OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN
Ramy Mohammed Aiesh Qaisi, Farhad Fouladinia, Abdulilah Mohammad Mayet, et al.
Sensors (2023) Vol. 23, Iss. 15, pp. 6959-6959
Open Access | Times Cited: 8

Showing 8 citing articles:

An optimised and novel capacitance-based sensor design for measuring void fraction in gas–oil two-phase flow systems
Abdullah M. Iliyasu, Mohammad Hossein Shahsavari, Abdullah S. Benselama, et al.
Nondestructive Testing And Evaluation (2024) Vol. 39, Iss. 8, pp. 2450-2466
Open Access | Times Cited: 5

Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors
Abdulilah Mohammad Mayet, Farhad Fouladinia, Seyed Mehdi Alizadeh, et al.
PLoS ONE (2024) Vol. 19, Iss. 5, pp. e0301437-e0301437
Open Access | Times Cited: 5

A novel metering system consists of capacitance-based sensor, gamma-ray sensor and ANN for measuring volume fractions of three-phase homogeneous flows
Farhad Fouladinia, Seyed Mehdi Alizadeh, Evgeniya Ilyinichna Gorelkina, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-27
Closed Access | Times Cited: 3

Multiphase Flow’s Volume Fractions Intelligent Measurement by a Compound Method Employing Cesium-137, Photon Attenuation Sensor, and Capacitance-Based Sensor
Abdulilah Mohammad Mayet, Farhad Fouladinia, Robert Hanus, et al.
Energies (2024) Vol. 17, Iss. 14, pp. 3519-3519
Open Access | Times Cited: 1

Utilizing Artificial Neural Networks and Combined Capacitance-Based Sensors to Predict Void Fraction in Two-Phase Annular Fluids Regardless of Liquid Phase Type
Mustafa Al‐Fayoumi, Hani Almimi, Aryan Veisi, et al.
IEEE Access (2023) Vol. 11, pp. 143745-143756
Open Access | Times Cited: 3

Comparison of Backscattered and Transmitted Gamma Rays Spectra for Prediction of Volume Fraction of Three-Phase Flows Using Machine Learning Model
S. Z. Islami rad, R. Gholipour Peyvandi
Journal of Nondestructive Evaluation (2024) Vol. 43, Iss. 4
Closed Access

AI-Based Evaluation of Homogeneous Flow Volume Fractions Independent of Scale Using Capacitance and Photon Sensors
Abdulilah Mohammad Mayet, Salman Arafath Mohammed, Shamimul Qamar, et al.
ARO-The Scientific Journal of Koya University (2024) Vol. 12, Iss. 2, pp. 167-178
Open Access

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