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Record W4405426760 · doi:10.5206/mase/20987

Dual phase lag two temperature fractional thermoelasticity in the context of Green Naghdi type II

2024· article· en· W4405426760 on OpenAlexvenueno aff
Sagar Ningonda Sankeshwari, V. S. Kulkarni

Bibliographic record

VenueMathematics in Applied Sciences and Engineering · 2024
Typearticle
Languageen
FieldMathematics
TopicFractional Differential Equations Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsLagContext (archaeology)Phase lagType (biology)Fractional calculusDual (grammatical number)MathematicsPhase (matter)Mathematical analysisPhysicsMaterials scienceComputer sciencePhilosophyGeologyQuantum mechanics

Abstract

fetched live from OpenAlex

The linear thermoelasticity theory without energy dissipation developed by Green Naghdi has been reconstructed using two-phase lags and two temperature theory in the framework of the fractional time derivative. In half space, the mathematical model for one dimensional wave propagation subject to thermal shock on the bounding surface is discussed. Assume that the bounding surface is traction free. The analytical solutions have been obtained in the Laplace domain. The Gaver-Stehfest technique is simple, efficient, and robust. It has been numerically used to perform an inversion of the Laplace transform, satisfying Kuznetsov’s convergence condition in the time domain. The significance of the fractional order parameter on variations of various fields inside the medium is addressed graphically. The utilization of delay time translations in heat flux vector and thermal displacement gradient causes the finite speed of wave propagation and depicts microscopic responses more precisely.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.329
Teacher spread0.279 · 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 designTheoretical or conceptual
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

Citations2
Published2024
Admission routes1
Has abstractyes

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