The aim of this investigatation is to compare the utility of machine learning algorithms in distinguishing between untreated and processed mint beside in predicting the spray day of the insecticide. Within seven days, mint treated samples with the malathion insecticide are collected, and their aromas are Studied using a laboratory-manufactured sensor array system based on commercial metallic semiconductor (MOS) gas sensors. To distinguish the mint type, some results of machine learning algorithms were compared to know the decision trees (DT), Naive Bayes, support vector machines (SVM), and ensemble classifier. Furthermore, to predict the treatment day support vector machines regression (SVMR) and partial least squares regression (PLSR) were compared. Regarding the best results, in the discrimination case, a success rate of 92.9% was achieved by the ensemble classifier while in the prediction case, a correlation coefficient of R=0.82 was reached by the SVMR. Good results are achieved if the right gas sensor array system is designed and realized coupled with a good choice of the appropriate machine learning algorithms.

In this work, we investigate the atomic-scale mechanical behavior of key metals (Al, Au, Cu, and Ni) used in drilling alloy and structural components for smart mining systems. Molecular dynamics (MD) simulations were performed to explore their stress-strain behavior under uniaxial compression from 300 K to 1000 K. The results display a linear decrease in uniaxial compressive strength (UCS) and Young’s modulus with increasing temperature due to lattice expansion and weakened interatomic potential derived from the Embedded Atom Method (EAM) formalism. Among all the metal studied, Ni exhibits the highest mechanical performance, maintaining a compressive strength of 7.40 GPa at 1000 K, which makes it a promising candidate for high-performance drilling alloys operating in severe thermal environments. Cu shows moderate thermal stability with a UCS of 5.54 GPa at 1000 K, whereas Au demonstrates the lowest thermal resistance and the greatest softening tendency, reaching only 2.21 GPa at the same temperature. Common neighbor analysis (CNA) and Radial Distribution Function (RDF) reveal that Ni preserves stronger atomic ordering because of its higher cohesive energy, while structural degradation is associated with the reduction of perfect Face Centered Cubic (FCC) configurations and the formation of defects and dislocations under compression. In comparison, Al shows stable defect recovery on heating, giving better resistance to thermal fatigue in automated mining sensor housings. These atomistic results that are temperature dependent are valuable input parameters for predictive algorithms and digital twins in advanced mining engineering.