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Record W1995059176 · doi:10.1109/have.2010.5623989

An improved realistic mass-spring model for surgery simulation

2010· article· en· W1995059176 on OpenAlexaff
Shaoping Xu, Hua Zhang, Linyan Hu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsCarleton University
Fundersnot available
KeywordsSpring (device)Nonlinear systemFinite element methodViscoelasticityComputer scienceElasticity (physics)ComputationAnisotropySimple (philosophy)Effective mass (spring–mass system)SimulationNonlinear elasticityApplied mathematicsMechanicsStructural engineeringMaterials scienceAlgorithmClassical mechanicsPhysicsMathematicsEngineeringComposite materialOptics

Abstract

fetched live from OpenAlex

An improved realistic mass-spring model, which is mainly based on the 3D finite strain nonlinear anisotropic elasticity theory, is presented for virtual reality based surgery simulation. Compared with the conversional mass-spring model, the proposed model is able to describe typical behaviors of living tissues such as incompressibility, nonlinearity and anisotropy. The nonlinear viscoelasticity is also incorporated into the soft tissue model by employing a numerical scheme. In terms of implementation, the model proposed can be seen as a mixture of finite-element and mass-spring models, which enables it to still maintain the advantage of mass-spring model, such as simple architectures, low memory usage and fast computation. An example to use this model to simulate human kidney is given to demonstrate its capability of describing the typical behaviors of soft tissue.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.252
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2010
Admission routes1
Has abstractyes

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