Web of Science (WoS) of Clarivate Analytics in the 2021 Report for Koya University shows two Highly Cited papers in the Field and one Hot Paper in the Field. The two highly cited papers got 32 and 54 citations, whereas the hot paper got 54 citation. The published papers are in the field of Engineering Multidiciplinary  and Nuclear Science Technology.

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According to WoS definition, Highly cited paper in the filed means; Highly Cited Papers are papers that perform in the top 1% based on the number of citations received when compared to other papers published in the same field in the same year. Click on the Highly Cited Paper icon for more details about an item. Hot paper in the field means; A paper is selected as a Hot Paper if it meets a citation-frequency threshold determined for its field and bimonthly group. Citation-frequency distributions are compiled for each field and cohort. Thresholds are set by finding the closest citation count that would select the top fraction of papers in each field and period.

Both published papers [1] [2] are open access. Open access costs a high article proccessing fee but it is available free for the public and brings high number of citations.

Highly Cited Papers in the Field:

1. Proposing a gamma radiation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products
By: Roshani, Mohammadmehdi; Phan, Giang; Faraj, Rezhna Hassan; et al.
NUCLEAR ENGINEERING AND TECHNOLOGY   Volume: ‏ 53   Issue: ‏ 4   Pages: ‏ 1277-1283   Published: ‏ APR 2021

Abstract

It is important for operators of poly-pipelines in petroleum industry to continuously monitor characteristics of transferred fluid such as its type and amount. To achieve this aim, in this study a dual energy gamma attenuation technique in combination with artificial neural network (ANN) is proposed to simultaneously determine type and amount of four different petroleum by-products. The detection system is composed of a dual energy gamma source, including americium-241 and barium-133 radioisotopes, and one 2.54 cm x 2.54 cm sodium iodide detector for recording the transmitted photons. Two signals recorded in transmission detector, namely the counts under photo peak of Americium-241 with energy of 59.5 keV and the counts under photo peak of Barium-133 with energy of 356 keV, were applied to the ANN as the two inputs and volume percentages of petroleum by-products were assigned as the outputs.

(c) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Times Cited: 32
(from Web of Science Core Collection)

2. Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline's scale layer thickness
By: Roshani, Mohammadmehdi; Phan, Giang T. T.; Ali, Peshawa Jammal Muhammad; et al.
ALEXANDRIA ENGINEERING JOURNAL   Volume: ‏ 60   Issue: ‏ 1   Pages: ‏ 1955-1966   Published: ‏ FEB 2021

Abstract
The main objective of the present research is to combine the effect of scale thickness on the flow pattern and characteristics of two-phase flow that is used in oil industry. In this regard, an intelligent nondestructive technique based on combination of gamma radiation attenuation and artificial intelligence is proposed to determine the type of flow pattern and gas volume percentage in two phase flow independent of petroleum pipeline's scale layer thickness. The proposed system includes a dual energy gamma source, composed of Barium-133 and Cesium-137 radioisotopes, and two sodium iodide detectors for recording the transmitted and scattered photons. Support Vector Machine was implemented for regime identification and Multi-Layer Perceptron with Levenberg Marquardt algorithm was utilized for void fraction prediction. Total count in the scattering detector and counts under photo peaks of Barium-133 and Cesium-137 were assigned as the inputs of networks. The results show the ability of presented system to identify the annular regime and measure the void fraction independent of petroleum pipeline's scale layer thickness. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Times Cited: 54
(from Web of Science Core Collection)

Hot Paper in the Field:

2. Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline's scale layer thickness
By: Roshani, Mohammadmehdi; Phan, Giang T. T.; Ali, Peshawa Jammal Muhammad; et al.
ALEXANDRIA ENGINEERING JOURNAL   Volume: ‏ 60   Issue: ‏ 1   Pages: ‏ 1955-1966   Published: ‏ FEB 2021

Abstract
The main objective of the present research is to combine the effect of scale thickness on the flow pattern and characteristics of two-phase flow that is used in oil industry. In this regard, an intelligent nondestructive technique based on combination of gamma radiation attenuation and artificial intelligence is proposed to determine the type of flow pattern and gas volume percentage in two phase flow independent of petroleum pipeline's scale layer thickness. The proposed system includes a dual energy gamma source, composed of Barium-133 and Cesium-137 radioisotopes, and two sodium iodide detectors for recording the transmitted and scattered photons. Support Vector Machine was implemented for regime identification and Multi-Layer Perceptron with Levenberg Marquardt algorithm was utilized for void fraction prediction. Total count in the scattering detector and counts under photo peaks of Barium-133 and Cesium-137 were assigned as the inputs of networks. The results show the ability of presented system to identify the annular regime and measure the void fraction independent of petroleum pipeline's scale layer thickness. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Times Cited: 54
(from Web of Science Core Collection)

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Koya University (KOU) is located in the city of Koya (Koy Sanjaq) which is 1.0 hr drive to the East of the Kurdistan Region capital Erbil (Arbil, Hewlér) in Kurdistan Region of F.R. Iraq. It is on the foothills of beautiful high mountain. Its campus has been carefully laid out to embrace the beautiful mountainous nature. There are 4 Faculties and 2 Schools in KOU; Faculty of Engineering (FENG), Faculty of Science and Health (FSCH), Faculty of Education (FEDU), Faculty of Humanities and Social Silences (FHSS), Shcool of Physical Education (SPHE) and School of Medicine (SMED). Also, there are two research centers; Genome Center and Malai Gawra Center. Moreover, at KOU there is an English Language Center (BELC) at KOU has been opened with the sponsorship of IREX and American embassy in Baghdad as well as with the support of Spring International Language Center of The University of Arkansas. KOU has two Scientific Journals; ARO-The Scientific Journal of Koya University, which is indexed by Clarivate Analytics (ESCI), and Koya University Journal of Humanities and Social Sciences (KUJHSS). KOU is a proactive member of Erasmus/ Marhaba Project and Erasmus+. KOU signed many Memorandum of Understandings (MoU) with many International Universities, e.g., The University of Arkansas (June 2015). The Lulea University in Sweden (April 2014), The University of Nottingham in the UK, The University of Buckingham in the UK (Oct 2008), Belkin University in Turkey (Sep 2009) and The University of Greenwich in the UK.