a less experienced operator who is not well acquainted with the donor hepatectomy plane. PMID:28759989

Does the Length of Elbow Flexors and Visual Feedback Have Effect on Accuracy of Isometric Muscle Contraction in Men after Stroke?

PubMed Central

Juodzbaliene, Vilma; Darbutas, Tomas; Skurvydas, Albertas

2016-01-01

The aim of the study was to determine the effect of different muscle length and visual feedback information (VFI) on accuracy of isometric contraction of elbow flexors in men after an ischemic stroke (IS). Materials and Methods. Maximum voluntary muscle contraction force (MVMCF) and accurate determinate muscle force (20% of MVMCF) developed during an isometric contraction of elbow flexors in 90° and 60° of elbow flexion were measured by an isokinetic dynamometer in healthy subjects (MH, n = 20) and subjects after an IS during their postrehabilitation period (MS, n = 20). Results. In order to evaluate the accuracy of the isometric contraction of the elbow flexors absolute errors were calculated. The absolute errors provided information about the difference between determinate and achieved muscle force. Conclusions. There is a tendency that greater absolute errors generating determinate force are made by MH and MS subjects in case of a greater elbow flexors length despite presence of VFI. Absolute errors also increase in both groups in case of a greater elbow flexors length without VFI. MS subjects make greater absolute errors generating determinate force without VFI in comparison with MH in shorter elbow flexors length. PMID:27042670

Effects of extended-release niacin with laropiprant in high-risk patients.

PubMed

Landray, Martin J; Haynes, Richard; Hopewell, Jemma C; Parish, Sarah; Aung, Theingi; Tomson, Joseph; Wallendszus, Karl; Craig, Martin; Jiang, Lixin; Collins, Rory; Armitage, Jane

2014-07-17

Patients with evidence of vascular disease are at increased risk for subsequent vascular events despite effective use of statins to lower the low-density lipoprotein (LDL) cholesterol level. Niacin lowers the LDL cholesterol level and raises the high-density lipoprotein (HDL) cholesterol level, but its clinical efficacy and safety are uncertain. After a prerandomization run-in phase to standardize the background statin-based LDL cholesterol-lowering therapy and to establish participants' ability to take extended-release niacin without clinically significant adverse effects, we randomly assigned 25,673 adults with vascular disease to receive 2 g of extended-release niacin and 40 mg of laropiprant or a matching placebo daily. The primary outcome was the first major vascular event (nonfatal myocardial infarction, death from coronary causes, stroke, or arterial revascularization). During a median follow-up period of 3.9 years, participants who were assigned to extended-release niacin-laropiprant had an LDL cholesterol level that was an average of 10 mg per deciliter (0.25 mmol per liter as measured in the central laboratory) lower and an HDL cholesterol level that was an average of 6 mg per deciliter (0.16 mmol per liter) higher than the levels in those assigned to placebo. Assignment to niacin-laropiprant, as compared with assignment to placebo, had no significant effect on the incidence of major vascular events (13.2% and 13.7% of participants with an event, respectively; rate ratio, 0.96; 95% confidence interval [CI], 0.90 to 1.03; P=0.29). Niacin-laropiprant was associated with an increased incidence of disturbances in diabetes control that were considered to be serious (absolute excess as compared with placebo, 3.7 percentage points; P<0.001) and with an increased incidence of diabetes diagnoses (absolute excess, 1.3 percentage points; P<0.001), as well as increases in serious adverse events associated with the gastrointestinal system (absolute excess, 1.0 percentage point; P<0.001), musculoskeletal system (absolute excess, 0.7 percentage points; P<0.001), skin (absolute excess, 0.3 percentage points; P=0.003), and unexpectedly, infection (absolute excess, 1.4 percentage points; P<0.001) and bleeding (absolute excess, 0.7 percentage points; P<0.001). Among participants with atherosclerotic vascular disease, the addition of extended-release niacin-laropiprant to statin-based LDL cholesterol-lowering therapy did not significantly reduce the risk of major vascular events but did increase the risk of serious adverse events. (Funded by Merck and others; HPS2-THRIVE ClinicalTrials.gov number, NCT00461630.).

Fluid dynamic design and experimental study of an aspirated temperature measurement platform used in climate observation.

PubMed

Yang, Jie; Liu, Qingquan; Dai, Wei; Ding, Renhui

2016-08-01

Due to the solar radiation effect, current air temperature sensors inside a thermometer screen or radiation shield may produce measurement errors that are 0.8 °C or higher. To improve the observation accuracy, an aspirated temperature measurement platform is designed. A computational fluid dynamics (CFD) method is implemented to analyze and calculate the radiation error of the aspirated temperature measurement platform under various environmental conditions. Then, a radiation error correction equation is obtained by fitting the CFD results using a genetic algorithm (GA) method. In order to verify the performance of the temperature sensor, the aspirated temperature measurement platform, temperature sensors with a naturally ventilated radiation shield, and a thermometer screen are characterized in the same environment to conduct the intercomparison. The average radiation errors of the sensors in the naturally ventilated radiation shield and the thermometer screen are 0.44 °C and 0.25 °C, respectively. In contrast, the radiation error of the aspirated temperature measurement platform is as low as 0.05 °C. This aspirated temperature sensor allows the radiation error to be reduced by approximately 88.6% compared to the naturally ventilated radiation shield, and allows the error to be reduced by a percentage of approximately 80% compared to the thermometer screen. The mean absolute error and root mean square error between the correction equation and experimental results are 0.032 °C and 0.036 °C, respectively, which demonstrates the accuracy of the CFD and GA methods proposed in this research.

Fluid dynamic design and experimental study of an aspirated temperature measurement platform used in climate observation

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

Yang, Jie, E-mail: yangjie396768@163.com; School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044; Liu, Qingquan

Due to the solar radiation effect, current air temperature sensors inside a thermometer screen or radiation shield may produce measurement errors that are 0.8 °C or higher. To improve the observation accuracy, an aspirated temperature measurement platform is designed. A computational fluid dynamics (CFD) method is implemented to analyze and calculate the radiation error of the aspirated temperature measurement platform under various environmental conditions. Then, a radiation error correction equation is obtained by fitting the CFD results using a genetic algorithm (GA) method. In order to verify the performance of the temperature sensor, the aspirated temperature measurement platform, temperature sensors withmore » a naturally ventilated radiation shield, and a thermometer screen are characterized in the same environment to conduct the intercomparison. The average radiation errors of the sensors in the naturally ventilated radiation shield and the thermometer screen are 0.44 °C and 0.25 °C, respectively. In contrast, the radiation error of the aspirated temperature measurement platform is as low as 0.05 °C. This aspirated temperature sensor allows the radiation error to be reduced by approximately 88.6% compared to the naturally ventilated radiation shield, and allows the error to be reduced by a percentage of approximately 80% compared to the thermometer screen. The mean absolute error and root mean square error between the correction equation and experimental results are 0.032 °C and 0.036 °C, respectively, which demonstrates the accuracy of the CFD and GA methods proposed in this research.« less

Reliability and validity of ten consumer activity trackers.

PubMed

Kooiman, Thea J M; Dontje, Manon L; Sprenger, Siska R; Krijnen, Wim P; van der Schans, Cees P; de Groot, Martijn

2015-01-01

Activity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions. Healthy adult volunteers (n = 33) walked twice on a treadmill (4.8 km/h) for 30 min while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots. Test-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (-0.2, -0.4), Fibit Flex (-5.7, 3.7), Jawbone Up (-1.0, 1.4), Nike+ Fuelband (-18, -24), Misfit Shine (0.2, 1.1), Withings Pulse (-0.5, -7.9), Fitbit Zip (-0.3, 1.2), Omron (2.5, -0.4), Digiwalker (-1.2, -5.9), and Moves app (9.6, -37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition. The reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.

Stochastic modelling of infectious diseases for heterogeneous populations.

PubMed

Ming, Rui-Xing; Liu, Ji-Ming; W Cheung, William K; Wan, Xiang

2016-12-22

Infectious diseases such as SARS and H1N1 can significantly impact people's lives and cause severe social and economic damages. Recent outbreaks have stressed the urgency of effective research on the dynamics of infectious disease spread. However, it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission, and some of them may be unknown. One feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical centres. The accumulated surveillance data, including temporal, spatial, clinical, and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease spread. The aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous populations. We test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong Kong. In the simulation experiment, our model achieves high accuracy in parameter estimation (less than 10.0 % mean absolute percentage error). In terms of the forward prediction of case incidence, the mean absolute percentage errors are 17.3 % for the simulation experiment and 20.0 % for the experiment on the real surveillance data. We propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance data. The proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction accuracy. We believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.

MO-FG-CAMPUS-IeP2-01: Characterization of Beam Shaping Filters and Photon Spectra From HVL Profiles in CT

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

Bujila, R; Royal Institute of Technology, Stockholm; Kull, L

Purpose: Advanced dosimetry in CT (e.g. the Monte Carlo method) requires an accurate characterization of the shaped filter and radiation quality used during a scan. The purpose of this work was to develop a method where half value layer (HVL) profiles along shaped filters could be made. From the HVL profiles the beam shaping properties and effective photon spectrum for a particular scan can be inferred. Methods: A measurement rig was developed to allow determinations of the HVL under a scatter-free narrow-beam geometry and constant focal spot to ionization chamber distance for different fan angles. For each fan angle themore » HVL is obtained by fitting the transmission of radiation through different thicknesses of an Al absorber (type 1100) using an appropriate model. The effective Al thickness of shaped filters and effective photon spectra are estimated using a model of photon emission from a Tungsten anode. This method is used to obtain the effective photon spectra and effective Al thickness of shaped filters for a CT scanner recently introduced to the market. Results: This study resulted in a set of effective photon spectra (central ray) for each kVp along with effective Al thicknesses of the different shaped filters. The effective photon spectra and effective Al thicknesses of shaped filters were used to obtain numerically approximated HVL profiles and compared to measured HVL profiles (mean absolute percentage error = 0.02). The central axis HVL found in the vendor’s technical documentation were compared to approximated HVL values (mean absolute percentage error = 0.03). Conclusion: This work has resulted in a unique method of measuring HVL profiles along shaped filters in CT. Further the effective photon spectra and the effective Al thicknesses of shaped filters that were obtained can be incorporated into Monte Carlo simulations.« less

Bayesian dynamic modeling of time series of dengue disease case counts

PubMed Central

López-Quílez, Antonio; Torres-Prieto, Alexander

2017-01-01

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health. PMID:28671941

Estimating Traffic Accidents in Turkey Using Differential Evolution Algorithm

NASA Astrophysics Data System (ADS)

Akgüngör, Ali Payıdar; Korkmaz, Ersin

2017-06-01

Estimating traffic accidents play a vital role to apply road safety procedures. This study proposes Differential Evolution Algorithm (DEA) models to estimate the number of accidents in Turkey. In the model development, population (P) and the number of vehicles (N) are selected as model parameters. Three model forms, linear, exponential and semi-quadratic models, are developed using DEA with the data covering from 2000 to 2014. Developed models are statistically compared to select the best fit model. The results of the DE models show that the linear model form is suitable to estimate the number of accidents. The statistics of this form is better than other forms in terms of performance criteria which are the Mean Absolute Percentage Errors (MAPE) and the Root Mean Square Errors (RMSE). To investigate the performance of linear DE model for future estimations, a ten-year period from 2015 to 2024 is considered. The results obtained from future estimations reveal the suitability of DE method for road safety applications.

Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

NASA Astrophysics Data System (ADS)

Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi

2012-07-01

The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.

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

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.

Prediction of stream volatilization coefficients

USGS Publications Warehouse

Rathbun, Ronald E.

1990-01-01

Equations are developed for predicting the liquid-film and gas-film reference-substance parameters for quantifying volatilization of organic solutes from streams. Molecular weight and molecular-diffusion coefficients of the solute are used as correlating parameters. Equations for predicting molecular-diffusion coefficients of organic solutes in water and air are developed, with molecular weight and molal volume as parameters. Mean absolute errors of prediction for diffusion coefficients in water are 9.97% for the molecular-weight equation, 6.45% for the molal-volume equation. The mean absolute error for the diffusion coefficient in air is 5.79% for the molal-volume equation. Molecular weight is not a satisfactory correlating parameter for diffusion in air because two equations are necessary to describe the values in the data set. The best predictive equation for the liquid-film reference-substance parameter has a mean absolute error of 5.74%, with molal volume as the correlating parameter. The best equation for the gas-film parameter has a mean absolute error of 7.80%, with molecular weight as the correlating parameter.

Fringe order correction for the absolute phase recovered by two selected spatial frequency fringe projections in fringe projection profilometry.

PubMed

Ding, Yi; Peng, Kai; Yu, Miao; Lu, Lei; Zhao, Kun

2017-08-01

The performance of the two selected spatial frequency phase unwrapping methods is limited by a phase error bound beyond which errors will occur in the fringe order leading to a significant error in the recovered absolute phase map. In this paper, we propose a method to detect and correct the wrong fringe orders. Two constraints are introduced during the fringe order determination of two selected spatial frequency phase unwrapping methods. A strategy to detect and correct the wrong fringe orders is also described. Compared with the existing methods, we do not need to estimate the threshold associated with absolute phase values to determine the fringe order error, thus making it more reliable and avoiding the procedure of search in detecting and correcting successive fringe order errors. The effectiveness of the proposed method is validated by the experimental results.

Time series model for forecasting the number of new admission inpatients.

PubMed

Zhou, Lingling; Zhao, Ping; Wu, Dongdong; Cheng, Cheng; Huang, Hao

2018-06-15

Hospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding. We used the single seasonal ARIMA (SARIMA), NARNN and the hybrid SARIMA-NARNN model to fit and forecast the monthly and daily number of new admission inpatients. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the forecasting performance among the three models. The modeling time range of monthly data included was from January 2010 to June 2016, July to October 2016 as the corresponding testing data set. The daily modeling data set was from January 4 to September 4, 2016, while the testing time range included was from September 5 to October 2, 2016. For the monthly data, the modeling RMSE and the testing RMSE, MAE and MAPE of SARIMA-NARNN model were less than those obtained from the single SARIMA or NARNN model, but the MAE and MAPE of modeling performance of SARIMA-NARNN model did not improve. For the daily data, all RMSE, MAE and MAPE of NARNN model were the lowest both in modeling stage and testing stage. Hybrid model does not necessarily outperform its constituents' performances. It is worth attempting to explore the reliable model to forecast the number of new admission inpatients from different data.

A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

NASA Astrophysics Data System (ADS)

Shamshirband, Shahaboddin; Mohammadi, Kasra; Tong, Chong Wen; Zamani, Mazdak; Motamedi, Shervin; Ch, Sudheer

2016-07-01

In this study, a hybrid support vector machine-firefly optimization algorithm (SVM-FFA) model is proposed to estimate monthly mean horizontal global solar radiation (HGSR). The merit of SVM-FFA is assessed statistically by comparing its performance with three previously used approaches. Using each approach and long-term measured HGSR, three models are calibrated by considering different sets of meteorological parameters measured for Bandar Abbass situated in Iran. It is found that the model (3) utilizing the combination of relative sunshine duration, difference between maximum and minimum temperatures, relative humidity, water vapor pressure, average temperature, and extraterrestrial solar radiation shows superior performance based upon all approaches. Moreover, the extraterrestrial radiation is introduced as a significant parameter to accurately estimate the global solar radiation. The survey results reveal that the developed SVM-FFA approach is greatly capable to provide favorable predictions with significantly higher precision than other examined techniques. For the SVM-FFA (3), the statistical indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination ( R 2) are 3.3252 %, 0.1859 kWh/m2, 3.7350 %, and 0.9737, respectively which according to the RRMSE has an excellent performance. As a more evaluation of SVM-FFA (3), the ratio of estimated to measured values is computed and found that 47 out of 48 months considered as testing data fall between 0.90 and 1.10. Also, by performing a further verification, it is concluded that SVM-FFA (3) offers absolute superiority over the empirical models using relatively similar input parameters. In a nutshell, the hybrid SVM-FFA approach would be considered highly efficient to estimate the HGSR.

On-line vs off-line electrical conductivity characterization. Polycarbonate composites developed with multiwalled carbon nanotubes by compounding technology

NASA Astrophysics Data System (ADS)

Llorens-Chiralt, R.; Weiss, P.; Mikonsaari, I.

2014-05-01

Material characterization is one of the key steps when conductive polymers are developed. The dispersion of carbon nanotubes (CNTs) in a polymeric matrix using melt mixing influence final composite properties. The compounding becomes trial and error using a huge amount of materials, spending time and money to obtain competitive composites. Traditional methods to carry out electrical conductivity characterization include compression and injection molding. Both methods need extra equipments and moulds to obtain standard bars. This study aims to investigate the accuracy of the data obtained from absolute resistance recorded during the melt compounding, using an on-line setup developed by our group, and to correlate these values with off-line characterization and processing parameters (screw/barrel configuration, throughput, screw speed, temperature profile and CNTs percentage). Compounds developed with different percentages of multi walled carbon nanotubes (MWCNTs) and polycarbonate has been characterized during and after extrusion. Measurements, on-line resistance and off-line resistivity, showed parallel response and reproducibility, confirming method validity. The significance of the results obtained stems from the fact that we are able to measure on-line resistance and to change compounding parameters during production to achieve reference values reducing production/testing cost and ensuring material quality. Also, this method removes errors which can be found in test bars development, showing better correlation with compounding parameters.

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input variable of different ANN structures [3]. The performance of each ANN structure is evaluated by the voting analysis based on eleven criteria, which are the root mean square error (RMSE), the correlation index (R), the mean absolute percentage error (MAPE), the mean percentage error (MPE), the mean percentage error (ME), the percentage volume in errors (VE), the percentage error in peak (MF), the normalized mean bias error (NMBE), the normalized root mean bias error (NRMSE), the Nash-Sutcliffe model efficiency coefficient (E) and the modified Nash-Sutcliffe model efficiency coefficient (E1). The next day flow for the test set is calculated using the best ANN structure's model. Consequently, the confidence intervals of various confidence levels for training, evaluation and test sets are compared in order to explore the generalisation dynamics of confidence intervals from training and evaluation sets. [1] H.S. Hippert, C.E. Pedreira, R.C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans. on Power Systems, vol. 16, no. 1, 2001, pp. 44-55. [2] G. J. Tsekouras, N.E. Mastorakis, F.D. Kanellos, V.T. Kontargyri, C.D. Tsirekis, I.S. Karanasiou, Ch.N. Elias, A.D. Salis, P.A. Kontaxis, A.A. Gialketsi: "Short term load forecasting in Greek interconnected power system using ANN: Confidence Interval using a novel re-sampling technique with corrective Factor", WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, (CSECS '10), Vouliagmeni, Athens, Greece, December 29-31, 2010. [3] D. Panagoulia, I. Trichakis, G. J. Tsekouras: "Flow Forecasting via Artificial Neural Networks - A Study for Input Variables conditioned on atmospheric circulation", European Geosciences Union, General Assembly 2012 (NH1.1 / AS1.16 - Extreme meteorological and hydrological events induced by severe weather and climate change), Vienna, Austria, 22-27 April 2012.

Generalized approach for using unbiased symmetric metrics with negative values: normalized mean bias factor and normalized mean absolute error factor

EPA Science Inventory

Unbiased symmetric metrics provide a useful measure to quickly compare two datasets, with similar interpretations for both under and overestimations. Two examples include the normalized mean bias factor and normalized mean absolute error factor. However, the original formulations...

Measurement effects of seasonal and monthly variability on pedometer-determined data.

PubMed

Kang, Minsoo; Bassett, David R; Barreira, Tiago V; Tudor-Locke, Catrine; Ainsworth, Barbara E

2012-03-01

The seasonal and monthly variability of pedometer-determined physical activity and its effects on accurate measurement have not been examined. The purpose of the study was to reduce measurement error in step-count data by controlling a) the length of the measurement period and b) the season or month of the year in which sampling was conducted. Twenty-three middle-aged adults were instructed to wear a Yamax SW-200 pedometer over 365 consecutive days. The step-count measurement periods of various lengths (eg, 2, 3, 4, 5, 6, 7 days, etc.) were randomly selected 10 times for each season and month. To determine accurate estimates of yearly step-count measurement, mean absolute percentage error (MAPE) and bias were calculated. The year-round average was considered as a criterion measure. A smaller MAPE and bias represent a better estimate. Differences in MAPE and bias among seasons were trivial; however, they varied among different months. The months in which seasonal changes occur presented the highest MAPE and bias. Targeting the data collection during certain months (eg, May) may reduce pedometer measurement error and provide more accurate estimates of year-round averages.

Cirrus cloud retrieval from MSG/SEVIRI during day and night using artificial neural networks

NASA Astrophysics Data System (ADS)

Strandgren, Johan; Bugliaro, Luca

2017-04-01

By covering a large part of the Earth, cirrus clouds play an important role in climate as they reflect incoming solar radiation and absorb outgoing thermal radiation. Nevertheless, the cirrus clouds remain one of the largest uncertainties in atmospheric research and the understanding of the physical processes that govern their life cycle is still poorly understood, as is their representation in climate models. To monitor and better understand the properties and physical processes of cirrus clouds, it's essential that those tenuous clouds can be observed from geostationary spaceborne imagers like SEVIRI (Spinning Enhanced Visible and InfraRed Imager), that possess a high temporal resolution together with a large field of view and play an important role besides in-situ observations for the investigation of cirrus cloud processes. CiPS (Cirrus Properties from Seviri) is a new algorithm targeting thin cirrus clouds. CiPS is an artificial neural network trained with coincident SEVIRI and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations in order to retrieve a cirrus cloud mask along with the cloud top height (CTH), ice optical thickness (IOT) and ice water path (IWP) from SEVIRI. By utilizing only the thermal/IR channels of SEVIRI, CiPS can be used during day and night making it a powerful tool for the cirrus life cycle analysis. Despite the great challenge of detecting thin cirrus clouds and retrieving their properties from a geostationary imager using only the thermal/IR wavelengths, CiPS performs well. Among the cirrus clouds detected by CALIOP, CiPS detects 70 and 95 % of the clouds with an optical thickness of 0.1 and 1.0 respectively. Among the cirrus free pixels, CiPS classify 96 % correctly. For the CTH retrieval, CiPS has a mean absolute percentage error of 10 % or less with respect to CALIOP for cirrus clouds with a CTH greater than 8 km. For the IOT retrieval, CiPS has a mean absolute percentage error of 100 % or less with respect to CALIOP for cirrus clouds with an optical thickness down to 0.07. For such thin cirrus clouds an error of 100 % should be regarded as low from a geostationary imager like SEVIRI. The IWP retrieved by CiPS shows a similar performance, but has larger deviations for the thinner cirrus clouds.

Spot measurement of heart rate based on morphology of PhotoPlethysmoGraphic (PPG) signals.

PubMed

Madhan Mohan, P; Nagarajan, V; Vignesh, J C

2017-02-01

Due to increasing health consciousness among people, it is imperative to have low-cost health care devices to measure the vital parameters like heart rate and arterial oxygen saturation (SpO 2 ). In this paper, an efficient heart rate monitoring algorithm based on the morphology of photoplethysmography (PPG) signals to measure the spot heart rate (HR) and its real-time implementation is proposed. The algorithm does pre-processing and detects the onsets and systolic peaks of the PPG signal to estimate the heart rate of the subject. Since the algorithm is based on the morphology of the signal, it works well when the subject is not moving, which is a typical test case. So, this algorithm is developed mainly to measure the heart rate at on-demand applications. Real-time experimental results indicate the heart rate accuracy of 99.5%, mean absolute percentage error (MAPE) of 1.65%, mean absolute error (MAE) of 1.18 BPM and reference closeness factor (RCF) of 0.988. The results further show that the average response time of the algorithm to give the spot HR is 6.85 s, so that the users need not wait longer to see their HR. The hardware implementation results show that the algorithm only requires 18 KBytes of total memory and runs at high speed with 0.85 MIPS. So, this algorithm can be targeted to low-cost embedded platforms.

Predicting clicks of PubMed articles.

PubMed

Mao, Yuqing; Lu, Zhiyong

2013-01-01

Predicting the popularity or access usage of an article has the potential to improve the quality of PubMed searches. We can model the click trend of each article as its access changes over time by mining the PubMed query logs, which contain the previous access history for all articles. In this article, we examine the access patterns produced by PubMed users in two years (July 2009 to July 2011). We explore the time series of accesses for each article in the query logs, model the trends with regression approaches, and subsequently use the models for prediction. We show that the click trends of PubMed articles are best fitted with a log-normal regression model. This model allows the number of accesses an article receives and the time since it first becomes available in PubMed to be related via quadratic and logistic functions, with the model parameters to be estimated via maximum likelihood. Our experiments predicting the number of accesses for an article based on its past usage demonstrate that the mean absolute error and mean absolute percentage error of our model are 4.0% and 8.1% lower than the power-law regression model, respectively. The log-normal distribution is also shown to perform significantly better than a previous prediction method based on a human memory theory in cognitive science. This work warrants further investigation on the utility of such a log-normal regression approach towards improving information access in PubMed.

Predicting clicks of PubMed articles

PubMed Central

Mao, Yuqing; Lu, Zhiyong

2013-01-01

Predicting the popularity or access usage of an article has the potential to improve the quality of PubMed searches. We can model the click trend of each article as its access changes over time by mining the PubMed query logs, which contain the previous access history for all articles. In this article, we examine the access patterns produced by PubMed users in two years (July 2009 to July 2011). We explore the time series of accesses for each article in the query logs, model the trends with regression approaches, and subsequently use the models for prediction. We show that the click trends of PubMed articles are best fitted with a log-normal regression model. This model allows the number of accesses an article receives and the time since it first becomes available in PubMed to be related via quadratic and logistic functions, with the model parameters to be estimated via maximum likelihood. Our experiments predicting the number of accesses for an article based on its past usage demonstrate that the mean absolute error and mean absolute percentage error of our model are 4.0% and 8.1% lower than the power-law regression model, respectively. The log-normal distribution is also shown to perform significantly better than a previous prediction method based on a human memory theory in cognitive science. This work warrants further investigation on the utility of such a log-normal regression approach towards improving information access in PubMed. PMID:24551386

IMPROVEMENT OF SMVGEAR II ON VECTOR AND SCALAR MACHINES THROUGH ABSOLUTE ERROR TOLERANCE CONTROL (R823186)

EPA Science Inventory

The computer speed of SMVGEAR II was improved markedly on scalar and vector machines with relatively little loss in accuracy. The improvement was due to a method of frequently recalculating the absolute error tolerance instead of keeping it constant for a given set of chemistry. ...

3D measurement using combined Gray code and dual-frequency phase-shifting approach

NASA Astrophysics Data System (ADS)

Yu, Shuang; Zhang, Jing; Yu, Xiaoyang; Sun, Xiaoming; Wu, Haibin; Liu, Xin

2018-04-01

The combined Gray code and phase-shifting approach is a commonly used 3D measurement technique. In this technique, an error that equals integer multiples of the phase-shifted fringe period, i.e. period jump error, often exists in the absolute analog code, which can lead to gross measurement errors. To overcome this problem, the present paper proposes 3D measurement using a combined Gray code and dual-frequency phase-shifting approach. Based on 3D measurement using the combined Gray code and phase-shifting approach, one set of low-frequency phase-shifted fringe patterns with an odd-numbered multiple of the original phase-shifted fringe period is added. Thus, the absolute analog code measured value can be obtained by the combined Gray code and phase-shifting approach, and the low-frequency absolute analog code measured value can also be obtained by adding low-frequency phase-shifted fringe patterns. Then, the corrected absolute analog code measured value can be obtained by correcting the former by the latter, and the period jump errors can be eliminated, resulting in reliable analog code unwrapping. For the proposed approach, we established its measurement model, analyzed its measurement principle, expounded the mechanism of eliminating period jump errors by error analysis, and determined its applicable conditions. Theoretical analysis and experimental results show that the proposed approach can effectively eliminate period jump errors, reliably perform analog code unwrapping, and improve the measurement accuracy.

Error Budget for a Calibration Demonstration System for the Reflected Solar Instrument for the Climate Absolute Radiance and Refractivity Observatory

NASA Technical Reports Server (NTRS)

Thome, Kurtis; McCorkel, Joel; McAndrew, Brendan

2013-01-01

A goal of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is to observe highaccuracy, long-term climate change trends over decadal time scales. The key to such a goal is to improving the accuracy of SI traceable absolute calibration across infrared and reflected solar wavelengths allowing climate change to be separated from the limit of natural variability. The advances required to reach on-orbit absolute accuracy to allow climate change observations to survive data gaps exist at NIST in the laboratory, but still need demonstration that the advances can move successfully from to NASA and/or instrument vendor capabilities for spaceborne instruments. The current work describes the radiometric calibration error budget for the Solar, Lunar for Absolute Reflectance Imaging Spectroradiometer (SOLARIS) which is the calibration demonstration system (CDS) for the reflected solar portion of CLARREO. The goal of the CDS is to allow the testing and evaluation of calibration approaches, alternate design and/or implementation approaches and components for the CLARREO mission. SOLARIS also provides a test-bed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. The resulting SI-traceable error budget for reflectance retrieval using solar irradiance as a reference and methods for laboratory-based, absolute calibration suitable for climatequality data collections is given. Key components in the error budget are geometry differences between the solar and earth views, knowledge of attenuator behavior when viewing the sun, and sensor behavior such as detector linearity and noise behavior. Methods for demonstrating this error budget are also presented.

Proprioceptive deficit in individuals with unilateral tearing of the anterior cruciate ligament after active evaluation of the sense of joint position.

PubMed

Cossich, Victor; Mallrich, Frédéric; Titonelli, Victor; de Sousa, Eduardo Branco; Velasques, Bruna; Salles, José Inácio

2014-01-01

To ascertain whether the proprioceptive deficit in the sense of joint position continues to be present when patients with a limb presenting a deficient anterior cruciate ligament (ACL) are assessed by testing their active reproduction of joint position, in comparison with the contralateral limb. Twenty patients with unilateral ACL tearing participated in the study. Their active reproduction of joint position in the limb with the deficient ACL and in the healthy contralateral limb was tested. Meta-positions of 20% and 50% of the maximum joint range of motion were used. Proprioceptive performance was determined through the values of the absolute error, variable error and constant error. Significant differences in absolute error were found at both of the positions evaluated, and in constant error at 50% of the maximum joint range of motion. When evaluated in terms of absolute error, the proprioceptive deficit continues to be present even when an active evaluation of the sense of joint position is made. Consequently, this sense involves activity of both intramuscular and tendon receptors.

Resection plane-dependent error in computed tomography volumetry of the right hepatic lobe in living liver donors.

PubMed

Kwon, Heon-Ju; Kim, Kyoung Won; Kim, Bohyun; Kim, So Yeon; Lee, Chul Seung; Lee, Jeongjin; Song, Gi Won; Lee, Sung Gyu

2018-03-01

Computed tomography (CT) hepatic volumetry is currently accepted as the most reliable method for preoperative estimation of graft weight in living donor liver transplantation (LDLT). However, several factors can cause inaccuracies in CT volumetry compared to real graft weight. The purpose of this study was to determine the frequency and degree of resection plane-dependent error in CT volumetry of the right hepatic lobe in LDLT. Forty-six living liver donors underwent CT before donor surgery and on postoperative day 7. Prospective CT volumetry (V P ) was measured via the assumptive hepatectomy plane. Retrospective liver volume (V R ) was measured using the actual plane by comparing preoperative and postoperative CT. Compared with intraoperatively measured weight (W), errors in percentage (%) V P and V R were evaluated. Plane-dependent error in V P was defined as the absolute difference between V P and V R . % plane-dependent error was defined as follows: |V P -V R |/W∙100. Mean V P , V R , and W were 761.9 mL, 755.0 mL, and 696.9 g. Mean and % errors in V P were 73.3 mL and 10.7%. Mean error and % error in V R were 64.4 mL and 9.3%. Mean plane-dependent error in V P was 32.4 mL. Mean % plane-dependent error was 4.7%. Plane-dependent error in V P exceeded 10% of W in approximately 10% of the subjects in our study. There was approximately 5% plane-dependent error in liver V P on CT volumetry. Plane-dependent error in V P exceeded 10% of W in approximately 10% of LDLT donors in our study. This error should be considered, especially when CT volumetry is performed by a less experienced operator who is not well acquainted with the donor hepatectomy plane.

Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors

PubMed Central

Storm, Fabio A.; Heller, Ben W.; Mazzà, Claudia

2015-01-01

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications. PMID:25789630

Vestibular schwannomas: Accuracy of tumor volume estimated by ice cream cone formula using thin-sliced MR images.

PubMed

Ho, Hsing-Hao; Li, Ya-Hui; Lee, Jih-Chin; Wang, Chih-Wei; Yu, Yi-Lin; Hueng, Dueng-Yuan; Ma, Hsin-I; Hsu, Hsian-He; Juan, Chun-Jung

2018-01-01

We estimated the volume of vestibular schwannomas by an ice cream cone formula using thin-sliced magnetic resonance images (MRI) and compared the estimation accuracy among different estimating formulas and between different models. The study was approved by a local institutional review board. A total of 100 patients with vestibular schwannomas examined by MRI between January 2011 and November 2015 were enrolled retrospectively. Informed consent was waived. Volumes of vestibular schwannomas were estimated by cuboidal, ellipsoidal, and spherical formulas based on a one-component model, and cuboidal, ellipsoidal, Linskey's, and ice cream cone formulas based on a two-component model. The estimated volumes were compared to the volumes measured by planimetry. Intraobserver reproducibility and interobserver agreement was tested. Estimation error, including absolute percentage error (APE) and percentage error (PE), was calculated. Statistical analysis included intraclass correlation coefficient (ICC), linear regression analysis, one-way analysis of variance, and paired t-tests with P < 0.05 considered statistically significant. Overall tumor size was 4.80 ± 6.8 mL (mean ±standard deviation). All ICCs were no less than 0.992, suggestive of high intraobserver reproducibility and high interobserver agreement. Cuboidal formulas significantly overestimated the tumor volume by a factor of 1.9 to 2.4 (P ≤ 0.001). The one-component ellipsoidal and spherical formulas overestimated the tumor volume with an APE of 20.3% and 29.2%, respectively. The two-component ice cream cone method, and ellipsoidal and Linskey's formulas significantly reduced the APE to 11.0%, 10.1%, and 12.5%, respectively (all P < 0.001). The ice cream cone method and other two-component formulas including the ellipsoidal and Linskey's formulas allow for estimation of vestibular schwannoma volume more accurately than all one-component formulas.

Step detection and activity recognition accuracy of seven physical activity monitors.

PubMed

Storm, Fabio A; Heller, Ben W; Mazzà, Claudia

2015-01-01

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

Mammographic Density Phenotypes and Risk of Breast Cancer: A Meta-analysis

PubMed Central

Graff, Rebecca E.; Ursin, Giske; dos Santos Silva, Isabel; McCormack, Valerie; Baglietto, Laura; Vachon, Celine; Bakker, Marije F.; Giles, Graham G.; Chia, Kee Seng; Czene, Kamila; Eriksson, Louise; Hall, Per; Hartman, Mikael; Warren, Ruth M. L.; Hislop, Greg; Chiarelli, Anna M.; Hopper, John L.; Krishnan, Kavitha; Li, Jingmei; Li, Qing; Pagano, Ian; Rosner, Bernard A.; Wong, Chia Siong; Scott, Christopher; Stone, Jennifer; Maskarinec, Gertraud; Boyd, Norman F.; van Gils, Carla H.

2014-01-01

Background Fibroglandular breast tissue appears dense on mammogram, whereas fat appears nondense. It is unclear whether absolute or percentage dense area more strongly predicts breast cancer risk and whether absolute nondense area is independently associated with risk. Methods We conducted a meta-analysis of 13 case–control studies providing results from logistic regressions for associations between one standard deviation (SD) increments in mammographic density phenotypes and breast cancer risk. We used random-effects models to calculate pooled odds ratios and 95% confidence intervals (CIs). All tests were two-sided with P less than .05 considered to be statistically significant. Results Among premenopausal women (n = 1776 case patients; n = 2834 control subjects), summary odds ratios were 1.37 (95% CI = 1.29 to 1.47) for absolute dense area, 0.78 (95% CI = 0.71 to 0.86) for absolute nondense area, and 1.52 (95% CI = 1.39 to 1.66) for percentage dense area when pooling estimates adjusted for age, body mass index, and parity. Corresponding odds ratios among postmenopausal women (n = 6643 case patients; n = 11187 control subjects) were 1.38 (95% CI = 1.31 to 1.44), 0.79 (95% CI = 0.73 to 0.85), and 1.53 (95% CI = 1.44 to 1.64). After additional adjustment for absolute dense area, associations between absolute nondense area and breast cancer became attenuated or null in several studies and summary odds ratios became 0.82 (95% CI = 0.71 to 0.94; P heterogeneity = .02) for premenopausal and 0.85 (95% CI = 0.75 to 0.96; P heterogeneity < .01) for postmenopausal women. Conclusions The results suggest that percentage dense area is a stronger breast cancer risk factor than absolute dense area. Absolute nondense area was inversely associated with breast cancer risk, but it is unclear whether the association is independent of absolute dense area. PMID:24816206

Empirical Approach in Developing Vs/Vp Ratio for Predicting S-Wave Velocity, Study Case; Sungai Batu, Kedah

NASA Astrophysics Data System (ADS)

Sabrian, T. A.; Saad, R.; Saidin, M.; Muhammad, S. B.; Yusoh, R.

2018-04-01

In recognition of the difficulties in acquiring seismic refraction shear wave data and the ambiguities involved in its processing, Vs/Vp ratio for sedimentary areas of Sungai Batu have been developed and assessed in this study. Two seismic refraction survey line L1 and L2 were conducted using P- and S-wave were acquired and processed along the same line regarding study area. The resulting velocities were extracted from seismic tomography profile to compute specific ratios after linearity and correlation checks. It is found that Vs is linearly related to Vp, with coefficients of determination (R2) of about 0.74 and 0.52 for L1 and L2 respectively. The specific ratios were computed as 0.3 and 0.4 for L1 and L2 respectively Another data sets acquired along different lines were used to validate the ratios. The mean absolute percentage errors were calculated for both modelling and validation data sets and found that the different percentage between Vs measured and Vs calculated is 20.7% and 22% respectively.

Absolute calibration of optical flats

DOEpatents

Sommargren, Gary E.

2005-04-05

The invention uses the phase shifting diffraction interferometer (PSDI) to provide a true point-by-point measurement of absolute flatness over the surface of optical flats. Beams exiting the fiber optics in a PSDI have perfect spherical wavefronts. The measurement beam is reflected from the optical flat and passed through an auxiliary optic to then be combined with the reference beam on a CCD. The combined beams include phase errors due to both the optic under test and the auxiliary optic. Standard phase extraction algorithms are used to calculate this combined phase error. The optical flat is then removed from the system and the measurement fiber is moved to recombine the two beams. The newly combined beams include only the phase errors due to the auxiliary optic. When the second phase measurement is subtracted from the first phase measurement, the absolute phase error of the optical flat is obtained.

Absolute Reliability and Concurrent Validity of Hand Held Dynamometry and Isokinetic Dynamometry in the Hip, Knee and Ankle Joint: Systematic Review and Meta-analysis

PubMed Central

Chamorro, Claudio; Armijo-Olivo, Susan; De la Fuente, Carlos; Fuentes, Javiera; Javier Chirosa, Luis

2017-01-01

Abstract The purpose of the study is to establish absolute reliability and concurrent validity between hand-held dynamometers (HHDs) and isokinetic dynamometers (IDs) in lower extremity peak torque assessment. Medline, Embase, CINAHL databases were searched for studies related to psychometric properties in muscle dynamometry. Studies considering standard error of measurement SEM (%) or limit of agreement LOA (%) expressed as percentage of the mean, were considered to establish absolute reliability while studies using intra-class correlation coefficient (ICC) were considered to establish concurrent validity between dynamometers. In total, 17 studies were included in the meta-analysis. The COSMIN checklist classified them between fair and poor. Using HHDs, knee extension LOA (%) was 33.59%, 95% confidence interval (CI) 23.91 to 43.26 and ankle plantar flexion LOA (%) was 48.87%, CI 35.19 to 62.56. Using IDs, hip adduction and extension; knee flexion and extension; and ankle dorsiflexion showed LOA (%) under 15%. Lower hip, knee, and ankle LOA (%) were obtained using an ID compared to HHD. ICC between devices ranged between 0.62, CI (0.37 to 0.87) for ankle dorsiflexion to 0.94, IC (0.91to 0.98) for hip adduction. Very high correlation were found for hip adductors and hip flexors and moderate correlations for knee flexors/extensors and ankle plantar/dorsiflexors. PMID:29071305

Relative and Absolute Error Control in a Finite-Difference Method Solution of Poisson's Equation

ERIC Educational Resources Information Center

Prentice, J. S. C.

2012-01-01

An algorithm for error control (absolute and relative) in the five-point finite-difference method applied to Poisson's equation is described. The algorithm is based on discretization of the domain of the problem by means of three rectilinear grids, each of different resolution. We discuss some hardware limitations associated with the algorithm,…

Assessing Suturing Skills in a Self-Guided Learning Setting: Absolute Symmetry Error

ERIC Educational Resources Information Center

Brydges, Ryan; Carnahan, Heather; Dubrowski, Adam

2009-01-01

Directed self-guidance, whereby trainees independently practice a skill-set in a structured setting, may be an effective technique for novice training. Currently, however, most evaluation methods require an expert to be present during practice. The study aim was to determine if absolute symmetry error, a clinically important measure that can be…

Adaptive aperture for Geiger mode avalanche photodiode flash ladar systems.

PubMed

Wang, Liang; Han, Shaokun; Xia, Wenze; Lei, Jieyu

2018-02-01

Although the Geiger-mode avalanche photodiode (GM-APD) flash ladar system offers the advantages of high sensitivity and simple construction, its detection performance is influenced not only by the incoming signal-to-noise ratio but also by the absolute number of noise photons. In this paper, we deduce a hyperbolic approximation to estimate the noise-photon number from the false-firing percentage in a GM-APD flash ladar system under dark conditions. By using this hyperbolic approximation function, we introduce a method to adapt the aperture to reduce the number of incoming background-noise photons. Finally, the simulation results show that the adaptive-aperture method decreases the false probability in all cases, increases the detection probability provided that the signal exceeds the noise, and decreases the average ranging error per frame.

Adaptive aperture for Geiger mode avalanche photodiode flash ladar systems

NASA Astrophysics Data System (ADS)

Wang, Liang; Han, Shaokun; Xia, Wenze; Lei, Jieyu

2018-02-01

Although the Geiger-mode avalanche photodiode (GM-APD) flash ladar system offers the advantages of high sensitivity and simple construction, its detection performance is influenced not only by the incoming signal-to-noise ratio but also by the absolute number of noise photons. In this paper, we deduce a hyperbolic approximation to estimate the noise-photon number from the false-firing percentage in a GM-APD flash ladar system under dark conditions. By using this hyperbolic approximation function, we introduce a method to adapt the aperture to reduce the number of incoming background-noise photons. Finally, the simulation results show that the adaptive-aperture method decreases the false probability in all cases, increases the detection probability provided that the signal exceeds the noise, and decreases the average ranging error per frame.

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

PubMed

Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed

2017-11-01

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

Local plate/rod descriptors of 3D trabecular bone micro-CT images from medial axis topologic analysis

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

Peyrin, Francoise; Attali, Dominique; Chappard, Christine

Purpose: Trabecular bone microarchitecture is made of a complex network of plate and rod structures evolving with age and disease. The purpose of this article is to propose a new 3D local analysis method for the quantitative assessment of parameters related to the geometry of trabecular bone microarchitecture. Methods: The method is based on the topologic classification of the medial axis of the 3D image into branches, rods, and plates. Thanks to the reversibility of the medial axis, the classification is next extended to the whole 3D image. Finally, the percentages of rods and plates as well as their meanmore » thicknesses are calculated. The method was applied both to simulated test images and 3D micro-CT images of human trabecular bone. Results: The classification of simulated phantoms made of plates and rods shows that the maximum error in the quantitative percentages of plate and rods is less than 6% and smaller than with the structure model index (SMI). Micro-CT images of human femoral bone taken in osteoporosis and early or advanced osteoarthritis were analyzed. Despite the large physiological variability, the present method avoids the underestimation of rods observed with other local methods. The relative percentages of rods and plates were not significantly different between osteoarthritis and osteoporotic groups, whereas their absolute percentages were in relation to an increase of rod and plate thicknesses in advanced osteoarthritis with also higher relative and absolute number of nodes. Conclusions: The proposed method is model-independent, robust to surface irregularities, and enables geometrical characterization of not only skeletal structures but entire 3D images. Its application provided more accurate results than the standard SMI on simple simulated phantoms, but the discrepancy observed on the advanced osteoarthritis group raises questions that will require further investigations. The systematic use of such a local method in the characterization of trabecular bone samples could provide new insight in bone microarchitecture changes related to bone diseases or to those induced by drugs or therapy.« less

Use of a Tracing Task to Assess Visuomotor Performance: Effects of Age, Sex, and Handedness

PubMed Central

2013-01-01

Background. Visuomotor abnormalities are common in aging and age-related disease, yet difficult to quantify. This study investigated the effects of healthy aging, sex, and handedness on the performance of a tracing task. Participants (n = 150, aged 21–95 years, 75 females) used a stylus to follow a moving target around a circle on a tablet computer with their dominant and nondominant hands. Participants also performed the Trail Making Test (a measure of executive function). Methods. Deviations from the circular path were computed to derive an “error” time series. For each time series, absolute mean, variance, and complexity index (a proposed measure of system functionality and adaptability) were calculated. Using the moving target and stylus coordinates, the percentage of task time within the target region and the cumulative micropause duration (a measure of motion continuity) were computed. Results. All measures showed significant effects of aging (p < .0005). Post hoc age group comparisons showed that with increasing age, the absolute mean and variance of the error increased, complexity index decreased, percentage of time within the target region decreased, and cumulative micropause duration increased. Only complexity index showed a significant difference between dominant versus nondominant hands within each age group (p < .0005). All measures showed relationships to the Trail Making Test (p < .05). Conclusions. Measures derived from a tracing task identified performance differences in healthy individuals as a function of age, sex, and handedness. Studies in populations with specific neuromotor syndromes are warranted to test the utility of measures based on the dynamics of tracking a target as a clinical assessment tool. PMID:23388876

Awareness of surgical costs: a multicenter cross-sectional survey.

PubMed

Bade, Kim; Hoogerbrug, Jonathan

2015-01-01

Resource scarcity continues to be an important problem in modern surgical practice. Studies in North America and Europe have found that medical professionals have limited understanding of the costs of medical care. No cost awareness studies have been undertaken in Australasia or specifically focusing on the surgical team. This study determined the cost of a range of commonly used diagnostic tests, procedures, and hospital resources associated with care of the surgical patient. The surgical teams' awareness of these costs was then assessed in a multicenter cross-sectional survey. In total, 14 general surgical consultants, 14 registrars, and 25 house officers working in three New Zealand hospitals were asked to estimate the costs of 14 items commonly associated with patient care. Cost estimations were considered correct if within 25% plus or minus of the actual cost. Accuracy was assessed by calculating the median, mean, and absolute percentage discrepancy. A total of 57 surveys were completed. Of which, four were incomplete and were not included in the analysis. Cost awareness was generally poor, and members of the surgical team were rarely able to estimate the costs to within 25%. The mean absolute percentage error was 0.87 (95% CI: 0.58-1.18) and underestimates were most common. There was no significant difference in estimate accuracy between consultants, registrars, or house officers, or between consultants working in both public/private practice compared with those working in public practice alone. There is poor awareness of surgical costs among consultant surgeons, registrars, and junior physicians working in Australasia. Copyright © 2014 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Evaluation and Applications of the Prediction of Intensity Model Error (PRIME) Model

NASA Astrophysics Data System (ADS)

Bhatia, K. T.; Nolan, D. S.; Demaria, M.; Schumacher, A.

2015-12-01

Forecasters and end users of tropical cyclone (TC) intensity forecasts would greatly benefit from a reliable expectation of model error to counteract the lack of consistency in TC intensity forecast performance. As a first step towards producing error predictions to accompany each TC intensity forecast, Bhatia and Nolan (2013) studied the relationship between synoptic parameters, TC attributes, and forecast errors. In this study, we build on previous results of Bhatia and Nolan (2013) by testing the ability of the Prediction of Intensity Model Error (PRIME) model to forecast the absolute error and bias of four leading intensity models available for guidance in the Atlantic basin. PRIME forecasts are independently evaluated at each 12-hour interval from 12 to 120 hours during the 2007-2014 Atlantic hurricane seasons. The absolute error and bias predictions of PRIME are compared to their respective climatologies to determine their skill. In addition to these results, we will present the performance of the operational version of PRIME run during the 2015 hurricane season. PRIME verification results show that it can reliably anticipate situations where particular models excel, and therefore could lead to a more informed protocol for hurricane evacuations and storm preparations. These positive conclusions suggest that PRIME forecasts also have the potential to lower the error in the original intensity forecasts of each model. As a result, two techniques are proposed to develop a post-processing procedure for a multimodel ensemble based on PRIME. The first approach is to inverse-weight models using PRIME absolute error predictions (higher predicted absolute error corresponds to lower weights). The second multimodel ensemble applies PRIME bias predictions to each model's intensity forecast and the mean of the corrected models is evaluated. The forecasts of both of these experimental ensembles are compared to those of the equal-weight ICON ensemble, which currently provides the most reliable forecasts in the Atlantic basin.

Three calculations of free cortisol versus measured values in the critically ill.

PubMed

Molenaar, Nienke; Groeneveld, A B Johan; de Jong, Margriet F C

2015-11-01

To investigate the agreement between the calculated free cortisol levels according to widely applied Coolens and adjusted Södergård equations with measured levels in the critically ill. A prospective study in a mixed intensive care unit. We consecutively included 103 patients with treatment-insensitive hypotension in whom an adrenocorticotropic hormone (ACTH) test (250μg) was performed. Serum total and free cortisol (equilibrium dialysis), corticosteroid-binding globulin and albumin were assessed. Free cortisol was estimated by the Coolens method (C) and two adjusted Södergård (S1 and S2) equations. Bland Altman plots were made. The bias for absolute (t=0, 30 and 60min after ACTH injection) cortisol levels was 38, -24, 41nmol/L when the C, S1 and S2 equations were used, with 95% limits of agreement between -65-142, -182-135, and -57-139nmol/L and percentage errors of 66, 85, and 64%, respectively. Bias for delta (peak-baseline) cortisol was 14, -31 and 16nmol/L, with 95% limits of agreement between -80-108, -157-95, and -74-105nmol/L, and percentage errors of 107, 114, and 100% for C, S1 and S2 equations, respectively. Calculated free cortisol levels have too high bias and imprecision to allow for acceptable use in the critically ill. Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Using a dry electrode EEG device during balance tasks in healthy young-adult males: Test-retest reliability analysis.

PubMed

Collado-Mateo, Daniel; Adsuar, Jose C; Olivares, Pedro R; Cano-Plasencia, Ricardo; Gusi, Narcis

2015-01-01

The analysis of brain activity during balance is an important topic in different fields of science. Given that all measurements involve an error that is caused by different agents, like the instrument, the researcher, or the natural human variability, a test-retest reliability evaluation of the electroencephalographic assessment is a needed starting point. However, there is a lack of information about the reliability of electroencephalographic measurements, especially in a new wireless device with dry electrodes. The current study aims to analyze the reliability of electroencephalographic measurements from a wireless device using dry electrodes during two different balance tests. Seventeen healthy male volunteers performed two different static balance tasks on a Biodex Balance Platform: (a) with two feet on the platform and (b) with one foot on the platform. Electroencephalographic data was recorded using Enobio (Neuroelectrics). The mean power spectrum of the alpha band of the central and frontal channels was calculated. Relative and absolute indices of reliability were also calculated. In general terms, the intraclass correlation coefficient (ICC) values of all the assessed channels can be classified as excellent (>0.90). The percentage standard error of measurement oscillated from 0.54% to 1.02% and the percentage smallest real difference ranged from 1.50% to 2.82%. Electroencephalographic assessment through an Enobio device during balance tasks has an excellent reliability. However, its utility was not demonstrated because responsiveness was not assessed.

Neural network cloud top pressure and height for MODIS

NASA Astrophysics Data System (ADS)

Håkansson, Nina; Adok, Claudia; Thoss, Anke; Scheirer, Ronald; Hörnquist, Sara

2018-06-01

Cloud top height retrieval from imager instruments is important for nowcasting and for satellite climate data records. A neural network approach for cloud top height retrieval from the imager instrument MODIS (Moderate Resolution Imaging Spectroradiometer) is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) dataset. Results are compared with two operational reference algorithms for cloud top height: the MODIS Collection 6 Level 2 height product and the cloud top temperature and height algorithm in the 2014 version of the NWC SAF (EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting) PPS (Polar Platform System). All three techniques are evaluated using both CALIOP and CPR (Cloud Profiling Radar for CloudSat (CLOUD SATellite)) height. Instruments like AVHRR (Advanced Very High Resolution Radiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) contain fewer channels useful for cloud top height retrievals than MODIS, therefore several different neural networks are investigated to test how infrared channel selection influences retrieval performance. Also a network with only channels available for the AVHRR1 instrument is trained and evaluated. To examine the contribution of different variables, networks with fewer variables are trained. It is shown that variables containing imager information for neighboring pixels are very important. The error distributions of the involved cloud top height algorithms are found to be non-Gaussian. Different descriptive statistic measures are presented and it is exemplified that bias and SD (standard deviation) can be misleading for non-Gaussian distributions. The median and mode are found to better describe the tendency of the error distributions and IQR (interquartile range) and MAE (mean absolute error) are found to give the most useful information of the spread of the errors. For all descriptive statistics presented MAE, IQR, RMSE (root mean square error), SD, mode, median, bias and percentage of absolute errors above 0.25, 0.5, 1 and 2 km the neural network perform better than the reference algorithms both validated with CALIOP and CPR (CloudSat). The neural networks using the brightness temperatures at 11 and 12 µm show at least 32 % (or 623 m) lower MAE compared to the two operational reference algorithms when validating with CALIOP height. Validation with CPR (CloudSat) height gives at least 25 % (or 430 m) reduction of MAE.

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

PubMed

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

Non-destructive mobile monitoring of microbial contaminations on meat surfaces using porphyrin fluorescence intensities.

PubMed

Durek, J; Fröhling, A; Bolling, J; Thomasius, R; Durek, P; Schlüter, O K

2016-05-01

A non-destructive mobile system for meat quality monitoring was developed and investigated for the possible application along the whole production chain of fresh meat. Pork and lamb meat was stored at 5 °C for up to 20 days post mortem and measured with a fluorescence spectrometer. Additionally, the bacterial influence on the fluorescence signals was evaluated by different experimental procedures. Fluorescence of NADH and different porphyrins could be correlated to the growth of diverse bacteria and hence used for contamination monitoring. The increase of porphyrin fluorescence started after 9 days p.m. for pork and after 2 days p.m. for lamb meat. Based on the results, a mobile fluorescence system was built and compared with the laboratory system. The corrected function of the meat slices showed a root mean square error of 1156.97 r.u. and a mean absolute percentage error of 12.59%; for lamb the values were 470.81 r.u. and 15.55%, respectively. A mobile and non-invasive measurement system would improve the microbial security of fresh meat. Copyright © 2016 Elsevier Ltd. All rights reserved.

Form and Objective of the Decision Rule in Absolute Identification

NASA Technical Reports Server (NTRS)

Balakrishnan, J. D.

1997-01-01

In several conditions of a line length identification experiment, the subjects' decision making strategies were systematically biased against the responses on the edges of the stimulus range. When the range and number of the stimuli were small, the bias caused the percentage of correct responses to be highest in the center and lowest on the extremes of the range. Two general classes of decision rules that would explain these results are considered. The first class assumes that subjects intend to adopt an optimal decision rule, but systematically misrepresent one or more parameters of the decision making context. The second class assumes that subjects use a different measure of performance than the one assumed by the experimenter: instead of maximizing the chances of a correct response, the subject attempts to minimize the expected size of the response error (a "fidelity criterion"). In a second experiment, extended experience and feedback did not diminish the bias effect, but explicitly penalizing all response errors equally, regardless of their size, did reduce or eliminate it in some subjects. Both results favor the fidelity criterion over the optimal rule.

