scholarly journals Estimation Leaf Area by Composite Leaves of Canavalia rosea Seedlings Through Linear Dimensions From Last Leaflet

2019 ◽  
Vol 11 (9) ◽  
pp. 299
Author(s):  
Ana Paula Braido Pinheiro ◽  
Vinicius de Souza Oliveira ◽  
Karina Tiemi Hassuda dos Santos ◽  
Jéssica Sayuri Hassuda Santos ◽  
Gleyce Pereira Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Mean Absolute Error ◽  
Linear Equations ◽  
Absolute Error ◽  
Mean Values ◽  
Linear Dimensions ◽  
Power Models ◽  
Independent Variable ◽  
The Mean ◽  
Non Linear Equations

The objective of this work was to propose models of equations from measurements of the linear dimensions of the last leaflet for the estimation of the leaf area of the composite leaves of Canavalia rosea. For this purpose, 441 composite leaves of 198 seedlings were used, 45 days after sowing, produced in nursery and belonging to the Federal University of Espírito Santo, Campus São Mateus, located in the municipality of São Mateus, North of the State of Espírito Santo, Brazil. The length (L) along the main midrib and the maximum leaf width (W) of the last leaflet of each composite leaf, as well as the leaf area of all leaflets, were measured. Subsequently, it was determined the product of the multiplication of the length with the width (LW) and leaf area observed (OLA) from the sum of leaf area of leaflets in front of these measures were adjusted linear and non-linear equations of linear first degree, quadratic and power models, where, OLA was used as a dependent variable in function of L, W and LW as independent variable. Based on the models tested, we obtained equations for the estimated leaf area (ELA). The mean values of ELA and OLA were compared by Student's t test 5% probability. The mean absolute error (MAE), the root mean square error (RMSE) and the Willmott d index, were determined as criteria for validation. The best adjusted equation was chosen through the non-significant values in the comparison of the means of ELA and OLA, values of MAE and RMSE closer to zero, value of the index d near the unitary and higher values of R2. Thus, the leaf area of the composite leaf of C. rosea seedlings can be estimated by the power model represented by equation ELA = 2.2951 (LW)0.9474 quickly, easily and non-destructively.

scholarly journals Alometric Model for Estimation of Leaf Area of Garcinia brasiliensis Mart. Through Non-destructive Method

2019 ◽  
Vol 11 (10) ◽  
pp. 154
Author(s):  
Vinicius de Souza Oliveira ◽  
Cássio Francisco Moreira de Carvalho ◽  
Juliany Morosini França ◽  
Flávia Barreto Pinto ◽  
Karina Tiemi Hassuda dos Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Linear Equations ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Maximum Width ◽  
Linear Dimensions ◽  
Length Width ◽  
Independent Variable ◽  
The Mean ◽  
Non Destructive

The objective of the present study was to test and establish mathematical models to estimate the leaf area of Garcinia brasiliensis Mart. through linear dimensions of the length, width and product of both measurements. In this way, 500 leaves of trees with age between 4 and 6 years were collected from all the cardinal points of the plant in the municipality of São Mateus, North of the State of Espírito Santo, Brazil. The length (L) along the main midrib, the maximum width (W), the product of the length with the width (LW) and the observed leaf area (OLA) were obtained for all leaves. From these measurements were adjusted linear equations of first degree, quadratic and power, in which OLA was used as dependent variable as function of L, W and LW as independent variable. For the validation, the values of L, W and LW of 100 random leaves were substituted in the equations generated in the modeling, thus obtaining the estimated leaf area (ELA). The values of the means of ELA and OLA were tested by Student’s t test 5% of probability. The mean absolute error (MAE), root mean square error (RMSE) and Willmott’s index d for all proposed models were also determined. The choice of the best model was based on the non significant values in the comparison of the means of ELA and OLA, values of MAE and RMSE closer to zero and value of the index d and coefficient of determination (R2) close to unity. The equation that best estimates leaf area of Garcinia brasiliensis Mart. in a way non-destructive is the power model represented by por ELA = 0.7470(LW)0.9842 and R2 = 0.9949.

scholarly journals Estimation of Leaf Area of Jackfruit Through Non-destructive Method

2019 ◽  
Vol 11 (6) ◽  
pp. 77
Author(s):  
Vinicius de Souza Oliveira ◽  
Leonardo Raasch Hell ◽  
Karina Tiemi Hassuda dos Santos ◽  
Hugo Rebonato Pelegrini ◽  
Jéssica Sayuri Hassuda Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Linear Equations ◽  
Absolute Error ◽  
Order Model ◽  
Sanitary Condition ◽  
Federal Institute ◽  
Independent Variable ◽  
Mathematical Equations ◽  
The Mean ◽  
Non Destructive

