SH-Wave Propagation in Functionally Graded Magneto-Electro-Elastic Substrate at Irregular Boundaries
Bibliographic record
Abstract
The current study examines the behavior of an SH wave traveling over a functionally graded magneto-elastic substrate arrangement.At the substrate-vacuum interface, two irregularities with different shapes-rectangular and parabolically shaped-are considered in electrically and magnetically open cases and electrically and magnetically short cases.A study is also done on the combined impact of inhomogeneity, depth source, and irregularity.With the help of the Fourier transform, inverse Fourier transform, and perturbation technique, complex frequency relation has been derived for each type of irregular interface.The results' key characteristics are highlighted.In order to know the impact of the parameters involved, a particular model consisting of BaTiO3-CoFe2O4 magneto-electro-elastic material has been taken.The findings were presented in the form of graphs, which were created using Mathematica 7. Graphs are plotted for variations in wavenumber and phase velocity.This calculation model could be the ideal match for laminated FGMEE structures utilized as surface acoustic wave devices since the variation of the film's magneto-electromechanical characteristics changes gradually with depth and throughout the production process (SAW).As a result, it can serve as a theoretical foundation for the design of high-performance SAW devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".