An Adaptive Indoor Air Quality Control Scheme for Minimizing Volatile Organic Compounds Density
Bibliographic record
Abstract
Volatile organic compounds (VOCs) such as toluene, xylene, and formaldehyde are commonly found in indoor and the VOCs will yield human health's issue. The compounds are crucial in determining the indoor air quality (IAQ) and hence being how to manage IAQ becomes an important topic. Most human may spend most of time living in poor IAQ environment and it may result in excess life risk to respiratory symptoms and billion US dollars cost annually. VOC degrades IAQ and high VOC density indoor is not uncommon. The World Health Organization (WHO) and the Government of Canada provided benchmarks on the harm levels and the benchmarks indicated the potential health risk caused by hazardous substances. In this paper, a new comprehensive control scheme, namely fuzzy genetic multi-layer control scheme (FGMLCS), is designed to manage the IAQ. The multilayer control structure is designed which includes fuzzy logic together with genetic algorithm and multi-objective optimization to give an optimal control for a better IAQ. Q factor is defined based on the “harm levels” set by the benchmarks to give a unified standard for various VOCs with different “harm levels”. FGMLCS has achieved VOC density better than the “harm levels” by over 57%, which is superior to the benchmarks and is able to lower the risk of health deterioration and thus aiding habitant to be less carcinogenic.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".