The objective of this study was to determine mathematical equations that estimate the leaf area of jackfruit (Artocarpus heterophyllus) in an easy and non-destructive way based on linear dimensions. In this way, 300 leaves of different sizes and in good sanitary condition of adult plants were collected at the Federal Institute of Espírito Santo, Campus Itapina, located in Colatina, municipality north of the State of Espírito Santo, Brazil. Were measured The length (L) along the midrib and the maximum leaf width (W), observed leaf area (OLA), besides the product of the multiplication of length with width (LW), length with length (LL) and width with width (WW). The models of linear equations of first degree, quadratic and power and their respective R2 were adjusted using OLA as dependent variable in function of L, W and LW, LL and WW as independent variable. The data were validated and the estimated leaf area (ELA) was obtained. The means of ELA and OLA were compared by Student’s t test (5% probability) and were evaluated by the mean absolute error (MAE) and root mean square error (RMSE) criteria. The choice of the best model was based on non-significant comparative values of ELA and OLA, in addition to the closest values of zero of EAM and RQME. The jackfruit leaf area estimate can be determined quickly, accurately and non-destructively by the linear first-order model with LW as the independent variable by equation ELA = 1.07451 + 0.71181(LW).

scholarly journals Non-destructive Method for Estimating the Leaf Area of Pear cv. ‘Triunfo’

2019 ◽  
Vol 11 (7) ◽  
pp. 14
Author(s):  
Vinicius de Souza Oliveira ◽  
Karina Tiemi Hassuda dos Santos ◽  
Andréia Lopes de Morais ◽  
Gleyce Pereira Santos ◽  
Jéssica Sayuri Hassuda Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Mean Squared Error ◽  
Linear Equations ◽  
Absolute Error ◽  
Equation Model ◽  
Maximum Width ◽  
Northern Side ◽  
Mathematical Equations ◽  
The Mean ◽  
High Degree

The present study had as objective to determine mathematical equations to estimate the leaf area of pear cv. ‘Triunfo’ using linear dimensions of the leaves. For that, 300 healthy leaves of different sizes from each quadrant of plants from the small farm of Boa Vista located in the city of Montanha, at the northern side of the State of Espírito Santo, Brazil were used. The length (L) along the main vein was measured, along with the maximum width (W) of the leaf blade and observed leaf area (OLA), in addition to the product of the length and width (LW) of each leaf. From these measurements models of linear equations of first degree, quadratic and power were adjusted and their respective R2, using OLA as dependent variable and L, W and LW as independent variable. Based on the proposed equations, the data were validated obtaining the estimated leaf area (ELA). The mean of the ELA and OLA were compared by Student t test 5% probability. The mean error (E), the mean absolute error (MAE) and the root mean squared error (RMSE) was also used as validation criterion. The best equation model was defined based on the non-significant values from the comparison of means of ELA and OLA, E, MAE and RMSE values closer to zero and highest R2. The leaf area of pear cv. ‘Triunfo’ can be estimated by the equation ELA = -0.432338 + 0.712862(LW) non-destructively and with a high degree of precision.

scholarly journals Estimation of Leaf Area by Linear Dimensions in Coffea dewevrei

2019 ◽  
pp. 1-8
Author(s):  
Omar Schmildt ◽  
Enilton Nascimento de Santana ◽  
Vinicius de Souza Oliveira ◽  
Rafael Ruy Gouvea ◽  
Lucas Corrêa Souza ◽  
...  
Keyword(s):  
Leaf Area ◽  
Technical Assistance ◽  
Absolute Error ◽  
Equation Model ◽  
Quadratic Model ◽  
Linear Dimensions ◽  
Independent Variable ◽  
Student’S T ◽  
Simple Linear Equation ◽  
The City

The objective of this research was to select the equation that best estimates the leaf area of the coffee tree Coffea dewevrei, from the linear dimensions of the leaves. For this purpose, 140 leaves of adult plants were collected from the Capixaba Institute for Research, Technical Assistance and Rural Extension, in the city of Linhares, North of the State of Espírito Santo, Brazil. The length (L), the width (W), the product of the multiplication between the length and width (LW) and the leaf area observed (OLA) were determined from all leaves. For the modeling, a 100 leaves sample was used, where OLA was used as a dependent variable in function of L, W and LW as independent variable, being obtained the following models: linear first degree, quadratic and power. For the validation, a sample of 40 leaves was used, where the values of L, W LW were substituted in the equations generated in the modeling, thus obtaining the estimated leaf area (ELA). A simple linear equation model was fitted for each modeling equation relating ELA in function of OLA. The hypotheses H0: β0 = 0 versus Ha: β0 ≠ 0 and H0: β1 = 1 versus Ha: β1 ≠ 1, were tested using Student's t test at 5% probability. The mean absolute error (MAE), root mean square error (RMSE) and Willmott's index d for all equations were also determined. The best model that estimates the area of Coffea dewevrei was chosen through the following criteria: β0 not different from zero, β1 not different from one, MAE and RMSE values closer to zero and index d closer to the unit. The area of the leaves can be determined by its greater width (W), through the quadratic model equation ELA=-10.255+1.020(W)+1.293(W)2.

