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In addition, an artificial neural network (ANN) model was designed to predict the mass transfer performance.
The model was designed to predict the major component of a heterogeneous mixture of CHO-derived glycoforms of a recombinant protein under normal growth conditions.
A model was designed to predict the effect of pH and ionic strength on fouling in the cross-flow ultrafiltration (UF of protein solutions.
The model was designed to predict the effect of different management operations on fish yield, identify and quantify the cause, effect and relationships between water quality parameters, and suggest remedial actions for impaired systems.
The model was designed to predict the hourly heating requirements based on the input of greenhouse indoor environmental control parameters, physical and thermal properties of crops and construction materials, and hourly weather data including temperature, relative humidity, wind speed, and cloud cover.
The model was designed to predict the impact of these events on adult N e in an An.
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In addition, the support vector regression (SVR) model is designed to predict the traffic flow.
The neural network model "neuro-modeler" is designed to predict the response parameters of dam.
Regression models were designed to predict the desired outputs as functions of selected inputs.
The BBD-BC model thus provided improved risk classification compared to the BCRAT; however, the BBD-BC model was designed to predict risk for all women with BBD.
The model was designed to predict both instantaneous and time-averaged pheromone concentrations.
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