In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function lnL(φ1, θ1) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (φ1, θ1) for the ARMA(1,1) model. Anot

The consensus algorithm is the core mechanism of blockchain and is used to ensure data consistency among blockchain nodes. The PBFT consensus algorithm is widely used in alliance chains because it is resistant to Byzantine errors. However, the present PBFT (Practical Byzantine Fault Tolerance) still has issues with master node selection that is random and complicated communication. The IBFT consensus technique, which is enhanced, is proposed in this study and is based on node trust value and BLS (Boneh-Lynn-Shacham) aggregate signature. In IBFT, multi-level indicators are used to calculate the trust value of each node, and some nodes are selected to take part in network consensus as a result of this calculation. The master node is chosen

BP 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.

 Finding a path solution in a dynamic environment represents a challenge for the robotics researchers, furthermore, it is the main issue for autonomous robots and manipulators since nowadays the world is looking forward to this challenge. The collision free path for robot in an environment with moving obstacles such as different objects, humans, animals or other robots is considered as an actual problem that needs to be solved.  In addition, the local minima and sharp edges are the most common problems in all path planning algorithms. The main objective of this work is to overcome these problems by demonstrating the robot path planning and obstacle avoidance using D star (D*) algorithm based on Particle Swarm Optimization (PSO)

Background:Ankylosing spondylitis (AS) is a progressive, chronic inflammatory illness with an unclear etiology that explicitly targets the vertebral column, peripheral joints, and extraarticular tissues. The purpose of this research was to investigate if the existence of single nucleotide polymorphisms (SNPs) in the promoter region of the tumor necrosis factor-alpha (TNF-α) gene at positions -1031T/C (rs199964), -857C/T (rs1799724) and -806C/T (rs4248158) in a sample of Iraqi AS patients could influence the patients' outcomes with etanercept.

Methods:Sixty patients with established AS receiving only etanercept were selected to enroll in this study, with a mean age of 40.75±8.6

Interleukin-38 (IL-38), an inflammatory cytokine discovered in recent years, has been implicated in the pathogenesis of systemic lupus erythematosus (SLE). IL-38 is encoded by the IL1F10 (interleukin 1 family member 10) gene. Genetic variants of this gene have been associated with susceptibility to a number of autoimmune and inflammatory diseases, while their association with SLE risk has not been explored. In this case–control study, two novel variants of the 5 prime untranslated region (5′UTR) of the IL1F10 gene, rs3811050 C/T and rs3811051 T/G, were investigated

 BACKGROUND: Vascular tumors are a heterogeneous group of diseases with biological behavior ranging from a hamartomatous growth to frank malignant. The pathophysiology of lymphangioma, vascular malformation and hemangioma is interconnected, blood vessels known to be the site of origin of hamartomas, venous malformations and some neoplasms as benign, tumor-like growth of vessels (hemangiomas). Angiogenesis is the process of formation of new blood vessels from an existing structure.

Aims of study Assessment of angiogenic potential in benign vascular lesions (hemangioma, lymphangioma and lobular capillary hemangioma) of head and neck region.

 Materials and Methods: Twenty-two formalin-fixed paraffin-embedd