scholarly journals Sinusoidal function to describe the growth curve of dairy heifers

2019 ◽  
Vol 59 (6) ◽  
pp. 1039
Author(s):  
H. Darmani Kuhi ◽  
N. Ghavi Hossein-Zadeh ◽  
S. López ◽  
S. Falahi ◽  
J. France
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Dairy Heifers ◽  
Growth Functions ◽  
Best Fit ◽  
Sinusoidal Function

The objective of the present study is to introduce a sinusoidal function into dairy research and production by applying it to bodyweight records (from 1 to 24 months) from six dairy cow breeds reported by the Dairy Heifer Evaluation Project of Penn State Extension (USA) from 1991 to 1992. The function was evaluated with regard to its ability to describe the relationship between bodyweight and age in dairy heifers, and then compared with seven standard growth functions, namely monomolecular, logistic, Gompertz, von Bertalanffy, Richards, Schumacher and Morgan. The models were fitted to monthly bodyweight records of dairy heifers using non-linear regression to derive estimates of the parameters of each function. The models were tested for goodness of fit by using adjusted coefficient of determination, root mean square error, Akaike’s information criterion and Bayesian information criterion. Values of adjusted coefficient of determination were generally high for all models, suggesting the generally appropriate fit of the models to the data. The sinusoidal function provided the best fit of the growth curves for Brown Swiss, Guernsey and Milking Shorthorn breeds due to the lowest values of root mean square error, Akaike’s information criterion and Bayesian information criterion. According to the chosen statistical criteria, the Richards function provided the best fit for Ayrshire heifers, and the monomolecular the best for Holstein and Jersey. The least accurate estimates were obtained with the logistic. In conclusion, the sinusoidal function introduced here can be considered as an appropriate alternative to standard growth functions when modelling growth patterns in dairy heifers.

scholarly journals Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

2020 ◽  
pp. 23
Author(s):  
Anggita Rosiana Putri ◽  
Abdul Rohman ◽  
Sugeng Riyanto ◽  
Widiastuti Setyaningsih
Keyword(s):  
Root Mean Square Error ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

Estimating Center of Mass Kinematics During Perturbed Human Standing Using Accelerometers

2021 ◽  
pp. 1-10
Author(s):  
Sandra K. Hnat ◽  
Musa L. Audu ◽  
Ronald J. Triolo ◽  
Roger D. Quinn
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Squared Error ◽  
Balance Control ◽  
Center Of Mass ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Cord Injury ◽  
Movement Type

Estimating center of mass (COM) through sensor measurements is done to maintain walking and standing stability with exoskeletons. The authors present a method for estimating COM kinematics through an artificial neural network, which was trained by minimizing the mean squared error between COM displacements measured by a gold-standard motion capture system and recorded acceleration signals from body-mounted accelerometers. A total of 5 able-bodied participants were destabilized during standing through: (1) unexpected perturbations caused by 4 linear actuators pulling on the waist and (2) volitionally moving weighted jars on a shelf. Each movement type was averaged across all participants. The algorithm’s performance was quantified by the root mean square error and coefficient of determination (R2) calculated from both the entire trial and during each perturbation type. Throughout the trials and movement types, the average coefficient of determination was 0.83, with 89% of the movements with R2 > .70, while the average root mean square error ranged between 7.3% and 22.0%, corresponding to 0.5- and 0.94-cm error in both the coronal and sagittal planes. COM can be estimated in real time for balance control of exoskeletons for individuals with a spinal cord injury, and the procedure can be generalized for other gait studies.

scholarly journals Modeling of COVID-19 Outbreak Indicators in China Between January and June

2020 ◽  
pp. 1-9
Author(s):  
Senol Celik ◽  
Handan Ankarali ◽  
Ozge Pasin
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Bayesian Information Criterion ◽  
Linear Trend ◽  
Growth Curves ◽  
Information Criterion ◽  
Time Series Models ◽  
Cumulative Number ◽  
Mean Square ◽  
Nonlinear Growth

ABSTRACT Objectives: The objective of this study is to compare the various nonlinear and time series models in describing the course of the coronavirus disease 2019 (COVID-19) outbreak in China. To this aim, we focus on 2 indicators: the number of total cases diagnosed with the disease, and the death toll. Methods: The data used for this study are based on the reports of China between January 22 and June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model. Results: Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths. Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85,589 and 4639, respectively. Conclusion: The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations.

scholarly journals Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 43 ◽  
Author(s):  
Dariusz Młyński ◽  
Andrzej Wałęga ◽  
Andrea Petroselli ◽  
Flavia Tauro ◽  
Marta Cebulska
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Daily Precipitation ◽  
Probability Distributions ◽  
Random Variables ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Probability Of Exceedance

