The Influence of Quarantine and Uprooting Control Measures in Reducing Rice Tungro Disease through a Mathematical Model
Bibliographic record
Abstract
Tungro virus is one of the most important diseases that affect the rice plant, as it is known as cancer because of the severe damage it causes both in quantity and quality in production. This disease is transmitted by the green leafhoppers (Nephotettix virescens), which are the most responsible vector for the disease's transmission. In this paper, we consider a mathematical model that describes the transmission dynamics of vector-borne rice tungro disease (RTD), which represents the predator-prey interaction between insect vectors and biological agents. Moreover, we incorporated two control efforts to formulate the optimal control model (OCM) in order to examine the best strategy for reducing the infection of RTD. The description of the two implementing controls is quarantine control ( ) such as uprooting and burning infected plants and chemical control ( ) such as using insecticides, respectively. The Hamiltonian and necessary optimality conditions (NOCs) are presented based on Pontryagin’s maximum principle (PMP). We show numerical simulations in some figures by using the forward-backward sweep method (FBSM) to investigate the suggested control strategies. The results demonstrate that each integrated strategy can reduce infection transmission, but the combination of the two controls is the best strategy for the others.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".