Difficult and expensive financing has always been a problem for domestic and foreign
enterprises, and how to effectively improve financing efficiency and improve the financing …

Abstract Machine learning (ML) models provide fast and accurate predictions of material
properties at a low computational cost. Herein, the mechanical properties of multiscale poly …

Currently, hydrogen is produced primarily through steam methane reforming, a gray
hydrogen production process that generates CO 2 as a by-product. Thus, it is crucial to …

Among the various physical properties of polypropylene composites (PPCs), heat deflection
temperature (HDT) during PPC production is significant because it is directly related to the …

Conventional methods for developing heterogeneous catalysts are inefficient in time and
cost, often relying on trial-and-error. The integration of machine-learning (ML) in catalysis …

In wet flue gas desulfurization system, the resource depletion of high-grade limestone, used
as conventional SO x absorbent, is becoming serious for SO x capture and utilization. This …

Manufacturing polypropylene (PP) composites to meet customers' needs is difficult, time-
consuming, and costly, owing to the ever-increasing diversity and complexity of the …

Missing values are unavoidable in lubricant formulation data in the chemical industry owing
to the complexity of lubricant manufacturing. Therefore, imputing missing values using …

Herein, machine learning approaches were employed to predict the kinetic constant of the
organic pollutant degradation process in a peroxymonosulfate environment with a typical Fe …

Flash floods are one of the worst natural disasters, causing massive economic losses and
many deaths. Creating a flood susceptibility map (FSM) that pinpoints the areas most at risk …