The monthly time series of the Total Suspended Solids (TSS) concentrations in Euphrates River at Nasria was analyzed as a time series. The data used for the analysis was the monthly series during (1977-2000).

The series was tested for nonhomogenity and found to be nonhomogeneous. A significant positive jump was observed after 1988. This nonhomogenity was removed using a method suggested by Yevichevich (7). The homogeneous series was then normalized using Box and Cox (2) transformation. The periodic component of the series was fitted using harmonic analyses, and removed from the series to obtain the dependent stochastic component. This component was then modeled using first order autoregressive model (Markovian chain). The above a

A numerical study of the double-diffusive laminar natural convection in a right triangular solar collector has been investigated in present work. The base (absorber) and glass cover of the collector are isothermal and isoconcentration surfaces, while the vertical wall is considered adiabatic and impermeable. Both aiding and opposing buoyancy forces have been studied. Governing equations in vorticity-stream function form are discretized via finite-difference method and are solved numerically by iterative successive under relaxation (SUR) technique. Computer code for MATLAB software has been developed and written to solve mathematical model. Results in the form of streamlines, isotherms, isoconcentration, average Nusselt, and average Sherw

Objectives: To identify the effectiveness program on nurse- midwife practice concerning performed
cardiotocography to pregnant women and to find out the relationship between nurse- midwife practice and
certain studied variables.
Methodology: A quasi-experimental design (pretest-post test approach) was conducted at three sector AlRussafa
directorate, AL- Karckh directorate and Medical City Directorate from the period of March, 26th 2014
to August, 30th 2015. A non-probability sample consisted of (130) nurse -midwives were selected and divided
into two groups (65) nurses-midwives (case group) who exposed to the educational program and (65 ) nursesmidwives
who didn't expose to the program considered as control group . D

Biodiesel is an environmentally friendly fuel and a good substitution for the fossil fuel. However, the purity of this fuel is a major concern that challenges researchers. In this study, a calcium oxide based catalyst has been prepared from local waste eggshells by the calcination method and tested in production biodiesel. The eggshells were powdered and calcined at different temperatures (700, 750, 800, 850 and 900 °C) and periods of time (1, 2, 3, 4 and 5 hr.). The effect of calcination temperature and calcination time on the structure and activity of the solid catalyst were examined by X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Brunaure-Emmett-Teller (BET). The optimum catalyst performance was obtained at 900 °C

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve