Geng Academic Integrity Report: 'Analysis of the Causes of Data Uncertainty in the Process of System Failure Evolution' (DOI: 10.12404/j.issn.1671-1815.2503411)

Academic fraud report · Geng Detector

Summary

Verdict: Highly suspicious. This theoretical/conceptual paper by Cui Tiejun and Li Shasha, published in Science Technology and Engineering (2026, Vol. 26, No. 10), exhibits multiple serious integrity concerns. The most damaging finding is a fundamental mismatch between the paper's main body and its 'Innovation' section: while the body discusses quantum mechanics formalism, state superposition, and time delay, Section 5.3 lists innovation points referencing 'random errors', 'systematic bias', 'statistical analysis', and a 'quantitative analysis framework'—none of which appear in the preceding text, suggesting templated copy-paste or AI-generated content. Methodologically, the authors graft quantum Dirac notation onto safety-system failure evolution without performing any actual probabilistic amplitude calculations, openly stating these are 'of no use to this research.' Citation analysis shows 7 of 20 references (35%) are self-citations by the same author group, indicating a closed self-referential literature pool. The paper is a purely theoretical piece, so traditional image or numerical data manipulation checks are not applicable. Confidence is moderate; the AI-generation/template-mismatch pattern is strongly suggestive but not definitively proven.

Verdict

🟠 Highly suspicious. The paper presents integrity concerns consistent with template-based fabrication or AI-assisted generation, most notably a direct mismatch between the theoretical content of the main body and the claimed 'innovations' in Section 5.3. Because the paper contains no experimental figures, Western blots, or datasets, conventional image/data fabrication checks do not apply. Concerns are concentrated in content consistency, methodological rigor, and citation patterns.

Key findings

🔴 Critical: Internal contradiction between main text and innovation claims. Sections 1–4 and the conclusion discuss quantum mechanics, state superposition, time delay, and formal logic. Section 5.3 (Innovations) claims the paper: (1) clarifies the intrinsic relationships of multi-source uncertainty such as 'random errors' and 'systematic bias' from a failure-evolution dynamic perspective; (2) integrates 'statistical analysis' with fault diagnosis theory to build a 'quantitative analysis framework'; (3) reveals the influence of uncertainty on 'fault diagnosis accuracy.' None of these elements—random error, systematic bias, statistical analysis, quantitative framework, or diagnostic accuracy—appear in the body of the paper.
🟠 Methodological abuse of quantum formalism. The authors apply Dirac notation (e.g., $|S\rangle \sim |L\rangle + |W\rangle$ in Equations 1–2) to system failure evolution (SFEP) but explicitly concede that determining probability amplitudes is 'difficult and of no use to this research,' reducing the equations to decorative symbolism without operational meaning.
🟡 Self-citation concentration. Of 20 references, 7 (35%) are authored by Cui Tiejun and/or Li Shasha (entries [1], [2], [3], [14], [15], [16], [20]), all published 2020–2025 on narrowly segmented topics in 'system failure evolution process,' forming a closed self-referential citation network.
✅ Timeline plausibility. Submission 2025-05-10, revision 2026-01-05; cited 2025 papers by the same group are already published. No temporal anomalies such as citations to non-existent future works detected.

Evidence highlights

Section 5.3 vs. body mismatch (quote, innovations): '① 首次从故障演化动态视角,厘清随机误差、系统偏差等多源不确定性的内在关联;② 融合统计分析与故障诊断理论,构建数据不确定性本质原因的量化分析框架;③ 揭示不确定性对故障诊断精度的影响规律.' The terms '随机误差' (random error), '系统偏差' (systematic bias), '统计分析' (statistical analysis), '量化分析框架' (quantitative analysis framework), and '故障诊断精度' (fault diagnosis accuracy) are absent from the rest of the manuscript.
Equations 1–2: Quantum Dirac notation is introduced but the authors state in-text that probability amplitudes cannot be determined and '对本次研究无作用' (are not useful for this research).
Reference list: 7/20 = 35% self-citation rate; references [1], [2], [3], [14], [15], [16], [20] all authored by Cui Tiejun and/or Li Shasha.
DOI retained for verification: 10.12404/j.issn.1671-1815.2503411

Notes

The paper is theoretical/conceptual, so image-manipulation and dataset-fabrication detectors are not applicable; integrity assessment relies on textual and structural analysis.
The mismatch between the innovation section and the actual paper body is the strongest red flag and is independently sufficient to warrant editorial investigation.
All numeric evidence (reference counts, percentages, dates, DOI) is reproduced verbatim from the source report.
This is an AI-assisted preliminary assessment; formal determination of misconduct requires institutional investigation. False positives and false negatives are possible.