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Record W2144464048 · doi:10.1109/tbme.2002.803555

Modeling of dynamic cardiovascular responses during G-transition-induced orthostatic stress in pitch and roll rotations

2002· article· en· W2144464048 on OpenAlexaff
William Melek, Ziren Lu, A. Kapps, Bob Cheung

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2002
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsOrthostatic vital signsAccelerationJet (fluid)SimulationEngineeringControl theory (sociology)Computer sciencePhysicsAerospace engineeringBlood pressureArtificial intelligenceMedicineInternal medicineClassical mechanics

Abstract

fetched live from OpenAlex

Dynamic and fuzzy models for a typical subject's cardiovascular response to the orthostatic stress have been developed based on experimental data. In our original study (Cheung et al., 1999), arterial blood pressure (BP) time-series data were obtained using a man-rated tilt table that applies gigahertz-acceleration transitions from +0.861 Gz [head-up (HU)] to -0.707 G [head-down (HD)] and back to +0.861 Gz (HU) using either pitch or roll rotations (Cheung et al., 1999). G transitions of different duration and onset rates are common in fighter maneuvers. Based on these data, two types of predictive models have been developed in this paper: 1) second-order discrete-time models that predict BP dynamics during pitch and roll rotations and 2) fuzzy logic models that predict important variations in a subject's cardiovascular dynamics induced by HU-to-HD and HD-to-HU transitions. These two types of models assist in providing an operationally important predictive view on the characteristics of BP responses to orthostatic stress induced by pitch and roll rotations of a fighter jet pilot. The new models are being currently utilized in the design of operational recommendations for more G-tolerant operational flight regimes (e.g., split-S tactical maneuver) than the ones currently in use for modern combat aircraft.

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 score0.282
Threshold uncertainty score0.586

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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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

Citations14
Published2002
Admission routes1
Has abstractyes

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