Considering the security problem experienced in the world in the supply of critical raw
materials within the scope of energy transformation, it would be extremely strategic for …

This paper presents a thorough evaluation of health outcomes linked to water-related
challenges in Islamic nations across East Asia and Central Asia from 2020 to 2030. It has …

This study examines the application of machine learning in predicting price risk boundaries
for industrial metals and critical minerals, emphasizing the role of statistical dependencies …

Base metals are key materials for various industrial sectors such as electronics, construction,
manufacturing, etc. Their selling price is important both for the profitability of the mining and …

Purpose of this research is effectiveness the exponential smoothing for predict the time
series data which has trend and non-seasonal characteristic. In this research using data …

Artificial neural networks are popular data-driven models extensively used for predicting the
prices of precious metals. This study suggests an optimized artificial neural network model …

Membrane life and performance are the key determining factors in the adoption of
membrane-based processes for water treatment and separations. This work investigated …

Traditional yield curve models, such as the Nelson–Siegel parsimonious model, excel in
accurately depicting the interest rate term structure when applied to smoothly evolving yield …

Traditional yield curve models such as Nelson–Sigeal parsimonious model represent the
interest rate term structure at high accuracy with smoothed yield curve data. The model …

Predictive path loss modelling is essential in designing wireless communication systems.
However, the empirical methods of path loss prediction are inaccurate as the empirical …