Supplemental Material for Examining the Reliability of Interval Level Data Using Root Mean Square Differences and Concordance Correlation Coefficients

This study aimed to analyze the intersession reliability of global positioning system (GPS-based) distances and accelerometer-based (acceleration) variables in small-sided soccer games (SSG) with and without the offside rule, as well as compare variables between the tasks. Twenty-four high-level U-17 soccer athletes played 3 versus 3 (plus goalkeepers) SSG in two formats (with and without the offside rule). SSG were performed on eight consecutive weeks (4 weeks for each group), twice a week. The physical demands were recorded using a GPS with an embedded triaxial accelerometer. GPS-based variables (total distance, average speed, and distances covered at different speeds) and accelerometer-based variables (Player Load™, root mean square of the acceleration recorded in each movement axis, and the root mean square of resultant acceleration) were calculated. Results showed that the inclusion of the offside rule reduced the total distance covered (large effect) and the distances covered at moderate speed zones (7–12.9 km/h – moderate effect; 13–17.9 km/h – large effect). In both SSG formats, GPS-based variables presented good to excellent reliability (intraclass correlation coefficients – ICC > 0.62) and accelerometer-based variables presented excellent reliability (ICC values > 0.89). Based on the results of this study, the offside rule decreases the physical demand of 3 versus 3 SSG and the physical demands required in these SSG present high intersession reliability.

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Red Wine Age Estimation by the Alteration of Its Color Parameters: Fourier Transform Infrared Spectroscopy as a Tool to Monitor Wine Maturation Time

Journal of Analytical Methods in Chemistry ◽

10.1155/2017/5767613 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

M. Basalekou ◽

C. Pappas ◽

Y. Kotseridis ◽

P. A. Tarantilis ◽

E. Kontaxakis ◽

...

Keyword(s):

Fourier Transform ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Correlation Coefficients ◽

Fourier Transform Infrared ◽

Mode Analysis ◽

Mean Square ◽

Chemical Reagents ◽

Grape Varieties

Color, phenolic content, and chemical age values of red wines made from Cretan grape varieties (Kotsifali, Mandilari) were evaluated over nine months of maturation in different containers for two vintages. The wines differed greatly on their anthocyanin profiles. Mid-IR spectra were also recorded with the use of a Fourier Transform Infrared Spectrophotometer in ZnSe disk mode. Analysis of Variance was used to explore the parameter’s dependency on time. Determination models were developed for the chemical age indexes using Partial Least Squares (PLS) (TQ Analyst software) considering the spectral region 1830–1500 cm−1. The correlation coefficients (r) for chemical age index i were 0.86 for Kotsifali (Root Mean Square Error of Calibration (RMSEC) = 0.067, Root Mean Square Error of Prediction (RMSEP) = 0,115, and Root Mean Square Error of Validation (RMSECV) = 0.164) and 0.90 for Mandilari (RMSEC = 0.050, RMSEP = 0.040, and RMSECV = 0.089). For chemical age index ii the correlation coefficients (r) were 0.86 and 0.97 for Kotsifali (RMSEC 0.044, RMSEP = 0.087, and RMSECV = 0.214) and Mandilari (RMSEC = 0.024, RMSEP = 0.033, and RMSECV = 0.078), respectively. The proposed method is simpler, less time consuming, and more economical and does not require chemical reagents.

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Comparison of neural networks and genetic algorithms to determine missing precipitation data (Case study: the city of Sari)

Revista de la Universidad del Zulia ◽

10.46925//rdluz.29.08 ◽

2020 ◽

Vol 11 (29) ◽

pp. 114-128

Author(s):

Ali Mahdavi ◽

Mohsen Najarchi ◽

Emadoddin Hazaveie ◽

Seyed Mohammad Mirhosayni Hazave ◽

Seyed Mohammad Mahdai Najafizadeh

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Genetic Programming ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Correlation Coefficients ◽

Mean Square ◽

Artificial Neural ◽

The City

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

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Turbulent Plane Couette Flow and Scalar Transport at Low Reynolds Number

Journal of Heat Transfer ◽

10.1115/1.1571084 ◽

2003 ◽

Vol 125 (6) ◽

pp. 988-998 ◽

Cited By ~ 14

Author(s):

Chun-Ho Liu

Keyword(s):

