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Record W6998432057

Advancing the Extraction of Mechanical Properties from Biaxial Data

2023· dissertation· en· W6998432057 on OpenAlexaff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsBlackberry (Canada)
Fundersnot available
KeywordsDeformation (meteorology)AnisotropyDisplacement (psychology)Consistency (knowledge bases)Characterization (materials science)PlanarIsotropyRange (aeronautics)
DOInot available

Abstract

fetched live from OpenAlex

Mechanical characterization is vital to understand soft tissue behaviour in health and pathology. In aortic aneurysms, for instance, it is used to develop techniques for rupture risk assessment. In skin, for instance, it is used to assess the effects of freezing and anatomic location, the information useful for donor tissue banks. Planar biaxial testing is one of the most common tools for the mechanical characterization of soft tissues. In this experiment, loadings such as displacements or forces are applied to the edges of the square specimens yielding deformations at the center of the specimens that are measured digitally. Biaxial testing is a great tool as it captures anisotropy and nonlinearity of soft tissues’ behaviour and is capable of exploring a wide range of deformation states as it can apply different combinations of loadings. However, because the deformations at the center of the specimens are not controlled, no two mechanical tests are equivalent, complicating the consistency in data extraction and comparison.
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\nIn this study, we propose a new approach for biaxial data analysis. First, a surface is fitted to the biaxial data. Second, the mechanical response is interpolated at the true equi-biaxial stretch deformation state (the state at which the deformations at the center of the sample are equal). Third, the effective mechanical properties such as high/low elastic moduli, and transition stretches/stress are extracted from the interpolated response. Other studies, in contrast, extract properties at the equi-biaxial displacement deformation state (the state at which equal strains are applied at the sample edges), which is due to the anisotropy of soft tissues and experimental setup, varies from specimen to specimen. We argue that our proposed approach of data extraction is more robust from the mechanical point of view.
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\nTo demonstrate that our proposed approach can result in drastically different data sets, we apply it to previously tested aortic tissues from human donors and pigs. We compare effective mechanical properties extracted from the interpolated equi-biaxial stretch deformation state and conventionally used equi-biaxial displacement deformation state. Statistical analysis shows a significant difference between two groups of mechanical measures whether the measures are compared individually within each group or the general comparison of groups is conducted. Particularly, measures related to the transition zones (stress/stretch) linked to collagen fibres’ engagement behaviour were affected the most. Overall, the results indicate that the way the data is extracted can impact the outcome of biaxial studies. This further highlights the advantage of using the proposed approach of biaxial data extraction at equivalent deformation states versus the conventional approach.
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\nThe proposed approach of the data extraction was also applied to human skin samples that came from the same donor’s back. Prior to biaxial testing, the samples were frozen/stored using three different freezing protocols (wet freezing in Phosphate Buffered Salin alone and with the cryoprotectant Glycerol as well as dry freezing using Liquid Nitrogen). Then, after testing, the effective mechanical properties were extracted and the effects of freezing and the anatomic locations were evaluated. We found very little quantitive evidence that the freezing storage approach mattered, although some qualitative observations were made to highlight the distinct behaviour of the samples frozen using Liquid Nitrogen. In the case of heterogeneity analysis, samples closer to the spine were different from samples further away from the spine, with the transition zone properties affected the most, especially for the samples subjected to Liquid Nitrogen freezing protocol. Future studies should assess each of the effect of heterogeneity and the effects of freezing separately, however, the overall approach of data extraction seems promising for intra-patient analysis.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.209
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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