Investigation of mesomorphic properties of new 1,3,4-thiadiazolines (which are synthesised via many steps in Scheme 1) was carried out in this study. These compounds are designed to have a heterocyclic unit, a carboxylate linkage group and a polar ether chain at the end of the molecule adjacent to the benzene ring, which enhance the dipolar interactions forces (varied from one to eight carbons) to investigate the association properties of their phases. The structure of the target compounds and the intermediates were confirmed by 1H NMR, 13C NMR, mass and FTIR spectral techniques. Polarised microscopic studies revealed that all the compounds in the series exhibited enantiotropic liquid crystalline properties. This was further confirmed using

     As a result of changes in the chemical properties of the Shatt al-Arab River, especially in the last decade, as well as the lack of rainfall and the effect of seawater intrusion into the Shatt al-Arab, this study was conducted to investigate the possible changes in the water pipelines like corrosive and scaling which Shatt al-Arab River is the source of water supply for domestic use. Domestic water samples were collected from 10 various locations in Basrah to study the water's tendency to be corrosive or form scales along the pipelines. The Langelier Index, Ryznar Index, Larson-Skold Index, and Saturation Index were used to determine the corrosivity potential of water based on physical and chemical parameters. Most domestic wate

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

     The Rock Wool Board (RWB) is widely used in the building exterior insulation all over world. As polymer affects hardening of plate, meaning it acted as a basic binder in this work and gave a smooth texture, ease of carrying and workmanship in different sizes and shapes. PVA was studied at the rates of PVA %4, %5, %6.66, %10, %20, results showed that 6.66% is the best percentage, through tensile and bending tests, this percentage was use with polyurethane with rates of PU 1, 0.5, 0.333, 0.25, 0.2 It is found that the ratio 0.333 is the best ratio to give good mechanical properties of bending, tensile and elongation. This percentage was fixed on different percentages of polymer (PU/PVA) as %2.5, %5, %10, %15, %20 and examinations

In this research, the program SEEP / W was used to compute the value of seepage through the homogenous and non-homogeneous earth dam with known dimensions. The results show that the relationship between the seepage and water height in upstream of the dam to its length for saturated soil was nonlinear when the dam is homogenous. For the non-homogeneous dam, the relationship was linear and the amount of seepage increase with the height of water in upstream to its length. Also the quantity of seepage was calculated using the method of (Fredlund and Xing, 1994) and (Van Genuchten, 1980) when the soil is saturated – unsaturated, the results referred to that the higher value of seepage when the soil is saturated and the lowe

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

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