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Predicting Tensile Properties of UV Degraded Nylon66/Polyester Woven Fabric Using Regression and Artificial Neural Network Models
Predicting Tensile Properties of UV Degraded Nylon66/Polyester Woven Fabric Using Regression and Artificial Neural Network Models
Modeling and Optimization of Precipitation Hardening Heat Treatment Factors of the Al2024 Alloy Using a Two-Level Full Factorial Design
Dehydration behaviour mathematical modelling energy efficiency and essential oil yield of peppermint leaves undergoing microwave and hot air treatments
A finite element model for extension and shear modes of piezo-laminated beams based on von Karman s nonlinear displacement-strain relation
A finite element model for extension and shear modes of piezo-laminated beams based on von Karman s nonlinear displacement-strain relation
A finite element model for extension and shear modes of piezo-laminated beams based on von Karman s nonlinear displacement-strain relation
application of power- law frequency fractal model in determination of vertical geochemical distribution of cu in kahang porphyry deposit central iran
Melt Processing of Acrylonitrile-butadiene-styrene (ABS) on Liquid Deposition Modeling (LDM) 3D Printing and Injection Molding: Process Feasibility
Modeling impacts of climate change on stream flow and sediment yield implications for adaptive measures on soil and water conservation in north of Iran
Comparing artificial neural network and multivariate linear regression models to predict soil organic carbon using digital terrain attributes
Predicting Radial and Tangential No-Load Air-Gap Flux Density in IPMSM Using a Novel Conformal Mapping and Lumped Parameter Model
INVESTIGATION ON MANUFACTURABILITY REPEATABILITY AND MECHANICAL PROPERTIES OF LIGHTWEIGHT POLYLACTIC ACID BCC-Z CELLULAR LATTICE STRUCTURES FABRICATED BY FUSED DEPOSITION MODELING
INVESTIGATION ON MANUFACTURABILITY REPEATABILITY AND MECHANICAL PROPERTIES OF LIGHTWEIGHT POLYLACTIC ACID BCC-Z CELLULAR LATTICE STRUCTURES FABRICATED BY FUSED DEPOSITION MODELING
Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models