首页|Findings on Machine Learning Detailed by Investigators at University of Dayton ( Prediction of Hydrocarbons Ignition Performances Using Machine Learning Modeling )
Findings on Machine Learning Detailed by Investigators at University of Dayton ( Prediction of Hydrocarbons Ignition Performances Using Machine Learning Modeling )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting originatingfrom Dayton, Ohio, by NewsRx correspondents, research stated, “This study presents a computationalmethodolog y for determining the Derived Cetane Number (DCN) of practical aviation fuels. T he proposedapproach integrates a novel Quantitative Structure-Property Relation ship (QSPR) model designedto predict DCN for hydrocarbon species and mixtures w ith fuel composition analysis obtained throughTwo-Dimensional Gas Chromatograph y (GCxGC).”
DaytonOhioUnited StatesNorth and C entral AmericaCyborgsEmerging TechnologiesHydrocarbonsMachine LearningOrganic ChemicalsUniversity of Dayton
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