Bridging technical, ecological and social–economic knowledge in engineering design
Bibliographic record
Abstract
Sustainable engineering design requires the joint consideration of technical, ecological, economic and social aspects in the provision of societal demands, such as water and energy supply. A growing number of concepts and methods for integrated assessment and sustainable design have been developed in recent decades and pose new challenges to the engineering profession. This paper proposes the use of system design concepts and methods to link engineering design to ecological and social–economic knowledge. This study expands the conventional functional analysis approach from systems engineering with a technical focus towards a more integrated perspective that allows for the joint consideration of technical, ecological and social–economic solutions in engineering design. Participatory systems thinking and system dynamics modelling are used for conceptual and preliminary system design by analysing the hierarchy and flows of functions to meet system requirements. The methodology consists of three steps: requirement analysis (step 1), functional organisation analysis (step 2) and functional flow analysis (step 3). An application of the methodology is provided using sustainable water management in Cyprus as an example. The results demonstrate the synergies and trade-offs between technical, ecological and social solutions in water management that provide important information for the subsequent detailed system design phase.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".