Lead selenide PbSe thin films of different thicknesses (300, 500, and 700 nm) were deposited under vacuum using thermal evaporation method on glass substrates. X-ray diffraction measurements showed that increasing of thickness lead to well crystallize the prepared samples, such that the crystallite size increases while the dislocation density decreases with thickness increasing. A.C conductivity, dielectric constants, and loss tangent are studied as function to thickness, frequency (10kHz-10MHz) and temperatures (293K-493K). The conductivity measurements confirm confirmed that hopping is the mechanism responsible for the conduction process. Increasing of thickness decreases the thermal activation energy estimated from Arhinus equation is

In this study, the results of x-ray diffraction methods were used to determine the Crystallite size and Lattice strain of Cu2O nanoparticles then to compare the results obtained by using variance analysis method, Scherrer method and Williamson-Hall method. The results of these methods of the same powder which is cuprous oxide, using equations during the determination the crystallite size and lattice strain, It was found that the results obtained the values of the crystallite size (28.302nm) and the lattice strain (0.03541) of the variance analysis method respectively and for the Williamson-Hall method were the results of the crystallite size (21.678nm) and lattice strain (0.00317) respectively, and Scherrer method which gives the value of c

Type 2 diabetes mellitus (T2DM) became the most prevalent health problem. Almost half of the world's people are ignorant that have diabetes. Menopause occurs as an important alteration in women through which take place the change in sex hormones, distribution in fat،s body, and metabolism, altogether which participate in the metabolism disease such as type 2 diabetes mellitus. Several studies have appeared the association between the TCF7L2 gene and different diseases like type 2 diabetes mellitus (T2DM). This study aimed to detect the relation of the genetic variation polymorphism for the TCF7L2 gene (rs12255372 G/T) in Iraqi women menopausal with T2DM. The outcomes indicated the increased levels of biochemical characteristics including H

Gestational Diabetes Mellitus is known as carbohydrate intolerance first detected during pregnancy. Pregnancy is periods of intense hormonal changes. The aim of the present study was to investigate a possible relation between the changes in serum hormones such as Luteinizing hormone (LH) , follicle stimulating hormone(FSH), Progesterone, and Prolactin with gestational diabetes mellitus. Thirty patients with gestational diabetes mellitus aged (22 -40) year attending the national center for treatment and research of diabetes/ AL-Mustansiriya University in Baghdad and 29 controls aged (20-39) year were participated. Hormonal tests including, FSH, LH, Progesterone, and Prolactin were detected by using Enzyme Linked Fluorescent Assay (ELFA) k

Background: Orthodontic mini-implants are increasingly used in orthodontics and the bone density is a very important factor in stabilization and success of mini-implant. The aim of this study was to observe the relationship among maximum bite force (MBF); body mass index (BMI); face width, height and type; and bone density in an attempt to predict bone density from these variables to eliminate the need for CT scan which have a highly hazard on patient. Materials and Methods: Computed tomographic (CT) images were obtained for 70 patients (24 males and 46 females) with age range 18-30 years. The maxillary and mandibular buccal cortical and cancellous bone densities were measured between 2nd premolar and 1st molar at two levels from the alveol

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121