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Record W2551169814 · doi:10.1016/j.gheart.2016.03.301

PM100 Study of Heart Failure: Criteria Derived From Espvr

2016· article· en· W2551169814 on OpenAlexaff
Rachad M. Shoucri

Bibliographic record

VenueGlobal Heart · 2016
Typearticle
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsMagnetorheological fluidPressure dropSolenoidActuatorMagnetic fieldMechanical engineeringSkyhookVibrationHydraulic cylinderCurrent (fluid)Computer scienceDamperAcousticsMechanicsControl engineeringEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Vibration control in the systems of precise positioning is an important problem that nanotechnology sector is facing. This problem challenges the engineers to develop advance positioning mechanisms such as hydraulic magnetorheological (MR) actuators or MR modules. These modules combine the characteristics of hydraulic systems and electromagnetic control because of the use of magnetorheological fluids instead of traditional hydraulic fluid. This allows to avoid using inertial valves that results in higher accuracy and dynamic characteristics as compared with conventional systems. The main element of a MR valve is a solenoid that creates a magnetic field to control viscosity and rheological behavior of the fluid due to structuring of the disperse phase of magnetic particles in magnetic field.The positioning error of the MR module depends, to great extent, on the minimum current which should be applied to the coil to start the motion. This work is aimed at the experimental study of the response of the MR module on the applied current. The response was measured as the pressure drop in the fluid at the exit of the MR module.It was found that the maximum magnetic field in the working gap of the module of 0.04 T corresponded to the pressure drop 0.12 MPa. The results form the base for design of MR modules of automatic control systems operating under semi-active and active vibration control modes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.374

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 head0.019
GPT teacher head0.324
Teacher spread0.305 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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