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Abstract Salinization and land degradation are significant challenges in the southern regions of Kazakhstan. These issues arise due to climate change, unequal water resource distribution, and human impact. The primary concern revolves around water resources, which are influenced by the area’s trans boundary flow of major rivers. The low level of water and food security has pushed the development of new approaches based on remote sensing monitoring and geographic information systems (GIS) to provide solutions for soil salinity. The research aims to focus on utilizing high-resolution radar images. This data type is effective for cloudy weather and can be useful for continued monitoring of some areas. Machine learning methods can solve the problem of automatic mapping of agricultural land salinity in Kazakhstan’s southern regions. The precise mapping of the salinity area helps prevent or decrease salinity’s impact on agriculture. The experiment realized that complex models such as LightGBM do not have significant accuracy performance over simple models on a small dataset compared with Ridge regression. The results allow us to recommend an approach for further improvement with ground-based measurement data and other deep-learning methods for mapping the salinity of agricultural lands.

AbstractAs a large social structure, the health care system is often reflected in media publications. This creates a significant impact on society’s attitude towards the system and the state in general. In order to predict and correct state policies, media actions, and identify media shortcomings, it is necessary to analyze the image portrayed by the media and the public’s attitude towards it. In this article, we present the results of a multidirectional analysis of a corpus of media publications related to health care. We propose a method for analyzing the information image of health care formed by the mass media based on a topic model of a text corpus. The method evaluates reader interest in various healthcare topics, the dynamics of changes in publication sentiment, and the main information trends. The article presents the results of analyzing a corpus of mass media publications in Kazakhstan from January 2020 to January 2023.

Introduction: The Covid-19 pandemic has boosted the development of online retail and has created an urgent need to transform digital marketing tools. The purpose of the study is to find ways to optimize advertising in Google and Facebook, which are the superior digital advertising platforms as their top digital advertising platforms in terms of both usage and performance. Methods: An experiment conducted as part of the study showed that the use of the dynamic optimization tool allows you to enrich existing feed data with additional 1st and 3rd party data and automatically compile visuals with additional information obtained from the feed and additional graphical overlays. The proposed method was tested on Facebook and Google with an analysis of click through rate, conversion rate and conversion cost indicators. Findings: The research results showed that the use of the dynamic optimization tool improves the effectiveness of online advertising, all the studied indicators has improved on both platforms. The results obtained complement to the few literature sources devoted to creating enriched feeds on various internet companies, so the work is of significant interest for marketers and business owners who work in the field of e-commerce. Originality: It has been established that in order to increase the conversion rates of ad campaigns and improve the quality of communication with the audience, a deeper study of the behavioral characteristics of visitors of internet resources, as well as high-quality visual content of the ads, are required.

Islet transplantation represents a promising therapeutic approach for diabetes management, yet the isolation and evaluation of pancreatic islets remain challenging. This study focuses on the isolation of islets from rabbit pancreases, followed by a comprehensive assessment of their viability and functionality. We developed a novel method for isolating islet cells from the pancreas of adult rabbits. We successfully isolated viable islets, which were subsequently evaluated through a combination of viability assays, an insulin enzyme-linked immunosorbent assay (ELISA), and fluorescence lifetime imaging microscopy (FLIM). The viability assays indicated a high percentage of intact islets post-isolation, while the insulin ELISA demonstrated robust insulin secretion in response to glucose stimulation. FLIM provided insights into the metabolic state of the islets, revealing distinct fluorescence lifetime signatures correlating with functional viability. Our findings underscore the potential of rabbit islets as a model for studying islet biology and diabetes therapy, highlighting the efficacy of combining traditional assays with advanced imaging techniques for comprehensive functional assessments. This research contributes to the optimization of islet isolation protocols and enhances our understanding of islet functional activity dynamics in preclinical settings.

Soil salinity assessment methods based on remote sensing data are a common topic of scientific research. However, the developed methods, as a rule, estimate relatively small areas of the land surface at certain moments of the season, tied to the timing of ground surveys. Considerable variability of weather conditions and the state of the earth surface makes it difficult to assess the salinity level with the help of remote sensing data and to verify it within a year. At the same time, the assessment of salinity on the basis of multiyear data allows reducing the level of seasonal fluctuations to a considerable extent and revealing the statistically stable characteristics of cultivated areas of land surface. Such an approach allows, in our opinion, the processes of mapping the salinity of large areas of cultivated lands to be automated considerably. The authors propose an approach to assess the salinization of cultivated and non-cultivated soils of arid zones on the basis of long-term averaged values of vegetation indices and salinity indices. This approach allows revealing the consistent relationships between the characteristics of spectral indices and salinization parameters. Based on this approach, this paper presents a mapping method including the use of multiyear data and machine learning algorithms to classify soil salinity levels in one of the regions of South Kazakhstan. Verification of the method was carried out by comparing the obtained salinity assessment with the expert data and the results of laboratory tests of soil samples. The percentage of “gross” errors of the method, in other words, errors when the predicted salinity class differs by more than one position compared to the actual one, is 22–28% (accuracy is 0.78–0.72). The obtained results allow recommending the developed method for the assessment of long-term trends of secondary salinization of irrigated arable land in arid areas.

A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively.

Transcatheter aortic valve implantation (TAVI) is commonly accepted method of treatment of aortic stenosis in most patients with currently increasing number of TAVI procedures. Patients after TAVI frequently have indications for percutaneous coronary intervention (PCI) that might be technically challenging due to the presence of transcatheter valve. Compromising coronary ostia involves such issues as design of the prosthesis and its position in the aortic root with respect to the commissures of native aortic valve. Since TAVI with commissural alignment can mitigate risk of compromising coronary ostia, this option should be considered while planning TAVI, especially in younger patients with coronary atherosclerosis and relatively high probability of PCI in the future. The algorithm of commissural alignment in clinical practice of TAVI up to date has been proposed only for several transcatheter heart valves reported in this paper.

