Modeling and Optimization of Thermal Transfer and Mechanical Properties of Bio-composite Using Response Surface Methodology
Bibliographic record
Abstract
In recent years, scientists have begun to search for more sustainable biomaterials.Although many studies have been conducted on different fiber-reinforced composites, much remains to be done.Using environmentally friendly composite materials for building insulation is a practical solution to reduce energy consumption.In this study, an advanced statistical approach using JMP software was adopted to manage a complex problem involving multiple parameters.This method was applied to optimize the thermal insulation characteristics of a bio-composite.By following a precisely designed experimental program.the study focuses on analyzing the impact of varying concentrations of date palm fibers (DPF) on the thermal properties of the material.The tested samples contained between 0% and 30% DPF. with a fiber length set at 7 mm.The findings of this study clearly illustrate that the thermal conductivity of the biocomposite decreases with an increase in the percentage of DPF.This phenomenon occurs because the incorporation of fibers into the composite enhances the porosity within the matrix.consequently, reducing its density.Thus.these results underscore the advantageous effect of DPF on the insulation properties of the material.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".