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Record W4390713358 · doi:10.47001/irjiet/2023.712012

Design of a Cost Effective Biaxial Tensile Testing Device for Soft Tissues

2023· article· en· W4390713358 on OpenAlexaff
Mahmut Arıkan, Ahmet Can

Bibliographic record

VenueInternational Research Journal of Innovations in Engineering and Technology · 2023
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsUniversity of Calgary
FundersTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsSoft tissueUltimate tensile strengthTensile testingMaterials scienceStructural engineeringComputer scienceComposite materialBiomedical engineeringEngineeringMedicineSurgery

Abstract

fetched live from OpenAlex

This study focuses on the development of a specifically designed biaxial tensile device to characterize the mechanical properties of soft tissues.Soft tissues play an important role in medicine, biological research, and materials engineering projects.Therefore, a sensitive and customized testing device was needed to study the mechanical behavior of such tissues.Within the scope of the study, the design, production, assembly, and testing of the device were carried out.The device is optimized for performing biaxial tensile tests of soft tissues.These tests are used to understand the tension and deformation behavior of tissues, to determine their biomechanical properties, and to provide important data for medical applications.Experiments show that the device operates with high precision and produces reliable results for characterizing the mechanical behavior of soft tissues.This biaxial tensile device could have a wide range of applications, from medical research to biomaterial development, contributing to progress in the field of soft tissue mechanics.This study highlights the successful development and testing of a biaxial tensile device specifically designed to perform mechanical testing of soft tissues.This device can be used as an important tool in the characterization of soft tissues and biomedical research.

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)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.122
GPT teacher head0.412
Teacher spread0.290 · 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.

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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