Monitoring biological processes using univariate statistical process control
Bibliographic record
Abstract
Abstract Biological modelling is a challenging task specifically when state variables are difficult or even impossible to be measured. Consequently, monitoring quality of biological process will be impacted negatively due to the lack of an accurate model capable of reflecting precisely the process dynamics. Moreover, the faults in such systems cannot be detected robustly. The current work proposes a novel approach that combines state estimation with process monitoring techniques. The developed approach, named as particle filter (PF)'based multiscale maximum double exponentially weighted moving average (MS‐M‐DEWMA) chart, includes two main phases. In the first phase, the PF technique is applied to estimate the unknown nonlinear states of the biological processes. In the second phase, the statistical univariate chart, MS‐M‐DEWMA is adopted to address fault detection in biological processes. Therefore, in this work, we propose a monitoring approach capable of detecting shifts in mean and/or variance in biological systems (pre‐defined structure obtained using material and energy balances) where the variables are estimated using state estimation techniques. The detection chart MS‐M‐DEWMA is applied to the residuals computed using the PF. The advantages of PF‐based MS‐M‐DEWMA method are threefold: (i) extract features and decorrelate measurements using dynamical multiscale representation; (ii) estimate the state of nonlinear biological processes using the PF technique; and (iii) enhance monitoring of biological processes through detecting shifts of both variance and mean using MS‐M‐DEWMA chart. The proposed approach is validated using a Cad system in E. coli (CSEC) model.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".