Development of improved methods for the synthesis of metal oxide nanoparticles are of high priority for the advancement of material science and technology. Herein, the biosynthesis of ZnO using hydrahelix of beta vulgaris and the seed of abrus precatorius as an aqueaus extracts adduced respectivily as stablizer and reductant reagent. The support are characterized by spectroscopic methods ( Ft-IR, Uv-vis ).The FTIR confirmed the presence of ZnO band. The Uv-visible showed absorption peak at corresponds to the ZnO nanostructures. X-ray diffraction, scaning electron microscopy (SEM), dispersive X-ray spectroscopy (EDX) techniques are taken to investigation the size, structure and composition of synthesised ZnO nanocrystals. The XRD pattern mat

 
      The process of soil classification in Iraq for industrial purposes is important topics that need to be extensive and specialized studies. In order for the advancement of reality service and industrial in our dear country, that a lot of scientific research touched upon the soil classification in the agricultural, commercial and other fields. No source and research can be found that touched upon the classification of land for industrial purposes directly. In this research specialized programs have been used such as geographic information system software The geographical information system permits the study of local distribution of phenomena, activities and the aims that can be determined in the loca

This study aims at examining the effectiveness of using the narrative approach in teaching the Interpretation of the Qur'an course in the development of conceptual comprehension among first-grade middle school female students. To achieve the objective of this study, a quantitative quasi-experimental design has been used. The sample consisted of first-grade middle school female students at "the third middle school" in Buraidah city, as this school suits the objective of the study. A test of conceptual understanding has been built by the researchers according to a list of conceptual understating skills at a significance level of α ≤ 0.05. Results have shown that there are statistically significant differences at the level (α ≤ 0,05)

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi