Development of Cotton, Hemp and Silk Blended Curtains for Designer Home Interiors
Bibliographic record
Abstract
Background and Objective: Curtains are now used for more than just providing seclusion and blocking light.Nowadays, people use curtains in their residences or places of business to express their personal style or to give the space a little extra flair.More people than ever are interested in interior design and many like employing contemporary colours and ideas to decorate their homes or other unique spaces.Materials and Methods: In this research, cotton-silk blend and hemp-silk blend yarns were used to develop curtain fabric with a blend ratio of (60:40).These six curtain fabrics were made on a rapier jacquard loom with a twill structure.Physical tests have been done in this research work according to the ISO/AATCC standard, like colour fastness to perspiration, colour fastness to dry cleaning, colour fastness to rubbing, light fastness and a tensile strength test.Results: Color fastness when rubbed under both dry and wet conditions differs only slightly between all samples of cotton and silk blend and all hemp and silk blends in this research work.Color fastness to dry cleaning is found to be of good scale grade in all samples of cotton-silk blend textiles when compared to all samples hemp-silk blend fabrics.Overall, color fastness other related tested performance shows slight variation for cotton-silk and hemp-silk blend curtain dyes fabrics.Conclusion: Hemp-silk and cotton-silk blend curtain textiles offer great performance and colour appearance overall.So that curtain fabric can be used more effectively in the field of house interior design.
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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 | 0.001 | 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".