Multi-physics computer simulation of radio frequency heating to control pest insects in stored-wheat
Bibliographic record
Abstract
Radio frequency (RF) selective heating, a novel method to control insect pests in the stored-grains, has many advantages over the existing methods that use pesticides, fumigants, heat, cold, and mechanical pressure. However, there are many variables that can affect the effectiveness of RF selective heating. The finite element method based COMSOL Multiphysics software was used to simulate the selective heating of rusty grain beetle (Cryptolestes ferrungineus, S) in the bulk stored-wheat at 12, 15, and 18% moisture content. The multi-physics – the electric model, and the non-isothermal fluid flow/heat transfer model were coupled, and the transient electrical and the thermal properties of the insect and wheat were used. Only one quadrant of the RF system including the sample was simulated because of the geometric symmetry in the system. The differences between the experimental and the simulated temperatures for the bulk wheat at MC of 12, 15, and 18% were not more than 13.3, 10.2 and 18.1% respectively. The temperature of the insect was 14.1 °C (maximum) higher than the temperature of the host grain. Therefore, there is a potential of this environmentally friendly method in controlling the insect pests in the stored-grains. The non-uniform heating of the samples was observed, and some recommendations to improve the heating uniformity are presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".