The aim of this study was to determine the best probability distributions for calculating the maximum annual daily precipitation with the specific probability of exceedance (Pmaxp%). The novelty of this study lies in using the peak-weighted root mean square error (PWRMSE), the root mean square error (RMSE), and the coefficient of determination (R2) for assessing the fit of empirical and theoretical distributions. The input data included maximum daily precipitation records collected in the years 1971–2014 at 51 rainfall stations from the Upper Vistula Basin, Southern Poland. The value of Pmaxp% was determined based on the following probability distributions of random variables: Pearson’s type III (PIII), Weibull’s (W), log-normal, generalized extreme value (GEV), and Gumbel’s (G). Our outcomes showed a lack of significant trends in the observation series of the investigated random variables for a majority of the rainfall stations in the Upper Vistula Basin. We found that the peak-weighted root mean square error (PWRMSE) method, a commonly used metric for quality assessment of rainfall-runoff models, is useful for identifying the statistical distributions of the best fit. In fact, our findings demonstrated the consistency of this approach with the RMSE goodness-of-fit metrics. We also identified the GEV distribution as recommended for calculating the maximum daily precipitation with the specific probability of exceedance in the catchments of the Upper Vistula Basin.

scholarly journals Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

2020 ◽  
Vol 12 (11) ◽  
pp. 1814
Author(s):  
Phamchimai Phan ◽  
Nengcheng Chen ◽  
Lei Xu ◽  
Zeqiang Chen
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Vegetation Index ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

scholarly journals Applicability of Near Infrared Reflectance Spectroscopy to Predict Amylose Contents of Single-Grain Maize

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2463
Author(s):  
Qing Dong ◽  
Qianqian Xu ◽  
Jiandong Wu ◽  
Beijiu Cheng ◽  
Haiyang Jiang
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Near Infrared Reflectance ◽  
Single Grain ◽  
Single Seeds

Near infrared reflectance spectroscopy (NIRS) and reference data were used to determine the amylose contents of single maize seeds to enable rapid, effective selection of individual seeds with desired traits. To predict the amylose contents of a single seed, a total of 1069 (865 as calibration set, 204 as validation set) single seeds representing 120 maize varieties were analyzed using chemical methods and performed calibration and external validation of the 150 single seeds set in parallel. Compared to various spectral pretreatments, the regression of partial least squares (PLS) with mathematical treatment of Harmonization showed the final optimization. The single-seed amylose contents showed the root mean square error of calibration (RMSEC) of 2.899, coefficient of determination for calibration (R2) of 0.902, and root mean square error of validation (RMSEV) of 2.948. In external validations, the coefficient of determination in cross-validation (r2), root mean square error of the prediction (RMSEP) and ratio of the standard deviation to SEP (RPD) were 0.892, 2.975 and 3.086 in the range of 20–30%, respectively. Therefore, NIRS will be helpful to breeders for determining the amylose contents of single-grain maize.

scholarly journals ARTIFICIAL NEURAL NETWORK APPLIED IN FORECASTING THE COMPOSITION OF MUNICIPAL SOLID WASTE IN IASI, ROMANIA

2021 ◽  
Vol 29 (3) ◽  
pp. 368-380
Author(s):  
Cristina Ghinea ◽  
Petronela Cozma ◽  
Maria Gavrilescu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Root Mean Square Error ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Mean Square Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Ann Model

Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.

scholarly journals Growth Curves of Texel Male Lambs

2018 ◽  
Vol 44 (1) ◽  
pp. 6
Author(s):  
Michelli De Fátima Sieklicki ◽  
Victor Breno Pedrosa ◽  
Caroline Gomes Rocha ◽  
Raphael Patrick Moreira ◽  
Paula Roberta Falcão ◽  
...  
Keyword(s):  
Growth Rate ◽  
Mean Square Error ◽  
Nonlinear Models ◽  
Growth Curves ◽  
Coefficient Of Determination ◽  
Good Growth ◽  
Von Bertalanffy ◽  
Mean Square ◽  
The Mean ◽  
Best Fit

