This scientific research focuses on the study of the disinfection process of harmful microorganisms found in the Vyacheslav Reservoir using the ETRO-02 ozonator installation based on pilot electrical corona discharge and ozone technology. The objective of the research is to determine the effectiveness of ozone technology in eliminating microorganisms in water and compare it with other disinfection methods. The materials and methods section reveals that ozone eliminates total coliform bacteria within 2-4 minutes at a rate of 99-100%, which is significantly faster than chloramine (which takes 15-30 minutes). Additionally, it was found that ozone decomposes into oxygen after use, leaving no harmful residues. The research results showed that ozone eliminates Clostridium spores by 85-90% within 10-15 minutes, while chloramine requires more time to reach the same efficiency. Ozone technology demonstrated an effectiveness of 95-99% at a pH range of 4.0-5.0, proving its suitability for different water conditions. Moreover, ozone works effectively under high temperatures and pressure. The findings indicated that at an ozone concentration of 1.0 mg/L, all harmful microorganisms in water are completely eliminated within 20 minutes. The research was conducted both theoretically and experimentally, with the mathematical model of water disinfection coded in Python. Mathematical calculations were performed using the SMath Solver software. In conclusion, ozone technology has been proven to be an effective, safe, and environmentally friendly method for water purification. The primary objective of this study is to evaluate the effectiveness of ozone technology for water disinfection and to compare its efficiency with other disinfection methods. © 2025, Zibeline International Publishing Sdn. Bhd.. All rights reserved.

The destabilization of the geopolitical, socio-economic and security situation in the world has exacerbated the issue of sustainable development of regional economies and deepening their interaction. Ensuring the growth of the national economy as a whole and individual regions in particular makes the search for mechanisms aimed specifically at internal sources relevant. Spatial development is gaining particular importance due to the increasing role of transport infrastructure in ensuring the economic growth of regions. The socio-economic heterogeneity of regional systems plays a decisive role in the formation of mechanisms for ensuring economic growth, which determines the diversity and contradictions of the effects of transport infrastructure on them. This is expressed in the fact that similar infrastructure facilities in different regions can have different organizational and economic effects. Thus, the appearance of a road can lead to the acceleration of material flows, thereby contributing to the development of the region's economy, and on the other hand, can stimulate an accelerated outflow of population. At the same time, the principles of managing social development and economic growth of regions obtained in practice do not allow to take into account the functional diversity and inconsistency of the effects of transport infrastructure and thereby complicate the search for effective mechanisms for ensuring regional development. growth based on the development of transport infrastructure. Therefore, the study focuses on the actualization of the need to introduce innovative mechanisms into the economy of regions by determining the conditions necessary and sufficient for the implementation of the role of transport infrastructure as one of the sources of sustainable economic growth. In this regard, the knowledge of the essence and patterns of the mutual influence of transport and regional economic development is of great theoretical and practical interest. © Author(s) 2025. © Author(s) 2025. All rights reserved.

Sustainable copper extraction presents significant challenges due to waste generation and environmental impacts, requiring advanced predictive methodologies to optimize production processes. This study addresses a gap in applying deep learning to forecast hydrometallurgical copper production by comparing six recurrent neural network architectures: Vanilla LSTM, Stacked LSTM, Bidirectional LSTM, GRU, CNN-LSTM, and Attention LSTM. Using time-series data from a full-scale industrial operation, we implemented a data augmentation approach to overcome data scarcity limitations. The models were evaluated through rigorous metrics and multi-step forecasting tests. The results demonstrated remarkable performance from five architectures, with Bidirectional LSTM and Attention LSTM achieving the highest accuracy (RMSE < 0.004, R2 > 0.999, MAPE < 1%). These models successfully captured and reproduced complex cyclical patterns in copper mass production for up to 500 time steps ahead. The findings validate our data augmentation strategy for enabling models to learn complex known cyclical patterns from limited initial data and establish a promising foundation for implementing AI-driven predictive systems that can enhance process control, reduce waste, and advance sustainability in hydrometallurgical operations. However, these performance metrics reflect the models’ ability to reproduce patterns inherent in the augmented dataset derived from a single operational cycle; validation on entirely independent operational data is crucial for assessing true generalization and is a critical next step.

