The issues of digitalization of the mining industry, including open-pit mining, are considered. The issues of the use of semantic systems as an element of information flows, allowing to determine and select the right decisions in the process of mining management, in particular, in the mining of minerals by open mining, are covered. The article formulates requirements for semantic networks for the description of material in computer-aided learning tools and analyzes the most well-known models of semantic networks.

This work is devoted to computer modeling of wave processes. As an example, we took an ideal elastic body with distributed transient normal load. A mathematical model of the wave process has been constructed, a method has been determined to analyse wave propagation. We have defined the point scheme and selected the pattern. The conditions have been determined for the interior, boundary and corner points of the area. A software had been developed and the result of calculations was checked. Oscillograms of longitudinal velocities and normal stresses, as well as isolines of normal stresses have been obtained.

Machine-learning surrogates predict composite properties but report no calibrated reliability and may violate physics. Bound-constrained conformal prediction confines intervals to physical bounds but assumes them exact. This paper keeps the corridor as a hard constraint and characterizes what a decision-support system guarantees when it is wrong. If a fraction $\eta$ of responses lies outside the corridor, no admissible interval covers more than $1-\eta$, so the nominal level $1-\alpha$ is attainable only if $\eta \le \alpha$. A projection-aware calibration scores out-of-corridor points as infinitely nonconforming. Its marginal coverage is at least $1-\alpha -\eta {\pi }_{\infty }$ at every calibration size (${\pi }_{\infty }$ the fallback probability); its coverage across calibrations follows an exact Beta law. In the limit it reaches the nominal level whenever attainable; calibrate-then-clip loses up to $\eta$. For $\eta <\alpha$ the system thus delivers admissible intervals at the nominal level for a small, quantified width premium, with a feasibility verdict and a Shapley explanation. Across eight datasets it recovers the nominal level on a woven laminate with a misspecified rule-of-mixtures bound (90.3% against 85.6% for naive projection) and tracks the frontier under normative bounds the data contradict. Under exact Voigt–Reuss bounds it removes all violations and narrows intervals thirteen-fold. Width, paid inside the corridor, is the price.