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.

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 ESTIMATING MOISTURE ABSORPTION KINETICS OF SWORD BEANS DURING SOAKING USING MIDILLI MODEL

2020 ◽  
Vol 5 (2) ◽  
pp. 76-80
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 ◽  
Moisture Absorption ◽  
Moisture Diffusivity ◽  
Moisture Ratio ◽  
Coefficient Of Determination ◽  
Diffusivity Coefficient ◽  
Mean Square ◽  
Kinetics Of

Midilli model was used to estimate the moisture ratio of four varieties of Sword beans. The experiment was carried out using distilled water at temperature of 530C for 100 minutes. The standard model of water absorption was fitted into the experimental data. Coefficient of determination [R2] and root mean square error were used to evaluate the model. The Midilli model was chosen based on maximum value of Coefficient of determination and minimum value of root mean square error. The result showed that Midilli model is the most appropriate for TCG-4 with R2 value of 0.9924 and RMSE value of 0.1758 to estimate moisture ratio changes versus time in soaking. The moisture ratio against soaking time was plotted in each case using Midilli equation. The plotted curves of each variety of bean indicated that moisture ratio decreases with increasing in time. The effective moisture diffusivity coefficient of four varieties of varieties of Sword beans increased.

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 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 Adaptação transcultural Portugal-Brasil do Inventário de Burnout de Maslach para estudantes

2012 ◽  
Vol 46 (5) ◽  
pp. 816-824 ◽  
Author(s):  
Juliana Alvares Duarte Bonini Campos ◽  
João Maroco
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Mean Square ◽  
Fit Index

OBJETIVO: Realizar a adaptação transcultural da versão em português do Inventário de Burnout de Maslach para estudantes e investigar sua confiabilidade, validade e invariância transcultural. MÉTODOS: A validação de face envolveu participação de equipe multidisciplinar. Foi realizada validação de conteúdo. A versão em português foi preenchida em 2009, pela internet, por 958 estudantes universitários brasileiros e 556 portugueses da zona urbana. Realizou-se análise fatorial confirmatória utilizando-se como índices de ajustamento o χ²/df, o comparative fit index (CFI), goodness of fit index (GFI) e o root mean square error of approximation (RMSEA). Para verificação da estabilidade da solução fatorial conforme a versão original em inglês, realizou-se validação cruzada em 2/3 da amostra total e replicada no 1/3 restante. A validade convergente foi estimada pela variância extraída média e confiabilidade composta. Avaliou-se a validade discriminante e a consistência interna foi estimada pelo coeficiente alfa de Cronbach. A validade concorrente foi estimada por análise correlacional da versão em português e dos escores médios do Inventário de Burnout de Copenhague; a divergente foi comparada à Escala de Depressão de Beck. Foi avaliada a invariância do modelo entre a amostra brasileira e a portuguesa. RESULTADOS: O modelo trifatorial de Exaustão, Descrença e Eficácia apresentou ajustamento adequado (χ²/df = 8,498; CFI = 0,916; GFI = 0,902; RMSEA = 0,086). A estrutura fatorial foi estável (λ: χ²dif = 11,383, p = 0,50; Cov: χ²dif = 6,479, p = 0,372; Resíduos: χ²dif = 21,514, p = 0,121). Observou-se adequada validade convergente (VEM = 0,45;0,64, CC = 0,82;0,88), discriminante (ρ² = 0,06;0,33) e consistência interna (α = 0,83;0,88). A validade concorrente da versão em português com o Inventário de Copenhague foi adequada (r = 0,21;0,74). A avaliação da validade divergente do instrumento foi prejudicada pela aproximação do conceito teórico das dimensões Exaustão e Descrença da versão em português com a Escala de Beck. Não se observou invariância do instrumento entre as amostras brasileiras e portuguesas (λ:χ²dif = 84,768, p < 0,001; Cov: χ²dif = 129,206, p < 0,001; Resíduos: χ²dif = 518,760, p < 0,001). CONCLUSÕES: A versão em português do Inventário de Burnout de Maslach para estudantes apresentou adequada confiabilidade e validade, mas sua estrutura fatorial não foi invariante entre os países, apontando ausência de estabilidade transcultural.

