Thermal Comfort and Cognitive Performance under Glass Facade Buildings
Bibliographic record
Abstract
Glass is one of the most adaptable modern materials. However, the use of glass can have unfavorable effects such as glare, heat, and discomfort. This study focuses on the issue of designing contemporary glass buildings and facades in humid tropical environments to evaluate the thermal comfort of building occupants and assess its effects on the completion of cognitive tasks. The Wet-Bulb Globe Temperature (WBGT) parameter was used to measure thermal comfort, and the ASHRAE (American Society of Heating, Refrigerating, and Air-Conditioning Engineers) scale was used to assess thermal comfort perception. The Montreal Cognitive Assessment was used to assess eight female respondents’ cognitive abilities, and the cut-off point was 26. The WBGT has a temperature range of 25, 54°–28, 83°C, in terms of thermal sensation, 54% of respondents feel hot and 62% are uncomfortable. In the completion of cognitive tasks, 75% received a score of less than 26. The results show that there is an effect of thermal comfort on cognitive performance. Due to respondents feeling uncomfortable in high-rise buildings with glass facades, as well as with cognitive outcomes that are below the cut-off point, recommendations for improvement are needed to increase thermal comfort, such as double glass facades.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".