Ultrasonic Characterization of Biomimetic Porous Scaffold Using Machine Learning: Application of Biot’s Theory
Bibliographic record
Abstract
Abstract A two-dimensional infinite length porous slab is employed to simulate biomimetic porous scaffold. The pores of slab are saturated with a relatively low and high viscous fluids such as air and bone marrow. Ultrasonic waves based on the Biot-JKD formulation travel through the porous slab and create viscous exchanges between the skeletal frame and the fluid. The Biot-JKD formulation focuses on the parameters, biomarkers of the biomimetic porous scaffold, which are sensitive to the transmission and reflection signals. These parameters include porosity, tortuosity, viscous characteristic length, Young’s modulus, and Poisson’s ratio. An artificial neural network (ANN) based on a set of the biomarkers is rendered to model the transmitted and reflected waves from the porous slab. The validation of the proposed analytical approach and released artificial neural network is evaluated by the pertinent literature. The output of the artificial neural network, the transmitted-reflected waves, is inversely applied to the analytical expression to estimate the biomarkers associated with bone regeneration. The results show that for a medium filled with a relatively high viscous fluid the longitudinal waves are more prone to estimate mechanical properties of the medium such as Young’s modulus and Poisson’s ratio while the transverse waves, in addition to longitudinal waves, are essential to estimate the physical properties of the medium including porosity, tortuosity, and viscous characteristic length. Furthermore, it is also concluded that for the medium filled with a relatively low viscous fluid such as air the longitudinal waves alone is able to estimate the biomarkers, which reduce significantly the computational efforts.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".