This paper describes the application of consensus optimization for Wireless Sensor Network (WSN) system. Consensus algorithm is usually conducted within a certain number of iterations for a given graph topology. Nevertheless, the best Number of Iterations (NOI) to reach consensus is varied in accordance with any change in number of nodes or other parameters of . graph topology. As a result, a time consuming trial and error procedure will necessary be applied
to obtain best NOI. The implementation of an intellig ent optimization can effectively help to get the optimal NOI. The performance of the consensus algorithm has considerably been improved by the inclusion of Particle Swarm Optimization (PSO). As a case s

This paper includes the application of Queuing theory with of Particle swarm algorithm or is called (Intelligence swarm) to solve the problem of The queues and developed for General commission for taxes /branch Karkh center in the service stage of the Department of calculators composed of six  employees , and it was chosen queuing model is a single-service channel  M / M / 1 according to the nature of the circuit work mentioned above and it will be divided according to the letters system for each employee, and  it was composed of data collection times (arrival time , service time, departure time)

This paper presents an improved technique on Ant Colony Optimization (ACO) algorithm. The procedure is applied on Single Machine with Infinite Bus (SMIB) system with power system stabilizer (PSS) at three different loading regimes. The simulations are made by using MATLAB software. The results show that by using Improved Ant Colony Optimization (IACO) the system will give better performance with less number of iterations as it compared with a previous modification on ACO. In addition, the probability of selecting the arc depends on the best ant performance and the evaporation rate.

This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO) for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In ord

The high mobility group A1 gene (HMGA1) rs139876191 variant has been related to metabolic syndrome and type 2 diabetes, but data are lacking in Middle Eastern populations. The study aimed to assess whether the HMGA1 rs139876191 variant is associated with metabolic syndrome risk and whether this variant predicts the risk of insulin resistance. This case-control study was carried out at single center in Kirkuk city/ Iraq from February to August 2022. Polymorphisms in HMGA1 and genotyping were identified by Sanger sequencing of genomic DNA obtained from 91 Iraqi participants (61 patients with metabolic syndrome and 30 control). Lipid profile, serum (glucose and insulin), glycated hemoglobin, blood pressure, body mass index, and waist circumfer

Background: Hypothyroidism is the most abundant thyroid disorder worldwide. For decades, levothyroxine was the main effective pharmacological treatment for hypothyroidism. A variety of factors can influence levothyroxine dose, such as genetic variations. Studying the impact of genetic polymorphisms on the administration of medications was risen remarkably. Different genetic variations were investigated that might affect levothyroxine dose requirements, especially the deiodinase enzymes.  Deiodinase type 2 genetic polymorphisms’ impact on levothyroxine dose was studied in different populations. Objective: To examine the association of the two single nucleotide polymorphism (SNP)s of deiodinase type 2 (rs225013 and rs225014) and le