Trapezoidal Fuzzy Numbers for Eco-Design Assessments in Conceptual Design
Bibliographic record
Abstract
The research context is about eco-design improvement that focuses on modifying an existing product for reducing its environmental impacts. In this context, engineers may propose various design concepts that can potentially make the product more environmentally friendly. At this point, the research problem is how to assess environmental impacts of each concept given the uncertainty of design information at the conceptual design stage. To address this research problem, the trapezoidal fuzzy numbers are first applied to capture imprecise design information. Then, the centroid concept is applied to model different views of imprecision (i.e., pessimistic, balanced and optimistic) associated in fuzzy impact assessment. Accordingly, a decision scheme is developed for assessing different design concepts and suggesting the potential areas of a design concept to reduce environmental impacts. In an application, a coffee maker has been decomposed and analyzed to propose three possible concepts for eco-design improvements. These three concepts are then assessed by the proposed method of this paper to demonstrate the methodical workflow and utility that assists engineers to make eco-design decisions at the early design stage.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".