Magnetic levitation-based electromagnetic energy harvesting: a semi-analytical non-linear model for energy transduction

NASA Astrophysics Data System (ADS)

Soares Dos Santos, Marco P.; Ferreira, Jorge A. F.; Simões, José A. O.; Pascoal, Ricardo; Torrão, João; Xue, Xiaozheng; Furlani, Edward P.

2016-01-01

Magnetic levitation has been used to implement low-cost and maintenance-free electromagnetic energy harvesting. The ability of levitation-based harvesting systems to operate autonomously for long periods of time makes them well-suited for self-powering a broad range of technologies. In this paper, a combined theoretical and experimental study is presented of a harvester configuration that utilizes the motion of a levitated hard-magnetic element to generate electrical power. A semi-analytical, non-linear model is introduced that enables accurate and efficient analysis of energy transduction. The model predicts the transient and steady-state response of the harvester a function of its motion (amplitude and frequency) and load impedance. Very good agreement is obtained between simulation and experiment with energy errors lower than 14.15% (mean absolute percentage error of 6.02%) and cross-correlations higher than 86%. The model provides unique insight into fundamental mechanisms of energy transduction and enables the geometric optimization of harvesters prior to fabrication and the rational design of intelligent energy harvesters.

Magnetic levitation-based electromagnetic energy harvesting: a semi-analytical non-linear model for energy transduction

PubMed Central

Soares dos Santos, Marco P.; Ferreira, Jorge A. F.; Simões, José A. O.; Pascoal, Ricardo; Torrão, João; Xue, Xiaozheng; Furlani, Edward P.

2016-01-01

Magnetic levitation has been used to implement low-cost and maintenance-free electromagnetic energy harvesting. The ability of levitation-based harvesting systems to operate autonomously for long periods of time makes them well-suited for self-powering a broad range of technologies. In this paper, a combined theoretical and experimental study is presented of a harvester configuration that utilizes the motion of a levitated hard-magnetic element to generate electrical power. A semi-analytical, non-linear model is introduced that enables accurate and efficient analysis of energy transduction. The model predicts the transient and steady-state response of the harvester a function of its motion (amplitude and frequency) and load impedance. Very good agreement is obtained between simulation and experiment with energy errors lower than 14.15% (mean absolute percentage error of 6.02%) and cross-correlations higher than 86%. The model provides unique insight into fundamental mechanisms of energy transduction and enables the geometric optimization of harvesters prior to fabrication and the rational design of intelligent energy harvesters. PMID:26725842

Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence

NASA Astrophysics Data System (ADS)

Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd

2018-04-01

Air overpressure (AOp) resulting from blasting can cause damage and nuisance to nearby civilians. Thus, it is important to be able to predict AOp accurately. In this study, 8 different Artificial Neural Network (ANN) were developed for the purpose of prediction of AOp. The ANN models were trained using different variants of Particle Swarm Optimization (PSO) algorithm. AOp predictions were also made using an empirical equation, as suggested by United States Bureau of Mines (USBM), to serve as a benchmark. In order to develop the models, 76 blasting operations in Hulu Langat were investigated. All the ANN models were found to outperform the USBM equation in three performance metrics; root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Using a performance ranking method, MSO-Rand-Mut was determined to be the best prediction model for AOp with a performance metric of RMSE=2.18, MAPE=1.73% and R2=0.97. The result shows that ANN models trained using PSO are capable of predicting AOp with great accuracy.

Assessment of errors in static electrical impedance tomography with adjacent and trigonometric current patterns.

PubMed

Kolehmainen, V; Vauhkonen, M; Karjalainen, P A; Kaipio, J P

1997-11-01

In electrical impedance tomography (EIT), difference imaging is often preferred over static imaging. This is because of the many unknowns in the forward modelling which make it difficult to obtain reliable absolute resistivity estimates. However, static imaging and absolute resistivity values are needed in some potential applications of EIT. In this paper we demonstrate by simulation the effects of different error components that are included in the reconstruction of static EIT images. All simulations are carried out in two dimensions with the so-called complete electrode model. Errors that are considered are the modelling error in the boundary shape of an object, errors in the electrode sizes and localizations and errors in the contact impedances under the electrodes. Results using both adjacent and trigonometric current patterns are given.

Pyrometer with tracking balancing

NASA Astrophysics Data System (ADS)

Ponomarev, D. B.; Zakharenko, V. A.; Shkaev, A. G.

2018-04-01

Currently, one of the main metrological noncontact temperature measurement challenges is the emissivity uncertainty. This paper describes a pyrometer with emissivity effect diminishing through the use of a measuring scheme with tracking balancing in which the radiation receiver is a null-indicator. In this paper the results of the prototype pyrometer absolute error study in surfaces temperature measurement of aluminum and nickel samples are presented. There is absolute error calculated values comparison considering the emissivity table values with errors on the results of experimental measurements by the proposed method. The practical implementation of the proposed technical solution has allowed two times to reduce the error due to the emissivity uncertainty.

Changes in relative and absolute concentrations of plasma phospholipid fatty acids observed in a randomized trial of Omega-3 fatty acids supplementation in Uganda.

PubMed

Song, Xiaoling; Diep, Pho; Schenk, Jeannette M; Casper, Corey; Orem, Jackson; Makhoul, Zeina; Lampe, Johanna W; Neuhouser, Marian L

2016-11-01

Expressing circulating phospholipid fatty acids (PLFAs) in relative concentrations has some limitations: the total of all fatty acids are summed to 100%; therefore, the values of individual fatty acid are not independent. In this study we examined if both relative and absolute metrics could effectively measure changes in circulating PLFA concentrations in an intervention trial. 66 HIV and HHV8 infected patients in Uganda were randomized to take 3g/d of either long-chain omega-3 fatty acids (1856mg EPA and 1232mg DHA) or high-oleic safflower oil in a 12-week double-blind trial. Plasma samples were collected at baseline and end of trial. Relative weight percentage and absolute concentrations of 41 plasma PLFAs were measured using gas chromatography. Total cholesterol was also measured. Intervention-effect changes in concentrations were calculated as differences between end of 12-week trial and baseline. Pearson correlations of relative and absolute concentration changes in individual PLFAs were high (>0.6) for 37 of the 41 PLFAs analyzed. In the intervention arm, 17 PLFAs changed significantly in relative concentration and 16 in absolute concentration, 15 of which were identical. Absolute concentration of total PLFAs decreased 95.1mg/L (95% CI: 26.0, 164.2; P=0.0085), but total cholesterol did not change significantly in the intervention arm. No significant change was observed in any of the measurements in the placebo arm. Both relative weight percentage and absolute concentrations could effectively measure changes in plasma PLFA concentrations. EPA and DHA supplementation changes the concentrations of multiple plasma PLFAs besides EPA and DHA.Both relative weight percentage and absolute concentrations could effectively measure changes in plasma phospholipid fatty acid (PLFA) concentrations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Uncertainty analysis technique for OMEGA Dante measurements

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

May, M. J.; Widmann, K.; Sorce, C.

2010-10-15

The Dante is an 18 channel x-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g., hohlraums, etc.) at x-ray energies between 50 eV and 10 keV. It is a main diagnostic installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the x-ray diodes, filters and mirrors, and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determinedmore » flux using a Monte Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.« less

Uncertainty Analysis Technique for OMEGA Dante Measurements

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

May, M J; Widmann, K; Sorce, C

2010-05-07

The Dante is an 18 channel X-ray filtered diode array which records the spectrally and temporally resolved radiation flux from various targets (e.g. hohlraums, etc.) at X-ray energies between 50 eV to 10 keV. It is a main diagnostics installed on the OMEGA laser facility at the Laboratory for Laser Energetics, University of Rochester. The absolute flux is determined from the photometric calibration of the X-ray diodes, filters and mirrors and an unfold algorithm. Understanding the errors on this absolute measurement is critical for understanding hohlraum energetic physics. We present a new method for quantifying the uncertainties on the determinedmore » flux using a Monte-Carlo parameter variation technique. This technique combines the uncertainties in both the unfold algorithm and the error from the absolute calibration of each channel into a one sigma Gaussian error function. One thousand test voltage sets are created using these error functions and processed by the unfold algorithm to produce individual spectra and fluxes. Statistical methods are applied to the resultant set of fluxes to estimate error bars on the measurements.« less

Mapping the absolute magnetic field and evaluating the quadratic Zeeman-effect-induced systematic error in an atom interferometer gravimeter

NASA Astrophysics Data System (ADS)

Hu, Qing-Qing; Freier, Christian; Leykauf, Bastian; Schkolnik, Vladimir; Yang, Jun; Krutzik, Markus; Peters, Achim

2017-09-01

Precisely evaluating the systematic error induced by the quadratic Zeeman effect is important for developing atom interferometer gravimeters aiming at an accuracy in the μ Gal regime (1 μ Gal =10-8m /s2 ≈10-9g ). This paper reports on the experimental investigation of Raman spectroscopy-based magnetic field measurements and the evaluation of the systematic error in the gravimetric atom interferometer (GAIN) due to quadratic Zeeman effect. We discuss Raman duration and frequency step-size-dependent magnetic field measurement uncertainty, present vector light shift and tensor light shift induced magnetic field measurement offset, and map the absolute magnetic field inside the interferometer chamber of GAIN with an uncertainty of 0.72 nT and a spatial resolution of 12.8 mm. We evaluate the quadratic Zeeman-effect-induced gravity measurement error in GAIN as 2.04 μ Gal . The methods shown in this paper are important for precisely mapping the absolute magnetic field in vacuum and reducing the quadratic Zeeman-effect-induced systematic error in Raman transition-based precision measurements, such as atomic interferometer gravimeters.

Application of Artificial Neural Network and Response Surface Methodology in Modeling of Surface Roughness in WS2 Solid Lubricant Assisted MQL Turning of Inconel 718

NASA Astrophysics Data System (ADS)

Maheshwera Reddy Paturi, Uma; Devarasetti, Harish; Abimbola Fadare, David; Reddy Narala, Suresh Kumar

2018-04-01

In the present paper, the artificial neural network (ANN) and response surface methodology (RSM) are used in modeling of surface roughness in WS2 (tungsten disulphide) solid lubricant assisted minimal quantity lubrication (MQL) machining. The real time MQL turning of Inconel 718 experimental data considered in this paper was available in the literature [1]. In ANN modeling, performance parameters such as mean square error (MSE), mean absolute percentage error (MAPE) and average error in prediction (AEP) for the experimental data were determined based on Levenberg–Marquardt (LM) feed forward back propagation training algorithm with tansig as transfer function. The MATLAB tool box has been utilized in training and testing of neural network model. Neural network model with three input neurons, one hidden layer with five neurons and one output neuron (3-5-1 architecture) is found to be most confidence and optimal. The coefficient of determination (R2) for both the ANN and RSM model were seen to be 0.998 and 0.982 respectively. The surface roughness predictions from ANN and RSM model were related with experimentally measured values and found to be in good agreement with each other. However, the prediction efficacy of ANN model is relatively high when compared with RSM model predictions.

Sub-nanometer periodic nonlinearity error in absolute distance interferometers

NASA Astrophysics Data System (ADS)

Yang, Hongxing; Huang, Kaiqi; Hu, Pengcheng; Zhu, Pengfei; Tan, Jiubin; Fan, Zhigang

2015-05-01

Periodic nonlinearity which can result in error in nanometer scale has become a main problem limiting the absolute distance measurement accuracy. In order to eliminate this error, a new integrated interferometer with non-polarizing beam splitter is developed. This leads to disappearing of the frequency and/or polarization mixing. Furthermore, a strict requirement on the laser source polarization is highly reduced. By combining retro-reflector and angel prism, reference and measuring beams can be spatially separated, and therefore, their optical paths are not overlapped. So, the main cause of the periodic nonlinearity error, i.e., the frequency and/or polarization mixing and leakage of beam, is eliminated. Experimental results indicate that the periodic phase error is kept within 0.0018°.

Models for estimating daily rainfall erosivity in China

NASA Astrophysics Data System (ADS)

Xie, Yun; Yin, Shui-qing; Liu, Bao-yuan; Nearing, Mark A.; Zhao, Ying

2016-04-01

The rainfall erosivity factor (R) represents the multiplication of rainfall energy and maximum 30 min intensity by event (EI30) and year. This rainfall erosivity index is widely used for empirical soil loss prediction. Its calculation, however, requires high temporal resolution rainfall data that are not readily available in many parts of the world. The purpose of this study was to parameterize models suitable for estimating erosivity from daily rainfall data, which are more widely available. One-minute resolution rainfall data recorded in sixteen stations over the eastern water erosion impacted regions of China were analyzed. The R-factor ranged from 781.9 to 8258.5 MJ mm ha-1 h-1 y-1. A total of 5942 erosive events from one-minute resolution rainfall data of ten stations were used to parameterize three models, and 4949 erosive events from the other six stations were used for validation. A threshold of daily rainfall between days classified as erosive and non-erosive was suggested to be 9.7 mm based on these data. Two of the models (I and II) used power law functions that required only daily rainfall totals. Model I used different model coefficients in the cool season (Oct.-Apr.) and warm season (May-Sept.), and Model II was fitted with a sinusoidal curve of seasonal variation. Both Model I and Model II estimated the erosivity index for average annual, yearly, and half-month temporal scales reasonably well, with the symmetric mean absolute percentage error MAPEsym ranging from 10.8% to 32.1%. Model II predicted slightly better than Model I. However, the prediction efficiency for the daily erosivity index was limited, with the symmetric mean absolute percentage error being 68.0% (Model I) and 65.7% (Model II) and Nash-Sutcliffe model efficiency being 0.55 (Model I) and 0.57 (Model II). Model III, which used the combination of daily rainfall amount and daily maximum 60-min rainfall, improved predictions significantly, and produced a Nash-Sutcliffe model efficiency for daily erosivity index prediction of 0.93. Thus daily rainfall data was generally sufficient for estimating annual average, yearly, and half-monthly time scales, while sub-daily data was needed when estimating daily erosivity values.

Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model

PubMed Central

2011-01-01

Background China is a country that is most seriously affected by hemorrhagic fever with renal syndrome (HFRS) with 90% of HFRS cases reported globally. At present, HFRS is getting worse with increasing cases and natural foci in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence to make the control of HFRS more effective. In this study, we applied a stochastic autoregressive integrated moving average (ARIMA) model with the objective of monitoring and short-term forecasting HFRS incidence in China. Methods Chinese HFRS data from 1975 to 2008 were used to fit ARIMA model. Akaike Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. Subsequently, the fitted ARIMA model was applied to obtain the fitted HFRS incidence from 1978 to 2008 and contrast with corresponding observed values. To assess the validity of the proposed model, the mean absolute percentage error (MAPE) between the observed and fitted HFRS incidence (1978-2008) was calculated. Finally, the fitted ARIMA model was used to forecast the incidence of HFRS of the years 2009 to 2011. All analyses were performed using SAS9.1 with a significant level of p < 0.05. Results The goodness-of-fit test of the optimum ARIMA (0,3,1) model showed non-significant autocorrelations in the residuals of the model (Ljung-Box Q statistic = 5.95,P = 0.3113). The fitted values made by ARIMA (0,3,1) model for years 1978-2008 closely followed the observed values for the same years, with a mean absolute percentage error (MAPE) of 12.20%. The forecast values from 2009 to 2011 were 0.69, 0.86, and 1.21per 100,000 population, respectively. Conclusion ARIMA models applied to historical HFRS incidence data are an important tool for HFRS surveillance in China. This study shows that accurate forecasting of the HFRS incidence is possible using an ARIMA model. If predicted values from this study are accurate, China can expect a rise in HFRS incidence. PMID:21838933

Demand Controlled Economizer Cycles: A Direct Digital Control Scheme for Heating, Ventilating, and Air Conditioning Systems,

DTIC Science & Technology

1984-05-01

Control Ignored any error of 1/10th degree or less. This was done by setting the error term E and the integral sum PREINT to zero If then absolute value of...signs of two errors jeq tdiff if equal, jump clr @preint else zero integal sum tdiff mov @diff,rl fetch absolute value of OAT-RAT ci rl,25 is...includes a heating coil and thermostatic control to maintain the air in this path at an elevated temperature, typically around 80 degrees Farenheit (80 F

Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors

NASA Astrophysics Data System (ADS)

Pernot, Pascal; Savin, Andreas

2018-06-01

Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulative distribution function of unsigned errors, namely, (1) the probability for a new calculation to have an absolute error below a chosen threshold and (2) the maximal amplitude of errors one can expect with a chosen high confidence level. Those statistics are also shown to be well suited for benchmarking and ranking studies. Moreover, the standard error on all benchmarking statistics depends on the size of the reference dataset. Systematic publication of these standard errors would be very helpful to assess the statistical reliability of benchmarking conclusions.

Reliability of the Swedish version of the Exercise Self-Efficacy Scale (S-ESES): a test-retest study in adults with neurological disease.

PubMed

Ahlström, Isabell; Hellström, Karin; Emtner, Margareta; Anens, Elisabeth

2015-03-01

To examine the test-retest reliability of the Swedish translated version of the Exercise Self-Efficacy Scale (S-ESES) in people with neurological disease and to examine internal consistency. Test-retest study. A total of 30 adults with neurological diseases including: Parkinson's disease; Multiple Sclerosis; Cervical Dystonia; and Charcot-Marie-Tooth disease. The S-ESES was sent twice by surface mail. Completion interval mean was 16 days apart. Weighted kappa, intraclass correlation coefficient 2,1 [ICC (2,1)], standard error of measurement (SEM), also expressed as a percentage value (SEM%), and Cronbach's alpha were calculated. The relative reliability of the test-retest results showed substantial agreement measured using weighted kappa (MD = 0.62) and a very high-reliability ICC (2,1) (0.92). Absolute reliability measured using SEM was 5.3 and SEM% was 20.7. Excellent internal consistency was shown, with an alpha coefficient of 0.91 (test 1) and 0.93 (test 2). The S-ESES is recommended for use in research and in clinical work for people with neurological diseases. The low-absolute reliability, however, indicates a limited ability to measure changes on an individual level.

Temporal expectation in focal hand dystonia.

PubMed

Avanzino, Laura; Martino, Davide; Martino, Isadora; Pelosin, Elisa; Vicario, Carmelo M; Bove, Marco; Defazio, Gianni; Abbruzzese, Giovanni

2013-02-01

Patients with writer's cramp present sensory and representational abnormalities relevant to motor control, such as impairment in the temporal discrimination between tactile stimuli and in pure motor imagery tasks, like the mental rotation of corporeal and inanimate objects. However, only limited information is available on the ability of patients with dystonia to process the time-dependent features (e.g. speed) of movement in real time. The processing of time-dependent features of movement has a crucial role in predicting whether the outcome of a complex motor sequence, such as handwriting or playing a musical passage, will be consistent with its ultimate goal, or results instead in an execution error. In this study, we sought to evaluate the implicit ability to perceive the temporal outcome of different movements in a group of patients with writer's cramp. Fourteen patients affected by writer's cramp in the right hand and 17 age- and gender-matched healthy subjects were recruited for the study. Subjects were asked to perform a temporal expectation task by predicting the end of visually perceived human body motion (handwriting, i.e. the action performed by the human body segment specifically affected by writer's cramp) or inanimate object motion (a moving circle reaching a spatial target). Videos representing movements were shown in full before experimental trials; the actual tasks consisted of watching the same videos, but interrupted after a variable interval ('pre-dark') from its onset by a dark interval of variable duration. During the 'dark' interval, subjects were asked to indicate when the movement represented in the video reached its end by clicking on the space bar of the keyboard. We also included a visual working memory task. Performance on the timing task was analysed measuring the absolute value of timing error, the coefficient of variability and the percentage of anticipation responses. Patients with writer's cramp exhibited greater absolute timing error compared with control subjects in the human body motion task (whereas no difference was observed in the inanimate object motion task). No effect of group was documented on the visual working memory tasks. Absolute timing error on the human body motion task did not significantly correlate with symptom severity, disease duration or writing speed. Our findings suggest an alteration of the writing movement representation at a central level and are consistent with the view that dystonia is not a purely motor disorder, but it also involves non-motor (sensory, cognitive) aspects related to movement processing and planning.

Optical Coherence Tomography–Based Corneal Power Measurement and Intraocular Lens Power Calculation Following Laser Vision Correction (An American Ophthalmological Society Thesis)

PubMed Central

Huang, David; Tang, Maolong; Wang, Li; Zhang, Xinbo; Armour, Rebecca L.; Gattey, Devin M.; Lombardi, Lorinna H.; Koch, Douglas D.

2013-01-01

Purpose: To use optical coherence tomography (OCT) to measure corneal power and improve the selection of intraocular lens (IOL) power in cataract surgeries after laser vision correction. Methods: Patients with previous myopic laser vision corrections were enrolled in this prospective study from two eye centers. Corneal thickness and power were measured by Fourier-domain OCT. Axial length, anterior chamber depth, and automated keratometry were measured by a partial coherence interferometer. An OCT-based IOL formula was developed. The mean absolute error of the OCT-based formula in predicting postoperative refraction was compared to two regression-based IOL formulae for eyes with previous laser vision correction. Results: Forty-six eyes of 46 patients all had uncomplicated cataract surgery with monofocal IOL implantation. The mean arithmetic prediction error of postoperative refraction was 0.05 ± 0.65 diopter (D) for the OCT formula, 0.14 ± 0.83 D for the Haigis-L formula, and 0.24 ± 0.82 D for the no-history Shammas-PL formula. The mean absolute error was 0.50 D for OCT compared to a mean absolute error of 0.67 D for Haigis-L and 0.67 D for Shammas-PL. The adjusted mean absolute error (average prediction error removed) was 0.49 D for OCT, 0.65 D for Haigis-L (P=.031), and 0.62 D for Shammas-PL (P=.044). For OCT, 61% of the eyes were within 0.5 D of prediction error, whereas 46% were within 0.5 D for both Haigis-L and Shammas-PL (P=.034). Conclusions: The predictive accuracy of OCT-based IOL power calculation was better than Haigis-L and Shammas-PL formulas in eyes after laser vision correction. PMID:24167323

The cerebellum predicts the temporal consequences of observed motor acts.

PubMed

Avanzino, Laura; Bove, Marco; Pelosin, Elisa; Ogliastro, Carla; Lagravinese, Giovanna; Martino, Davide

2015-01-01

It is increasingly clear that we extract patterns of temporal regularity between events to optimize information processing. The ability to extract temporal patterns and regularity of events is referred as temporal expectation. Temporal expectation activates the same cerebral network usually engaged in action selection, comprising cerebellum. However, it is unclear whether the cerebellum is directly involved in temporal expectation, when timing information is processed to make predictions on the outcome of a motor act. Healthy volunteers received one session of either active (inhibitory, 1 Hz) or sham repetitive transcranial magnetic stimulation covering the right lateral cerebellum prior the execution of a temporal expectation task. Subjects were asked to predict the end of a visually perceived human body motion (right hand handwriting) and of an inanimate object motion (a moving circle reaching a target). Videos representing movements were shown in full; the actual tasks consisted of watching the same videos, but interrupted after a variable interval from its onset by a dark interval of variable duration. During the 'dark' interval, subjects were asked to indicate when the movement represented in the video reached its end by clicking on the spacebar of the keyboard. Performance on the timing task was analyzed measuring the absolute value of timing error, the coefficient of variability and the percentage of anticipation responses. The active group exhibited greater absolute timing error compared with the sham group only in the human body motion task. Our findings suggest that the cerebellum is engaged in cognitive and perceptual domains that are strictly connected to motor control.

The valuation of equity warrants under the fractional Vasicek process of the short-term interest rate

NASA Astrophysics Data System (ADS)

Xiao, Weilin; Zhang, Weiguo; Zhang, Xili; Chen, Xiaoyan

2014-01-01

Motivated by the empirical evidence of long range dependence in short-term interest rates and considering the long maturities of equity warrants, we propose the fractional Vasicek model to describe the dynamics of the short rate in the pricing environment of equity warrants. Using the partial differential equation approach, we present a valuation model for equity warrants under the assumption that the short rate follows the fractional Vasicek process. After identifying the pricing model for equity warrants, we provide the parameter estimation procedure for the proposed pricing model. Since obtaining the values of equity warrants from the proposed model needs to solve a nonlinear equation, we employ a hybrid intelligent algorithm to get around this optimization problem. Furthermore, to illustrate the practicality of our proposed model, we conduct an empirical study to ascertain the performance of our proposed model using the data from China’s warrant market and the China Foreign Exchange Trade System. The comparison of traditional models (such as the Black-Scholes model, the Noreen-Wolfson model, the Lauterbach-Schultz model, and the Ukhov model) with our proposed model is also presented. The empirical results show that the mean absolute percentage error of our pricing model is 10.30%. By contrast, the Black-Scholes model, the Noreen-Wolfson model, the Lauterbach-Schultz model, and the Ukhov model applied to the same warrant produce mean absolute errors of 35.26%, 37.67%, 33.40%, 32.81%, respectively. Thus the long memory property in stochastic interest rates cannot be ignored in determining the valuation of equity warrants.

Noise-Enhanced Eversion Force Sense in Ankles With or Without Functional Instability.

PubMed

Ross, Scott E; Linens, Shelley W; Wright, Cynthia J; Arnold, Brent L

2015-08-01

Force sense impairments are associated with functional ankle instability. Stochastic resonance stimulation (SRS) may have implications for correcting these force sense deficits. To determine if SRS improved force sense. Case-control study. Research laboratory. Twelve people with functional ankle instability (age = 23 ± 3 years, height = 174 ± 8 cm, mass = 69 ± 10 kg) and 12 people with stable ankles (age = 22 ± 2 years, height = 170 ± 7 cm, mass = 64 ± 10 kg). The eversion force sense protocol required participants to reproduce a targeted muscle tension (10% of maximum voluntary isometric contraction). This protocol was assessed under SRSon and SRSoff (control) conditions. During SRSon, random subsensory mechanical noise was applied to the lower leg at a customized optimal intensity for each participant. Constant error, absolute error, and variable error measures quantified accuracy, overall performance, and consistency of force reproduction, respectively. With SRS, we observed main effects for force sense absolute error (SRSoff = 1.01 ± 0.67 N, SRSon = 0.69 ± 0.42 N) and variable error (SRSoff = 1.11 ± 0.64 N, SRSon = 0.78 ± 0.56 N) (P < .05). No other main effects or treatment-by-group interactions were found (P > .05). Although SRS reduced the overall magnitude (absolute error) and variability (variable error) of force sense errors, it had no effect on the directionality (constant error). Clinically, SRS may enhance muscle tension ability, which could have treatment implications for ankle stability.

Relevant reduction effect with a modified thermoplastic mask of rotational error for glottic cancer in IMRT

NASA Astrophysics Data System (ADS)

Jung, Jae Hong; Jung, Joo-Young; Cho, Kwang Hwan; Ryu, Mi Ryeong; Bae, Sun Hyun; Moon, Seong Kwon; Kim, Yong Ho; Choe, Bo-Young; Suh, Tae Suk

2017-02-01

The purpose of this study was to analyze the glottis rotational error (GRE) by using a thermoplastic mask for patients with the glottic cancer undergoing intensity-modulated radiation therapy (IMRT). We selected 20 patients with glottic cancer who had received IMRT by using the tomotherapy. The image modalities with both kilovoltage computed tomography (planning kVCT) and megavoltage CT (daily MVCT) images were used for evaluating the error. Six anatomical landmarks in the image were defined to evaluate a correlation between the absolute GRE (°) and the length of contact with the underlying skin of the patient by the mask (mask, mm). We also statistically analyzed the results by using the Pearson's correlation coefficient and a linear regression analysis ( P <0.05). The mask and the absolute GRE were verified to have a statistical correlation ( P < 0.01). We found a statistical significance for each parameter in the linear regression analysis (mask versus absolute roll: P = 0.004 [ P < 0.05]; mask versus 3D-error: P = 0.000 [ P < 0.05]). The range of the 3D-errors with contact by the mask was from 1.2% - 39.7% between the maximumand no-contact case in this study. A thermoplastic mask with a tight, increased contact area may possibly contribute to the uncertainty of the reproducibility as a variation of the absolute GRE. Thus, we suggest that a modified mask, such as one that covers only the glottis area, can significantly reduce the patients' setup errors during the treatment.

Online absolute pose compensation and steering control of industrial robot based on six degrees of freedom laser measurement

NASA Astrophysics Data System (ADS)

Yang, Juqing; Wang, Dayong; Fan, Baixing; Dong, Dengfeng; Zhou, Weihu

2017-03-01

In-situ intelligent manufacturing for large-volume equipment requires industrial robots with absolute high-accuracy positioning and orientation steering control. Conventional robots mainly employ an offline calibration technology to identify and compensate key robotic parameters. However, the dynamic and static parameters of a robot change nonlinearly. It is not possible to acquire a robot's actual parameters and control the absolute pose of the robot with a high accuracy within a large workspace by offline calibration in real-time. This study proposes a real-time online absolute pose steering control method for an industrial robot based on six degrees of freedom laser tracking measurement, which adopts comprehensive compensation and correction of differential movement variables. First, the pose steering control system and robot kinematics error model are constructed, and then the pose error compensation mechanism and algorithm are introduced in detail. By accurately achieving the position and orientation of the robot end-tool, mapping the computed Jacobian matrix of the joint variable and correcting the joint variable, the real-time online absolute pose compensation for an industrial robot is accurately implemented in simulations and experimental tests. The average positioning error is 0.048 mm and orientation accuracy is better than 0.01 deg. The results demonstrate that the proposed method is feasible, and the online absolute accuracy of a robot is sufficiently enhanced.

Spinal intra-operative three-dimensional navigation with infra-red tool tracking: correlation between clinical and absolute engineering accuracy

NASA Astrophysics Data System (ADS)

Guha, Daipayan; Jakubovic, Raphael; Gupta, Shaurya; Yang, Victor X. D.

2017-02-01

Computer-assisted navigation (CAN) may guide spinal surgeries, reliably reducing screw breach rates. Definitions of screw breach, if reported, vary widely across studies. Absolute quantitative error is theoretically a more precise and generalizable metric of navigation accuracy, but has been computed variably and reported in fewer than 25% of clinical studies of CAN-guided pedicle screw accuracy. We reviewed a prospectively-collected series of 209 pedicle screws placed with CAN guidance to characterize the correlation between clinical pedicle screw accuracy, based on postoperative imaging, and absolute quantitative navigation accuracy. We found that acceptable screw accuracy was achieved for significantly fewer screws based on 2mm grade vs. Heary grade, particularly in the lumbar spine. Inter-rater agreement was good for the Heary classification and moderate for the 2mm grade, significantly greater among radiologists than surgeon raters. Mean absolute translational/angular accuracies were 1.75mm/3.13° and 1.20mm/3.64° in the axial and sagittal planes, respectively. There was no correlation between clinical and absolute navigation accuracy, in part because surgeons appear to compensate for perceived translational navigation error by adjusting screw medialization angle. Future studies of navigation accuracy should therefore report absolute translational and angular errors. Clinical screw grades based on post-operative imaging, if reported, may be more reliable if performed in multiple by radiologist raters.

45 CFR 98.100 - Error Rate Report.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Welfare DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL ADMINISTRATION CHILD CARE AND DEVELOPMENT FUND... rates, which is defined as the percentage of cases with an error (expressed as the total number of cases with an error compared to the total number of cases); the percentage of cases with an improper payment...

Predicting Air Permeability of Handloom Fabrics: A Comparative Analysis of Regression and Artificial Neural Network Models

NASA Astrophysics Data System (ADS)

Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya

2013-03-01

This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.

The PMA Catalogue: 420 million positions and absolute proper motions

NASA Astrophysics Data System (ADS)

Akhmetov, V. S.; Fedorov, P. N.; Velichko, A. B.; Shulga, V. M.

2017-07-01

We present a catalogue that contains about 420 million absolute proper motions of stars. It was derived from the combination of positions from Gaia DR1 and 2MASS, with a mean difference of epochs of about 15 yr. Most of the systematic zonal errors inherent in the 2MASS Catalogue were eliminated before deriving the absolute proper motions. The absolute calibration procedure (zero-pointing of the proper motions) was carried out using about 1.6 million positions of extragalactic sources. The mean formal error of the absolute calibration is less than 0.35 mas yr-1. The derived proper motions cover the whole celestial sphere without gaps for a range of stellar magnitudes from 8 to 21 mag. In the sky areas where the extragalactic sources are invisible (the avoidance zone), a dedicated procedure was used that transforms the relative proper motions into absolute ones. The rms error of proper motions depends on stellar magnitude and ranges from 2-5 mas yr-1 for stars with 10 mag < G < 17 mag to 5-10 mas yr-1 for faint ones. The present catalogue contains the Gaia DR1 positions of stars for the J2015 epoch. The system of the PMA proper motions does not depend on the systematic errors of the 2MASS positions, and in the range from 14 to 21 mag represents an independent realization of a quasi-inertial reference frame in the optical and near-infrared wavelength range. The Catalogue also contains stellar magnitudes taken from the Gaia DR1 and 2MASS catalogues. A comparison of the PMA proper motions of stars with similar data from certain recent catalogues has been undertaken.

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

NASA Astrophysics Data System (ADS)

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

2012-05-01

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

Error Analysis of Wind Measurements for the University of Illinois Sodium Doppler Temperature System

NASA Technical Reports Server (NTRS)

Pfenninger, W. Matthew; Papen, George C.

1992-01-01

Four-frequency lidar measurements of temperature and wind velocity require accurate frequency tuning to an absolute reference and long term frequency stability. We quantify frequency tuning errors for the Illinois sodium system, to measure absolute frequencies and a reference interferometer to measure relative frequencies. To determine laser tuning errors, we monitor the vapor cell and interferometer during lidar data acquisition and analyze the two signals for variations as functions of time. Both sodium cell and interferometer are the same as those used to frequency tune the laser. By quantifying the frequency variations of the laser during data acquisition, an error analysis of temperature and wind measurements can be calculated. These error bounds determine the confidence in the calculated temperatures and wind velocities.

Application of a Hybrid Model for Predicting the Incidence of Tuberculosis in Hubei, China

PubMed Central

Zhang, Guoliang; Huang, Shuqiong; Duan, Qionghong; Shu, Wen; Hou, Yongchun; Zhu, Shiyu; Miao, Xiaoping; Nie, Shaofa; Wei, Sheng; Guo, Nan; Shan, Hua; Xu, Yihua

2013-01-01

Background A prediction model for tuberculosis incidence is needed in China which may be used as a decision-supportive tool for planning health interventions and allocating health resources. Methods The autoregressive integrated moving average (ARIMA) model was first constructed with the data of tuberculosis report rate in Hubei Province from Jan 2004 to Dec 2011.The data from Jan 2012 to Jun 2012 were used to validate the model. Then the generalized regression neural network (GRNN)-ARIMA combination model was established based on the constructed ARIMA model. Finally, the fitting and prediction accuracy of the two models was evaluated. Results A total of 465,960 cases were reported between Jan 2004 and Dec 2011 in Hubei Province. The report rate of tuberculosis was highest in 2005 (119.932 per 100,000 population) and lowest in 2010 (84.724 per 100,000 population). The time series of tuberculosis report rate show a gradual secular decline and a striking seasonal variation. The ARIMA (2, 1, 0) × (0, 1, 1)12 model was selected from several plausible ARIMA models. The residual mean square error of the GRNN-ARIMA model and ARIMA model were 0.4467 and 0.6521 in training part, and 0.0958 and 0.1133 in validation part, respectively. The mean absolute error and mean absolute percentage error of the hybrid model were also less than the ARIMA model. Discussion and Conclusions The gradual decline in tuberculosis report rate may be attributed to the effect of intensive measures on tuberculosis. The striking seasonal variation may have resulted from several factors. We suppose that a delay in the surveillance system may also have contributed to the variation. According to the fitting and prediction accuracy, the hybrid model outperforms the traditional ARIMA model, which may facilitate the allocation of health resources in China. PMID:24223232

Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations

NASA Astrophysics Data System (ADS)

Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.

2015-01-01

The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.

Measurement and modeling of particulate matter concentrations: Applying spatial analysis and regression techniques to assess air quality.

PubMed

Sajjadi, Seyed Ali; Zolfaghari, Ghasem; Adab, Hamed; Allahabadi, Ahmad; Delsouz, Mehri

2017-01-01

This paper presented the levels of PM 2.5 and PM 10 in different stations at the city of Sabzevar, Iran. Furthermore, this study was an attempt to evaluate spatial interpolation methods for determining the PM 2.5 and PM 10 concentrations in the city of Sabzevar. Particulate matters were measured by Haz-Dust EPAM at 48 stations. Then, four interpolating models, including Radial Basis Functions (RBF), Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Universal Kriging (UK) were used to investigate the status of air pollution in the city. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were employed to compare the four models. The results showed that the PM 2.5 concentrations in the stations were between 10 and 500 μg/m 3 . Furthermore, the PM 10 concentrations for all of 48 stations ranged from 20 to 1500 μg/m 3 . The concentrations obtained for the period of nine months were greater than the standard limits. There was difference in the values of MAPE, RMSE, MBE, and MAE. The results indicated that the MAPE in IDW method was lower than other methods: (41.05 for PM 2.5 and 25.89 for PM 10 ). The best interpolation method for the particulate matter (PM 2.5 and PM 10 ) seemed to be IDW method. •The PM 10 and PM 2.5 concentration measurements were performed in the period of warm and risky in terms of particulate matter at 2016.•Concentrations of PM 2.5 and PM 10 were measured by a monitoring device, environmental dust model Haz-Dust EPAM 5000.•Interpolation is used to convert data from observation points to continuous fields to compare spatial patterns sampled by these measurements with spatial patterns of other spatial entities.

A digital photographic measurement method for quantifying foot posture: validity, reliability, and descriptive data.

PubMed

Cobb, Stephen C; James, C Roger; Hjertstedt, Matthew; Kruk, James

2011-01-01

Although abnormal foot posture long has been associated with lower extremity injury risk, the evidence is equivocal. Poor intertester reliability of traditional foot measures might contribute to the inconsistency. To investigate the validity and reliability of a digital photographic measurement method (DPMM) technology, the reliability of DPMM-quantified foot measures, and the concurrent validity of the DPMM with clinical-measurement methods (CMMs) and to report descriptive data for DPMM measures with moderate to high intratester and intertester reliability. Descriptive laboratory study. Biomechanics research laboratory. A total of 159 people participated in 3 groups. Twenty-eight people (11 men, 17 women; age  =  25 ± 5 years, height  =  1.71 ± 0.10 m, mass  =  77.6 ± 17.3 kg) were recruited for investigation of intratester and intertester reliability of the DPMM technology; 20 (10 men, 10 women; age  =  24 ± 2 years, height  =  1.71 ± 0.09 m, mass  =  76 ± 16 kg) for investigation of DPMM and CMM reliability and concurrent validity; and 111 (42 men, 69 women; age  =  22.8 ± 4.7 years, height  =  168.5 ± 10.4 cm, mass  =  69.8 ± 13.3 kg) for development of a descriptive data set of the DPMM foot measurements with moderate to high intratester and intertester reliabilities. The dimensions of 10 model rectangles and the 28 participants' feet were measured, and DPMM foot posture was measured in the 111 participants. Two clinicians assessed the DPMM and CMM foot measures of the 20 participants. Validity and reliability were evaluated using mean absolute and percentage errors and intraclass correlation coefficients. Descriptive data were computed from the DPMM foot posture measures. The DPMM technology intratester and intertester reliability intraclass correlation coefficients were 1.0 for each tester and variable. Mean absolute errors were equal to or less than 0.2 mm for the bottom and right-side variables and 0.1° for the calculated angle variable. Mean percentage errors between the DPMM and criterion reference values were equal to or less than 0.4%. Intratester and intertester reliabilities of DPMM-computed structural measures of arch and navicular indices were moderate to high (>0.78), and concurrent validity was moderate to strong. The DPMM is a valid and reliable clinical and research tool for quantifying foot structure. The DPMM and the descriptive data might be used to define groups in future studies in which the relationship between foot posture and function or injury risk is investigated.

State-of-the-Art pH Electrode Quality Control for Measurements of Acidic, Low Ionic Strength Waters.

ERIC Educational Resources Information Center

Stapanian, Martin A.; Metcalf, Richard C.

1990-01-01

Described is the derivation of the relationship between the pH measurement error and the resulting percentage error in hydrogen ion concentration including the use of variable activity coefficients. The relative influence of the ionic strength of the solution on the percentage error is shown. (CW)

Predictability of the Arctic sea ice edge

NASA Astrophysics Data System (ADS)

Goessling, H. F.; Tietsche, S.; Day, J. J.; Hawkins, E.; Jung, T.

2016-02-01

Skillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the "truth" disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often-neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end-user relevant forecasts.

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

Ellefson, S; Department of Human Oncology, University of Wisconsin, Madison, WI; Culberson, W

Purpose: Discrepancies in absolute dose values have been detected between the ViewRay treatment planning system and ArcCHECK readings when performing delivery quality assurance on the ViewRay system with the ArcCHECK-MR diode array (SunNuclear Corporation). In this work, we investigate whether these discrepancies are due to errors in the ViewRay planning and/or delivery system or due to errors in the ArcCHECK’s readings. Methods: Gamma analysis was performed on 19 ViewRay patient plans using the ArcCHECK. Frequency analysis on the dose differences was performed. To investigate whether discrepancies were due to measurement or delivery error, 10 diodes in low-gradient dose regions weremore » chosen to compare with ion chamber measurements in a PMMA phantom with the same size and shape as the ArcCHECK, provided by SunNuclear. The diodes chosen all had significant discrepancies in absolute dose values compared to the ViewRay TPS. Absolute doses to PMMA were compared between the ViewRay TPS calculations, ArcCHECK measurements, and measurements in the PMMA phantom. Results: Three of the 19 patient plans had 3%/3mm gamma passing rates less than 95%, and ten of the 19 plans had 2%/2mm passing rates less than 95%. Frequency analysis implied a non-random error process. Out of the 10 diode locations measured, ion chamber measurements were all within 2.2% error relative to the TPS and had a mean error of 1.2%. ArcCHECK measurements ranged from 4.5% to over 15% error relative to the TPS and had a mean error of 8.0%. Conclusion: The ArcCHECK performs well for quality assurance on the ViewRay under most circumstances. However, under certain conditions the absolute dose readings are significantly higher compared to the planned doses. As the ion chamber measurements consistently agree with the TPS, it can be concluded that the discrepancies are due to ArcCHECK measurement error and not TPS or delivery system error. This work was funded by the Bhudatt Paliwal Professorship and the University of Wisconsin Medical Radiation Research Center.« less

Low-Cost Ultrasonic Distance Sensor Arrays with Networked Error Correction

PubMed Central

Dai, Hongjun; Zhao, Shulin; Jia, Zhiping; Chen, Tianzhou

2013-01-01

Distance has been one of the basic factors in manufacturing and control fields, and ultrasonic distance sensors have been widely used as a low-cost measuring tool. However, the propagation of ultrasonic waves is greatly affected by environmental factors such as temperature, humidity and atmospheric pressure. In order to solve the problem of inaccurate measurement, which is significant within industry, this paper presents a novel ultrasonic distance sensor model using networked error correction (NEC) trained on experimental data. This is more accurate than other existing approaches because it uses information from indirect association with neighboring sensors, which has not been considered before. The NEC technique, focusing on optimization of the relationship of the topological structure of sensor arrays, is implemented for the compensation of erroneous measurements caused by the environment. We apply the maximum likelihood method to determine the optimal fusion data set and use a neighbor discovery algorithm to identify neighbor nodes at the top speed. Furthermore, we adopt the NEC optimization algorithm, which takes full advantage of the correlation coefficients for neighbor sensors. The experimental results demonstrate that the ranging errors of the NEC system are within 2.20%; furthermore, the mean absolute percentage error is reduced to 0.01% after three iterations of this method, which means that the proposed method performs extremely well. The optimized method of distance measurement we propose, with the capability of NEC, would bring a significant advantage for intelligent industrial automation. PMID:24013491

Accuracy of a Basketball Indoor Tracking System Based on Standard Bluetooth Low Energy Channels (NBN23®).

PubMed

Figueira, Bruno; Gonçalves, Bruno; Folgado, Hugo; Masiulis, Nerijus; Calleja-González, Julio; Sampaio, Jaime

2018-06-14

The present study aims to identify the accuracy of the NBN23 ® system, an indoor tracking system based on radio-frequency and standard Bluetooth Low Energy channels. Twelve capture tags were attached to a custom cart with fixed distances of 0.5, 1.0, 1.5, and 1.8 m. The cart was pushed along a predetermined course following the lines of a standard dimensions Basketball court. The course was performed at low speed (<10.0 km/h), medium speed (>10.0 km/h and <20.0 km/h) and high speed (>20.0 km/h). Root mean square error (RMSE) and percentage of variance accounted for (%VAF) were used as accuracy measures. The obtained data showed acceptable accuracy results for both RMSE and %VAF, despite the expected degree of error in position measurement at higher speeds. The RMSE for all the distances and velocities presented an average absolute error of 0.30 ± 0.13 cm with 90.61 ± 8.34 of %VAF, in line with most available systems, and considered acceptable for indoor sports. The processing of data with filter correction seemed to reduce the noise and promote a lower relative error, increasing the %VAF for each measured distance. Research using positional-derived variables in Basketball is still very scarce; thus, this independent test of the NBN23 ® tracking system provides accuracy details and opens up opportunities to develop new performance indicators that help to optimize training adaptations and performance.

Evaluating Laboratory Performance on Point-of-Care Glucose Testing with Six Sigma Metric for 151 Institutions in China.

PubMed

Fei, Yang; Wang, Wei; He, Falin; Zhong, Kun; Wang, Zhiguo

2015-10-01

The aim of this study was to use Six Sigma(SM) (Motorola Trademark Holdings, Libertyville, IL) techniques to analyze the quality of point-of-care (POC) glucose testing measurements quantitatively and to provide suggestions for improvement. In total, 151 laboratories in China were included in this investigation in 2014. Bias and coefficient of variation were collected from an external quality assessment and an internal quality control program, respectively, for POC glucose testing organized by the National Center for Clinical Laboratories. The σ values and the Quality Goal Index were used to evaluate the performance of POC glucose meters. There were 27, 30, 57, and 37 participants in the groups using Optium Xceed™ (Abbott Diabetes Care, Alameda, CA), Accu-Chek(®) Performa (Roche, Basel, Switzerland), One Touch Ultra(®) (Abbott), and "other" meters, respectively. The median of the absolute value of percentage difference varied among different lots and different groups. Among all the groups, the Abbott One Touch Ultra group had the smallest median of absolute value of percentage difference except for lot 201411, whereas the "other" group had the largest median in all five lots. More than 85% of participate laboratories satisfied the total allowable error (TEa) requirement in International Organization for Standardization standard 15197:2013, and 85.43% (129/151) of laboratories obtained intralaboratory coefficient of variations less than 1/3TEa. However, Six Sigma techniques suggested that 41.72% (63/151) to 65.56% (99/151) of the laboratories needed to improve their POC glucose testing performance, in either precision, trueness, or both. Laboratories should pay more attention on the practice of POC glucose testing and take actions to improve their performance. Only in this way can POC glucose testing really function well in clinical practice.

Systematic errors of EIT systems determined by easily-scalable resistive phantoms.

PubMed

Hahn, G; Just, A; Dittmar, J; Hellige, G

2008-06-01

We present a simple method to determine systematic errors that will occur in the measurements by EIT systems. The approach is based on very simple scalable resistive phantoms for EIT systems using a 16 electrode adjacent drive pattern. The output voltage of the phantoms is constant for all combinations of current injection and voltage measurements and the trans-impedance of each phantom is determined by only one component. It can be chosen independently from the input and output impedance, which can be set in order to simulate measurements on the human thorax. Additional serial adapters allow investigation of the influence of the contact impedance at the electrodes on resulting errors. Since real errors depend on the dynamic properties of an EIT system, the following parameters are accessible: crosstalk, the absolute error of each driving/sensing channel and the signal to noise ratio in each channel. Measurements were performed on a Goe-MF II EIT system under four different simulated operational conditions. We found that systematic measurement errors always exceeded the error level of stochastic noise since the Goe-MF II system had been optimized for a sufficient signal to noise ratio but not for accuracy. In time difference imaging and functional EIT (f-EIT) systematic errors are reduced to a minimum by dividing the raw data by reference data. This is not the case in absolute EIT (a-EIT) where the resistivity of the examined object is determined on an absolute scale. We conclude that a reduction of systematic errors has to be one major goal in future system design.

45 CFR 98.102 - Content of Error Rate Reports.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Funds and State Matching and Maintenance-of-Effort (MOE Funds): (1) Percentage of cases with an error... cases in the sample with an error compared to the total number of cases in the sample; (2) Percentage of cases with an improper payment (both over and under payments), expressed as the total number of cases in...

Development and evaluation of a gyroscope-based wheel rotation monitor for manual wheelchair users.

PubMed

Hiremath, Shivayogi V; Ding, Dan; Cooper, Rory A

2013-07-01

To develop and evaluate a wireless gyroscope-based wheel rotation monitor (G-WRM) that can estimate speeds and distances traveled by wheelchair users during regular wheelchair propulsion as well as wheelchair sports such as handcycling, and provide users with real-time feedback through a smartphone application. The speeds and the distances estimated by the G-WRM were compared with the criterion measures by calculating absolute difference, mean difference, and percentage errors during a series of laboratory-based tests. Intraclass correlations (ICC) and the Bland-Altman plots were also used to assess the agreements between the G-WRM and the criterion measures. In addition, battery life and wireless data transmission tests under a number of usage conditions were performed. The percentage errors for the angular velocities, speeds, and distances obtained from three prototype G-WRMs were less than 3% for all the test trials. The high ICC values (ICC (3,1) > 0.94) and the Bland-Altman plots indicate excellent agreement between the estimated speeds and distances by the G-WRMs and the criterion measures. The battery life tests showed that the device could last for 35 hours in wireless mode and 139 hours in secure digital card mode. The wireless data transmission tests indicated less than 0.3% of data loss. The results indicate that the G-WRM is an appropriate tool for tracking a spectrum of wheelchair-related activities from regular wheelchair propulsion to wheelchair sports such as handcycling. The real-time feedback provided by the G-WRM can help wheelchair users self-monitor their everyday activities.

Abdominal adipose tissue compartments vary with ethnicity in Asian neonates: Growing Up in Singapore Toward Healthy Outcomes birth cohort study.

PubMed

Tint, Mya Thway; Fortier, Marielle V; Godfrey, Keith M; Shuter, Borys; Kapur, Jeevesh; Rajadurai, Victor S; Agarwal, Pratibha; Chinnadurai, Amutha; Niduvaje, Krishnamoorthy; Chan, Yiong-Huak; Aris, Izzuddin Bin Mohd; Soh, Shu-E; Yap, Fabian; Saw, Seang-Mei; Kramer, Michael S; Gluckman, Peter D; Chong, Yap-Seng; Lee, Yung-Seng

2016-05-01

A susceptibility to metabolic diseases is associated with abdominal adipose tissue distribution and varies between ethnic groups. The distribution of abdominal adipose tissue at birth may give insights into whether ethnicity-associated variations in metabolic risk originate partly in utero. We assessed the influence of ethnicity on abdominal adipose tissue compartments in Asian neonates in the Growing Up in Singapore Toward Healthy Outcomes mother-offspring cohort. MRI was performed at ≤2 wk after birth in 333 neonates born at ≥34 wk of gestation and with birth weights ≥2000 g. Abdominal superficial subcutaneous tissue (sSAT), deep subcutaneous tissue (dSAT), and internal adipose tissue (IAT) compartment volumes (absolute and as a percentage of the total abdominal volume) were quantified. In multivariate analyses that were controlled for sex, age, and parity, the absolute and percentage of dSAT and the percentage of sSAT (but not absolute sSAT) were greater, whereas absolute IAT (but not the percentage of IAT) was lower, in Indian neonates than in Chinese neonates. Compared with Chinese neonates, Malay neonates had greater percentages of sSAT and dSAT but similar percentages of IAT. Marginal structural model analyses largely confirmed the results on the basis of volume percentages with controlled direct effects of ethnicity on abdominal adipose tissue; dSAT was significantly greater (1.45 mL; 95% CI: 0.49, 2.41 mL, P = 0.003) in non-Chinese (Indian or Malay) neonates than in Chinese neonates. However, ethnic differences in sSAT and IAT were NS [3.06 mL (95% CI:-0.27, 6.39 mL; P = 0.0712) for sSAT and -1.30 mL (95% CI: -2.64, 0.04 mL; P = 0.057) for IAT in non-Chinese compared with Chinese neonates, respectively]. Indian and Malay neonates have a greater dSAT volume than do Chinese neonates. This finding supports the notion that in utero influences may contribute to higher cardiometabolic risk observed in Indian and Malay persons in our population. If such differences persist in the longitudinal tracking of adipose tissue growth, these differences may contribute to the ethnic disparities in risks of cardiometabolic diseases. This trial was registered at clinicaltrials.gov as NCT01174875. © 2016 American Society for Nutrition.

Wideband Arrhythmia-Insensitive-Rapid (AIR) Pulse Sequence for Cardiac T1 mapping without Image Artifacts induced by ICD

PubMed Central

Hong, KyungPyo; Jeong, Eun-Kee; Wall, T. Scott; Drakos, Stavros G.; Kim, Daniel

2015-01-01

Purpose To develop and evaluate a wideband arrhythmia-insensitive-rapid (AIR) pulse sequence for cardiac T1 mapping without image artifacts induced by implantable-cardioverter-defibrillator (ICD). Methods We developed a wideband AIR pulse sequence by incorporating a saturation pulse with wide frequency bandwidth (8.9 kHz), in order to achieve uniform T1 weighting in the heart with ICD. We tested the performance of original and “wideband” AIR cardiac T1 mapping pulse sequences in phantom and human experiments at 1.5T. Results In 5 phantoms representing native myocardium and blood and post-contrast blood/tissue T1 values, compared with the control T1 values measured with an inversion-recovery pulse sequence without ICD, T1 values measured with original AIR with ICD were considerably lower (absolute percent error >29%), whereas T1 values measured with wideband AIR with ICD were similar (absolute percent error <5%). Similarly, in 11 human subjects, compared with the control T1 values measured with original AIR without ICD, T1 measured with original AIR with ICD was significantly lower (absolute percent error >10.1%), whereas T1 measured with wideband AIR with ICD was similar (absolute percent error <2.0%). Conclusion This study demonstrates the feasibility of a wideband pulse sequence for cardiac T1 mapping without significant image artifacts induced by ICD. PMID:25975192

Absolute color scale for improved diagnostics with wavefront error mapping.

PubMed

Smolek, Michael K; Klyce, Stephen D

2007-11-01

Wavefront data are expressed in micrometers and referenced to the pupil plane, but current methods to map wavefront error lack standardization. Many use normalized or floating scales that may confuse the user by generating ambiguous, noisy, or varying information. An absolute scale that combines consistent clinical information with statistical relevance is needed for wavefront error mapping. The color contours should correspond better to current corneal topography standards to improve clinical interpretation. Retrospective analysis of wavefront error data. Historic ophthalmic medical records. Topographic modeling system topographical examinations of 120 corneas across 12 categories were used. Corneal wavefront error data in micrometers from each topography map were extracted at 8 Zernike polynomial orders and for 3 pupil diameters expressed in millimeters (3, 5, and 7 mm). Both total aberrations (orders 2 through 8) and higher-order aberrations (orders 3 through 8) were expressed in the form of frequency histograms to determine the working range of the scale across all categories. The standard deviation of the mean error of normal corneas determined the map contour resolution. Map colors were based on corneal topography color standards and on the ability to distinguish adjacent color contours through contrast. Higher-order and total wavefront error contour maps for different corneal conditions. An absolute color scale was produced that encompassed a range of +/-6.5 microm and a contour interval of 0.5 microm. All aberrations in the categorical database were plotted with no loss of clinical information necessary for classification. In the few instances where mapped information was beyond the range of the scale, the type and severity of aberration remained legible. When wavefront data are expressed in micrometers, this absolute scale facilitates the determination of the severity of aberrations present compared with a floating scale, particularly for distinguishing normal from abnormal levels of wavefront error. The new color palette makes it easier to identify disorders. The corneal mapping method can be extended to mapping whole eye wavefront errors. When refraction data are expressed in diopters, the previously published corneal topography scale is suggested.

