Phase Synchronization of Nonidentical Light-Sensitive Belousov−Zhabotinsky Systems Induced by Variability in a High−Low Illumination Program
Bibliographic record
Abstract
Phase synchronization of two systems with different dynamical parameters driven by a common external signal is studied using a model of the photosensitive Belousov−Zhabotinsky reaction. Complex dynamics, including chaos, arise when the external light intensity is periodically switched between two levels. Two dynamical conditions are investigated here: (a) the two systems are driven between two limit cycles and (b) both systems are driven between excitable and oscillatory states. Phase synchronization is achieved with both Gaussian-distributed and dichotomous noise when the random variation is added to the duration of the periodic forcing. In the case that noise is added to the intensity of the periodic forcing, perfect phase synchronization is achieved with dichotomous noise, whereas only transient synchronization is observed with Gaussian-distributed random variation. Studies with correlated noise show that the compound system may have two attractors, one corresponding to the phase synchronized state and one to unsynchronized oscillations (lag-synchronized or chaotic, depending on the parameters). This suggests that transient synchronization is due to noise-induced transitions between the synchronized attractor and the neighborhood of a second invariant set which may in some cases also be an attractor. The synchronization mechanism is also studied using a return map.
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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 | 0.000 |
| 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) | 0.001 | 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".