The current research includes a look at the participation of Iraqi women in the combat roles, which starts from the assumption of the democratic transition that must be led - in one of its aspect - until the army or the military foundation to become a "citizen army," the matter which is represented a demand increasingly needed in the experiences of Democratic transformation that facing serious security challenges such as in Iraq, this means that the army or security foundation - which is involved in counterterrorism - should not reflect a specific group in society , and hence embody the most important democratic principles, which are equality and equal opportunities, especially gender equality. On the other hand, the influence of armed conf

Background: Dyslipidemia is defined as an abnormally high level of various lipids in the blood. It is considered a major risk for atherosclerosis and coronary artery disease. Genetic susceptibility can have a significant influence on the development and progression of dyslipidemia. ApoB-100 R3500Q mutation and ApoE variants are among those genetic risks for dyslipidemia. This study aims to assess the possible contribution of ApoB and ApoE variants on lipid profile among a group of early-onset ischemic heart disease (IHD) patients in comparison to a group of controls. Methods: Forty patients with dyslipidemia and early-onset IHD without chronic conditions likely to cause derangement of lipid levels were recruited to this case-control study

The aim of the present study is to evaluate the change in the levels of glucagon, GLP-1 and GPCR in diabetic patient's and diabetic with dyslipidemia as metabolic syndrome. The study included 75 male aged ranged (30-50) years and with BMI (25-29) kg/m2 which divided into three groups as follows: group one (G1): consist of 25 subjects as healthy control group. Group two (G2): consist of 25 patient's with diabetes mellitus and group three (G3): consist of 25 patient's with diabetic and dyslipidemia as metabolic syndrome. Serum was used in determination of FBG, lipid profile, insulin, glucagon, GLP-1 and GPCR. Whole blood was determination of HbA1c. The results revealed significant elevation in FBG and HbA1c in G2 and G3 comparing to G1. While

In the last few years, the Internet of Things (IoT) is gaining remarkable attention in both academic and industrial worlds. The main goal of the IoT is laying on describing everyday objects with different capabilities in an interconnected fashion to the Internet to share resources and to carry out the assigned tasks. Most of the IoT objects are heterogeneous in terms of the amount of energy, processing ability, memory storage, etc. However, one of the most important challenges facing the IoT networks is the energy-efficient task allocation. An efficient task allocation protocol in the IoT network should ensure the fair and efficient distribution of resources for all objects to collaborate dynamically with limited energy. The canonic

Introduction Periodontal diseases are ranked among the most common health problems affecting mankind. These conditions are initiated by bacterial biofilm, which is further modulated by several risk factors. Objectives To investigate the association of different risk factors with periodontal...

Objective: The evaluation of serum osteocalcin (OSN) for Iraqi infertile patients to see the effect of osteocalcin insufficiency, which may lead to a decreased level of testosterone production in males that may cause infertility. Methods: Forty two newly diagnosed infertile males age range (24–47) years and thirty two apparently healthy males as controls age range (25–58) years. Serum levels of testosterone (TEST), stimulating follicle hormone (FSH) and luteinizing hormone (LH), prolactin (PROL), osteocalcin OSN, and fasting blood sugar (FBS) were performed in both patients and controls. Estimation of serum OSN by Immulite1000 auto-analyzer, TEST, FSH, LH, PROL, and FBS by Immulite2000 auto-analyzer. Results: Infertile patients

Accurate prediction and optimization of morphological traits in Roselle are essential for enhancing crop productivity and adaptability to diverse environments. In the present study, a machine learning framework was developed using Random Forest and Multi-layer Perceptron algorithms to model and predict key morphological traits, branch number, growth period, boll number, and seed number per plant, based on genotype and planting date. The dataset was generated from a field experiment involving ten Roselle genotypes and five planting dates. Both RF and MLP exhibited robust predictive capabilities; however, RF (R² = 0.84) demonstrated superior performance compared to MLP (R² = 0.80), underscoring its efficacy in capturing the nonlinear genoty