Optimal Adaptable Design Considering Changes of Requirements, Configurations and Parameters in the Whole Product Life-Cycle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Adaptable design is a new design approach to create an adaptable product to replace multiple products for satisfying the different requirements in the product life-cycle. In this research, a method to identify the optimal product considering changes of requirements, configurations and parameters in the whole product life-cycle is introduced. The requirements, configurations and parameters of the adaptable product are modeled as functions of the life-cycle time parameter. The adaptable product is changed to different configurations and parameters to satisfy the different requirements in different life-cycle time periods. The evaluation measures, which are achieved from configurations and parameters, are also changed in different life-cycle time periods. The optimal product, modeled by its configurations and parameters, considering the whole product life-cycle is identified through optimization. A case study is provided to demonstrate how the introduced method can be employed for solving engineering problems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it