A Distributed Simulation Approach to Integrate AnyLogic and Unity for Virtual Reality Applications: Case of COVID-19 Modelling and Training in a Dialysis Unit
Bibliographic record
Abstract
Different heterogeneous simulation components can be integrated to produce a more effective complex global system. The IEEE High-Level Architecture (HLA) is an international standard that promotes interoperability and reusability for distributed simulation (DS). This paper proposes a DS system that integrates an agent-based and discrete-event simulator with a 3D game engine to build virtual reality (VR) applications that replicate real environments. In this case study, AnyLogic is used as an agent-based and discrete event simulator to simulate the process flow and COVID-19 transmission inside the University Health Network dialysis unit, Toronto, Canada. Unity game engine delivers the 3D modelling replicating the real architecture and environment of the dialysis unit. The HLA standard plays a major role in the integration of AnyLogic and Unity to produce a more effective and powerful DS system for VR applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".