Mathematical study and simulation on stenosed carotid arteries with the help of a two‐phase blood flow model
Bibliographic record
Abstract
Abstract The present study is focused on a medical problem called stenosed carotid artery. The problem is formulated with the help of a two‐phase blood flow model. The non‐Newtonian nature of blood is considered that hold power law. Physical quantities were expressed in tensorial form. Analytical and numerical methods are used to solve equations under given boundary conditions. The effects of various parameters on blood flow like stenosis size, flow flux, resistance, haematocrit, pressure drop, etc. were studied and shown through various graphs. Parameter , which ensures that the fluid is Newtonian or non‐Newtonian; its impact on pressure drop; resistance to flow; and flow flux were obtained during the disease and presented through the graph. A relationship between pressure drop and haematocrit was obtained, which was helpful to predict fluctuation in blood flow during stenosis. We have also given a medical use for this model with the help of pathological data. We also analyzed steady and laminar flow in a carotid artery for different heights of stenosis. The study of various physiological parameters has been performed on the basis of blockage percentage and concentration of haematocrit. The nature of the red blood corpuscle (RBC) phase is considered liquid packets in a semi‐permeable membrane, which makes this model close to reality.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".