Measurement and Performance Evaluation of an IoT-Integrated Dehumidification Control System for Airborne Infection Isolation Rooms: A Case Study at Betong Hospital
Bibliographic record
Abstract
This study presents the measurement, and performance evaluation of an IoT-integrated humidity control system for airborne infection isolation rooms (AIIRs), developed in accordance with ASHRAE Standard 170.The system features two silica gel panels that operate alternately between dehumidification and regeneration, allowing continuous and efficient humidity regulation.Environmental monitoring is carried out using a BME280 sensor and an ESP32 microcontroller, providing real-time measurements of temperature and relative humidity.Data are transmitted to Google Firebase and Sheets for cloud-based storage, analysis, and visualization, and are used to assess system performance.A notification system with a front display and smartphone application alerts users when environmental conditions exceed acceptable thresholds.Experimental results show that the system maintained an average temperature of 22.63℃ and relative humidity at 54.37% RH, both within recommended limits for infection control.The humidity ratio was 0.00882 kg/kg•da, below the ASHRAE threshold of 0.012 kg/kg•da.Each silica gel panel sustained RH below 60% for approximately 1.3 hours and was fully regenerated at 100℃ within 55 minutes.Overall, the system demonstrates reliable, real-time environmental control and offers a cost-effective solution for enhancing indoor air quality and strengthening safety for both patients and healthcare personnel in airborne infection isolation rooms.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".