Control of Temporal Dynamics of Stable Cavitation by a Real-time Proportional Feedback Method
Bibliographic record
Abstract
The objective of this work is to design an optimal controller to modulate the stable cavitation activity of microbubbles induced by pulsed ultrasound. During therapy, the microbubbles inflow the target region in the blood vessel, receive ultrasound exposure and produce cavitation activities, and then outflow the therapy region. The stability of the bubbles could be affected by acoustic pulses and flowing environment, leading to the decrease of bubble number and the nonuniformly temporal distribution of stable cavitation activity in the target region. Previous researches regulated peak negative pressure (PNP) to effectively control stable cavitation activity whereas some undesirable consequences (e.g. hemorrhage) were found after applying PNP regulation. We proposed a novel proportional feedback controller based on both PNP and pulse length (PL) regulation. In order to rapidly elevate stable cavitation intensity from baseline to the pre-expected level, in our controller, a proportional factor of P1was firstly used to real-time adjust PNP from the initial 0.01 MPa to an appropriate level of experiential stable cavitation range. The obtained PNP was fixed in the subsequent regulation, and another factor of P2was used to modulate PL from the initial 20 μs to the obtained expected intensity and minimize the overshoot of the stable cavitation intensity. P3was then applied to keep the temporal stability of stable cavitation activity (±10% fluctuation range of the expected cavitation intensity) by further modulating PL. Finally, three parameters (rising time, stability ratio and concentration ratio) were used to evaluate the performance of the proposed method. Experimental results validated that the proposed method could control temporal distribution of stable cavitation intensity in the target region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".