Effect of intracortical bone properties on the guided wave propagation using low-frequency axial transmission technique: Simulation study
Bibliographic record
Abstract
In this work, a semi-analytical finite-element method was on the propagation of acoustic guided wave. The simulations were performed with a realistic bone cross-sectional geometry by considering an axial transmission configuration. A point source excitation was applied using a toneburst centered at 35 kHz as the input signal. The propagating waves were monitored using out-of-plane displacement in the axial direction at 32 positions separated 4 mm apart. The properties of the endosteal region of cortical bone were varied from healthy to early osteoporotic conditions. The use of the two-dimensional Fourier transform (2DFFT) allowed the measurement of individual mode velocity, which was compared to the velocity typically measured using first arrival signal (FAS). The results have shown that variations in the velocity measured using 2DFFT were mainly associated to a low order flexural-like mode among the eight modes supported by the waveguide. Furthermore, the measured velocity decreased approximately 10/% due to a degradation of 20/% of the biomechanical properties in the endosteal region. In contrast, the FAS velocity has shown to be affected by four modes, showing a non-linear variation on the velocity. The flexural mode measured using 2DFFT can therefore be considered promising to monitor changes in the endosteal region.
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 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 | 0.000 |
| 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".