Reynolds Number ◽

Couette Flow ◽

Root Mean Square ◽

Correlation Coefficients ◽

Streamwise Velocity ◽

Conductive Heat ◽

Mean Square ◽

Plane Couette Flow ◽

Maximum Root ◽

Channel Center

The turbulence structure and passive scalar (heat) transport in plane Couette flow at Reynolds number equal to 3000 (based on the relative speed and distance between the walls) are studied using direct numerical simulation (DNS). The numerical model is a three-dimensional trilinear Galerkin finite element code. It is found that the structures of the mean velocity and temperature in plane Couette flow are similar to those in forced channel flow, but the empirical coefficients are different. The total (turbulent and viscous) shear stress and total (turbulent and conductive) heat flux are constant throughout the channel. The locations of maximum root-mean-square streamwise velocity and temperature fluctuations are close to the walls, while the location of maximum root-mean-square spanwise and vertical velocity fluctuations are at the channel center. The correlation coefficients between velocities and temperature are fairly constant in the center core of the channel. In particular, the streamwise velocity is highly correlated with temperature (correlation coefficient ≈−0.9). At the channel center, the turbulence production is unable to counterbalance the dissipation, in which the diffusion terms (both turbulent and viscous) bring turbulent kinetic energy from the near-wall regions toward the channel center. The snapshots of the DNS database help explain the nature of the correlation coefficients. The elongated wall streaks for both streamwise velocity and temperature in the viscous sublayer are well simulated. Moreover, the current DNS shows organized large-scale eddies (secondary rotations) perpendicular to the direction of mean flow at the channel center.

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Determination of suitable drying curve model for bread moisture loss during baking

International Agrophysics ◽

10.2478/v10247-012-0090-y ◽

2013 ◽

Vol 27 (2) ◽

pp. 233-237 ◽

Cited By ~ 4

Author(s):

A.R. Soleimani Pour-Damanab ◽

A. Jafary ◽

S. Rafiee

Keyword(s):

Regression Analysis ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Estimation Error ◽

Linear Regression Analysis ◽

Correlation Coefficients ◽

Moisture Loss ◽

Mean Square ◽

Chi Squared

Abstract This study presents mathematical modelling of bread moisture loss or drying during baking in a conventional bread baking process. In order to estimate and select the appropriate moisture loss curve equation, 11 different models, semi-theoretical and empirical, were applied to the experimental data and compared according to their correlation coefficients, chi-squared test and root mean square error which were predicted by nonlinear regression analysis. Consequently, of all the drying models, a Page model was selected as the best one, according to the correlation coefficients, chi-squared test, and root mean square error values and its simplicity. Mean absolute estimation error of the proposed model by linear regression analysis for natural and forced convection modes was 2.43, 4.74%, respectively.

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Comparison of iohexol plasma clearance formulas vs. inulin urinary clearance for measuring glomerular filtration rate

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2020-0770 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Laurence Dubourg ◽

Sandrine Lemoine ◽

Brune Joannard ◽

Laurence Chardon ◽

Vandréa de Souza ◽

...

Keyword(s):

Glomerular Filtration Rate ◽

Root Mean Square Error ◽

Glomerular Filtration ◽

Mean Square Error ◽

Root Mean Square ◽

Filtration Rate ◽

Plasma Clearance ◽

Mean Square ◽

Concordance Correlation ◽

Iohexol Plasma Clearance

AbstractObjectivesThe one-compartment iohexol plasma clearance has been proposed as a reliable alternative to renal inulin clearance. However, this method’s performance depends on the formula used to calculate glomerular filtration rate (GFR). This study reports on performance comparisons between various mathematical formulas proposed for iohexol plasma clearance vs. inulin urinary clearance.MethodsGFR was simultaneously determined by inulin and iohexol clearance in 144 participants (age: 10–84 years; glomerular filtration rate: 15–169 mL/min/1.73 m2). A retrospective cross-sectional study evaluated the performance of four formulas proposed to calculate plasma iohexol clearance (Brøchner–Mortensen, Fleming et al., Jødal–Brøchner–Mortensen, and Ng–Schwartz–Munoz). The performance of each formula was assessed using bias, precision (standard deviation of the bias), accuracy (percentage iohexol within 5, 10, and 15%), root mean square error, and concordance correlation coefficient vs. renal inulin clearance as reference.ResultsRegarding accuracy, there was no difference in root mean square error (RMSE), P5, P10, or P15 between the four formulas. The four concordance correlation coefficients (CCC) between the value from each formula and in-GFR were high and not significantly different. At in-GFR ≥90 mL/min/1.73 m2, Ng–Schwartz–Munoz formula performed slightly better than other formulas regarding median bias (−0.5; 95% CI [−3.0 to 2.0] and accuracy P15 (95.0; 95% CI [88.0–100.0]).ConclusionsThe studied formulas were found equivalent in terms of precision and accuracy, but the Ng–Schwartz–Munoz formula improved the accuracy at higher levels of in-GFR.

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Development and validation of the Oxford Pandemic Attitude Scale-COVID-19 (OPAS-C): an internet-based cross-sectional study in the UK and USA

BMJ Open ◽

10.1136/bmjopen-2020-043758 ◽

2021 ◽

Vol 11 (7) ◽

pp. e043758

Author(s):

Bella Nichole Kantor ◽

Jonathan Kantor

Keyword(s):

