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This study aims to predict MVI preoperatively using quantitative image analysis.
This study aims to predict the coercivity of cobalt nanowires fabricated by Alternating Current (AC) pulse.
This study aims to predict different affective states via physiological measures with three types of computational models.
This study aims to predict 2D temperature distribution in thermal manifestation zone of the Puga Valley and thermal gradients at ungauged well locations.
Based on the principle of two-stage method, this study aims to predict the tunnel deflection due to adjacent pit excavations more accurately and rapidly.
This study aims to predict the early-age crack width of thin fiber reinforced concrete (FRC) overlays through the modified prediction equation and finite element modeling.
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This study aimed to predict extubation outcome in a consecutive series of patients by measuring CPF from the ventilator flow-meter.
This study aimed to predict the best cosolvents for the supercritical CO2 extraction of oleic and linoleic acids using the Hansen solubility theory.
This study aimed to predict wave parameters from fetch length and meteorological factors by using triple diagram methodology based on Kriging principles.
Therefore, this study aimed to predict physical, chemical, and mineralogical soil properties using vis NIR (350 2500 nm) and mid-IR (4000 400 cm− 1) spectral libraries and statistically compare their modeling performances.
This study aimed to predict the 6-year incidence of metabolic syndrome (MetS) using an artificial neural network (ANN) system and multiple logistic regression (MLR) analysis based on clinical factors, including the insulin resistance index calculated by homeostasis model assessment (HOMA-IR).
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