In context of this paper we prepare high purity powder ZnO nanostructures by chemical method at low temperature solution and study the effect off annealing at high temperature, ZnO nanoparticles have been successfully synthesized by chemical method at 0Cᵒ solution. In this method, suddenly reaction is occurred between zinc acetate solution and sodium hydroxide solution at 0Cᵒ, annealing temperature of powder product surfactant plays an important role in morphological changes. The nanostructures have been characterized by X-ray diffraction (XRD), Scanning Electron Microscope (SEM), differential scanning calorimetry(DSC) and UV-visible .analysis Effect of annealing temperatures on the morphology , structure and optical properties is di

.Curcumin (Cur) and L phenylalanine (Phy) compounds were used to prepare two mixed ligand complexes with Cr (III) and Fe (III) ions. The synthesized complexes are characterized by using conductivity measurement and different spectral methods like FT-IR and UV- Vis .Molar conductance and analytical studies confirmed that the complexes exhibit octahedral geometry., suggest that the complexes are formed in 1: 1 :2 [ L : Metal : 2phe ] ratio and they proposed to have the general formulae [M(Cur)(phe)2] Cl (M= Cr (III) and Fe (III) The compound dyeing method was studied and applied to acrylic fabric.The antibacterial activity of curcumin, phenylalanine and their mixed ligand complexes were examined on pathogenic bacterial strains and showed good

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

م.د. فاطمة حميد ،أ.م.د وفاء صباح محمد الخفاجي, International Journal of Psychosocial Rehabilitation,, 2020 - Cited by 1

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

Background: Assessment is an important part of the learning cascade in education. Students realize it as an influential motivator to direct and guide their learning. The method of assessment determines the way the students reach high levels of learning. It has been documented that one of factor affecting students’ choice of learning approach is the way how assessment is being performed. Many methods of assessment namely multiple choice questions, essay questions and others are mainly used to assess basic science knowledge in undergraduate education. Objectives: The aim of this study is to compare multiple choice questions (MCQ) and essay questions (EQ) (record the success and failure rate of multiple choice questions (MCQ) and essay quest