Gestational diabetes mellitus (GDM) is a growing health concern that usually appears during the second and third trimester stage of pregnancy and is characterized by carbohydrate intolerance of variable severity. The aim of the present study was to scrutinize the relationship between the G972R polymorphism of the insulin receptor substrate-1 (IRS-1) gene with GDM in the Iraqi female population. One hundred and twenty of blood samples taken from healthy women (control) and women with gestational diabetes mellitus in 3rd trimester stage of pregnancy, fasting blood glucose (FBG) and HbA1c% measured to diagnose GDM, lipid profile (cholesterol, triglyceride, HDL, LDL, and VLDL), insulin concentration, insulin resistance and beta cell function to
... Show MoreTench is a cyprinid fish that has undergone human-induced translocations. The natural populations of the species are on the decline due to habitat loss and spawning grounds degradation. The genetic diversity of seven natural populations was investigated to establish the genetic knowledge base for successful conservation efforts and for selective breeding. Twelve microsatellite markers, the sequencing of a 615 bp section of mtDNA (Cytb) and PCR-RFLP analysis of two nuclear markers (Act) and (RpS7) were used to analyze the genetic variation and structure among 175 individuals. All microsatellite loci were found to have moderate levels of polymorphism. The pairwise Fst values between population pairings were moderate; the populations w
... Show Moreيعتبر "تاج الأشواك" أو نبات شوكة المسيح، وهو من نباتات الزينة الطبية ، ينتمي إلى جنس يوفوربيا. E. milii يحتوي كميات وفيرة من المركبات الفينولية ، التربينات، الستيرويدات والقلويدات. كانت الأهداف الرئيسية لهذه الدراسة هي فحص مستخلصات الفلافونويد والنانو فلافونويد ضد نوعين من خطوط الخلايا السرطانية. تم تصنيع مركبات الفلافونويد النانوية عن طريق تفاعل مركب الكيتوسان والماليك اسد. تم تحليل مركبات الفلافونويد ال
... Show MoreBP algorithm is the most widely used supervised training algorithms for multi-layered feedforward neural net works. However, BP takes long time to converge and quite sensitive to the initial weights of a network. In this paper, a modified cuckoo search algorithm is used to get the optimal set of initial weights that will be used by BP algorithm. And changing the value of BP learning rate to improve the error convergence. The performance of the proposed hybrid algorithm is compared with the stan dard BP using simple data sets. The simulation result show that the proposed algorithm has improved the BP training in terms of quick convergence of the solution depending on the slope of the error graph.
Three-dimensional (3D) reconstruction from images is a most beneficial method of object regeneration by using a photo-realistic way that can be used in many fields. For industrial fields, it can be used to visualize the cracks within alloys or walls. In medical fields, it has been used as 3D scanner to reconstruct some human organs such as internal nose for plastic surgery or to reconstruct ear canal for fabricating a hearing aid device, and others. These applications need high accuracy details and measurement that represent the main issue which should be taken in consideration, also the other issues are cost, movability, and ease of use which should be taken into consideration. This work has presented an approach for design and construc
... Show MoreIt is an established fact that substantial amounts of oil usually remain in a reservoir after primary and secondary processes. Therefore; there is an ongoing effort to sweep that remaining oil. Field optimization includes many techniques. Horizontal wells are one of the most motivating factors for field optimization. The selection of new horizontal wells must be accompanied with the right selection of the well locations. However, modeling horizontal well locations by a trial and error method is a time consuming method. Therefore; a method of Artificial Neural Network (ANN) has been employed which helps to predict the optimum performance via proposed new wells locations by incorporatin
Emergency vehicle (EV) services save lives around the world. The necessary fast response of EVs requires minimising travel time. Preempting traffic signals can enable EVs to reach the desired location quickly. Most of the current research tries to decrease EV delays but neglects the resulting negative impacts of the preemption on other vehicles in the side roads. This paper proposes a dynamic preemption algorithm to control the traffic signal by adjusting some cycles to balance between the two critical goals: minimal delay for EVs with no stop, and a small additional delay to the vehicles on the side roads. This method is applicable to preempt traffic lights for EVs through an Intelli
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)
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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
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