Poroelastic model of the lungs at low frequencies predicted by Biot's theory
Bibliographic record
Abstract
Biot's theory of poroelastic wave propagation inherently lends itself to elucidate the characteristics of a biphasic medium comprising solid and fluid components, such as biological tissues. One of the intricately complex biological tissues that remains poorly understood is the lungs since their properties diversify significantly through their pore geometries affected by inspiratory positive airway pressure (IPAP) and applied frequency range. One hypothesizes that the vibroacoustic behaviour of the lungs can be predicted by Biot's theory, as the nature of the lungs aligns with the principles of the theory at low frequencies. This study aims to analytically investigate the vibroacoustic behaviour of the lungs, considering 10 and 20-cm H;2O IPAP. Utilizing a fractional derivative formulation, one predicts the complex-valued shear wave speed, as well as the fast and slow compression wave speeds, for frequencies ranging from 5 to 100 Hz. A 3D digital thorax twin study using these predicted wave speeds, particularly at 28 Hz and 20 cm H;2O IPAP, is validated against experimental data from the literature. Consequently, applying Biot's theory provides a valuable framework for understanding the dynamic vibroacoustic behaviour of the lung tissues in response to varying IPAP and low frequencies.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".