In Kazakhstan, air pollution in industrial cities poses a significant challenge that requires urgent attention. This study investigates the dispersion of harmful pollutants in the air across nine prominent industrial cities in Kazakhstan. The research involves modeling the emissions from major pollution sources for each city, which provides a comprehensive view of how these substances spread through the atmosphere. The study also examines the distribution patterns of these pollutants to gauge their concentration levels in each urban area. Additionally, it addresses the inverse problem of data assimilation from automated monitoring stations (AMS), aiming to refine the information on pollution sources. By utilizing the conjugate equations method, the study successfully converged to an accurate solution. Detailed visualizations for Almaty, Ust-Kamenogorsk, and Pavlodar illustrate the pollution dynamics and pinpoint the most affected regions. These findings are crucial for formulating strategies to mitigate the adverse effects of industrial emissions on both the environment and public health.

The structural and positional analysis of planar linkage mechanisms containing higher-class Assur groups requires the generation and solution of systems of algebraic kinematic equations. This paper presents algorithms for the automated analysis of such mechanisms based on an information matrix model. The proposed representation describes a mechanism as a system of material points connected by geometric constraints and provides a unified framework for algorithmic processing of mechanism structure and parameters. A square-matrix information model is developed to store structural and geometric information of a mechanism. Based on this model, three algorithms are proposed: (i) an algorithm for structural analysis and determination of mechanism mobility, (ii) an algorithm for automatic generation of kinematic equations, and (iii) an algorithm for automatic construction of a positional analysis procedure for solving the generated system of algebraic equations. For mechanisms containing higher-class Assur groups, the developed approach automatically generates an ordered sequence of two-link kinematic chains. When direct decomposition is not possible, conditional driving links are introduced by selecting conditionally disconnected links and defining corresponding driving coordinates. The proposed algorithms have been implemented in the SAPRM system. The applicability of the approach is demonstrated through positional analysis of three planar linkage mechanisms containing higher-class Assur groups.

The increasing need for accurate prediction of geochemical anomalies requires methods capable of capturing complex spatial patterns that traditional approaches often fail to represent adequately. For N datasets of the form (X_i,Y_i) representing the geographic coordinates of sampling points and C_i denoting the geochemical measurement, training multilayer perceptrons (MLPs) presents a challenge. The low informativeness of the input features and their weak correlation with the target variable result in excessively simplified predictions. Analysis of a baseline model trained only on geographic coordinates showed that, while the loss function converges rapidly, the resulting values become overly “compressed” and fail to reflect the actual concentration range. To address this, a preprocessing method based on anisotropy was developed to enhance the correlation between input and output variables. This approach constructs, for each prediction point, a structured informational model that incorporates the direction and magnitude of spatial variability through sectoral and radial partitioning of the nearest sampling data. The transformed features are then used in a dual-MLP architecture, where the first network produces sectoral estimates, and the second aggregates them into the final prediction. The results show that anisotropic feature transformation significantly improves neural network prediction capabilities in geochemical analysis.

This work presents the corporate geoinformation system Mineral AI, developed to support spatial-analytical decision-making for subsoil use in the East Kazakhstan region. The system is implemented on the ArcGIS Enterprise platform and integrated with the ArcGIS Pro desktop application, providing a unified technological workflow from data acquisition to publication of analytical results. Mineral AI supports the full lifecycle of geodata management, including field acquisition and spatial referencing of geological, lithogeochemical, and geophysical observations; ingestion into the corporate environment; data normalization, cleaning, and classification; anomaly detection; and harmonization of indicators for subsequent analysis. Following preprocessing, the data are subjected to spatial analysis, geostatistical interpolation, 3D modeling, and predictive assessment of prospective mineralized zones. Particular attention is given to constructing probabilistic prospectivity layers based on the integration of geological, lithogeochemical, and structural factors. The analytical outputs are published as managed geoservices with controlled access via the ArcGIS Enterprise portal, ensuring data consistency and version control across expert units. The results demonstrate that this architecture transforms the assessment of mineral resource potential from a fragmented expert-driven activity into a formalized, reproducible, institutionally supported procedure suitable for exploration planning, optimization of geological survey resource allocation, and justification of subsoil-use management decisions.