Contact surface and material nonlinearity modeling of human lungs
Bibliographic record
Abstract
A finite element model has been developed to investigate the effect of contact surfaces and hyperelastic material properties on the mechanical behavior of human lungs of one lung cancer patient. The three-dimensional model consists of four parts, namely the left lung, right lung, tumor in the left lung and chest wall. The interaction between the lungs and chest wall was modeled using frictionless surface-based contact. Hyperelastic material properties of the lungs are used in the model. The effect of the two parameters is investigated by tracking the tumor movement, and by comparing the analytical results to the patient bifurcation points: 45 points in each lung and 18 points around the tumor. The accuracy of the model is improved by including the contact surface and hyperelastic material properties. The average error and the standard deviation (SD) in modeling the displacement in the SI direction are reduced from 0.68 (SD = 0.34) cm in the elastic model to 0.09 (0.21) cm in the contact-hyperelastic model. Similarly, the average error (SD) of tumor location decreases from 0.71 (0.21) cm in the elastic material without contact to -0.03 (0.24) cm in the hyperelastic material with contact model.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".