Abstract

This research aims to develop a unit as part of a sixth-grade Arabic language textbook and measure its effectiveness in the development of twenty-first-century skills of female students. The author adopted the experimental approach with a quasi-experimental design of the pre-post single-group. A list of the major skills was derived from the framework for the 21st-century skills of the mother tongue that was developed by the Partnership for 21st-Century Skills and reviewed and adjusted by some specialists. According to their views, the unit was developed. The study targeted 15 sub-skills falling under three main skills. The results of the study showed the effectiveness of the developed unit in the develo

The two body model of (Core+n) within the radial wave functions of the cosh potential has been used to investigate the ground state features such as the proton, neutron and matter densities, the root mean square (RMS) nuclear proton, neutron, charge and mass radii of unstable neutron-rich 14B, 15C, 19C and 22N nuclei. The calculated results show that the two body model with the radial wave functions of the cosh potential succeeds in reproducing neutron halo in these nuclei.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Background: Tooth extraction is one of the most commonly performed procedures in dentistry. It is usually a traumatic process often resulting in immediate destruction and loss of alveolar bone and surrounding soft tissues. Various instruments have been described to perform atraumatic extractions which can prevent damage to the paradental structures. The physics forceps is one of those innovations in dental extraction technologies that claim to provide an efficient means for atraumatic dental extractions. Materials and method: A randomized clinical trial was conducted to compare the physics forceps with the conventional forceps for the removal of 28 mandibular single rooted teeth under the following parameters: incidence of crown, root, b

Background: Acute myeloid leukemia (AML) is a genetically heterogeneous leukemia characterized by abnormal myeloid blast accumulation, disrupting normal hematopoiesis and leading to rapid progression. Objective: To investigate SNPs within the 3’UTR of the CCAAT/enhancer-binding protein alpha (CEBPA) gene and its association with AML in Iraqi patients. Methods: The study was carried out on 120 AML patients classified into newly diagnosed, induction chemotherapy, and consolidation chemotherapy stages (40 each), and 40 individuals as a control group. Genomic DNA was extracted from AML patients and controls, followed by PCR amplification and Sanger sequencing of the 3’UTR region of the CEBPA gene. The AML patients were characterized

The aim: to evaluate combined microscopy techniques for determining the morphological and optical properties of methadone hydrochloride (MDN) crystals. Materials and methods: MDN crystal formation was optimized using a closed container method and crystals were characterized using polarized light microscope (PLM), scanning electron microscopy (SEM) and confocal microscopy (CM). SEM and CM were used to determine MDN crystal thickness and study its relationship with crystal retardation colours using the Michel-Levy Birefringence approach. Results: Dimensions (mean±SD) of diamond shaped MDN crystals were confirmed using SEM and CM. Crystals were 46.4±15.2 Vs 32.0±8.3 µm long, 28.03±8.2 Vs 20.85±5.5 µm wide, and 6.62±

A total of 54 out of 67 (80.59%) of burn wound swab showed growth of one, or two, or three bacterial pathogens. Pseudomonas aeruginosa was the commonest pathogen, isolated in 48.14% of swab samples, followed by Klebsiella pneumoniae (31.48%), Staphylococcus aureus (27.77%), Acinetobacter baumanii (14.81%), Escherichia coli (7.40%), and Citrobacter freundii, Providencia stuartii, Enterobacter cloacae, with 1.85% isolation percentage for each. All bacterial isolates were tested against 19 antibiotics, and showed multi-drug resistance to 10 antibiotics, or more. The most effective antibiotics were the fifth-generation cephalosporin, ceftobiprole, and and antibiotic combinations, as Ceftazidime / clavulanic acid, and Cefoperazone /sulbactam, an