Design of a Cost Effective Biaxial Tensile Testing Device for Soft Tissues
Bibliographic record
Abstract
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".