Numerical Analysis of Energy Density Distribution in the Human Lungs Under Low-Frequency Acoustic Excitation
Bibliographic record
Abstract
According to the 2018 annual report of Canada, 3.8 million people are living with asthma, and 2 million are living with chronic obstructive pulmonary disease (COPD), both of which can impact a person's ability to breathe because of excessive mucus accumulation in bronchioles. However, most people with COPD can achieve reasonable symptoms to control and quality of life with the help of an acoustic airway clearance device, which supplies vibrations to act on bronchial mucus's viscoelastic, shear-thinning, and thixotropic properties, liquefying it to ease expectoration. Yet since, no 3D complex and whole thorax geometry is used to understand and/or optimize the lungs' behaviour numerically. This study aims at investigating the human lungs under airway clearance therapy (ACT) with a 3D validated realistic computed tomography-based numerical finite element analysis CT/FEA of the human thorax. We study the lungs under ACT in the frequency domain (5-100 Hz) using COMSOL 6.1 Multiphysics®. Results show that the lungs are most affected at approximately 30 Hz, which is consistent with former experimental studies. Furthermore, the mean value of the strain energy density follows the mean value of kinetic energy density by 32 Hz. Therefore, this study provides valuable insights into optimizing treatment for respiratory conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".