HE-Cool V1.0: Control Model of Hybrid Evaporative Cooler Prototype
Bibliographic record
Abstract
During the summer in Indonesia, the climatic conditions continually facilitate the development of one from developing the use of an evaporation-based air cooler to more effective use for sustainable users over long distances. Therefore, this research developed an evaporative cooler prototype system based on HE-COOL V1.0 control model with a design for speed regulation control. The design was conducted with an Arduino microcontroller with various buttons: button 1 represents a low speed, button 2 represents a medium speed, and button 3 represents a high speed. The voltage used in the control model was 9 volts which is a voltage drop from the power supply unit of 220 V, and the port on the Arduino pinout has a 5 V supply. Humidity was detected by a DHT11 sensor with an average percentage of 56% usage factor monitored during an air-conditioning tests process. In addition, the input uses an application installed on an Android smartphone that makes users control the Arduino (device) from a distance of 0 to 21 meters without barriers and 19 meters with barriers using Bluetooth communication between devices. Users can also store and observe the humidity output data through a real-time firebase web database system feature.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".