MétaCan
Menu
Back to cohort
Record W2136826956 · doi:10.1080/02656730210139591

An edge-element based finite element model of microwave heating in hyperthermia: method and verification

2002· article· en· W2136826956 on OpenAlexaff
J. Carl Kumaradas, M.D. Sherar

Bibliographic record

VenueInternational Journal of Hyperthermia · 2002
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoOntario Institute for Cancer Research
FundersNational Cancer Institute
KeywordsFinite element methodBasis functionMicrowaveMinificationMicrowave heatingBasis (linear algebra)Mie scatteringBolus (digestion)Scalar (mathematics)Computer scienceScatteringMathematicsPhysicsMathematical optimizationMathematical analysisOpticsEngineeringStructural engineeringGeometrySurgeryLight scattering

Abstract

fetched live from OpenAlex

Hyperthermia has been shown to improve local tumour control of superficial and deep seated lesions when combined with radiotherapy. There remains difficulty in heating larger tumours with conventional applicators, but this is being addressed by several new applicator designs. This paper presents a new numerical model of microwave heating which is designed to aid in the development of new applicators for superficial heating. The model is based on a finite element method which utilises vector valued basis functions instead of the more conventional scalar valued basis functions. These basis functions were chosen since they are inherently suited for the solution of Maxwell's equations due to their vector nature. The model was successfully verified against an analytic solution to the Mie scattering problem as well as against previously published measurements of heating from a modified water bolus attached to a conventional waveguide applicator. An accompanying paper describes an application of this model to the design optimization of this modified bolus.

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: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.582

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 head0.023
GPT teacher head0.264
Teacher spread0.241 · 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

Citations10
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of HyperthermiaSame topicUltrasound and Hyperthermia ApplicationsFrench-language works237,207