This study was conducted to assess the applicability of artificial neural networks (ANN) for forecasting the dynamics of uranium extraction over exploitation time during the process of In Situ Leaching (ISL). Currently, ISL process simulation involves multiple steps, starting with geostatistical interpolation, followed by computational fluid dynamics (CFD) and reactive transport simulation. While extensive research exists detailing each of these steps, machine learning techniques may offer the potential to directly obtain extraction curves (i.e., the concentration of the mineral produced over the exploitation time of the deposit), thereby bypassing these computationally expensive steps. As a basis, both an empirical experimental configuration and reactive transport simulations were used to generate training data for the neural network model. An ANN was constructed, trained, and tested on several test cases with different initial parameters, then the expected outcomes were compared to those derived from conventional modeling techniques. The results indicate that for the employed experimental configuration and a limited number of features, artificial intelligence technologies, specifically regression-based neural networks can model the recovery rate (or extraction degree) of the ISL process for mineral production, achieving a high degree of accuracy compared to traditional CFD and mass transport models. © 2024 by the authors.

This study investigates the feasibility of predicting students’ critical thinking levels using machine learning techniques applied to academic and behavioral data. Recognizing critical thinking as a core competency in modern education is yet notoriously difficult to measure directly. This research aims to establish relationship between critical thinking proficiency and quantifiable variables such as an academic performance, extracurricular involvement, and course selection. A dataset comprising 500 anonymized student records was compiled and preprocessed to extract relevant features. Three predictive models—Linear Regression, Decision Tree, and Random Forest Regressor—were trained and evaluated using standard performance metrics. Among the three, the Random Forest model achieved the highest predictive accuracy with an R2 score of 0.84, substantially outperforming the Decision Tree (0.65) and Linear Regression (0.37) models. The results indicate that patterns in students’ course preferences, levels of academic achievement, and engagement in non-academic activities collectively provide meaningful insights into their critical thinking capacity. These findings demonstrate that viability of data-driven frameworks for indirectly assessing cognitive skills and have potential applications in curriculum design, early intervention systems, and educational approach policy development. By leveraging accessible education data, the proposed approach contributes to more scalable, objective, and personalized evaluation strategies within broader domain of learning analytics

Roll-front uranium deposits are ore mineralizations that occur in sandstones or arkoses downstream from redox fronts or reduced/oxidized geochemical barriers. They are often bounded above and below by impermeable shaly/muddy layers making them ideal for in-situ leaching exploitation. Several stochastic simulations were previously investigated either to characterize the ore grade distribution within roll-front type deposits, or for describing geological processes involved in their formation. This work suggests some modifications/improvements of conventional geostatistical algorithms for honoring hydrodynamic constraints that govern fluid flows in ore bearing layers. In particular, instead of using the classical Euclidian or curvilinear (for Sgrid) distance for computing the variogram, it is proposed to calculate the variogram accounting for the time of flight (TOF) of water particles down the streamlines together with available well data. Non-deterministic streamline-based methods seem to provide more accurate interpolation results and resource estimation compared to a traditional geostatistical approach when applied to roll-front deposits. © 2022 by the authors.

This review analyzes the diversity and distribution of Arbuscular Mycorrhizal Fungi in grasslands and cattle-grazing ecosystems of Argentina and Brazil. The authors examine published studies to assess the role of AMF in sustainable agriculture, soil health, and ecosystem functioning. The review highlights the importance of AMF as environmentally friendly biofertilizers that support plant growth and biodiversity. The findings indicate that soil pH is a major factor influencing AMF diversity, with grasslands predominantly hosting members of the Glomeraceae family, while pastures are characterized mainly by Acaulosporaceae. Brazilian ecosystems exhibited substantially greater AMF richness than those of Argentina.

The aim of this study is to determine the differences in the damaged layer degradation kinetics in two-phase lithium-containing ceramics based on Li2ZrO3 and Li4SiO4 compounds in the case of irradiation with protons and helium ions, simulating the gas swelling and blistering processes, as well as the accumulation of radiolysis products in the damaged layer. During the conducted studies it was established that in the case of proton irradiation, the dominant role at fluences of 1015–1017 cm−2 is played by oxygen vacancies, the change in the concentration of which upon reaching critical values causes a decrease in the thermophysical properties, and disordering of the damaged layer. In this case, the accumulation of radiolysis products in the form of HC2 – and Zr3+ -defects in the structure of the damaged layer is observed at fluences of 5 × 1017 cm−2, while when irradiated with He2+ ions, the formation of these types of HC2 – and Zr3+ -defects is observed at a fluence of 1017 cm−2. Comparison of the concentration dependences of defects in the damaged layer on the atomic displacement value under irradiation with protons and He2+ ions revealed that the formation of oxygen vacancies under irradiation with He2+ ions is more intense than in the case of irradiation with protons, which in turn results in more pronounced processes of accumulation of radiolysis products in the case of high-dose irradiation.

