Investigations on the Grasping Contact Analysis of Biological Tissues With Applications in Minimally Invasive Surgery
Bibliographic record
Abstract
Analytical and finite element methods are employed to determine the contact pressure on the surface of a tissue being grasped by an endoscopic grasper, in Minimally Invasive Surgery (MIS).Normally, an endoscopic grasper is corrugated (teeth-like) in order to grasp slippery tissues.It is highly important to avoid damage to the tissues while grasping and manipulation during endoscopic surgery.Therefore, it is essential to determine the exact contact pressure on the surface of the tissue.To this end, initially a comprehensive closed form, finite element and experimental analysis of grasping contact pressure on viscoelastic materials which have similar properties as that of biological tissues is studied.The behavior of a grasper with wedge-like teeth, when pressed into a linear viscoelastic material is examined.Initially, a single wedge penetrating into a solid is studied and then is extended to the grasper.The elastic wedge indentation is the basis of the closed form analysis and the effects of time are included in the equations by considering the corresponding integral operator from viscoelastic stress-stain relations.In addition, a finite element analysis is carried out in Ansys-10 software.Finally, the experimental results are presented to validate both analytical and FEM results.The results of this study provide a closed form expression for grasping contact pressure force and contact area along with the variations of stress in tissue obtained through FEM analysis.The variation of contact pressure and the rate of growth of the contact area with time are presented.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".