Reliability study of biometrics "do not contact" in myopia.

PubMed

Migliorini, R; Fratipietro, M; Comberiati, A M; Pattavina, L; Arrico, L

The aim of the study is a comparison between the actually achieved after surgery condition versus the expected refractive condition of the eye as calculated via a biometer. The study was conducted in a random group of 38 eyes of patients undergoing surgery by phacoemulsification. The mean absolute error was calculated between the predicted values from the measurements with the optical biometer and those obtained in the post-operative error which was at around 0.47% Our study shows results not far from those reported in the literature, and in relation, to the mean absolute error is among the lowest values at 0.47 ± 0.11 SEM.

Automated estimation of abdominal effective diameter for body size normalization of CT dose.

PubMed

Cheng, Phillip M

2013-06-01

Most CT dose data aggregation methods do not currently adjust dose values for patient size. This work proposes a simple heuristic for reliably computing an effective diameter of a patient from an abdominal CT image. Evaluation of this method on 106 patients scanned on Philips Brilliance 64 and Brilliance Big Bore scanners demonstrates close correspondence between computed and manually measured patient effective diameters, with a mean absolute error of 1.0 cm (error range +2.2 to -0.4 cm). This level of correspondence was also demonstrated for 60 patients on Siemens, General Electric, and Toshiba scanners. A calculated effective diameter in the middle slice of an abdominal CT study was found to be a close approximation of the mean calculated effective diameter for the study, with a mean absolute error of approximately 1.0 cm (error range +3.5 to -2.2 cm). Furthermore, the mean absolute error for an adjusted mean volume computed tomography dose index (CTDIvol) using a mid-study calculated effective diameter, versus a mean per-slice adjusted CTDIvol based on the calculated effective diameter of each slice, was 0.59 mGy (error range 1.64 to -3.12 mGy). These results are used to calculate approximate normalized dose length product values in an abdominal CT dose database of 12,506 studies.

Comparison of spatial interpolation methods for soil moisture and its application for monitoring drought.

PubMed

Chen, Hui; Fan, Li; Wu, Wei; Liu, Hong-Bin

2017-09-26

Soil moisture data can reflect valuable information on soil properties, terrain features, and drought condition. The current study compared and assessed the performance of different interpolation methods for estimating soil moisture in an area with complex topography in southwest China. The approaches were inverse distance weighting, multifarious forms of kriging, regularized spline with tension, and thin plate spline. The 5-day soil moisture observed at 167 stations and daily temperature recorded at 33 stations during the period of 2010-2014 were used in the current work. Model performance was tested with accuracy indicators of determination coefficient (R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and modeling efficiency (ME). The results indicated that inverse distance weighting had the best performance with R 2 , MAPE, RMSE, RRMSE, and ME of 0.32, 14.37, 13.02%, 0.16, and 0.30, respectively. Based on the best method, a spatial database of soil moisture was developed and used to investigate drought condition over the study area. The results showed that the distribution of drought was characterized by evidently regional difference. Besides, drought mainly occurred in August and September in the 5 years and was prone to happening in the western and central parts rather than in the northeastern and southeastern areas.

Development and application of GIS-based PRISM integration through a plugin approach

NASA Astrophysics Data System (ADS)

Lee, Woo-Seop; Chun, Jong Ahn; Kang, Kwangmin

2014-05-01

A PRISM (Parameter-elevation Regressions on Independent Slopes Model) QGIS-plugin was developed on Quantum GIS platform in this study. This Quantum GIS plugin system provides user-friendly graphic user interfaces (GUIs) so that users can obtain gridded meteorological data of high resolutions (1 km × 1 km). Also, this software is designed to run on a personal computer so that it does not require an internet access or a sophisticated computer system. This module is a user-friendly system that a user can generate PRISM data with ease. The proposed PRISM QGIS-plugin is a hybrid statistical-geographic model system that uses coarse resolution datasets (APHRODITE datasets in this study) with digital elevation data to generate the fine-resolution gridded precipitation. To validate the performance of the software, Prek Thnot River Basin in Kandal, Cambodia is selected for application. Overall statistical analysis shows promising outputs generated by the proposed plugin. Error measures such as RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) were used to evaluate the performance of the developed PRISM QGIS-plugin. Evaluation results using RMSE and MAPE were 2.76 mm and 4.2%, respectively. This study suggested that the plugin can be used to generate high resolution precipitation datasets for hydrological and climatological studies at a watershed where observed weather datasets are limited.

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

PubMed Central

Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M.

2016-01-01

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover. PMID:27438600

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks.

PubMed

Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M

2016-01-01

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.

A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes.

PubMed

Ebtehaj, Isa; Bonakdari, Hossein

2016-01-01

Sediment transport without deposition is an essential consideration in the optimum design of sewer pipes. In this study, a novel method based on a combination of support vector regression (SVR) and the firefly algorithm (FFA) is proposed to predict the minimum velocity required to avoid sediment settling in pipe channels, which is expressed as the densimetric Froude number (Fr). The efficiency of support vector machine (SVM) models depends on the suitable selection of SVM parameters. In this particular study, FFA is used by determining these SVM parameters. The actual effective parameters on Fr calculation are generally identified by employing dimensional analysis. The different dimensionless variables along with the models are introduced. The best performance is attributed to the model that employs the sediment volumetric concentration (C(V)), ratio of relative median diameter of particles to hydraulic radius (d/R), dimensionless particle number (D(gr)) and overall sediment friction factor (λ(s)) parameters to estimate Fr. The performance of the SVR-FFA model is compared with genetic programming, artificial neural network and existing regression-based equations. The results indicate the superior performance of SVR-FFA (mean absolute percentage error = 2.123%; root mean square error =0.116) compared with other methods.

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

NASA Astrophysics Data System (ADS)

Wang, Wen-Chuan; Chau, Kwok-Wing; Cheng, Chun-Tian; Qiu, Lin

2009-08-01

SummaryDeveloping a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R), Nash-Sutcliffe efficiency coefficient ( E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.

Decomposition Analyses Applied to a Complex Ultradian Biorhythm: The Oscillating NADH Oxidase Activity of Plasma Membranes Having a Potential Time-Keeping (Clock) Function

PubMed Central

Foster, Ken; Anwar, Nasim; Pogue, Rhea; Morré, Dorothy M.; Keenan, T. W.; Morré, D. James

2003-01-01

Seasonal decomposition analyses were applied to the statistical evaluation of an oscillating activity for a plasma membrane NADH oxidase activity with a temperature compensated period of 24 min. The decomposition fits were used to validate the cyclic oscillatory pattern. Three measured values, average percentage error (MAPE), a measure of the periodic oscillation, mean average deviation (MAD), a measure of the absolute average deviations from the fitted values, and mean standard deviation (MSD), the measure of standard deviation from the fitted values plus R-squared and the Henriksson-Merton p value were used to evaluate accuracy. Decomposition was carried out by fitting a trend line to the data, then detrending the data if necessary, by subtracting the trend component. The data, with or without detrending, were then smoothed by subtracting a centered moving average of length equal to the period length determined by Fourier analysis. Finally, the time series were decomposed into cyclic and error components. The findings not only validate the periodic nature of the major oscillations but suggest, as well, that the minor intervening fluctuations also recur within each period with a reproducible pattern of recurrence. PMID:19330112

Vestibular schwannomas: Accuracy of tumor volume estimated by ice cream cone formula using thin-sliced MR images

PubMed Central

Ho, Hsing-Hao; Li, Ya-Hui; Lee, Jih-Chin; Wang, Chih-Wei; Yu, Yi-Lin; Hueng, Dueng-Yuan; Hsu, Hsian-He

2018-01-01

Purpose We estimated the volume of vestibular schwannomas by an ice cream cone formula using thin-sliced magnetic resonance images (MRI) and compared the estimation accuracy among different estimating formulas and between different models. Methods The study was approved by a local institutional review board. A total of 100 patients with vestibular schwannomas examined by MRI between January 2011 and November 2015 were enrolled retrospectively. Informed consent was waived. Volumes of vestibular schwannomas were estimated by cuboidal, ellipsoidal, and spherical formulas based on a one-component model, and cuboidal, ellipsoidal, Linskey’s, and ice cream cone formulas based on a two-component model. The estimated volumes were compared to the volumes measured by planimetry. Intraobserver reproducibility and interobserver agreement was tested. Estimation error, including absolute percentage error (APE) and percentage error (PE), was calculated. Statistical analysis included intraclass correlation coefficient (ICC), linear regression analysis, one-way analysis of variance, and paired t-tests with P < 0.05 considered statistically significant. Results Overall tumor size was 4.80 ± 6.8 mL (mean ±standard deviation). All ICCs were no less than 0.992, suggestive of high intraobserver reproducibility and high interobserver agreement. Cuboidal formulas significantly overestimated the tumor volume by a factor of 1.9 to 2.4 (P ≤ 0.001). The one-component ellipsoidal and spherical formulas overestimated the tumor volume with an APE of 20.3% and 29.2%, respectively. The two-component ice cream cone method, and ellipsoidal and Linskey’s formulas significantly reduced the APE to 11.0%, 10.1%, and 12.5%, respectively (all P < 0.001). Conclusion The ice cream cone method and other two-component formulas including the ellipsoidal and Linskey’s formulas allow for estimation of vestibular schwannoma volume more accurately than all one-component formulas. PMID:29438424

Estimation of Right-Lobe Graft Weight From Computed Tomographic Volumetry for Living Donor Liver Transplantation.

PubMed

Yang, X; Chu, C W; Yang, J D; Yang, K H; Yu, H C; Cho, B H; You, H

2017-03-01

The objective of the study was to establish a right-lobe graft weight (GW) estimation formula for living donor liver transplantation (LDLT) from right-lobe graft volume without veins (GV w/o_veins ), including portal vein and hepatic vein measured by computed tomographic (CT) volumetry, and to compare its estimation accuracy with those of existing formulas. Right-lobe GW estimation formulas established with the use of graft volume with veins (GV w_veins ) sacrifice accuracy because GW measured intra-operatively excludes the weight of blood in the veins. Right-lobe GW estimation formulas have been established with the use of right-lobe GV w/o_veins , but a more accurate formula must be developed. The present study developed right-lobe GW estimation formulas based on GV w/o_veins as well as GV w_veins , using 40 cases of Korean donors: GW = 29.1 + 0.943 × GV w/o_veins (adjusted R 2  = 0.94) and GW = 74.7 + 0.773 × GV w_veins (adjusted R 2  = 0.87). The proposed GW estimation formulas were compared with existing GV w_veins - and GV w/o_veins -based models, using 43 cases additionally obtained from two medical centers for cross-validation. The GV w/o_veins -based formula developed in the present study was most preferred (absolute error = 21.5 ± 16.5 g and percentage of absolute error = 3.0 ± 2.3%). The GV w/o_veins -based formula is preferred to the GV w_veins -based formula in GW estimation. Accurate CT volumetry and alignment between planned and actual surgical cutting lines are crucial in the establishment of a better GW estimation formula. Copyright © 2016 Elsevier Inc. All rights reserved.

Clinical time series prediction: Toward a hierarchical dynamical system framework.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Comparison of the Pentacam equivalent keratometry reading and IOL Master keratometry measurement in intraocular lens power calculations.

PubMed

Karunaratne, Nicholas

2013-12-01

To compare the accuracy of the Pentacam Holladay equivalent keratometry readings with the IOL Master 500 keratometry in calculating intraocular lens power. Non-randomized, prospective clinical study conducted in private practice. Forty-five consecutive normal patients undergoing cataract surgery. Forty-five consecutive patients had Pentacam equivalent keratometry readings at the 2-, 3 and 4.5-mm corneal zone and IOL Master keratometry measurements prior to cataract surgery. For each Pentacam equivalent keratometry reading zone and IOL Master measurement the difference between the observed and expected refractive error was calculated using the Holladay 2 and Sanders, Retzlaff and Kraff theoretic (SRKT) formulas. Mean keratometric value and mean absolute refractive error. There was a statistically significantly difference between the mean keratometric values of the IOL Master, Pentacam equivalent keratometry reading 2-, 3- and 4.5-mm measurements (P < 0.0001, analysis of variance). There was no statistically significant difference between the mean absolute refraction error for the IOL Master and equivalent keratometry readings 2 mm, 3 mm and 4.5 mm zones for either the Holladay 2 formula (P = 0.14) or SRKT formula (P = 0.47). The lowest mean absolute refraction error for Holladay 2 equivalent keratometry reading was the 4.5 mm zone (mean 0.25 D ± 0.17 D). The lowest mean absolute refraction error for SRKT equivalent keratometry reading was the 4.5 mm zone (mean 0.25 D ± 0.19 D). Comparing the absolute refraction error of IOL Master and Pentacam equivalent keratometry reading, best agreement was with Holladay 2 and equivalent keratometry reading 4.5 mm, with mean of the difference of 0.02 D and 95% limits of agreement of -0.35 and 0.39 D. The IOL Master keratometry and Pentacam equivalent keratometry reading were not equivalent when used only for corneal power measurements. However, the keratometry measurements of the IOL Master and Pentacam equivalent keratometry reading 4.5 mm may be similarly effective when used in intraocular lens power calculation formulas, following constant optimization. © 2013 Royal Australian and New Zealand College of Ophthalmologists.

Volatility of bitumen prices and implications for the industry

USGS Publications Warehouse

Attanasi, E.D.

2008-01-01

Sustained crude oil price increases have led to increased investment in and production of Canadian bitumen to supplement North American oil supplies. For new projects, the evaluation of profitability is based on a prediction of the future price path of bitumen and ultimately light/medium crude oil. This article examines the relationship between the bitumen and light crude oil prices in the context of a simple error-correction economic-adjustment model. The analysis shows bitumen prices to be significantly more volatile than light crude prices. Also, the dominant effect of an oil price shock on bitumen prices is immediate and is amplified, both in absolute terms and percentage price changes. It is argued that the bitumen industry response to such market risks will likely be a realignment toward vertical integration via new downstream construction, mergers, or on a de facto basis by the establishment of alliances. ?? 2008 International Association for Mathematical Geology.

Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

NASA Astrophysics Data System (ADS)

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

2010-02-01

This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

Exploiting data representation for fault tolerance

DOE PAGES

Hoemmen, Mark Frederick; Elliott, J.; Sandia National Lab.; ...

2015-01-06

Incorrect computer hardware behavior may corrupt intermediate computations in numerical algorithms, possibly resulting in incorrect answers. Prior work models misbehaving hardware by randomly flipping bits in memory. We start by accepting this premise, and present an analytic model for the error introduced by a bit flip in an IEEE 754 floating-point number. We then relate this finding to the linear algebra concepts of normalization and matrix equilibration. In particular, we present a case study illustrating that normalizing both vector inputs of a dot product minimizes the probability of a single bit flip causing a large error in the dot product'smore » result. Moreover, the absolute error is either less than one or very large, which allows detection of large errors. Then, we apply this to the GMRES iterative solver. We count all possible errors that can be introduced through faults in arithmetic in the computationally intensive orthogonalization phase of GMRES, and show that when the matrix is equilibrated, the absolute error is bounded above by one.« less

Enumeration of antigen-specific CD8+ T lymphocytes by single-platform, HLA tetramer-based flow cytometry: a European multicenter evaluation.

PubMed

Heijnen, Ingmar A F M; Barnett, David; Arroz, Maria J; Barry, Simon M; Bonneville, Marc; Brando, Bruno; D'hautcourt, Jean-Luc; Kern, Florian; Tötterman, Thomas H; Marijt, Erik W A; Bossy, David; Preijers, Frank W M B; Rothe, Gregor; Gratama, Jan W

2004-11-01

HLA class I peptide tetramers represent powerful diagnostic tools for detection and monitoring of antigen-specific CD8(+) T cells. The impetus for the current multicenter study is the critical need to standardize tetramer flow cytometry if it is to be implemented as a routine diagnostic assay. Hence, the European Working Group on Clinical Cell Analysis set out to develop and evaluate a single-platform tetramer-based method that used cytomegalovirus (CMV) as the antigenic model. Absolute numbers of CMV-specific CD8(+) T cells were obtained by combining the percentage of tetramer-binding cells with the absolute CD8(+) T-cell count. Six send-outs of stabilized blood from healthy individuals or CMV-carrying donors with CMV-specific CD8(+) T-cell counts of 3 to 10 cells/microl were distributed to 7 to 16 clinical sites. These sites were requested to enumerate CD8(+) T cells and, in the case of CMV-positive donors, CMV-specific subsets on three separate occasions using the standard method. Between-site coefficients of variation of less than 10% (absolute CD8(+) T-cell counts) and approximately 30% (percentage and absolute numbers of CMV-specific CD8(+) T cells) were achieved. Within-site coefficients of variation were approximately 5% (absolute CD8(+) T-cell counts), approximately 9% (percentage CMV-specific CD8(+) T cells), and approximately 17% (absolute CMV-specific CD8(+) T-cell counts). The degree of variation tended to correlate inversely with the proportion of CMV-specific CD8(+) T-cell subsets. The single-platform MHC tetramer-based method for antigen-specific CD8(+) T-cell counting has been evaluated by a European group of laboratories and can be considered a reproducible assay for routine enumeration of antigen-specific CD8(+) T cells. (c) 2004 Wiley-Liss, Inc.

A suggestion for computing objective function in model calibration

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang

2014-01-01

A parameter-optimization process (model calibration) is usually required for numerical model applications, which involves the use of an objective function to determine the model cost (model-data errors). The sum of square errors (SSR) has been widely adopted as the objective function in various optimization procedures. However, ‘square error’ calculation was found to be more sensitive to extreme or high values. Thus, we proposed that the sum of absolute errors (SAR) may be a better option than SSR for model calibration. To test this hypothesis, we used two case studies—a hydrological model calibration and a biogeochemical model calibration—to investigate the behavior of a group of potential objective functions: SSR, SAR, sum of squared relative deviation (SSRD), and sum of absolute relative deviation (SARD). Mathematical evaluation of model performance demonstrates that ‘absolute error’ (SAR and SARD) are superior to ‘square error’ (SSR and SSRD) in calculating objective function for model calibration, and SAR behaved the best (with the least error and highest efficiency). This study suggests that SSR might be overly used in real applications, and SAR may be a reasonable choice in common optimization implementations without emphasizing either high or low values (e.g., modeling for supporting resources management).

Wechsler Adult Intelligence Scale-Revised Block Design broken configuration errors in nonpenetrating traumatic brain injury.

PubMed

Wilde, M C; Boake, C; Sherer, M

2000-01-01

Final broken configuration errors on the Wechsler Adult Intelligence Scale-Revised (WAIS-R; Wechsler, 1981) Block Design subtest were examined in 50 moderate and severe nonpenetrating traumatically brain injured adults. Patients were divided into left (n = 15) and right hemisphere (n = 19) groups based on a history of unilateral craniotomy for treatment of an intracranial lesion and were compared to a group with diffuse or negative brain CT scan findings and no history of neurosurgery (n = 16). The percentage of final broken configuration errors was related to injury severity, Benton Visual Form Discrimination Test (VFD; Benton, Hamsher, Varney, & Spreen, 1983) total score and the number of VFD rotation and peripheral errors. The percentage of final broken configuration errors was higher in the patients with right craniotomies than in the left or no craniotomy groups, which did not differ. Broken configuration errors did not occur more frequently on designs without an embedded grid pattern. Right craniotomy patients did not show a greater percentage of broken configuration errors on nongrid designs as compared to grid designs.

Estimation of lower flammability limits of C-H compounds in air at atmospheric pressure, evaluation of temperature dependence and diluent effect.

PubMed

Mendiburu, Andrés Z; de Carvalho, João A; Coronado, Christian R

2015-03-21

Estimation of the lower flammability limits of C-H compounds at 25 °C and 1 atm; at moderate temperatures and in presence of diluent was the objective of this study. A set of 120 C-H compounds was divided into a correlation set and a prediction set of 60 compounds each. The absolute average relative error for the total set was 7.89%; for the correlation set, it was 6.09%; and for the prediction set it was 9.68%. However, it was shown that by considering different sources of experimental data the values were reduced to 6.5% for the prediction set and to 6.29% for the total set. The method showed consistency with Le Chatelier's law for binary mixtures of C-H compounds. When tested for a temperature range from 5 °C to 100 °C, the absolute average relative errors were 2.41% for methane; 4.78% for propane; 0.29% for iso-butane and 3.86% for propylene. When nitrogen was added, the absolute average relative errors were 2.48% for methane; 5.13% for propane; 0.11% for iso-butane and 0.15% for propylene. When carbon dioxide was added, the absolute relative errors were 1.80% for methane; 5.38% for propane; 0.86% for iso-butane and 1.06% for propylene. Copyright © 2014 Elsevier B.V. All rights reserved.

Fundamental principles of absolute radiometry and the philosophy of this NBS program (1968 to 1971)

NASA Technical Reports Server (NTRS)

Geist, J.

1972-01-01

A description is given work performed on a program to develop an electrically calibrated detector (also called absolute radiometer, absolute detector, and electrically calibrated radiometer) that could be used to realize, maintain, and transfer a scale of total irradiance. The program includes a comprehensive investigation of the theoretical basis of absolute detector radiometry, as well as the design and construction of a number of detectors. A theoretical analysis of the sources of error is also included.

The effect of modeled absolute timing variability and relative timing variability on observational learning.

PubMed

Grierson, Lawrence E M; Roberts, James W; Welsher, Arthur M

2017-05-01

There is much evidence to suggest that skill learning is enhanced by skill observation. Recent research on this phenomenon indicates a benefit of observing variable/erred demonstrations. In this study, we explore whether it is variability within the relative organization or absolute parameterization of a movement that facilitates skill learning through observation. To do so, participants were randomly allocated into groups that observed a model with no variability, absolute timing variability, relative timing variability, or variability in both absolute and relative timing. All participants performed a four-segment movement pattern with specific absolute and relative timing goals prior to and following the observational intervention, as well as in a 24h retention test and transfers tests that featured new relative and absolute timing goals. Absolute timing error indicated that all groups initially acquired the absolute timing, maintained their performance at 24h retention, and exhibited performance deterioration in both transfer tests. Relative timing error revealed that the observation of no variability and relative timing variability produced greater performance at the post-test, 24h retention and relative timing transfer tests, but for the no variability group, deteriorated at absolute timing transfer test. The results suggest that the learning of absolute timing following observation unfolds irrespective of model variability. However, the learning of relative timing benefits from holding the absolute features constant, while the observation of no variability partially fails in transfer. We suggest learning by observing no variability and variable/erred models unfolds via similar neural mechanisms, although the latter benefits from the additional coding of information pertaining to movements that require a correction. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash

PubMed Central

Safiuddin, Md.; Raman, Sudharshan N.; Abdus Salam, Md.; Jumaat, Mohd. Zamin

2016-01-01

Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination (R2) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN. PMID:28773520

Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash.

PubMed

Safiuddin, Md; Raman, Sudharshan N; Abdus Salam, Md; Jumaat, Mohd Zamin

2016-05-20

Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination ( R ²) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN.

Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses.

PubMed

Leng, Xiang'zi; Wang, Jinhua; Ji, Haibo; Wang, Qin'geng; Li, Huiming; Qian, Xin; Li, Fengying; Yang, Meng

2017-08-01

Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM 2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75 μg/m 3 . Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (ø≤2.5 μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (ø > 2.5 μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating Evaporative Fraction From Readily Obtainable Variables in Mangrove Forests of the Everglades, U.S.A.

NASA Technical Reports Server (NTRS)

Yagci, Ali Levent; Santanello, Joseph A.; Jones, John; Barr, Jordan

2017-01-01

A remote-sensing-based model to estimate evaporative fraction (EF) the ratio of latent heat (LE; energy equivalent of evapotranspiration -ET-) to total available energy from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micro-meteorological and flux tower observations, or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature [T(sub s)] normalized difference vegetation index (NDVI)and daily maximum air temperature [T(sub a)]. The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using T(sub s) and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the T(sub s) from Landsat relative to the T(sub s) from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.

USGS Publications Warehouse

Yagci, Ali Levent; Santanello, Joseph A.; Jones, John W.; Barr, Jordan G.

2017-01-01

A remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature (Ts) normalized difference vegetation index (NDVI) and daily maximum air temperature (Ta). The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using Ts and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the Ts from Landsat relative to the Ts from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

An investigation of abdominal muscle recruitment for sustained phonation in 25 healthy singers.

PubMed

Macdonald, Ian; Rubin, John S; Blake, Ed; Hirani, Shashi; Epstein, Ruth

2012-11-01

The purpose of this study was to investigate the baseline muscle thickness and recruitment patterns of the transversus abdominis muscle (TAM) and the internal oblique muscle (IOM) during semisupine phonation in a group of healthy performers. This was a 2 × 3×2 within-group, repeated-measure study in which 25 professional vocalists--12 male and 13 female performed a series of sustained pitches in differing vocal qualities. Measurements were taken with ultrasound (Sonosite Micromaxx Ultrasound System) of the baseline thickness and % recruitment during voicing, of two deep abdominal muscles--TAM and the IOM. Correlations between TAM and IOM absolute change scores, TAM and IOM percentage change scores, and changes in muscle thickness (absolute and percentage) and age were examined using Spearman's correlations. Gender differences in the four types of change scores within each combination of pitch and quality were conducted with one-way analysis of variances. Differences in muscle thickness change 1) absolute scores and 2) percentage change in TAM and IOM, by pitch and quality (and their interactions) were analyzed using linear mixed models, using restricted maximum likelihood estimations, employing a Toeplitz variance-covariance matrix structure in SPSS (IBM, 2011). Post hoc analyses for independent variable group differences used Sidak's correction for multiple comparisons. Alpha level was set to 0.05. In terms of absolute contractions (changes in the actual millimeter thickness of the muscle), the IOM was greater than the TAM. However in terms of percentage changes in muscles during phonation, the TAM was always greater than the IOM. The TAM as a percentage change was recruited preferentially and significantly in most vocal qualities tested. Although there were differences in muscle mass and recruitment patterns between genders, and males had thicker muscle mass at rest, differences due to muscle mass were not conclusive. Overall this study supports the argument that the peri-abdominal muscles do indeed play a role in supporting the "performing" or athletic voice in healthy subjects, and will hopefully act as a database for further research in individuals with healthy and injured voices. Copyright © 2012 The Voice Foundation. Published by Mosby, Inc. All rights reserved.

A novel validation and calibration method for motion capture systems based on micro-triangulation.

PubMed

Nagymáté, Gergely; Tuchband, Tamás; Kiss, Rita M

2018-06-06

Motion capture systems are widely used to measure human kinematics. Nevertheless, users must consider system errors when evaluating their results. Most validation techniques for these systems are based on relative distance and displacement measurements. In contrast, our study aimed to analyse the absolute volume accuracy of optical motion capture systems by means of engineering surveying reference measurement of the marker coordinates (uncertainty: 0.75 mm). The method is exemplified on an 18 camera OptiTrack Flex13 motion capture system. The absolute accuracy was defined by the root mean square error (RMSE) between the coordinates measured by the camera system and by engineering surveying (micro-triangulation). The original RMSE of 1.82 mm due to scaling error was managed to be reduced to 0.77 mm while the correlation of errors to their distance from the origin reduced from 0.855 to 0.209. A simply feasible but less accurate absolute accuracy compensation method using tape measure on large distances was also tested, which resulted in similar scaling compensation compared to the surveying method or direct wand size compensation by a high precision 3D scanner. The presented validation methods can be less precise in some respects as compared to previous techniques, but they address an error type, which has not been and cannot be studied with the previous validation methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Digital Photographic Measurement Method for Quantifying Foot Posture: Validity, Reliability, and Descriptive Data

PubMed Central

Cobb, Stephen C.; James, C. Roger; Hjertstedt, Matthew; Kruk, James

2011-01-01

Abstract Context: Although abnormal foot posture long has been associated with lower extremity injury risk, the evidence is equivocal. Poor intertester reliability of traditional foot measures might contribute to the inconsistency. Objectives: To investigate the validity and reliability of a digital photographic measurement method (DPMM) technology, the reliability of DPMM-quantified foot measures, and the concurrent validity of the DPMM with clinical-measurement methods (CMMs) and to report descriptive data for DPMM measures with moderate to high intratester and intertester reliability. Design: Descriptive laboratory study. Setting: Biomechanics research laboratory. Patients or Other Participants: A total of 159 people participated in 3 groups. Twenty-eight people (11 men, 17 women; age  =  25 ± 5 years, height  =  1.71 ± 0.10 m, mass  =  77.6 ± 17.3 kg) were recruited for investigation of intratester and intertester reliability of the DPMM technology; 20 (10 men, 10 women; age  =  24 ± 2 years, height  =  1.71 ± 0.09 m, mass  =  76 ± 16 kg) for investigation of DPMM and CMM reliability and concurrent validity; and 111 (42 men, 69 women; age  =  22.8 ± 4.7 years, height  =  168.5 ± 10.4 cm, mass  =  69.8 ± 13.3 kg) for development of a descriptive data set of the DPMM foot measurements with moderate to high intratester and intertester reliabilities. Intervention(s): The dimensions of 10 model rectangles and the 28 participants' feet were measured, and DPMM foot posture was measured in the 111 participants. Two clinicians assessed the DPMM and CMM foot measures of the 20 participants. Main Outcome Measure(s): Validity and reliability were evaluated using mean absolute and percentage errors and intraclass correlation coefficients. Descriptive data were computed from the DPMM foot posture measures. Results: The DPMM technology intratester and intertester reliability intraclass correlation coefficients were 1.0 for each tester and variable. Mean absolute errors were equal to or less than 0.2 mm for the bottom and right-side variables and 0.1° for the calculated angle variable. Mean percentage errors between the DPMM and criterion reference values were equal to or less than 0.4%. Intratester and intertester reliabilities of DPMM-computed structural measures of arch and navicular indices were moderate to high (>0.78), and concurrent validity was moderate to strong. Conclusions: The DPMM is a valid and reliable clinical and research tool for quantifying foot structure. The DPMM and the descriptive data might be used to define groups in future studies in which the relationship between foot posture and function or injury risk is investigated. PMID:21214347

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

PubMed Central

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-01-01

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting. PMID:29883381

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

PubMed

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-05-21

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

75 FR 30370 - Certain Coated Paper Suitable For High-Quality Print Graphics Using Sheet-Fed Presses from the...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-01

...) a change of at least five absolute percentage points in, but not less than 25 percent of, the... between a countervailable subsidy rate of zero (or de minimis) and a countervailable subsidy rate of... absolute points and not less than 25 percent of the originally calculated margin. Thus, the ministerial...

Special electronic distance meter calibration for precise engineering surveying industrial applications

NASA Astrophysics Data System (ADS)

Braun, Jaroslav; Štroner, Martin; Urban, Rudolf

2015-05-01

All surveying instruments and their measurements suffer from some errors. To refine the measurement results, it is necessary to use procedures restricting influence of the instrument errors on the measured values or to implement numerical corrections. In precise engineering surveying industrial applications the accuracy of the distances usually realized on relatively short distance is a key parameter limiting the resulting accuracy of the determined values (coordinates, etc.). To determine the size of systematic and random errors of the measured distances were made test with the idea of the suppression of the random error by the averaging of the repeating measurement, and reducing systematic errors influence of by identifying their absolute size on the absolute baseline realized in geodetic laboratory at the Faculty of Civil Engineering CTU in Prague. The 16 concrete pillars with forced centerings were set up and the absolute distances between the points were determined with a standard deviation of 0.02 millimetre using a Leica Absolute Tracker AT401. For any distance measured by the calibrated instruments (up to the length of the testing baseline, i.e. 38.6 m) can now be determined the size of error correction of the distance meter in two ways: Firstly by the interpolation on the raw data, or secondly using correction function derived by previous FFT transformation usage. The quality of this calibration and correction procedure was tested on three instruments (Trimble S6 HP, Topcon GPT-7501, Trimble M3) experimentally using Leica Absolute Tracker AT401. By the correction procedure was the standard deviation of the measured distances reduced significantly to less than 0.6 mm. In case of Topcon GPT-7501 is the nominal standard deviation 2 mm, achieved (without corrections) 2.8 mm and after corrections 0.55 mm; in case of Trimble M3 is nominal standard deviation 3 mm, achieved (without corrections) 1.1 mm and after corrections 0.58 mm; and finally in case of Trimble S6 is nominal standard deviation 1 mm, achieved (without corrections) 1.2 mm and after corrections 0.51 mm. Proposed procedure of the calibration and correction is in our opinion very suitable for increasing of the accuracy of the electronic distance measurement and allows the use of the common surveying instrument to achieve uncommonly high precision.

Classification of collected trot, passage and piaffe based on temporal variables.

PubMed

Clayton, H M

1997-05-01

The objective was to determine whether collected trot, passage and piaffe could be distinguished as separate gaits on the basis of temporal variables. Sagittal plane, 60 Hz videotapes of 10 finalists in the dressage competitions at the 1992 Olympic Games were analysed to measure the temporal variables in absolute terms and as percentages of stride duration. Classification was based on analysis of variance, a graphical method and discriminant analysis. Stride duration was sufficient to distinguish collected trot from passage and piaffe in all horses. The analysis of variance showed that the mean values of most variables differed significantly between passage and piaffe. When hindlimb stance percentage was plotted against diagonal advanced placement percentage, some overlap was found between all 3 movements indicating that individual horses could not be classified reliably in this manner. Using hindlimb stance percentage and diagonal advanced placement percentage as input in a discriminant analysis, 80% of the cases were classified correctly, but at least one horse was misclassified in each movement. When the absolute, rather than percentage, values of the 2 variables were used as input in the discriminant analysis, 90% of the cases were correctly classified and the only misclassifications were between passage and piaffe. However, the 2 horses in which piaffe was misclassified as passage were the gold and silver medallists. In general, higher placed horses tended toward longer diagonal advanced placements, especially in collected trot and passage, and shorter hindlimb stance percentages in passage and piaffe.

Performance evaluation of Abbott CELL-DYN Ruby for routine use.

PubMed

Lehto, T; Hedberg, P

2008-10-01

CELL-DYN Ruby is a new automated hematology analyzer suitable for routine use in small laboratories and as a back-up or emergency analyzer in medium- to high-volume laboratories. The analyzer was evaluated by comparing the results from the CELL-DYN((R)) Ruby with the results obtained from CELL-DYN Sapphire . Precision, linearity, and carryover between patient samples were also assessed. Precision was good at all levels for the routine cell blood count (CBC) parameters, CV% being or= 0.98) with CELL-DYN Sapphire for the CBC parameters. For the absolute reticulocyte count, R(2) was 0.82. In the white blood cell (WBC) differentials, the between-days precision was good for all parameters (CV%: or= 0.97), and the correlation coefficient for absolute monocyte count and monocyte percentage were 0.91 and 0.87, respectively. For absolute basophil count and basophil percentage the correlations were weaker (R(2) = 0.46 and 0.34, respectively). Carryover was minimal for all the parameters studied. The linearities of WBC, red blood cell, PLTs, and hemoglobin were acceptable within the tested ranges. In conclusion, the results of the evaluation showed the performance of CELL-DYN Ruby to be good.

Bench-to-bedside review: the importance of the precision of the reference technique in method comparison studies--with specific reference to the measurement of cardiac output.

PubMed

Cecconi, Maurizio; Rhodes, Andrew; Poloniecki, Jan; Della Rocca, Giorgio; Grounds, R Michael

2009-01-01

Bland-Altman analysis is used for assessing agreement between two measurements of the same clinical variable. In the field of cardiac output monitoring, its results, in terms of bias and limits of agreement, are often difficult to interpret, leading clinicians to use a cutoff of 30% in the percentage error in order to decide whether a new technique may be considered a good alternative. This percentage error of +/- 30% arises from the assumption that the commonly used reference technique, intermittent thermodilution, has a precision of +/- 20% or less. The combination of two precisions of +/- 20% equates to a total error of +/- 28.3%, which is commonly rounded up to +/- 30%. Thus, finding a percentage error of less than +/- 30% should equate to the new tested technique having an error similar to the reference, which therefore should be acceptable. In a worked example in this paper, we discuss the limitations of this approach, in particular in regard to the situation in which the reference technique may be either more or less precise than would normally be expected. This can lead to inappropriate conclusions being drawn from data acquired in validation studies of new monitoring technologies. We conclude that it is not acceptable to present comparison studies quoting percentage error as an acceptability criteria without reporting the precision of the reference technique.

Demand Forecasting: An Evaluation of DODs Accuracy Metric and Navys Procedures

DTIC Science & Technology

2016-06-01

inventory management improvement plan, mean of absolute scaled error, lead time adjusted squared error, forecast accuracy, benchmarking, naïve method...Manager JASA Journal of the American Statistical Association LASE Lead-time Adjusted Squared Error LCI Life Cycle Indicator MA Moving Average MAE...Mean Squared Error xvi NAVSUP Naval Supply Systems Command NDAA National Defense Authorization Act NIIN National Individual Identification Number

Twice cutting method reduces tibial cutting error in unicompartmental knee arthroplasty.

PubMed

Inui, Hiroshi; Taketomi, Shuji; Yamagami, Ryota; Sanada, Takaki; Tanaka, Sakae

2016-01-01

Bone cutting error can be one of the causes of malalignment in unicompartmental knee arthroplasty (UKA). The amount of cutting error in total knee arthroplasty has been reported. However, none have investigated cutting error in UKA. The purpose of this study was to reveal the amount of cutting error in UKA when open cutting guide was used and clarify whether cutting the tibia horizontally twice using the same cutting guide reduced the cutting errors in UKA. We measured the alignment of the tibial cutting guides, the first-cut cutting surfaces and the second cut cutting surfaces using the navigation system in 50 UKAs. Cutting error was defined as the angular difference between the cutting guide and cutting surface. The mean absolute first-cut cutting error was 1.9° (1.1° varus) in the coronal plane and 1.1° (0.6° anterior slope) in the sagittal plane, whereas the mean absolute second-cut cutting error was 1.1° (0.6° varus) in the coronal plane and 1.1° (0.4° anterior slope) in the sagittal plane. Cutting the tibia horizontally twice reduced the cutting errors in the coronal plane significantly (P<0.05). Our study demonstrated that in UKA, cutting the tibia horizontally twice using the same cutting guide reduced cutting error in the coronal plane. Copyright © 2014 Elsevier B.V. All rights reserved.

Validation of SenseWear Armband in children, adolescents, and adults.

PubMed

Lopez, G A; Brønd, J C; Andersen, L B; Dencker, M; Arvidsson, D

2018-02-01

SenseWear Armband (SW) is a multisensor monitor to assess physical activity and energy expenditure. Its prediction algorithms have been updated periodically. The aim was to validate SW in children, adolescents, and adults. The most recent SW algorithm 5.2 (SW5.2) and the previous version 2.2 (SW2.2) were evaluated for estimation of energy expenditure during semi-structured activities in 35 children, 31 adolescents, and 36 adults with indirect calorimetry as reference. Energy expenditure estimated from waist-worn ActiGraph GT3X+ data (AG) was used for comparison. Improvements in measurement errors were demonstrated with SW5.2 compared to SW2.2, especially in children and for biking. The overall mean absolute percent error with SW5.2 was 24% in children, 23% in adolescents, and 20% in adults. The error was larger for sitting and standing (23%-32%) and for basketball and biking (19%-35%), compared to walking and running (8%-20%). The overall mean absolute error with AG was 28% in children, 22% in adolescents, and 28% in adults. The absolute percent error for biking was 32%-74% with AG. In general, SW and AG underestimated energy expenditure. However, both methods demonstrated a proportional bias, with increasing underestimation for increasing energy expenditure level, in addition to the large individual error. SW provides measures of energy expenditure level with similar accuracy in children, adolescents, and adults with the improvements in the updated algorithms. Although SW captures biking better than AG, these methods share remaining measurements errors requiring further improvements for accurate measures of physical activity and energy expenditure in clinical and epidemiological research. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid

2018-04-01

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

Quality Aware Compression of Electrocardiogram Using Principal Component Analysis.

PubMed

Gupta, Rajarshi

2016-05-01

Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.

A new method to estimate average hourly global solar radiation on the horizontal surface

NASA Astrophysics Data System (ADS)

Pandey, Pramod K.; Soupir, Michelle L.

2012-10-01

A new model, Global Solar Radiation on Horizontal Surface (GSRHS), was developed to estimate the average hourly global solar radiation on the horizontal surfaces (Gh). The GSRHS model uses the transmission function (Tf,ij), which was developed to control hourly global solar radiation, for predicting solar radiation. The inputs of the model were: hour of day, day (Julian) of year, optimized parameter values, solar constant (H0), latitude, and longitude of the location of interest. The parameter values used in the model were optimized at a location (Albuquerque, NM), and these values were applied into the model for predicting average hourly global solar radiations at four different locations (Austin, TX; El Paso, TX; Desert Rock, NV; Seattle, WA) of the United States. The model performance was assessed using correlation coefficient (r), Mean Absolute Bias Error (MABE), Root Mean Square Error (RMSE), and coefficient of determinations (R2). The sensitivities of parameter to prediction were estimated. Results show that the model performed very well. The correlation coefficients (r) range from 0.96 to 0.99, while coefficients of determination (R2) range from 0.92 to 0.98. For daily and monthly prediction, error percentages (i.e. MABE and RMSE) were less than 20%. The approach we proposed here can be potentially useful for predicting average hourly global solar radiation on the horizontal surface for different locations, with the use of readily available data (i.e. latitude and longitude of the location) as inputs.

Altitude Registration of Limb-Scattered Radiation

NASA Technical Reports Server (NTRS)

Moy, Leslie; Bhartia, Pawan K.; Jaross, Glen; Loughman, Robert; Kramarova, Natalya; Chen, Zhong; Taha, Ghassan; Chen, Grace; Xu, Philippe

2017-01-01

One of the largest constraints to the retrieval of accurate ozone profiles from UV backscatter limb sounding sensors is altitude registration. Two methods, the Rayleigh scattering attitude sensing (RSAS) and absolute radiance residual method (ARRM), are able to determine altitude registration to the accuracy necessary for long-term ozone monitoring. The methods compare model calculations of radiances to measured radiances and are independent of onboard tracking devices. RSAS determines absolute altitude errors, but, because the method is susceptible to aerosol interference, it is limited to latitudes and time periods with minimal aerosol contamination. ARRM, a new technique introduced in this paper, can be applied across all seasons and altitudes. However, it is only appropriate for relative altitude error estimates. The application of RSAS to Limb Profiler (LP) measurements from the Ozone Mapping and Profiler Suite (OMPS) on board the Suomi NPP (SNPP) satellite indicates tangent height (TH) errors greater than 1 km with an absolute accuracy of +/-200 m. Results using ARRM indicate a approx. 300 to 400m intra-orbital TH change varying seasonally +/-100 m, likely due to either errors in the spacecraft pointing or in the geopotential height (GPH) data that we use in our analysis. ARRM shows a change of approx. 200m over 5 years with a relative accuracy (a long-term accuracy) of 100m outside the polar regions.

Visual symptoms associated with refractive errors among Thangka artists of Kathmandu valley.

PubMed

Dhungel, Deepa; Shrestha, Gauri Shankar

2017-12-21

Prolong near work, especially among people with uncorrected refractive error is considered a potential source of visual symptoms. The present study aims to determine the visual symptoms and the association of those with refractive errors among Thangka artists. In a descriptive cross-sectional study, 242 (46.1%) participants of 525 thangka artists examined, with age ranged between 16 years to 39 years which comprised of 112 participants with significant refractive errors and 130 absolutely emmetropic participants, were enrolled from six Thangka painting schools. The visual symptoms were assessed using a structured questionnaire consisting of nine items and scoring from 0 to 6 consecutive scales. The eye examination included detailed anterior and posterior segment examination, objective and subjective refraction, and assessment of heterophoria, vergence and accommodation. Symptoms were presented in percentage and median. Variation in distribution of participants and symptoms was analysed using the Kruskal Wallis test for mean, and the correlation with the Pearson correlation coefficient. A significance level of 0.05 was applied for 95% confidence interval. The majority of participants (65.1%) among refractive error group (REG) were above the age of 30 years, with a male predominance (61.6%), compared to the participants in the normal cohort group (NCG), where majority of them (72.3%) were below 30 years of age (72.3%) and female (51.5%). Overall, the visual symptoms are high among Thangka artists. However, blurred vision (p = 0.003) and dry eye (p = 0.004) are higher among the REG than the NCG. Females have slightly higher symptoms than males. Most of the symptoms, such as sore/aching eye (p = 0.003), feeling dry (p = 0.005) and blurred vision (p = 0.02) are significantly associated with astigmatism. Thangka artists present with significant proportion of refractive error and visual symptoms, especially among females. The most commonly reported symptoms are blurred vision, dry eye and watering of the eye. The visual symptoms are more correlated with astigmatism.

Changes in relative and absolute concentrations of plasma phospholipid fatty acids observed in a randomized trial of Omega-3 fatty acids supplementation in Uganda

PubMed Central

Song, Xiaoling; Diep, Pho; Schenk, Jeannette M; Casper, Corey; Orem, Jackson; Makhoul, Zeina; Lampe, Johanna W; Neuhouser, Marian L.

2016-01-01

Expressing circulating phospholipid fatty acids (PLFAs) in relative concentrations has some limitations: the total of all fatty acids are summed to 100%; therefore, the values of individual fatty acid are not independent. In this study we examined if both relative and absolute metrics could effectively measure changes in circulating PLFA concentrations in an intervention trial. 66 HIV and HHV8 infected patients in Uganda were randomized to take 3g/d of either long-chain omega-3 fatty acids (1,856 mg EPA and 1,232 mg DHA) or high—oleic safflower oil in a 12-week double-blind trial. Plasma samples were collected at baseline and end of trial. Relative weight percentage and absolute concentrations of 41 plasma PLFAs were measured using gas chromatography. Total cholesterol was also measured. Intervention-effect changes in concentrations were calculated as differences between end of 12-week trial and baseline. Pearson correlations of relative and absolute concentration changes in individual PLFAs were high (>0.6) for 37 of the 41 PLFAs analyzed. In the intervention arm, 17 PLFAs changed significantly in relative concentration and 16 in absolute concentration, 15 of which were identical. Absolute concentration of total PLFAs decreased 95.1 mg/L (95% CI: 26.0, 164.2; P = 0.0085), but total cholesterol did not change significantly in the intervention arm. No significant change was observed in any of the measurements in the placebo arm. Both relative weight percentage and absolute concentrations could effectively measure changes in plasma PLFA concentrations. EPA and DHA supplementation changes the concentrations of multiple plasma PLFAs besides EPA and DHA. PMID:27926458

Sensitivity of feedforward neural networks to weight errors

NASA Technical Reports Server (NTRS)

Stevenson, Maryhelen; Widrow, Bernard; Winter, Rodney

1990-01-01

An analysis is made of the sensitivity of feedforward layered networks of Adaline elements (threshold logic units) to weight errors. An approximation is derived which expresses the probability of error for an output neuron of a large network (a network with many neurons per layer) as a function of the percentage change in the weights. As would be expected, the probability of error increases with the number of layers in the network and with the percentage change in the weights. The probability of error is essentially independent of the number of weights per neuron and of the number of neurons per layer, as long as these numbers are large (on the order of 100 or more).

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.

Characterization of performance-emission indices of a diesel engine using ANFIS operating in dual-fuel mode with LPG

NASA Astrophysics Data System (ADS)

Chakraborty, Amitav; Roy, Sumit; Banerjee, Rahul

2018-03-01

This experimental work highlights the inherent capability of an adaptive-neuro fuzzy inference system (ANFIS) based model to act as a robust system identification tool (SIT) in prognosticating the performance and emission parameters of an existing diesel engine running of diesel-LPG dual fuel mode. The developed model proved its adeptness by successfully harnessing the effects of the input parameters of load, injection duration and LPG energy share on output parameters of BSFCEQ, BTE, NOX, SOOT, CO and HC. Successive evaluation of the ANFIS model, revealed high levels of resemblance with the already forecasted ANN results for the same input parameters and it was evident that similar to ANN, ANFIS also has the innate ability to act as a robust SIT. The ANFIS predicted data harmonized the experimental data with high overall accuracy. The correlation coefficient (R) values are stretched in between 0.99207 to 0.999988. The mean absolute percentage error (MAPE) tallies were recorded in the range of 0.02-0.173% with the root mean square errors (RMSE) in acceptable margins. Hence the developed model is capable of emulating the actual engine parameters with commendable ranges of accuracy, which in turn would act as a robust prediction platform in the future domains of optimization.

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.

Time-Series Approaches for Forecasting the Number of Hospital Daily Discharged Inpatients.

PubMed

Ting Zhu; Li Luo; Xinli Zhang; Yingkang Shi; Wenwu Shen

2017-03-01

For hospitals where decisions regarding acceptable rates of elective admissions are made in advance based on expected available bed capacity and emergency requests, accurate predictions of inpatient bed capacity are especially useful for capacity reservation purposes. As given, the remaining unoccupied beds at the end of each day, bed capacity of the next day can be obtained by examining the forecasts of the number of discharged patients during the next day. The features of fluctuations in daily discharges like trend, seasonal cycles, special-day effects, and autocorrelation complicate decision optimizing, while time-series models can capture these features well. This research compares three models: a model combining seasonal regression and ARIMA, a multiplicative seasonal ARIMA (MSARIMA) model, and a combinatorial model based on MSARIMA and weighted Markov Chain models in generating forecasts of daily discharges. The models are applied to three years of discharge data of an entire hospital. Several performance measures like the direction of the symmetry value, normalized mean squared error, and mean absolute percentage error are utilized to capture the under- and overprediction in model selection. The findings indicate that daily discharges can be forecast by using the proposed models. A number of important practical implications are discussed, such as the use of accurate forecasts in discharge planning, admission scheduling, and capacity reservation.

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

NASA Astrophysics Data System (ADS)

Ebtehaj, Isa; Bonakdari, Hossein

2017-12-01

Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number ( Fr) parameters includes the volumetric sediment concentration ( C V ), ratio of median particle diameter to hydraulic radius ( d/R), ratio of median particle diameter to pipe diameter ( d/D) and overall friction factor of sediment ( λ s ). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy ( R 2 = 0.965) than the ANFIS model and regression-based equations.

The absolute radiometric calibration of the advanced very high resolution radiometer

NASA Technical Reports Server (NTRS)

Slater, P. N.; Teillet, P. M.; Ding, Y.

1988-01-01

The need for independent, redundant absolute radiometric calibration methods is discussed with reference to the Thematic Mapper. Uncertainty requirements for absolute calibration of between 0.5 and 4 percent are defined based on the accuracy of reflectance retrievals at an agricultural site. It is shown that even very approximate atmospheric corrections can reduce the error in reflectance retrieval to 0.02 over the reflectance range 0 to 0.4.

Accuracy of Robotic Radiosurgical Liver Treatment Throughout the Respiratory Cycle

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

Winter, Jeff D.; Wong, Raimond; Swaminath, Anand

Purpose: To quantify random uncertainties in robotic radiosurgical treatment of liver lesions with real-time respiratory motion management. Methods and Materials: We conducted a retrospective analysis of 27 liver cancer patients treated with robotic radiosurgery over 118 fractions. The robotic radiosurgical system uses orthogonal x-ray images to determine internal target position and correlates this position with an external surrogate to provide robotic corrections of linear accelerator positioning. Verification and update of this internal–external correlation model was achieved using periodic x-ray images collected throughout treatment. To quantify random uncertainties in targeting, we analyzed logged tracking information and isolated x-ray images collected immediately beforemore » beam delivery. For translational correlation errors, we quantified the difference between correlation model–estimated target position and actual position determined by periodic x-ray imaging. To quantify prediction errors, we computed the mean absolute difference between the predicted coordinates and actual modeled position calculated 115 milliseconds later. We estimated overall random uncertainty by quadratically summing correlation, prediction, and end-to-end targeting errors. We also investigated relationships between tracking errors and motion amplitude using linear regression. Results: The 95th percentile absolute correlation errors in each direction were 2.1 mm left–right, 1.8 mm anterior–posterior, 3.3 mm cranio–caudal, and 3.9 mm 3-dimensional radial, whereas 95th percentile absolute radial prediction errors were 0.5 mm. Overall 95th percentile random uncertainty was 4 mm in the radial direction. Prediction errors were strongly correlated with modeled target amplitude (r=0.53-0.66, P<.001), whereas only weak correlations existed for correlation errors. Conclusions: Study results demonstrate that model correlation errors are the primary random source of uncertainty in Cyberknife liver treatment and, unlike prediction errors, are not strongly correlated with target motion amplitude. Aggregate 3-dimensional radial position errors presented here suggest the target will be within 4 mm of the target volume for 95% of the beam delivery.« less

Measurement of cardiac index by transpulmonary thermodilution using an implanted central venous access port: a prospective study in patients scheduled for oncologic high-risk surgery.

PubMed

Suria, Stéphanie; Wyniecki, Anne; Eghiaian, Alexandre; Monnet, Xavier; Weil, Grégoire

2014-01-01

Transpulmonary thermodilution allows the measurement of cardiac index for high risk surgical patients. Oncologic patients often have a central venous access (port-a-catheter) for chronic treatment. The validity of the measurement by a port-a-catheter of the absolute cardiac index and the detection of changes in cardiac index induced by fluid challenge are unknown. We conducted a monocentric prospective study. 27 patients were enrolled. 250 ml colloid volume expansions for fluid challenge were performed during ovarian cytoreductive surgery. The volume expansion-induced changes in cardiac index measured by transpulmonary thermodilution by a central venous access (CIcvc) and by a port-a-catheter (CIport) were recorded. 23 patients were analyzed with 123 pairs of measurements. Using a Bland and Altman for repeated measurements, the bias (lower and upper limits of agreement) between CIport and CIcvc was 0.14 (-0.59 to 0.88) L/min/m2. The percentage error was 22%. The concordance between the changes in CIport and CIcvc observed during volume expansion was 92% with an r = 0.7 (with exclusion zone). No complications (included sepsis) were observed during the follow up period. The transpulmonary thermodilution by a port-a-catheter is reliable for absolute values estimation of cardiac index and for measurement of the variation after fluid challenge. clinicaltrials.gov NCT02063009.

Error Analysis of non-TLD HDR Brachytherapy Dosimetric Techniques

NASA Astrophysics Data System (ADS)

Amoush, Ahmad

The American Association of Physicists in Medicine Task Group Report43 (AAPM-TG43) and its updated version TG-43U1 rely on the LiF TLD detector to determine the experimental absolute dose rate for brachytherapy. The recommended uncertainty estimates associated with TLD experimental dosimetry include 5% for statistical errors (Type A) and 7% for systematic errors (Type B). TG-43U1 protocol does not include recommendation for other experimental dosimetric techniques to calculate the absolute dose for brachytherapy. This research used two independent experimental methods and Monte Carlo simulations to investigate and analyze uncertainties and errors associated with absolute dosimetry of HDR brachytherapy for a Tandem applicator. An A16 MicroChamber* and one dose MOSFET detectors† were selected to meet the TG-43U1 recommendations for experimental dosimetry. Statistical and systematic uncertainty analyses associated with each experimental technique were analyzed quantitatively using MCNPX 2.6‡ to evaluate source positional error, Tandem positional error, the source spectrum, phantom size effect, reproducibility, temperature and pressure effects, volume averaging, stem and wall effects, and Tandem effect. Absolute dose calculations for clinical use are based on Treatment Planning System (TPS) with no corrections for the above uncertainties. Absolute dose and uncertainties along the transverse plane were predicted for the A16 microchamber. The generated overall uncertainties are 22%, 17%, 15%, 15%, 16%, 17%, and 19% at 1cm, 2cm, 3cm, 4cm, and 5cm, respectively. Predicting the dose beyond 5cm is complicated due to low signal-to-noise ratio, cable effect, and stem effect for the A16 microchamber. Since dose beyond 5cm adds no clinical information, it has been ignored in this study. The absolute dose was predicted for the MOSFET detector from 1cm to 7cm along the transverse plane. The generated overall uncertainties are 23%, 11%, 8%, 7%, 7%, 9%, and 8% at 1cm, 2cm, 3cm, and 4cm, 5cm, 6cm, and 7cm, respectively. The Nucletron Freiburg flap applicator is used with the Nucletron remote afterloader HDR machine to deliver dose to surface cancers. Dosimetric data for the Nucletron 192Ir source were generated using Monte Carlo simulation and compared with the published data. Two dimensional dosimetric data were calculated at two source positions; at the center of the sphere of the applicator and between two adjacent spheres. Unlike the TPS dose algorithm, The Monte Carlo code developed for this research accounts for the applicator material, secondary electrons and delta particles, and the air gap between the skin and the applicator. *Standard Imaging, Inc., Middleton, Wisconsin USA † OneDose MOSFET, Sicel Technologies, Morrisville NC ‡ Los Alamos National Laboratory, NM USA

Accounting for hardware imperfections in EIT image reconstruction algorithms.

