В работе проведена оценка качества питьевой воды Майского района Павлодарской области Республики Казахстан. Исследованы органолептические, обобщенные и микробиологические показатели, а также содержание органических и неорганических веществ в воде до и после очистки на водоочистной станции и в водопроводной воде в соответствии с нормативными требованиями. Установлено, что вода относится к пресным с минерализацией и имеет среднюю жесткость гидрокарбонатного типа. Выявлены превышения ПДК по мутности и содержанию марганца до очистки. Микробиологический анализ показал несоответствие гигиеническим нормам, а обнаружение Pseudomonas aeruginosa инфекция свидетельствует о санитарно-эпидемиологической опасности и влиянии антропогенных и хозяйственно-бытовых факторов на качество питьевой воды.

The article presents the results of experimental studies of the effect of the stabilizing MgO dopant using the electron spin resonance (ESP) method on enhancement of the stability of Li2ZrO3 ceramics to defect formation processes and accumulation of radiolysis products in the near-surface layer in the case of high-dose irradiation with protons simulating the hydrogenation effects characteristic of processes associated with tritium production. During the conducted studies, it was established that the addition of the stabilizing MgO dopant results in formation of inclusions in the form of the tetragonal MgLi2ZrO4 phase, which leads to an increase in the resistance of the near-surface layers to destructive damage due to the accumulation of structural damage (oxygen vacancies and point defects), as well as products of the physicochemical processes of radiolysis, characteristic of high irradiation fluence values. It was found that in the case of unmodified Li2ZrO3 ceramics, the formation of HC2 – centers is observed at a fluence of 1016 proton/cm2, while for two-phase ceramics, the formation of HC2 – centers is observed at higher fluences, while the intensity of the bands is significantly less than in the case of single-phase unmodified ceramics. The difference in the nature of changes in the intensities of singlet bands responsible for the presence of vacancy defects in the damaged layer, as well as HC2 – centers for single-phase and two-phase ceramics is a direct confirmation of the inhibition of structural degradation mechanisms in two-phase ceramics.

During the conducted studies, it was established that the use of ion modification by irradiation with O+ and Ar+ ions makes it possible to elevate the degradation resistance of anode materials due to the effect of vacancy defect creation, the density of which varies with the irradiation fluence. At the same time, the analysis of changes in the band gap and the optical density value, expressing changes in structural distortions, revealed that ion irradiation leads to a rise in the stability of the preservation of electronic properties during long-term resource tests, which are inextricably linked with the degradation of ZnO/CoZn nanostructures due to oxidation processes as a result of lithiation. During assessment of changes in the parameters of the band gap and optical density of the samples after resource tests, it was found that the observed growth in these indicators is due to oxidation processes and partial amorphization due to the formation of oxide inclusions in the structure of nanowires, the presence of which is due to the interaction of nanostructures with the electrolyte over a long period of time during charging/discharging, which results in near-surface layer degradation due to the introduction of oxygen, and in the case of a long service life, to the formation of oxide inclusions that elevate the density of defects and vacancies in the structure. According to tests of synthesized ZnO/CoZn nanostructures as anode materials, it was found that the use of O+ and Ar+ ions not only leads to a growth in the degradation resistance of capacitive characteristics during long-term tests, but also to the stability maintenance of indicators with a reversible decrease in the charging rate at charging rate variation.

This study evaluates the applicability of optical spectroscopy and X-ray diffraction techniques for determining the lower detection limit of latent track density in polymer nuclear-track detectors exposed to α-particles from simulated radon decay products using Americium-241 sources. The results demonstrate that both UV optical spectroscopy and X-ray diffraction can detect latent track densities as low as 10⁴ tracks/cm². The research further reveals that increasing track density leads to significant structural and optical changes in the polymer, including anisotropic variations in electron density, deformation-induced stresses, and molecular structure distortions. A strong correlation was found between optical and structural characteristics across different irradiation levels.

Typical leg exoskeletons employ open-loop kinematic chains with motors placed directly on movable joints; while this design offers flexibility, it leads to increased costs and heightened control complexity due to the high number of degrees of freedom. The use of heavy servo-motors to handle torque in active joints results in complex and bulky designs, as highlighted in the existing literature. In this analytical study, we introduced a novel synthesis method with analytical solutions provided for synthesizing the lower-limb exoskeleton. Furthermore, we proposed a mathematical model of multicriteria optimization; as a result, we obtained several lower-limb exoskeleton mechanisms comprising only six links, well-suited to the human anatomical structure, exhibit superior trajectory accuracy, efficient force transmission, satisfactory step height, and having internal transfer segment of the foot. © 2023 by the authors.

Educational Recommender Systems (ERS) have become a critical component of modern digital learning environments, offering personali􀁝ed learning pathways tailored to individual student needs, preferences, and behaviors. These systems utili􀁝e a wide range of data sources􀂲including demographic information, academic performance, cognitive characteristics, and behavioral patterns􀂲to recommend relevant courses, learning materials, and strategies that support student engagement and success. This paper presents a comprehensive review of current approaches used in the development of ERS, including collaborative filtering, content-based filtering, hybrid recommendation models, machine learning, deep learning algorithms, 􀁎nowledge graphs, and e􀁛plainable artificial intelligence (XAI). Particular attention is given to the advantages and limitations of each approach, as well as 􀁎ey challenges that persist in the implementation of ERS. These include the cold-start problem, data sparsity, scalability issues, lack of transparency in decision-ma􀁎ing, and concerns regarding user privacy and algorithmic fairness. The paper also e􀁛plores practical applications of ERS in higher education and large-scale online platforms such as MOOCs, where such systems have demonstrated positive impacts on learner motivation, retention

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.