This study aimed to provide a theoretical framework for the impact of the Turkish intervention in northern Syria and the extent of its impact on the relations between the two countries.

It also sought to answer the study's main question: What is the impact of the Turkish intervention in northern Syria on Turkish-Syrian relations during the period 2011-2019 ?.   

In this study, the historical method, the descriptive method, and the systems analysis method were used, with the aim of arriving at an accurate and realistic understanding of the Turkish-Syrian relations.

The study reached a set of results, the most important of which was that the Syrian-Turkish relations are based on interests, and that all alliances and

Objective(s): To evaluate nurses’ Practice toward neonatal endotracheal suctioning procedure, and to determine the effectiveness of the interventional program on nurses’ practices, as well as to find out the relationship between nurses’ practice and their demographic characteristics.

Methodology: A Pre-experimental, one group design, was carried out to achieve the objectives of the current study using the evaluation approach and the implementation of the education program for the period from January 17 to June 31, 2022. A non- probability, purposive sample of (24) nurses were selected from the Neonatal Intensive Care Unit at Pediatric Teaching Hospital/ Medical City Department. A checklist w

The world is moving towards greening business in general and production systems in particular. At the same time, economic units seek to enhance their productivity and find any variables that can contribute to improving their elements. Economic units should not ignore the green dimension of cost management techniques because of its role in containing the green dimension of the production system and the product. However the few researches dealt with the subject of the green kaizen showed its role in reducing costs and improving the environment. Those researches did not address its contribution to raising the level of productivity. Productivity is an important indicator of economic units that expresses their level of success and progre

The purpose of this work is to concurrently estimate the UVvisible spectra of binary combinations of piroxicam and mefenamic acid using the chemometric approach. To create the model, spectral data from 73 samples (with wavelengths between 200 and 400 nm) were employed. A two-layer artificial neural network model was created, with two neurons in the output layer and fourteen neurons in the hidden layer. The model was trained to simulate the concentrations and spectra of piroxicam and mefenamic acid. For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt algorithm with feed-forward back-propagation learning produced root mean square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients of determination of

Density functional theory (DFT) calculations were used to evaluate the capability of Glutamine (Gln) and its derivative chemicals as inhibitors for the anti-corrosive behavior of iron. The current work is devoted to scrutinizing reactivity descriptors (both local and global) of Gln, two states of neutral and protonated. Also, the change of Gln upon the incorporation into dipeptides was investigated. Since the number of reaction centers has increased, an enhancement in dipeptides’ inhibitory effect was observed. Thus, the adsorption of small-scale peptides and glutamine amino acids on Fe surfaces (1 1 1) was performed, and characteristics such as adsorption energies and the configuration with the highest stability and lowest energy were ca

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p