Characterization Of A Tissue-Like Hyperelastic Polymer
Why this work is in the frame
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Bibliographic record
Abstract
Abdominal Aortic Aneurysms (AAAs) are an often fatal medical condition that frequently remains asymptomatic. There is considerable interest in research associated with AAAs as they have a reported mortality rate of up to 90% in the event of rupture. Most present studies completed on AAAs are in silico due to the challenges associated with using human tissue, or human participants. These studies mainly consist of computer simulations, modelling the AAA as a hyperelastic material, many of which rely on the same material strain energy function (SEF) that was introduced in literature. However, to date no in vitro studies have been completed to validate these computational models. This will part one of a two part investigation into validating the computational models most frequently utilized in literature. This first part of the investigation focuses on the process of selecting and characterizing a tissue-like material (i.e., hyperelastic behavior) that could be used in an in vitro test. The second part will concentrate on validating the existing computational models. In several articles, silicone has been used as a tissue-like material exhibiting hyperelastic behavior. Accordingly, in the present work, silicone is adopted for experimental testing. Two molds of different thicknesses and a die cutter were manufactured to prepare specimens for tensile testing. Different thicknesses' specimens were tested under uniaxial tension. The reported testing results were analyzed to determine the true stress-strain behavior, so that the mechanical behavior could be compared to that of the aortic tissue reported in literature. The same methodology used in literature to determine the hyperelastic coefficients of aortic tissue was then applied to determine the silicone hyperelastic coefficients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it