Hybrid architecture of ZnO nanorods/graphene oxide ZnO-NRs@GO synthesized by electrostatic self-assembly methods. The morphological, optical and luminescence characteristics of ZnO-NRs@GO and ZnO-NRs thin films have been described by FESEM, TEM, HRTEM, and AFM, which refers to graphene oxide have been coated ZnO-NRs with five layers. Here we synthesis ZnO-NRs@GO by simple, cheap and environmentally friendly method, which made it favorable for huge -scale preparation in many applications such as photocatalyst. ZnO-NRs@GO was applied as a photocatalyst Rodamin 6 G (R6G) dye from water using 532 nm diode laser-induced photocatalytic process. Overall degradation of R6G/ ZnO-NRs@GO was achieved after 90 minutes of laser irradiation while it ne

Objective This study aims to investigate the impact of integrated training on kinematics variables and defensive accuracy in volleyball, focusing on enhancing balance and muscle tension control through proprioceptive neuromuscular facilitation (PNF) exercises. Methods The sample consisted of 14 male volleyball athletes from the first volleyball league of Al-Jaish Sports Club were divided into experimental (n=7) and control group (n=7). In the pre- and post-intervention periods, dynamic balance, muscle tension control and kinematic variables (during a lateral reaching task) as well as defensive performance accuracy upon fatigue onset of recoil laser strikes were assessed. Exposure the intervention program was carried out for six weeks, and t

            The variation in wing morphological features was investigated using geometric morphometric technique of the Sand Fly from two Iraqi provinces Babylon and Diyala . We distributed eleven landmarks on the wings of Sand Fly species. By using the centroid size and shape together, all species were clearly distinguished.  It is clear from these results that the wing analysis is an essential method for future geometric morphometry studies to distinguish the species of Sand Flies in Iraq.

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

Phytochemical Screening and Antibacterial Effect of Stevia Rebaudiana (Bertoni) Alcoholic Leaves Extract on Streptococcus Oralis (Dental Plaque’s Primary Colonizer), Manar Ibrahim