Investigation of the Effect of Waste Materials on the Properties of the Composite Polymer Prosthetics Limbs
Bibliographic record
Abstract
Nowadays, the trend to benefit from environmentally friendly waste and recycle it instead of incineration processes that can cause pollution in the environment to have a clean and green environment that is free from pollution and can work for a long time without consumption and damage. This study is designed to manufacture prosthetics limbs from PMMA (polymethyl methacrylate) reinforced with different weight percentages of waste paper layers (0%, 3%, 6%, 9%, and 12%) by the “hand lay” method. The tensile, flexural, impact, compression, and hardness of these composites are tested before and after adding weight percentages waste paper layers to determine the mechanical properties behavior. The average values of all the samples are collected and analyzed by one-way analysis of variance (ANOVA) to interpret the results. When discussing the results of mechanical tests, it is found that the addition (12%-waste paper layers) to (PMMA) increased the average values (tensile, flexural, impact, compression, and hardness) by 142.1%, 532.3%, 146%, 99.2%, and 6.37%; respectively, compared to the mean values of other samples. It is clear from this work there is an improvement in the properties of PMMA after strengthening with waste paper layers.
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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 | 0.000 |
| 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) | 0.001 | 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".