SmartWUDHU’: Recycling Ablution Water for Sustainable Living in Malaysia
Bibliographic record
Abstract
The ablution ritual consumes large amount of water, especially in musollas and mosques, where the greywater is allowed to run free and drain away. As quoted in the Hadith, Prophet Muhammad reminded Muslims to avoid wastage, even when performing the cleansing ritual or ablution prior to prayer. The ritual, locally known as known as wudhu’, requires a Muslim to wash exposed body parts with clean water. In Malaysia, most ablution system consists simply of a row of water taps with a drainage trough to carry the greywater to main drains. As the tap is usually left running, much good water is wasted in the process. Considering the unnecessary wastage, a simple recycling system can be designed to collect, treat and reuse the ablution water within a close-loop system for non-potable water applications, such as toilet flushing, general washing, plants watering and flowerbed cultivation. This approach does not only introduce practical engineering solutions in promoting sustainable living, it is also in-line with the Islamic principles of using natural resources in a prudent manner. By referring the University’s own mosque, a study was conducted to develop and verify a conceptual model of the ablution water recycling system, named SmartWUDHU’, which fulfills the requirements of Islamic teachings yet viable from the engineering perspective. A simple ablution water output prediction model was next proposed to more accurately quantify the capacity and efficiency of the close-loop water recycling system. Water quality check was also carried out to gauge the effectiveness of treatment against regulated standards as well as religious provisions. The SmartWUDHU’ system, retrofitted or installed new, exemplifies a successful merge between engineering know-how and religious doctrines for enhanced quality living now, and into the future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".