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Record W4414015504 · doi:10.11159/icbes25.118

Monte Carlo Simulation and Optofluidic Techniques for Detecting Enterococcus faecalis and Enterococcus faecium

2025· article· en· W4414015504 on OpenAlexvenueno aff
Quoc-Thinh Dinh, Hsin-Yu Chuang, Dang Khoa Tong, Weipai Chuang, Cheng‐Yen Kao, Cheng‐Yang Liu

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsnot available
FundersNational Science and Technology Council
KeywordsEnterococcus faecalisEnterococcus faeciumMonte Carlo methodEnterococcusComputer scienceMicrobiologyBiologyMathematicsStatisticsBacteriaAntibiotics

Abstract

fetched live from OpenAlex

This study presents a novel optofluidic system enhanced with Monte Carlo simulations for the optical characterization of bacterial suspensions, focusing on Enterococcus faecalis and Enterococcus faecium.The integration of optofluidics and advanced simulation techniques enables precise measurements of optical properties, including scattering, absorption, transmission, and refractive index (R.I.), which are critical for microbial detection.The system demonstrated the ability to distinguish between the two bacterial species, with R.I. values ranging from 1.405-1.410for E. faecalis and 1.395-1.400for E. faecium.Experimental results showed consistent trends of reduced light transmission with increasing bacterial concentrations (125-500 ppm) and extended optical path lengths (6-18 mm).Monte Carlo simulations validated the findings with error margins below 5%, highlighting the robustness of this approach.This method provides a scalable solution for bacterial diagnostics in clinical and environmental applications by achieving accurate, reproducible results and offering unique refractive index determination capabilities.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.498

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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