Multiple Input–Single Output (MISO) Framework for Low Velocity Impact Response of Hybrid Gongronema latifolium/S-Glass Fibre Epoxy Composites
Bibliographic record
Abstract
Sustainable composites are vital for impact-critical aerospace, automotive, and defense applications. This study used Multiple Input–Single Output (MISO) experimental approach to assess how hybrid ratio, mass fraction, and fiber orientation influence the low-velocity impact behavior of Gongronema/S-glass epoxy composites. Gongronema fibers and S-glass were combined with ER-F292 epoxy and molded into ASTMstandard samples. Charpy impact tests measured energy absorption. A 60-run design evaluated input variable combinations, and Multiple Linear Regression identified significant predictors using p-values and confidence intervals. Results showed that the mean values for hybridization ratio, mass fraction, fiber orientation, and low velocity impact were (2.50), (27.79%), (67.90°), and (3.82 J), respectively. It was found that the mass fraction had significant negative correlation with low velocity impact (r = -0.455; p = 0.000), as did the fiber orientation (r = -0.853; p = 0.000). The results for R = (0.994), R² = (0.989), F = (1607.390), and Durbin-Watson = (2.213) show that the regression model is highly predictive. Regression coefficients indicated negative effects from hybridization ratio (-0.357), mass fraction (-0.032), and fiber orientation (- 0.017), all statistically significant (p = 0.000). Residual plots confirmed model validity. The TEM images of confirmation test sample 1 reveal fiber-matrix interfaces with particle sizes between 10.02–26.40 nm. Variations in scale (100 nm and 50 nm) show microstructural differences, suggesting strong adhesion, dispersion aggregation, and anisotropic behavior due to 90-degree fiber orientation within epoxy matrix. The study concludes that strategic optimization of input parameters significantly enhances the impact resistance of hybrid biocomposites.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".