Semi-analytical finite-element based method for inverse characterization of cortical bone using low-frequency guided waves
Bibliographic record
Abstract
Axial transmission research has demonstrated that low-frequency ultrasonic guided waves are sensitive to changes in the intracortical bone, which is of interest since the resorption in the endosteal region is associated to early-stage osteoporosis. Current methods rely on inversion schemes used to match experimental data with the theoretical data obtained from simplified models. However, due to the importance of the cross-sectional curvature of the cortical bone at low-frequency (e.g., <200 kHz), the implementation of a more elaborate model remains an open issue. Thus, the aim of this paper is to introduce a semi-analytical finite-element (SAFE) model to be used along with a genetic algorithm for the inverse characterization of cortical bone. Our proposal is to validate an inverse scheme using laboratory-controlled measurements on bone-mimicking phantoms at low frequency. An arbitrary cross-sectional geometry, instead of a plate or cylinder simplification, was implemented. Despite a computationally expensive SAFE routine, the results show that the model outputs estimated by the genetic algorithm are in good agreement with the reference values obtained by µCT images. The possibility of implementing parallel computation using graphics processing units in order to increase the level of complexity of the SAFE model may now be investigated.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".