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Preface

2021· article· en· W4213349336 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)MechatronicsLibrary scienceEngineeringField (mathematics)Engineering ethicsComputer scienceArtificial intelligenceMedicineMathematics

Abstract

fetched live from OpenAlex

It is highly delighted to introduce the proceedings dedicated to the 2020 International Conference on Applied Mechanics and Mechanical Engineering (ICAMME 2020), which was successfully held on Sep. 25-27, 2020 in Hulun Buir, China, and hosted by AEIC Academic Exchange Information Centre. ICAMME 2020 is to bring together innovative academics and industrial experts in the field of applied mechanics, mechanical engineering, electrical and automation engineering to a common forum. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. An ideal platform for seeking global partners had been established. About 60 participants from academic, high-education institutes and other organizations took part in the Conference. The conference model was divided into two sessions, including oral presentations and keynote speeches. In the first part, some scholars, whose submissions were selected as the excellent papers, were given 15 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. We were very honored to have Prof. Simon X. Yang from University of Guelph, Canada and Prof. Wei Min Huang from Nanyang Technological University, Singapore as our Chairman. In the keynote presentation part, we invited three professors as our keynote speakers. The first keynote speaker, Prof. Simon X. Yang, our beloved Chairman, shared a speech: Bioinspired Intelligent Approaches to Various Mechatronic Systems. In this talk, he started with a very brief introduction to biologically inspired computation and learning, and some bio-inspired computational techniques. After that, he presented several practical applications in engineering systems. The second keynote speaker, Prof. Wei Min Huang from Nanyang Technological University, Singapore. He delivered a speech: Morphing Via Shape Memory Materials and Technology. In this talk, he presented a brief introduction of various shape memory phenomena firstly. After that, he showed a range of morphing applications in aero/space missions, biomedical devices etc. utilizing shape memory alloy and shape memory polymer. Our finale keynote speaker, Prof. Junxi Bi, from Inner Mongolia University of Technology, China shared a speech on Reliability Analysis and Optimization of Key Parts of Low Pressure Cantilever Casting Machine. The proceedings present a selection of high-quality papers submitted to the conference by researchers from universities, research institutes, and industry. All papers were subjected to peer-review by conference committee members and international reviewers. The papers were selected based on their quality and their relevance to the conference. The proceedings present recent advances in the fields of Applied Mechanics and Engineering Mechanics, Mechanical Engineering, Materials Science, Electrical and Automation Engineering and Other related research. I would like to express special gratitude to members of the conference committee and organizers of the conference. I would also like to thank the reviewers for their valuable time and advice which helped in improving the quality of the papers selected for presentation at the conference and for publication in the proceedings. Finally, I want to thank the authors, the members of the organizing committee, the reviewers, the chairpersons, sponsors, and all other conference participants for their support of ICAMME 2020. The Organizing Committee of ICAMME 2020 List of Committee member 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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.210
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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