Expression of concern: AI-driven propulsion optimization with a graph neural network-based framework for service level agreement prediction and performance enhancement in aerospace systems

The publisher of Aircraft Engineering and Aerospace Technology is issuing an Expression of Concern for the following article Fang, J. (2025), “AI-driven propulsion optimization with a graph neural network-based framework for service level agreement prediction and performance enhancement in aerospace systems”, Aircraft Engineering and Aerospace Technology, Vol. ahead-of-print No. ahead-of-print. Link to the cited article., which was submitted to the guest-edited special issue “AI Applications in High-Speed Aerospace Propulsion Systems”, to inform readers that concerns have been raised regarding its authorship. This special issue has subsequently been cancelled. An investigation by the publisher found a number of articles with multiple concerns, including but not limited to lack of suitability for the scope of the journal and special issue, evidence of third-party involvement, and the inclusion of irrelevant citations. The following citation has been identified as anomalous and inappropriate; the author was unable to provide sufficient justification for its presence in the article: Qureshi, H.N., Manalastas, M., Zaidi, S.M., Imran, A. and Al Kalaa, M.O. (2020), “Service level agreements for 5G andbeyond: overview, challenges and enablers of 5G-healthcare systems”, IEEE Access, Vol. 9, pp. 1044–1061. An investigation is ongoing and is currently unresolved. Further information will be provided by Aircraft Engineering and Aerospace Technology as it becomes available. The publisher of the journal sincerely apologizes to the readers of this article.

Publication Details

Journal
Aircraft Engineering and Aerospace Technology
Published
2026-02-18
DOI
https://doi.org/10.1108/aeat-02-2026-0063
Primary Topic
Air Traffic Management and Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Expression of concern: AI-driven propulsion optimization with a graph neural network-based framework for service level agreement prediction and performance enhancement in aerospace systems

Aircraft Engineering and Aerospace Technology
Air Traffic Management and Optimization
article

Expression of concern: AI-driven propulsion optimization with a graph neural network-based framework for service level agreement prediction and performance enhancement in aerospace systems

article en

Abstract

The publisher of Aircraft Engineering and Aerospace Technology is issuing an Expression of Concern for the following article Fang, J. (2025), “AI-driven propulsion optimization with a graph neural network-based framework for service level agreement prediction and performance enhancement in aerospace systems”, Aircraft Engineering and Aerospace Technology, Vol. ahead-of-print No. ahead-of-print. Link to the cited article., which was submitted to the guest-edited special issue “AI Applications in High-Speed Aerospace Propulsion Systems”, to inform readers that concerns have been raised regarding its authorship. This special issue has subsequently been cancelled. An investigation by the publisher found a number of articles with multiple concerns, including but not limited to lack of suitability for the scope of the journal and special issue, evidence of third-party involvement, and the inclusion of irrelevant citations. The following citation has been identified as anomalous and inappropriate; the author was unable to provide sufficient justification for its presence in the article: Qureshi, H.N., Manalastas, M., Zaidi, S.M., Imran, A. and Al Kalaa, M.O. (2020), “Service level agreements for 5G andbeyond: overview, challenges and enablers of 5G-healthcare systems”, IEEE Access, Vol. 9, pp. 1044–1061. An investigation is ongoing and is currently unresolved. Further information will be provided by Aircraft Engineering and Aerospace Technology as it becomes available. The publisher of the journal sincerely apologizes to the readers of this article.

Aircraft Engineering and Aerospace Technology
Openalex Percentile: Top 52%
Air Traffic Management and Optimization
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