Relevance. The relevance of the study is due to the need to introduce a new educational approach to improve the quality of education. Purpose. Based on this, the purpose of the study was to analyse the functioning of innovative educational strategies that will provide an opportunity to form the necessary professional skills and abilities in students. Methodology. The following methods were used: the logical analysis, functional analysis, synthesis, deduction, induction and others. In the course of the study, it was characterized the meta-subject approach; its main features, signs and principles of functioning in the educational environment were identified. Results. It has been found that students are given the opportunity to find new ways to solve problems due to the use of this method in the study of foreign languages. In turn, this allows students to develop communicative and professional skills, optimize the resources used and improve the efficiency of functioning of the educational sector. It has been considered various methods, approaches and techniques that allow students to form the necessary meta-subject skills. In the study, it was proposed a cognitive strategy for introducing the economic discourse “foreign economic activity”. It is worth noting that the study has provided the frame “negotiation strategy”, which has the prospect of being effectively implemented in the educational process. In addition, it has been proposed recommendations that would allow implementing the proposed strategies correctly and expediently in the training process and form meta-subject skills. Conclusions. The practical value of the results obtained lies in providing a plan and strategies to ensure the formation of meta-subject skills in students in a foreign language, which makes it possible to increase the efficiency of educational system and form the future competitive specialists, considering the conditions modern of labor market. Copyright © The Author(s).

Nowadays, there is an interest in biomedical and nanobiotechnological studies, such as studies on carotenoids as antioxidants and studies on molecular markers for cardiovascular, endocrine, and oncological diseases. Moreover, interest in industrial production of microalgal biomass for biofuels and bioproducts has stimulated studies on microalgal physiology and mechanisms of synthesis and accumulation of valuable biomolecules in algal cells. Biomolecules such as neutral lipids and carotenoids are being actively explored by the biotechnology community. Raman spectroscopy (RS) has become an important tool for researchers to understand biological processes at the cellular level in medicine and biotechnology. This review provides a brief analysis of existing studies on the application of RS for investigation of biological, medical, analytical, photosynthetic, and algal research, particularly to understand how the technique can be used for lipids, carotenoids, and cellular research. First, the review article shows the main applications of the modified Raman spectroscopy in medicine and biotechnology. Research works in the field of medicine and biotechnology are analysed in terms of showing the common connections of some studies as caretenoids and lipids. Second, this article summarises some of the recent advances in Raman microspectroscopy applications in areas related to microalgal detection. Strategies based on Raman spectroscopy provide potential for biochemical-composition analysis and imaging of living microalgal cells, in situ and in vivo. Finally, current approaches used in the papers presented show the advantages, perspectives, and other essential specifics of the method applied to plants and other species/objects. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

In this study, carbonized apricot stones and rice husk were utilized as feedstock for the synthesis of CuO-loaded carbonized sorbents for the removal of carbon dioxide (CO2 ) from gas mixtures. The specific surface area of carbonized sorbents increased with increasing carbonization temperature, resulting in a porous structure with enhanced sorption capacity. The presence of pores and the development of porosity in the sorbents were confirmed by SEM images. CuO nanoparticles were well-dispersed on the surface of carbonized sorbents, and the particle sizes were between 60‒100 nm. Chemical interactions between acidic carbon dioxide and basic copper oxide led to improved adsorption properties. The sorption characteristics of the carbonized sorbents were studied under dynamic conditions, and the results showed that CuO-loaded carbonized apricot stones and rice husk had the maximum sorption capacity for CO2, with efficiencies of 98% and 91%, respectively. These findings indicate that carbonized apricot stones and rice husk can be utilized as low-cost and eco-friendly feedstock for the production of efficient CO2 sorbents. © 2023 The Author(s).

Soil salinization poses severe abiotic stress that adversely affects plant growth and development, ultimately threatening global food security by inducing physiological abnormalities. In response to escalating nutrient demands, with global requirements quantified at 76 % for nitrogen and 87 % for phosphorus, modern agriculture is increasingly adopting sustainable practices to enhance nutrient recycling and reduce reliance on external inputs. Emerging sources of plant phytostimulants, such as microalgal and cyanobacterial biomass, show promise in augmenting crop yields and bolstering plant resistance to various abiotic factors, including salt stress. The efficacy of these microorganisms stems from their simplistic cellular structure, superior photosynthetic efficiency, capacity for heterotrophic growth, adaptability to varying environmental conditions, potential for metabolic engineering, and the abundance of valuable biomolecules (such as soluble amino acids, micronutrients, polysaccharides, and phytohormones) within their biomass. This review provides an analysis of the current research landscape concerning microalgae- and cyanobacteria-derived phytostimulants, highlighting their promise as an innovative and sustainable alternative to synthetic fertilizers in the agricultural sector. Moreover, it identifies various adaptive responses of plants to salinity stress and assesses the potential and challenges associated with the use of microalgae and cyanobacteria-based metabolites for developing new sustainable strategies to enhance crop tolerance to salinity stress. © 2024

