Hyers-Ulam Stability of Fractional Differential Equations Using West Nile Virus
Bibliographic record
Abstract
In this paper, we use the Atangana-Baleanu Caputo derivative to design and assess a fractional-order infection model for West Nile virus. The model offers insights into the evolution of the virus and takes into account its intricate dynamics of transmission. We investigate the system’s qualitative behavior and prove existence and uniqueness findings using fixed-point theory. Additionally, we examine the suggested model’s stability in terms of Hyers-Ulam stability. Euler’s approach for the Atangana-Baleanu integral is used to numerically simulate the fractional-order model in order to visualize the effects of different parameters. The theoretical findings are verified, and the impact of fractional-order derivatives on the dynamics of the system is demonstrated using MATLAB. The study emphasizes the use of fractional calculus in epidemiological modeling, which offers a more realistic depiction of the spread of illness in the actual world.
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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 | 0.002 |
| 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) | 0.001 | 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".