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Record W4405433912 · doi:10.3934/dcdsb.2024185

Stability of a semi-Markovian hybrid switching system with application to an epidemic model

2024· article· en· W4405433912 on OpenAlexaff
Feng Chen, Jing Hu, Yuming Chen, Qimin Zhang

Bibliographic record

VenueDiscrete and Continuous Dynamical Systems - B · 2024
Typearticle
Languageen
FieldEngineering
TopicStability and Control of Uncertain Systems
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsStability (learning theory)Epidemic modelMarkov processComputer scienceStatistical physicsMathematicsApplied mathematicsControl theory (sociology)Mathematical economicsStatisticsPhysicsArtificial intelligenceDemographySociologyMachine learningControl (management)

Abstract

fetched live from OpenAlex

In this paper, we are concerned with a semi-Markovian hybrid switching system, where the hybrid process includes a semi-Markov process and mode-dependent fixed dwell time. We obtain sufficient conditions on its stochastic asymptotic stability in the large by the multiple Lyapunov function approach and ergodicity of the hybrid process. The theoretical results are then applied to the transmission of COVID-19. A critical value is derived, which depends on the stationary distribution of the hybrid process and determines whether the disease dies out or not.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.210
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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