The paper studies the distribution of relative displacement of a composite plate with integrated fiber Bragg gratings. The analysis of the methods for manufacturing composite plates with embedded optical fibers containing FBG sensors, as well as the spectral characteristics of the gratings under various bending conditions, are performed. The effect of sensor arrangement on the accuracy of determining stresses and relative elongations of the material is experimentally studied. The features of spectral shifts that occur under non-uniform stresses are revealed, which can reduce the accuracy of measurements when using interrogators. The patterns of change in the central wavelength of Bragg gratings depending on the type and magnitude of plate bending are established. The research results confirm that the use of a network of embedded FBG sensors allows one to accurately determine the areas of maximum deformations, as well as the nature and magnitude of bending of composite structures. The data obtained can be used to develop more accurate systems for monitoring the stress–strain state of composite materials. © 2025 by the authors.

In the arid and semi-arid climate of Southern Kazakhstan, groundwater is the primary and most resilient source of water for pasture irrigation. This study provides an integrated assessment of the predicted, natural, and operational groundwater resources across five administrative regions—Almaty, Zhetysu, Zhambyl, Kyzylorda, and Turkestan—considering water quality (total dissolved solids, TDS), potential well yield, and aquifer depth. Hydrogeological maps at 1:200,000 and 1:1,000,000 scales, a regional well inventory, and GIS-based spatial analysis were combined to classify resource availability and identify surplus and deficit zones. Results show that 92.5% of predicted exploitable resources (totaling 1155.2 m3/s) have TDS ≤ 3 g/L, making them suitable for domestic and livestock use. Regional disparities are pronounced: Zhetysu, Almaty, and Zhambyl exhibit resource surpluses, Kyzylorda approaches balance, while Turkestan faces a marked deficit. The developed groundwater availability map integrates mineralization, well productivity, and recommended drilling depth, enabling the design of water intake systems without costly field exploration. This decision-support tool has practical value for optimizing water allocation, reducing operational costs, and improving the sustainability of pasture management under the constraints of limited surface water resources. © 2025 by the authors.

Climate change, uneven distribution of water resources and anthropogenic impact have led to salinization and land degradation in the southern regions of Kazakhstan. Identification of saline lands and their mapping is a laborious process associated with a complex of ground measurements. Data from remote sensing are widely used to solve this problem. In this paper, the problem of assessing the salinity of the lands of the South Kazakhstan region using remote sensing data is considered. The aim of the study is to analyze the applicability of machine learning methods to assess the salinity of agricultural lands in southern Kazakhstan based on remote sensing. The authors present a salinity dataset obtained from field studies and containing more than 200 laboratory measurements of soil salinity. Moreover, the authors describe the results of applying several regression reconstruction algorithms (XGBoost, LightGBM, random forest, Support vector machines, Elastic net, etc.), where synthetic aperture radar (SAR) data from the Sentinel-1 satellite and optical data in the form of spectral salinity indices are used as input data. The obtained results show that, in general, these input data can be used to estimate salinity of the wetted arable land. XGBoost regressor ((Formula presented.) = 0.282) showed the best results. Supplementing the radar data with the values of salinity spectral index improves the result significantly ((Formula presented.) = 0.356). For the local datasets, the best result shown by the model is (Formula presented.) = 0.473 (SAR) and (Formula presented.) = 0.654 (SAR with spectral indexes), respectively. The study also revealed a number of problems that justify the need for a broader range of ground surveys and consideration of multi-year factors affecting soil salinity. Key results of the article: (i) a set of salinity data for different geographical zones of southern Kazakhstan is presented for the first time; (ii) a method is proposed for determining soil salinity on the basis of synthetic aperture radar supplemented with optical data, and this resulted in the improved prediction of the results for the region under consideration; (iii) a comparison of several types of machine learning models was made and it was found that boosted models give, on average, the best prediction result; (iv) a method for optimizing the number of model input parameters using explainable machine learning is proposed; (v) it is shown that the results obtained in this work are in better agreement with ground-based measurements of electrical conductivity than the results of the previously proposed global model. © 2023 by the authors.