Background: Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder affecting women of reproductive age. Oxidative stress may contribute to its pathogenesis. Paraoxonase-1 (PON1) is an antioxidant enzyme associated with high-density lipoprotein, and its genetic polymorphisms may alter enzyme activity. This study aimed to investigate the association between PON1 L55M polymorphism (rs854560) and PCOS in Iraqi women.Methods: This case-control study included 80 women aged 20–35 years who were in good general health   and matched for age and body mass index (BMI). Genomic DNA was extracted using a commercial kit. The PON1 L55M polymorphism was amplified by polymerase chain reaction and genotyped by direct sequen

     Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age, characterized by anovulatory, infertility and metabolic disturbances. This study aimed to assess the diagnostic accuracy of Leucine-rich alpha-2-glycoprotein-1 (LRG1) and xanthine oxidase (XO) activity as novel biomarkers for PCOS, as well as to explore the relationship between LRG1 and XO activity across different groups. A total of 150 married women, aged 18-46 years were enrolled and divided into three groups: 50 with PCOS under treatment, 50 PCOS without treatment conditions shared by women with polycystic ovary conditions, and 50 healthy women. Serum samples were analysed to measure LRG1, xanthine oxidase (XO) activity,

A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co

planning is among the most significant in the field of robotics research.  As it is linked to finding a safe and efficient route in a cluttered environment for wheeled mobile robots and is considered a significant prerequisite for any such mobile robot project to be a success. This paper proposes the optimal path planning of the wheeled mobile robot with collision avoidance by using an algorithm called grey wolf optimization (GWO) as a method for finding the shortest and safe. The research goals in this study for identify the best path while taking into account the effect of the number of obstacles and design parameters on performance for the algorithm to find the best path. The simulations are run in the MATLAB environment to test the

Many industrial systems involve multiple criteria and objectives, and they are very complex problems in computational science, such as task scheduling. We propose bi-criteria and bi-objective scheduling problems, which are solved by two nature-inspired evolutionary algorithms, such as Simulated Annealing (SA) and Bee Algorithm (BA). This problem is characterized by scheduling a batch of tasks on multiple machines, and it is fundamental because the solution should focus on the simultaneous optimization of two conflicting objectives: the makespan minimization and the total tardiness minimization. This problem is NP-Hard, and therefore, two evolutionary methods were used to search for solutions intelligently in this huge, very complex

Some degree of noise is always present in any electronic device that
transmits or receives a signal . For televisions, this signal i has been to s the
broadcast data transmitted over cable-or received at the antenna; for digital
cameras, the signal is the light which hits the camera sensor. At any case, noise
is unavoidable. In this paper, an electronic noise has been generate on
TV-satellite images by using variable resistors connected to the transmitting cable
. The contrast of edges has been determined. This method has been applied by
capturing images from TV-satellite images (Al-arabiya channel) channel with
different resistors. The results show that when increasing resistance always
produced higher noise f

Future wireless networks will require advance physical-layer techniques to meet the requirements of Internet of Everything (IoE) applications and massive communication systems. To this end, a massive MIMO (m-MIMO) system is to date considered one of the key technologies for future wireless networks. This is due to the capability of m-MIMO to bring a significant improvement in the spectral efficiency and energy efficiency. However, designing an efficient downlink (DL) training sequence for fast channel state information (CSI) estimation, i.e., with limited coherence time, in a frequency division duplex (FDD) m-MIMO system when users exhibit different correlation patterns, i.e., span distinct channel covariance matrices, is to date ve