Effect of bone degradation on axially transmitted low frequency (<500 kHz) ultrasonic guided waves
Bibliographic record
Abstract
The early diagnosis of osteoporosis through bone quality assessment has been extensively studied in the past few years. Research in axial transmission in long cortical bone using ultrasonic guided waves was shown to be sensitive to variations of the bone properties such as the geometry and the mechanical properties. The aim of this presentation is, therefore, to simulate a complex bone-like geometry using 3D finite elements (FE) in order to investigate the influence of the cortical thickness mechanical properties degradation. Three parameters were investigated: (1) the thickness of the periosteal region, (2) the degradation of the endosteal region, and (3) the position of the transition zone from periosteal to endosteal regions. Moreover, the influence of soft tissue was investigated on a plate-like structure using semi-analytical iso-geometric analysis method (SAIGA). Two cortical bone phantom plates with different mechanical properties covered with a soft tissue mimicking material were used to perform axial transmission measurement. The dispersion curves of the propagating modes were experimentally measured and then were compared to the solutions obtained with the SAIGA method. The results showed the possibility to retrieve the properties of the bone phantom plates through an inversion of the dispersion curves.
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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 | 0.002 |
| 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 | 0.001 |
| 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".