Internet of Twins Approach: Digital-Twin-as-a-Platform Architecture
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
Digital twins (DTs) are becoming integral in sectors such as energy and manufacturing, catalyzing applications from monitoring and analysis to optimization and autonomous management. However, data in different formats, volumes, and qualities require high processing capabilities for integration. Moreover, the functionalities in DTs must work together, bringing interoperability challenges that have necessitated extensive manual programming. To address these, we propose the Internet of Twins, a disaggregated DT deployed in a distributed cloud architecture where interconnections are made using a remote procedure call framework, gRPC. Using this, we develop DT as a platform with knowledge-graph-based orchestration. Here, we implement a recursive execution algorithm to ensure necessary data are processed in the correct sequence autonomously. Then, we implement a wind energy pilot application and evaluate the performance. The results show that our approach lowers data preprocessing and line-of-code overhead up to 20% and 26%, creating a flexible and scalable architecture.
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 | 0.000 | 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 | 0.001 |
| 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