Modelling of greenhouse climate parameters with artificial neural network and multivariate adaptive regression splines approach

In this study, it is aimed to model some greenhouse climate parameters by using two different prediction tools based on machine learning and artificial intelligence. For this purpose, in the first part of the study, the data set which consists of indoor and outdoor measurements taken from 7 different points of the greenhouse for 12 months from a region with terrestrial climate was adapted for modeling. In the second part, the functional relationship between input (independent) and output (dependent) variables was examined by artificial neural network (ANN) and multivariate adaptive regression splines (MARS) methods. In the third part, the models were evaluated with performance criteria and the best estimation model is selected. Comparison of ANN and MARS models indicated that MARS performs better than ANN with lesser values of MAPE (mean absolute percentage error), RMSE (root mean square error) and MAD (mean absolute deviation), and slightly higher value of R2 (coefficient of determination) in order to predict mean temperature (Tmcan, °C) and relative humidity (RHmcan, %). Based on these findings, it was observed that MARS method could provide a more detailed modeling as an alternative to ANN in developing comprehensive greenhouse climate mechanization. © 2019 Parlar Scientific Publications. All rights reserved

Establishment of optimum regression models and determination of relationships between body measurements and slaughter traits in Japanese quails by path analysis

Path analysis was used to investigate direct, indirect and total effects of some morphological measurements on slaughter and carcass traits in Japanese quails. Bodyweight, shank length, shank diameter, breast circumference and body length measurements were taken from 219 Japanese quails. Bivariate correlations between carcass weight and morphological traits in quails ranged from 0.405 to 0.864. The direct effect of bodyweight on carcass weight was the strongest in the study and (path coefficient of 0.85) positively influenced carcass weight (P 0.05). These traits were indirectly realised mostly by shank diameter. Thus, they were dropped from the final regression equations to obtain much more simplified prediction models. The optimum multiple regression equation for Japanese quails included bodyweight, with coefficient of determination (R2) of 0.7463. The correlation between characters was determined in more detail by using path analysis in this study. Thus, it was shown that path analysis could be used for selecting a variable. The forecast indices obtained in this study could aid in weight estimation, selection and breeding programs. © 2015 CSIRO

-> L-3 and L-2 -> L-3) for Hg and Bi in molecules

The f(12), f(13) and f(23) Coster-Kronig (CK) transitions (that is L-1 --> L-2, L-1 --> L-3 and L-2 --> L-3 transitions) in Hg and Bi in molecules were studied at 59.5 keV excitation energy using an Si(Li) detector. A change in the CK transition probabilities was observed for different molecules. The change in the values for Hg compounds were greater than those for Bi compounds. Because we did not have pure Bi in the laboratory and also were not able to have specimen preparation conditions for pure Hg, we did not obtain experimental values for elemental Bi and Hg. Copyright (C) 2002 John Wiley Sons, Ltd

-> L-3 and L-2 -> L-3) for Hg and Bi in molecules

The f(12), f(13) and f(23) Coster-Kronig (CK) transitions (that is L-1 --> L-2, L-1 --> L-3 and L-2 --> L-3 transitions) in Hg and Bi in molecules were studied at 59.5 keV excitation energy using an Si(Li) detector. A change in the CK transition probabilities was observed for different molecules. The change in the values for Hg compounds were greater than those for Bi compounds. Because we did not have pure Bi in the laboratory and also were not able to have specimen preparation conditions for pure Hg, we did not obtain experimental values for elemental Bi and Hg. Copyright (C) 2002 John Wiley Sons, Ltd

transitions for elements with 20 <= Z <= 90

Most probable values of M-i subshell fluorescence yields (i = 1-5) and Coster-Kronig (CK) transition S-ij probabilities (S-12, S-13, S-14, S-15, S-23, S-24, S-35, S-34, S-35 and f(45)) were determined by using least squares fitted to obtain the polynomials, which were plotted by using McGuire's values, representing them as a function of atomic number. Values of M-i subshell fluorescence yields (i = 1-5) and (&omega;) over bar (M) and CK transition S-ij probabilities as above are presented in tables. Copyright (C) 2002 John Wiley Sons, Ltd

transitions for elements with 20 <= Z <= 90

Most probable values of M-i subshell fluorescence yields (i = 1-5) and Coster-Kronig (CK) transition S-ij probabilities (S-12, S-13, S-14, S-15, S-23, S-24, S-35, S-34, S-35 and f(45)) were determined by using least squares fitted to obtain the polynomials, which were plotted by using McGuire's values, representing them as a function of atomic number. Values of M-i subshell fluorescence yields (i = 1-5) and (&omega;) over bar (M) and CK transition S-ij probabilities as above are presented in tables. Copyright (C) 2002 John Wiley Sons, Ltd