Compression and tension behavior of the prosthetic foam materials polyurethane, EVA, Pelite™ and a combination of polyurethane and EVA: a preliminary study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Materials with low-strength and low-impedance properties, such as elastomers and polymeric foams are major contributors to prosthetic liner design. Polyethylene-Light (Pelite™) is a foam liner that is the most frequently used in prosthetics but it does not cater to all amputees' limb and skin conditions. The study aims to investigate the newly modified Foam Liner, a combination of two different types of foams (EVA + PU + EVA) as the newly modified Foam Liner in terms of compressive and tensile properties in comparison to Pelite™, polyurethane (PU) foam, and ethylene-vinyl acetate (EVA) foam. Universal testing machine (AGS-X, Shimadzu, Kyoto, Japan) has been used to measure the tensile and compressive stress. Pelite™ had the highest compressive stress at 566.63 kPa and tensile stress at 1145 kPa. Foam Liner fell between EVA and Pelite™ with 551.83 kPa at compression and 715.40 kPa at tension. PU foam had the lowest compressive stress at 2.80 kPa and tensile stress at 33.93 kPa. Foam Liner has intermediate compressive elasticity but has high tensile elasticity compared to EVA and Pelite™. Pelite™ remains the highest in compressive and tensile stiffness. Although it is good for amputees with bony prominence, constant pressure might result in skin breakdown or ulcer. Foam Liner would be the best for amputees with soft tissues on the residual limbs to accommodate movement.
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.
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