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Record W4406488755 · doi:10.1051/e3sconf/202560503013

Enhancing cognitive performance through thermal comfort: Insights from classroom renovation at Diponegoro University

2025· article· en· W4406488755 on OpenAlexaboutno aff
Ratna Purwaningsih, Eka Lailita Eti Varina, Manik Mahachandra, Ade Aisyah Arifna Putri, Novie Susanto

Bibliographic record

VenueE3S Web of Conferences · 2025
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCognitionThermal comfortArchitectural engineeringEngineeringNeurosciencePhysicsMeteorology

Abstract

fetched live from OpenAlex

International Undergraduate Program (IUP) of Industrial Engineering Department classrooms face challenges when the Wet-bulb Globe Temperature (WBGT) reaches 27.09°C and a temperature of 32.69°C, primarily due to the extensive use of glass. Direct sunlight through glass windows can raise indoor temperature. A renovation project was initiated to enhance overall comfort by closing the glass surface using wooden material. This research aims to assess the WBGT in the classroom before and after renovation to analyze the effect of the thermal comfort increase on cognitive performance. Post-renovation measurements revealed a reduction in WBGT to 24.58°C, accompanied by a decrease in temperature and humidity. The cognitive performance is measured using the Montreal Cognitive Assessment (MoCA). Cognitive performance, including response time, showed improvement after the renovation, with the introduction of plants further enhancing this effect. Beyond improving classroom comfort, the renovation also presents opportunities for energy savings by reducing reliance on air conditioning. This study demonstrates how renovations can contribute to sustainable building practices, offering both immediate educational benefits and reductions in energy consumption, particularly in tropical climates.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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