Compression and tension behavior of the prosthetic foam materials polyurethane, EVA, Pelite™ and a combination of polyurethane and EVA: a preliminary study
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".