The development of critical thinking skills has become a central priority in contemporary education, especially in preparing students for complex problem-solving, informed decision-making, and lifelong learning. This paper presents the design and initial deployment of a bilingual (Kazakh–Russian) AI-powered educational tool aimed at fostering critical thinking among secondary school students. The application leverages NLP and large-language models to analyze literary or instructional texts and generate Bloom-aligned questions at all cognitive levels, from basic recall to analysis, evaluation, and creative synthesis. The system enables teachers to upload text-based content, upon which the AI automatically produces structured level-specific questions tailored to the material. An integrated scoring and feedback mechanism supports formative assessment and encourages self-regulated learning. The preliminary evaluation includes an expert review of 30 generated items (three teachers of the literature) and early engagement metrics from a pilot with Class 6A (n=18). Baseline data collection is underway to allow a comparative analysis of the impact of the tool on the development of critical thinking. This case study illustrates the potential of AI to deliver personalized and cognitively engaging learning experiences in formal education. The paper details the system architecture, instructional logic (including retrieval-augmented, prompt-driven generation), and pilot evaluation framework, providing a foundation for future empirical studies and scalable implementation in diverse educational contexts.

This article presents the results of research work devoted to improving the characteristics of paint and varnish coatings based on aqueous dispersions of polyacrylates; it is proposed to modify them by introducing mineral raw materials as fillers and hydrated lime, with subsequent processing in a vortex layer apparatus. The introduction of activated diatomite does not cause the deterioration of covering power, adhesion or an increase in the porosity of the paint material. The modification of coatings contributes to an increase in their operational properties, which can be associated with a reduction in the free volume in the composite and the formation of polymer boundary layers with modified physical and chemical properties. The aim of this study is to obtain a water-dispersion paint and varnish composition containing modified diatomite on a polyacrylate basis and, subsequently, study its main physical and mechanical parameters. The work has been carried out by the following method: determination of porosity, adhesion, elasticity and covering power of the control composition; determination of porosity, adhesion, elasticity and covering power of the obtained composites using modified filler; investigation of the influence of radiation on the infrared spectrum of the paint coating surface using a FLIRB620 thermal imager. As a result of this research work, it was noticed that the modification of water dispersions with silica-activated diatomite helps to eliminate the main disadvantages of materials and coatings based on acrylate binders—low water resistance and low physical and mechanical characteristics. The introduction of modified diatomite into water-emulsion paint on an acrylate base does not lead to the deterioration of the main performance characteristics of paint coatings—porosity, adhesion, elasticity and covering.

This article discusses the importance of ecosystem restoration in response to increasing environmental pressures such as intensive agriculture, deforestation, resource overexploitation, and climate change. Special attention is given to the role of beneficial soil microorganisms, particularly mycorrhizal fungi and rhizobia, in supporting plant establishment, biodiversity recovery, and ecosystem resilience. The study highlights the significance of plant–microbe symbioses in restoration programs and identifies glomalin content as a reliable indicator of soil stabilization and restoration success, with higher levels observed in undisturbed and restored ecosystems than in disturbed sites.

Reactive transport modeling is known to be computationally intensive when applied to 3D problems. Transforming sequential computing on the computer processor units (CPU) into parallelized computation on the high-performance parallel graphic processor units (GPU) is a classical approach to increasing computational performance. Another complementary approach is to decompose a complex 3D modeling problem into a set of simpler 1D problems using streamline approaches which can be easily parallelized, therefore reducing computation time. This paper investigates solutions to the equations governing dissolution and transport using streamlines coupled with a parallelization approach. In addition, an analytical solution to the dissolution and transfer equations of uranium describing the In-Situ Leaching (ISL) mining recovery is found using an approximation series to the 2nd order. The analytical solution is compared to the 1D numerical resolution along the streamlines and to the 3D simulation results superimposed on the streamline. Both approaches give similar results with a relative error of <2 % (2%). The proposed methodology is then applied to a case study in which the classical 3D resolution is compared to the newly suggested streamline solution, demonstrating that the streamline approach increases computational performances by a factor ranging from hundred to thousand depending on the complexity of the grid-block model. © 2023 The Authors

The research aims to enhance methods for distributing freight flows across a railway network to ensure unhindered access to public railway transportation services for shippers. The study was conducted using the methodological framework of railway operation theory in conjunction with operations research methods. During the research, a method for distributing freight flows across a railway network, taking into account capacity constraints of specific sections, as well as a method for allocating additional transportation costs among shippers, is proposed. The originality of the research lies in improving the methodology for distributing freight flows across railway networks under capacity constraints. The proposed method makes it possible to consider the interests of individual shippers when using public railway transport by identifying sections with capacity constraints and adjusting tariff distances accordingly. The applied relevance is confirmed by the fact that implementing the results can improve methods of organizing wagon flows and determining freight tariffs, taking into account the interests of both railways and their customers.