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Prediction of the small-k behavior of the structure factor (S(k)) for Rubidium and Cesium via a new model for the Direct correlation function and evaluation of some reported effective pair potentials
Three Dimentional Image Analysis for Prediction of Vinyl Triethoxysilane Concentration and Porosity Percent of Methacrylate/Ethylene Glycol Dimethacrylate Copolymers using Scanning Electron Microscopy
Comparison of enhanced neural network and response surface models in predicting bio-dissolution of aluminum and vanadium from bauxite residue by isolated Aspergillus niger strains
Comparison of enhanced neural network and response surface models in predicting bio-dissolution of aluminum and vanadium from bauxite residue by isolated Aspergillus niger strains
Three-Dimensional Analysis of Seepage in Fractured Rock Masses and Evaluation of the Accuracy of Empirical Methods for Predicting Permeability in Cutoff Walls: A Case Study of Chamshir Dam
Three-Dimensional Analysis of Seepage in Fractured Rock Masses and Evaluation of the Accuracy of Empirical Methods for Predicting Permeability in Cutoff Walls: A Case Study of Chamshir Dam
Experimental and RSM-based multi-objective optimization of low-carbon high-performance concrete incorporating silica fume and slag: Compressive strength, durability, environmental impact, and cost analysis
Phosphine as a superconductor?
Zahra Manzari Tavakoli
publication
Applying a modified model predictive current control method to improve surface-mounted permanent magnet synchronous motor drives performance in transient and steady-state operations
Applying a modified model predictive current control method to improve surface-mounted permanent magnet synchronous motor drives performance in transient and steady-state operations
Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment
Use of the time series and multi-temporal features of Sentinel-1/2 satellite imagery to predict soil inorganic and organic carbon in a low-relief area with a semi-arid environment
Application of a Bayesian Artificial Neural Network and the Reversible Jump Markov Chain Monte Carlo Method to predict the grain size of hot strip low carbon steels