PubMed

Hartinger, Alzbeta E; Gagnon, Hervé; Guardo, Robert

2007-07-01

Electrical impedance tomography (EIT) is a non-invasive technique for imaging the conductivity distribution of a body section. Different types of EIT images can be reconstructed: absolute, time difference and frequency difference. Reconstruction algorithms are sensitive to many errors which translate into image artefacts. These errors generally result from incorrect modelling or inaccurate measurements. Every reconstruction algorithm incorporates a model of the physical set-up which must be as accurate as possible since any discrepancy with the actual set-up will cause image artefacts. Several methods have been proposed in the literature to improve the model realism, such as creating anatomical-shaped meshes, adding a complete electrode model and tracking changes in electrode contact impedances and positions. Absolute and frequency difference reconstruction algorithms are particularly sensitive to measurement errors and generally assume that measurements are made with an ideal EIT system. Real EIT systems have hardware imperfections that cause measurement errors. These errors translate into image artefacts since the reconstruction algorithm cannot properly discriminate genuine measurement variations produced by the medium under study from those caused by hardware imperfections. We therefore propose a method for eliminating these artefacts by integrating a model of the system hardware imperfections into the reconstruction algorithms. The effectiveness of the method has been evaluated by reconstructing absolute, time difference and frequency difference images with and without the hardware model from data acquired on a resistor mesh phantom. Results have shown that artefacts are smaller for images reconstructed with the model, especially for frequency difference imaging.

Intranasal agomelatine solid lipid nanoparticles to enhance brain delivery: formulation, optimization and in vivo pharmacokinetics

PubMed Central

Fatouh, Ahmed M; Elshafeey, Ahmed H; Abdelbary, Ahmed

2017-01-01

Purpose Agomelatine is a novel antidepressant drug suffering from an extensive first-pass metabolism leading to a diminished absolute bioavailability. The aim of the study is: first to enhance its absolute bioavailability, and second to increase its brain delivery. Methods To achieve these aims, the nasal route was adopted to exploit first its avoidance of the hepatic first-pass metabolism to increase the absolute bioavailability, and second the direct nose-to-brain pathway to enhance the brain drug delivery. Solid lipid nanoparticles were selected as a drug delivery system to enhance agomelatine permeability across the blood–brain barrier and therefore its brain delivery. Results The optimum solid lipid nanoparticles have a particle size of 167.70 nm ±0.42, zeta potential of −17.90 mV ±2.70, polydispersity index of 0.12±0.10, entrapment efficiency % of 91.25%±1.70%, the percentage released after 1 h of 35.40%±1.13% and the percentage released after 8 h of 80.87%±5.16%. The pharmacokinetic study of the optimized solid lipid nanoparticles revealed a significant increase in each of the plasma peak concentration, the AUC(0–360 min) and the absolute bioavailability compared to that of the oral suspension of Valdoxan® with the values of 759.00 ng/mL, 7,805.69 ng⋅min/mL and 44.44%, respectively. The optimized solid lipid nanoparticles gave a drug-targeting efficiency of 190.02, which revealed more successful brain targeting by the intranasal route compared with the intravenous route. The optimized solid lipid nanoparticles had a direct transport percentage of 47.37, which indicates a significant contribution of the direct nose-to-brain pathway in the brain drug delivery. Conclusion The intranasal administration of agomelatine solid lipid nanoparticles has effectively enhanced both the absolute bioavailability and the brain delivery of agomelatine. PMID:28684900

Compensating additional optical power in the central zone of a multifocal contact lens forminimization of the shrinkage error of the shell mold in the injection molding process.

PubMed

Vu, Lien T; Chen, Chao-Chang A; Lee, Chia-Cheng; Yu, Chia-Wei

2018-04-20

This study aims to develop a compensating method to minimize the shrinkage error of the shell mold (SM) in the injection molding (IM) process to obtain uniform optical power in the central optical zone of soft axial symmetric multifocal contact lenses (CL). The Z-shrinkage error along the Z axis or axial axis of the anterior SM corresponding to the anterior surface of a dry contact lens in the IM process can be minimized by optimizing IM process parameters and then by compensating for additional (Add) powers in the central zone of the original lens design. First, the shrinkage error is minimized by optimizing three levels of four IM parameters, including mold temperature, injection velocity, packing pressure, and cooling time in 18 IM simulations based on an orthogonal array L 18 (2 1 ×3 4 ). Then, based on the Z-shrinkage error from IM simulation, three new contact lens designs are obtained by increasing the Add power in the central zone of the original multifocal CL design to compensate for the optical power errors. Results obtained from IM process simulations and the optical simulations show that the new CL design with 0.1 D increasing in Add power has the closest shrinkage profile to the original anterior SM profile with percentage of reduction in absolute Z-shrinkage error of 55% and more uniform power in the central zone than in the other two cases. Moreover, actual experiments of IM of SM for casting soft multifocal CLs have been performed. The final product of wet CLs has been completed for the original design and the new design. Results of the optical performance have verified the improvement of the compensated design of CLs. The feasibility of this compensating method has been proven based on the measurement results of the produced soft multifocal CLs of the new design. Results of this study can be further applied to predict or compensate for the total optical power errors of the soft multifocal CLs.

Comparison of intraocular lens power prediction using immersion ultrasound and optical biometry with and without formula optimization.

PubMed

Nemeth, Gabor; Nagy, Attila; Berta, Andras; Modis, Laszlo

2012-09-01

Comparison of postoperative refraction results using ultrasound biometry with closed immersion shell and optical biometry. Three hundred and sixty-four eyes of 306 patients (age: 70.6 ± 12.8 years) underwent cataract surgery where intraocular lenses calculated by SRK/T formula were implanted. In 159 cases immersion ultrasonic biometry, in 205 eyes optical biometry was used. Differences between predicted and actual postoperative refractions were calculated both prior to and after optimization with the SRK/T formula, after which we analysed the similar data in the case of Holladay, Haigis, and Hoffer-Q formulas. Mean absolute error (MAE) and the percentage rate of patients within ±0.5 and ±1.0 D difference in the predicted error were calculated with these four formulas. MAE was 0.5-0.7 D in cases of both methods with SRK/T, Holladay, and Hoffer-Q formula, but higher with Haigis formula. With no optimization, 60-65 % of the patients were under 0.5 D error in the immersion group (except for Haigis formula). Using the optical method, this value was slightly higher (62-67 %), however, in this case, Haigis formula also did not perform so well (45 %). Refraction results significantly improved with Holladay, Hoffer-Q, and Haigis formulas in both groups. The rate of patients under 0.5 D error increased to 65 % by the immersion technique, and up to 80 % by the optical one. According to our results, optical biometry offers only slightly better outcomes compared to those of immersion shell with no optimized formulas. However, in case of new generation formulas with both methods, the optimization of IOL-constants give significantly better results.

Validation of GPU based TomoTherapy dose calculation engine.

PubMed

Chen, Quan; Lu, Weiguo; Chen, Yu; Chen, Mingli; Henderson, Douglas; Sterpin, Edmond

2012-04-01

The graphic processing unit (GPU) based TomoTherapy convolution/superposition(C/S) dose engine (GPU dose engine) achieves a dramatic performance improvement over the traditional CPU-cluster based TomoTherapy dose engine (CPU dose engine). Besides the architecture difference between the GPU and CPU, there are several algorithm changes from the CPU dose engine to the GPU dose engine. These changes made the GPU dose slightly different from the CPU-cluster dose. In order for the commercial release of the GPU dose engine, its accuracy has to be validated. Thirty eight TomoTherapy phantom plans and 19 patient plans were calculated with both dose engines to evaluate the equivalency between the two dose engines. Gamma indices (Γ) were used for the equivalency evaluation. The GPU dose was further verified with the absolute point dose measurement with ion chamber and film measurements for phantom plans. Monte Carlo calculation was used as a reference for both dose engines in the accuracy evaluation in heterogeneous phantom and actual patients. The GPU dose engine showed excellent agreement with the current CPU dose engine. The majority of cases had over 99.99% of voxels with Γ(1%, 1 mm) < 1. The worst case observed in the phantom had 0.22% voxels violating the criterion. In patient cases, the worst percentage of voxels violating the criterion was 0.57%. For absolute point dose verification, all cases agreed with measurement to within ±3% with average error magnitude within 1%. All cases passed the acceptance criterion that more than 95% of the pixels have Γ(3%, 3 mm) < 1 in film measurement, and the average passing pixel percentage is 98.5%-99%. The GPU dose engine also showed similar degree of accuracy in heterogeneous media as the current TomoTherapy dose engine. It is verified and validated that the ultrafast TomoTherapy GPU dose engine can safely replace the existing TomoTherapy cluster based dose engine without degradation in dose accuracy.

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Increased Regulatory T-Cell Percentage Contributes to Poor CD4(+) Lymphocytes Recovery: A 2-Year Prospective Study After Introduction of Antiretroviral Therapy.

PubMed

Saison, Julien; Maucort Boulch, Delphine; Chidiac, Christian; Demaret, Julie; Malcus, Christophe; Cotte, Laurent; Poitevin-Later, Francoise; Miailhes, Patrick; Venet, Fabienne; Trabaud, Mary Anne; Monneret, Guillaume; Ferry, Tristan

2015-04-01

Background.  The primary aim of this study was to determine the impact of regulatory T cells (Tregs) percentage on immune recovery in human immunodeficiency virus (HIV)-infected patients after antiretroviral therapy introduction. Methods.  A 2-year prospective study was conducted in HIV-1 chronically infected naive patients with CD4 count <500 cells/mm(3). Regulatory T cells were identified as CD4(+)CD25(high)CD127(low) cells among CD4(+) lymphocytes. Effect of Treg percentage at inclusion on CD4 evolution overtime was analyzed using a mixed-effect Poisson regression for count data. Results.  Fifty-eight patients were included (median CD4 = 293/mm(3), median Treg percentage = 6.1%). Percentage of Treg at baseline and CD4 nadir were independently related to the evolution of CD4 absolute value according to time: (1) at any given nadir CD4 count, 1% increase of initial Treg was associated with a 1.9% lower CD4 absolute value at month 24; (2) at any given Treg percentage at baseline, 10 cell/mm(3) increase of CD4 nadir was associated with a 2.4% increase of CD4 at month 24; and (3) both effects did not attenuate with time. The effect of Treg at baseline on CD4 evolution was as low as the CD4 nadir was high. Conclusions.  Regulatory T-cell percentage at baseline is a strong independent prognostic factor of immune recovery, particularly among patients with low CD4 nadir.

First Impressions of CARTOSAT-1

NASA Technical Reports Server (NTRS)

Lutes, James

2007-01-01

CARTOSAT-1 RPCs need special handling. Absolute accuracy of uncontrolled scenes is poor (biases > 300 m). Noticeable cross-track scale error (+/- 3-4 m across stereo pair). Most errors are either biases or linear in line/sample (These are easier to correct with ground control).

A digital, constant-frequency pulsed phase-locked-loop instrument for real-time, absolute ultrasonic phase measurements

NASA Astrophysics Data System (ADS)

Haldren, H. A.; Perey, D. F.; Yost, W. T.; Cramer, K. E.; Gupta, M. C.

2018-05-01

A digitally controlled instrument for conducting single-frequency and swept-frequency ultrasonic phase measurements has been developed based on a constant-frequency pulsed phase-locked-loop (CFPPLL) design. This instrument uses a pair of direct digital synthesizers to generate an ultrasonically transceived tone-burst and an internal reference wave for phase comparison. Real-time, constant-frequency phase tracking in an interrogated specimen is possible with a resolution of 0.000 38 rad (0.022°), and swept-frequency phase measurements can be obtained. Using phase measurements, an absolute thickness in borosilicate glass is presented to show the instrument's efficacy, and these results are compared to conventional ultrasonic pulse-echo time-of-flight (ToF) measurements. The newly developed instrument predicted the thickness with a mean error of -0.04 μm and a standard deviation of error of 1.35 μm. Additionally, the CFPPLL instrument shows a lower measured phase error in the absence of changing temperature and couplant thickness than high-resolution cross-correlation ToF measurements at a similar signal-to-noise ratio. By showing higher accuracy and precision than conventional pulse-echo ToF measurements and lower phase errors than cross-correlation ToF measurements, the new digitally controlled CFPPLL instrument provides high-resolution absolute ultrasonic velocity or path-length measurements in solids or liquids, as well as tracking of material property changes with high sensitivity. The ability to obtain absolute phase measurements allows for many new applications than possible with previous ultrasonic pulsed phase-locked loop instruments. In addition to improved resolution, swept-frequency phase measurements add useful capability in measuring properties of layered structures, such as bonded joints, or materials which exhibit non-linear frequency-dependent behavior, such as dispersive media.

A Simple Model Predicting Individual Weight Change in Humans

PubMed Central

Thomas, Diana M.; Martin, Corby K.; Heymsfield, Steven; Redman, Leanne M.; Schoeller, Dale A.; Levine, James A.

2010-01-01

Excessive weight in adults is a national concern with over 2/3 of the US population deemed overweight. Because being overweight has been correlated to numerous diseases such as heart disease and type 2 diabetes, there is a need to understand mechanisms and predict outcomes of weight change and weight maintenance. A simple mathematical model that accurately predicts individual weight change offers opportunities to understand how individuals lose and gain weight and can be used to foster patient adherence to diets in clinical settings. For this purpose, we developed a one dimensional differential equation model of weight change based on the energy balance equation is paired to an algebraic relationship between fat free mass and fat mass derived from a large nationally representative sample of recently released data collected by the Centers for Disease Control. We validate the model's ability to predict individual participants’ weight change by comparing model estimates of final weight data from two recent underfeeding studies and one overfeeding study. Mean absolute error and standard deviation between model predictions and observed measurements of final weights are less than 1.8 ± 1.3 kg for the underfeeding studies and 2.5 ± 1.6 kg for the overfeeding study. Comparison of the model predictions to other one dimensional models of weight change shows improvement in mean absolute error, standard deviation of mean absolute error, and group mean predictions. The maximum absolute individual error decreased by approximately 60% substantiating reliability in individual weight change predictions. The model provides a viable method for estimating individual weight change as a result of changes in intake and determining individual dietary adherence during weight change studies. PMID:24707319

Modeling the compliance of polyurethane nanofiber tubes for artificial common bile duct

NASA Astrophysics Data System (ADS)

Moazeni, Najmeh; Vadood, Morteza; Semnani, Dariush; Hasani, Hossein

2018-02-01

The common bile duct is one of the body’s most sensitive organs and a polyurethane nanofiber tube can be used as a prosthetic of the common bile duct. The compliance is one of the most important properties of prosthetic which should be adequately compliant as long as possible to keep the behavioral integrity of prosthetic. In the present paper, the prosthetic compliance was measured and modeled using regression method and artificial neural network (ANN) based on the electrospinning process parameters such as polymer concentration, voltage, tip-to-collector distance and flow rate. Whereas, the ANN model contains different parameters affecting on the prediction accuracy directly, the genetic algorithm (GA) was used to optimize the ANN parameters. Finally, it was observed that the optimized ANN model by GA can predict the compliance with high accuracy (mean absolute percentage error = 8.57%). Moreover, the contribution of variables on the compliance was investigated through relative importance analysis and the optimum values of parameters for ideal compliance were determined.

Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model

PubMed Central

Yamamoto, Yumi; Välitalo, Pyry A.; Huntjens, Dymphy R.; Proost, Johannes H.; Vermeulen, An; Krauwinkel, Walter; Beukers, Margot W.; van den Berg, Dirk‐Jan; Hartman, Robin; Wong, Yin Cheong; Danhof, Meindert; van Hasselt, John G. C.

2017-01-01

Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System‐specific and drug‐specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration‐time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration‐time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development. PMID:28891201

Developing Automatic Water Table Control System for Reducing Greenhouse Gas Emissions from Paddy Fields

NASA Astrophysics Data System (ADS)

Arif, C.; Fauzan, M. I.; Satyanto, K. S.; Budi, I. S.; Masaru, M.

2018-05-01

Water table in rice fields play important role to mitigate greenhouse gas (GHG) emissions from paddy fields. Continuous flooding by maintenance water table 2-5 cm above soil surface is not effective and release more GHG emissions. System of Rice Intensification (SRI) as alternative rice farming apply intermittent irrigation by maintaining lower water table is proven can reduce GHG emissions reducing productivity significantly. The objectives of this study were to develop automatic water table control system for SRI application and then evaluate the performances. The control system was developed based on fuzzy logic algorithms using the mini PC of Raspberry Pi. Based on laboratory and field tests, the developed system was working well as indicated by lower MAPE (mean absolute percentage error) values. MAPE values for simulation and field tests were 16.88% and 15.80%, respectively. This system can save irrigation water up to 42.54% without reducing productivity significantly when compared to manual irrigation systems.

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

NASA Astrophysics Data System (ADS)

Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

2018-05-01

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

Forecasting the value of credit scoring

NASA Astrophysics Data System (ADS)

Saad, Shakila; Ahmad, Noryati; Jaffar, Maheran Mohd

2017-08-01

Nowadays, credit scoring system plays an important role in banking sector. This process is important in assessing the creditworthiness of customers requesting credit from banks or other financial institutions. Usually, the credit scoring is used when customers send the application for credit facilities. Based on the score from credit scoring, bank will be able to segregate the "good" clients from "bad" clients. However, in most cases the score is useful at that specific time only and cannot be used to forecast the credit worthiness of the same applicant after that. Hence, bank will not know if "good" clients will always be good all the time or "bad" clients may become "good" clients after certain time. To fill up the gap, this study proposes an equation to forecast the credit scoring of the potential borrowers at a certain time by using the historical score related to the assumption. The Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the forecast scoring. Result shows the forecast scoring is highly accurate as compared to actual credit scoring.

Calculation of 125Te NMR Chemical Shifts at the Full Four-Component Relativistic Level with Taking into Account Solvent and Vibrational Corrections: A Gateway to Better Agreement with Experiment.

PubMed

Rusakova, Irina L; Rusakov, Yuriy Yu; Krivdin, Leonid B

2017-06-29

Four-component relativistic calculations of 125 Te NMR chemical shifts were performed in the series of 13 organotellurium compounds, potential precursors of the biologically active species, at the density functional theory level under the nonrelativistic and four-component fully relativistic conditions using locally dense basis set scheme derived from relativistic Dyall's basis sets. The relativistic effects in tellurium chemical shifts were found to be of as much as 20-25% of the total calculated values. The vibrational and solvent corrections to 125 Te NMR chemical shifts are about, accordingly, 6 and 8% of their total values. The PBE0 exchange-correlation functional turned out to give the best agreement of calculated tellurium shifts with their experimental values giving the mean absolute percentage error of 4% in the range of ∼1000 ppm, provided all corrections are taken into account.

Absolute Parameters for the F-type Eclipsing Binary BW Aquarii

NASA Astrophysics Data System (ADS)

Maxted, P. F. L.

2018-05-01

BW Aqr is a bright eclipsing binary star containing a pair of F7V stars. The absolute parameters of this binary (masses, radii, etc.) are known to good precision so they are often used to test stellar models, particularly in studies of convective overshooting. ... Maxted & Hutcheon (2018) analysed the Kepler K2 data for BW Aqr and noted that it shows variability between the eclipses that may be caused by tidally induced pulsations. ... Table 1 shows the absolute parameters for BW Aqr derived from an improved analysis of the Kepler K2 light curve plus the RV measurements from both Imbert (1979) and Lester & Gies (2018). ... The values in Table 1 with their robust error estimates from the standard deviation of the mean are consistent with the values and errors from Maxted & Hutcheon (2018) based on the PPD calculated using emcee for a fit to the entire K2 light curve.

Absolute measurement of the extreme UV solar flux

NASA Technical Reports Server (NTRS)

Carlson, R. W.; Ogawa, H. S.; Judge, D. L.; Phillips, E.

1984-01-01

A windowless rare-gas ionization chamber has been developed to measure the absolute value of the solar extreme UV flux in the 50-575-A region. Successful results were obtained on a solar-pointing sounding rocket. The ionization chamber, operated in total absorption, is an inherently stable absolute detector of ionizing UV radiation and was designed to be independent of effects from secondary ionization and gas effusion. The net error of the measurement is + or - 7.3 percent, which is primarily due to residual outgassing in the instrument, other errors such as multiple ionization, photoelectron collection, and extrapolation to the zero atmospheric optical depth being small in comparison. For the day of the flight, Aug. 10, 1982, the solar irradiance (50-575 A), normalized to unit solar distance, was found to be 5.71 + or - 0.42 x 10 to the 10th photons per sq cm sec.

Altitude registration of limb-scattered radiation

NASA Astrophysics Data System (ADS)

Moy, Leslie; Bhartia, Pawan K.; Jaross, Glen; Loughman, Robert; Kramarova, Natalya; Chen, Zhong; Taha, Ghassan; Chen, Grace; Xu, Philippe

2017-01-01

One of the largest constraints to the retrieval of accurate ozone profiles from UV backscatter limb sounding sensors is altitude registration. Two methods, the Rayleigh scattering attitude sensing (RSAS) and absolute radiance residual method (ARRM), are able to determine altitude registration to the accuracy necessary for long-term ozone monitoring. The methods compare model calculations of radiances to measured radiances and are independent of onboard tracking devices. RSAS determines absolute altitude errors, but, because the method is susceptible to aerosol interference, it is limited to latitudes and time periods with minimal aerosol contamination. ARRM, a new technique introduced in this paper, can be applied across all seasons and altitudes. However, it is only appropriate for relative altitude error estimates. The application of RSAS to Limb Profiler (LP) measurements from the Ozone Mapping and Profiler Suite (OMPS) on board the Suomi NPP (SNPP) satellite indicates tangent height (TH) errors greater than 1 km with an absolute accuracy of ±200 m. Results using ARRM indicate a ˜ 300 to 400 m intra-orbital TH change varying seasonally ±100 m, likely due to either errors in the spacecraft pointing or in the geopotential height (GPH) data that we use in our analysis. ARRM shows a change of ˜ 200 m over ˜ 5 years with a relative accuracy (a long-term accuracy) of ±100 m outside the polar regions.

44 CFR 67.6 - Basis of appeal.

Code of Federal Regulations, 2010 CFR

2010-10-01

... absolute (except where mathematical or measurement error or changed physical conditions can be demonstrated... a mathematical or measurement error or changed physical conditions, then the specific source of the... registered professional engineer or licensed land surveyor, of the new data necessary for FEMA to conduct a...

[Comparison study on sampling methods of Oncomelania hupensis snail survey in marshland schistosomiasis epidemic areas in China].

PubMed

An, Zhao; Wen-Xin, Zhang; Zhong, Yao; Yu-Kuan, Ma; Qing, Liu; Hou-Lang, Duan; Yi-di, Shang

2016-06-29

To optimize and simplify the survey method of Oncomelania hupensis snail in marshland endemic region of schistosomiasis and increase the precision, efficiency and economy of the snail survey. A quadrate experimental field was selected as the subject of 50 m×50 m size in Chayegang marshland near Henghu farm in the Poyang Lake region and a whole-covered method was adopted to survey the snails. The simple random sampling, systematic sampling and stratified random sampling methods were applied to calculate the minimum sample size, relative sampling error and absolute sampling error. The minimum sample sizes of the simple random sampling, systematic sampling and stratified random sampling methods were 300, 300 and 225, respectively. The relative sampling errors of three methods were all less than 15%. The absolute sampling errors were 0.221 7, 0.302 4 and 0.047 8, respectively. The spatial stratified sampling with altitude as the stratum variable is an efficient approach of lower cost and higher precision for the snail survey.

Discrete distributed strain sensing of intelligent structures

NASA Technical Reports Server (NTRS)

Anderson, Mark S.; Crawley, Edward F.

1992-01-01

Techniques are developed for the design of discrete highly distributed sensor systems for use in intelligent structures. First the functional requirements for such a system are presented. Discrete spatially averaging strain sensors are then identified as satisfying the functional requirements. A variety of spatial weightings for spatially averaging sensors are examined, and their wave number characteristics are determined. Preferable spatial weightings are identified. Several numerical integration rules used to integrate such sensors in order to determine the global deflection of the structure are discussed. A numerical simulation is conducted using point and rectangular sensors mounted on a cantilevered beam under static loading. Gage factor and sensor position uncertainties are incorporated to assess the absolute error and standard deviation of the error in the estimated tip displacement found by numerically integrating the sensor outputs. An experiment is carried out using a statically loaded cantilevered beam with five point sensors. It is found that in most cases the actual experimental error is within one standard deviation of the absolute error as found in the numerical simulation.

Novel Downhole Electromagnetic Flowmeter for Oil-Water Two-Phase Flow in High-Water-Cut Oil-Producing Wells.

PubMed

Wang, Yanjun; Li, Haoyu; Liu, Xingbin; Zhang, Yuhui; Xie, Ronghua; Huang, Chunhui; Hu, Jinhai; Deng, Gang

2016-10-14

First, the measuring principle, the weight function, and the magnetic field of the novel downhole inserted electromagnetic flowmeter (EMF) are described. Second, the basic design of the EMF is described. Third, the dynamic experiments of two EMFs in oil-water two-phase flow are carried out. The experimental errors are analyzed in detail. The experimental results show that the maximum absolute value of the full-scale errors is better than 5%, the total flowrate is 5-60 m³/d, and the water-cut is higher than 60%. The maximum absolute value of the full-scale errors is better than 7%, the total flowrate is 2-60 m³/d, and the water-cut is higher than 70%. Finally, onsite experiments in high-water-cut oil-producing wells are conducted, and the possible reasons for the errors in the onsite experiments are analyzed. It is found that the EMF can provide an effective technology for measuring downhole oil-water two-phase flow.

Novel Downhole Electromagnetic Flowmeter for Oil-Water Two-Phase Flow in High-Water-Cut Oil-Producing Wells

PubMed Central

Wang, Yanjun; Li, Haoyu; Liu, Xingbin; Zhang, Yuhui; Xie, Ronghua; Huang, Chunhui; Hu, Jinhai; Deng, Gang

2016-01-01

First, the measuring principle, the weight function, and the magnetic field of the novel downhole inserted electromagnetic flowmeter (EMF) are described. Second, the basic design of the EMF is described. Third, the dynamic experiments of two EMFs in oil-water two-phase flow are carried out. The experimental errors are analyzed in detail. The experimental results show that the maximum absolute value of the full-scale errors is better than 5%, the total flowrate is 5–60 m3/d, and the water-cut is higher than 60%. The maximum absolute value of the full-scale errors is better than 7%, the total flowrate is 2–60 m3/d, and the water-cut is higher than 70%. Finally, onsite experiments in high-water-cut oil-producing wells are conducted, and the possible reasons for the errors in the onsite experiments are analyzed. It is found that the EMF can provide an effective technology for measuring downhole oil-water two-phase flow. PMID:27754412

Spacecraft methods and structures with enhanced attitude control that facilitates gyroscope substitutions

NASA Technical Reports Server (NTRS)

Li, Rongsheng (Inventor); Kurland, Jeffrey A. (Inventor); Dawson, Alec M. (Inventor); Wu, Yeong-Wei A. (Inventor); Uetrecht, David S. (Inventor)

2004-01-01

Methods and structures are provided that enhance attitude control during gyroscope substitutions by insuring that a spacecraft's attitude control system does not drive its absolute-attitude sensors out of their capture ranges. In a method embodiment, an operational process-noise covariance Q of a Kalman filter is temporarily replaced with a substantially greater interim process-noise covariance Q. This replacement increases the weight given to the most recent attitude measurements and hastens the reduction of attitude errors and gyroscope bias errors. The error effect of the substituted gyroscopes is reduced and the absolute-attitude sensors are not driven out of their capture range. In another method embodiment, this replacement is preceded by the temporary replacement of an operational measurement-noise variance R with a substantially larger interim measurement-noise variance R to reduce transients during the gyroscope substitutions.

Radiometric properties of the NS001 Thematic Mapper Simulator aircraft multispectral scanner

NASA Technical Reports Server (NTRS)

Markham, Brian L.; Ahmad, Suraiya P.

1990-01-01

Laboratory tests of the NS001 TM are described emphasizing absolute calibration to determine the radiometry of the simulator's reflective channels. In-flight calibration of the data is accomplished with the NS001 internal integrating-sphere source because instabilities in the source can limit the absolute calibration. The data from 1987-89 indicate uncertainties of up to 25 percent with an apparent average uncertainty of about 15 percent. Also identified are dark current drift and sensitivity changes along the scan line, random noise, and nonlinearity which contribute errors of 1-2 percent. Uncertainties similar to hysteresis are also noted especially in the 2.08-2.35-micron range which can reduce sensitivity and cause errors. The NS001 TM Simulator demonstrates a polarization sensitivity that can generate errors of up to about 10 percent depending on the wavelength.

Optimal quantum error correcting codes from absolutely maximally entangled states

NASA Astrophysics Data System (ADS)

Raissi, Zahra; Gogolin, Christian; Riera, Arnau; Acín, Antonio

2018-02-01

Absolutely maximally entangled (AME) states are pure multi-partite generalizations of the bipartite maximally entangled states with the property that all reduced states of at most half the system size are in the maximally mixed state. AME states are of interest for multipartite teleportation and quantum secret sharing and have recently found new applications in the context of high-energy physics in toy models realizing the AdS/CFT-correspondence. We work out in detail the connection between AME states of minimal support and classical maximum distance separable (MDS) error correcting codes and, in particular, provide explicit closed form expressions for AME states of n parties with local dimension \

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

Li, Heng, E-mail: hengli@mdanderson.org; Zhu, X. Ronald; Zhang, Xiaodong

Purpose: To develop and validate a novel delivery strategy for reducing the respiratory motion–induced dose uncertainty of spot-scanning proton therapy. Methods and Materials: The spot delivery sequence was optimized to reduce dose uncertainty. The effectiveness of the delivery sequence optimization was evaluated using measurements and patient simulation. One hundred ninety-one 2-dimensional measurements using different delivery sequences of a single-layer uniform pattern were obtained with a detector array on a 1-dimensional moving platform. Intensity modulated proton therapy plans were generated for 10 lung cancer patients, and dose uncertainties for different delivery sequences were evaluated by simulation. Results: Without delivery sequence optimization,more » the maximum absolute dose error can be up to 97.2% in a single measurement, whereas the optimized delivery sequence results in a maximum absolute dose error of ≤11.8%. In patient simulation, the optimized delivery sequence reduces the mean of fractional maximum absolute dose error compared with the regular delivery sequence by 3.3% to 10.6% (32.5-68.0% relative reduction) for different patients. Conclusions: Optimizing the delivery sequence can reduce dose uncertainty due to respiratory motion in spot-scanning proton therapy, assuming the 4-dimensional CT is a true representation of the patients' breathing patterns.« less

Quantitative endoscopy: initial accuracy measurements.

PubMed

Truitt, T O; Adelman, R A; Kelly, D H; Willging, J P

2000-02-01

The geometric optics of an endoscope can be used to determine the absolute size of an object in an endoscopic field without knowing the actual distance from the object. This study explores the accuracy of a technique that estimates absolute object size from endoscopic images. Quantitative endoscopy involves calibrating a rigid endoscope to produce size estimates from 2 images taken with a known traveled distance between the images. The heights of 12 samples, ranging in size from 0.78 to 11.80 mm, were estimated with this calibrated endoscope. Backup distances of 5 mm and 10 mm were used for comparison. The mean percent error for all estimated measurements when compared with the actual object sizes was 1.12%. The mean errors for 5-mm and 10-mm backup distances were 0.76% and 1.65%, respectively. The mean errors for objects <2 mm and > or =2 mm were 0.94% and 1.18%, respectively. Quantitative endoscopy estimates endoscopic image size to within 5% of the actual object size. This method remains promising for quantitatively evaluating object size from endoscopic images. It does not require knowledge of the absolute distance of the endoscope from the object, rather, only the distance traveled by the endoscope between images.

Clinical time series prediction: towards a hierarchical dynamical system framework

PubMed Central

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. PMID:25534671

Influence of non-level walking on pedometer accuracy.

PubMed

Leicht, Anthony S; Crowther, Robert G

2009-05-01

The YAMAX Digiwalker pedometer has been previously confirmed as a valid and reliable monitor during level walking, however, little is known about its accuracy during non-level walking activities or between genders. Subsequently, this study examined the influence of non-level walking and gender on pedometer accuracy. Forty-six healthy adults completed 3-min bouts of treadmill walking at their normal walking pace during 11 inclines (0-10%) while another 123 healthy adults completed walking up and down 47 stairs. During walking, participants wore a YAMAX Digiwalker SW-700 pedometer with the number of steps taken and registered by the pedometer recorded. Pedometer difference (steps registered-steps taken), net error (% of steps taken), absolute error (absolute % of steps taken) and gender were examined by repeated measures two-way ANOVA and Tukey's post hoc tests. During incline walking, pedometer accuracy indices were similar between inclines and gender except for a significantly greater step difference (-7+/-5 steps vs. 1+/-4 steps) and net error (-2.4+/-1.8% for 9% vs. 0.4+/-1.2% for 2%). Step difference and net error were significantly greater during stair descent compared to stair ascent while absolute error was significantly greater during stair ascent compared to stair descent. The current study demonstrated that the YAMAX Digiwalker SW-700 pedometer exhibited good accuracy during incline walking up to 10% while it overestimated steps taken during stair ascent/descent with greater overestimation during stair descent. Stair walking activity should be documented in field studies as the YAMAX Digiwalker SW-700 pedometer overestimates this activity type.

Satellite SAR geocoding with refined RPC model

NASA Astrophysics Data System (ADS)

Zhang, Lu; Balz, Timo; Liao, Mingsheng

2012-04-01

Recent studies have proved that the Rational Polynomial Camera (RPC) model is able to act as a reliable replacement of the rigorous Range-Doppler (RD) model for the geometric processing of satellite SAR datasets. But its capability in absolute geolocation of SAR images has not been evaluated quantitatively. Therefore, in this article the problems of error analysis and refinement of SAR RPC model are primarily investigated to improve the absolute accuracy of SAR geolocation. Range propagation delay and azimuth timing error are identified as two major error sources for SAR geolocation. An approach based on SAR image simulation and real-to-simulated image matching is developed to estimate and correct these two errors. Afterwards a refined RPC model can be built from the error-corrected RD model and then used in satellite SAR geocoding. Three experiments with different settings are designed and conducted to comprehensively evaluate the accuracies of SAR geolocation with both ordinary and refined RPC models. All the experimental results demonstrate that with RPC model refinement the absolute location accuracies of geocoded SAR images can be improved significantly, particularly in Easting direction. In another experiment the computation efficiencies of SAR geocoding with both RD and RPC models are compared quantitatively. The results show that by using the RPC model such efficiency can be remarkably improved by at least 16 times. In addition the problem of DEM data selection for SAR image simulation in RPC model refinement is studied by a comparative experiment. The results reveal that the best choice should be using the proper DEM datasets of spatial resolution comparable to that of the SAR images.

New principle for measuring arterial blood oxygenation, enabling motion-robust remote monitoring.

PubMed

van Gastel, Mark; Stuijk, Sander; de Haan, Gerard

2016-12-07

Finger-oximeters are ubiquitously used for patient monitoring in hospitals worldwide. Recently, remote measurement of arterial blood oxygenation (SpO 2 ) with a camera has been demonstrated. Both contact and remote measurements, however, require the subject to remain static for accurate SpO 2 values. This is due to the use of the common ratio-of-ratios measurement principle that measures the relative pulsatility at different wavelengths. Since the amplitudes are small, they are easily corrupted by motion-induced variations. We introduce a new principle that allows accurate remote measurements even during significant subject motion. We demonstrate the main advantage of the principle, i.e. that the optimal signature remains the same even when the SNR of the PPG signal drops significantly due to motion or limited measurement area. The evaluation uses recordings with breath-holding events, which induce hypoxemia in healthy moving subjects. The events lead to clinically relevant SpO 2 levels in the range 80-100%. The new principle is shown to greatly outperform current remote ratio-of-ratios based methods. The mean-absolute SpO 2 -error (MAE) is about 2 percentage-points during head movements, where the benchmark method shows a MAE of 24 percentage-points. Consequently, we claim ours to be the first method to reliably measure SpO 2 remotely during significant subject motion.

External quality-assurance results for the National Atmospheric Deposition Program/National Trends Network, 2002-03

USGS Publications Warehouse

Wetherbee, Gregory A.; Latysh, Natalie E.; Burke, Kevin P.

2005-01-01

Six external quality-assurance programs were operated by the U.S. Geological Survey (USGS) External Quality-Assurance (QA) Project for the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) from 2002 through 2003. Each program measured specific components of the overall error inherent in NADP/NTN wet-deposition measurements. The intersite-comparison program assessed the variability and bias of pH and specific conductance determinations made by NADP/NTN site operators twice per year with respect to accuracy goals. The percentage of site operators that met the pH accuracy goals decreased from 92.0 percent in spring 2002 to 86.3 percent in spring 2003. In these same four intersite-comparison studies, the percentage of site operators that met the accuracy goals for specific conductance ranged from 94.4 to 97.5 percent. The blind-audit program and the sample-handling evaluation (SHE) program evaluated the effects of routine sample handling, processing, and shipping on the chemistry of weekly NADP/NTN samples. The blind-audit program data indicated that the variability introduced by sample handling might be environmentally significant to data users for sodium, potassium, chloride, and hydrogen ion concentrations during 2002. In 2003, the blind-audit program was modified and replaced by the SHE program. The SHE program was designed to control the effects of laboratory-analysis variability. The 2003 SHE data had less overall variability than the 2002 blind-audit data. The SHE data indicated that sample handling buffers the pH of the precipitation samples and, in turn, results in slightly lower conductivity. Otherwise, the SHE data provided error estimates that were not environmentally significant to data users. The field-audit program was designed to evaluate the effects of onsite exposure, sample handling, and shipping on the chemistry of NADP/NTN precipitation samples. Field-audit results indicated that exposure of NADP/NTN wet-deposition samples to onsite conditions tended to neutralize the acidity of the samples by less than 1.0 microequivalent per liter. Onsite exposure of the sampling bucket appeared to slightly increase the concentration of most of the analytes but not to an extent that was environmentally significant to NADP data users. An interlaboratory-comparison program was used to estimate the analytical variability and bias of the NADP Central Analytical Laboratory (CAL) during 2002-03. Bias was identified in the CAL data for calcium, magnesium, sodium, potassium, ammonium, chloride, nitrate, sulfate, hydrogen ion, and specific conductance, but the absolute value of the bias was less than analytical minimum detection limits for all constituents except magnesium, nitrate, sulfate, and specific conductance. Control charts showed that CAL results were within statistical control approximately 90 percent of the time. Data for the analysis of ultrapure deionized-water samples indicated that CAL did not have problems with laboratory contamination. During 2002-03, the overall variability of data from the NADP/NTN precipitation-monitoring system was estimated using data from three collocated monitoring sites. Measurement differences of constituent concentration and deposition for paired samples from the collocated samplers were evaluated to compute error terms. The medians of the absolute percentage errors (MAEs) for the paired samples generally were larger for cations (approximately 8 to 50 percent) than for anions (approximately 3 to 33 percent). MAEs were approximately 16 to 30 percent for hydrogen-ion concentration, less than 10 percent for specific conductance, less than 5 percent for sample volume, and less than 8 percent for precipitation depth. The variability attributed to each component of the sample-collection and analysis processes, as estimated by USGS quality-assurance programs, varied among analytes. Laboratory analysis variability accounted for approximately 2 percent of the

Mean Expected Error in Prediction of Total Body Water: A True Accuracy Comparison between Bioimpedance Spectroscopy and Single Frequency Regression Equations

PubMed Central

Abtahi, Shirin; Abtahi, Farhad; Ellegård, Lars; Johannsson, Gudmundur; Bosaeus, Ingvar

2015-01-01

For several decades electrical bioimpedance (EBI) has been used to assess body fluid distribution and body composition. Despite the development of several different approaches for assessing total body water (TBW), it remains uncertain whether bioimpedance spectroscopic (BIS) approaches are more accurate than single frequency regression equations. The main objective of this study was to answer this question by calculating the expected accuracy of a single measurement for different EBI methods. The results of this study showed that all methods produced similarly high correlation and concordance coefficients, indicating good accuracy as a method. Even the limits of agreement produced from the Bland-Altman analysis indicated that the performance of single frequency, Sun's prediction equations, at population level was close to the performance of both BIS methods; however, when comparing the Mean Absolute Percentage Error value between the single frequency prediction equations and the BIS methods, a significant difference was obtained, indicating slightly better accuracy for the BIS methods. Despite the higher accuracy of BIS methods over 50 kHz prediction equations at both population and individual level, the magnitude of the improvement was small. Such slight improvement in accuracy of BIS methods is suggested insufficient to warrant their clinical use where the most accurate predictions of TBW are required, for example, when assessing over-fluidic status on dialysis. To reach expected errors below 4-5%, novel and individualized approaches must be developed to improve the accuracy of bioimpedance-based methods for the advent of innovative personalized health monitoring applications. PMID:26137489

Clinical value of CT-based preoperative software assisted lung lobe volumetry for predicting postoperative pulmonary function after lung surgery

NASA Astrophysics Data System (ADS)

Wormanns, Dag; Beyer, Florian; Hoffknecht, Petra; Dicken, Volker; Kuhnigk, Jan-Martin; Lange, Tobias; Thomas, Michael; Heindel, Walter

2005-04-01

This study was aimed to evaluate a morphology-based approach for prediction of postoperative forced expiratory volume in one second (FEV1) after lung resection from preoperative CT scans. Fifteen Patients with surgically treated (lobectomy or pneumonectomy) bronchogenic carcinoma were enrolled in the study. A preoperative chest CT and pulmonary function tests before and after surgery were performed. CT scans were analyzed by prototype software: automated segmentation and volumetry of lung lobes was performed with minimal user interaction. Determined volumes of different lung lobes were used to predict postoperative FEV1 as percentage of the preoperative values. Predicted FEV1 values were compared to the observed postoperative values as standard of reference. Patients underwent lobectomy in twelve cases (6 upper lobes; 1 middle lobe; 5 lower lobes; 6 right side; 6 left side) and pneumonectomy in three cases. Automated calculation of predicted postoperative lung function was successful in all cases. Predicted FEV1 ranged from 54% to 95% (mean 75% +/- 11%) of the preoperative values. Two cases with obviously erroneous LFT were excluded from analysis. Mean error of predicted FEV1 was 20 +/- 160 ml, indicating absence of systematic error; mean absolute error was 7.4 +/- 3.3% respective 137 +/- 77 ml/s. The 200 ml reproducibility criterion for FEV1 was met in 11 of 13 cases (85%). In conclusion, software-assisted prediction of postoperative lung function yielded a clinically acceptable agreement with the observed postoperative values. This method might add useful information for evaluation of functional operability of patients with lung cancer.

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

NASA Astrophysics Data System (ADS)

Liang, Zhongmin; Li, Yujie; Hu, Yiming; Li, Binquan; Wang, Jun

2017-06-01

Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

Anomalous annealing of floating gate errors due to heavy ion irradiation

NASA Astrophysics Data System (ADS)

Yin, Yanan; Liu, Jie; Sun, Youmei; Hou, Mingdong; Liu, Tianqi; Ye, Bing; Ji, Qinggang; Luo, Jie; Zhao, Peixiong

2018-03-01

Using the heavy ions provided by the Heavy Ion Research Facility in Lanzhou (HIRFL), the annealing of heavy-ion induced floating gate (FG) errors in 34 nm and 25 nm NAND Flash memories has been studied. The single event upset (SEU) cross section of FG and the evolution of the errors after irradiation depending on the ion linear energy transfer (LET) values, data pattern and feature size of the device are presented. Different rates of annealing for different ion LET and different pattern are observed in 34 nm and 25 nm memories. The variation of the percentage of different error patterns in 34 nm and 25 nm memories with annealing time shows that the annealing of FG errors induced by heavy-ion in memories will mainly take place in the cells directly hit under low LET ion exposure and other cells affected by heavy ions when the ion LET is higher. The influence of Multiple Cell Upsets (MCUs) on the annealing of FG errors is analyzed. MCUs with high error multiplicity which account for the majority of the errors can induce a large percentage of annealed errors.

Nutrition labels in bar graph format deemed most useful for consumer purchase decisions using adaptive conjoint analysis.

PubMed

Geiger, C J; Wyse, B W; Parent, C R; Hansen, R G

1991-07-01

This study estimated the effects of changing multiple levels and combinations of nutrition information format, load, expression, and order on consumers' perceptions of label usefulness in purchase decisions using adaptive conjoint analysis. A shopping mall intercept survey, which was administered by a marketing research firm, assessed consumer preferences for 12 label alternatives produced on Campbell's soup cans to portray nutrition information realistically; 252 of 258 respondents completed the computer interactive interview. Consumers significantly preferred the bar graph format to the bar graph/nutrient density and traditional label formats. Consumers considered the bar graph/nutrient density format to be as useful as the traditional label format. There was a highly significant difference among the three levels of information load; the most information load was preferred regardless of nutrient importance. Consumers significantly preferred nutrition information stated in absolute numbers and percentages vs in absolute numbers only in traditional, or in percentages only expressions. There was a significant difference between consumer preferences for the two types of information order. The findings indicate that consumers clearly preferred the nutrition label that displayed all nutrient values using a bar graph format, offered the most information load, and expressed nutrient values using both absolute numbers and percentages. Consumers also preferred nutrition information rearranged in an order that grouped nutrients that should be consumed in adequate amounts on the top, calories in the middle, and nutrients that should be consumed in lesser amounts on the bottom of the label.

WE-G-BRA-04: Common Errors and Deficiencies in Radiation Oncology Practice

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

Kry, S; Dromgoole, L; Alvarez, P

Purpose: Dosimetric errors in radiotherapy dose delivery lead to suboptimal treatments and outcomes. This work reviews the frequency and severity of dosimetric and programmatic errors identified by on-site audits performed by the IROC Houston QA center. Methods: IROC Houston on-site audits evaluate absolute beam calibration, relative dosimetry data compared to the treatment planning system data, and processes such as machine QA. Audits conducted from 2000-present were abstracted for recommendations, including type of recommendation and magnitude of error when applicable. Dosimetric recommendations corresponded to absolute dose errors >3% and relative dosimetry errors >2%. On-site audits of 1020 accelerators at 409 institutionsmore » were reviewed. Results: A total of 1280 recommendations were made (average 3.1/institution). The most common recommendation was for inadequate QA procedures per TG-40 and/or TG-142 (82% of institutions) with the most commonly noted deficiency being x-ray and electron off-axis constancy versus gantry angle. Dosimetrically, the most common errors in relative dosimetry were in small-field output factors (59% of institutions), wedge factors (33% of institutions), off-axis factors (21% of institutions), and photon PDD (18% of institutions). Errors in calibration were also problematic: 20% of institutions had an error in electron beam calibration, 8% had an error in photon beam calibration, and 7% had an error in brachytherapy source calibration. Almost all types of data reviewed included errors up to 7% although 20 institutions had errors in excess of 10%, and 5 had errors in excess of 20%. The frequency of electron calibration errors decreased significantly with time, but all other errors show non-significant changes. Conclusion: There are many common and often serious errors made during the establishment and maintenance of a radiotherapy program that can be identified through independent peer review. Physicists should be cautious, particularly in areas highlighted herein that show a tendency for errors.« less

Proprioceptive deficit in patients with complete tearing of the anterior cruciate ligament.

PubMed

Godinho, Pedro; Nicoliche, Eduardo; Cossich, Victor; de Sousa, Eduardo Branco; Velasques, Bruna; Salles, José Inácio

2014-01-01

To investigate the existence of proprioceptive deficits between the injured limb and the uninjured (i.e. contralateral normal) limb, in individuals who suffered complete tearing of the anterior cruciate ligament (ACL), using a strength reproduction test. Sixteen patients with complete tearing of the ACL participated in the study. A voluntary maximum isometric strength test was performed, with reproduction of the muscle strength in the limb with complete tearing of the ACL and the healthy contralateral limb, with the knee flexed at 60°. The meta-intensity was used for the procedure of 20% of the voluntary maximum isometric strength. The proprioceptive performance was determined by means of absolute error, variable error and constant error values. Significant differences were found between the control group and ACL group for the variables of absolute error (p = 0.05) and constant error (p = 0.01). No difference was found in relation to variable error (p = 0.83). Our data corroborate the hypothesis that there is a proprioceptive deficit in subjects with complete tearing of the ACL in an injured limb, in comparison with the uninjured limb, during evaluation of the sense of strength. This deficit can be explained in terms of partial or total loss of the mechanoreceptors of the ACL.

Verification of Pharmacogenetics-Based Warfarin Dosing Algorithms in Han-Chinese Patients Undertaking Mechanic Heart Valve Replacement

PubMed Central

Zhao, Li; Chen, Chunxia; Li, Bei; Dong, Li; Guo, Yingqiang; Xiao, Xijun; Zhang, Eryong; Qin, Li

2014-01-01

Objective To study the performance of pharmacogenetics-based warfarin dosing algorithms in the initial and the stable warfarin treatment phases in a cohort of Han-Chinese patients undertaking mechanic heart valve replacement. Methods We searched PubMed, Chinese National Knowledge Infrastructure and Wanfang databases for selecting pharmacogenetics-based warfarin dosing models. Patients with mechanic heart valve replacement were consecutively recruited between March 2012 and July 2012. The predicted warfarin dose of each patient was calculated and compared with the observed initial and stable warfarin doses. The percentage of patients whose predicted dose fell within 20% of their actual therapeutic dose (percentage within 20%), and the mean absolute error (MAE) were utilized to evaluate the predictive accuracy of all the selected algorithms. Results A total of 8 algorithms including Du, Huang, Miao, Wei, Zhang, Lou, Gage, and International Warfarin Pharmacogenetics Consortium (IWPC) model, were tested in 181 patients. The MAE of the Gage, IWPC and 6 Han-Chinese pharmacogenetics-based warfarin dosing algorithms was less than 0.6 mg/day in accuracy and the percentage within 20% exceeded 45% in all of the selected models in both the initial and the stable treatment stages. When patients were stratified according to the warfarin dose range, all of the equations demonstrated better performance in the ideal-dose range (1.88–4.38 mg/day) than the low-dose range (<1.88 mg/day). Among the 8 algorithms compared, the algorithms of Wei, Huang, and Miao showed a lower MAE and higher percentage within 20% in both the initial and the stable warfarin dose prediction and in the low-dose and the ideal-dose ranges. Conclusions All of the selected pharmacogenetics-based warfarin dosing regimens performed similarly in our cohort. However, the algorithms of Wei, Huang, and Miao showed a better potential for warfarin prediction in the initial and the stable treatment phases in Han-Chinese patients undertaking mechanic heart valve replacement. PMID:24728385

Verification of pharmacogenetics-based warfarin dosing algorithms in Han-Chinese patients undertaking mechanic heart valve replacement.

PubMed

Zhao, Li; Chen, Chunxia; Li, Bei; Dong, Li; Guo, Yingqiang; Xiao, Xijun; Zhang, Eryong; Qin, Li

2014-01-01

To study the performance of pharmacogenetics-based warfarin dosing algorithms in the initial and the stable warfarin treatment phases in a cohort of Han-Chinese patients undertaking mechanic heart valve replacement. We searched PubMed, Chinese National Knowledge Infrastructure and Wanfang databases for selecting pharmacogenetics-based warfarin dosing models. Patients with mechanic heart valve replacement were consecutively recruited between March 2012 and July 2012. The predicted warfarin dose of each patient was calculated and compared with the observed initial and stable warfarin doses. The percentage of patients whose predicted dose fell within 20% of their actual therapeutic dose (percentage within 20%), and the mean absolute error (MAE) were utilized to evaluate the predictive accuracy of all the selected algorithms. A total of 8 algorithms including Du, Huang, Miao, Wei, Zhang, Lou, Gage, and International Warfarin Pharmacogenetics Consortium (IWPC) model, were tested in 181 patients. The MAE of the Gage, IWPC and 6 Han-Chinese pharmacogenetics-based warfarin dosing algorithms was less than 0.6 mg/day in accuracy and the percentage within 20% exceeded 45% in all of the selected models in both the initial and the stable treatment stages. When patients were stratified according to the warfarin dose range, all of the equations demonstrated better performance in the ideal-dose range (1.88-4.38 mg/day) than the low-dose range (<1.88 mg/day). Among the 8 algorithms compared, the algorithms of Wei, Huang, and Miao showed a lower MAE and higher percentage within 20% in both the initial and the stable warfarin dose prediction and in the low-dose and the ideal-dose ranges. All of the selected pharmacogenetics-based warfarin dosing regimens performed similarly in our cohort. However, the algorithms of Wei, Huang, and Miao showed a better potential for warfarin prediction in the initial and the stable treatment phases in Han-Chinese patients undertaking mechanic heart valve replacement.

[Comparison of three daily global solar radiation models].

PubMed

Yang, Jin-Ming; Fan, Wen-Yi; Zhao, Ying-Hui

2014-08-01

Three daily global solar radiation estimation models ( Å-P model, Thornton-Running model and model provided by Liu Ke-qun et al.) were analyzed and compared using data of 13 weather stations from 1982 to 2012 from three northeastern provinces and eastern Inner Mongolia. After cross-validation analysis, the result showed that mean absolute error (MAE) for each model was 1.71, 2.83 and 1.68 MJ x m(-2) x d(-1) respectively, showing that Å-P model and model provided by Liu Ke-qun et al. which used percentage of sunshine had an advantage over Thornton-Running model which didn't use percentage of sunshine. Model provided by Liu Ke-qun et al. played a good effect on the situation of non-sunshine, and its MAE and bias percentage were 18.5% and 33.8% smaller than those of Å-P model, respectively. High precision results could be obtained by using the simple linear model of Å-P. Å-P model, Thornton-Running model and model provided by Liu Ke-qun et al. overvalued daily global solar radiation by 12.2%, 19.2% and 9.9% respectively. MAE for each station varied little with the spatial change of location, and annual MAE decreased with the advance of years. The reason for this might be that the change of observation accuracy caused by the replacement of radiation instrument in 1993. MAEs for rainy days, non-sunshine days and warm seasons of the three models were greater than those for days without rain, sunshine days and cold seasons respectively, showing that different methods should be used for different weather conditions on estimating solar radiation with meteorological elements.

Comparison of the Performance of the Warfarin Pharmacogenetics Algorithms in Patients with Surgery of Heart Valve Replacement and Heart Valvuloplasty.

PubMed

Xu, Hang; Su, Shi; Tang, Wuji; Wei, Meng; Wang, Tao; Wang, Dongjin; Ge, Weihong

2015-09-01

A large number of warfarin pharmacogenetics algorithms have been published. Our research was aimed to evaluate the performance of the selected pharmacogenetic algorithms in patients with surgery of heart valve replacement and heart valvuloplasty during the phase of initial and stable anticoagulation treatment. 10 pharmacogenetic algorithms were selected by searching PubMed. We compared the performance of the selected algorithms in a cohort of 193 patients during the phase of initial and stable anticoagulation therapy. Predicted dose was compared to therapeutic dose by using a predicted dose percentage that falls within 20% threshold of the actual dose (percentage within 20%) and mean absolute error (MAE). The average warfarin dose for patients was 3.05±1.23mg/day for initial treatment and 3.45±1.18mg/day for stable treatment. The percentages of the predicted dose within 20% of the therapeutic dose were 44.0±8.8% and 44.6±9.7% for the initial and stable phases, respectively. The MAEs of the selected algorithms were 0.85±0.18mg/day and 0.93±0.19mg/day, respectively. All algorithms had better performance in the ideal group than in the low dose and high dose groups. The only exception is the Wadelius et al. algorithm, which had better performance in the high dose group. The algorithms had similar performance except for the Wadelius et al. and Miao et al. algorithms, which had poor accuracy in our study cohort. The Gage et al. algorithm had better performance in both phases of initial and stable treatment. Algorithms had relatively higher accuracy in the >50years group of patients on the stable phase. Copyright © 2015 Elsevier Ltd. All rights reserved.

