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Record W4224989547 · doi:10.18280/mmep.090206

Materials and Economic Aspects of Designing Microwave Electrical Installations

2022· article· en· W4224989547 on OpenAlexvenueno aff
Midhat I. Tukhvatullin, Yuri Arkhangelsky, Rustam Aipov, Eduard Khasanov

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMicrowaveDielectricElectromagnetic fieldField (mathematics)Mechanical engineeringThermalDielectric heatingProcess engineeringMicrowave heatingEngineering physicsElectrical engineeringMaterials scienceElectronic engineeringComputer scienceEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Ultrahigh-frequency electro-technical installations are capable of performing thermal microwave modification of dielectric and non-thermal microwave modification of polymer materials and products, as a result of which new properties and parameters appear in the object processed in the working chamber of such an installation. When designing microwave electrical installations, it is necessary to consider the relationship between the dielectric parameters of the processed object and the parameters of the microwave electromagnetic field of the working chamber of the installation. The paper considers the influence of the parameters of the processed dielectrics on the synthesis of working chambers of microwave electrical installations and mathematical modelling of heat treatment in a microwave electromagnetic field, the structure and parameters of the installation on its economic efficiency. Consideration of the materials science and economic aspects of microwave electrical technology allows to accelerate and reduce the cost of design in microwave electrical technology.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.200
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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