Immersive Innovation: Exploring Interactive Virtual Reality through Distributed Simulations
Bibliographic record
Abstract
This chapter explores the significant role of modeling and simulation techniques in various sectors, focusing particularly on distributed simulation (DS). The increasing importance of DS has been emphasized in response to evolving industrial, healthcare, and services settings. By leveraging DS, the integration of heterogeneous simulations enhances the effectiveness and efficiency of individual and classical simulations. In particular, this chapter introduces a DS that seamlessly combines two distinct simulation methods within a virtual reality (VR) environment. This integration enables users to fully immerse themselves in a 3D digital twin environment. Two case studies were conducted to evaluate the effectiveness of the developed DS system. The first case study focused on the implementation of DS in a hemodialysis unit, while the second case study examined its application in an intensive care unit. AnyLogic has been utilized for developing both discrete event and agent-based simulations, while the Unity platform has been employed for VR environment creation. In order to ensure smooth integration and synchronization, as well as address the demanding computational requirements, a network-based DS system has been implemented based on the high-level architecture—an IEEE standard for DS.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".