No abstract

"A study was conducted in Al-Seba Reserve / Basra Governorate to study the effect of nitrogen and potassium fertilization on growth of stevia plants during 2018-2019 agricultural season, in pots. A sandy mixture of soil was used and two factors were studied: the first factor was urea fertilizer with five levels of nitrogen (N0 0, N1100, N2150, N3 200, and N4 250 kg / ha) and the second factor was potassium sulfate with three levels of potassium (K0 0, K1 75 and K3150 kg/h( . The experiment was experimental factor using a complete randomized design (C.R.D) with three replications. The results showed a significant effect of adding nitrogen and potassium fertilizers and there interaction on: plant height, number of branches, leaf area index, and the treatment N3K2 gave the highest yield reached (1.27 tons. h-1), and N4K2 recorded a highest content of Rebaudioside A (53.26 ppm). *Part of Ph.D. thesis of the first author Corresponding author: E-mail(khawla_74@yahoo.com) Al- Muthanna University All rights reserved"

It is so much noticeable that initialization of architectural parameters has a great impact on whole learnability stream so that knowing  mathematical properties of dataset results in providing neural network architecture a better expressivity and capacity. In this paper, five random samples of the Volve field dataset were taken. Then a training set was specified and the persistent homology of the dataset was calculated to show impact of data complexity on selection of multilayer perceptron regressor (MLPR) architecture. By using the proposed method that provides a well-rounded strategy to compute data complexity. Our method is a compound algorithm composed of the t-SNE method, alpha-complexity algorithm, and a persistence barcode reading method to extract the Betti number of a dataset. After that,  MLPR were trained using that dataset using a single hidden layer with increased hidden neurons. Then, increased both hidden layers and hidden neurons. Our empirical analysis has shown that the training efficiency of MLPR severely depends on its architecture’s ability to express the homology of the dataset.

No abstract

"This study was conducted in the experimental field of Agriculture Faculty, Wasit University (Wasit Governorate Center) in the autumn season 2018, to study the effect of four planting dates (June 25, July 10, July 25 and August 10) and four spacing between hills (10, 15, 20 and 25 cm) on growth and forage yield of Sudan grass Hybrid. The experiment was carried out by using split-plots with R.C.B.D design with three replicates, the planting dates were put in the main plots, and distances between hills were placed in the sub plots. Two cuts were taken from all treatments ,the following traits were studied ,Plant height,, number of tillers per plant, number of leaves per plant, plant leaf area, leaves/stems ratio, and green forage yield. Planting date on 25 July at 1st cutting gave the highest studied characteristics and green yield were 89.452 t. ha-1, Planting date on 25 June at the 2nd cutting gave the highest studied characteristics and gave green forage yield about 86.090 t.ha-1. Planting at 20cm showed a significant effect among the other distances in most growth characteristics at the 1st and 2nd cutting and gave the highest green forage yield were 85.255 and 58.900 t. ha-1. The distance of 20 cm at the date of June 25 gave the highest green yield, which were 93.200 t. ha-1. The interaction between studied factors showed a significant effect on the green forage yield at 1st cutting, the distance of 20 cm at the date of June 25 gave the highest green yield, which were 93.200 t. ha-1, with an increase of 37.67% over than June 25 date, at a distance of 10 cm, the lowest yield of green fodder was recorded at 67.707 t. ha-1 *Part of M.Sc. thesis of the first author"