scholarly journals Mathematical Modeling to Estimation Leaf Area of Pimenta dioica from Linear Dimensions

2019 ◽  
pp. 1-8
Author(s):  
Vinicius De Souza Oliveira ◽  
Lucas Caetano Gonçalves ◽  
Amanda Costa ◽  
Karina Tiemi Hassuda dos Santos ◽  
Jéssica Sayuri Hassuda Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Linear Equations ◽  
Selection Criterion ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Maximum Width ◽  
Independent Variable ◽  
Pimenta Dioica ◽  
Non Destructive

The objective of this work was to obtain regression equations and to indicate the most appropriate from different mathematical models for the estimation of the leaf area of ​​ Allspice (Pimenta dioica) by non - destructive method. 500 leaves of plants located in the municipality of São Mateus, North of Espírito Santo State, Brazil, were collected, 400 of which were used to adjust the equations and 100 for validation. The length (L) along the main midrib, the maximum width (W), the product of the length with the width (LW) and the observed leaf area (OLA) were measured from all leaves. We fitted models of linear equations of first degree, quadratic and power, where OLA was the dependent variable in function of L, W and LW. From the 100 sheets intended for validation, and using the adjusted equations for each mathematical model, the estimated leaf area (ELA) was obtained. Subsequently, a simple linear regression was fitted for each model of the proposed equation in which ELA was the dependent variable and OLA the independent variable. The mean absolute error (MAE), the root mean square error (RMSE) and Willmott's index d also determined. The best fit had as selection criterion the non-significance of the comparative means of ELA and OLA, MAE and RMSE values ​​closer to zero and value of the coefficient of determination coefficient (R2) close to one. Thus, the power model (ELA = 0.7605(LW)0.9926, R2 = 0.9764, MAE = 1.0066, RMSE = 1.7759 and d = 0.9950) based on the product of length and width (LW) is the most appropriate for estimating the leaf area of ​​Pimenta dioica.

scholarly journals Determination of the Leaflet Area of Schinus terebinthifolius Raddi in Function of Linear Dimensions

2019 ◽  
Vol 11 (14) ◽  
pp. 198
Author(s):  
Vinicius de Souza Oliveira ◽  
André Monzoli Covre ◽  
Drielly Stephania Gouvea ◽  
Luciano Canal ◽  
Karina Tiemi Hassuda dos Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Absolute Error ◽  
T Test ◽  
Quadratic Model ◽  
Angular Coefficient ◽  
Schinus Terebinthifolius ◽  
Linear Coefficient ◽  
Mean Values ◽  
Mathematical Equations ◽  
The Mean

The objective of this study was to select mathematical equations that best fit the estimation of the leaf area of pink pepper (Schinus terebinthifolius Raddi) from the linear leaflet dimensions. 500 leaflets with different physiological ages of a commercial plantation were collected, located in the region of Gameleira, municipality of São Mateus, North of the State of Espírito Santo, Brazil. Was measured the length (L) along the main midrib, the largest width (W) and the observed leaf area (OLA) of each sheet. The product of the multiplication between L and W of the leaflets (LW) was determined. For the modeling the measurements of 400 leaflets were used, where OLA was used in function of L, W or LW. Based on the models found, we obtained the estimated leaf area (ELA). A simple linear regression was fitted for each proposed model of OLA in function of ELA. We tested the hypotheses H0: β0 = 0 versus Ha: β0 ≠ 0 and H0: β1 = 1 versus Ha: β1 ≠ 1, using Student’s t test at 5% probability. The mean values of ELA and OLA were compared by Student’s t test 5% probability. It was determined the mean error (E), mean absolute error (MAE), root mean square error (RMSE) and Willmott d index. The best adjusted equation was chosen by linear coefficient (β0) not different from zero, angular coefficient (β1) not unlike one, non-significant values of ELA and OLA, E, EAM and RQME closer to zero and Willmott’s index d closer to one. In this way, the leaf area of leaflets of Schinus terebinthifolius Raddi can be estimated by the quadratic model equation ELA = -2.6646 + 2.2124W + 1.3953(W)2 , using only the width of the leaves as a measure.