Empirical models validation to estimate global solar irradiance on a horizontal plan in Ouargla, Algeria

NASA Astrophysics Data System (ADS)

Gougui, Abdelmoumen; Djafour, Ahmed; Khelfaoui, Narimane; Boutelli, Halima

2018-05-01

In this paper a comparison between three models for predicting the total solar flux falling on a horizontal surface has been processed. Capderou, Perrin & Brichambaut and Hottel models used to estimate the global solar radiation, the models are identified and evaluated using MATLAB environment. The recorded data have been obtained from a small weather station installed at the LAGE laboratory of Ouargla University, Algeria. Solar radiation data have been recorded using four sample days, every 15thday of the month, (March, April, May and October). The Root Mean Square Error (RMSE), Correlation Coefficient (CC) and Mean Absolute Percentage Error (MAPE) have been also calculated so as that to test the reliability of the proposed models. A comparisons between the measured and the calculated values have been made. The results obtained in this study depict that Perrin & Brichambaut and Capderou models are more effective to estimate the total solar intensity on a horizontal surface for clear sky over Ouargla city (Latitude of 31° 95' N, Longitude of 5° 24' E, and Altitude of 0.141km above Mean Sea Level), these models dedicated from meteorological parameters, geographical location and number of days since the first January. Perrin & Brichambaut and Capderou models give the best tendency with a CC of 0.985-0.999 and 0.932-0.995 consecutively further, Hottel give's a CC of 0.617-0.942.

Microgrid optimal scheduling considering impact of high penetration wind generation

NASA Astrophysics Data System (ADS)

Alanazi, Abdulaziz

The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations.

Development of an accident duration prediction model on the Korean Freeway Systems.

PubMed

Chung, Younshik

2010-01-01

Since duration prediction is one of the most important steps in an accident management process, there have been several approaches developed for modeling accident duration. This paper presents a model for the purpose of accident duration prediction based on accurately recorded and large accident dataset from the Korean Freeway Systems. To develop the duration prediction model, this study utilizes the log-logistic accelerated failure time (AFT) metric model and a 2-year accident duration dataset from 2006 to 2007. Specifically, the 2006 dataset is utilized to develop the prediction model and then, the 2007 dataset was employed to test the temporal transferability of the 2006 model. Although the duration prediction model has limitations such as large prediction error due to the individual differences of the accident treatment teams in terms of clearing similar accidents, the results from the 2006 model yielded a reasonable prediction based on the mean absolute percentage error (MAPE) scale. Additionally, the results of the statistical test for temporal transferability indicated that the estimated parameters in the duration prediction model are stable over time. Thus, this temporal stability suggests that the model may have potential to be used as a basis for making rational diversion and dispatching decisions in the event of an accident. Ultimately, such information will beneficially help in mitigating traffic congestion due to accidents.

Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs.

PubMed

Kaur, Gurmanik; Arora, Ajat Shatru; Jain, Vijender Kumar

2017-01-01

Crossing the legs at the knees, during BP measurement, is one of the several physiological stimuli that considerably influence the accuracy of BP measurements. Therefore, it is paramount to develop an appropriate prediction model for interpreting influence of crossed legs on BP. This research work described the use of principal component analysis- (PCA-) fused forward stepwise regression (FSWR), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and least squares support vector machine (LS-SVM) models for prediction of BP reactivity to crossed legs among the normotensive and hypertensive participants. The evaluation of the performance of the proposed prediction models using appropriate statistical indices showed that the PCA-based LS-SVM (PCA-LS-SVM) model has the highest prediction accuracy with coefficient of determination ( R 2 ) = 93.16%, root mean square error (RMSE) = 0.27, and mean absolute percentage error (MAPE) = 5.71 for SBP prediction in normotensive subjects. Furthermore, R 2  = 96.46%, RMSE = 0.19, and MAPE = 1.76 for SBP prediction and R 2  = 95.44%, RMSE = 0.21, and MAPE = 2.78 for DBP prediction in hypertensive subjects using the PCA-LSSVM model. This assessment presents the importance and advantages posed by hybrid computing models for the prediction of variables in biomedical research studies.

An optimisation methodology of artificial neural network models for predicting solar radiation: a case study

NASA Astrophysics Data System (ADS)

Rezrazi, Ahmed; Hanini, Salah; Laidi, Maamar

2016-02-01

The right design and the high efficiency of solar energy systems require accurate information on the availability of solar radiation. Due to the cost of purchase and maintenance of the radiometers, these data are not readily available. Therefore, there is a need to develop alternative ways of generating such data. Artificial neural networks (ANNs) are excellent and effective tools for learning, pinpointing or generalising data regularities, as they have the ability to model nonlinear functions; they can also cope with complex `noisy' data. The main objective of this paper is to show how to reach an optimal model of ANNs for applying in prediction of solar radiation. The measured data of the year 2007 in Ghardaïa city (Algeria) are used to demonstrate the optimisation methodology. The performance evaluation and the comparison of results of ANN models with measured data are made on the basis of mean absolute percentage error (MAPE). It is found that MAPE in the ANN optimal model reaches 1.17 %. Also, this model yields a root mean square error (RMSE) of 14.06 % and an MBE of 0.12. The accuracy of the outputs exceeded 97 % and reached up 99.29 %. Results obtained indicate that the optimisation strategy satisfies practical requirements. It can successfully be generalised for any location in the world and be used in other fields than solar radiation estimation.

Measurement of Cardiac Index by Transpulmonary Thermodilution Using an Implanted Central Venous Access Port: A Prospective Study in Patients Scheduled for Oncologic High-Risk Surgery

PubMed Central

Suria, Stéphanie; Wyniecki, Anne; Eghiaian, Alexandre; Monnet, Xavier; Weil, Grégoire

2014-01-01

Background Transpulmonary thermodilution allows the measurement of cardiac index for high risk surgical patients. Oncologic patients often have a central venous access (port-a-catheter) for chronic treatment. The validity of the measurement by a port-a-catheter of the absolute cardiac index and the detection of changes in cardiac index induced by fluid challenge are unknown. Methods We conducted a monocentric prospective study. 27 patients were enrolled. 250 ml colloid volume expansions for fluid challenge were performed during ovarian cytoreductive surgery. The volume expansion-induced changes in cardiac index measured by transpulmonary thermodilution by a central venous access (CIcvc) and by a port-a-catheter (CIport) were recorded. Results 23 patients were analyzed with 123 pairs of measurements. Using a Bland and Altman for repeated measurements, the bias (lower and upper limits of agreement) between CIport and CIcvc was 0.14 (−0.59 to 0.88) L/min/m2. The percentage error was 22%. The concordance between the changes in CIport and CIcvc observed during volume expansion was 92% with an r = 0.7 (with exclusion zone). No complications (included sepsis) were observed during the follow up period. Conclusions The transpulmonary thermodilution by a port-a-catheter is reliable for absolute values estimation of cardiac index and for measurement of the variation after fluid challenge. Trial Registration clinicaltrials.gov NCT02063009 PMID:25136951

Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator.

PubMed

Hannula, Manne; Huttunen, Kerttu; Koskelo, Jukka; Laitinen, Tomi; Leino, Tuomo

2008-01-01

In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor fighter pilots during complex simulated F/A-18 Hornet air battles. In our data, the mean absolute error of the ANN estimate was 11.4 as a visual analog scale score, being 13-23% better than the mean absolute error of the MLR model in the estimation of cognitive workload.

Systematic error of the Gaia DR1 TGAS parallaxes from data for the red giant clump

NASA Astrophysics Data System (ADS)

Gontcharov, G. A.

2017-08-01

Based on the Gaia DR1 TGAS parallaxes and photometry from the Tycho-2, Gaia, 2MASS, andWISE catalogues, we have produced a sample of 100 000 clump red giants within 800 pc of the Sun. The systematic variations of the mode of their absolute magnitude as a function of the distance, magnitude, and other parameters have been analyzed. We show that these variations reach 0.7 mag and cannot be explained by variations in the interstellar extinction or intrinsic properties of stars and by selection. The only explanation seems to be a systematic error of the Gaia DR1 TGAS parallax dependent on the square of the observed distance in kpc: 0.18 R 2 mas. Allowance for this error reduces significantly the systematic dependences of the absolute magnitude mode on all parameters. This error reaches 0.1 mas within 800 pc of the Sun and allows an upper limit for the accuracy of the TGAS parallaxes to be estimated as 0.2 mas. A careful allowance for such errors is needed to use clump red giants as "standard candles." This eliminates all discrepancies between the theoretical and empirical estimates of the characteristics of these stars and allows us to obtain the first estimates of the modes of their absolute magnitudes from the Gaia parallaxes: mode( M H ) = -1.49 m ± 0.04 m , mode( M Ks ) = -1.63 m ± 0.03 m , mode( M W1) = -1.67 m ± 0.05 m mode( M W2) = -1.67 m ± 0.05 m , mode( M W3) = -1.66 m ± 0.02 m , mode( M W4) = -1.73 m ± 0.03 m , as well as the corresponding estimates of their de-reddened colors.

Aquatic habitat mapping with an acoustic doppler current profiler: Considerations for data quality

USGS Publications Warehouse

Gaeuman, David; Jacobson, Robert B.

2005-01-01

When mounted on a boat or other moving platform, acoustic Doppler current profilers (ADCPs) can be used to map a wide range of ecologically significant phenomena, including measures of fluid shear, turbulence, vorticity, and near-bed sediment transport. However, the instrument movement necessary for mapping applications can generate significant errors, many of which have not been inadequately described. This report focuses on the mechanisms by which moving-platform errors are generated, and quantifies their magnitudes under typical habitat-mapping conditions. The potential for velocity errors caused by mis-alignment of the instrument?s internal compass are widely recognized, but has not previously been quantified for moving instruments. Numerical analyses show that even relatively minor compass mis-alignments can produce significant velocity errors, depending on the ratio of absolute instrument velocity to the target velocity and on the relative directions of instrument and target motion. A maximum absolute instrument velocity of about 1 m/s is recommended for most mapping applications. Lower velocities are appropriate when making bed velocity measurements, an emerging application that makes use of ADCP bottom-tracking to measure the velocity of sediment particles at the bed. The mechanisms by which heterogeneities in the flow velocity field generate horizontal velocities errors are also quantified, and some basic limitations in the effectiveness of standard error-detection criteria for identifying these errors are described. Bed velocity measurements may be particularly vulnerable to errors caused by spatial variability in the sediment transport field.

Finding the most accurate method to measure head circumference for fetal weight estimation.

PubMed

Schmidt, Ulrike; Temerinac, Dunja; Bildstein, Katharina; Tuschy, Benjamin; Mayer, Jade; Sütterlin, Marc; Siemer, Jörn; Kehl, Sven

2014-07-01

Accurate measurement of fetal head biometry is important for fetal weight estimation (FWE) and is therefore an important prognostic parameter for neonatal morbidity and mortality and a valuable tool for determining the further obstetric management. Measurement of the head circumference (HC) in particular is employed in many commonly used weight equations. The aim of the present study was to find the most accurate method to measure head circumference for fetal weight estimation. This prospective study included 481 term pregnancies. Inclusion criteria were a singleton pregnancy and ultrasound examination with complete fetal biometric parameters within 3 days of delivery, and an absence of structural or chromosomal malformations. Different methods were used for ultrasound measurement of the HC (ellipse-traced, ellipse-calculated, and circle-calculated). As a reference method, HC was also determined using a measuring tape immediately after birth. FWE was carried out with Hadlock formulas, including either HC or biparietal diameter (BPD), and differences were compared using percentage error (PE), absolute percentage error (APE), limits of agreement (LOA), and cumulative distribution. The ellipse-traced method showed the best results for FWE among all of the ultrasound methods assessed. It had the lowest median APE and the narrowest LOA. With regard to the cumulative distribution, it included the largest number of cases at a discrepancy level of ±10%. The accuracy of BPD was similar to that of the ellipse-traced method when it was used instead of HC for weight estimation. Differences between the three techniques for calculating HC were small but significant. For clinical use, the ellipse-traced method should be recommended. However, when BPD is used instead of HC for FWE, the accuracy is similar to that of the ellipse-traced method. The BPD might therefore be a good alternative to head measurements in estimating fetal weight. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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

Zhang, R; Bai, W

Purpose: Because of statistical noise in Monte Carlo dose calculations, effective point doses may not be accurate. Volume spheres are useful for evaluating dose in Monte Carlo plans, which have an inherent statistical uncertainty.We use a user-defined sphere volume instead of a point, take sphere sampling around effective point make the dose statistics to decrease the stochastic errors. Methods: Direct dose measurements were made using a 0.125cc Semiflex ion chamber (IC) 31010 isocentrically placed in the center of a homogeneous Cylindric sliced RW3 phantom (PTW, Germany).In the scanned CT phantom series the sensitive volume length of the IC (6.5mm) weremore » delineated and defined the isocenter as the simulation effective points. All beams were simulated in Monaco in accordance to the measured model. In our simulation using 2mm voxels calculation grid spacing and choose calculate dose to medium and request the relative standard deviation ≤0.5%. Taking three different assigned IC over densities (air electron density(ED) as 0.01g/cm3 default CT scanned ED and Esophageal lumen ED 0.21g/cm3) were tested at different sampling sphere radius (2.5, 2, 1.5 and 1 mm) statistics dose were compared with the measured does. Results: The results show that in the Monaco TPS for the IC using Esophageal lumen ED 0.21g/cm3 and sampling sphere radius 1.5mm the statistical value is the best accordance with the measured value, the absolute average percentage deviation is 0.49%. And when the IC using air electron density(ED) as 0.01g/cm3 and default CT scanned EDthe recommented statistical sampling sphere radius is 2.5mm, the percentage deviation are 0.61% and 0.70%, respectivly. Conclusion: In Monaco treatment planning system for the ionization chamber 31010 recommend air cavity using ED 0.21g/cm3 and sampling 1.5mm sphere volume instead of a point dose to decrease the stochastic errors. Funding Support No.C201505006.« less

Uncertainty Analysis of Downscaled CMIP5 Precipitation Data for Louisiana, USA

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Tamanna, M.; Chivoiu, B.; Habib, E. H.

2014-12-01

The downscaled CMIP3 and CMIP5 Climate and Hydrology Projections dataset contains fine spatial resolution translations of climate projections over the contiguous United States developed using two downscaling techniques (monthly Bias Correction Spatial Disaggregation (BCSD) and daily Bias Correction Constructed Analogs (BCCA)). The objective of this study is to assess the uncertainty of the CMIP5 downscaled general circulation models (GCM). We performed an analysis of the daily, monthly, seasonal and annual variability of precipitation downloaded from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections website for the state of Louisiana, USA at 0.125° x 0.125° resolution. A data set of daily gridded observations of precipitation of a rectangular boundary covering Louisiana is used to assess the validity of 21 downscaled GCMs for the 1950-1999 period. The following statistics are computed using the CMIP5 observed dataset with respect to the 21 models: the correlation coefficient, the bias, the normalized bias, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). A measure of variability simulated by each model is computed as the ratio of its standard deviation, in both space and time, to the corresponding standard deviation of the observation. The correlation and MAPE statistics are also computed for each of the nine climate divisions of Louisiana. Some of the patterns that we observed are: 1) Average annual precipitation rate shows similar spatial distribution for all the models within a range of 3.27 to 4.75 mm/day from Northwest to Southeast. 2) Standard deviation of summer (JJA) precipitation (mm/day) for the models maintains lower value than the observation whereas they have similar spatial patterns and range of values in winter (NDJ). 3) Correlation coefficients of annual precipitation of models against observation have a range of -0.48 to 0.36 with variable spatial distribution by model. 4) Most of the models show negative correlation coefficients in summer and positive in winter. 5) MAE shows similar spatial distribution for all the models within a range of 5.20 to 7.43 mm/day from Northwest to Southeast of Louisiana. 6) Highest values of correlation coefficients are found at seasonal scale within a range of 0.36 to 0.46.

Investigation of Non-Equilibrium Radiation for Earth Entry

NASA Technical Reports Server (NTRS)

Brandis, Aaron; Johnston, Chris; Cruden, Brett

2016-01-01

This paper presents measurements and simulations of non-equilibrium shock layer radiation relevant to high-speed Earth entry data obtained in the NASA Ames Research Center's Electric Arc Shock Tube (EAST) facility. The experiments were aimed at measuring the spatially and spectrally resolved radiance at relevant entry conditions for both an approximate Earth atmosphere (79 N2 : 21 O2) as well as a more accurate composition featuring the trace species Ar and CO2 (78.08 N2 : 20.95 O2 : 0.04 CO2 : 0.93 Ar). The experiments were configured to target a wide range of conditions, of which shots from 8 to 11.5 km/s at 0.2 Torr (26.7 Pa) are examined in this paper. The non-equilibrium component was chosen to be the focus of this study as it can account for a significant percentage of the emitted radiation for Earth entry, and more importantly, non-equilibrium has traditionally been assigned a large uncertainty for vehicle design. The main goals of this study are to present the shock tube data in the form of a non-equilibrium metric, evaluate the level of agreement between the experiment and simulations, identify key discrepancies and to promote discussion about various aspects of modeling non-equilibrium radiating flows. Radiance profiles integrated over discreet wavelength regions, ranging from the VUV through to the NIR, were compared in order to maximize both the spectral coverage and the number of experiments that could be used in the analysis. A previously defined non-equilibrium metric has been used to allow comparisons with several shots and reveal trends in the data. Overall, LAURAHARA is shown to under-predict EAST by as much as 50 and over-predict by as much as 20 depending on the shock speed. DPLRNEQAIR is shown to under-predict EAST by as much as 40 and over-predict by as much as 12 depending on the shock speed. In terms of an upper bound estimate for the absolute error in wall-directed heat flux, at the lower speeds investigated in this paper, 8 to 9 km/s, even though there are some large relative differences, the absolute error in radiance will be less then 1 Wcm2. At the highest shock speed of 11 km/s, the error will be less than 20 W/ sq cm.

SU-F-T-330: Characterization of the Clinically Released ScandiDos Discover Diode Array for In-Vivo Dose Monitoring

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

Saenz, D; Gutierrez, A

Purpose: The ScandiDos Discover has obtained FDA clearance and is now clinically released. We studied the essential attenuation and beam hardening components as well as tested the diode array’s ability to detect changes in absolute dose and MLC leaf positions. Methods: The ScandiDos Discover was mounted on the heads of an Elekta VersaHD and a Varian 23EX. Beam attenuation measurements were made at 10 cm depth for 6 MV and 18 MV beam energies. The PDD(10) was measured as a metric for the effect on beam quality. Next, a plan consisting of two orthogonal 10 × 10 cm2 fields wasmore » used to adjust the dose per fraction by scaling monitor units to test the absolute dose detection sensitivity of the Discover. A second plan (conformal arc) was then delivered several times independently on the Elekta VersaHD. Artificially introduced MLC position errors in the four central leaves were then added. The errors were incrementally increased from 1 mm to 4 mm and back across seven control points. Results: The absolute dose measured at 10 cm depth decreased by 1.2% and 0.7% for 6 MV and 18 MV beam with the Discover, respectively. Attenuation depended slightly on the field size but only changed the attenuation by 0.1% across 5 × 5 cm{sup 2} and 20 − 20 cm{sup 2} fields. The change in PDD(10) for a 10 − 10 cm{sup 2} field was +0.1% and +0.6% for 6 MV and 18 MV, respectively. Changes in monitor units from −5.0% to 5.0% were faithfully detected. Detected leaf errors were within 1.0 mm of intended errors. Conclusion: A novel in-vivo dosimeter monitoring the radiation beam during treatment was examined through its attenuation and beam hardening characteristics. The device tracked with changes in absolute dose as well as introduced leaf position deviations.« less

Sonographic estimation of fetal weight: comparison of bias, precision and consistency using 12 different formulae.

PubMed

Anderson, N G; Jolley, I J; Wells, J E

2007-08-01

To determine the major sources of error in ultrasonographic assessment of fetal weight and whether they have changed over the last decade. We performed a prospective observational study in 1991 and again in 2000 of a mixed-risk pregnancy population, estimating fetal weight within 7 days of delivery. In 1991, the Rose and McCallum formula was used for 72 deliveries. Inter- and intraobserver agreement was assessed within this group. Bland-Altman measures of agreement from log data were calculated as ratios. We repeated the study in 2000 in 208 consecutive deliveries, comparing predicted and actual weights for 12 published equations using Bland-Altman and percentage error methods. We compared bias (mean percentage error), precision (SD percentage error), and their consistency across the weight ranges. 95% limits of agreement ranged from - 4.4% to + 3.3% for inter- and intraobserver estimates, but were - 18.0% to 24.0% for estimated and actual birth weight. There was no improvement in accuracy between 1991 and 2000. In 2000 only six of the 12 published formulae had overall bias within 7% and precision within 15%. There was greater bias and poorer precision in nearly all equations if the birth weight was < 1,000 g. Observer error is a relatively minor component of the error in estimating fetal weight; error due to the equation is a larger source of error. Improvements in ultrasound technology have not improved the accuracy of estimating fetal weight. Comparison of methods of estimating fetal weight requires statistical methods that can separate out bias, precision and consistency. Estimating fetal weight in the very low birth weight infant is subject to much greater error than it is in larger babies. Copyright (c) 2007 ISUOG. Published by John Wiley & Sons, Ltd.

A Model of Self-Monitoring Blood Glucose Measurement Error.

PubMed

Vettoretti, Martina; Facchinetti, Andrea; Sparacino, Giovanni; Cobelli, Claudio

2017-07-01

A reliable model of the probability density function (PDF) of self-monitoring of blood glucose (SMBG) measurement error would be important for several applications in diabetes, like testing in silico insulin therapies. In the literature, the PDF of SMBG error is usually described by a Gaussian function, whose symmetry and simplicity are unable to properly describe the variability of experimental data. Here, we propose a new methodology to derive more realistic models of SMBG error PDF. The blood glucose range is divided into zones where error (absolute or relative) presents a constant standard deviation (SD). In each zone, a suitable PDF model is fitted by maximum-likelihood to experimental data. Model validation is performed by goodness-of-fit tests. The method is tested on two databases collected by the One Touch Ultra 2 (OTU2; Lifescan Inc, Milpitas, CA) and the Bayer Contour Next USB (BCN; Bayer HealthCare LLC, Diabetes Care, Whippany, NJ). In both cases, skew-normal and exponential models are used to describe the distribution of errors and outliers, respectively. Two zones were identified: zone 1 with constant SD absolute error; zone 2 with constant SD relative error. Goodness-of-fit tests confirmed that identified PDF models are valid and superior to Gaussian models used so far in the literature. The proposed methodology allows to derive realistic models of SMBG error PDF. These models can be used in several investigations of present interest in the scientific community, for example, to perform in silico clinical trials to compare SMBG-based with nonadjunctive CGM-based insulin treatments.

Automated drug dispensing system reduces medication errors in an intensive care setting.

PubMed

Chapuis, Claire; Roustit, Matthieu; Bal, Gaëlle; Schwebel, Carole; Pansu, Pascal; David-Tchouda, Sandra; Foroni, Luc; Calop, Jean; Timsit, Jean-François; Allenet, Benoît; Bosson, Jean-Luc; Bedouch, Pierrick

2010-12-01

We aimed to assess the impact of an automated dispensing system on the incidence of medication errors related to picking, preparation, and administration of drugs in a medical intensive care unit. We also evaluated the clinical significance of such errors and user satisfaction. Preintervention and postintervention study involving a control and an intervention medical intensive care unit. Two medical intensive care units in the same department of a 2,000-bed university hospital. Adult medical intensive care patients. After a 2-month observation period, we implemented an automated dispensing system in one of the units (study unit) chosen randomly, with the other unit being the control. The overall error rate was expressed as a percentage of total opportunities for error. The severity of errors was classified according to National Coordinating Council for Medication Error Reporting and Prevention categories by an expert committee. User satisfaction was assessed through self-administered questionnaires completed by nurses. A total of 1,476 medications for 115 patients were observed. After automated dispensing system implementation, we observed a reduced percentage of total opportunities for error in the study compared to the control unit (13.5% and 18.6%, respectively; p<.05); however, no significant difference was observed before automated dispensing system implementation (20.4% and 19.3%, respectively; not significant). Before-and-after comparisons in the study unit also showed a significantly reduced percentage of total opportunities for error (20.4% and 13.5%; p<.01). An analysis of detailed opportunities for error showed a significant impact of the automated dispensing system in reducing preparation errors (p<.05). Most errors caused no harm (National Coordinating Council for Medication Error Reporting and Prevention category C). The automated dispensing system did not reduce errors causing harm. Finally, the mean for working conditions improved from 1.0±0.8 to 2.5±0.8 on the four-point Likert scale. The implementation of an automated dispensing system reduced overall medication errors related to picking, preparation, and administration of drugs in the intensive care unit. Furthermore, most nurses favored the new drug dispensation organization.

Assessing and Ensuring GOES-R Magnetometer Accuracy

NASA Technical Reports Server (NTRS)

Carter, Delano R.; Todirita, Monica; Kronenwetter, Jeffrey; Chu, Donald

2016-01-01

The GOES-R magnetometer subsystem accuracy requirement is 1.7 nanoteslas (nT). During quiet times (100 nT), accuracy is defined as absolute mean plus 3 sigma. During storms (300 nT), accuracy is defined as absolute mean plus 2 sigma. Error comes both from outside the magnetometers, e.g. spacecraft fields and misalignments, as well as inside, e.g. zero offset and scale factor errors. Because zero offset and scale factor drift over time, it will be necessary to perform annual calibration maneuvers. To predict performance before launch, we have used Monte Carlo simulations and covariance analysis. Both behave as expected, and their accuracy predictions agree within 30%. With the proposed calibration regimen, both suggest that the GOES-R magnetometer subsystem will meet its accuracy requirements.

Thyroid Autoimmunity is Associated with Decreased Cytotoxicity T Cells in Women with Repeated Implantation Failure

PubMed Central

Huang, Chunyu; Liang, Peiyan; Diao, Lianghui; Liu, Cuicui; Chen, Xian; Li, Guangui; Chen, Cong; Zeng, Yong

2015-01-01

Thyroid autoimmunity (TAI), which is defined as the presence of autoantibodies against thyroid peroxidase (TPO) and/or thyroglobulin (TG), is related to repeated implantation failure (RIF). It is reported that TAI was involved in reproductive failure not only through leading thyroid function abnormality, but it can also be accompanied with immune imbalance. Therefore, this study was designed to investigate the association of thyroid function, immune status and TAI in women with RIF. Blood samples were drawn from 72 women with RIF to evaluate the prevalence of TAI, the thyroid function, the absolute numbers and percentages of lymphocytes. The prevalence of thyroid function abnormality in RIF women with TAI was not significantly different from that in RIF women without TAI (χ2 = 0.484, p > 0.05). The absolute number and percentage of T cells, T helper (Th) cells, B cells and natural killer (NK) cells were not significantly different in RIF women with TAI compared to those without TAI (all p > 0.05). The percentage of T cytotoxicity (Tc) cells was significantly decreased in RIF women with TAI compared to those without TAI (p < 0.05). Meanwhile, Th/Tc ratio was significantly increased (p < 0.05). These results indicated that the decreased Tc percentage and increased Th/Tc ratio may be another influential factor of adverse pregnancy outcomes in RIF women with TAI. PMID:26308040

40 CFR 1065.1005 - Symbols, abbreviations, acronyms, and units of measure.

Code of Federal Regulations, 2014 CFR

2014-07-01

... of diameters meter per meter m/m 1 b atomic oxygen-to-carbon ratio mole per mole mol/mol 1 C # number... error between a quantity and its reference e brake-specific emission or fuel consumption gram per... standard deviation S Sutherland constant kelvin K K SEE standard estimate of error T absolute temperature...

Metric Identification and Protocol Development for Characterizing DNAPL Source Zone Architecture and Associated Plume Response

DTIC Science & Technology

2013-09-01

M.4.1. Two-dimensional domains cropped out of three-dimensional numerically generated realizations; (a) 3D PCE-NAPL realizations generated by UTCHEM...165 Figure R.3.2. The absolute error vs relative error scatter plots of pM and gM from SGS data set- 4 using multi-task manifold...error scatter plots of pM and gM from TP/MC data set using multi- task manifold regression

Impact of spot charge inaccuracies in IMPT treatments.

PubMed

Kraan, Aafke C; Depauw, Nicolas; Clasie, Ben; Giunta, Marina; Madden, Tom; Kooy, Hanne M

2017-08-01

Spot charge is one parameter of pencil-beam scanning dose delivery system whose accuracy is typically high but whose required value has not been investigated. In this work we quantify the dose impact of spot charge inaccuracies on the dose distribution in patients. Knowing the effect of charge errors is relevant for conventional proton machines, as well as for new generation proton machines, where ensuring accurate charge may be challenging. Through perturbation of spot charge in treatment plans for seven patients and a phantom, we evaluated the dose impact of absolute (up to 5× 10 6 protons) and relative (up to 30%) charge errors. We investigated the dependence on beam width by studying scenarios with small, medium and large beam sizes. Treatment plan statistics included the Γ passing rate, dose-volume-histograms and dose differences. The allowable absolute charge error for small spot plans was about 2× 10 6 protons. Larger limits would be allowed if larger spots were used. For relative errors, the maximum allowable error size for small, medium and large spots was about 13%, 8% and 6% for small, medium and large spots, respectively. Dose distributions turned out to be surprisingly robust against random spot charge perturbation. Our study suggests that ensuring spot charge errors as small as 1-2% as is commonly aimed at in conventional proton therapy machines, is clinically not strictly needed. © 2017 American Association of Physicists in Medicine.

Association between outcome and changes in plasma lactate concentration during presurgical treatment in dogs with gastric dilatation-volvulus: 64 cases (2002-2008).

PubMed

Zacher, Laurie A; Berg, John; Shaw, Scott P; Kudej, Raymond K

2010-04-15

To determine whether changes in presurgical plasma lactate concentration (before and after initial fluid resuscitation and gastric decompression) were associated with short-term outcome for dogs with gastric dilatation-volvulus (GDV). Retrospective case series. 64 dogs. Medical records were reviewed, and signalment, history, resuscitative treatments, serial presurgical lactate concentrations, surgical findings, and short-term outcome were obtained for dogs with confirmed GDV. 36 of 40 (90%) dogs with an initial lactate concentration 9.0 mmol/L). Within HIL dogs, there was no difference in mean +/- SD initial lactate concentration between survivors and nonsurvivors (10.6 +/- 2.3 mmol/L vs 11.2 +/- 2.3 mmol/L, respectively); however, there were significant differences in post-treatment lactate concentration, absolute change in lactate concentration, and percentage change in lactate concentration following resuscitative treatment. By use of optimal cutoff values within HIL dogs, survival rates for dogs with final lactate concentration > 6.4 mmol/L (23%), absolute change in lactate concentration 4 mmol/L (86%), or percentage change in lactate concentration > 42.5% (100%). Calculating changes in plasma lactate concentration following initial treatment in dogs with GDV may assist in determining prognosis and identifying patients that require more aggressive treatment.

Error analysis on spinal motion measurement using skin mounted sensors.

PubMed

Yang, Zhengyi; Ma, Heather Ting; Wang, Deming; Lee, Raymond

2008-01-01

Measurement errors of skin-mounted sensors in measuring forward bending movement of the lumbar spines are investigated. In this investigation, radiographic images capturing the entire lumbar spines' positions were acquired and used as a 'gold' standard. Seventeen young male volunteers (21 (SD 1) years old) agreed to participate in the study. Light-weight miniature sensors of the electromagnetic tracking systems-Fastrak were attached to the skin overlying the spinous processes of the lumbar spine. With the sensors attached, the subjects were requested to take lateral radiographs in two postures: neutral upright and full flexion. The ranges of motions of lumbar spine were calculated from two sets of digitized data: the bony markers of vertebral bodies and the sensors and compared. The differences between the two sets of results were then analyzed. The relative movement between sensor and vertebrae was decomposed into sensor sliding and titling, from which sliding error and titling error were introduced. Gross motion range of forward bending of lumbar spine measured from bony markers of vertebrae is 67.8 degrees (SD 10.6 degrees ) and that from sensors is 62.8 degrees (SD 12.8 degrees ). The error and absolute error for gross motion range were 5.0 degrees (SD 7.2 degrees ) and 7.7 degrees (SD 3.9 degrees ). The contributions of sensors placed on S1 and L1 to the absolute error were 3.9 degrees (SD 2.9 degrees ) and 4.4 degrees (SD 2.8 degrees ), respectively.

Anatomical frame identification and reconstruction for repeatable lower limb joint kinematics estimates.

PubMed

Donati, Marco; Camomilla, Valentina; Vannozzi, Giuseppe; Cappozzo, Aurelio

2008-07-19

The quantitative description of joint mechanics during movement requires the reconstruction of the position and orientation of selected anatomical axes with respect to a laboratory reference frame. These anatomical axes are identified through an ad hoc anatomical calibration procedure and their position and orientation are reconstructed relative to bone-embedded frames normally derived from photogrammetric marker positions and used to describe movement. The repeatability of anatomical calibration, both within and between subjects, is crucial for kinematic and kinetic end results. This paper illustrates an anatomical calibration approach, which does not require anatomical landmark manual palpation, described in the literature to be prone to great indeterminacy. This approach allows for the estimate of subject-specific bone morphology and automatic anatomical frame identification. The experimental procedure consists of digitization through photogrammetry of superficial points selected over the areas of the bone covered with a thin layer of soft tissue. Information concerning the location of internal anatomical landmarks, such as a joint center obtained using a functional approach, may also be added. The data thus acquired are matched with the digital model of a deformable template bone. Consequently, the repeatability of pelvis, knee and hip joint angles is determined. Five volunteers, each of whom performed five walking trials, and six operators, with no specific knowledge of anatomy, participated in the study. Descriptive statistics analysis was performed during upright posture, showing a limited dispersion of all angles (less than 3 deg) except for hip and knee internal-external rotation (6 deg and 9 deg, respectively). During level walking, the ratio of inter-operator and inter-trial error and an absolute subject-specific repeatability were assessed. For pelvic and hip angles, and knee flexion-extension the inter-operator error was equal to the inter-trial error-the absolute error ranging from 0.1 deg to 0.9 deg. Knee internal-external rotation and ab-adduction showed, on average, inter-operator errors, which were 8% and 28% greater than the relevant inter-trial errors, respectively. The absolute error was in the range 0.9-2.9 deg.

Optimized keratometry and total corneal astigmatism for toric intraocular lens calculation.

PubMed

Savini, Giacomo; Næser, Kristian; Schiano-Lomoriello, Domenico; Ducoli, Pietro

2017-09-01

To compare keratometric astigmatism (KA) and different modalities of measuring total corneal astigmatism (TCA) for toric intraocular lens (IOL) calculation and optimize corneal measurements to eliminate the residual refractive astigmatism. G.B. Bietti Foundation IRCCS, Rome, Italy. Prospective case series. Patients who had a toric IOL were enrolled. Preoperatively, a Scheimpflug camera (Pentacam HR) was used to measure TCA through ray tracing. Different combinations of measurements at a 3.0 mm diameter, centered on the pupil or the corneal vertex and performed along a ring or within it, were compared. Keratometric astigmatism was measured using the same Scheimpflug camera and a corneal topographer (Keratron). Astigmatism was analyzed with Næser's polar value method. The optimized preoperative corneal astigmatism was back-calculated from the postoperative refractive astigmatism. The study comprised 62 patients (64 eyes). With both devices, KA produced an overcorrection of with-the-rule (WTR) astigmatism by 0.6 diopter (D) and an undercorrection of against-the-rule (ATR) astigmatism by 0.3 D. The lowest meridional error in refractive astigmatism was achieved by the TCA pupil/zone measurement in WTR eyes (0.27 D overcorrection) and the TCA apex/zone measurement in ATR eyes (0.07 D undercorrection). In the whole sample, no measurement allowed more than 43.75% of eyes to yield an absolute error in astigmatism magnitude lower than 0.5 D. Optimized astigmatism values increased the percentage of eyes with this error up to 57.81%, with no difference compared with the Barrett calculator and the Abulafia-Koch calculator. Compared with KA, TCA improved calculations for toric IOLs; however, optimization of corneal astigmatism measurements led to more accurate results. Copyright © 2017 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

The sensitivity of patient specific IMRT QC to systematic MLC leaf bank offset errors.

PubMed

Rangel, Alejandra; Palte, Gesa; Dunscombe, Peter

2010-07-01

Patient specific IMRT QC is performed routinely in many clinics as a safeguard against errors and inaccuracies which may be introduced during the complex planning, data transfer, and delivery phases of this type of treatment. The purpose of this work is to evaluate the feasibility of detecting systematic errors in MLC leaf bank position with patient specific checks. 9 head and neck (H&N) and 14 prostate IMRT beams were delivered using MLC files containing systematic offsets (+/- 1 mm in two banks, +/- 0.5 mm in two banks, and 1 mm in one bank of leaves). The beams were measured using both MAPCHECK (Sun Nuclear Corp., Melbourne, FL) and the aS1000 electronic portal imaging device (Varian Medical Systems, Palo Alto, CA). Comparisons with calculated fields, without offsets, were made using commonly adopted criteria including absolute dose (AD) difference, relative dose difference, distance to agreement (DTA), and the gamma index. The criteria most sensitive to systematic leaf bank offsets were the 3% AD, 3 mm DTA for MAPCHECK and the gamma index with 2% AD and 2 mm DTA for the EPID. The criterion based on the relative dose measurements was the least sensitive to MLC offsets. More highly modulated fields, i.e., H&N, showed greater changes in the percentage of passing points due to systematic MLC inaccuracy than prostate fields. None of the techniques or criteria tested is sufficiently sensitive, with the population of IMRT fields, to detect a systematic MLC offset at a clinically significant level on an individual field. Patient specific QC cannot, therefore, substitute for routine QC of the MLC itself.

Validation on MERSI/FY-3A precipitable water vapor product

NASA Astrophysics Data System (ADS)

Gong, Shaoqi; Fiifi Hagan, Daniel; Lu, Jing; Wang, Guojie

2018-01-01

The precipitable water vapor is one of the most active gases in the atmosphere which strongly affects the climate. China's second-generation polar orbit meteorological satellite FY-3A equipped with a Medium Resolution Spectral Imager (MERSI) is able to detect atmospheric water vapor. In this paper, water vapor data from AERONET, radiosonde and MODIS were used to validate the accuracy of the MERSI water vapor product in the different seasons and climatic regions of East Asia. The results show that the values of MERSI water vapor product are relatively lower than that of the other instruments and its accuracy is generally lower. The mean bias (MB) was -0.8 to -12.7 mm, the root mean square error (RMSE) was 2.2-17.0 mm, and the mean absolute percentage error (MAPE) varied from 31.8% to 44.1%. On the spatial variation, the accuracy of MERSI water vapor product in a descending order was from North China, West China, Japan -Korea, East China, to South China, while the seasonal variation of accuracy was the best for winter, followed by spring, then in autumn and the lowest in summer. It was found that the errors of MERSI water vapor product was mainly due to the low accuracy of radiation calibration of the MERSI absorption channel, along with the inaccurate look-up table of apparent reflectance and water vapor within the water vapor retrieved algorithm. In addition, the surface reflectance, the mixed pixels of image cloud, the humidity and temperature of atmospheric vertical profile and the haze were also found to have affected the accuracy of MERSI water vapor product.

Household and Living Arrangement Projections at the Subnational Level: An Extended Cohort-Component Approach

PubMed Central

Zeng, Yi; Land, Kenneth C.; Wang, Zhenglian; Gu, Danan

2012-01-01

This article presents the core methodological ideas, empirical assessments, and applications of an extended cohort-component approach (known as the “ProFamy model”) to simultaneously project household composition, living arrangements, and population sizes at the subnational level in the United States. Comparisons of projections from 1990 to 2000 using this approach with census counts in 2000 for each of the 50 states and Washington, DC show that 68.0 %, 17.0 %, 11.2 %, and 3.8 % of the absolute percentage errors are <3.0 %, 3.0 % to 4.99 %, 5.0 % to 9.99 %, and ≥10.0 %, respectively. Another analysis compares average forecast errors between the extended cohort-component approach and the still widely used classic headship-rate method, by projecting number-of-bedrooms–specific housing demands from 1990 to 2000 and then comparing those projections with census counts in 2000 for each of the 50 states and Washington, DC. The results demonstrate that, compared with the extended cohort-component approach, the headship-rate method produces substantially more serious forecast errors because it cannot project households by size while the extended cohort-component approach projects detailed household sizes. We also present illustrative household and living arrangement projections for the five decades from 2000 to 2050, with medium-, small-, and large-family scenarios for each of the 50 states; Washington, DC; six counties of southern California, and the Minneapolis–St. Paul metropolitan area. Among many interesting numerical outcomes of household and living arrangement projections with medium, low, and high bounds, the aging of American households over the next few decades across all states/areas is particularly striking. Finally, the limitations of the present study and potential future lines of research are discussed. PMID:23208782

Improving the quality of preclinical research echocardiography: Observations, training and guidelines for measurement.

PubMed

Donner, Daniel G; Kiriazis, Helen; Du, Xiao-Jun; Marwick, Thomas H; McMullen, Julie R

2018-04-20

Informal training in preclinical research may be a contributor to the poor reproducibility of preclinical cardiology research and low rates of translation into clinical research and practice. Mouse echocardiography is a widely used technique to assess cardiac structure and function in drug intervention studies using disease models. The inter-observer variability (IOV) of clinical echocardiographic measurements has been shown to improve with formalized training, but preclinical echocardiography lacks similarly critical standardization of training. The aims of this investigation were to assess the IOV of echocardiographic measurements from studies in mice, and address any technical impediments to reproducibility by implementing standardized guidelines through formalized training. In this prospective, single-site, observational cohort study, 13 scientists performing preclinical echocardiographic image analysis were assessed for measurement of short-axis M-mode-derived dimensions and calculated left ventricular mass (LVMass). Ten M-mode images of mouse hearts acquired and analyzed by an expert researcher with a spectrum of LVMass were selected for assessment, and validated by autopsy weight. Following the initial observation, a structured formal training program was introduced, and accuracy and reproducibility were re-evaluated. Mean absolute percentage error (MAPE) for Expert-calculated LVMass was 6{plus minus}4% compared to autopsy LVMass, and 25{plus minus}21% for participants before training. Standardized formal training improved participant MAPE by approximately 30% relative to expert-calculated LVMass (p<0.001). Participants initially categorized with high-range error (25-45%) improved to low-moderate error ranges (<15-25%). This report reveals an example of technical skill training insufficiency likely endemic to preclinical research and provides validated guidelines for echocardiographic measurement for adaptation to formalized in-training programs.

Time series analysis of gold production in Malaysia

NASA Astrophysics Data System (ADS)

Muda, Nora; Hoon, Lee Yuen

2012-05-01

Gold is a soft, malleable, bright yellow metallic element and unaffected by air or most reagents. It is highly valued as an asset or investment commodity and is extensively used in jewellery, industrial application, dentistry and medical applications. In Malaysia, gold mining is limited in several areas such as Pahang, Kelantan, Terengganu, Johor and Sarawak. The main purpose of this case study is to obtain a suitable model for the production of gold in Malaysia. The model can also be used to predict the data of Malaysia's gold production in the future. Box-Jenkins time series method was used to perform time series analysis with the following steps: identification, estimation, diagnostic checking and forecasting. In addition, the accuracy of prediction is tested using mean absolute percentage error (MAPE). From the analysis, the ARIMA (3,1,1) model was found to be the best fitted model with MAPE equals to 3.704%, indicating the prediction is very accurate. Hence, this model can be used for forecasting. This study is expected to help the private and public sectors to understand the gold production scenario and later plan the gold mining activities in Malaysia.

Computational analysis of PET by AIBL (CapAIBL): a cloud-based processing pipeline for the quantification of PET images

NASA Astrophysics Data System (ADS)

Bourgeat, Pierrick; Dore, Vincent; Fripp, Jurgen; Villemagne, Victor L.; Rowe, Chris C.; Salvado, Olivier

2015-03-01

With the advances of PET tracers for β-Amyloid (Aβ) detection in neurodegenerative diseases, automated quantification methods are desirable. For clinical use, there is a great need for PET-only quantification method, as MR images are not always available. In this paper, we validate a previously developed PET-only quantification method against MR-based quantification using 6 tracers: 18F-Florbetaben (N=148), 18F-Florbetapir (N=171), 18F-NAV4694 (N=47), 18F-Flutemetamol (N=180), 11C-PiB (N=381) and 18F-FDG (N=34). The results show an overall mean absolute percentage error of less than 5% for each tracer. The method has been implemented as a remote service called CapAIBL (http://milxcloud.csiro.au/capaibl). PET images are uploaded to a cloud platform where they are spatially normalised to a standard template and quantified. A report containing global as well as local quantification, along with surface projection of the β-Amyloid deposition is automatically generated at the end of the pipeline and emailed to the user.

A dual growing method for the automatic extraction of individual trees from mobile laser scanning data

NASA Astrophysics Data System (ADS)

Li, Lin; Li, Dalin; Zhu, Haihong; Li, You

2016-10-01

Street trees interlaced with other objects in cluttered point clouds of urban scenes inhibit the automatic extraction of individual trees. This paper proposes a method for the automatic extraction of individual trees from mobile laser scanning data, according to the general constitution of trees. Two components of each individual tree - a trunk and a crown can be extracted by the dual growing method. This method consists of coarse classification, through which most of artifacts are removed; the automatic selection of appropriate seeds for individual trees, by which the common manual initial setting is avoided; a dual growing process that separates one tree from others by circumscribing a trunk in an adaptive growing radius and segmenting a crown in constrained growing regions; and a refining process that draws a singular trunk from the interlaced other objects. The method is verified by two datasets with over 98% completeness and over 96% correctness. The low mean absolute percentage errors in capturing the morphological parameters of individual trees indicate that this method can output individual trees with high precision.

A Hybrid FEM-ANN Approach for Slope Instability Prediction

NASA Astrophysics Data System (ADS)

Verma, A. K.; Singh, T. N.; Chauhan, Nikhil Kumar; Sarkar, K.

2016-09-01

Assessment of slope stability is one of the most critical aspects for the life of a slope. In any slope vulnerability appraisal, Factor Of Safety (FOS) is the widely accepted index to understand, how close or far a slope from the failure. In this work, an attempt has been made to simulate a road cut slope in a landslide prone area in Rudrapryag, Uttarakhand, India which lies near Himalayan geodynamic mountain belt. A combination of Finite Element Method (FEM) and Artificial Neural Network (ANN) has been adopted to predict FOS of the slope. In ANN, a three layer, feed- forward back-propagation neural network with one input layer and one hidden layer with three neurons and one output layer has been considered and trained using datasets generated from numerical analysis of the slope and validated with new set of field slope data. Mean absolute percentage error estimated as 1.04 with coefficient of correlation between the FOS of FEM and ANN as 0.973, which indicates that the system is very vigorous and fast to predict FOS for any slope.

Estimation of suspended particulate matter in turbid coastal waters: application to hyperspectral satellite imagery.

PubMed

Zhao, Jun; Cao, Wenxi; Xu, Zhantang; Ye, Haibin; Yang, Yuezhong; Wang, Guifen; Zhou, Wen; Sun, Zhaohua

2018-04-16

An empirical algorithm is proposed to estimate suspended particulate matter (SPM) ranging from 0.675 to 25.7 mg L -1 in the turbid Pearl River estuary (PRE). Comparisons between model predicted and in situ measured SPM resulted in R 2 s of 0.97 and 0.88 and mean absolute percentage errors (MAPEs) of 23.96% and 29.69% by using the calibration and validation data sets, respectively. The developed algorithm demonstrated the highest accuracy when compared with existing ones for turbid coastal waters. The diurnal dynamics of SPM was revealed by applying the proposed algorithm to reflectance data collected by a moored buoy in the PRE. The established algorithm was implemented to Hyperspectral Imager for the Coastal Ocean (HICO) data and the distribution pattern of SPM in the PRE was elucidated. Validation of HICO-derived reflectance data by using concurrent MODIS/Aqua data as a benchmark indicated their reliability. Factors influencing variability of SPM in the PRE were analyzed, which implicated the combined effects of wind, tide, rainfall, and circulation as the cause.

Application of a Line Laser Scanner for Bed Form Tracking in a Laboratory Flume

NASA Astrophysics Data System (ADS)

de Ruijsscher, T. V.; Hoitink, A. J. F.; Dinnissen, S.; Vermeulen, B.; Hazenberg, P.

2018-03-01

A new measurement method for continuous detection of bed forms in movable bed laboratory experiments is presented and tested. The device consists of a line laser coupled to a 3-D camera, which makes use of triangulation. This allows to measure bed forms during morphodynamic experiments, without removing the water from the flume. A correction is applied for the effect of laser refraction at the air-water interface. We conclude that the absolute measurement error increases with increasing flow velocity, its standard deviation increases with water depth and flow velocity, and the percentage of missing values increases with water depth. Although 71% of the data is lost in a pilot moving bed experiment with sand, still high agreement between flowing water and dry bed measurements is found when a robust LOcally weighted regrESSion (LOESS) procedure is applied. This is promising for bed form tracking applications in laboratory experiments, especially when lightweight sediments like polystyrene are used, which require smaller flow velocities to achieve dynamic similarity to the prototype. This is confirmed in a moving bed experiment with polystyrene.

Modeling stock prices in a portfolio using multidimensional geometric brownian motion

NASA Astrophysics Data System (ADS)

Maruddani, Di Asih I.; Trimono

2018-05-01

Modeling and forecasting stock prices of public corporates are important studies in financial analysis, due to their stock price characteristics. Stocks investments give a wide variety of risks. Taking a portfolio of several stocks is one way to minimize risk. Stochastic process of single stock price movements model can be formulated in Geometric Brownian Motion (GBM) model. But for a portfolio that consist more than one corporate stock, we need an expansion of GBM Model. In this paper, we use multidimensional Geometric Brownian Motion model. This paper aims to model and forecast two stock prices in a portfolio. These are PT. Matahari Department Store Tbk and PT. Telekomunikasi Indonesia Tbk on period January 4, 2016 until April 21, 2017. The goodness of stock price forecast value is based on Mean Absolute Percentage Error (MAPE). As the results, we conclude that forecast two stock prices in a portfolio using multidimensional GBM give less MAPE than using GBM for single stock price respectively. We conclude that multidimensional GBM is more appropriate for modeling stock prices, because the price of each stock affects each other.

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

PubMed

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

2012-01-01

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

Modelling tourists arrival using time varying parameter

NASA Astrophysics Data System (ADS)

Suciptawati, P.; Sukarsa, K. G.; Kencana, Eka N.

2017-06-01

The importance of tourism and its related sectors to support economic development and poverty reduction in many countries increase researchers’ attentions to study and model tourists’ arrival. This work is aimed to demonstrate time varying parameter (TVP) technique to model the arrival of Korean’s tourists to Bali. The number of Korean tourists whom visiting Bali for period January 2010 to December 2015 were used to model the number of Korean’s tourists to Bali (KOR) as dependent variable. The predictors are the exchange rate of Won to IDR (WON), the inflation rate in Korea (INFKR), and the inflation rate in Indonesia (INFID). Observing tourists visit to Bali tend to fluctuate by their nationality, then the model was built by applying TVP and its parameters were approximated using Kalman Filter algorithm. The results showed all of predictor variables (WON, INFKR, INFID) significantly affect KOR. For in-sample and out-of-sample forecast with ARIMA’s forecasted values for the predictors, TVP model gave mean absolute percentage error (MAPE) as much as 11.24 percent and 12.86 percent, respectively.

A Novel Model for Stock Price Prediction Using Hybrid Neural Network

NASA Astrophysics Data System (ADS)

Senapati, Manas Ranjan; Das, Sumanjit; Mishra, Sarojananda

2018-06-01

The foremost challenge for investors is to select stock price by analyzing financial data which is a menial task as of distort associated and massive pattern. Thereby, selecting stock poses one of the greatest difficulties for investors. Nowadays, prediction of financial market like stock market, exchange rate and share value are very challenging field of research. The prediction and scrutinization of stock price is also a potential area of research due to its vital significance in decision making by financial investors. This paper presents an intelligent and an optimal model for prophecy of stock market price using hybridization of Adaline Neural Network (ANN) and modified Particle Swarm Optimization (PSO). The connoted model hybrid of Adaline and PSO uses fluctuations of stock market as a factor and employs PSO to optimize and update weights of Adaline representation to depict open price of Bombay stock exchange. The prediction performance of the proposed model is compared with different representations like interval measurements, CMS-PSO and Bayesian-ANN. The result indicates that proposed scheme has an edge over all the juxtaposed schemes in terms of mean absolute percentage error.

Multi-step prediction for influenza outbreak by an adjusted long short-term memory.

PubMed

Zhang, J; Nawata, K

2018-05-01

Influenza results in approximately 3-5 million annual cases of severe illness and 250 000-500 000 deaths. We urgently need an accurate multi-step-ahead time-series forecasting model to help hospitals to perform dynamical assignments of beds to influenza patients for the annually varied influenza season, and aid pharmaceutical companies to formulate a flexible plan of manufacturing vaccine for the yearly different influenza vaccine. In this study, we utilised four different multi-step prediction algorithms in the long short-term memory (LSTM). The result showed that implementing multiple single-output prediction in a six-layer LSTM structure achieved the best accuracy. The mean absolute percentage errors from two- to 13-step-ahead prediction for the US influenza-like illness rates were all <15%, averagely 12.930%. To the best of our knowledge, it is the first time that LSTM has been applied and refined to perform multi-step-ahead prediction for influenza outbreaks. Hopefully, this modelling methodology can be applied in other countries and therefore help prevent and control influenza worldwide.

Is a shift from research on individual medical error to research on health information technology underway? A 40-year analysis of publication trends in medical journals.

PubMed

Erlewein, Daniel; Bruni, Tommaso; Gadebusch Bondio, Mariacarla

2018-06-07

In 1983, McIntyre and Popper underscored the need for more openness in dealing with errors in medicine. Since then, much has been written on individual medical errors. Furthermore, at the beginning of the 21st century, researchers and medical practitioners increasingly approached individual medical errors through health information technology. Hence, the question arises whether the attention of biomedical researchers shifted from individual medical errors to health information technology. We ran a study to determine publication trends concerning individual medical errors and health information technology in medical journals over the last 40 years. We used the Medical Subject Headings (MeSH) taxonomy in the database MEDLINE. Each year, we analyzed the percentage of relevant publications to the total number of publications in MEDLINE. The trends identified were tested for statistical significance. Our analysis showed that the percentage of publications dealing with individual medical errors increased from 1976 until the beginning of the 21st century but began to drop in 2003. Both the upward and the downward trends were statistically significant (P < 0.001). A breakdown by country revealed that it was the weight of the US and British publications that determined the overall downward trend after 2003. On the other hand, the percentage of publications dealing with health information technology doubled between 2003 and 2015. The upward trend was statistically significant (P < 0.001). The identified trends suggest that the attention of biomedical researchers partially shifted from individual medical errors to health information technology in the USA and the UK. © 2018 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.

