Event‐triggered data‐ and knowledge‐driven adaptive quality iterative learning control with uncertainty for a pharmaceutical cyber‐physical system
Bibliographic record
Abstract
Abstract In the context of Pharma 4.0, a cyber‐physical systems (CPSs)‐based pharmaceutical quality control (PQC) mode holds a critical position in ensuring the quality of drug products. This paper is concerned with a PQC problem with uncertainty embodied in ever‐changing critical material attributes, which present new challenges related to costs and efficiency during pharmaceutical development. So, an event‐triggered data‐ and knowledge‐driven adaptive PQC framework is proposed. First, a data‐ and knowledge‐driven adaptive iterative learning control‐based PQC scheme is developed with the assistance of process knowledge that also contains much additional information reflecting the laws and trends governing process operations. Second, an event‐triggering condition suitable for the PQC tasks is designed and embedded in the controller design to reduce some unnecessary computing and communication loads. Furthermore, the integration of process data and knowledge is used for the adaptive adjustment of control parameters and the determination of initial control directions. Finally, several data experiments illustrate the effectiveness of the proposed methods.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".