Acoustic precursor wave propagation in viscoelastic media
Bibliographic record
Abstract
Precursor field theory has been developed to describe the dynamics of electromagnetic field evolution in causally attenuative and dispersive media. In Debye dielectrics, the so-called Brillouin precursor exhibits an algebraic attenuation rate that makes it an ideal pulse waveform for communication, sensing, and imaging applications. Inspired by these studies in the electromagnetic domain, the present paper explores the propagation of acoustic precursors in dispersive media, with emphasis on biological media. To this end, a recently proposed causal dispersive model is employed, based on its interpretation as the acoustic counterpart of the Cole¿Cole model for dielectrics. The model stems from the fractional stress¿strain relation, which is consistent with the empirically known frequency power-law attenuation in viscoelastic media. It is shown that viscoelastic media described by this model, including human blood, support the formation and propagation of Brillouin precursors. The amplitude of these precursors exhibits a sub-exponential attenuation rate as a function of distance, actually being proportional to z(-p), where z is the distance traveled within the medium and 0.5 <p < 1. The precursors identified in this work facilitate the design of optimal waveforms for propagation in complex media, creating new possibilities for acoustic-pulse-based communication and imaging systems.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".