Poster Presentation: Optical Test of NGST Developmental Mirrors

NASA Technical Reports Server (NTRS)

Hadaway, James B.; Geary, Joseph; Reardon, Patrick; Peters, Bruce; Keidel, John; Chavers, Greg

2000-01-01

An Optical Testing System (OTS) has been developed to measure the figure and radius of curvature of NGST developmental mirrors in the vacuum, cryogenic environment of the X-Ray Calibration Facility (XRCF) at Marshall Space Flight Center (MSFC). The OTS consists of a WaveScope Shack-Hartmann sensor from Adaptive Optics Associates as the main instrument, a Point Diffraction Interferometer (PDI), a Point Spread Function (PSF) imager, an alignment system, a Leica Disto Pro distance measurement instrument, and a laser source palette (632.8 nm wavelength) that is fiber-coupled to the sensor instruments. All of the instruments except the laser source palette are located on a single breadboard known as the Wavefront Sensor Pallet (WSP). The WSP is located on top of a 5-DOF motion system located at the center of curvature of the test mirror. Two PC's are used to control the OTS. The error in the figure measurement is dominated by the WaveScope's measurement error. An analysis using the absolute wavefront gradient error of 1/50 wave P-V (at 0.6328 microns) provided by the manufacturer leads to a total surface figure measurement error of approximately 1/100 wave rms. This easily meets the requirement of 1/10 wave P-V. The error in radius of curvature is dominated by the Leica's absolute measurement error of VI.5 mm and the focus setting error of Vi.4 mm, giving an overall error of V2 mm. The OTS is currently being used to test the NGST Mirror System Demonstrators (NMSD's) and the Subscale Beryllium Mirror Demonstrator (SBNM).

Is adult gait less susceptible than paediatric gait to hip joint centre regression equation error?

PubMed

Kiernan, D; Hosking, J; O'Brien, T

2016-03-01

Hip joint centre (HJC) regression equation error during paediatric gait has recently been shown to have clinical significance. In relation to adult gait, it has been inferred that comparable errors with children in absolute HJC position may in fact result in less significant kinematic and kinetic error. This study investigated the clinical agreement of three commonly used regression equation sets (Bell et al., Davis et al. and Orthotrak) for adult subjects against the equations of Harrington et al. The relationship between HJC position error and subject size was also investigated for the Davis et al. set. Full 3-dimensional gait analysis was performed on 12 healthy adult subjects with data for each set compared to Harrington et al. The Gait Profile Score, Gait Variable Score and GDI-kinetic were used to assess clinical significance while differences in HJC position between the Davis and Harrington sets were compared to leg length and subject height using regression analysis. A number of statistically significant differences were present in absolute HJC position. However, all sets fell below the clinically significant thresholds (GPS <1.6°, GDI-Kinetic <3.6 points). Linear regression revealed a statistically significant relationship for both increasing leg length and increasing subject height with decreasing error in anterior/posterior and superior/inferior directions. Results confirm a negligible clinical error for adult subjects suggesting that any of the examined sets could be used interchangeably. Decreasing error with both increasing leg length and increasing subject height suggests that the Davis set should be used cautiously on smaller subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

Prescription errors before and after introduction of electronic medication alert system in a pediatric emergency department.

PubMed

Sethuraman, Usha; Kannikeswaran, Nirupama; Murray, Kyle P; Zidan, Marwan A; Chamberlain, James M

2015-06-01

Prescription errors occur frequently in pediatric emergency departments (PEDs).The effect of computerized physician order entry (CPOE) with electronic medication alert system (EMAS) on these is unknown. The objective was to compare prescription errors rates before and after introduction of CPOE with EMAS in a PED. The hypothesis was that CPOE with EMAS would significantly reduce the rate and severity of prescription errors in the PED. A prospective comparison of a sample of outpatient, medication prescriptions 5 months before and after CPOE with EMAS implementation (7,268 before and 7,292 after) was performed. Error types and rates, alert types and significance, and physician response were noted. Medication errors were deemed significant if there was a potential to cause life-threatening injury, failure of therapy, or an adverse drug effect. There was a significant reduction in the errors per 100 prescriptions (10.4 before vs. 7.3 after; absolute risk reduction = 3.1, 95% confidence interval [CI] = 2.2 to 4.0). Drug dosing error rates decreased from 8 to 5.4 per 100 (absolute risk reduction = 2.6, 95% CI = 1.8 to 3.4). Alerts were generated for 29.6% of prescriptions, with 45% involving drug dose range checking. The sensitivity of CPOE with EMAS in identifying errors in prescriptions was 45.1% (95% CI = 40.8% to 49.6%), and the specificity was 57% (95% CI = 55.6% to 58.5%). Prescribers modified 20% of the dosing alerts, resulting in the error not reaching the patient. Conversely, 11% of true dosing alerts for medication errors were overridden by the prescribers: 88 (11.3%) resulted in medication errors, and 684 (88.6%) were false-positive alerts. A CPOE with EMAS was associated with a decrease in overall prescription errors in our PED. Further system refinements are required to reduce the high false-positive alert rates. © 2015 by the Society for Academic Emergency Medicine.

Performance Evaluation of sUAS Equipped with Velodyne HDL-32E LiDAR Sensor

NASA Astrophysics Data System (ADS)

Jozkow, G.; Wieczorek, P.; Karpina, M.; Walicka, A.; Borkowski, A.

2017-08-01

The Velodyne HDL-32E laser scanner is used more frequently as main mapping sensor in small commercial UASs. However, there is still little information about the actual accuracy of point clouds collected with such UASs. This work evaluates empirically the accuracy of the point cloud collected with such UAS. Accuracy assessment was conducted in four aspects: impact of sensors on theoretical point cloud accuracy, trajectory reconstruction quality, and internal and absolute point cloud accuracies. Theoretical point cloud accuracy was evaluated by calculating 3D position error knowing errors of used sensors. The quality of trajectory reconstruction was assessed by comparing position and attitude differences from forward and reverse EKF solution. Internal and absolute accuracies were evaluated by fitting planes to 8 point cloud samples extracted for planar surfaces. In addition, the absolute accuracy was also determined by calculating point 3D distances between LiDAR UAS and reference TLS point clouds. Test data consisted of point clouds collected in two separate flights performed over the same area. Executed experiments showed that in tested UAS, the trajectory reconstruction, especially attitude, has significant impact on point cloud accuracy. Estimated absolute accuracy of point clouds collected during both test flights was better than 10 cm, thus investigated UAS fits mapping-grade category.

Demonstrating the Error Budget for the Climate Absolute Radiance and Refractivity Observatory Through Solar Irradiance Measurements

NASA Technical Reports Server (NTRS)

Thome, Kurtis; McCorkel, Joel; McAndrew, Brendan

2016-01-01

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission addresses the need to observe highaccuracy, long-term climate change trends and to use decadal change observations as a method to determine the accuracy of climate change. A CLARREO objective is to improve the accuracy of SI-traceable, absolute calibration at infrared and reflected solar wavelengths to reach on-orbit accuracies required to allow climate change observations to survive data gaps and observe climate change at the limit of natural variability. Such an effort will also demonstrate National Institute of Standards and Technology (NIST) approaches for use in future spaceborne instruments. The current work describes the results of laboratory and field measurements with the Solar, Lunar for Absolute Reflectance Imaging Spectroradiometer (SOLARIS) which is the calibration demonstration system (CDS) for the reflected solar portion of CLARREO. SOLARIS allows testing and evaluation of calibration approaches, alternate design and/or implementation approaches and components for the CLARREO mission. SOLARIS also provides a test-bed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. Results of laboratory calibration measurements are provided to demonstrate key assumptions about instrument behavior that are needed to achieve CLARREO's climate measurement requirements. Absolute radiometric response is determined using laser-based calibration sources and applied to direct solar views for comparison with accepted solar irradiance models to demonstrate accuracy values giving confidence in the error budget for the CLARREO reflectance retrieval.

Corsica: A Multi-Mission Absolute Calibration Site

NASA Astrophysics Data System (ADS)

Bonnefond, P.; Exertier, P.; Laurain, O.; Guinle, T.; Femenias, P.

2013-09-01

In collaboration with the CNES and NASA oceanographic projects (TOPEX/Poseidon and Jason), the OCA (Observatoire de la Côte d'Azur) developed a verification site in Corsica since 1996, operational since 1998. CALibration/VALidation embraces a wide variety of activities, ranging from the interpretation of information from internal-calibration modes of the sensors to validation of the fully corrected estimates of the reflector heights using in situ data. Now, Corsica is, like the Harvest platform (NASA side) [14], an operating calibration site able to support a continuous monitoring with a high level of accuracy: a 'point calibration' which yields instantaneous bias estimates with a 10-day repeatability of 30 mm (standard deviation) and mean errors of 4 mm (standard error). For a 35-day repeatability (ERS, Envisat), due to a smaller time series, the standard error is about the double ( 7 mm).In this paper, we will present updated results of the absolute Sea Surface Height (SSH) biases for TOPEX/Poseidon (T/P), Jason-1, Jason-2, ERS-2 and Envisat.

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

Fuangrod, T; Simpson, J; Greer, P

Purpose: A real-time patient treatment delivery verification system using EPID (Watchdog) has been developed as an advanced patient safety tool. In a pilot study data was acquired for 119 prostate and head and neck (HN) IMRT patient deliveries to generate body-site specific action limits using statistical process control. The purpose of this study is to determine the sensitivity of Watchdog to detect clinically significant errors during treatment delivery. Methods: Watchdog utilizes a physics-based model to generate a series of predicted transit cine EPID images as a reference data set, and compares these in real-time to measured transit cine-EPID images acquiredmore » during treatment using chi comparison (4%, 4mm criteria) after the initial 2s of treatment to allow for dose ramp-up. Four study cases were used; dosimetric (monitor unit) errors in prostate (7 fields) and HN (9 fields) IMRT treatments of (5%, 7%, 10%) and positioning (systematic displacement) errors in the same treatments of (5mm, 7mm, 10mm). These errors were introduced by modifying the patient CT scan and re-calculating the predicted EPID data set. The error embedded predicted EPID data sets were compared to the measured EPID data acquired during patient treatment. The treatment delivery percentage (measured from 2s) where Watchdog detected the error was determined. Results: Watchdog detected all simulated errors for all fields during delivery. The dosimetric errors were detected at average treatment delivery percentage of (4%, 0%, 0%) and (7%, 0%, 0%) for prostate and HN respectively. For patient positional errors, the average treatment delivery percentage was (52%, 43%, 25%) and (39%, 16%, 6%). Conclusion: These results suggest that Watchdog can detect significant dosimetric and positioning errors in prostate and HN IMRT treatments in real-time allowing for treatment interruption. Displacements of the patient require longer to detect however incorrect body site or very large geographic misses will be detected rapidly.« less

Computational tests of quantum chemical models for excited and ionized states of molecules with phosphorus and sulfur atoms.

PubMed

Hahn, David K; RaghuVeer, Krishans; Ortiz, J V

2014-05-15

Time-dependent density functional theory (TD-DFT) and electron propagator theory (EPT) are used to calculate the electronic transition energies and ionization energies, respectively, of species containing phosphorus or sulfur. The accuracy of TD-DFT and EPT, in conjunction with various basis sets, is assessed with data from gas-phase spectroscopy. TD-DFT is tested using 11 prominent exchange-correlation functionals on a set of 37 vertical and 19 adiabatic transitions. For vertical transitions, TD-CAM-B3LYP calculations performed with the MG3S basis set are lowest in overall error, having a mean absolute deviation from experiment of 0.22 eV, or 0.23 eV over valence transitions and 0.21 eV over Rydberg transitions. Using a larger basis set, aug-pc3, improves accuracy over the valence transitions via hybrid functionals, but improved accuracy over the Rydberg transitions is only obtained via the BMK functional. For adiabatic transitions, all hybrid functionals paired with the MG3S basis set perform well, and B98 is best, with a mean absolute deviation from experiment of 0.09 eV. The testing of EPT used the Outer Valence Green's Function (OVGF) approximation and the Partial Third Order (P3) approximation on 37 vertical first ionization energies. It is found that OVGF outperforms P3 when basis sets of at least triple-ζ quality in the polarization functions are used. The largest basis set used in this study, aug-pc3, obtained the best mean absolute error from both methods -0.08 eV for OVGF and 0.18 eV for P3. The OVGF/6-31+G(2df,p) level of theory is particularly cost-effective, yielding a mean absolute error of 0.11 eV.

Novel isotopic N, N-Dimethyl Leucine (iDiLeu) Reagents Enable Absolute Quantification of Peptides and Proteins Using a Standard Curve Approach

NASA Astrophysics Data System (ADS)

Greer, Tyler; Lietz, Christopher B.; Xiang, Feng; Li, Lingjun

2015-01-01

Absolute quantification of protein targets using liquid chromatography-mass spectrometry (LC-MS) is a key component of candidate biomarker validation. One popular method combines multiple reaction monitoring (MRM) using a triple quadrupole instrument with stable isotope-labeled standards (SIS) for absolute quantification (AQUA). LC-MRM AQUA assays are sensitive and specific, but they are also expensive because of the cost of synthesizing stable isotope peptide standards. While the chemical modification approach using mass differential tags for relative and absolute quantification (mTRAQ) represents a more economical approach when quantifying large numbers of peptides, these reagents are costly and still suffer from lower throughput because only two concentration values per peptide can be obtained in a single LC-MS run. Here, we have developed and applied a set of five novel mass difference reagents, isotopic N, N-dimethyl leucine (iDiLeu). These labels contain an amine reactive group, triazine ester, are cost effective because of their synthetic simplicity, and have increased throughput compared with previous LC-MS quantification methods by allowing construction of a four-point standard curve in one run. iDiLeu-labeled peptides show remarkably similar retention time shifts, slightly lower energy thresholds for higher-energy collisional dissociation (HCD) fragmentation, and high quantification accuracy for trypsin-digested protein samples (median errors <15%). By spiking in an iDiLeu-labeled neuropeptide, allatostatin, into mouse urine matrix, two quantification methods are validated. The first uses one labeled peptide as an internal standard to normalize labeled peptide peak areas across runs (<19% error), whereas the second enables standard curve creation and analyte quantification in one run (<8% error).

Procrustes-based geometric morphometrics on MRI images: An example of inter-operator bias in 3D landmarks and its impact on big datasets.

PubMed

Daboul, Amro; Ivanovska, Tatyana; Bülow, Robin; Biffar, Reiner; Cardini, Andrea

2018-01-01

Using 3D anatomical landmarks from adult human head MRIs, we assessed the magnitude of inter-operator differences in Procrustes-based geometric morphometric analyses. An in depth analysis of both absolute and relative error was performed in a subsample of individuals with replicated digitization by three different operators. The effect of inter-operator differences was also explored in a large sample of more than 900 individuals. Although absolute error was not unusual for MRI measurements, including bone landmarks, shape was particularly affected by differences among operators, with up to more than 30% of sample variation accounted for by this type of error. The magnitude of the bias was such that it dominated the main pattern of bone and total (all landmarks included) shape variation, largely surpassing the effect of sex differences between hundreds of men and women. In contrast, however, we found higher reproducibility in soft-tissue nasal landmarks, despite relatively larger errors in estimates of nasal size. Our study exemplifies the assessment of measurement error using geometric morphometrics on landmarks from MRIs and stresses the importance of relating it to total sample variance within the specific methodological framework being used. In summary, precise landmarks may not necessarily imply negligible errors, especially in shape data; indeed, size and shape may be differentially impacted by measurement error and different types of landmarks may have relatively larger or smaller errors. Importantly, and consistently with other recent studies using geometric morphometrics on digital images (which, however, were not specific to MRI data), this study showed that inter-operator biases can be a major source of error in the analysis of large samples, as those that are becoming increasingly common in the 'era of big data'.

Procrustes-based geometric morphometrics on MRI images: An example of inter-operator bias in 3D landmarks and its impact on big datasets

PubMed Central

Ivanovska, Tatyana; Bülow, Robin; Biffar, Reiner; Cardini, Andrea

2018-01-01

Using 3D anatomical landmarks from adult human head MRIs, we assessed the magnitude of inter-operator differences in Procrustes-based geometric morphometric analyses. An in depth analysis of both absolute and relative error was performed in a subsample of individuals with replicated digitization by three different operators. The effect of inter-operator differences was also explored in a large sample of more than 900 individuals. Although absolute error was not unusual for MRI measurements, including bone landmarks, shape was particularly affected by differences among operators, with up to more than 30% of sample variation accounted for by this type of error. The magnitude of the bias was such that it dominated the main pattern of bone and total (all landmarks included) shape variation, largely surpassing the effect of sex differences between hundreds of men and women. In contrast, however, we found higher reproducibility in soft-tissue nasal landmarks, despite relatively larger errors in estimates of nasal size. Our study exemplifies the assessment of measurement error using geometric morphometrics on landmarks from MRIs and stresses the importance of relating it to total sample variance within the specific methodological framework being used. In summary, precise landmarks may not necessarily imply negligible errors, especially in shape data; indeed, size and shape may be differentially impacted by measurement error and different types of landmarks may have relatively larger or smaller errors. Importantly, and consistently with other recent studies using geometric morphometrics on digital images (which, however, were not specific to MRI data), this study showed that inter-operator biases can be a major source of error in the analysis of large samples, as those that are becoming increasingly common in the 'era of big data'. PMID:29787586

A new age-based formula for estimating weight of Korean children.

PubMed

Park, Jungho; Kwak, Young Ho; Kim, Do Kyun; Jung, Jae Yun; Lee, Jin Hee; Jang, Hye Young; Kim, Hahn Bom; Hong, Ki Jeong

2012-09-01

The objective of this study was to develop and validate a new age-based formula for estimating body weights of Korean children. We obtained body weight and age data from a survey conducted in 2005 by the Korean Pediatric Society that was performed to establish normative values for Korean children. Children aged 0-14 were enrolled, and they were divided into three groups according to age: infants (<12 months), preschool-aged (1-4 years) and school-aged children (5-14 years). Seventy-five percent of all subjects were randomly selected to make a derivation set. Regression analysis was performed in order to produce equations that predict the weight from the age for each group. The linear equations derived from this analysis were simplified to create a weight estimating formula for Korean children. This formula was then validated using the remaining 25% of the study subjects with mean percentage error and absolute error. To determine whether a new formula accurately predicts actual weights of Korean children, we also compared this new formula to other weight estimation methods (APLS, Shann formula, Leffler formula, Nelson formula and Broselow tape). A total of 124,095 children's data were enrolled, and 19,854 (16.0%), 40,612 (32.7%) and 63,629 (51.3%) were classified as infants, preschool-aged and school-aged groups, respectively. Three equations, (age in months+9)/2, 2×(age in years)+9 and 4×(age in years)-1 were derived for infants, pre-school and school-aged groups, respectively. When these equations were applied to the validation set, the actual average weight of those children was 0.4kg heavier than our estimated weight (95% CI=0.37-0.43, p<0.001). The mean percentage error of our model (+0.9%) was lower than APLS (-11.5%), Shann formula (-8.6%), Leffler formula (-1.7%), Nelson formula (-10.0%), Best Guess formula (+5.0%) and Broselow tape (-4.8%) for all age groups. We developed and validated a simple formula to estimate body weight from the age of Korean children and found that this new formula was more accurate than other weight estimating methods. However, care should be taken when applying this formula to older children because of a large standard deviation of estimated weight. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Computerized pharmaceutical intervention to reduce reconciliation errors at hospital discharge in Spain: an interrupted time-series study.

PubMed

García-Molina Sáez, C; Urbieta Sanz, E; Madrigal de Torres, M; Vicente Vera, T; Pérez Cárceles, M D

2016-04-01

It is well known that medication reconciliation at discharge is a key strategy to ensure proper drug prescription and the effectiveness and safety of any treatment. Different types of interventions to reduce reconciliation errors at discharge have been tested, many of which are based on the use of electronic tools as they are useful to optimize the medication reconciliation process. However, not all countries are progressing at the same speed in this task and not all tools are equally effective. So it is important to collate updated country-specific data in order to identify possible strategies for improvement in each particular region. Our aim therefore was to analyse the effectiveness of a computerized pharmaceutical intervention to reduce reconciliation errors at discharge in Spain. A quasi-experimental interrupted time-series study was carried out in the cardio-pneumology unit of a general hospital from February to April 2013. The study consisted of three phases: pre-intervention, intervention and post-intervention, each involving 23 days of observations. At the intervention period, a pharmacist was included in the medical team and entered the patient's pre-admission medication in a computerized tool integrated into the electronic clinical history of the patient. The effectiveness was evaluated by the differences between the mean percentages of reconciliation errors in each period using a Mann-Whitney U test accompanied by Bonferroni correction, eliminating autocorrelation of the data by first using an ARIMA analysis. In addition, the types of error identified and their potential seriousness were analysed. A total of 321 patients (119, 105 and 97 in each phase, respectively) were included in the study. For the 3966 medicaments recorded, 1087 reconciliation errors were identified in 77·9% of the patients. The mean percentage of reconciliation errors per patient in the first period of the study was 42·18%, falling to 19·82% during the intervention period (P = 0·000). When the intervention was withdrawn, the mean percentage of reconciliation errors increased again to 27·72% (P = 0·008). The difference between the percentages of pre- and post-intervention periods was statistically significant (P = 0·000). Most reconciliation errors were due to omission (46·7%) or incomplete prescription (43·8%), and 35·3% of which could have caused harm to the patient. A computerized pharmaceutical intervention is shown to reduce reconciliation errors in the context of a high incidence of such errors. © 2016 John Wiley & Sons Ltd.

Comparing alchemical and physical pathway methods for computing the absolute binding free energy of charged ligands.

PubMed

Deng, Nanjie; Cui, Di; Zhang, Bin W; Xia, Junchao; Cruz, Jeffrey; Levy, Ronald

2018-06-13

Accurately predicting absolute binding free energies of protein-ligand complexes is important as a fundamental problem in both computational biophysics and pharmaceutical discovery. Calculating binding free energies for charged ligands is generally considered to be challenging because of the strong electrostatic interactions between the ligand and its environment in aqueous solution. In this work, we compare the performance of the potential of mean force (PMF) method and the double decoupling method (DDM) for computing absolute binding free energies for charged ligands. We first clarify an unresolved issue concerning the explicit use of the binding site volume to define the complexed state in DDM together with the use of harmonic restraints. We also provide an alternative derivation for the formula for absolute binding free energy using the PMF approach. We use these formulas to compute the binding free energy of charged ligands at an allosteric site of HIV-1 integrase, which has emerged in recent years as a promising target for developing antiviral therapy. As compared with the experimental results, the absolute binding free energies obtained by using the PMF approach show unsigned errors of 1.5-3.4 kcal mol-1, which are somewhat better than the results from DDM (unsigned errors of 1.6-4.3 kcal mol-1) using the same amount of CPU time. According to the DDM decomposition of the binding free energy, the ligand binding appears to be dominated by nonpolar interactions despite the presence of very large and favorable intermolecular ligand-receptor electrostatic interactions, which are almost completely cancelled out by the equally large free energy cost of desolvation of the charged moiety of the ligands in solution. We discuss the relative strengths of computing absolute binding free energies using the alchemical and physical pathway methods.

Correcting for Optimistic Prediction in Small Data Sets

PubMed Central

Smith, Gordon C. S.; Seaman, Shaun R.; Wood, Angela M.; Royston, Patrick; White, Ian R.

2014-01-01

The C statistic is a commonly reported measure of screening test performance. Optimistic estimation of the C statistic is a frequent problem because of overfitting of statistical models in small data sets, and methods exist to correct for this issue. However, many studies do not use such methods, and those that do correct for optimism use diverse methods, some of which are known to be biased. We used clinical data sets (United Kingdom Down syndrome screening data from Glasgow (1991–2003), Edinburgh (1999–2003), and Cambridge (1990–2006), as well as Scottish national pregnancy discharge data (2004–2007)) to evaluate different approaches to adjustment for optimism. We found that sample splitting, cross-validation without replication, and leave-1-out cross-validation produced optimism-adjusted estimates of the C statistic that were biased and/or associated with greater absolute error than other available methods. Cross-validation with replication, bootstrapping, and a new method (leave-pair-out cross-validation) all generated unbiased optimism-adjusted estimates of the C statistic and had similar absolute errors in the clinical data set. Larger simulation studies confirmed that all 3 methods performed similarly with 10 or more events per variable, or when the C statistic was 0.9 or greater. However, with lower events per variable or lower C statistics, bootstrapping tended to be optimistic but with lower absolute and mean squared errors than both methods of cross-validation. PMID:24966219

Validation of cardiac output studies from the Mostcare compared to a pulmonary artery catheter in septic patients.

PubMed

Gopal, S; Do, T; Pooni, J S; Martinelli, G

2014-03-01

The Mostcare monitor is a non-invasive cardiac output monitor. It has been well validated in cardiac surgical patients but there is limited evidence on its use in patients with severe sepsis and septic shock. The study included the first 22 consecutive patients with severe sepsis and septic shock in whom the floatation of a pulmonary artery catheter was deemed necessary to guide clinical management. Cardiac output measurements including cardiac output, cardiac index and stroke volume were simultaneously calculated and recorded from a thermodilution pulmonary artery catheter and from the Mostcare monitor respectively. The two methods of measuring cardiac output were compared by Bland-Altman statistics and linear regression analysis. A percentage error of less than 30% was defined as acceptable for this study. Bland-Altman analysis for cardiac output showed a Bias of 0.31 L.min-1, precision (=SD) of 1.97 L.min-1 and a percentage error of 62.54%. For Cardiac Index the bias was 0.21 L.min-1.m-2, precision of 1.10 L.min-1.m-2 and a percentage error of 64%. For stroke volume the bias was 5 mL, precision of 24.46 mL and percentage error of 70.21%. Linear regression produced a correlation coefficient r2 for cardiac output, cardiac index, and stroke volume, of 0.403, 0.306, and 0.3 respectively. Compared to thermodilution cardiac output, cardiac output studies obtained from the Mostcare monitor have an unacceptably high error rate. The Mostcare monitor demonstrated to be an unreliable monitoring device to measure cardiac output in patients with severe sepsis and septic shock on an intensive care unit.

Ultrahigh-resolution mapping of peatland microform using ground-based structure from motion with multiview stereo

NASA Astrophysics Data System (ADS)

Mercer, Jason J.; Westbrook, Cherie J.

2016-11-01

Microform is important in understanding wetland functions and processes. But collecting imagery of and mapping the physical structure of peatlands is often expensive and requires specialized equipment. We assessed the utility of coupling computer vision-based structure from motion with multiview stereo photogrammetry (SfM-MVS) and ground-based photos to map peatland topography. The SfM-MVS technique was tested on an alpine peatland in Banff National Park, Canada, and guidance was provided on minimizing errors. We found that coupling SfM-MVS with ground-based photos taken with a point and shoot camera is a viable and competitive technique for generating ultrahigh-resolution elevations (i.e., <0.01 m, mean absolute error of 0.083 m). In evaluating 100+ viable SfM-MVS data collection and processing scenarios, vegetation was found to considerably influence accuracy. Vegetation class, when accounted for, reduced absolute error by as much as 50%. The logistic flexibility of ground-based SfM-MVS paired with its high resolution, low error, and low cost makes it a research area worth developing as well as a useful addition to the wetland scientists' toolkit.

[Absolute and relative strength-endurance of the knee flexor and extensor muscles: a reliability study using the IsoMed 2000-dynamometer].

PubMed

Dirnberger, J; Wiesinger, H P; Stöggl, T; Kösters, A; Müller, E

2012-09-01

Isokinetic devices are highly rated in strength-related performance diagnosis. A few years ago, the broad variety of existing products was extended by the IsoMed 2000-dynamometer. In order for an isokinetic device to be clinically useful, the reliability of specific applications must be established. Although there have already been single studies on this topic for the IsoMed 2000 concerning maximum strength measurements, there has been no study regarding the assessment of strength-endurance so far. The aim of the present study was to establish the reliability for various methods of quantification of strength-endurance using the IsoMed 2000. A sample of 33 healthy young subjects (age: 23.8 ± 2.6 years) participated in one familiarisation and two testing sessions, 3-4 days apart. Testing consisted of a series 30 full effort concentric extension-flexion cycles of the right knee muscles at an angular velocity of 180 °/s. Based on the parameters Peak, Torque and Work for each repetition, indices of absolute (KADabs) and relative (KADrel) strength-endurance were derived. KADabs was calculated as the mean value of all testing repetitions, KADrel was determined in two ways: on the one hand, as the percentage decrease between the first and the last 5 repetitions (KADrelA) and on the other, as the negative slope derived from the linear regression equitation of all repetitions (KADrelB). Detection of systematic errors was performed using paired sample t-tests, relative and absolute reliability were examined using intraclass correlation coefficient (ICC 2.1) and standard error of measurement (SEM%), respectively. In general, for extension measurements concerning KADabs and - in an weakened form - KADrel high ICC -values of 0.76-0.89 combined with clinically acceptable values of SEM% of 1.2-5.9 % could be found. For flexion measurements this only applies to KADabs, whereas results for KADrel turned out to be clearly weaker with ICC- and SEM% values of 0.42-0.62 and 9.6-17.7 % and leave considerable doubts on the clinical usefulness. However, if there should be after all a need to measure KADrel for flexion, it is - in view of the stronger reliability results - recommended (i) to concentrate on the calculation of KADrelB, (ii) to use the parameter Work and - in view of considerable familiariszation and learning effects of ≈10 % - (iii) to include a familiarisation period that extends exceeds the familiarisation session conducted in the present study. Georg Thieme Verlag KG Stuttgart · New York.

Seepage investigation and selected hydrologic data for the Escalante River drainage basin, Garfield and Kane Counties, Utah, 1909-2002

USGS Publications Warehouse

Wilberg, Dale E.; Stolp, Bernard J.

2005-01-01

This report contains the results of an October 2001 seepage investigation conducted along a reach of the Escalante River in Utah extending from the U.S. Geological Survey streamflow-gaging station near Escalante to the mouth of Stevens Canyon. Discharge was measured at 16 individual sites along 15 consecutive reaches. Total reach length was about 86 miles. A reconnaissance-level sampling of water for tritium and chlorofluorcarbons was also done. In addition, hydrologic and water-quality data previously collected and published by the U.S. Geological Survey for the 2,020-square-mile Escalante River drainage basin was compiled and is presented in 12 tables. These data were collected from 64 surface-water sites and 28 springs from 1909 to 2002.None of the 15 consecutive reaches along the Escalante River had a measured loss or gain that exceeded the measurement error. All discharge measurements taken during the seepage investigation were assigned a qualitative rating of accuracy that ranged from 5 percent to greater than 8 percent of the actual flow. Summing the potential error for each measurement and dividing by the maximum of either the upstream discharge and any tributary inflow, or the downstream discharge, determined the normalized error for a reach. This was compared to the computed loss or gain that also was normalized to the maximum discharge. A loss or gain for a specified reach is considered significant when the loss or gain (normalized percentage difference) is greater than the measurement error (normalized percentage error). The percentage difference and percentage error were normalized to allow comparison between reaches with different amounts of discharge.The plate that accompanies the report is 36" by 40" and can be printed in 16 tiles, 8.5 by 11 inches. An index for the tiles is located on the lower left-hand side of the plate. Using Adobe Acrobat, the plate can be viewed independent of the report; all Acrobat functions are available.

48 CFR 52.241-6 - Service Provisions.

Code of Federal Regulations, 2014 CFR

2014-10-01

... such errors. However, any meter which registers not more than __ percent slow or fast shall be deemed... the Government if the percentage of errors is found to be not more than __ percent slow or fast. (3...

48 CFR 52.241-6 - Service Provisions.

Code of Federal Regulations, 2010 CFR

2010-10-01

... such errors. However, any meter which registers not more than __ percent slow or fast shall be deemed... the Government if the percentage of errors is found to be not more than __ percent slow or fast. (3...

48 CFR 52.241-6 - Service Provisions.

Code of Federal Regulations, 2012 CFR

2012-10-01

... such errors. However, any meter which registers not more than __ percent slow or fast shall be deemed... the Government if the percentage of errors is found to be not more than __ percent slow or fast. (3...

48 CFR 52.241-6 - Service Provisions.

Code of Federal Regulations, 2011 CFR

2011-10-01

... such errors. However, any meter which registers not more than __ percent slow or fast shall be deemed... the Government if the percentage of errors is found to be not more than __ percent slow or fast. (3...

48 CFR 52.241-6 - Service Provisions.

Code of Federal Regulations, 2013 CFR

2013-10-01

... such errors. However, any meter which registers not more than __ percent slow or fast shall be deemed... the Government if the percentage of errors is found to be not more than __ percent slow or fast. (3...

Using total quality management approach to improve patient safety by preventing medication error incidences*.

PubMed

Yousef, Nadin; Yousef, Farah

2017-09-04

Whereas one of the predominant causes of medication errors is a drug administration error, a previous study related to our investigations and reviews estimated that the incidences of medication errors constituted 6.7 out of 100 administrated medication doses. Therefore, we aimed by using six sigma approach to propose a way that reduces these errors to become less than 1 out of 100 administrated medication doses by improving healthcare professional education and clearer handwritten prescriptions. The study was held in a General Government Hospital. First, we systematically studied the current medication use process. Second, we used six sigma approach by utilizing the five-step DMAIC process (Define, Measure, Analyze, Implement, Control) to find out the real reasons behind such errors. This was to figure out a useful solution to avoid medication error incidences in daily healthcare professional practice. Data sheet was used in Data tool and Pareto diagrams were used in Analyzing tool. In our investigation, we reached out the real cause behind administrated medication errors. As Pareto diagrams used in our study showed that the fault percentage in administrated phase was 24.8%, while the percentage of errors related to prescribing phase was 42.8%, 1.7 folds. This means that the mistakes in prescribing phase, especially because of the poor handwritten prescriptions whose percentage in this phase was 17.6%, are responsible for the consequent) mistakes in this treatment process later on. Therefore, we proposed in this study an effective low cost strategy based on the behavior of healthcare workers as Guideline Recommendations to be followed by the physicians. This method can be a prior caution to decrease errors in prescribing phase which may lead to decrease the administrated medication error incidences to less than 1%. This improvement way of behavior can be efficient to improve hand written prescriptions and decrease the consequent errors related to administrated medication doses to less than the global standard; as a result, it enhances patient safety. However, we hope other studies will be made later in hospitals to practically evaluate how much effective our proposed systematic strategy really is in comparison with other suggested remedies in this field.

Examining Impulse-Variability Theory and the Speed-Accuracy Trade-Off in Children's Overarm Throwing Performance.

PubMed

Molina, Sergio L; Stodden, David F

2018-04-01

This study examined variability in throwing speed and spatial error to test the prediction of an inverted-U function (i.e., impulse-variability [IV] theory) and the speed-accuracy trade-off. Forty-five 9- to 11-year-old children were instructed to throw at a specified percentage of maximum speed (45%, 65%, 85%, and 100%) and hit the wall target. Results indicated no statistically significant differences in variable error across the target conditions (p = .72), failing to support the inverted-U hypothesis. Spatial accuracy results indicated no statistically significant differences with mean radial error (p = .18), centroid radial error (p = .13), and bivariate variable error (p = .08) also failing to support the speed-accuracy trade-off in overarm throwing. As neither throwing performance variability nor accuracy changed across percentages of maximum speed in this sample of children as well as in a previous adult sample, current policy and practices of practitioners may need to be reevaluated.

Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model.

PubMed

Liu, L; Luan, R S; Yin, F; Zhu, X P; Lü, Q

2016-01-01

Hand, foot and mouth disease (HFMD) is an infectious disease caused by enteroviruses, which usually occurs in children aged <5 years. In China, the HFMD situation is worsening, with increasing number of cases nationwide. Therefore, monitoring and predicting HFMD incidence are urgently needed to make control measures more effective. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast HFMD incidence in Sichuan province, China. HFMD infection data from January 2010 to June 2014 were used to fit the ARIMA model. The coefficient of determination (R 2), normalized Bayesian Information Criterion (BIC) and mean absolute percentage of error (MAPE) were used to evaluate the goodness-of-fit of the constructed models. The fitted ARIMA model was applied to forecast the incidence of HMFD from April to June 2014. The goodness-of-fit test generated the optimum general multiplicative seasonal ARIMA (1,0,1) × (0,1,0)12 model (R 2 = 0·692, MAPE = 15·982, BIC = 5·265), which also showed non-significant autocorrelations in the residuals of the model (P = 0·893). The forecast incidence values of the ARIMA (1,0,1) × (0,1,0)12 model from July to December 2014 were 4103-9987, which were proximate forecasts. The ARIMA model could be applied to forecast HMFD incidence trend and provide support for HMFD prevention and control. Further observations should be carried out continually into the time sequence, and the parameters of the models could be adjusted because HMFD incidence will not be absolutely stationary in the future.

Support vector machine for day ahead electricity price forecasting

NASA Astrophysics Data System (ADS)

Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti

2015-05-01

Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.

Estimates of the absolute error and a scheme for an approximate solution to scheduling problems

NASA Astrophysics Data System (ADS)

Lazarev, A. A.

2009-02-01

An approach is proposed for estimating absolute errors and finding approximate solutions to classical NP-hard scheduling problems of minimizing the maximum lateness for one or many machines and makespan is minimized. The concept of a metric (distance) between instances of the problem is introduced. The idea behind the approach is, given the problem instance, to construct another instance for which an optimal or approximate solution can be found at the minimum distance from the initial instance in the metric introduced. Instead of solving the original problem (instance), a set of approximating polynomially/pseudopolynomially solvable problems (instances) are considered, an instance at the minimum distance from the given one is chosen, and the resulting schedule is then applied to the original instance.

Inertial Measures of Motion for Clinical Biomechanics: Comparative Assessment of Accuracy under Controlled Conditions - Effect of Velocity

PubMed Central

Lebel, Karina; Boissy, Patrick; Hamel, Mathieu; Duval, Christian

2013-01-01

Background Inertial measurement of motion with Attitude and Heading Reference Systems (AHRS) is emerging as an alternative to 3D motion capture systems in biomechanics. The objectives of this study are: 1) to describe the absolute and relative accuracy of multiple units of commercially available AHRS under various types of motion; and 2) to evaluate the effect of motion velocity on the accuracy of these measurements. Methods The criterion validity of accuracy was established under controlled conditions using an instrumented Gimbal table. AHRS modules were carefully attached to the center plate of the Gimbal table and put through experimental static and dynamic conditions. Static and absolute accuracy was assessed by comparing the AHRS orientation measurement to those obtained using an optical gold standard. Relative accuracy was assessed by measuring the variation in relative orientation between modules during trials. Findings Evaluated AHRS systems demonstrated good absolute static accuracy (mean error < 0.5o) and clinically acceptable absolute accuracy under condition of slow motions (mean error between 0.5o and 3.1o). In slow motions, relative accuracy varied from 2o to 7o depending on the type of AHRS and the type of rotation. Absolute and relative accuracy were significantly affected (p<0.05) by velocity during sustained motions. The extent of that effect varied across AHRS. Interpretation Absolute and relative accuracy of AHRS are affected by environmental magnetic perturbations and conditions of motions. Relative accuracy of AHRS is mostly affected by the ability of all modules to locate the same global reference coordinate system at all time. Conclusions Existing AHRS systems can be considered for use in clinical biomechanics under constrained conditions of use. While their individual capacity to track absolute motion is relatively consistent, the use of multiple AHRS modules to compute relative motion between rigid bodies needs to be optimized according to the conditions of operation. PMID:24260324

Thermal performances of vertical hybrid PV/T air collector

NASA Astrophysics Data System (ADS)

Tabet, I.; Touafek, K.; Bellel, N.; Khelifa, A.

2016-11-01

In this work, numerical analyses and the experimental validation of the thermal behavior of a vertical photovoltaic thermal air collector are investigated. The thermal model is developed using the energy balance equations of the PV/T air collector. Experimental tests are conducted to validate our mathematical model. The tests are performed in the southern Algerian region (Ghardaïa) under clear sky conditions. The prototype of the PV/T air collector is vertically erected and south oriented. The absorber upper plate temperature, glass cover temperature, air temperature in the inlet and outlet of the collector, ambient temperature, wind speed, and solar radiation are measured. The efficiency of the collector increases with increase in mass flow of air, but the increase in mass flow of air reduces the temperature of the system. The increase in efficiency of the PV/T air collector is due to the increase in the number of fins added. In the experiments, the air temperature difference between the inlet and the outlet of the PV/T air collector reaches 10 ° C on November 21, 2014, the interval time is between 10:00 and 14:00, and the temperature of the upper plate reaches 45 ° C at noon. The mathematical model describing the dynamic behavior of the typical PV/T air collector is evaluated by calculating the root mean square error and mean absolute percentage error. A good agreement between the experiment and the simulation results is obtained.

Does Medial Meniscal Allograft Transplantation With the Bone-Plug Technique Restore the Anatomic Location of the Native Medial Meniscus?

PubMed

Kim, Nam-Ki; Bin, Seong-Il; Kim, Jong-Min; Lee, Chang-Rack

2015-12-01

Previous work has shown the importance of restoring the normal structure of the native meniscus with meniscal allograft transplantation. The purpose of this study was to compare the anatomic positions of the anterior horn and posterior horn between the preoperative medial meniscus and the postoperative meniscal allograft after medial meniscal allograft transplantation with the bone-plug technique. The hypothesis was that the bone-plug technique could restore the preoperative structure of the native medial meniscus. Case series; Level of evidence, 4. Between December 1999 and December 2013, a total of 59 patients (49 male, 10 female) underwent medial meniscal allograft transplantation by use of the bone-plug technique. The anatomic positions of both horns in the native medial meniscus and in the meniscal allograft were measured via MRI. The percentage reference method was used to measure the locations of both horns. On coronal MRI, the mean absolute distance of the posterior horn from the lateral border of the tibial plateau changed from 45.2 ± 3.3 to 48.1 ± 4.2 mm (P < .05), and the percentage distance of the posterior horn changed from 59.6% to 63.0% (P < .05). On sagittal MRI, the mean absolute distance of the posterior horn from the anterior reference point changed from 40.3 ± 3.0 to 42.0 ± 3.5 mm (P < .05), and the mean percentage distance of the posterior horn changed from 76.5% to 79.4% (P <.05). On coronal MRI, the mean absolute distance of the anterior horn from the lateral border of the tibial plateau changed from 41.3 ± 4.2 to 48.5 ± 5.6 mm (P < .05), and the mean percentage distance of the anterior horn changed from 54.5% to 63.8% (P < .05). On sagittal MRI, the mean absolute distance of the anterior horn from the anterior reference point changed from 5.5 ± 1.0 to 9.9 ± 2.9 mm (P < .05), and the mean percentage distance of the anterior horn changed from 10.6% to 19.0% (P < .05). Despite attempts to place the meniscal allograft in the same position as the native meniscus, the anatomic locations of both horns were shifted posteromedially compared with those of the native medial meniscus. There were significant differences, attributed to several limitations in the bone-plug technique, between the preoperative and postoperative values of both horns. However, the posterior horn showed a location change of <5 mm, on average, in both the coronal and sagittal planes, whereas the anterior horn showed a location change of ≥ 5 mm in the coronal plane but <5 mm in the sagittal plane. © 2015 The Author(s).

Effects of the liver volume and donor steatosis on errors in the estimated standard liver volume.

PubMed

Siriwardana, Rohan Chaminda; Chan, See Ching; Chok, Kenneth Siu Ho; Lo, Chung Mau; Fan, Sheung Tat

2011-12-01

An accurate assessment of donor and recipient liver volumes is essential in living donor liver transplantation. Many liver donors are affected by mild to moderate steatosis, and steatotic livers are known to have larger volumes. This study analyzes errors in liver volume estimation by commonly used formulas and the effects of donor steatosis on these errors. Three hundred twenty-five Asian donors who underwent right lobe donor hepatectomy were the subjects of this study. The percentage differences between the liver volumes from computed tomography (CT) and the liver volumes estimated with each formula (ie, the error percentages) were calculated. Five popular formulas were tested. The degrees of steatosis were categorized as follows: no steatosis [n = 178 (54.8%)], ≤ 10% steatosis [n = 128 (39.4%)], and >10% to 20% steatosis [n = 19 (5.8%)]. The median errors ranged from 0.6% (7 mL) to 24.6% (360 mL). The lowest was seen with the locally derived formula. All the formulas showed a significant association between the error percentage and the CT liver volume (P < 0.001). Overestimation was seen with smaller liver volumes, whereas underestimation was seen with larger volumes. The locally derived formula was most accurate when the liver volume was 1001 to 1250 mL. A multivariate analysis showed that the estimation error was dependent on the liver volume (P = 0.001) and the anthropometric measurement that was used in the calculation (P < 0.001) rather than steatosis (P ≥ 0.07). In conclusion, all the formulas have a similar pattern of error that is possibly related to the anthropometric measurement. Clinicians should be aware of this pattern of error and the liver volume with which their formula is most accurate. Copyright © 2011 American Association for the Study of Liver Diseases.

Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling: A case study in environmental remote sensing

NASA Astrophysics Data System (ADS)

Gao, Jing; Burt, James E.

2017-12-01

This study investigates the usefulness of a per-pixel bias-variance error decomposition (BVD) for understanding and improving spatially-explicit data-driven models of continuous variables in environmental remote sensing (ERS). BVD is a model evaluation method originated from machine learning and have not been examined for ERS applications. Demonstrated with a showcase regression tree model mapping land imperviousness (0-100%) using Landsat images, our results showed that BVD can reveal sources of estimation errors, map how these sources vary across space, reveal the effects of various model characteristics on estimation accuracy, and enable in-depth comparison of different error metrics. Specifically, BVD bias maps can help analysts identify and delineate model spatial non-stationarity; BVD variance maps can indicate potential effects of ensemble methods (e.g. bagging), and inform efficient training sample allocation - training samples should capture the full complexity of the modeled process, and more samples should be allocated to regions with more complex underlying processes rather than regions covering larger areas. Through examining the relationships between model characteristics and their effects on estimation accuracy revealed by BVD for both absolute and squared errors (i.e. error is the absolute or the squared value of the difference between observation and estimate), we found that the two error metrics embody different diagnostic emphases, can lead to different conclusions about the same model, and may suggest different solutions for performance improvement. We emphasize BVD's strength in revealing the connection between model characteristics and estimation accuracy, as understanding this relationship empowers analysts to effectively steer performance through model adjustments.

Validation of the ASTER Global Digital Elevation Model Version 2 over the conterminous United States

USGS Publications Warehouse

Gesch, Dean B.; Oimoen, Michael J.; Zhang, Zheng; Meyer, David J.; Danielson, Jeffrey J.

2012-01-01

The ASTER Global Digital Elevation Model Version 2 (GDEM v2) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009. The absolute vertical accuracy of GDEM v2 was calculated by comparison with more than 18,000 independent reference geodetic ground control points from the National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v2 is 8.68 meters. This compares with the RMSE of 9.34 meters for GDEM v1. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v2 mean error of -0.20 meters is a significant improvement over the GDEM v1 mean error of -3.69 meters. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover to examine the effects of cover types on measured errors. The GDEM v2 mean errors by land cover class verify that the presence of aboveground features (tree canopies and built structures) cause a positive elevation bias, as would be expected for an imaging system like ASTER. In open ground classes (little or no vegetation with significant aboveground height), GDEM v2 exhibits a negative bias on the order of 1 meter. GDEM v2 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v2 has elevations that are higher in the canopy than SRTM.

A prediction model of short-term ionospheric foF2 based on AdaBoost

NASA Astrophysics Data System (ADS)

Zhao, Xiukuan; Ning, Baiqi; Liu, Libo; Song, Gangbing

2014-02-01

In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years' foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years' data were used as a training dataset and the second eleven years' data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.

What does the MADRS mean? Equipercentile linking with the CGI using a company database of mirtazapine studies.

PubMed

Leucht, Stefan; Fennema, Hein; Engel, Rolf R; Kaspers-Janssen, Marion; Lepping, Peter; Szegedi, Armin

2017-03-01

Little is known about the clinical relevance of the Montgomery Asberg Depression Rating Scale (MADRS) total scores. It is unclear how total scores translate into clinical severity, or how commonly used measures for response (reduction from baseline of ≥50% in the total score) translate into clinical relevance. Moreover, MADRS based definitions of remission vary. We therefore compared: a/ the MADRS total score with the Clinical Global Impression - Severity Score (CGI-S) b/ the percentage and absolute change in the MADRS total scores with Clinical Global Impression - Improvement (CGI-I); c/ the absolute and percentage change in the MADRS total scores with CGI-S absolute change. The method used was equipercentile linking of MADRS and CGI ratings from 22 drug trials in patients with Major Depressive Disorder (MDD) (n=3288). Our results confirm the validity of the commonly used measures for response in MDD trials: a CGI-I score of 2 ('much improved') corresponded to a percentage MADRS reduction from baseline of 48-57%, and a CGI-I score of 1 ('very much improved') to a reduction of 80-84%. If a state of almost complete absence of symptoms were required for a definition of remission, a MADRS total score would be <8, because such scores corresponded to a CGI-S score of 2 ('borderline mentally ill'). Although our analysis is based on a large number of patients, the original trials were not specifically designed to examine our research question. The results might contribute to a better understanding and improved interpretation of clinical trial results in MDD. Copyright © 2017 Elsevier B.V. All rights reserved.

Multisite evaluation of the BD™ Stem Cell Enumeration (SCE) Kit for CD34+ cell enumeration on the BD FACSCanto II™ and BD FACSCalibur™ flow cytometers

PubMed Central

Preti, Robert A; Chan, Wai Shun; Kurtzberg, Joanne; Dornsife, Ronna E.; Wallace, Paul K.; Furlange, Rosemary; Lin, Anna; Omana-Zapata, Imelda; Bonig, Halvard; Tonn, Thorsten

2018-01-01

Background Evaluation of the BD™ Stem Cell Enumeration (SCE) Kit was conducted at four clinical sites with flow cytometry CD34+ enumeration, to assess agreement between two investigational methods, the BD FACSCanto™ II and BD FACSCalibur™ systems, and the predicate method (Beckman Coulter Stem-Kit™ reagents). Methods Leftover and delinked specimens (n = 1,032) from clinical flow cytometry testing were analyzed on the BD FACSCanto II (n = 918) and BD FACSCalibur (n = 905) in normal and mobilized blood, frozen and thawed bone marrow, and leucopheresis and cord blood anticoagulated with CPD, ACD-A, heparin, and EDTA alone or in combination. Fresh leucopheresis analysis addressed site equivalency for sample preparation, testing, and analysis. Results The mean relative bias showed agreement within predefined parameters for the BD FACSCanto II (−2.81 to 4.31 ±7.1) and BD FACSCalibur (−2.69 to 5.2 ±7.9). Results are reported as absolute and relative differences compared to the predicate for viable CD34+, percentage of CD34+ in CD45+, and viable CD45+ populations (or gates). Bias analyses of the distribution of the predicate low, mid, and high bin values were done using BD FACSCanto II optimal gating and BD FACSCalibur manual gating for viable CD34+, percentage of CD34+ in CD45+, and viable CD45+. Bias results from both investigational methods show agreement. Deming regression analyses showed a linear relationship with R2 >0.92 for both investigational methods. Discussion In conclusion, the results from both investigational methods demonstrated agreement and equivalence with the predicate method for enumeration of absolute viable CD34+, percentage of viable CD34+ in CD45+, and absolute viable CD45+ populations. PMID:24927716

Protease-modulating polyacrylate-based hydrogel stimulates wound bed preparation in venous leg ulcers – a randomized controlled trial

PubMed Central

Humbert, P; Faivre, B; Véran, Y; Debure, C; Truchetet, F; Bécherel, P-A; Plantin, P; Kerihuel, J-C; Eming, SA; Dissemond, J; Weyandt, G; Kaspar, D; Smola, H; Zöllner, P

2014-01-01

Background Stringent control of proteolytic activity represents a major therapeutic approach for wound-bed preparation. Objectives We tested whether a protease-modulating polyacrylate- (PA-) containing hydrogel resulted in a more efficient wound-bed preparation of venous leg ulcers when compared to an amorphous hydrogel without known protease-modulating properties. Methods Patients were randomized to the polyacrylate-based hydrogel (n = 34) or to an amorphous hydrogel (n = 41). Wound beds were evaluated by three blinded experts using photographs taken on days 0, 7 and 14. Results After 14 days of treatment there was an absolute decrease in fibrin and necrotic tissue of 37.6 ± 29.9 percentage points in the PA-based hydrogel group and by 16.8 ± 23.0 percentage points in the amorphous hydrogel group. The absolute increase in the proportion of ulcer area covered by granulation tissue was 36.0 ± 27.4 percentage points in the PA-based hydrogel group and 14.5 ± 22.0 percentage points in the control group. The differences between the groups were significant (decrease in fibrin and necrotic tissue P = 0.004 and increase in granulation tissue P = 0.0005, respectively). Conclusion In particular, long-standing wounds profited from the treatment with the PA-based hydrogel. These data suggest that PA-based hydrogel dressings can stimulate normalization of the wound environment, particularly in hard-to-heal ulcers. PMID:24612304

Increases in Plasma Lutein through Supplementation Are Correlated with Increases in Physical Activity and Reductions in Sedentary Time in Older Adults

PubMed Central

Thomson, Rebecca L.; Coates, Alison M.; Howe, Peter R. C.; Bryan, Janet; Matsumoto, Megumi; Buckley, Jonathan D.

2014-01-01

Cross-sectional studies have reported positive relationships between serum lutein concentrations and higher physical activity levels. The purpose of the study was to determine whether increasing plasma lutein levels increases physical activity. Forty-four older adults (BMI, 25.3 ± 2.6 kg/m2; age, 68.8 ± 6.4 year) not meeting Australian physical activity guidelines (150 min/week of moderate to vigorous activity) were randomized to consume capsules containing 21 mg of lutein or placebo with 250 mL of full-cream milk per day for 4 weeks and encouraged to increase physical activity. Physical activity was assessed by self-report, pedometry and accelerometry (daily activity counts and sedentary time). Exercise self-efficacy was assessed by questionnaire. Thirty-nine participants competed the study (Lutein = 19, Placebo = 20). Lutein increased plasma lutein concentrations compared with placebo (p < 0.001). Absolute and percentage changes in plasma lutein were inversely associated with absolute (r = −0.36, p = 0.03) and percentage changes (r = −0.39, p = 0.02) in sedentary time. Percentage change in plasma lutein was positively associated with the percentage change in average daily activity counts (r = 0.36, p = 0.03). Exercise self-efficacy did not change (p = 0.16). Lutein increased plasma lutein, which was associated with increased physical activity and reduced sedentary time in older adults. Larger trials should evaluate whether Lutein can provide health benefits over the longer term. PMID:24594505

Long-Term Effectiveness of Sigmoidoscopy Screening on Colorectal Cancer Incidence and Mortality in Women and Men: A Randomized Trial.

PubMed

Holme, Øyvind; Løberg, Magnus; Kalager, Mette; Bretthauer, Michael; Hernán, Miguel A; Aas, Eline; Eide, Tor J; Skovlund, Eva; Lekven, Jon; Schneede, Jörn; Tveit, Kjell Magne; Vatn, Morten; Ursin, Giske; Hoff, Geir

2018-06-05

The long-term effects of sigmoidoscopy screening on colorectal cancer (CRC) incidence and mortality in women and men are unclear. To determine the effectiveness of flexible sigmoidoscopy screening after 15 years of follow-up in women and men. Randomized controlled trial. (ClinicalTrials.gov: NCT00119912). Oslo and Telemark County, Norway. Adults aged 50 to 64 years at baseline without prior CRC. Screening (between 1999 and 2001) with flexible sigmoidoscopy with and without additional fecal blood testing versus no screening. Participants with positive screening results were offered colonoscopy. Age-adjusted CRC incidence and mortality stratified by sex. Of 98 678 persons, 20 552 were randomly assigned to screening and 78 126 to no screening. Adherence rates were 64.7% in women and 61.4% in men. Median follow-up was 14.8 years. The absolute risks for CRC in women were 1.86% in the screening group and 2.05% in the control group (risk difference, -0.19 percentage point [95% CI, -0.49 to 0.11 percentage point]; HR, 0.92 [CI, 0.79 to 1.07]). In men, the corresponding risks were 1.72% and 2.50%, respectively (risk difference, -0.78 percentage point [CI, -1.08 to -0.48 percentage points]; hazard ratio [HR], 0.66 [CI, 0.57 to 0.78]) (P for heterogeneity = 0.004). The absolute risks for death from CRC in women were 0.60% in the screening group and 0.59% in the control group (risk difference, 0.01 percentage point [CI, -0.16 to 0.18 percentage point]; HR, 1.01 [CI, 0.77 to 1.33]). The corresponding risks for death from CRC in men were 0.49% and 0.81%, respectively (risk difference, -0.33 percentage point [CI, -0.49 to -0.16 percentage point]; HR, 0.63 [CI, 0.47 to 0.83]) (P for heterogeneity = 0.014). Follow-up through national registries. Offering sigmoidoscopy screening in Norway reduced CRC incidence and mortality in men but had little or no effect in women. Norwegian government and Norwegian Cancer Society.