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.

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.

scholarly journals Modelagem de Equações Estruturais e os testes de seleção – Caso do vestibular da Universidade Estadual do Ceará

2021 ◽  
Author(s):  
José Leudo Maia ◽  
Marcos Antonio Martins Lima
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Mean Square ◽  
Fit Index ◽  
Tucker Lewis Index

Resumo Este estudo teve como objetivo fazer uso da Modelagem de Equações Estruturais – MEE, para avaliar a qualidade do modelo empregado pela Universidade Estadual do Ceará (UECE), no processo de seleção de seus vestibulandos, o qual é baseado na Teoria Clássica dos Testes (TCT), assim como propor ajuste a esse modelo pelo uso da Análise Fatorial de Segunda Ordem e da Análise de Regressão, via MEE. Utilizou-se um banco de dados composto dos resultados das provas de 11.060 candidatos ao vestibular de 2018.1, cujo tratamento se deu por meio do software IBM SPSS Amos (2013, v.22), obtendo-se os seguintes indicadores de qualidade: CFI ( Comparative Fit Index ) = 0,925; GFI ( Goodness-of-fit Index ) = 0,965; TLI ( Tucker Lewis Index ) = 0,922, e RMSEA ( Root Mean Square Error of Aproximation ) = 0,019. Juntos, esses indicadores demonstraram que o modelo é robusto e bastante consistente, apresentando um R 2 (Coeficiente de Correlação de Pearson ) = 0,965, indicando que a proporção das covariâncias observadas entre as variáveis manifestas e explicada pelo modelo ajustado é bastante significativa. Todas as variáveis do modelo ajustado apresentaram elevados coeficientes de regressão com valores entre 0,87 e 0,99, permitindo uma boa discriminação entre as notas dos vestibulandos, principalmente aqueles com o mesmo número de questões respondidas corretamente.

scholarly journals Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes

2021 ◽  
Vol 12 ◽  
Author(s):  
Hui Sun ◽  
Meichen Feng ◽  
Lujie Xiao ◽  
Wude Yang ◽  
Guangwei Ding ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Winter Wheat ◽  
Water Content ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Coefficient Of Determination ◽  
Plant Water ◽  
Mean Square ◽  
Proximal Sensing ◽  
Sensing Data

Real-time, nondestructive, and accurate estimation of plant water status is important to the precision irrigation of winter wheat. The objective of this study was to develop a method to estimate plant water content (PWC) by using canopy spectral proximal sensing data. Two experiments under different water stresses were conducted in 2014–2015 and 2015–2016. The PWC and canopy reflectance of winter wheat were collected at different growth stages (the jointing, booting, heading, flowering, and filling stages in 2015 and the jointing, booting, flowering, and filling stages in 2016). The performance of different spectral transformation approaches was further compared. Based on the optimal pretreatment, partial least squares regression (PLSR) and four combination methods [i.e., PLSR-stepwise regression (SR), PLSR-successive projections algorithm (SPA), PLSR-random frog (RF), and PLSR-uninformative variables elimination (UVE)] were used to extract the sensitive bands of PWC. The results showed that all transformed spectra were closely correlated to PWC. The PLSR models based on the first derivative transformation method exhibited the best performance (coefficient of determination in calibration, R2C = 0.96; root mean square error in calibration, RMSEC = 20.49%; ratio of performance to interquartile distance in calibration, RPIQC = 9.19; and coefficient of determination in validation, R2V = 0.86; root mean square error in validation, RMSEV = 46.27%; ratio of performance to interquartile distance in validation, RPIQV = 4.34). Among the combination models, the PLSR model established with the sensitive bands from PLSR-RF demonstrated a good performance for calibration and validation (R2C = 0.99, RMSEC = 11.53%, and RPIQC = 16.34; and R2V = 0.84, RMSEV = 44.40%, and RPIQV = 4.52, respectively). This study provides a theoretical basis and a reference for estimating PWC of winter wheat by using canopy spectral proximal sensing data.

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