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The definition of glacial deposits (tillites) in Precambrian sections is a difficult task due to the frequent similarity of their composition and structure with other sedimentary rocks. This is due to the fact that Precambrian sediments have been losing their inherent geomorphological and lithological features over their long history. Tillites are important markers of climate change and are widely used in stratigraphic correlations and geodynamic reconstructions. The research in this article is aimed at studying tillites within the Bolshoy Karatau ridge in order to substantiate the composition and age of rocks on the eroded surface during the movement of ancient glacier. The age values of detrital zircons from the tillite matrix form the main intervals of 740-856 million years (32 grains), 1950-2040 million years (14 grains) and 2200-2630 million years (26 grains) with age peaks of 765, 835, 924, 2030 and 2435 Ma. The following ages were obtained for boulders from the tillite horizon: 746±4 million years (9 grains); 778±4 million years (9 grains); 746±3 million years (13 grains); 788±3 million years (16 grains). The obtained dates for boulders from the tillite horizon have similar age analogues within the Middle Tien Shan, Karatau-Talas, Zheltau, Chu-Kendyktas, and Ulutau blocks.

A software module for calculating the productivity and efficiency of discrete manufacturing has been developed. The module's business logic is implemented in the AnyLogic package, in which a simulation model was created and exported to a set of JAR files. The model was built for a production cell located in the IPIT-216 unit of Samara University. The simulation error was less than 15%. Unlike common commercial simulation software packages (Plant Simulation, FlexSim, Arena), the developed module enables the calculation of production KPIs in a simpler and more accessible interface, eliminating the need to purchase specialized software and undergo extensive training.

The paper considers the problem of estimating the state of charge (SoC) of lithium-ion batteries. The second-order Thevenin model based on the substitution scheme is used as a mathematical model of the battery. An extended information Kalman filter is used to estimate the state of charge of the battery

This article describes the results of a comparative analysis of classical and modified genetic algorithms for solving the problem the optimal runner position in a thermoplastic injection mold. Based on the comparison, it was found that the modified genetic algorithm converges 25% faster than the classical algorithm and allows for finding the minimum with 55% fewer calls to the criterion function, compared to the classical implementation of the genetic optimization algorithm. The issue of selecting the best criterion for solving the runner position optimization problem on a discrete finite element computational mesh is also examined.

This paper presents a method for designing support structures for 3 D printing based on topological optimization and the method of equivalent static loads. Information exchange between the optimization and numerical simulation modules of SLM process is organized for the computational process. The proposed support synthesis method effectively reduces the maximum shrinkage deflection from 0.731 mm to 0.630 mm, a reduction of 13.8%.

This article examines the dispersion coefficient of low-frequency electromagnetic waves known as whistling atmospherics or whistlers. A mathematical model describing the law of whistler frequency variation over time is proposed. An algorithm for calculating the whistler dispersion coefficient based on spectrogram analysis is also developed. This algorithm involves identifying the whistle trace using threshold filtering and clustering. The whistle trail is then straightened by transforming the coordinates, and a linear regression equation is constructed, where the cotangent of the slope provides an estimate of the dispersion. The algorithms are implemented in the ADWRK 1.0 software package using the Python programming language. The results obtained using this software package are compared with those obtained using the proposed mathematical model. It is shown that the calculation of the whistler dispersion coefficient can be more accurate using a combined method for extracting the whistler trail.

In this paper, we propose a method using conformal meshes to solve bimaterial topological optimization problems in linear and nonlinear formulations. The features of using conformal meshes in the case of solving contact problems are considered. The solution speed and results of bimaterial topological optimization on uniform and conformal meshes were compared. The results demonstrate that the use of conformal meshes increases the speed of solution of topological optimization problem by more than 12 times.

In this paper, the formation of perfect optical vortex beams is investigated based on numerical simulation for different parameters of the optical system, such as the axicon angle and the vortex beam order. The formation of beams during propagation in free space and in the focal plane is considered, which can be useful for optical capture and movement of microparticles.

This paper presents the results of developing a generator of chemically active oxygen species for air disinfection and purification for agricultural applications. The main functional element of the generator is a new highly efficient oxide photocatalyst of the Cu/ZnO-ZnAl₂O₄ system. The use of this photocatalyst ensures intensive generation of chemically active singlet oxygen, purification and disinfection of ambient air, and preservation of the high quality and freshness of agricultural products.

In recent decades, there has been a rapid increase in the use of deep machine learning tools based on artificial neural networks in various fields of science. Deep neural networks vary in their architecture, for example, in a convolutional neural network, different layers can use convolutional kernels to extract key features from an image and pool the layers to generalize these features. Recurrent neural networks process sequential data series and retain memory of past data by returning the output of a layer back to the same layer. Training a neural network involves optimizing the weights of connections in the network to minimize the prediction error. Deep learning has the potential to leverage information hidden in large datasets to provide innovative solutions to complex environmental challenges. Big data consists of images, audio, videos, or unstructured text, which can be challenging to analyze using traditional statistical methods. With an exponential increase in publications on the methods and results of deep learning on neural networks in various fields of knowledge, this review attempts to analyze some of its applications in the field of forest ecology. In particular, it presents the results of using artificial neural networks to solve certain problems in Russian forestry, such as combining heterogeneous data to estimate forest phytomass, mapping and predicting forest cover dynamics, and identifying plant roots in minirizotron images. The final section describes some of the achievements, challenges, and uncertainties of deep machine learning in ecosystem ecology.