Abstract

In this study, mucilage was extracted from Malabar spinach and tested for drag-reducing properties in aqueous liquids flowing through pipelines.  Friction produced by liquids flowing in turbulent mode through pipelines increase power consumption. Drag-reducing agents (DRA) such as polymers, suspended solids and surfactants are used to reduce power losses. There is a demand for natural, biodegradable DRA and mucilage is emerging as an attractive alternative to conventional DRAs. Literature review revealed that very little research has been done on the drag-reducing properties of this mucilage and there is an opportunity to explore the potential applications of mucilage from Malabar spinach. An experi

Abstract

Purpose: The research attempts to Stand on the reality of the effective application of of strategic information systems in telecommunications companies in the Kurdistan Region, and what is the amount of the impact of such systems on promoting of Strategic Intelligence.

Design/Methodology/Approach: The Applied method has been used, In order to achieve the objectives of the research has been the development of a questionnaire prepared for this purpose and then distributed to (11) Company of Iraqi communications operating in Kurdistan Region companies, it has been used questionnaire to collect data in order to develop

Presupposition is the background belief that is known by both the speaker and the addressee, it is tied to particular words and aspects of the surface structure that act as linguistic triggers. The present study aims at investigating whether Iraqi fourth -year university students are able to recognize the English presuppositions through the meaning of these linguistic triggers .To fulfil the basic requirements of the study, the researcher has conducted a test . The results of the study have validated the hypothesis of the work and it is found that the linguistic triggers are important tools in recognizing presuppositions.    

An experimental study was conducted on pressure drop of water flow through vertical cylindrical packed beds in turbulent region and the influence of the operating parameters on its behavior. The bed packing was made of spherical and non-spherical particles (spheres, Rasching rings and intalox saddle) with aspect ratio range 3.46  D/dp  8.486 obtaining bed porosities 0.396 0.84 and Reynolds number 1217  21758. The system is consisted of 5 cm inside diameter Perspex column, 50 cm long; distilled water was pumped through the bed with flow rate 875, 1000, 1125, 1250,1375 and 1500 l/h and inlet water temperature 20, 30, 40 and 50 ˚C. The packed bed system was monitored by using LabVIEW program, were the result

This study is concerned with the concept of offering a point of view as a narrative construction element, especially the element and point of view of the novel to the letter Alcinmatugrave both business taken from masterpieces and literary works in the foundation or set of cinema. On the whole, the narrator participate in mock narrative process and support the narrator visual image, especially stories that need to exist scenes prove added to the image that uses signs of reality to take those marks a new dimension not only across the image, but also the intervention of the narrator in the re-formation of this realism marks surrounding Palms perceived and visible and put markers regularly between what the narrative of privacy and image, fo

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