RSW-MCFP: A Resource-Oriented Solid Waste Management System for a Mixed Rural-Urban Area through Monte Carlo Simulation-Based Fuzzy Programming
Bibliographic record
Abstract
The growth of global population and economy continually increases the waste volumes and consequently creates challenges to handle and dispose solid wastes. It becomes more challenging in mixed rural-urban areas (i.e., areas of mixed land use for rural and urban purposes) where both agricultural waste (e.g., manure) and municipal solid waste are generated. The efficiency and confidence of decisions in current management practices significantly rely on the accurate information and subjective judgments, which are usually compromised by uncertainties. This study proposed a resource-oriented solid waste management system for mixed rural-urban areas. The system is featured by a novel Monte Carlo simulation-based fuzzy programming approach. The developed system was tested by a real-world case with consideration of various resource-oriented treatment technologies and the associated uncertainties. The modeling results indicated that the community-based bio-coal and household-based CH4facilities were necessary and would become predominant in the waste management system. The 95% confidence intervals of waste loadings to the CH4and bio-coal facilities were 387, 450 and 178, 215 tonne/day (mixed flow), respectively. In general, the developed system has high capability in supporting solid waste management for mixed rural-urban areas in a cost-efficient and sustainable manner under uncertainty.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".