Numerical Study of Patient Respiration Effect on Bacterial Dispersion in a Surgery Room
Bibliographic record
Abstract
This study aims to present a numerical investigation of respiration influence on the particle concentration in a surgery room.Controlling the temperature and contamination in the surgery room is essential for safe and risk-free surgical procedures.Generally, in many hospital cleanrooms, utilized for operations such as open-heart surgery, organ transplantation, and neurosurgery, the reduction of pollutant particles is vital as a factor that can lead to capillary clogging during an operation.Also, reducing the concentration of large particles is very important, because dust particles may contain various pathogenic bacteria and viruses.Therefore, particle distribution and temperature control were numerically investigated in this study.At first, the particle concentration at specific zones was investigated to obtain the stability of the respiratory cycle.Then, the concentration and aggregation of particles around the patient's head were measured on different pages along the coordinate axes while patient's breathing was quite stable.Furthermore, the effect of the air conditioning system of the room on temperature distribution control by was studied in a specific area.The simulation results showed a considerable decrease in the particle concentration, but the particles were not eliminated from the room completely.Moreover, the higher temperature of the area around the patient's head caused by his breathing had little effect on room temperature, and the inlet air controlled the room temperature properly.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".