A Framework for Integrating Reliability, Robustness, Resilience, and Vulnerability to Assess System Adaptivity
Bibliographic record
Abstract
Abstract The growing effort to improve a mechanical system’s performance with a sustainable perspective has created more complexity due to the need for additional technological subsystems. Increased complexity could result in new failure modes for systems making performance assessment more challenging. Therefore, it is essential to develop frameworks to assess performance based on a broader approach beyond single indicators. However, when considering reliability, resilience, robustness, and vulnerability (3RV) concepts as single mathematical-based models for assessing a system’s performance, designers are confronted by similarities between these concepts. In this regard, integrating these four concepts and developing a comprehensive variable (herein called system adaptivity) could better unify 3RV as a single objective function. Consequently, this study presents independent definitions for each concept and identifies common aspects and interrelationships between them. Finally, a system adaptivity objective function will be defined quantitatively by evaluating identified characteristics and internal and external relations for each concept in the previous step. This new prospect could represent a system’s adaptivity as an integrated framework towards different defined failure scenarios.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".