Latent heat from rice cooking to disinfect drinking water
Bibliographic record
Abstract
During the monsoon season the water distribution systems in the cities of Bangladesh, a developing country, has its distribution pipelines compromised due to water intrusion. This produces outbreaks of waterborne diseases. An easy solution is simply boiling the water before drinking. Unfortunately, this practice is limited due to fuel shortage or added fuel expense incurred. The affected population ends up using simple filters that is not effective in removing disease causing microorganisms from water. Every household in Bangladesh eats rice as their main staple and it is cooked twice a day for lunch and dinner. The method used is boiling and then simmering rice in a pot. The rice cooking process generates water vapor, which we are trapping and condensing by a stacked pot water heater/disinfector in a very simple way to utilize the latent heat to heat water to a required temperature and duration for disinfection. The efficacy of the process was evaluated in a controlled experimental program. The results indicated that our process would heat the water to temperatures that would inactivate pathogenic bacteria, viruses, and protozoa by raising the water temperature to 76.6 ±0.9oC, utilizing the latent heat generated from rice cooking. Further, if one wants to bring the water to rolling boiling, it requires ~ 3.0 minutes of additional direct heating after preheating with our system. This additional heating would require a normal expenditure of LPG of 0.073kg to bring to boil 10 L of water after preheating; whereas direct heating to bring 10 L of water to boil from an ambient temperature of 23.0oCwould require 0.24 Kg of LPG. We feel that this system will go a long way to address this public health crisis faced every year in Bangladesh and to address the safe water crisis faced by the million plus Rohyangya refugees from Burma living in makeshift camps in Bangladesh. In addition, our system can be utilized in other developing countries to provide safe drinking water.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".