POTRET PERILAKU RELIGIUS MAHASISWA UIN SAYARIF HIDAYATULLAH JAKARTA

Al-Risalah ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. 126-148
Author(s):  
Ay Maryani
Keyword(s):  
Religious Behavior ◽  
Male And Female ◽  
Mean Values ◽  
Significance Level ◽  
Average Value ◽  
Environment Variable ◽  
Independent Variable ◽  
The Mean ◽  
Gender Factor ◽  
Level 2

This study describes the religious behavior of UIN Syarif Hidayatullah Jakarta students. The variables used are internal and external environment as independent variable and religious behavior as dependent variable. The internal environment variable consists of gender factor, (male and female). The external environmental variables comprise the faculty environment, the present resident and the place to grow. The present resident consist of dormitories, boarding houses and parents' homes and the place to grow consist of urban and rural environment. Religious behavior variables consist of habluminallah behavior and habluminannas behavior. Habluminallah's behavior measured by (1) knowledge of faith and worship, (2) attitudes toward faith and worship, and (3) practice of faith and worship. The habluminannas variable measured by (1) Islamic behavior for them self, like honest, discipline and good work / studyethics, trust and concern on legality, (2) Islamic behavior with others, like generous, cooperation, caring, respect to the people's rights and tolerance and (3) Islamic behavior for the natural surroundings, like love of nature and nature conservation efforts. The methodology used was (1) Statistical descriptive, (2) MANOVA (Multivariate Analysis of Variance) and (3) Independent sample t-test. The results showed the religious behavior UIN Syarif Hidayatullah Jakarta students has a very good category. This is indicated by the mean values for the behavior of haluminallah and habluminannas of 158.85 and 178.76,  espectively. The average value of habluminallah behaviour in the range of values "145-180" with the category of "very good" and habluminannas behavior in the range value "165-205" with the category "very good. Habluminallah and habluminannas behavior are different for each faculty. This is indicated by the significance level of Pillai Trace, Wilk Lambda, Hotelling Trace, Roy's Largest Root of 0.00 (<0.05). Habluminallah and habluminannas behavior are the same for respondents who live in dormitories, boarding houses, and parents' homes. This is indicated by the value of F test and significance at Wilk's Lambda respectively for 2.055 and 0.085 (>0.05). Habluminallah and habluminannas behaviors are similar for urban and rural respondents. This is known from the sig level. (2-tailed) for habluminallah and habluminannas behavior of 0.317 and 0.245 (> 0.05), respectively. Habluminallah and habluminannas behaviors are similar for male and female. This is known from the sig level. (2-tailed) for habluminallah and habluminannas behavior of 0.950 and 0.307 (> 0.05),respectively. The results of this study are expected to be used to develop university policies that can enhance the Islamic values of UIN Syarif Hidayatullah Jakarta.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

Visualizing the Variance of a Random Variable

2011 ◽  
Vol 18 (01) ◽  
pp. 71-85
Author(s):  
Fabrizio Cacciafesta
Keyword(s):  
Stochastic Dominance ◽  
Mean Absolute Error ◽  
Random Variable ◽  
Absolute Error ◽  
Second Order ◽  
Taylor Formula ◽  
Order Stochastic Dominance ◽  
The Mean ◽  
Second Order Stochastic Dominance ◽  
Options Theory

We provide a simple way to visualize the variance and the mean absolute error of a random variable with finite mean. Some application to options theory and to second order stochastic dominance is given: we show, among other, that the "call-put parity" may be seen as a Taylor formula.

scholarly journals Direct Calculation of Thermodynamic Wet-Bulb Temperature as a Function of Pressure and Elevation

2013 ◽  
Vol 30 (8) ◽  
pp. 1757-1765 ◽  
Author(s):  
Sayed-Hossein Sadeghi ◽  
Troy R. Peters ◽  
Douglas R. Cobos ◽  
Henry W. Loescher ◽  
Colin S. Campbell
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Direct Calculation ◽  
Ambient Air ◽  
Absolute Error ◽  
Mean Square ◽  
Air Temperatures ◽  
Order Polynomial ◽  
The Mean ◽  
Very High

Abstract A simple analytical method was developed for directly calculating the thermodynamic wet-bulb temperature from air temperature and the vapor pressure (or relative humidity) at elevations up to 4500 m above MSL was developed. This methodology was based on the fact that the wet-bulb temperature can be closely approximated by a second-order polynomial in both the positive and negative ranges in ambient air temperature. The method in this study builds upon this understanding and provides results for the negative range of air temperatures (−17° to 0°C), so that the maximum observed error in this area is equal to or smaller than −0.17°C. For temperatures ≥0°C, wet-bulb temperature accuracy was ±0.65°C, and larger errors corresponded to very high temperatures (Ta ≥ 39°C) and/or very high or low relative humidities (5% &lt; RH &lt; 10% or RH &gt; 98%). The mean absolute error and the root-mean-square error were 0.15° and 0.2°C, respectively.

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