Machine learning-based prediction of adsorption capacity of metal-doped and undoped activated carbon: Assessing the role of metal doping.

This research developed five ensemble-based machine learning (ML) models to predict the adsorption capacity of both pristine and metal-doped activated carbon (AC) and identified key influencing features. Results indicated that Extreme Gradient Boosting (XGB) model provided the most accurate predictions for both types of AC, with metal-doped AC exhibiting 1.7 times higher adsorption capacity than pristine AC showing 254.

Intelligent inventory management with autonomation and service strategy.

The manufacturer's service to the customer is one of the critical factors in maximizing profit. This study proposes the innovative (, ) inventory policy integrated with autonomated inspection and service strategy for service-dependent demand. First, an advanced autonomated inspection makes the product error-free.