The percentage of nosocomial-related out of total hospitalizations for rotavirus gastroenteritis and its association with hand hygiene compliance.

PubMed

Waisbourd-Zinman, Orith; Ben-Ziony, Shiri; Solter, Ester; Chodick, Gabriel; Ashkenazi, Shai; Livni, Gilat

2011-03-01

Because the absolute numbers of both community-acquired and nosocomial rotavirus gastroenteritis (RVGE) vary, we studied the percentage of hospitalizations for RVGE that were transmitted nosocomially as an indicator of in-hospital acquisition of the infection. In a 4-year prospective study, the percentage of nosocomial RVGE declined steadily, from 20.3% in 2003 to 12.7% in 2006 (P = .001). Concomitantly, the rate of compliance with hand hygiene increased from 33.7% to 49% (P = .012), with a significant (P < .0001) inverse association noted between the two trends. Copyright © 2011 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

Improvement of GPS radio occultation retrieval error of E region electron density: COSMIC measurement and IRI model simulation

NASA Astrophysics Data System (ADS)

Wu, Kang-Hung; Su, Ching-Lun; Chu, Yen-Hsyang

2015-03-01

In this article, we use the International Reference Ionosphere (IRI) model to simulate temporal and spatial distributions of global E region electron densities retrieved by the FORMOSAT-3/COSMIC satellites by means of GPS radio occultation (RO) technique. Despite regional discrepancies in the magnitudes of the E region electron density, the IRI model simulations can, on the whole, describe the COSMIC measurements in quality and quantity. On the basis of global ionosonde network and the IRI model, the retrieval errors of the global COSMIC-measured E region peak electron density (NmE) from July 2006 to July 2011 are examined and simulated. The COSMIC measurement and the IRI model simulation both reveal that the magnitudes of the percentage error (PE) and root mean-square-error (RMSE) of the relative RO retrieval errors of the NmE values are dependent on local time (LT) and geomagnetic latitude, with minimum in the early morning and at high latitudes and maximum in the afternoon and at middle latitudes. In addition, the seasonal variation of PE and RMSE values seems to be latitude dependent. After removing the IRI model-simulated GPS RO retrieval errors from the original COSMIC measurements, the average values of the annual and monthly mean percentage errors of the RO retrieval errors of the COSMIC-measured E region electron density are, respectively, substantially reduced by a factor of about 2.95 and 3.35, and the corresponding root-mean-square errors show averaged decreases of 15.6% and 15.4%, respectively. It is found that, with this process, the largest reduction in the PE and RMSE of the COSMIC-measured NmE occurs at the equatorial anomaly latitudes 10°N-30°N in the afternoon from 14 to 18 LT, with a factor of 25 and 2, respectively. Statistics show that the residual errors that remained in the corrected COSMIC-measured NmE vary in a range of -20% to 38%, which are comparable to or larger than the percentage errors of the IRI-predicted NmE fluctuating in a range of -6.5% to 20%.

Aging of immune system: Immune signature from peripheral blood lymphocyte subsets in 1068 healthy adults.

PubMed

Qin, Ling; Jing, Xie; Qiu, Zhifeng; Cao, Wei; Jiao, Yang; Routy, Jean-Pierre; Li, Taisheng

2016-05-01

Aging is a major risk factor for several conditions including neurodegenerative, cardiovascular diseases and cancer. Functional impairments in cellular pathways controlling genomic stability, and immune control have been identified. Biomarker of immune senescence is needed to improve vaccine response and to develop therapy to improve immune control. To identify phenotypic signature of circulating immune cells with aging, we enrolled 1068 Chinese healthy volunteers ranging from 18 to 80 years old. The decreased naïve CD4+ and CD8+ T cells, increased memory CD4+ or CD8+ T cells, loss of CD28 expression on T cells and reverse trend of CD38 and HLA-DR, were significant for aging of immune system. Conversely, the absolute counts and percentage of NK cells and CD19+B cells maintained stable in aging individuals. The Chinese reference ranges of absolute counts and percentage of peripheral lymphocyte in this study might be useful for future clinical evaluation.

Design with limited anthropometric data: A method of interpreting sums of percentiles in anthropometric design.

PubMed

Albin, Thomas J

2017-07-01

Occasionally practitioners must work with single dimensions defined as combinations (sums or differences) of percentile values, but lack information (e.g. variances) to estimate the accommodation achieved. This paper describes methods to predict accommodation proportions for such combinations of percentile values, e.g. two 90th percentile values. Kreifeldt and Nah z-score multipliers were used to estimate the proportions accommodated by combinations of percentile values of 2-15 variables; two simplified versions required less information about variance and/or correlation. The estimates were compared to actual observed proportions; for combinations of 2-15 percentile values the average absolute differences ranged between 0.5 and 1.5 percentage points. The multipliers were also used to estimate adjusted percentile values, that, when combined, estimate a desired proportion of the combined measurements. For combinations of two and three adjusted variables, the average absolute difference between predicted and observed proportions ranged between 0.5 and 3.0 percentage points. Copyright © 2017 Elsevier Ltd. All rights reserved.

The AFGL (Air Force Geophysics Laboratory) Absolute Gravity System’s Error Budget Revisted.

DTIC Science & Technology

1985-05-08

also be induced by equipment not associated with the system. A systematic bias of 68 pgal was observed by the Istituto di Metrologia "G. Colonnetti...Laboratory Astrophysics, Univ. of Colo., Boulder, Colo. IMGC: Istituto di Metrologia "G. Colonnetti", Torino, Italy Table 1. Absolute Gravity Values...measurements were made with three Model D and three Model G La Coste-Romberg gravity meters. These instruments were operated by the following agencies

Instrumentation and First Results of the Reflected Solar Demonstration System for the Climate Absolute Radiance and Refractivity Observatory

NASA Technical Reports Server (NTRS)

McCorkel, Joel; Thome, Kurtis; Hair, Jason; McAndrew, Brendan; Jennings, Don; Rabin, Douglas; Daw, Adrian; Lundsford, Allen

2012-01-01

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission key goals include enabling observation of high accuracy long-term climate change trends, use of these observations to test and improve climate forecasts, and calibration of operational and research sensors. The spaceborne instrument suites include a reflected solar spectroradiometer, emitted infrared spectroradiometer, and radio occultation receivers. The requirement for the RS instrument is that derived reflectance must be traceable to Sl standards with an absolute uncertainty of <0.3% and the error budget that achieves this requirement is described in previo1L5 work. This work describes the Solar/Lunar Absolute Reflectance Imaging Spectroradiometer (SOLARIS), a calibration demonstration system for RS instrument, and presents initial calibration and characterization methods and results. SOLARIS is an Offner spectrometer with two separate focal planes each with its own entrance aperture and grating covering spectral ranges of 320-640, 600-2300 nm over a full field-of-view of 10 degrees with 0.27 milliradian sampling. Results from laboratory measurements including use of integrating spheres, transfer radiometers and spectral standards combined with field-based solar and lunar acquisitions are presented. These results will be used to assess the accuracy and repeatability of the radiometric and spectral characteristics of SOLARIS, which will be presented against the sensor-level requirements addressed in the CLARREO RS instrument error budget.

The Absolute Stability Analysis in Fuzzy Control Systems with Parametric Uncertainties and Reference Inputs

NASA Astrophysics Data System (ADS)

Wu, Bing-Fei; Ma, Li-Shan; Perng, Jau-Woei

This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur'e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

First identification of O,S-diethyl Thiocarbonate in Indian Cress absolute and odor evaluation of its synthesized homologues by GC-sniffing.

PubMed

Breme, Katharina; Guillamon, Nadine; Fernandez, Xavier; Tournayre, Pascal; Brevard, Hugues; Joulain, Daniel; Berdagué, Jean Louis; Meierhenrich, Uwe J

2009-03-25

Indian cress (Tropaeolum majus L.) absolute was studied by GC-olfactometry (VIDEO-Sniff method) in order to identify odor-active aroma compounds. Because of its fruity-sulfury odor note, a compound that has never been identified in plant extracts before stood out: O,S-diethyl thiocarbonate, present at 0.1% (percentage of the total GC/FID area) in the extract. GCxGC-TOFMS allowed for a clean mass spectrum to be obtained, and isolation by preparative GC followed by NMR studies allowed its identification. Here, we report on the first detection of O,S-diethyl thiocarbonate in Indian cress absolute by GC-olfactometry/VIDEO-Sniff and on its isolation and identification. The synthesis and odor evaluation of its homologues are presented.

Protection of SK-N-MC cells against β-amyloid peptide-induced degeneration using neuron growth factor-loaded liposomes with surface lactoferrin.

PubMed

Kuo, Yung-Chih; Wang, Cheng-Ting

2014-07-01

A liposomal system with surface lactoferrin (Lf) was developed for delivering neuron growth factor (NGF) across the blood-brain barrier (BBB) and improving the viability of neuron-like SK-N-MC cells with deposited β-amyloid peptide (Aβ). The Lf-grafted liposomes carrying NGF (Lf/NGF-liposomes) were applied to a monolayer of human brain-microvascular endothelial cells (HBMECs) regulated by human astrocytes (HAs) and to fibrillar Aβ1-42-insulted SK-N-MC cells. An increase in cholesterol mole percentage enhanced the particle size, absolute value of zeta potential, and physical stability, however, reduced the entrapment efficiency and release rate of NGF. In addition, an increase in Lf concentration increased the particle size, surface nitrogen percentage, NGF permeability across the BBB, and viability of HBMECs, HAs, and SK-N-MC cells, however, decreased the absolute value of zeta potential, surface phosphorus percentage, and loading efficiency of Lf. After treating with Lf/NGF-liposomes, a higher Aβ concentration yielded a lower survival of SK-N-MC cells. The current Lf/NGF-liposomes are efficacious drug carriers to target the BBB and inhibit the Aβ-induced neurotoxicity as potential pharmacotherapy for Alzheimer's disease. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effect of concentration gradients on biodegradation in bench-scale sand columns with HYDRUS modeling of hydrocarbon transport and degradation.

PubMed

Horel, Agota; Schiewer, Silke; Misra, Debasmita

2015-09-01

The present research investigated to what extent results obtained in small microcosm experiments can be extrapolated to larger settings with non-uniform concentrations. Microbial hydrocarbon degradation in sandy sediments was compared for column experiments versus homogenized microcosms with varying concentrations of diesel, Syntroleum, and fish biodiesel as contaminants. Syntroleum and fish biodiesel had higher degradation rates than diesel fuel. Microcosms showed significantly higher overall hydrocarbon mineralization percentages (p < 0.006) than columns. Oxygen levels and moisture content were likely not responsible for that difference, which could, however, be explained by a strong gradient of fuel and nutrient concentrations through the column. The mineralization percentage in the columns was similar to small-scale microcosms at high fuel concentrations. While absolute hydrocarbon degradation increased, mineralization percentages decreased with increasing fuel concentration which was corroborated by saturation kinetics; the absolute CO2 production reached a steady plateau value at high substrate concentrations. Numerical modeling using HYDRUS 2D/3D simulated the transport and degradation of the investigated fuels in vadose zone conditions similar to those in laboratory column experiments. The numerical model was used to evaluate the impact of different degradation rate constants from microcosm versus column experiments.

Application of Intra-Oral Dental Scanners in the Digital Workflow of Implantology

PubMed Central

van der Meer, Wicher J.; Andriessen, Frank S.; Wismeijer, Daniel; Ren, Yijin

2012-01-01

Intra-oral scanners will play a central role in digital dentistry in the near future. In this study the accuracy of three intra-oral scanners was compared. Materials and methods: A master model made of stone was fitted with three high precision manufactured PEEK cylinders and scanned with three intra-oral scanners: the CEREC (Sirona), the iTero (Cadent) and the Lava COS (3M). In software the digital files were imported and the distance between the centres of the cylinders and the angulation between the cylinders was assessed. These values were compared to the measurements made on a high accuracy 3D scan of the master model. Results: The distance errors were the smallest and most consistent for the Lava COS. The distance errors for the Cerec were the largest and least consistent. All the angulation errors were small. Conclusions: The Lava COS in combination with a high accuracy scanning protocol resulted in the smallest and most consistent errors of all three scanners tested when considering mean distance errors in full arch impressions both in absolute values and in consistency for both measured distances. For the mean angulation errors, the Lava COS had the smallest errors between cylinders 1–2 and the largest errors between cylinders 1–3, although the absolute difference with the smallest mean value (iTero) was very small (0,0529°). An expected increase in distance and/or angular errors over the length of the arch due to an accumulation of registration errors of the patched 3D surfaces could be observed in this study design, but the effects were statistically not significant. Clinical relevance For making impressions of implant cases for digital workflows, the most accurate scanner with the scanning protocol that will ensure the most accurate digital impression should be used. In our study model that was the Lava COS with the high accuracy scanning protocol. PMID:22937030

[Design and accuracy analysis of upper slicing system of MSCT].

PubMed

Jiang, Rongjian

2013-05-01

The upper slicing system is the main components of the optical system in MSCT. This paper focuses on the design of upper slicing system and its accuracy analysis to improve the accuracy of imaging. The error of slice thickness and ray center by bearings, screw and control system were analyzed and tested. In fact, the accumulated error measured is less than 1 microm, absolute error measured is less than 10 microm. Improving the accuracy of the upper slicing system contributes to the appropriate treatment methods and success rate of treatment.

Inherent Conservatism in Deterministic Quasi-Static Structural Analysis

NASA Technical Reports Server (NTRS)

Verderaime, V.

1997-01-01

The cause of the long-suspected excessive conservatism in the prevailing structural deterministic safety factor has been identified as an inherent violation of the error propagation laws when reducing statistical data to deterministic values and then combining them algebraically through successive structural computational processes. These errors are restricted to the applied stress computations, and because mean and variations of the tolerance limit format are added, the errors are positive, serially cumulative, and excessively conservative. Reliability methods circumvent these errors and provide more efficient and uniform safe structures. The document is a tutorial on the deficiencies and nature of the current safety factor and of its improvement and transition to absolute reliability.

The use of fractional orders in the determination of birefringence of highly dispersive materials by the channelled spectrum method

NASA Astrophysics Data System (ADS)

Nagarajan, K.; Shashidharan Nair, C. K.

2007-07-01

The channelled spectrum employing polarized light interference is a very convenient method for the study of dispersion of birefringence. However, while using this method, the absolute order of the polarized light interference fringes cannot be determined easily. Approximate methods are therefore used to estimate the order. One of the approximations is that the dispersion of birefringence across neighbouring integer order fringes is negligible. In this paper, we show how this approximation can cause errors. A modification is reported whereby the error in the determination of absolute fringe order can be reduced using fractional orders instead of integer orders. The theoretical background for this method supported with computer simulation is presented. An experimental arrangement implementing these modifications is described. This method uses a Constant Deviation Spectrometer (CDS) and a Soleil Babinet Compensator (SBC).

THE ACUTE EFFECTS OF CONCENTRIC VERSUS ECCENTRIC MUSCLE FATIGUE ON SHOULDER ACTIVE REPOSITIONING SENSE

PubMed Central

2017-01-01

Purpose/Background Shoulder proprioception is essential in the activities of daily living as well as in sports. Acute muscle fatigue is believed to cause a deterioration of proprioception, increasing the risk of injury. The purpose of this study was to evaluate if fatigue of the shoulder external rotators during eccentric versus concentric activity affects shoulder joint proprioception as determined by active reproduction of position. Study design Quasi-experimental trial. Methods Twenty-two healthy subjects with no recent history of shoulder pathology were randomly allocated to either a concentric or an eccentric exercise group for fatiguing the shoulder external rotators. Proprioception was assessed before and after the fatiguing protocol using an isokinetic dynamometer, by measuring active reproduction of position at 30 ° of shoulder external rotation, reported as absolute angular error. The fatiguing protocol consisted of sets of fifteen consecutive external rotator muscle contractions in either the concentric or eccentric action. The subjects were exercised until there was a 30% decline from the peak torque of the subjects’ maximal voluntary contraction over three consecutive muscle contractions. Results A one-way analysis of variance test revealed no statistical difference in absolute angular error (p > 0.05) between concentric and eccentric groups. Moreover, no statistical difference (p > 0.05) was found in absolute angular error between pre- and post-fatigue in either group. Conclusions Eccentric exercise does not seem to acutely affect shoulder proprioception to a larger extent than concentric exercise. Level of evidence 2b PMID:28515976

A new analytical method for quantification of olive and palm oil in blends with other vegetable edible oils based on the chromatographic fingerprints from the methyl-transesterified fraction.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the quantification of olive oil and palm oil in blends with other vegetable edible oils (canola, safflower, corn, peanut, seeds, grapeseed, linseed, sesame and soybean) using normal phase liquid chromatography, and applying chemometric tools was developed. The procedure for obtaining of chromatographic fingerprint from the methyl-transesterified fraction from each blend is described. The multivariate quantification methods used were Partial Least Square-Regression (PLS-R) and Support Vector Regression (SVR). The quantification results were evaluated by several parameters as the Root Mean Square Error of Validation (RMSEV), Mean Absolute Error of Validation (MAEV) and Median Absolute Error of Validation (MdAEV). It has to be highlighted that the new proposed analytical method, the chromatographic analysis takes only eight minutes and the results obtained showed the potential of this method and allowed quantification of mixtures of olive oil and palm oil with other vegetable oils. Copyright © 2016 Elsevier B.V. All rights reserved.

Dimensional Error in Rapid Prototyping with Open Source Software and Low-cost 3D-printer

PubMed Central

Andrade-Delgado, Laura; Telich-Tarriba, Jose E.; Fuente-del-Campo, Antonio; Altamirano-Arcos, Carlos A.

2018-01-01

Summary: Rapid prototyping models (RPMs) had been extensively used in craniofacial and maxillofacial surgery, especially in areas such as orthognathic surgery, posttraumatic or oncological reconstructions, and implantology. Economic limitations are higher in developing countries such as Mexico, where resources dedicated to health care are limited, therefore limiting the use of RPM to few selected centers. This article aims to determine the dimensional error of a low-cost fused deposition modeling 3D printer (Tronxy P802MA, Shenzhen, Tronxy Technology Co), with Open source software. An ordinary dry human mandible was scanned with a computed tomography device. The data were processed with open software to build a rapid prototype with a fused deposition machine. Linear measurements were performed to find the mean absolute and relative difference. The mean absolute and relative difference was 0.65 mm and 1.96%, respectively (P = 0.96). Low-cost FDM machines and Open Source Software are excellent options to manufacture RPM, with the benefit of low cost and a similar relative error than other more expensive technologies. PMID:29464171

Hydraulic head estimation at unobserved locations: Approximating the distribution of the absolute error based on geologic interpretations

NASA Astrophysics Data System (ADS)

Langousis, Andreas; Kaleris, Vassilios; Xeygeni, Vagia; Magkou, Foteini

2017-04-01

Assessing the availability of groundwater reserves at a regional level, requires accurate and robust hydraulic head estimation at multiple locations of an aquifer. To that extent, one needs groundwater observation networks that can provide sufficient information to estimate the hydraulic head at unobserved locations. The density of such networks is largely influenced by the spatial distribution of the hydraulic conductivity in the aquifer, and it is usually determined through trial-and-error, by solving the groundwater flow based on a properly selected set of alternative but physically plausible geologic structures. In this work, we use: 1) dimensional analysis, and b) a pulse-based stochastic model for simulation of synthetic aquifer structures, to calculate the distribution of the absolute error in hydraulic head estimation as a function of the standardized distance from the nearest measuring locations. The resulting distributions are proved to encompass all possible small-scale structural dependencies, exhibiting characteristics (bounds, multi-modal features etc.) that can be explained using simple geometric arguments. The obtained results are promising, pointing towards the direction of establishing design criteria based on large-scale geologic maps.

Elevation correction factor for absolute pressure measurements

NASA Technical Reports Server (NTRS)

Panek, Joseph W.; Sorrells, Mark R.

1996-01-01

With the arrival of highly accurate multi-port pressure measurement systems, conditions that previously did not affect overall system accuracy must now be scrutinized closely. Errors caused by elevation differences between pressure sensing elements and model pressure taps can be quantified and corrected. With multi-port pressure measurement systems, the sensing elements are connected to pressure taps that may be many feet away. The measurement system may be at a different elevation than the pressure taps due to laboratory space or test article constraints. This difference produces a pressure gradient that is inversely proportional to height within the interface tube. The pressure at the bottom of the tube will be higher than the pressure at the top due to the weight of the tube's column of air. Tubes with higher pressures will exhibit larger absolute errors due to the higher air density. The above effect is well documented but has generally been taken into account with large elevations only. With error analysis techniques, the loss in accuracy from elevation can be easily quantified. Correction factors can be applied to maintain the high accuracies of new pressure measurement systems.

Dimensional Error in Rapid Prototyping with Open Source Software and Low-cost 3D-printer.

PubMed

Rendón-Medina, Marco A; Andrade-Delgado, Laura; Telich-Tarriba, Jose E; Fuente-Del-Campo, Antonio; Altamirano-Arcos, Carlos A

2018-01-01

Rapid prototyping models (RPMs) had been extensively used in craniofacial and maxillofacial surgery, especially in areas such as orthognathic surgery, posttraumatic or oncological reconstructions, and implantology. Economic limitations are higher in developing countries such as Mexico, where resources dedicated to health care are limited, therefore limiting the use of RPM to few selected centers. This article aims to determine the dimensional error of a low-cost fused deposition modeling 3D printer (Tronxy P802MA, Shenzhen, Tronxy Technology Co), with Open source software. An ordinary dry human mandible was scanned with a computed tomography device. The data were processed with open software to build a rapid prototype with a fused deposition machine. Linear measurements were performed to find the mean absolute and relative difference. The mean absolute and relative difference was 0.65 mm and 1.96%, respectively ( P = 0.96). Low-cost FDM machines and Open Source Software are excellent options to manufacture RPM, with the benefit of low cost and a similar relative error than other more expensive technologies.

Building blocks for automated elucidation of metabolites: machine learning methods for NMR prediction.

PubMed

Kuhn, Stefan; Egert, Björn; Neumann, Steffen; Steinbeck, Christoph

2008-09-25

Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB. A mean absolute error of 0.18 ppm was achieved for the prediction of proton NMR shifts ranging from 0 to 11 ppm. Random forest, J48 decision tree and support vector machines achieved similar overall errors. HOSE codes being a notably simple method achieved a comparatively good result of 0.17 ppm mean absolute error. NMR prediction methods applied in the course of this work delivered precise predictions which can serve as a building block for Computer-Assisted Structure Elucidation for biological metabolites.

Consistency of gene starts among Burkholderia genomes

PubMed Central

2011-01-01

Background Evolutionary divergence in the position of the translational start site among orthologous genes can have significant functional impacts. Divergence can alter the translation rate, degradation rate, subcellular location, and function of the encoded proteins. Results Existing Genbank gene maps for Burkholderia genomes suggest that extensive divergence has occurred--53% of ortholog sets based on Genbank gene maps had inconsistent gene start sites. However, most of these inconsistencies appear to be gene-calling errors. Evolutionary divergence was the most plausible explanation for only 17% of the ortholog sets. Correcting probable errors in the Genbank gene maps decreased the percentage of ortholog sets with inconsistent starts by 68%, increased the percentage of ortholog sets with extractable upstream intergenic regions by 32%, increased the sequence similarity of intergenic regions and predicted proteins, and increased the number of proteins with identifiable signal peptides. Conclusions Our findings highlight an emerging problem in comparative genomics: single-digit percent errors in gene predictions can lead to double-digit percentages of inconsistent ortholog sets. The work demonstrates a simple approach to evaluate and improve the quality of gene maps. PMID:21342528

Which skills and factors better predict winning and losing in high-level men's volleyball?

PubMed

Peña, Javier; Rodríguez-Guerra, Jorge; Buscà, Bernat; Serra, Núria

2013-09-01

The aim of this study was to determine which skills and factors better predicted the outcomes of regular season volleyball matches in the Spanish "Superliga" and were significant for obtaining positive results in the game. The study sample consisted of 125 matches played during the 2010-11 Spanish men's first division volleyball championship. Matches were played by 12 teams composed of 148 players from 17 different nations from October 2010 to March 2011. The variables analyzed were the result of the game, team category, home/away court factors, points obtained in the break point phase, number of service errors, number of service aces, number of reception errors, percentage of positive receptions, percentage of perfect receptions, reception efficiency, number of attack errors, number of blocked attacks, attack points, percentage of attack points, attack efficiency, and number of blocks performed by both teams participating in the match. The results showed that the variables of team category, points obtained in the break point phase, number of reception errors, and number of blocked attacks by the opponent were significant predictors of winning or losing the matches. Odds ratios indicated that the odds of winning a volleyball match were 6.7 times greater for the teams belonging to higher rankings and that every additional point in Complex II increased the odds of winning a match by 1.5 times. Every reception and blocked ball error decreased the possibility of winning by 0.6 and 0.7 times, respectively.

Clinical implementation and error sensitivity of a 3D quality assurance protocol for prostate and thoracic IMRT

PubMed Central

Cotter, Christopher; Turcotte, Julie Catherine; Crawford, Bruce; Sharp, Gregory; Mah'D, Mufeed

2015-01-01

This work aims at three goals: first, to define a set of statistical parameters and plan structures for a 3D pretreatment thoracic and prostate intensity‐modulated radiation therapy (IMRT) quality assurance (QA) protocol; secondly, to test if the 3D QA protocol is able to detect certain clinical errors; and third, to compare the 3D QA method with QA performed with single ion chamber and 2D gamma test in detecting those errors. The 3D QA protocol measurements were performed on 13 prostate and 25 thoracic IMRT patients using IBA's COMPASS system. For each treatment planning structure included in the protocol, the following statistical parameters were evaluated: average absolute dose difference (AADD), percent structure volume with absolute dose difference greater than 6% (ADD6), and 3D gamma test. To test the 3D QA protocol error sensitivity, two prostate and two thoracic step‐and‐shoot IMRT patients were investigated. Errors introduced to each of the treatment plans included energy switched from 6 MV to 10 MV, multileaf collimator (MLC) leaf errors, linac jaws errors, monitor unit (MU) errors, MLC and gantry angle errors, and detector shift errors. QA was performed on each plan using a single ion chamber and 2D array of ion chambers for 2D and 3D QA. Based on the measurements performed, we established a uniform set of tolerance levels to determine if QA passes for each IMRT treatment plan structure: maximum allowed AADD is 6%; maximum 4% of any structure volume can be with ADD6 greater than 6%, and maximum 4% of any structure volume may fail 3D gamma test with test parameters 3%/3 mm DTA. Out of the three QA methods tested the single ion chamber performed the worst by detecting 4 out of 18 introduced errors, 2D QA detected 11 out of 18 errors, and 3D QA detected 14 out of 18 errors. PACS number: 87.56.Fc PMID:26699299

A novel diagnosis method for a Hall plates-based rotary encoder with a magnetic concentrator.

PubMed

Meng, Bumin; Wang, Yaonan; Sun, Wei; Yuan, Xiaofang

2014-07-31

In the last few years, rotary encoders based on two-dimensional complementary metal oxide semiconductors (CMOS) Hall plates with a magnetic concentrator have been developed to measure contactless absolute angle. There are various error factors influencing the measuring accuracy, which are difficult to locate after the assembly of encoder. In this paper, a model-based rapid diagnosis method is presented. Based on an analysis of the error mechanism, an error model is built to compare minimum residual angle error and to quantify the error factors. Additionally, a modified particle swarm optimization (PSO) algorithm is used to reduce the calculated amount. The simulation and experimental results show that this diagnosis method is feasible to quantify the causes of the error and to reduce iteration significantly.

Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

PubMed Central

Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao

2015-01-01

Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR. PMID:26305568

Time-resolved imaging refractometry of microbicidal films using quantitative phase microscopy.

PubMed

Rinehart, Matthew T; Drake, Tyler K; Robles, Francisco E; Rohan, Lisa C; Katz, David; Wax, Adam

2011-12-01

Quantitative phase microscopy is applied to image temporal changes in the refractive index (RI) distributions of solutions created by microbicidal films undergoing hydration. We present a novel method of using an engineered polydimethylsiloxane structure as a static phase reference to facilitate calibration of the absolute RI across the entire field. We present a study of dynamic structural changes in microbicidal films during hydration and subsequent dissolution. With assumptions about the smoothness of the phase changes induced by these films, we calculate absolute changes in the percentage of film in regions across the field of view.

Time-resolved imaging refractometry of microbicidal films using quantitative phase microscopy

PubMed Central

Rinehart, Matthew T.; Drake, Tyler K.; Robles, Francisco E.; Rohan, Lisa C.; Katz, David; Wax, Adam

2011-01-01

Quantitative phase microscopy is applied to image temporal changes in the refractive index (RI) distributions of solutions created by microbicidal films undergoing hydration. We present a novel method of using an engineered polydimethylsiloxane structure as a static phase reference to facilitate calibration of the absolute RI across the entire field. We present a study of dynamic structural changes in microbicidal films during hydration and subsequent dissolution. With assumptions about the smoothness of the phase changes induced by these films, we calculate absolute changes in the percentage of film in regions across the field of view. PMID:22191912

Network Adjustment of Orbit Errors in SAR Interferometry

NASA Astrophysics Data System (ADS)

Bahr, Hermann; Hanssen, Ramon

2010-03-01

Orbit errors can induce significant long wavelength error signals in synthetic aperture radar (SAR) interferograms and thus bias estimates of wide-scale deformation phenomena. The presented approach aims for correcting orbit errors in a preprocessing step to deformation analysis by modifying state vectors. Whereas absolute errors in the orbital trajectory are negligible, the influence of relative errors (baseline errors) is parametrised by their parallel and perpendicular component as a linear function of time. As the sensitivity of the interferometric phase is only significant with respect to the perpendicular base-line and the rate of change of the parallel baseline, the algorithm focuses on estimating updates to these two parameters. This is achieved by a least squares approach, where the unwrapped residual interferometric phase is observed and atmospheric contributions are considered to be stochastic with constant mean. To enhance reliability, baseline errors are adjusted in an overdetermined network of interferograms, yielding individual orbit corrections per acquisition.

Numerical model estimating the capabilities and limitations of the fast Fourier transform technique in absolute interferometry

NASA Astrophysics Data System (ADS)

Talamonti, James J.; Kay, Richard B.; Krebs, Danny J.

1996-05-01

A numerical model was developed to emulate the capabilities of systems performing noncontact absolute distance measurements. The model incorporates known methods to minimize signal processing and digital sampling errors and evaluates the accuracy limitations imposed by spectral peak isolation by using Hanning, Blackman, and Gaussian windows in the fast Fourier transform technique. We applied this model to the specific case of measuring the relative lengths of a compound Michelson interferometer. By processing computer-simulated data through our model, we project the ultimate precision for ideal data, and data containing AM-FM noise. The precision is shown to be limited by nonlinearities in the laser scan. absolute distance, interferometer.

Absolute magnitude calibration using trigonometric parallax - Incomplete, spectroscopic samples

NASA Technical Reports Server (NTRS)

Ratnatunga, Kavan U.; Casertano, Stefano

1991-01-01

A new numerical algorithm is used to calibrate the absolute magnitude of spectroscopically selected stars from their observed trigonometric parallax. This procedure, based on maximum-likelihood estimation, can retrieve unbiased estimates of the intrinsic absolute magnitude and its dispersion even from incomplete samples suffering from selection biases in apparent magnitude and color. It can also make full use of low accuracy and negative parallaxes and incorporate censorship on reported parallax values. Accurate error estimates are derived for each of the fitted parameters. The algorithm allows an a posteriori check of whether the fitted model gives a good representation of the observations. The procedure is described in general and applied to both real and simulated data.

Communicating data about the benefits and harms of treatment: a randomized trial.

PubMed

Woloshin, Steven; Schwartz, Lisa M

2011-07-19

Despite limited evidence, it is often asserted that natural frequencies (for example, 2 in 1000) are the best way to communicate absolute risks. To compare comprehension of treatment benefit and harm when absolute risks are presented as natural frequencies, percents, or both. Parallel-group randomized trial with central allocation and masking of investigators to group assignment, conducted through an Internet survey in September 2009. (ClinicalTrials.gov registration number: NCT00950014) National sample of U.S. adults randomly selected from a professional survey firm's research panel of about 30,000 households. 2944 adults aged 18 years or older (all with complete follow-up). Tables presenting absolute risks in 1 of 5 numeric formats: natural frequency (x in 1000), variable frequency (x in 100, x in 1000, or x in 10,000, as needed to keep the numerator >1), percent, percent plus natural frequency, or percent plus variable frequency. Comprehension as assessed by 18 questions (primary outcome) and judgment of treatment benefit and harm. The average number of comprehension questions answered correctly was lowest in the variable frequency group and highest in the percent group (13.1 vs. 13.8; difference, 0.7 [95% CI, 0.3 to 1.1]). The proportion of participants who "passed" the comprehension test (≥13 correct answers) was lowest in the natural and variable frequency groups and highest in the percent group (68% vs. 73%; difference, 5 percentage points [CI, 0 to 10 percentage points]). The largest format effect was seen for the 2 questions about absolute differences: the proportion correct in the natural frequency versus percent groups was 43% versus 72% (P < 0.001) and 73% versus 87% (P < 0.001). Even when data were presented in the percent format, one third of participants failed the comprehension test. Natural frequencies are not the best format for communicating the absolute benefits and harms of treatment. The more succinct percent format resulted in better comprehension: Comprehension was slightly better overall and notably better for absolute differences. Attorney General Consumer and Prescriber Education grant program, the Robert Wood Johnson Pioneer Program, and the National Cancer Institute.

Changing ethnic inequalities in mortality in New Zealand over 30 years: linked cohort studies with 68.9 million person-years of follow-up.

PubMed

Disney, George; Teng, Andrea; Atkinson, June; Wilson, Nick; Blakely, Tony

2017-01-01

Internationally, ethnic inequalities in mortality within countries are increasingly recognized as a public health concern. But few countries have data to monitor such inequalities. We aimed to provide a detailed description of ethnic inequalities (Māori [indigenous], Pacific, and European/Other) in mortality for a country with high quality ethnicity data, using both standard and novel visualization methods. Cohort studies of the entire New Zealand population were conducted, using probabilistically-linked Census and mortality data from 1981 to 2011 (68.9 million person years). Absolute (standardized rate difference) and relative (standardized rate ratio) inequalities were calculated, in 1-74-year-olds, for Māori and Pacific peoples in comparison to European/Other. All-cause mortality rates were highest for Māori, followed by Pacific peoples then European/Other, and declined in all three ethnic groups over time. Pacific peoples experienced the slowest annual percentage fall in mortality rates, then Māori, with European/Other having the highest percentage falls - resulting in widening relative inequalities. Absolute inequalities, however, for both Māori and Pacific males compared to European/Other have been falling since 1996. But for females, only Māori absolute inequalities (compared with European/Other) have been falling. Regarding cause of death, cancer is becoming a more important contributor than cardiovascular disease (CVD) to absolute inequalities, especially for Māori females. We found declines in all-cause mortality rates, over time, for each ethnic group of interest. Ethnic mortality inequalities are generally stable or even falling in absolute terms, but have increased on a relative scale. The drivers of these inequalities in mortality are transitioning over time, away from CVD to cancer and diabetes; such transitions are likely in other countries, and warrant further research. To address these inequalities, policymakers need to enhance prevention activities and health care delivery, but also support wider improvements in educational achievement and socioeconomic position for highest need populations.

Changing ethnic inequalities in mortality in New Zealand over 30 years: linked cohort studies with 68.9 million person-years of follow-up.

PubMed

Disney, George; Teng, Andrea; Atkinson, June; Wilson, Nick; Blakely, Tony

2017-04-26

Internationally, ethnic inequalities in mortality within countries are increasingly recognized as a public health concern. But few countries have data to monitor such inequalities. We aimed to provide a detailed description of ethnic inequalities (Māori [indigenous], Pacific, and European/Other) in mortality for a country with high quality ethnicity data, using both standard and novel visualization methods. Cohort studies of the entire New Zealand population were conducted, using probabilistically-linked Census and mortality data from 1981 to 2011 (68.9 million person years). Absolute (standardized rate difference) and relative (standardized rate ratio) inequalities were calculated, in 1-74-year-olds, for Māori and Pacific peoples in comparison to European/Other. All-cause mortality rates were highest for Māori, followed by Pacific peoples then European/Other, and declined in all three ethnic groups over time. Pacific peoples experienced the slowest annual percentage fall in mortality rates, then Māori, with European/Other having the highest percentage falls - resulting in widening relative inequalities. Absolute inequalities, however, for both Māori and Pacific males compared to European/Other have been falling since 1996. But for females, only Māori absolute inequalities (compared with European/Other) have been falling. Regarding cause of death, cancer is becoming a more important contributor than cardiovascular disease (CVD) to absolute inequalities, especially for Māori females. We found declines in all-cause mortality rates, over time, for each ethnic group of interest. Ethnic mortality inequalities are generally stable or even falling in absolute terms, but have increased on a relative scale. The drivers of these inequalities in mortality are transitioning over time, away from CVD to cancer and diabetes; such transitions are likely in other countries, and warrant further research. To address these inequalities, policymakers need to enhance prevention activities and health care delivery, but also support wider improvements in educational achievement and socioeconomic position for highest need populations.

Scandinavian research in anaesthesiology 1981-2000: visibility and impact in EU and world context.

PubMed

Skram, U; Larsen, B; Ingwersen, P; Viby-Mogensen, J

2004-09-01

We wished to assess the development in number and impact of publications in anaesthesiology and intensive care medicine from 1981 to 2000 in the four Scandinavian countries: Sweden, Norway, Finland, and Denmark. For comparison, we also analyzed data from the UK and the Netherlands. Publication and citation data from 1981 to 2000 were gathered from National Science Indicators (2001), covering 33 journals indexed in Current Contents. Data were analyzed in running 5-year periods. The following informetric indicators were used: absolute number of publications; absolute number of citations; absolute citation impact (average number of citations per publication per 5-year period); citation impact relative to the European Union and the world; and the percentage of cited papers from each country. The annual number of publications from Denmark was stable over the 20-year period. Sweden increased its production by 35%, while the remaining four countries showed increases from 100% to 146%. Thus, Sweden and Denmark lost visibility within the European Union (EU) and in world context. The EU and world citation shares of Finland and Norway increased slightly, whereas those of Sweden, Denmark, the UK, and the Netherlands all declined significantly. The absolute citation impact (ACI) increased for all the four Scandinavian countries. The ACI of the Netherlands did not change and was surpassed by all the Scandinavian countries by 1994-98, while the UK finished below the other five countries. (1) The annual number of publications from Sweden, Norway, Finland, the UK, and the Netherlands increased after the late eighties, whereas the net publication output from Denmark was stagnant over the 20-year period investigated; (2) the international publication and citation visibility of Finland and Norway increased slightly, as opposed to the significant decrease seen by the other four countries; (3) judging from the increase in absolute and relative citation impact and in the percentage of cited papers, the recognition of publications from the four Scandinavian countries increased over the past 20 years.

A comparative effectiveness analysis of three continuous glucose monitors.

PubMed

Damiano, Edward R; El-Khatib, Firas H; Zheng, Hui; Nathan, David M; Russell, Steven J

2013-02-01

To compare three continuous glucose monitoring (CGM) devices in subjects with type 1 diabetes under closed-loop blood glucose (BG) control. Six subjects with type 1 diabetes (age 52 ± 14 years, diabetes duration 32 ± 14 years) each participated in two 51-h closed-loop BG control experiments in the hospital. Venous plasma glucose (PG) measurements (GlucoScout, International Biomedical) obtained every 15 min (2,360 values) were paired in time with corresponding CGM glucose (CGMG) measurements obtained from three CGM devices, the Navigator (Abbott Diabetes Care), the Seven Plus (DexCom), and the Guardian (Medtronic), worn simultaneously by each subject. Errors in paired PG-CGMG measurements and data reporting percentages were obtained for each CGM device. The Navigator had the best overall accuracy, with an aggregate mean absolute relative difference (MARD) of all paired points of 11.8 ± 11.1% and an average MARD across all 12 experiments of 11.8 ± 3.8%. The Seven Plus and Guardian produced aggregate MARDs of all paired points of 16.5 ± 17.8% and 20.3 ± 18.0%, respectively, and average MARDs across all 12 experiments of 16.5 ± 6.7% and 20.2 ± 6.8%, respectively. Data reporting percentages, a measure of reliability, were 76% for the Seven Plus and nearly 100% for the Navigator and Guardian. A comprehensive head-to-head-to-head comparison of three CGM devices for BG values from 36 to 563 mg/dL revealed marked differences in performance characteristics that include accuracy, precision, and reliability. The Navigator outperformed the other two in these areas.

Chemodynamics of heavy metals in long-term contaminated soils: metal speciation in soil solution.

PubMed

Kim, Kwon-Rae; Owens, Gary

2009-01-01

The concentration and speciation of heavy metals in soil solution isolated from long-term contaminated soils were investigated. The soil solution was extracted at 70% maximum water holding capacity (MWHC) after equilibration for 24 h. The free metal concentrations (Cd2+, CU2+, Pb2+, and Zn2+) in soil solution were determined using the Donnan membrane technique (DMT). Initially the DMT was validated using artificial solutions where the percentage of free metal ions were significantly correlated with the percentages predicted using MINTEQA2. However, there was a significant difference between the absolute free ion concentrations predicted by MINTEQA2 and the values determined by the DMT. This was due to the significant metal adsorption onto the cation exchange membrane used in the DMT with 20%, 28%, 44%, and 8% mass loss of the initial total concentration of Cd, Cu, Pb, and Zn in solution, respectively. This could result in a significant error in the determination of free metal ions when using DMT if no allowance for membrane cation adsorption was made. Relative to the total soluble metal concentrations the amounts of free Cd2+ (3%-52%) and Zn2+ (11%-72%) in soil solutions were generally higher than those of Cu2+ (0.2%-30%) and Pb2+ (0.6%-10%). Among the key soil solution properties, dissolved heavy metal concentrations were the most significant factor governing free metal ion concentrations. Soil solution pH showed only a weak relationship with free metal ion partitioning coefficients (K(p)) and dissolved organic carbon did not show any significant influence on K(p).

An error criterion for determining sampling rates in closed-loop control systems

NASA Technical Reports Server (NTRS)

Brecher, S. M.

1972-01-01

The determination of an error criterion which will give a sampling rate for adequate performance of linear, time-invariant closed-loop, discrete-data control systems was studied. The proper modelling of the closed-loop control system for characterization of the error behavior, and the determination of an absolute error definition for performance of the two commonly used holding devices are discussed. The definition of an adequate relative error criterion as a function of the sampling rate and the parameters characterizing the system is established along with the determination of sampling rates. The validity of the expressions for the sampling interval was confirmed by computer simulations. Their application solves the problem of making a first choice in the selection of sampling rates.

Absolute emission cross sections for electron capture reactions of C2+, N3+, N4+ and O3+ ions in collisions with Li(2s) atoms

NASA Astrophysics Data System (ADS)

Rieger, G.; Pinnington, E. H.; Ciubotariu, C.

2000-12-01

Absolute photon emission cross sections following electron capture reactions have been measured for C2+, N3+, N4+ and O3+ ions colliding with Li(2s) atoms at keV energies. The results are compared with calculations using the extended classical over-the-barrier model by Niehaus. We explore the limits of our experimental method and present a detailed discussion of experimental errors.

CYP2C19*17 increases clopidogrel-mediated platelet inhibition but does not alter the pharmacokinetics of the active metabolite of clopidogrel.

PubMed

Pedersen, Rasmus Steen; Nielsen, Flemming; Stage, Tore Bjerregaard; Vinholt, Pernille Just; el Achwah, Alaa Bilal; Damkier, Per; Brosen, Kim

2014-11-01

The aim of the present study was to determine the impact of CYP2C19*17 on the pharmacokinetics and pharmacodynamics of the active metabolite of clopidogrel and the pharmacokinetics of proguanil. Thus, we conducted an open-label two-phase cross-over study in 31 healthy male volunteers (11 CYP2C19*1/*1, 11 CYP2C19*1/*17 and nine CYP2C19*17/*17). In Phase A, the pharmacokinetics of the derivatized active metabolite of clopidogrel (CAMD) and platelet function were determined after administration of a single oral dose of 600 mg clopidogrel (Plavix; Sanofi-Avensis, Horsholm, Denmark). In Phase B, the pharmacokinetics of proguanil and its metabolites cycloguanil and 4-chlorphenylbiguanide (4-CPB) were determined in 29 of 31 subjects after a single oral dose of 200 mg proguanil given as the combination drug Malarone (GlaxoSmithKline Pharma, Brondby, Denmark). Significant correlations were found between the area under the time-concentration curve (AUC0-∞ ) of CAMD and both the absolute ADP-induced P2Y12 receptor-activated platelet aggregation (r = -0.60, P = 0.0007) and the percentage inhibition of aggregation (r = 0.59, P = 0.0009). In addition, the CYP2C19*17/*17 and CYP2C19*1/*17 genotype groups had significantly higher percentage inhibition of platelet aggregation compared with the CYP2C19*1/*1 subjects (geometric mean percentage inhibition of 84%, 73% and 63%, respectively; P = 0.014). Neither the absolute ADP-induced P2Y12 receptor-activated platelet aggregation, exposure to CAMD nor the pharmacokinetic parameters of proguanil, cycloguanil and 4-CPB exhibited any significant differences among the genotype groups. In conclusion, carriers of CYP2C19*17 exhibit higher percentage inhibition of platelet aggregation, but do not have significantly lower absolute P2Y12 receptor-activated platelet aggregation or higher exposure to the active metabolite after a single oral administration of 600 mg clopidogrel. © 2014 Wiley Publishing Asia Pty Ltd.

Using lean to improve medication administration safety: in search of the "perfect dose".

PubMed

Ching, Joan M; Long, Christina; Williams, Barbara L; Blackmore, C Craig

2013-05-01

At Virginia Mason Medical Center (Seattle), the Collaborative Alliance for Nursing Outcomes (CALNOC) Medication Administration Accuracy Quality Study was used in combination with Lean quality improvement efforts to address medication administration safety. Lean interventions were targeted at improving the medication room layout, applying visual controls, and implementing nursing standard work. The interventions were designed to prevent medication administration errors through improving six safe practices: (1) comparing medication with medication administration record, (2) labeling medication, (3) checking two forms of patient identification, (4) explaining medication to patient, (5) charting medication immediately, and (6) protecting the process from distractions/interruptions. Trained nurse auditors observed 9,244 doses for 2,139 patients. Following the intervention, the number of safe-practice violations decreased from 83 violations/100 doses at baseline (January 2010-March 2010) to 42 violations/100 doses at final follow-up (July 2011-September 2011), resulting in an absolute risk reduction of 42 violations/100 doses (95% confidence interval [CI]: 35-48), p < .001). The number of medication administration errors decreased from 10.3 errors/100 doses at baseline to 2.8 errors/100 doses at final follow-up (absolute risk reduction: 7 violations/100 doses [95% CI: 5-10, p < .001]). The "perfect dose" score, reflecting compliance with all six safe practices and absence of any of the eight medication administration errors, improved from 37 in compliance/100 doses at baseline to 68 in compliance/100 doses at the final follow-up. Lean process improvements coupled with direct observation can contribute to substantial decreases in errors in nursing medication administration.

The Effects of Cryotherapy on Knee Joint Position Sense and Force Production Sense in Healthy Individuals

PubMed Central

Furmanek, Mariusz P.; Słomka, Kajetan J.; Sobiesiak, Andrzej; Rzepko, Marian; Juras, Grzegorz

2018-01-01

Abstract The proprioceptive information received from mechanoreceptors is potentially responsible for controlling the joint position and force differentiation. However, it is unknown whether cryotherapy influences this complex mechanism. Previously reported results are not universally conclusive and sometimes even contradictory. The main objective of this study was to investigate the impact of local cryotherapy on knee joint position sense (JPS) and force production sense (FPS). The study group consisted of 55 healthy participants (age: 21 ± 2 years, body height: 171.2 ± 9 cm, body mass: 63.3 ± 12 kg, BMI: 21.5 ± 2.6). Local cooling was achieved with the use of gel-packs cooled to -2 ± 2.5°C and applied simultaneously over the knee joint and the quadriceps femoris muscle for 20 minutes. JPS and FPS were evaluated using the Biodex System 4 Pro apparatus. Repeated measures analysis of variance (ANOVA) did not show any statistically significant changes of the JPS and FPS under application of cryotherapy for all analyzed variables: the JPS’s absolute error (p = 0.976), its relative error (p = 0.295), and its variable error (p = 0.489); the FPS’s absolute error (p = 0.688), its relative error (p = 0.193), and its variable error (p = 0.123). The results indicate that local cooling does not affect proprioceptive acuity of the healthy knee joint. They also suggest that local limited cooling before physical activity at low velocity did not present health or injury risk in this particular study group. PMID:29599858

46 CFR 67.31 - Stock or equity interest requirements.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., directly or indirectly, voting power; or non-citizens, by any means, exercise control over the entity. The... favor of non-citizens, non-citizen voting power, or non-citizen control exceeds the percentage of the... citizenship under this subpart, control of non-fishing industry vessels includes an absolute right to: Direct...

46 CFR 67.31 - Stock or equity interest requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., directly or indirectly, voting power; or non-citizens, by any means, exercise control over the entity. The... favor of non-citizens, non-citizen voting power, or non-citizen control exceeds the percentage of the... citizenship under this subpart, control of non-fishing industry vessels includes an absolute right to: Direct...

46 CFR 67.31 - Stock or equity interest requirements.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., directly or indirectly, voting power; or non-citizens, by any means, exercise control over the entity. The... favor of non-citizens, non-citizen voting power, or non-citizen control exceeds the percentage of the... citizenship under this subpart, control of non-fishing industry vessels includes an absolute right to: Direct...

46 CFR 67.31 - Stock or equity interest requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., directly or indirectly, voting power; or non-citizens, by any means, exercise control over the entity. The... favor of non-citizens, non-citizen voting power, or non-citizen control exceeds the percentage of the... citizenship under this subpart, control of non-fishing industry vessels includes an absolute right to: Direct...

46 CFR 67.31 - Stock or equity interest requirements.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., directly or indirectly, voting power; or non-citizens, by any means, exercise control over the entity. The... favor of non-citizens, non-citizen voting power, or non-citizen control exceeds the percentage of the... citizenship under this subpart, control of non-fishing industry vessels includes an absolute right to: Direct...

A feasibility study in adapting Shamos Bickel and Hodges Lehman estimator into T-Method for normalization

NASA Astrophysics Data System (ADS)

Harudin, N.; Jamaludin, K. R.; Muhtazaruddin, M. Nabil; Ramlie, F.; Muhamad, Wan Zuki Azman Wan

2018-03-01

T-Method is one of the techniques governed under Mahalanobis Taguchi System that developed specifically for multivariate data predictions. Prediction using T-Method is always possible even with very limited sample size. The user of T-Method required to clearly understanding the population data trend since this method is not considering the effect of outliers within it. Outliers may cause apparent non-normality and the entire classical methods breakdown. There exist robust parameter estimate that provide satisfactory results when the data contain outliers, as well as when the data are free of them. The robust parameter estimates of location and scale measure called Shamos Bickel (SB) and Hodges Lehman (HL) which are used as a comparable method to calculate the mean and standard deviation of classical statistic is part of it. Embedding these into T-Method normalize stage feasibly help in enhancing the accuracy of the T-Method as well as analysing the robustness of T-method itself. However, the result of higher sample size case study shows that T-method is having lowest average error percentages (3.09%) on data with extreme outliers. HL and SB is having lowest error percentages (4.67%) for data without extreme outliers with minimum error differences compared to T-Method. The error percentages prediction trend is vice versa for lower sample size case study. The result shows that with minimum sample size, which outliers always be at low risk, T-Method is much better on that, while higher sample size with extreme outliers, T-Method as well show better prediction compared to others. For the case studies conducted in this research, it shows that normalization of T-Method is showing satisfactory results and it is not feasible to adapt HL and SB or normal mean and standard deviation into it since it’s only provide minimum effect of percentages errors. Normalization using T-method is still considered having lower risk towards outlier’s effect.

Dynamic phenotypic restructuring of the CD4 and CD8 T-cell subsets with age in healthy humans: a compartmental model analysis.

PubMed

Jackola, D R; Hallgren, H M

1998-11-16

In healthy humans, phenotypic restructuring occurs with age within the CD3+ T-lymphocyte complement. This is characterized by a non-linear decrease of the percentage of 'naive' (CD45RA+) cells and a corresponding non-linear increase of the percentage of 'memory' (CD45R0+) cells among both the CD4+ and CD8+ T-cell subsets. We devised a simple compartmental model to study the age-dependent kinetics of phenotypic restructuring. We also derived differential equations whose parameters determined yearly gains minus losses of the percentage and absolute numbers of circulating naive cells, yearly gains minus losses of the percentage and absolute numbers of circulating memory cells, and the yearly rate of conversion of naive to memory cells. Solutions of these evaluative differential equations demonstrate the following: (1) the memory cell complement 'resides' within its compartment for a longer time than the naive cell complement within its compartment for both CD4 and CD8 cells; (2) the average, annual 'turnover rate' is the same for CD4 and CD8 naive cells. In contrast, the average, annual 'turnover rate' for memory CD8 cells is 1.5 times that of memory CD4 cells; (3) the average, annual conversion rate of CD4 naive cells to memory cells is twice that of the CD8 conversion rate; (4) a transition in dynamic restructuring occurs during the third decade of life that is due to these differences in turnover and conversion rates, between and from naive to memory cells.

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.

PubMed

Guelpa, Anina; Bevilacqua, Marta; Marini, Federico; O'Kennedy, Kim; Geladi, Paul; Manley, Marena

2015-04-15

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method. Copyright © 2014 Elsevier Ltd. All rights reserved.

Task-shifting of CD4 T cell count monitoring by the touchscreen-based Muse™ Auto CD4/CD4% single-platform system for CD4 T cell numeration: Implication for decentralization in resource-constrained settings.

PubMed

Kouabosso, André; Mossoro-Kpinde, Christian Diamant; Bouassa, Ralph-Sydney Mboumba; Longo, Jean De Dieu; Mbeko Simaleko, Marcel; Grésenguet, Gérard; Bélec, Laurent

2018-04-01

The accuracy of CD4 T cell monitoring by the recently developed flow cytometry-based CD4 T cell counting Muse™ Auto CD4/CD4% Assay analyzer (EMD Millipore Corporation, Merck Life Sciences, KGaA, Darmstadt, Germany) was evaluated in trained lay providers against laboratory technicians. After 2 days of training on the Muse™ Auto CD4/CD4% analyzer, EDTA-blood samples from 6 HIV-positive and 4 HIV-negative individuals were used for CD4 T cell counting in triplicate in parallel by 12 trained lay providers as compared to 10 lab technicians. Mean number of CD4 T cells in absolute number was 829 ± 380 cells/μl by lay providers and 794 ± 409 cells/μl by technicians (P > 0.05); and in percentage 36.2 ± 14.8%CD4 by lay providers and 36.1 ± 15.0%CD4 by laboratory technician (P > 0.05). The unweighted linear regression and Passing-Bablok regression analyses on CD4 T cell results expressed in absolute count revealed moderate correlation between CD4 T cell counts obtained by lay providers and lab technicians. The mean absolute bias measured by Bland-Altman analysis between CD4 T cell/μl obtained by lay providers and lab technicians was -3.41 cells/μl. Intra-assay coefficient of variance (CV) of Muse™ Auto CD4/CD4% in absolute number was 10.1% by lay providers and 8.5% by lab technicians (P > 0.05), and in percentage 5.5% by lay providers and 4.4% by lab technicians (P > 0.05). The inter-assay CV of Muse™ Auto CD4/CD4% in absolute number was 13.4% by lay providers and 10.3% by lab technicians (P > 0.05), and in percentage 7.8% by lay providers and 6.9% by lab technicians (P > 0.05). The study demonstrates the feasibility of CD4 T cell counting using the alternative flow cytometer Muse™ Auto CD4/CD4% analyzer by trained lay providers and therefore the practical possibility of decentralization CD4 T cell counting to health community centers. Copyright © 2018. Published by Elsevier B.V.

