Optimizing the Egress Route Using a New Smoke Emulator IoT System
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
The ability to find optimal egress routing is critical for safe and effective firefighting processes. This article presents a novel smoke emulator that can be used to provide this ability in firefighting processes. It has two fundamental components: 1) a sensor network based on the Internet of Things (IoT) and 2) a simplified computational fluid dynamics (CFD) smoke simulator based on Navier-Stokes equations. Supported by IoT, real-time events, such as door opening and window breaking, can be detected, and the relevant information can be used to update the variables in CFD to ensure high accuracy and relevancy. A long short-term memory (LSTM) is employed to evaluate the values of any sensors temporarily malfunctioning. A two-level of A* routing algorithm (Nosrati et al., 2012) has also been developed to optimize the egress routing for evacuees with an aim to minimize the time of smoke exposure. Simulation studies demonstrated that this smoke emulator helps significantly improve the firefighting process's safety and effectiveness.
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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 | 0.000 |
| 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