Nonlinear Dynamics of Human Aortas for Material Characterization
Bibliographic record
Abstract
Evaluating the nonlinear dynamics of human descending thoracic aortas is essential for building the next generation of vascular prostheses.This study characterizes the nonlinear dynamics, viscoelastic material properties, and fluid-structure interaction of 11 ex-vivo human descending thoracic aortas the full range of physiological heart rates.The aortic segments are harvested from heart-beating donors screened for transplants.A mock circulatory loop is developed to reproduce physiological pulsatile pressure and flow.The results show cyclic axisymmetric diameter changes, which are satisfactorily compared to in-vivo measurements at a resting pulse rate of 60 bpm, with an additional bending vibration.An increase of the dynamic stiffness (i.e., storage modulus) with age is also observed.This increase is accompanied by a strong reduction with age of the cyclic diameter change during the heart pulsation at 60 bpm and by a significant reduction of the loss factor (i.e., damping).Large dissipation is observed at higher pulse rates due to the combined effects of fluid-structure interaction and viscoelasticity of the aortic wall.This study presents data necessary for developing innovative grafts that better mimic the dynamics of the aorta.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".