Background: The consumption of lamb meat is growing due to improved farming methods. However, to be economically feasible, the animal should stand out for its precocity, fast finishing and muscular force, such as seen in Texel breed. Besides, knowledge about weight gain and development can facilitate the selection of the best animals, and allow a better fitting to farming systems. Growth curves are an effective method that describes animal development, modeling the relationship between weight and age and help to predict the growth rate. Thus, this study aimed to analyze which nonlinear model, including Brody, Gompertz, Von Bertalanffy and Logistic best describe the growth curve of Texel sheep.Materials, Methods & Results: In this experiment, the lambs were kept in confined system while the ewes, in a semi-extensive system. This study followed 42 Texel male lambs, which were confined from birth to slaughter, and fed concentrated feed (3% of body weight) and corn silage (average 1.5 kg/animal/day), 4 times a day. The lambs were weighed fortnightly, in different classes considered as follows, weight at birth (BW), 15 days (P15), 30 days (P30), 45 days (P45), 60 days (P60), 75 days (P75), 90 days (P90), 105 days (P105), and 120 days (P120), which was defined as the slaughtering weight. The growth curves were determined using the nonlinear models of Brody, Von Bertalanffy, Gompertz and Logistic. The following parameters were used in the curves, Y, slaughtering weight; A, asymptotic weight; k, growth rate, t, animal age; B, constant related to the initial weight; and, m, constant of the curve shape. The criteria used for selecting the model that best described the curve were the mean square error (MSE), which was calculated by dividing the sum of squared error by the number of observations, and also the coefficient of determination (R²), calculated as the square of the correlation between the observed and estimated weights. The average weights observed were as follows, 4.02 kg at birth, 21.68 kg at weaning (P60) and 32.55 kg at slaughtering (P120). The solution of the nonlinear models allows, thru the parameters, establish specific feeding programs and define the optimal slaughtering age. Furthermore, the coefficients of determination, with values close to 97.3%, showed good fits for all models. Still, considering the mean square error, where the lower value indicates the best fit to the data evaluated, the results were 13.1564 (Brody), 13.3421 (Von Bertalanffy), 13.4876 (Gompertz) and 13.6717 (Logistic). The results showed that Brody could be considered the model that best describes the growth rate up to 120 days old of Texel lambs.Discussion: Compared to other studies, the average weights obtained in the experiment varied widely. This large variation can be explained by the used rearing system that might favor or not the performance of lambs. However, the average weaning weight obtained was similar to several studies in the literature, confirming the potential of Texel breed. This breed demonstrated to be capable to provide a precocious animal, with good growth results from the early developmental stage until the slaughtering age. Regarding the growth curves, the Brody model was the best fit for the estimated and observed weights. Moreover, the coefficient of determination indicated good fits for all models. However, an important aspect is the negative correlation between the A and k parameters, demonstrating that the higher the animal growth rate, the lower its asymptotic size.

DRYING KINETICS AND MOISTURE DIFFUSIVITY OF FOUR VARIETIES OF BAMBARA BEANS

2021 ◽  
Vol 6 (1) ◽  
pp. 30-33
Author(s):  
E.O. Awotona ◽  
A.O. Alade ◽  
S.A. Adebanjo ◽  
O. Duduyemi ◽  
T.J. Afolabi
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Moisture Diffusivity ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Fit Statistics ◽  
Drying Behavior ◽  
Maximum Coefficient

Drying of bambara beans was studied at 40oC at every 30 minutes in a Laboratory oven. Effective moisture diffusivity ranges between 5.886 x 10-10 m2/s – 4.354 x 10-10 m2/s respectively. The statistical criteria used in evaluation of the model were maximum coefficient of determination R2 and minimum root mean square error [RMSE]. Determination for goodness of fit statistics for drying of the beans was carried out. Midilli model was used to predict the drying curve. The Midili model was found to produce accurate predictions for all the four varieties of bambara beans and the model was shown to be an excellent model for predicting drying behavior of TVSU-47 and the R2 value was 0.9971 and the value of root mean square error was 0.0149 respectively.

Detailed Dynamic Model of an Institutional Building in Hot and Humid Climate Conditions

2017 ◽  
Author(s):  
Junaid Bin Masood ◽  
Sajid Hussain ◽  
Ali AlAlili ◽  
Sara Zaidan ◽  
Ebrahim Al Hajri
Keyword(s):  
Energy Consumption ◽  
Root Mean Square Error ◽  
Dynamic Model ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Building Envelope ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Audit Process ◽  
Simulation Engine

This paper is an ASHRAE Level 3 study of the energy audit process carried out in an institutional building, The Umm Shaif Building, of The Petroleum Institute, Abu Dhabi, UAE. It undertakes the study by collecting data and conditional surveys. The energy loss locations are highlighted through psychrometric and infrared camera analysis. The detailed dynamic model has been simulated using the EnergyPlus® simulation engine. The details of the building envelope, and fenestration, the occupancy schedules, the equipment energy consumption and HVAC details are presented. The detailed building model is used to allocate the energy usage and identify key energy consumers. The main results are reported using monthly total energy consumption. The validation and calibration are performed through different statistical metrics including Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Coefficient of Variance Root Mean Square Error (CVRMSE). Finally, energy conservation measures are suggested with the energy and cost savings.

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