The concentrations of fine particulate matter (PM 2.5) constituents, which are very important
and essential information for the identification of air pollution sources, were predicted at …

Missing data is a common cause of uncertainty in reservoir characterization, especially in
core analysis of porosity and permeability. This is due to the high cost and time required to …

In the contemporary realm of solar energy research, accurately predicting Solar Irradiance
(SI) is critical for optimizing photovoltaic (PV) installations. This research delves into the …

Estimating missing petrophysical data, particularly in permeability/porosity core-analysis
measurements is a challenge. The data gaps substantially increase uncertainty in …

Missing values and incomplete observations can exist in just about ever type of recorded
data. With analytical modeling, and machine learning in particular, the quantity and quality of …

This study addresses the optimization of fracturing parameters in the fractured gas reservoirs
of the Tarim Basin, especially under the challenge of small sample sizes and high data …

Missing data occurs even in a well-designed and controlled research. It not only lowers the
statistical power, but also leads to erroneous findings due to biased estimates. This article …