TOPSIS Method for Optimization of an Apparatus for Water and Soil Treatments
Bibliographic record
Abstract
The article focuses on the design and application of a new apparatus for water and soil treatments and its optimization with the technique for order of preference by similarity to the ideal solution (TOPSIS). Achieving sustainability required developing experience with new devices, which improved the water and soil treatment outputs. Essential principles, including the irradiation process, treatment capability, and industrial development, were discussed. Water and soil treatments with new nanocomposites based on biocompatible and natural materials were also discussed. A well-implemented approach requires the consideration of creative design. The article addressed these issues by representing the apparatus characteristics of reliable decontamination of drinking water, wastewater, seawater, and soil. A user-centered design approach for apparatus development was also considered. The design for its industrial development was presented and discussed, emphasizing this approach’s commercial viability. The novelty of the proposed apparatus is in decreasing light reflection due to the oxygen uptake, which could be affected by water or soil extraction decontamination. The Fourier transform infrared spectroscopy showed the characteristic peak intensities of superparamagnetic iron oxide nanoparticles and silk fibroin nanoparticles. The predicted theoretical and experimental decrease in light reflection due to nanoparticle oxygen uptake was determined. Moreover, the analysis of the removal of water contaminants using the inductively coupled plasma mass spectrometry analysis showed a concentration decrease of 48 % for Cd and 50 % for Zn after water treatment with nanoparticles. The optimization results using the TOPSIS method showed that the choice of parameters corresponding to the designed apparatus (contaminants separation due to water treatment and experiment duration) and their weights could impact the candidates’ ranks. Moreover, the ranking could be changed due to improved water and soil treatment procedures. The designed apparatus based on the presented optimization can improve water and soil treatments and further applications in environmental science.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".