Assessment of cortical bone phantom properties using ultrasonic guided waves transduced with a multi-element transducer
Bibliographic record
Abstract
The past decade has seen extensive exploration of alternative methods for the early diagnosis of osteoporosis through the assessment of the the bone quality. Previous research on axial transmission of ultrasonic guided waves demonstrated their sensitivity to the intrinsic properties of elongated cortical bones. This study highlights the capacity of low-frequency guided waves to ascertain bone properties through the inversion of dispersion curves. The proposed inversion scheme relies on dispersion curves simulated using the semi-analytical iso-geometric analysis (SAIGA) method. The model incorporates the excitability of ultrasonic guided wave modes to ensure that the inversion uses both the dispersive trajectories and amplitudes of the modes. Two models were examined: (1) a cortical bone phantom plate covered with a soft tissue mimicking material and (2) a quasi-cylinder cortical bone phantom surrounded by a soft tissue mimicking material. A proprietary axial transmission multielement ultrasonic transducer, designed for exciting guided waves under 500 kHz, was employed to capture experimental dispersion curves on phantoms through the 2D-FFT. The mechanical properties of the bone phantoms were inferred by minimizing disparities between experimental and simulated dispersion curves. Inverse properties exhibited an error of less than 4% compared to reference values. The axial transmission probe demonstrated its proficiency in accurately measuring modes propagating inside the cortical layer.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".