Continuous glucose monitoring: quality of hypoglycaemia detection.

PubMed

Zijlstra, E; Heise, T; Nosek, L; Heinemann, L; Heckermann, S

2013-02-01

To evaluate the accuracy of a (widely used) continuous glucose monitoring (CGM)-system and its ability to detect hypoglycaemic events. A total of 18 patients with type 1 diabetes mellitus used continuous glucose monitoring (Guardian REAL-Time CGMS) during two 9-day in-house periods. A hypoglycaemic threshold alarm alerted patients to sensor readings <70 mg/dl. Continuous glucose monitoring sensor readings were compared to laboratory reference measurements taken every 4 h and in case of a hypoglycaemic alarm. A total of 2317 paired data points were evaluated. Overall, the mean absolute relative difference (MARD) was 16.7%. The percentage of data points in the clinically accurate or acceptable Clarke Error Grid zones A + B was 94.6%. In the hypoglycaemic range, accuracy worsened (MARD 38.8%) leading to a failure to detect more than half of the true hypoglycaemic events (sensitivity 37.5%). Furthermore, more than half of the alarms that warn patients for hypoglycaemia were false (false alert rate 53.3%). Above the low alert threshold, the sensor confirmed 2077 of 2182 reference values (specificity 95.2%). Patients using continuous glucose monitoring should be aware of its limitation to accurately detect hypoglycaemia. © 2012 Blackwell Publishing Ltd.

The Mental Number Line in Dyscalculia: Impaired Number Sense or Access From Symbolic Numbers?

PubMed

Lafay, Anne; St-Pierre, Marie-Catherine; Macoir, Joël

Numbers may be manipulated and represented mentally over a compressible number line oriented from left to right. According to numerous studies, one of the primary reasons for dyscalculia is related to improper understanding of the mental number line. Children with dyscalculia usually show difficulty when they have to place Arabic numbers on a physical number line. However, it remains unclear whether they have a deficit with the mental number line per se or a deficit with accessing it from nonsymbolic and/or symbolic numbers. Quebec French-speaking 8- to 9-year-old children with (24) and without (37) dyscalculia were assessed with transcoding tasks ( number-to-position and position-to-number) designed to assess the acuity of the mental number line with Arabic and spoken numbers as well as with analogic numerosities. Results showed that children with dyscalculia produced a larger percentage absolute error than children without mathematics difficulties in every task except the number-to-position transcoding task with analogic numerosities. Hence, these results suggested that children with dyscalculia do not have a general deficit of the mental number line but rather a deficit with accessing it from symbolic numbers.

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data.

PubMed

Liu, Kai; Cui, Meng-Ying; Cao, Peng; Wang, Jiang-Bo

2016-01-01

On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods.

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data

PubMed Central

Cui, Meng-Ying; Cao, Peng; Wang, Jiang-Bo

2016-01-01

On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods. PMID:27362654

Hierarchical time series bottom-up approach for forecast the export value in Central Java

NASA Astrophysics Data System (ADS)

Mahkya, D. A.; Ulama, B. S.; Suhartono

2017-10-01

The purpose of this study is Getting the best modeling and predicting the export value of Central Java using a Hierarchical Time Series. The export value is one variable injection in the economy of a country, meaning that if the export value of the country increases, the country’s economy will increase even more. Therefore, it is necessary appropriate modeling to predict the export value especially in Central Java. Export Value in Central Java are grouped into 21 commodities with each commodity has a different pattern. One approach that can be used time series is a hierarchical approach. Hierarchical Time Series is used Buttom-up. To Forecast the individual series at all levels using Autoregressive Integrated Moving Average (ARIMA), Radial Basis Function Neural Network (RBFNN), and Hybrid ARIMA-RBFNN. For the selection of the best models used Symmetric Mean Absolute Percentage Error (sMAPE). Results of the analysis showed that for the Export Value of Central Java, Bottom-up approach with Hybrid ARIMA-RBFNN modeling can be used for long-term predictions. As for the short and medium-term predictions, it can be used a bottom-up approach RBFNN modeling. Overall bottom-up approach with RBFNN modeling give the best result.

A fuzzy mathematical model of West Java population with logistic growth model

NASA Astrophysics Data System (ADS)

Nurkholipah, N. S.; Amarti, Z.; Anggriani, N.; Supriatna, A. K.

2018-03-01

In this paper we develop a mathematics model of population growth in the West Java Province Indonesia. The model takes the form as a logistic differential equation. We parameterize the model using several triples of data, and choose the best triple which has the smallest Mean Absolute Percentage Error (MAPE). The resulting model is able to predict the historical data with a high accuracy and it also able to predict the future of population number. Predicting the future population is among the important factors that affect the consideration is preparing a good management for the population. Several experiment are done to look at the effect of impreciseness in the data. This is done by considering a fuzzy initial value to the crisp model assuming that the model propagates the fuzziness of the independent variable to the dependent variable. We assume here a triangle fuzzy number representing the impreciseness in the data. We found that the fuzziness may disappear in the long-term. Other scenarios also investigated, such as the effect of fuzzy parameters to the crisp initial value of the population. The solution of the model is obtained numerically using the fourth-order Runge-Kutta scheme.

Accessing and constructing driving data to develop fuel consumption forecast model

NASA Astrophysics Data System (ADS)

Yamashita, Rei-Jo; Yao, Hsiu-Hsen; Hung, Shih-Wei; Hackman, Acquah

2018-02-01

In this study, we develop a forecasting models, to estimate fuel consumption based on the driving behavior, in which vehicles and routes are known. First, the driving data are collected via telematics and OBDII. Then, the driving fuel consumption formula is used to calculate the estimate fuel consumption, and driving behavior indicators are generated for analysis. Based on statistical analysis method, the driving fuel consumption forecasting model is constructed. Some field experiment results were done in this study to generate hundreds of driving behavior indicators. Based on data mining approach, the Pearson coefficient correlation analysis is used to filter highly fuel consumption related DBIs. Only highly correlated DBI will be used in the model. These DBIs are divided into four classes: speed class, acceleration class, Left/Right/U-turn class and the other category. We then use K-means cluster analysis to group to the driver class and the route class. Finally, more than 12 aggregate models are generated by those highly correlated DBIs, using the neural network model and regression analysis. Based on Mean Absolute Percentage Error (MAPE) to evaluate from the developed AMs. The best MAPE values among these AM is below 5%.

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

Prediction of Malaysian monthly GDP

NASA Astrophysics Data System (ADS)

Hin, Pooi Ah; Ching, Soo Huei; Yeing, Pan Wei

2015-12-01

The paper attempts to use a method based on multivariate power-normal distribution to predict the Malaysian Gross Domestic Product next month. Letting r(t) be the vector consisting of the month-t values on m selected macroeconomic variables, and GDP, we model the month-(t+1) GDP to be dependent on the present and l-1 past values r(t), r(t-1),…,r(t-l+1) via a conditional distribution which is derived from a [(m+1)l+1]-dimensional power-normal distribution. The 100(α/2)% and 100(1-α/2)% points of the conditional distribution may be used to form an out-of sample prediction interval. This interval together with the mean of the conditional distribution may be used to predict the month-(t+1) GDP. The mean absolute percentage error (MAPE), estimated coverage probability and average length of the prediction interval are used as the criterions for selecting the suitable lag value l-1 and the subset from a pool of 17 macroeconomic variables. It is found that the relatively better models would be those of which 2 ≤ l ≤ 3, and involving one or two of the macroeconomic variables given by Market Indicative Yield, Oil Prices, Exchange Rate and Import Trade.

A comparison of volume clamp method-based continuous noninvasive cardiac output (CNCO) measurement versus intermittent pulmonary artery thermodilution in postoperative cardiothoracic surgery patients.

PubMed

Wagner, Julia Y; Körner, Annmarie; Schulte-Uentrop, Leonie; Kubik, Mathias; Reichenspurner, Hermann; Kluge, Stefan; Reuter, Daniel A; Saugel, Bernd

2018-04-01

The CNAP technology (CNSystems Medizintechnik AG, Graz, Austria) allows continuous noninvasive arterial pressure waveform recording based on the volume clamp method and estimation of cardiac output (CO) by pulse contour analysis. We compared CNAP-derived CO measurements (CNCO) with intermittent invasive CO measurements (pulmonary artery catheter; PAC-CO) in postoperative cardiothoracic surgery patients. In 51 intensive care unit patients after cardiothoracic surgery, we measured PAC-CO (criterion standard) and CNCO at three different time points. We conducted two separate comparative analyses: (1) CNCO auto-calibrated to biometric patient data (CNCO bio ) versus PAC-CO and (2) CNCO calibrated to the first simultaneously measured PAC-CO value (CNCO cal ) versus PAC-CO. The agreement between the two methods was statistically assessed by Bland-Altman analysis and the percentage error. In a subgroup of patients, a passive leg raising maneuver was performed for clinical indications and we present the changes in PAC-CO and CNCO in four-quadrant plots (exclusion zone 0.5 L/min) in order to evaluate the trending ability of CNCO. The mean difference between CNCO bio and PAC-CO was +0.5 L/min (standard deviation ± 1.3 L/min; 95% limits of agreement -1.9 to +3.0 L/min). The percentage error was 49%. The concordance rate was 100%. For CNCOcal, the mean difference was -0.3 L/min (±0.5 L/min; -1.2 to +0.7 L/min) with a percentage error of 19%. In this clinical study in cardiothoracic surgery patients, CNCO cal showed good agreement when compared with PAC-CO. For CNCO bio , we observed a higher percentage error and good trending ability (concordance rate 100%).

Implementing parallel spreadsheet models for health policy decisions: The impact of unintentional errors on model projections

PubMed Central

Bailey, Stephanie L.; Bono, Rose S.; Nash, Denis; Kimmel, April D.

2018-01-01

Background Spreadsheet software is increasingly used to implement systems science models informing health policy decisions, both in academia and in practice where technical capacity may be limited. However, spreadsheet models are prone to unintentional errors that may not always be identified using standard error-checking techniques. Our objective was to illustrate, through a methodologic case study analysis, the impact of unintentional errors on model projections by implementing parallel model versions. Methods We leveraged a real-world need to revise an existing spreadsheet model designed to inform HIV policy. We developed three parallel versions of a previously validated spreadsheet-based model; versions differed by the spreadsheet cell-referencing approach (named single cells; column/row references; named matrices). For each version, we implemented three model revisions (re-entry into care; guideline-concordant treatment initiation; immediate treatment initiation). After standard error-checking, we identified unintentional errors by comparing model output across the three versions. Concordant model output across all versions was considered error-free. We calculated the impact of unintentional errors as the percentage difference in model projections between model versions with and without unintentional errors, using +/-5% difference to define a material error. Results We identified 58 original and 4,331 propagated unintentional errors across all model versions and revisions. Over 40% (24/58) of original unintentional errors occurred in the column/row reference model version; most (23/24) were due to incorrect cell references. Overall, >20% of model spreadsheet cells had material unintentional errors. When examining error impact along the HIV care continuum, the percentage difference between versions with and without unintentional errors ranged from +3% to +16% (named single cells), +26% to +76% (column/row reference), and 0% (named matrices). Conclusions Standard error-checking techniques may not identify all errors in spreadsheet-based models. Comparing parallel model versions can aid in identifying unintentional errors and promoting reliable model projections, particularly when resources are limited. PMID:29570737

Implementing parallel spreadsheet models for health policy decisions: The impact of unintentional errors on model projections.

PubMed

Bailey, Stephanie L; Bono, Rose S; Nash, Denis; Kimmel, April D

2018-01-01

Spreadsheet software is increasingly used to implement systems science models informing health policy decisions, both in academia and in practice where technical capacity may be limited. However, spreadsheet models are prone to unintentional errors that may not always be identified using standard error-checking techniques. Our objective was to illustrate, through a methodologic case study analysis, the impact of unintentional errors on model projections by implementing parallel model versions. We leveraged a real-world need to revise an existing spreadsheet model designed to inform HIV policy. We developed three parallel versions of a previously validated spreadsheet-based model; versions differed by the spreadsheet cell-referencing approach (named single cells; column/row references; named matrices). For each version, we implemented three model revisions (re-entry into care; guideline-concordant treatment initiation; immediate treatment initiation). After standard error-checking, we identified unintentional errors by comparing model output across the three versions. Concordant model output across all versions was considered error-free. We calculated the impact of unintentional errors as the percentage difference in model projections between model versions with and without unintentional errors, using +/-5% difference to define a material error. We identified 58 original and 4,331 propagated unintentional errors across all model versions and revisions. Over 40% (24/58) of original unintentional errors occurred in the column/row reference model version; most (23/24) were due to incorrect cell references. Overall, >20% of model spreadsheet cells had material unintentional errors. When examining error impact along the HIV care continuum, the percentage difference between versions with and without unintentional errors ranged from +3% to +16% (named single cells), +26% to +76% (column/row reference), and 0% (named matrices). Standard error-checking techniques may not identify all errors in spreadsheet-based models. Comparing parallel model versions can aid in identifying unintentional errors and promoting reliable model projections, particularly when resources are limited.

Real-Time and Meter-Scale Absolute Distance Measurement by Frequency-Comb-Referenced Multi-Wavelength Interferometry.

PubMed

Wang, Guochao; Tan, Lilong; Yan, Shuhua

2018-02-07

We report on a frequency-comb-referenced absolute interferometer which instantly measures long distance by integrating multi-wavelength interferometry with direct synthetic wavelength interferometry. The reported interferometer utilizes four different wavelengths, simultaneously calibrated to the frequency comb of a femtosecond laser, to implement subwavelength distance measurement, while direct synthetic wavelength interferometry is elaborately introduced by launching a fifth wavelength to extend a non-ambiguous range for meter-scale measurement. A linearity test performed comparatively with a He-Ne laser interferometer shows a residual error of less than 70.8 nm in peak-to-valley over a 3 m distance, and a 10 h distance comparison is demonstrated to gain fractional deviations of ~3 × 10 -8 versus 3 m distance. Test results reveal that the presented absolute interferometer enables precise, stable, and long-term distance measurements and facilitates absolute positioning applications such as large-scale manufacturing and space missions.

Real-Time and Meter-Scale Absolute Distance Measurement by Frequency-Comb-Referenced Multi-Wavelength Interferometry

PubMed Central

Tan, Lilong; Yan, Shuhua

2018-01-01

We report on a frequency-comb-referenced absolute interferometer which instantly measures long distance by integrating multi-wavelength interferometry with direct synthetic wavelength interferometry. The reported interferometer utilizes four different wavelengths, simultaneously calibrated to the frequency comb of a femtosecond laser, to implement subwavelength distance measurement, while direct synthetic wavelength interferometry is elaborately introduced by launching a fifth wavelength to extend a non-ambiguous range for meter-scale measurement. A linearity test performed comparatively with a He–Ne laser interferometer shows a residual error of less than 70.8 nm in peak-to-valley over a 3 m distance, and a 10 h distance comparison is demonstrated to gain fractional deviations of ~3 × 10−8 versus 3 m distance. Test results reveal that the presented absolute interferometer enables precise, stable, and long-term distance measurements and facilitates absolute positioning applications such as large-scale manufacturing and space missions. PMID:29414897

Improved modification for the density-functional theory calculation of thermodynamic properties for C-H-O composite compounds.

PubMed

Liu, Min Hsien; Chen, Cheng; Hong, Yaw Shun

2005-02-08

A three-parametric modification equation and the least-squares approach are adopted to calibrating hybrid density-functional theory energies of C(1)-C(10) straight-chain aldehydes, alcohols, and alkoxides to accurate enthalpies of formation DeltaH(f) and Gibbs free energies of formation DeltaG(f), respectively. All calculated energies of the C-H-O composite compounds were obtained based on B3LYP6-311++G(3df,2pd) single-point energies and the related thermal corrections of B3LYP6-31G(d,p) optimized geometries. This investigation revealed that all compounds had 0.05% average absolute relative error (ARE) for the atomization energies, with mean value of absolute error (MAE) of just 2.1 kJ/mol (0.5 kcal/mol) for the DeltaH(f) and 2.4 kJ/mol (0.6 kcal/mol) for the DeltaG(f) of formation.

How is the weather? Forecasting inpatient glycemic control

PubMed Central

Saulnier, George E; Castro, Janna C; Cook, Curtiss B; Thompson, Bithika M

2017-01-01

Aim: Apply methods of damped trend analysis to forecast inpatient glycemic control. Method: Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting. Results: The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries. Conclusion: Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement. PMID:29134125

Verifying Safeguards Declarations with INDEPTH: A Sensitivity Study

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

Grogan, Brandon R; Richards, Scott

2017-01-01

A series of ORIGEN calculations were used to simulate the irradiation and decay of a number of spent fuel assemblies. These simulations focused on variations in the irradiation history that achieved the same terminal burnup through a different set of cycle histories. Simulated NDA measurements were generated for each test case from the ORIGEN data. These simulated measurement types included relative gammas, absolute gammas, absolute gammas plus neutrons, and concentrations of a set of six isotopes commonly measured by NDA. The INDEPTH code was used to reconstruct the initial enrichment, cooling time, and burnup for each irradiation using each simulatedmore » measurement type. The results were then compared to the initial ORIGEN inputs to quantify the size of the errors induced by the variations in cycle histories. Errors were compared based on the underlying changes to the cycle history, as well as the data types used for the reconstructions.« less

Error analysis of multi-needle Langmuir probe measurement technique.

PubMed

Barjatya, Aroh; Merritt, William

2018-04-01

Multi-needle Langmuir probe is a fairly new instrument technique that has been flown on several recent sounding rockets and is slated to fly on a subset of QB50 CubeSat constellation. This paper takes a fundamental look into the data analysis procedures used for this instrument to derive absolute electron density. Our calculations suggest that while the technique remains promising, the current data analysis procedures could easily result in errors of 50% or more. We present a simple data analysis adjustment that can reduce errors by at least a factor of five in typical operation.

Error analysis of multi-needle Langmuir probe measurement technique

NASA Astrophysics Data System (ADS)

Barjatya, Aroh; Merritt, William

2018-04-01

Multi-needle Langmuir probe is a fairly new instrument technique that has been flown on several recent sounding rockets and is slated to fly on a subset of QB50 CubeSat constellation. This paper takes a fundamental look into the data analysis procedures used for this instrument to derive absolute electron density. Our calculations suggest that while the technique remains promising, the current data analysis procedures could easily result in errors of 50% or more. We present a simple data analysis adjustment that can reduce errors by at least a factor of five in typical operation.

Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

PubMed

Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

2018-03-01

Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error <30% and correlation (r) was at least 0.9339 in the same pool of healthy subjects. A 3-concentration-time points limited sampling model predicts the exposure of saroglitazar (ie, AUC 0-t ) within predefined acceptable bias and imprecision limit. Same model was also used to predict AUC 0-∞ . The same limited sampling model was found to predict the exposure of saroglitazar sulfoxide within predefined criteria. This model can find utility during late-phase clinical development of saroglitazar in the patient population. Copyright © 2018 Elsevier HS Journals, Inc. All rights reserved.

Error analysis of 3D-PTV through unsteady interfaces

NASA Astrophysics Data System (ADS)

Akutina, Yulia; Mydlarski, Laurent; Gaskin, Susan; Eiff, Olivier

2018-03-01

The feasibility of stereoscopic flow measurements through an unsteady optical interface is investigated. Position errors produced by a wavy optical surface are determined analytically, as are the optimal viewing angles of the cameras to minimize such errors. Two methods of measuring the resulting velocity errors are proposed. These methods are applied to 3D particle tracking velocimetry (3D-PTV) data obtained through the free surface of a water flow within a cavity adjacent to a shallow channel. The experiments were performed using two sets of conditions, one having no strong surface perturbations, and the other exhibiting surface gravity waves. In the latter case, the amplitude of the gravity waves was 6% of the water depth, resulting in water surface inclinations of about 0.2°. (The water depth is used herein as a relevant length scale, because the measurements are performed in the entire water column. In a more general case, the relevant scale is the maximum distance from the interface to the measurement plane, H, which here is the same as the water depth.) It was found that the contribution of the waves to the overall measurement error is low. The absolute position errors of the system were moderate (1.2% of H). However, given that the velocity is calculated from the relative displacement of a particle between two frames, the errors in the measured water velocities were reasonably small, because the error in the velocity is the relative position error over the average displacement distance. The relative position error was measured to be 0.04% of H, resulting in small velocity errors of 0.3% of the free-stream velocity (equivalent to 1.1% of the average velocity in the domain). It is concluded that even though the absolute positions to which the velocity vectors are assigned is distorted by the unsteady interface, the magnitude of the velocity vectors themselves remains accurate as long as the waves are slowly varying (have low curvature). The stronger the disturbances on the interface are (high amplitude, short wave length), the smaller is the distance from the interface at which the measurements can be performed.

The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study.

PubMed

Franklin, Bryony Dean; O'Grady, Kara; Donyai, Parastou; Jacklin, Ann; Barber, Nick

2007-08-01

To assess the impact of a closed-loop electronic prescribing, automated dispensing, barcode patient identification and electronic medication administration record (EMAR) system on prescribing and administration errors, confirmation of patient identity before administration, and staff time. Before-and-after study in a surgical ward of a teaching hospital, involving patients and staff of that ward. Closed-loop electronic prescribing, automated dispensing, barcode patient identification and EMAR system. Percentage of new medication orders with a prescribing error, percentage of doses with medication administration errors (MAEs) and percentage given without checking patient identity. Time spent prescribing and providing a ward pharmacy service. Nursing time on medication tasks. Prescribing errors were identified in 3.8% of 2450 medication orders pre-intervention and 2.0% of 2353 orders afterwards (p<0.001; chi(2) test). MAEs occurred in 7.0% of 1473 non-intravenous doses pre-intervention and 4.3% of 1139 afterwards (p = 0.005; chi(2) test). Patient identity was not checked for 82.6% of 1344 doses pre-intervention and 18.9% of 1291 afterwards (p<0.001; chi(2) test). Medical staff required 15 s to prescribe a regular inpatient drug pre-intervention and 39 s afterwards (p = 0.03; t test). Time spent providing a ward pharmacy service increased from 68 min to 98 min each weekday (p = 0.001; t test); 22% of drug charts were unavailable pre-intervention. Time per drug administration round decreased from 50 min to 40 min (p = 0.006; t test); nursing time on medication tasks outside of drug rounds increased from 21.1% to 28.7% (p = 0.006; chi(2) test). A closed-loop electronic prescribing, dispensing and barcode patient identification system reduced prescribing errors and MAEs, and increased confirmation of patient identity before administration. Time spent on medication-related tasks increased.

Test-retest reliability at the item level and total score level of the Norwegian version of the Spinal Cord Injury Falls Concern Scale (SCI-FCS).

PubMed

Roaldsen, Kirsti Skavberg; Måøy, Åsa Blad; Jørgensen, Vivien; Stanghelle, Johan Kvalvik

2016-05-01

Translation of the Spinal Cord Injury Falls Concern Scale (SCI-FCS), and investigation of test-retest reliability on item-level and total-score-level. Translation, adaptation and test-retest study. A specialized rehabilitation setting in Norway. Fifty-four wheelchair users with a spinal cord injury. The median age of the cohort was 49 years, and the median number of years after injury was 13. Interventions/measurements: The SCI-FCS was translated and back-translated according to guidelines. Individuals answered the SCI-FCS twice over the course of one week. We investigated item-level test-retest reliability using Svensson's rank-based statistical method for disagreement analysis of paired ordinal data. For relative reliability, we analyzed the total-score-level test-retest reliability with intraclass correlation coefficients (ICC2.1), the standard error of measurement (SEM), and the smallest detectable change (SDC) for absolute reliability/measurement-error assessment and Cronbach's alpha for internal consistency. All items showed satisfactory percentage agreement (≥69%) between test and retest. There were small but non-negligible systematic disagreements among three items; we recovered an 11-13% higher chance for a lower second score. There was no disagreement due to random variance. The test-retest agreement (ICC2.1) was excellent (0.83). The SEM was 2.6 (12%), and the SDC was 7.1 (32%). The Cronbach's alpha was high (0.88). The Norwegian SCI-FCS is highly reliable for wheelchair users with chronic spinal cord injuries.

Evaluation of Different Phenological Information to Map Crop Rotation in Complex Irrigated Indus Basin

NASA Astrophysics Data System (ADS)

Ismaeel, A.; Zhou, Q.

2018-04-01

Accurate information of crop rotation in large basin is essential for policy decisions on land, water and nutrient resources around the world. Crop area estimation using low spatial resolution remote sensing data is challenging in a large heterogeneous basin having more than one cropping seasons. This study aims to evaluate the accuracy of two phenological datasets individually and in combined form to map crop rotations in complex irrigated Indus basin without image segmentation. Phenology information derived from Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, having 8-day temporal and 1000 m spatial resolution, was used in the analysis. An unsupervised (temporal space clustering) to supervised (area knowledge and phenology behavior) classification approach was adopted to identify 13 crop rotations. Estimated crop area was compared with reported area collected by field census. Results reveal that combined dataset (NDVI*LAI) performs better in mapping wheat-rice, wheat-cotton and wheat-fodder rotation by attaining root mean square error (RMSE) of 34.55, 16.84, 20.58 and mean absolute percentage error (MAPE) of 24.56 %, 36.82 %, 30.21 % for wheat, rice and cotton crop respectively. For sugarcane crop mapping, LAI produce good results by achieving RMSE of 8.60 and MAPE of 34.58 %, as compared to NDVI (10.08, 40.53 %) and NDVI*LAI (10.83, 39.45 %). The availability of major crop rotation statistics provides insight to develop better strategies for land, water and nutrient accounting frameworks to improve agriculture productivity.

GOSAILT: A hybrid of GOMS and SAILT with topography consideration

NASA Astrophysics Data System (ADS)

Wu, S.; Wen, J.

2017-12-01

Heterogeneous terrain significantly complicated the energy, mass and momentum exchange between the atmosphere and terrestrial ecosystem. Understanding of topographic effect on the forest reflectance is critical for biophysical parameters retrieval over rugged area. In this paper, a new hybrid bidirectional reflectance distribution function (BRDF) model of geometric optical mutual shadowing and scattering-from-arbitrarily-inclined-leaves model coupled topography (GOSAILT) for sloping forest was proposed. The effects of slope, aspect, gravity field of tree crown, multiple scattering scheme, and diffuse skylight are considered. The area proportions of scene components estimated by the GOSAILT model were compared with the geometric optical model for sloping terrains (GOST) model. The 3-D discrete anisotropic radiative transfer (DART) simulations were used to evaluate the performance of GOSAILT. The results indicate that the canopy reflectance is distorted by the slopes with a maximum variation of 78.3% in the red band and 17.3% in the NIR band on a steep 60 º slope. Compared with the DART simulations, the proposed GOSAILT model can capture anisotropic reflectance well with a determine coefficient (R2) of 0.9720 and 0.6701, root-mean-square error (RMSE) of 0.0024 and 0.0393, mean absolute percentage error of 2.4% and 4.61% for the red and near-infrared (NIR) band. The comparison results indicate the GOSAIL model can accurately reproducing the angular feature of discrete canopy over rugged terrain conditions. The GOSAILT model is promising for the land surface biophysical parameters retrieval (e.g. albedo, leaf area index) over the heterogeneous terrain.

Segmentation of corneal endothelium images using a U-Net-based convolutional neural network.

PubMed

Fabijańska, Anna

2018-04-18

Diagnostic information regarding the health status of the corneal endothelium may be obtained by analyzing the size and the shape of the endothelial cells in specular microscopy images. Prior to the analysis, the endothelial cells need to be extracted from the image. Up to today, this has been performed manually or semi-automatically. Several approaches to automatic segmentation of endothelial cells exist; however, none of them is perfect. Therefore this paper proposes to perform cell segmentation using a U-Net-based convolutional neural network. Particularly, the network is trained to discriminate pixels located at the borders between cells. The edge probability map outputted by the network is next binarized and skeletonized in order to obtain one-pixel wide edges. The proposed solution was tested on a dataset consisting of 30 corneal endothelial images presenting cells of different sizes, achieving an AUROC level of 0.92. The resulting DICE is on average equal to 0.86, which is a good result, regarding the thickness of the compared edges. The corresponding mean absolute percentage error of cell number is at the level of 4.5% which confirms the high accuracy of the proposed approach. The resulting cell edges are well aligned to the ground truths and require a limited number of manual corrections. This also results in accurate values of the cell morphometric parameters. The corresponding errors range from 5.2% for endothelial cell density, through 6.2% for cell hexagonality to 11.93% for the coefficient of variation of the cell size. Copyright © 2018 Elsevier B.V. All rights reserved.

Case-Mix for Performance Management: A Risk Algorithm Based on ICD-10-CM.

PubMed

Gao, Jian; Moran, Eileen; Almenoff, Peter L

2018-06-01

Accurate risk adjustment is the key to a reliable comparison of cost and quality performance among providers and hospitals. However, the existing case-mix algorithms based on age, sex, and diagnoses can only explain up to 50% of the cost variation. More accurate risk adjustment is desired for provider performance assessment and improvement. To develop a case-mix algorithm that hospitals and payers can use to measure and compare cost and quality performance of their providers. All 6,048,895 patients with valid diagnoses and cost recorded in the US Veterans health care system in fiscal year 2016 were included in this study. The dependent variable was total cost at the patient level, and the explanatory variables were age, sex, and comorbidities represented by 762 clinically homogeneous groups, which were created by expanding the 283 categories from Clinical Classifications Software based on ICD-10-CM codes. The split-sample method was used to assess model overfitting and coefficient stability. The predictive power of the algorithms was ascertained by comparing the R, mean absolute percentage error, root mean square error, predictive ratios, and c-statistics. The expansion of the Clinical Classifications Software categories resulted in higher predictive power. The R reached 0.72 and 0.52 for the transformed and raw scale cost, respectively. The case-mix algorithm we developed based on age, sex, and diagnoses outperformed the existing case-mix models reported in the literature. The method developed in this study can be used by other health systems to produce tailored risk models for their specific purpose.

Pixel-based absolute surface metrology by three flat test with shifted and rotated maps

NASA Astrophysics Data System (ADS)

Zhai, Dede; Chen, Shanyong; Xue, Shuai; Yin, Ziqiang

2018-03-01

In traditional three flat test, it only provides the absolute profile along one surface diameter. In this paper, an absolute testing algorithm based on shift-rotation with three flat test has been proposed to reconstruct two-dimensional surface exactly. Pitch and yaw error during shift procedure is analyzed and compensated in our method. Compared with multi-rotation method proposed before, it only needs a 90° rotation and a shift, which is easy to carry out especially in condition of large size surface. It allows pixel level spatial resolution to be achieved without interpolation or assumption to the test surface. In addition, numerical simulations and optical tests are implemented and show the high accuracy recovery capability of the proposed method.

Piezocomposite Actuator Arrays for Correcting and Controlling Wavefront Error in Reflectors

NASA Technical Reports Server (NTRS)

Bradford, Samuel Case; Peterson, Lee D.; Ohara, Catherine M.; Shi, Fang; Agnes, Greg S.; Hoffman, Samuel M.; Wilkie, William Keats

2012-01-01

Three reflectors have been developed and tested to assess the performance of a distributed network of piezocomposite actuators for correcting thermal deformations and total wave-front error. The primary testbed article is an active composite reflector, composed of a spherically curved panel with a graphite face sheet and aluminum honeycomb core composite, and then augmented with a network of 90 distributed piezoelectric composite actuators. The piezoelectric actuator system may be used for correcting as-built residual shape errors, and for controlling low-order, thermally-induced quasi-static distortions of the panel. In this study, thermally-induced surface deformations of 1 to 5 microns were deliberately introduced onto the reflector, then measured using a speckle holography interferometer system. The reflector surface figure was subsequently corrected to a tolerance of 50 nm using the actuators embedded in the reflector's back face sheet. Two additional test articles were constructed: a borosilicate at window at 150 mm diameter with 18 actuators bonded to the back surface; and a direct metal laser sintered reflector with spherical curvature, 230 mm diameter, and 12 actuators bonded to the back surface. In the case of the glass reflector, absolute measurements were performed with an interferometer and the absolute surface was corrected. These test articles were evaluated to determine their absolute surface control capabilities, as well as to assess a multiphysics modeling effort developed under this program for the prediction of active reflector response. This paper will describe the design, construction, and testing of active reflector systems under thermal loads, and subsequent correction of surface shape via distributed peizeoelctric actuation.

Algorithms for spacecraft formation flying navigation based on wireless positioning system measurements

NASA Astrophysics Data System (ADS)

Goh, Shu Ting

Spacecraft formation flying navigation continues to receive a great deal of interest. The research presented in this dissertation focuses on developing methods for estimating spacecraft absolute and relative positions, assuming measurements of only relative positions using wireless sensors. The implementation of the extended Kalman filter to the spacecraft formation navigation problem results in high estimation errors and instabilities in state estimation at times. This is due to the high nonlinearities in the system dynamic model. Several approaches are attempted in this dissertation aiming at increasing the estimation stability and improving the estimation accuracy. A differential geometric filter is implemented for spacecraft positions estimation. The differential geometric filter avoids the linearization step (which is always carried out in the extended Kalman filter) through a mathematical transformation that converts the nonlinear system into a linear system. A linear estimator is designed in the linear domain, and then transformed back to the physical domain. This approach demonstrated better estimation stability for spacecraft formation positions estimation, as detailed in this dissertation. The constrained Kalman filter is also implemented for spacecraft formation flying absolute positions estimation. The orbital motion of a spacecraft is characterized by two range extrema (perigee and apogee). At the extremum, the rate of change of a spacecraft's range vanishes. This motion constraint can be used to improve the position estimation accuracy. The application of the constrained Kalman filter at only two points in the orbit causes filter instability. Two variables are introduced into the constrained Kalman filter to maintain the stability and improve the estimation accuracy. An extended Kalman filter is implemented as a benchmark for comparison with the constrained Kalman filter. Simulation results show that the constrained Kalman filter provides better estimation accuracy as compared with the extended Kalman filter. A Weighted Measurement Fusion Kalman Filter (WMFKF) is proposed in this dissertation. In wireless localizing sensors, a measurement error is proportional to the distance of the signal travels and sensor noise. In this proposed Weighted Measurement Fusion Kalman Filter, the signal traveling time delay is not modeled; however, each measurement is weighted based on the measured signal travel distance. The obtained estimation performance is compared to the standard Kalman filter in two scenarios. The first scenario assumes using a wireless local positioning system in a GPS denied environment. The second scenario assumes the availability of both the wireless local positioning system and GPS measurements. The simulation results show that the WMFKF has similar accuracy performance as the standard Kalman Filter (KF) in the GPS denied environment. However, the WMFKF maintains the position estimation error within its expected error boundary when the WLPS detection range limit is above 30km. In addition, the WMFKF has a better accuracy and stability performance when GPS is available. Also, the computational cost analysis shows that the WMFKF has less computational cost than the standard KF, and the WMFKF has higher ellipsoid error probable percentage than the standard Measurement Fusion method. A method to determine the relative attitudes between three spacecraft is developed. The method requires four direction measurements between the three spacecraft. The simulation results and covariance analysis show that the method's error falls within a three sigma boundary without exhibiting any singularity issues. A study of the accuracy of the proposed method with respect to the shape of the spacecraft formation is also presented.

Skin Cooling and Force Replication at the Ankle in Healthy Individuals: A Crossover Randomized Controlled Trial

PubMed Central

Haupenthal, Daniela Pacheco dos Santos; de Noronha, Marcos; Haupenthal, Alessandro; Ruschel, Caroline; Nunes, Guilherme S.

2015-01-01

Context Proprioception of the ankle is determined by the ability to perceive the sense of position of the ankle structures, as well as the speed and direction of movement. Few researchers have investigated proprioception by force-replication ability and particularly after skin cooling. Objective To analyze the ability of the ankle-dorsiflexor muscles to replicate isometric force after a period of skin cooling. Design Randomized controlled clinical trial. Setting Laboratory. Patients or Other Participants Twenty healthy individuals (10 men, 10 women; age = 26.8 ± 5.2 years, height = 171 ± 7 cm, mass = 66.8 ± 10.5 kg). Intervention(s) Skin cooling was carried out using 2 ice applications: (1) after maximal voluntary isometric contraction (MVIC) performance and before data collection for the first target force, maintained for 20 minutes; and (2) before data collection for the second target force, maintained for 10 minutes. We measured skin temperature before and after ice applications to ensure skin cooling. Main Outcome Measure(s) A load cell was placed under an inclined board for data collection, and 10 attempts of force replication were carried out for 2 values of MVIC (20%, 50%) in each condition (ice, no ice). We assessed force sense with absolute and root mean square errors (the difference between the force developed by the dorsiflexors and the target force measured with the raw data and after root mean square analysis, respectively) and variable error (the variance around the mean absolute error score). A repeated-measures multivariate analysis of variance was used for statistical analysis. Results The absolute error was greater for the ice than for the no-ice condition (F1,19 = 9.05, P = .007) and for the target force at 50% of MVIC than at 20% of MVIC (F1,19 = 26.01, P < .001). Conclusions The error was greater in the ice condition and at 50% of MVIC. Skin cooling reduced the proprioceptive ability of the ankle-dorsiflexor muscles to replicate isometric force. PMID:25761136

DFT benchmark study for the oxidative addition of CH 4 to Pd. Performance of various density functionals

NASA Astrophysics Data System (ADS)

de Jong, G. Theodoor; Geerke, Daan P.; Diefenbach, Axel; Matthias Bickelhaupt, F.

2005-06-01

We have evaluated the performance of 24 popular density functionals for describing the potential energy surface (PES) of the archetypal oxidative addition reaction of the methane C-H bond to the palladium atom by comparing the results with our recent ab initio [CCSD(T)] benchmark study of this reaction. The density functionals examined cover the local density approximation (LDA), the generalized gradient approximation (GGA), meta-GGAs as well as hybrid density functional theory. Relativistic effects are accounted for through the zeroth-order regular approximation (ZORA). The basis-set dependence of the density-functional-theory (DFT) results is assessed for the Becke-Lee-Yang-Parr (BLYP) functional using a hierarchical series of Slater-type orbital (STO) basis sets ranging from unpolarized double-ζ (DZ) to quadruply polarized quadruple-ζ quality (QZ4P). Stationary points on the reaction surface have been optimized using various GGA functionals, all of which yield geometries that differ only marginally. Counterpoise-corrected relative energies of stationary points are converged to within a few tenths of a kcal/mol if one uses the doubly polarized triple-ζ (TZ2P) basis set and the basis-set superposition error (BSSE) drops to 0.0 kcal/mol for our largest basis set (QZ4P). Best overall agreement with the ab initio benchmark PES is achieved by functionals of the GGA, meta-GGA, and hybrid-DFT type, with mean absolute errors of 1.3-1.4 kcal/mol and errors in activation energies ranging from +0.8 to -1.4 kcal/mol. Interestingly, the well-known BLYP functional compares very reasonably with an only slightly larger mean absolute error of 2.5 kcal/mol and an underestimation by -1.9 kcal/mol of the overall barrier (i.e., the difference in energy between the TS and the separate reactants). For comparison, with B3LYP we arrive at a mean absolute error of 3.8 kcal/mol and an overestimation of the overall barrier by 4.5 kcal/mol.

Test Plan for a Calibration Demonstration System for the Reflected Solar Instrument for the Climate Absolute Radiance and Refractivity Observatory

NASA Technical Reports Server (NTRS)

Thome, Kurtis; McCorkel, Joel; Hair, Jason; McAndrew, Brendan; Daw, Adrian; Jennings, Donald; Rabin, Douglas

2012-01-01

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission addresses the need to observe high-accuracy, long-term climate change trends and to use decadal change observations as the most critical method to determine the accuracy of climate change. One of the major objectives of CLARREO is to advance the accuracy of SI traceable absolute calibration at infrared and reflected solar wavelengths. This advance is required to reach the on-orbit absolute accuracy required to allow climate change observations to survive data gaps while remaining sufficiently accurate to observe climate change to within the uncertainty of the limit of natural variability. While these capabilities exist at NIST in the laboratory, there is a need to demonstrate that it can move successfully from NIST to NASA and/or instrument vendor capabilities for future spaceborne instruments. The current work describes the test plan for the Solar, Lunar for Absolute Reflectance Imaging Spectroradiometer (SOLARIS) which is the calibration demonstration system (CDS) for the reflected solar portion of CLARREO. The goal of the CDS is to allow the testing and evaluation of calibration approaches , alternate design and/or implementation approaches and components for the CLARREO mission. SOLARIS also provides a test-bed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. The end result of efforts with the SOLARIS CDS will be an SI-traceable error budget for reflectance retrieval using solar irradiance as a reference and methods for laboratory-based, absolute calibration suitable for climate-quality data collections. The CLARREO mission addresses the need to observe high-accuracy, long-term climate change trends and advance the accuracy of SI traceable absolute calibration. The current work describes the test plan for the SOLARIS which is the calibration demonstration system for the reflected solar portion of CLARREO. SOLARIS provides a test-bed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. The end result will be an SI-traceable error budget for reflectance retrieval using solar irradiance as a reference and methods for laboratory-based, absolute calibration suitable for climate-quality data collections.

Publisher Correction: Unravelling the immune signature of Plasmodium falciparum transmission-reducing immunity.

PubMed

Stone, Will J R; Campo, Joseph J; Ouédraogo, André Lin; Meerstein-Kessel, Lisette; Morlais, Isabelle; Da, Dari; Cohuet, Anna; Nsango, Sandrine; Sutherland, Colin J; van de Vegte-Bolmer, Marga; Siebelink-Stoter, Rianne; van Gemert, Geert-Jan; Graumans, Wouter; Lanke, Kjerstin; Shandling, Adam D; Pablo, Jozelyn V; Teng, Andy A; Jones, Sophie; de Jong, Roos M; Fabra-García, Amanda; Bradley, John; Roeffen, Will; Lasonder, Edwin; Gremo, Giuliana; Schwarzer, Evelin; Janse, Chris J; Singh, Susheel K; Theisen, Michael; Felgner, Phil; Marti, Matthias; Drakeley, Chris; Sauerwein, Robert; Bousema, Teun; Jore, Matthijs M

2018-04-11

The original version of this Article contained errors in Fig. 3. In panel a, bars from a chart depicting the percentage of antibody-positive individuals in non-infectious and infectious groups were inadvertently included in place of bars depicting the percentage of infectious individuals, as described in the Article and figure legend. However, the p values reported in the Figure and the resulting conclusions were based on the correct dataset. The corrected Fig. 3a now shows the percentage of infectious individuals in antibody-negative and -positive groups, in both the PDF and HTML versions of the Article. The incorrect and correct versions of Figure 3a are also presented for comparison in the accompanying Publisher Correction as Figure 1.The HTML version of the Article also omitted a link to Supplementary Data 6. The error has now been fixed and Supplementary Data 6 is available to download.

The Absolute Magnitude of the Sun in Several Filters

NASA Astrophysics Data System (ADS)

Willmer, Christopher N. A.

2018-06-01

This paper presents a table with estimates of the absolute magnitude of the Sun and the conversions from vegamag to the AB and ST systems for several wide-band filters used in ground-based and space-based observatories. These estimates use the dustless spectral energy distribution (SED) of Vega, calibrated absolutely using the SED of Sirius, to set the vegamag zero-points and a composite spectrum of the Sun that coadds space-based observations from the ultraviolet to the near-infrared with models of the Solar atmosphere. The uncertainty of the absolute magnitudes is estimated by comparing the synthetic colors with photometric measurements of solar analogs and is found to be ∼0.02 mag. Combined with the uncertainty of ∼2% in the calibration of the Vega SED, the errors of these absolute magnitudes are ∼3%–4%. Using these SEDs, for three of the most utilized filters in extragalactic work the estimated absolute magnitudes of the Sun are M B = 5.44, M V = 4.81, and M K = 3.27 mag in the vegamag system and M B = 5.31, M V = 4.80, and M K = 5.08 mag in AB.

The Zero Product Principle Error.

ERIC Educational Resources Information Center

Padula, Janice

1996-01-01

Argues that the challenge for teachers of algebra in Australia is to find ways of making the structural aspects of algebra accessible to a greater percentage of students. Uses the zero product principle to provide an example of a common student error grounded in the difficulty of understanding the structure of algebra. (DDR)

Algebra Students' Difficulty with Fractions: An Error Analysis

ERIC Educational Resources Information Center

Brown, George; Quinn, Robert J.

2006-01-01

An analysis of the 1990 National Assessment of Educational Progress (NAEP) found that only 46 percent of all high school seniors demonstrated success with a grasp of decimals, percentages, fractions and simple algebra. This article investigates error patterns that emerge as students attempt to answer questions involving the ability to apply…

Application of a soft computing technique in predicting the percentage of shear force carried by walls in a rectangular channel with non-homogeneous roughness.

PubMed

Khozani, Zohreh Sheikh; Bonakdari, Hossein; Zaji, Amir Hossein

2016-01-01

Two new soft computing models, namely genetic programming (GP) and genetic artificial algorithm (GAA) neural network (a combination of modified genetic algorithm and artificial neural network methods) were developed in order to predict the percentage of shear force in a rectangular channel with non-homogeneous roughness. The ability of these methods to estimate the percentage of shear force was investigated. Moreover, the independent parameters' effectiveness in predicting the percentage of shear force was determined using sensitivity analysis. According to the results, the GP model demonstrated superior performance to the GAA model. A comparison was also made between the GP program determined as the best model and five equations obtained in prior research. The GP model with the lowest error values (root mean square error ((RMSE) of 0.0515) had the best function compared with the other equations presented for rough and smooth channels as well as smooth ducts. The equation proposed for rectangular channels with rough boundaries (RMSE of 0.0642) outperformed the prior equations for smooth boundaries.

Evaluating the accuracy and large inaccuracy of two continuous glucose monitoring systems.

PubMed

Leelarathna, Lalantha; Nodale, Marianna; Allen, Janet M; Elleri, Daniela; Kumareswaran, Kavita; Haidar, Ahmad; Caldwell, Karen; Wilinska, Malgorzata E; Acerini, Carlo L; Evans, Mark L; Murphy, Helen R; Dunger, David B; Hovorka, Roman

2013-02-01

This study evaluated the accuracy and large inaccuracy of the Freestyle Navigator (FSN) (Abbott Diabetes Care, Alameda, CA) and Dexcom SEVEN PLUS (DSP) (Dexcom, Inc., San Diego, CA) continuous glucose monitoring (CGM) systems during closed-loop studies. Paired CGM and plasma glucose values (7,182 data pairs) were collected, every 15-60 min, from 32 adults (36.2±9.3 years) and 20 adolescents (15.3±1.5 years) with type 1 diabetes who participated in closed-loop studies. Levels 1, 2, and 3 of large sensor error with increasing severity were defined according to absolute relative deviation greater than or equal to ±40%, ±50%, and ±60% at a reference glucose level of ≥6 mmol/L or absolute deviation greater than or equal to ±2.4 mmol/L,±3.0 mmol/L, and ±3.6 mmol/L at a reference glucose level of <6 mmol/L. Median absolute relative deviation was 9.9% for FSN and 12.6% for DSP. Proportions of data points in Zones A and B of Clarke error grid analysis were similar (96.4% for FSN vs. 97.8% for DSP). Large sensor over-reading, which increases risk of insulin over-delivery and hypoglycemia, occurred two- to threefold more frequently with DSP than FSN (once every 2.5, 4.6, and 10.7 days of FSN use vs. 1.2, 2.0, and 3.7 days of DSP use for Level 1-3 errors, respectively). At levels 2 and 3, large sensor errors lasting 1 h or longer were absent with FSN but persisted with DSP. FSN and DSP differ substantially in the frequency and duration of large inaccuracy despite only modest differences in conventional measures of numerical and clinical accuracy. Further evaluations are required to confirm that FSN is more suitable for integration into closed-loop delivery systems.

Effect of proprioception training on knee joint position sense in female team handball players.

PubMed

Pánics, G; Tállay, A; Pavlik, A; Berkes, I

2008-06-01

A number of studies have shown that proprioception training can reduce the risk of injuries in pivoting sports, but the mechanism is not clearly understood. To determine the contributing effects of propioception on knee joint position sense among team handball players. Prospective cohort study. Two professional female handball teams were followed prospectively for the 2005-6 season. 20 players in the intervention team followed a prescribed proprioceptive training programme while 19 players in the control team did not have a specific propioceptive training programme. The coaches recorded all exposures of the individual players. The location and nature of injuries were recorded. Joint position sense (JPS) was measured by a goniometer on both knees in three angle intervals, testing each angle five times. Assessments were performed before and after the season by the same examiner for both teams. In the intervention team a third assessment was also performed during the season. Complete data were obtained for 15 subjects in the intervention team and 16 in the control team. Absolute error score, error of variation score and SEM were calculated and the results of the intervention and control teams were compared. The proprioception sensory function of the players in the intervention team was significantly improved between the assessments made at the start and the end of the season (mean (SD) absolute error 9.78-8.21 degrees (7.19-6.08 degrees ) vs 3.61-4.04 degrees (3.71-3.20 degrees ), p<0.05). No improvement was seen in the sensory function in the control team between the start and the end of the season (mean (SD) absolute error 6.31-6.22 degrees (6.12-3.59 degrees ) vs 6.13-6.69 degrees (7.46-6.49 degrees ), p>0.05). This is the first study to show that proprioception training improves the joint position sense in elite female handball players. This may explain the effect of neuromuscular training in reducing the injury rate.

Calculating tumor trajectory and dose-of-the-day using cone-beam CT projections

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

Jones, Bernard L., E-mail: bernard.jones@ucdenver.edu; Westerly, David; Miften, Moyed

2015-02-15

Purpose: Cone-beam CT (CBCT) projection images provide anatomical data in real-time over several respiratory cycles, forming a comprehensive picture of tumor movement. The authors developed and validated a method which uses these projections to determine the trajectory of and dose to highly mobile tumors during each fraction of treatment. Methods: CBCT images of a respiration phantom were acquired, the trajectory of which mimicked a lung tumor with high amplitude (up to 2.5 cm) and hysteresis. A template-matching algorithm was used to identify the location of a steel BB in each CBCT projection, and a Gaussian probability density function for themore » absolute BB position was calculated which best fit the observed trajectory of the BB in the imager geometry. Two modifications of the trajectory reconstruction were investigated: first, using respiratory phase information to refine the trajectory estimation (Phase), and second, using the Monte Carlo (MC) method to sample the estimated Gaussian tumor position distribution. The accuracies of the proposed methods were evaluated by comparing the known and calculated BB trajectories in phantom-simulated clinical scenarios using abdominal tumor volumes. Results: With all methods, the mean position of the BB was determined with accuracy better than 0.1 mm, and root-mean-square trajectory errors averaged 3.8% ± 1.1% of the marker amplitude. Dosimetric calculations using Phase methods were more accurate, with mean absolute error less than 0.5%, and with error less than 1% in the highest-noise trajectory. MC-based trajectories prevent the overestimation of dose, but when viewed in an absolute sense, add a small amount of dosimetric error (<0.1%). Conclusions: Marker trajectory and target dose-of-the-day were accurately calculated using CBCT projections. This technique provides a method to evaluate highly mobile tumors using ordinary CBCT data, and could facilitate better strategies to mitigate or compensate for motion during stereotactic body radiotherapy.« less

Investigation of advanced phase-shifting projected fringe profilometry techniques

NASA Astrophysics Data System (ADS)

Liu, Hongyu

1999-11-01

The phase-shifting projected fringe profilometry (PSPFP) technique is a powerful tool in the profile measurements of rough engineering surfaces. Compared with other competing techniques, this technique is notable for its full-field measurement capacity, system simplicity, high measurement speed, and low environmental vulnerability. The main purpose of this dissertation is to tackle three important problems, which severely limit the capability and the accuracy of the PSPFP technique, with some new approaches. Chapter 1 provides some background information of the PSPFP technique including the measurement principles, basic features, and related techniques is briefly introduced. The objectives and organization of the thesis are also outlined. Chapter 2 gives a theoretical treatment to the absolute PSPFP measurement. The mathematical formulations and basic requirements of the absolute PSPFP measurement and its supporting techniques are discussed in detail. Chapter 3 introduces the experimental verification of the proposed absolute PSPFP technique. Some design details of a prototype system are discussed as supplements to the previous theoretical analysis. Various fundamental experiments performed for concept verification and accuracy evaluation are introduced together with some brief comments. Chapter 4 presents the theoretical study of speckle- induced phase measurement errors. In this analysis, the expression for speckle-induced phase errors is first derived based on the multiplicative noise model of image- plane speckles. The statistics and the system dependence of speckle-induced phase errors are then thoroughly studied through numerical simulations and analytical derivations. Based on the analysis, some suggestions on the system design are given to improve measurement accuracy. Chapter 5 discusses a new technique combating surface reflectivity variations. The formula used for error compensation is first derived based on a simplified model of the detection process. The techniques coping with two major effects of surface reflectivity variations are then introduced. Some fundamental problems in the proposed technique are studied through simulations. Chapter 6 briefly summarizes the major contributions of the current work and provides some suggestions for the future research.

Cross sections for H(-) and Cl(-) production from HCl by dissociative electron attachment

NASA Technical Reports Server (NTRS)

Orient, O. J.; Srivastava, S. K.

1985-01-01

A crossed target beam-electron beam collision geometry and a quadrupole mass spectrometer have been used to conduct dissociative electron attachment cross section measurements for the case of H(-) and Cl(-) production from HCl. The relative flow technique is used to determine the absolute values of cross sections. A tabulation is given of the attachment energies corresponding to various cross section maxima. Error sources contributing to total errors are also estimated.

Modified Bat Algorithm for Feature Selection with the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset

PubMed

Jeyasingh, Suganthi; Veluchamy, Malathi

2017-05-01

Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature selection algorithms available in data mining. Feature selection is considered as a vital step to increase the classification accuracy. This paper proposes a Modified Bat Algorithm (MBA) for feature selection to eliminate irrelevant features from an original dataset. The Bat algorithm was modified using simple random sampling to select the random instances from the dataset. Ranking was with the global best features to recognize the predominant features available in the dataset. The selected features are used to train a Random Forest (RF) classification algorithm. The MBA feature selection algorithm enhanced the classification accuracy of RF in identifying the occurrence of breast cancer. The Wisconsin Diagnosis Breast Cancer Dataset (WDBC) was used for estimating the performance analysis of the proposed MBA feature selection algorithm. The proposed algorithm achieved better performance in terms of Kappa statistic, Mathew’s Correlation Coefficient, Precision, F-measure, Recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE). Creative Commons Attribution License

Comparison of total energy expenditure assessed by two devices in controlled and free-living conditions.

PubMed

Rousset, Sylvie; Fardet, Anthony; Lacomme, Philippe; Normand, Sylvie; Montaurier, Christophe; Boirie, Yves; Morio, Béatrice

2015-01-01

The objective of this study was to evaluate the validity of total energy expenditure (TEE) provided by Actiheart and Armband. Normal-weight adult volunteers wore both devices either for 17 hours in a calorimetric chamber (CC, n = 49) or for 10 days in free-living conditions (FLC) outside the laboratory (n = 41). The two devices and indirect calorimetry or doubly labelled water, respectively, were used to estimate TEE in the CC group and FLC group. In the CC, the relative value of TEE error was not significant (p > 0.05) for Actiheart but significantly different from zero for Armband, showing TEE underestimation (-4.9%, p < 0.0001). However, the mean absolute values of errors were significantly different between Actiheart and Armband: 8.6% and 6.7%, respectively (p = 0.05). Armband was more accurate for estimating TEE during sleeping, rest, recovery periods and sitting-standing. Actiheart provided better estimation during step and walking. In FLC, no significant error in relative value was detected. Nevertheless, Armband produced smaller errors in absolute value than Actiheart (8.6% vs. 12.8%). The distributions of differences were more scattered around the means, suggesting a higher inter-individual variability in TEE estimated by Actiheart than by Armband. Our results show that both monitors are appropriate for estimating TEE. Armband is more effective than Actiheart at the individual level for daily light-intensity activities.