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Record W4386225565 · doi:10.53555/sfs.v10i2.1412

An Analytical Study for Reducing Infection Transmission in Chest Hospitals in Egypt

2023· article· en· W4386225565 on OpenAlexvenueno aff
Hossam Said Mahmoud Ali, Ahmed Ahmed Fekry, Reham Eldessuky Hamed

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsNatural ventilationTransmission (telecommunications)Ventilation (architecture)Airborne transmissionMedicineGovernment (linguistics)EngineeringCoronavirus disease 2019 (COVID-19)DiseaseInternal medicineTelecommunicationsMechanical engineeringInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

With the spread of infection and an increase in the number of visitors and patients in developing countries, especially in governmental hospitals that provide affordable healthcare, there has been an exhaustion in energy resources accompanied by a global increase in energy prices. Since some buildings are unable to properly use mechanical ventilation, they have turned to using recycled air, which has aided in the spread of infection. Therefore, it has become mandatory for these buildings to turn towards natural ventilation, especially after the World Health Organization recognized natural ventilation as a solution to combat the spread of infection in 2007. There are also limited studies covering the relationship between natural ventilation, airborne transmission of infection, and architectural variables. In this research, we will focus on studying the effect of ventilation and architectural variables on the spread of airborne infections in government chest hospitals using Ansys®21 Fluent CFD solvers. Architectural variables such as height, width, and depth and their effect on the amount of infection inside the ward and its spread were studied. It was found that an increase in height had a positive effect on reducing infection by 12.14%,while the increase in width had 3.54% effect and finally, an increase in depth had a negative effect on infection by 10.75%. Then, an increase in height was studied once with an increase in width and it was found to lead to a decrease in infection by 31.73%, depending on the baseline case and the amount of infection in it. An increase in depth was found to lead to a decrease in infection by 2.37%. Additionally, the acquired infection for each patient decreased from a range of 6-6.40% to a range of 2.40-2.80% in the first case and 3.40-3.70% in the second case. Therefore, the experiments confirm the effect of architectural variables on the rate of ventilation and the proportion of infection and its spread.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.115
GPT teacher head0.304
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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