A New Laser Reflectance System Capable of Measuring Changing Cross-Sectional Area of Soft Tissues During Tensile Testing
Bibliographic record
Abstract
Determination of the biomechanical properties of soft tissues such as tendons and ligaments is dependent on the accurate measurement of their cross-sectional area (CSA). Measurement methods, which involve contact with the specimen, are problematic because soft tissues are easily deformed. Noncontact measurement methods are preferable in this regard, but may experience difficulty in dealing with the complex cross-sectional shapes and glistening surfaces seen in soft tissues. Additionally, existing CSA measurement systems are separated from the materials testing machine, resulting in the inability to measure CSA during testing. Furthermore, CSA measurements are usually made in a different orientation, and with a different preload, prior to testing. To overcome these problems, a noncontact laser reflectance system (LRS) was developed. Designed to fit in an Instron 8872 servohydraulic test machine, the system measures CSA by orbiting a laser transducer in a circular path around a soft tissue specimen held by tissue clamps. CSA measurements can be conducted before and during tensile testing. The system was validated using machined metallic specimens of various shapes and sizes, as well as different sizes of bovine tendons. The metallic specimens could be measured to within 4% accuracy, and the tendons to within an average error of 4.3%. Statistical analyses showed no significant differences between the measurements of the LRS and those of the casting method, an established measurement technique. The LRS was successfully used to measure the changing CSA of bovine tendons during uniaxial tensile testing. The LRS developed in this work represents a simple, quick, and accurate way of reconstructing complex cross-sectional profiles and calculating cross-sectional areas. In addition, the LRS represents the first system capable of automatically measuring changing CSA of soft tissues during tensile testing, facilitating the calculation of more accurate biomechanical properties.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".