Two factorial experiments were conducted. One of them was laboratory experiment which was carried out at the Laboratory of Agriculture and Marshes College, University of Thi-Qar during laboratories of certification and test of seeds office in Thi-Qar governorate–Nassiriyah district during 2015. The other was conducted at the lath house with used the pots during spring season of 2016. The aim was to investigate the effect of soybean seeds priming before sowing on seed vigour and seedling growth characteristics under salinity stress. The design of Lab. experiment was (CRD) while for the other experiment was (RCBD) with four replications. Each experiment consisted of two factors. The first factor included seeds soaking treatments for 24 hour

The Journal of Studies and Researches of Sport Education (JSRSE)

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

This study aimed at some of the criteria used to determine the form of the river basins, and exposed the need to modify some of its limitations. In which, the generalization of the elongation and roundness ratio coefficient criterion was modified, which was set in a range between (0-1). This range goes beyond determining the form of the basin, which gives it an elongated or rounded feature, and the ratio has been modified by making it more detailed and accurate in giving the basin a specific form, not only a general characteristic. So, we reached a standard for each of the basins' forms regarding the results of the elongation and circularity ratios. Thus, circular is (1-0.8), and square is (between 0.8-0.6), the blade or oval form is (0.6-0

Estimation the unknown parameters of a two-dimensional sinusoidal signal model is an important and a difficult problem , The importance of this model  in modeling Symmetric gray- scale texture image . In this paper, we propose employment Deferential Evaluation algorithm and the use of Sequential approach to estimate the unknown frequencies and amplitudes of the 2-D sinusoidal components when the signal is affected by noise. Numerical simulation are performed for different sample size, and various level of standard deviation to observe the performance of this method in estimate the parameters of 2-D sinusoidal signal model , This model was used for modeling  the Symmetric gray scale texture image and estimating by using

Sorghum cultivation is often accompanied by low field emergence rates and weak seedlings, which may be due to genetic or environmental stress. A factorial experiment was conducted in the spring and fall seasons of 2022 using a randomized complete block design with split-plot arrangement and four replications. Planting dates (spring season: Feb. 15th, Mar. 1st, 15th, and Apr. 1st, 15th; fall season: Jun. 15th, Jul. 1st, 15th, and Aug. 1st, 15th) were allocated to the main plots. Seeds stimulation treatments (35% banana peel extract + 100 mg L-1 citric acid and distilled water soaking treatment only) were allocated to the subplots. The interaction treatment (banana peel extract + citric acid) with the planting date of April 15 showed the high

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F