Modeling of Interconnected Infrastructures with Unified Interface Design toward Smart Cities
Bibliographic record
Abstract
In recent years, there have been tendencies to enable smart cities with interconnected infrastructures and communities. Current engineering design and operation practices are limited to handling individual systems with modeling and simulation, as well as control systems. This paper presents a holistic approach with engineering practice to design and operate interconnected systems as part of smart cities. The approach is based on modeling individual physical systems and associated processes and identifying key performance indicators to evaluate each system and interconnected systems with an understanding of the coupling among systems to increase the overall performance of interconnected systems. The multi-objective optimization technique is proposed to achieve the best performance based on system design, control, and operation parameters. Due to the multidimensional nature of the interconnected systems, a unified interface system with modular design is proposed to achieve the highest overall performance of the interconnected systems with standardized interactions among state variables and performance measures. The proposed approach can allow dynamic updates of the interconnected systems based on model libraries of each system and process. A case study is presented of interconnected energy–water–transportation–waste facilities, whereby modeling is discussed, and performance measures are evaluated for different scenarios using the unified interface 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".