Nitrogen (N) is a key growth and yield-limiting factor in cultivated rice areas. This study has been conducted to evaluate the effects of different conditions of N application on rice yield and yield components (Shiroudi cultivar) in Babol (Mazandaran, Iran) during the 2015- 2016 season. A factorial experiment executed of a Randomized Complete Block Design (RCBD) used in three iterations. In the first factor, treatments were four N amounts (including 50, 90, 130, and 170 kg N ha^-1), while in the second factor, the treatments consisted of four different fertilizer splitting methods, including T₁:70 % at the basal stage + 30 % at the maximum tillering stage, T₂:1/3 at the basal stage + 1/3 at the maximum ti

This study aims to find out the effect of the mediator on scaffolding fourth yearstudent- teachers' teaching competencies and their self-efficacy. The present study combines scaffolding and self-efficacy by using a mediator on scaffolding students affects teaching competencies and selfefficacy and from the results of which the existence of student-teachers’ selfawareness was ensured as an effect of the same independent variable. The model affects their teaching competencies and led them to be aware of the needs of their pupils and themselves. 

The present work establishes and validates HILIC strategies simple, accurate, exact and precise in pure form and inpharmaceutical dosage for separating and determining theophylline. These methods are developed on HILIC theophyllineseparation in columns ZIC2 and ZIC3. The eluent was prepared by mixing buffer (20% sodium acetate-40 mM, pH 5.5), 80%acetonitrile. The flow rate is 0.8 mL/min, with gradient elution and UV detection at 270 nm. In the ZIC2 and ZIC3 columns oftheophylline determining, the concentration range was 0.01-4μg.ml-1. The lower limit of detection and quantification fortheophylline were determined as 0.130, 0.190 μg.ml-1 and accuracy were 99.70%, 99.58% on ZIC2 and ZIC3, respectively. TheHILIC methods developed and validat

Background: Acute radiodermatitis is a common side effect during and after radiotherapy course in breast cancer patients treated by radiotherapy. This study assess the frequency of acute radiodermatitis and record the predictive factors for acute radiodermatitis. Patients and Methods: A descriptive case series study conducted at Baghdad, Iraq from August 2020 to September 2021. 70 female scheduled for radiotherapy sessions enrolled in this study. sociodemographic data were recorded and Skin examination before radiotherapy and weekly till the end of the radiotherapy sessions was done to report the frequency, risk factors, clinical picture and grades of acute radiodermatitis based on The National Cancer Institute’s Common Terminology Crite

This research represents a practical attempt applied to calibrate and verify a hydraulic model for the Blue Nile River. The calibration procedures are performed using the observed data for a previous period and comparing them with the calibration results while verification requirements are achieved with the application of the observed data for another future period and comparing them with the verification results. The study objective covered a relationship of the river terrain with the distance between the assumed points of the dam failures along the river length. The computed model values and the observed data should conform to the theoretical analysis and the overall verification performance of the model by comparing it with anothe

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet