Bibliographic record
Abstract
It is with great pleasure that we present the proceedings of the 8th International Conference on Aeronautical, Aerospace and Mechanical Engineering (AAME 2025). Building on the success of previous editions, AAME 2025 was held in Suzhou, China, from March 28 to 30, 2025, adopting a hybrid format to accommodate global participation while fostering in-person collaboration. The conference brought together leading researchers, engineers, and industry experts to share cutting-edge advancements and explore future directions in aeronautical, aerospace, and mechanical engineering. This year’s conference featured two distinguished keynote speakers who set the tone for the event. Prof. Zheng Hong (George) Zhu from York University, Canada, a renowned expert in space robotics and dynamics, shared groundbreaking insights into the latest developments in aerospace technologies. Prof. Lixi Huang from The University of Hong Kong, China, delivered an inspiring talk on innovations in mechanical engineering and their applications in sustainable systems. Additionally, six invited speakers from prestigious institutions contributed their expertise, further enriching the conference with diverse perspectives on emerging trends and challenges in the field. AAME 2025 attracted participants from several countries, including China, Canada, Russia, Japan and other countries. The conference provided a vibrant platform for delegates to present their research, engage in stimulating discussions, and forge collaborations. All submitted papers underwent a rigorous peer-review process by the International Technical Committee, ensuring the highest standards of quality and originality. The selected papers reflect the conference’s commitment to advancing knowledge and addressing real-world engineering problems. List of Conference Committees is available in this PDF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".