On Blasius Plate Solution of Particle Dispersion and Deposition in Human Respiratory Track
Bibliographic record
Abstract
This study investigates combust fuel aerosol contained in the atmosphere and their inhalation and deposition in the human respiratory tract, resulting to Environmental pollution, particularly in developing nations where emission control in vehicular devices are not well enforced.Effects of fuel density on the rate of aerosol deposition on the walls of a model respiratory tracts using two-dimensional consideration of the equations of momentum, energy and convection-advection and also characterized the dispersion and deposition of aerosols from selected combust fossil fuels in the respiratory tract by inertial impaction using relevant dimensionless numbers was considered.Using a similarity variable, the resulting partial differential equations were transformed into self-similar ordinary differential equations and the boundary value problem to an initial-value problem using the concept of shooting and fourth-order Runge-Kutta methods.The results showed that higher density fuels deposited more aerosols on the walls of the respiratory tract with significant reduction in respiratory tracts diameters as the aerosol deposition/concentration continues on the tracts.The study numerically presented deposition behaviour of a variety of fossil-fuel and bio-fuel combusts, deposition pattern of high density fuels deposit was greater in comparison to biofuels derived aerosols in the computational domain of the model tract.Thus, bio-fuels are presented as alternate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".