Root Mean Square ◽

Goodness Of Fit ◽

Discriminant Validity ◽

Correlation Coefficients ◽

Attitude Scale ◽

Mean Square ◽

Cross Sectional ◽

The Us ◽

The Usa ◽

The Uk

ObjectivesTo develop and validate the Oxford Pandemic Attitude Scale-COVID-19 (OPAS-C), a multidimensional scale that addresses seven domains over 20 items including stress, fear, loneliness, sense of community, belief that the pandemic is a hoax or exaggerated, the use of and attitude to non-pharmaceutical interventions and vaccine hesitancy, in a single measure.DesignCross-sectional validation study.SettingInternet based with respondents in the USA and UK.ParticipantsGeneral community respondents using the Prolific Academic platform.Main outcome measuresExploratory factor analyses with promax oblique rotation and confirmatory factor analysis including goodness of fit indices: root mean square error of approximation (RMSEA), standardised root mean square residual (SRMR) and comparative fit index (CFI). Reliability as internal consistency using Cronbach’s alpha. Convergent and discriminant validity using Pearson correlation coefficients.ResultsThe sample included 351 respondents in the USA and the factorial structure was confirmed using a separate set of 348 respondents in the UK. The OPAS-C had excellent goodness of fit characteristics, with an RMSEA of 0.047 (90% CI 0.037 to 0.056), SRMR of 0.043 and CFI of 0.962. Reliability was excellent, demonstrating Cronbach’s alpha of 0.87 in both the US and UK samples. Convergent validity showed correlation coefficients of 0.54 and 0.49 in the US and UK samples, respectively. Discriminant validity demonstrated correlations of 0.21 and 0.26 in the US and UK samples, respectively.ConclusionsThe OPAS-C represents the first validated scale that addresses mental health measures and public health-relevant responses to COVID-19, and may be a useful measure for use in future longitudinal and cross-sectional studies. Further international validation beyond the USA and UK may be helpful.

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Hyperspectral Imaging for the Nondestructive Quality Assessment of the Firmness of Nanguo Pears Under Different Freezing/Thawing Conditions

Sensors ◽

10.3390/s19143124 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3124 ◽

Cited By ~ 1

Author(s):

Zhang ◽

Shang ◽

Wang ◽

Zhang ◽

Yu ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Hyperspectral Imaging ◽

Correlation Coefficients ◽

Mean Square ◽

Critical Temperatures ◽

Standard Normal Variate ◽

Scattering Correction ◽

Standard Normal

Firmness changes in Nanguo pears under different freezing/thawing conditions have been characterized by hyperspectral imaging (HSI). Four different freezing/thawing conditions (the critical temperatures, numbers of cycles, holding time and cooling rates) were set in this experiment. Four different pretreatment methods were used: multivariate scattering correction (MSC), standard normal variate (SNV), Savitzky-Golay standard normal variate (S-G-SNV) and Savitzky-Golay multiplicative scattering correction (S-G-MSC). Combined with competitive adaptive reweighted sampling (CARS) to identify characteristic wavelengths, firmness prediction models of Nanguo pears under different freezing/thawing conditions were established by partial least squares (PLS) regression. The performance of the firmness model was analyzed quantitatively by the correlation coefficient (R), the root mean square error of calibration (RMSEC), the root mean square error of prediction (RMSEP) and the root mean square error of cross validation (RMSECV). The results showed that the MSC-PLS model has the highest accuracy at different cooling rates and holding times; the correlation coefficients of the calibration set (Rc) were 0.899 and 0.927, respectively, and the correlation coefficients of the validation set (Rp) were 0.911 and 0.948, respectively. The accuracy of the SNV-PLS model was the highest at different numbers of cycles, and the Rc and the Rp were 0.861 and 0.848, respectively. The RMSEC was 65.189, and the RMSEP was 65.404. The accuracy of the S-G-SNV-PLS model was the highest at different critical temperatures, with Rc and Rp values of 0.854 and 0.819, respectively, and RMSEC and RMSEP values of 74.567 and 79.158, respectively.

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Quantitative Model for Diosgenin in Dioscorea zingiberensis C.H.Wright by NIRS Combined with TQ Software

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.807-809.2085 ◽

2013 ◽

Vol 807-809 ◽

pp. 2085-2091 ◽

Cited By ~ 1

Author(s):

Yan Bai ◽

Hai Yan Gong ◽

Chun Fang Zuo ◽

Jing Wei Lei ◽

Xiao Yan Duan ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Near Infrared ◽

Correlation Coefficients ◽

Quantitative Model ◽

Calibration Model ◽

Mean Square ◽

Correction Model ◽

Dioscorea Zingiberensis

To determine the Diosgenin in Dioscorea zingiberensis C.H.Wright by near-infraed spectroscopy (NIRS) combined with TQ software. The near-infrared sprectra and HPLC values of the Diosgenin in Dioscorea zingiberensis C.H.Wright from different areas were collected, and the quantitative calibration model was established with TQ software. And then the prediction samples were anylized by the model. The correlation coefficients (R2), the root-mean-square error of calibration (RMSEC) and the root-mean-square error of cross-validation (RMSECV) of the quantitative calibration model for diosgenin were 0.96459, 0.0999 and 0.30041 respectively; the correlation coefficients of prediction (r2) and the root-mean-square error of prediction (RMSEP) were 0.9634 and 0.128. The method is fast, convenient, non-polluted and accurate. The correction model could be used to predict the diosgenin in Dioscorea zingiberensis C.H.Wright.

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