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Record W4400663346 · doi:10.1002/rcs.2663

Simulating blood accumulation with improved smoothed particle hydrodynamics in surgical simulation system

2024· article· en· W4400663346 on OpenAlex
Pengyu Sun, Peter Liu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Medical Robotics and Computer Assisted Surgery · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsCarleton University
Fundersnot available
KeywordsSmoothed-particle hydrodynamicsBoundary value problemBlood flowComputer scienceBlood lossSurgical simulationMechanicsSimulationKernel (algebra)SurgeryMedicineMathematicsPhysicsMathematical analysisCardiology

Abstract

fetched live from OpenAlex

BACKGROUND: Blood accumulation often occurs during bleeding in surgery. Simulating the blood accumulation in surgical simulation system not only enhances the realism and immersion of surgical training, but also helps researchers better understand the physical properties of blood flow. METHODS: To realistically simulate the blood accumulation during the bleeding, this paper proposes a novel kernel function with non-negative second derivatives to improve the SPH method. Meanwhile, a simple form of boundary force equation is constructed to impose the solid boundary condition. RESULTS: We simulate the blood accumulation during liver bleeding and vessel bleeding respectively in the surgical simulation system. The simulation results show that there is no occurrence of blood physically penetrating the boundary. CONCLUSIONS: Applying the solid boundary condition to the blood by using the method proposed in this paper is not only convenient but can also eliminate compression instability in the blood accumulation simulation.

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.

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)0.001
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: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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