This work investigates experimentally the effect of using a skirt with a square foundation of 100 mm width resting on dry gypseous soil (i.e., loose soil with 33% relative density), and subjected to an inclined load. Previous works did not study the use square skirted foundation rested on gypseous soil and subjected to inclined load. The investigated soil was brought from Tikrit city with 59% gypsum content. Standard physical and chemical tests on selected soil were carried out. Model laboratory tests were carried out to determine the effect of using a skirt with a square foundation on the load-settlement behavior of gypseous soil and subjected to inclined load with various Skirt depth (Ds) to foundation width (B) ratio

Background: Accurate detection of thyroid autoantibodies by enzyme linked immunosorbant assay technique namely thyroglobulin antibody, thyroid peroxides antibody is crucial in the differentiation of autoimmune thyroid disorders from other form of thyroid diseases.
Objective: Evaluation of the detection of thyroglobulin antibody and thyroid peroxides antibody in different thyroid diseases using enzyme linked immunosorbant assay technique.
Methods: - Seventy-five patients admitted to Surgical Units of Baghdad Medical City Hospital for the period between "October 2010 to June 2011" they were waiting to do thyroidectomy. They were chosen nonselectively for serological evaluation of above autoantibodies , and correlation of the results

Face recognition and identity verification are now critical components of current security and verification technology. The main objective of this review is to identify the most important deep learning techniques that have contributed to the improvement in the accuracy and reliability of facial recognition systems, as well as highlighting existing problems and potential future research areas. An extensive literature review was conducted with the assistance of leading scientific databases such as IEEE Xplore, ScienceDirect, and SpringerLink and covered studies from the period 2015 to 2024. The studies of interest were related to the application of deep neural networks, i.e., CNN, Siamese, and Transformer-based models, in face recogni