Background: The world health organization estimates that worldwide 2 billion people still have iodine deficiency Objectives: Is to make comparison between the effect of identification of recurrent laryngeal nerve (RLN) and non-identification of the nerve on incidence of recurrent laryngeal nerve injury (RLNI) in different thyroidectomy procedures.

Type of the study: cross –sectional study.

Methods: 132 patients with goiters underwent thyroidectomy .Identification of RLN visually by exposure were done for agroup of them and non-identification of the nerves for the other group. The outcomes of RLNI in the two groupsanalyzed statistically for the effect of

This study included isolation of some active materials from Curcuma longa such as tannins, saponins and volatile oils with percentage of 59%, 31%, and 9% respectively. Also the study included the determination of minerals in Curcuma longa such as " Na, Ca and K" using Flame photometer. The concentrations of these minerals were (14 ppm),(10 ppm) and )76 ppm) respectively. The anti-bacterial activity study was performed for the active materials isolated from Curcuma longa against two genus of pathogenic bacteria, Escherichia Coli and Staphylococcus aurous by using agar-well diffusion method. It appeared from this study that all of the extraction have inhibitory effect on bacteria was used. The inhibition zone diameter varies with

Background: Hodgkin's lymphoma (HL), formerly called Hodgkin's diseases is an uncommon form of lymphoma. The incidence of Hodgkin's lymphoma shows marked heterogeneity with respect to age, gender, race, geographic area, social class and histological subtype. This study was carried out in an attempt to evaluate the incidence of Hodgkin's lymphoma of head and neck in Baghdad city. Materials and Methods: The diagnosed cases of Hodgkin's lymphoma of head and neck region in Baghdad city between (1990-1999) were collected and analyzed according to age, gender, site and the histopathological subtypes of the tumor. Results: Out of (702) cases of Hodgkin's lymphoma of ten years between (1990-1999),(362 ) of them were occurred in the head and neck

In this research a new system identification algorithm is presented for obtaining an optimal set of mathematical models for system with perturbed coefficients, then this algorithm is applied practically by an “On Line System Identification Circuit”, based on real time speed response data of a permanent magnet DC motor. Such set of mathematical models represents the physical plant against all variation which may exist in its parameters, and forms a strong mathematical foundation for stability and performance analysis in control theory problems.

Abstract 

For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS, 

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in