Islet allotransplantation offers a promising cell therapy for type 1 diabetes, but challenges such as limited donor availability and immunosuppression persist. Microencapsulation of islets in polymer-coated alginate microcapsules is a favored strategy for immune protection and maintaining islet viability. This study introduces Poly [2-(methacryloyloxy)ethyl]trimethylammonium chloride (PMETAC) as an innovative coating material for microcapsules. PMETAC enhances biocompatibility and durability, marking a significant advancement in islet encapsulation. Our approach combines alginate with PMETAC to create Langerhans islet microcapsules, simplifying material composition and preparation and ultimately lowering costs and increasing clinical applicability. Our comprehensive evaluation of the stability (including osmotic stability, thermal stability, and culture condition stability) and cytotoxicity of a novel microencapsulation system based on alginate-PMETAC-alginate offers insights into its potential application in islet immunoisolation strategies. Microcapsules with PMETAC content ranging from 0.01 to 1% are explored in the current work. The results indicate that the coatings made with 0.4% PMETAC show the most promising outcomes, remaining stable in the mentioned tests and exhibiting the required permeability. It was shown that the islets encapsulated in this manner retain viability and functional activity. Thus, alginate microcapsules coated with 0.4% PMETAC are suitable for further animal trials. While our findings are promising, further studies, including animal testing, will be necessary to evaluate the clinical applicability of our encapsulation method.

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.

The traditional approach to monitoring observations of the technogenic processes development in oilfields, which consists in determining the concentration of marker pollutants in various natural environments, does not provide the necessary completeness of information and the efficiency of its receipt. The paper considers an example of expanding the range of observations due to unmanned aerial photography and a number of other methods. Interpretation signs (for panchromatic survey) were determined that register such consequences of technogenic transformation of the natural environment as mechanogenesis, bitumization, and halogenesis. Technogenic mechanogenesis is understood as a physical violation of the integrity of ecosystems, the movement of soils and grounds. Bitumization is expressed in the migration of petroleum hydrocarbons through soils, ground, surface, subsurface, and underground waters, and their destruction. Salt migration in these media is defined as halogenesis. The most reliable indicators are linearly elongated areas of dead forests, dark red spots in drying microdepressions and reservoirs. It was found out that the oilfield impact on the raised bog leads to anthropogenic eutrophication, the introduction of plant species, uncharacteristic coenotic groups, the replacement of subshrubs with grasses, and morphometric changes in forest pine. In the peat deposits of the disturbed area, an unusual interlayer of whitish, undecomposed moss was recorded. The moment of the beginning of a pronounced technogenic transformation was registered in the course of work with the archive of multispectral space images. Continuous remote sensing with the help of unmanned aerial photography and interpretation by sedimentological, geobotanical methods significantly expand the possibilities of studying the technogenic transformation of the natural environment. To ensure environmental safety, it is advisable to develop remote methods and technologies to include them in the environmental monitoring system.

Precision farming is one of the ways of transition to the intensive methods of agricultural production. The case of application of unmanned aerial vehicles (UAVs) for solving problems of agriculture and animal husbandry is among the actively studied issues. The UAV is capable of solving the tasks of monitoring, fertilizing, herbicides, etc. However, the effective use of UAV requires to solve the tasks of flight planning, taking into account the heterogeneity of the available attachments and the problem solved in the process of the overflight. This research investigates the problem of flight planning of a group of heterogeneous UAVs applied to solving the issues of coverage, which may arise both in the course of monitoring and in the process of the implementation of agrotechnical measures. The method of coverage path planning of heterogenic UAVs group based on a genetic algorithm is proposed; this method provides planning of flight by a group of UAVs using a moving ground platform on which UAVs are recharged and refueled (multi heterogenic UAVs coverage path planning with moving ground platform (mhCPPmp)). This method allows calculating a fly by to solve the task of covering fields of different shapes and permits selecting the optimal subset of UAVs from the available set of devices; it also provides a 10% reduction in the cost of a flyby compared to an algorithm that does not use heterogeneous UAVs or a moving platform.

Saponite is classified as a trioctahedral mineral of the smectite group.Natural saponite of the Lomonosov deposit (Arkhangelsk region, Russia) is represented as Na- or Ca- forms according to XRD analysis. The interlayer space of the structure contains water molecules. The saponite clay sampled at the deposit is represented by pulp. The clay powder for the research was obtained by the technology of concentrating fine clay material. The crystallochemical formula of saponite was calculated based on the data obtained from XRD, XRF and atomic emission analyses. The purpose of the research was to study the effect of mixed sodium carbonate-sulfate electrolyte (Na2SO4 - 80%; Na2CO3-20%), polyacrylamide (PAA) and low viscosity polyanionic cellulose (PAC L) on the structure, filtration and rheological properties of saponite suspension. The optimum solids content in the water suspension of saponite clay was determined to be in the range of 85-100 g/l. Going out of range leads to deterioration of filtration properties presented by significant increase in filtration loss or increased thickness of the filtration cake. Filtration loss at 85 g/l solids content in the water suspension was 22 cm³/30 min. Addition of mixed sodium carbonate-sulfate electrolyte to the saponite suspension improved rheological properties, but increased filtration loss to 34-36 cm³/30 min. The admixture of polyacrylamide leads rather quickly to flocculation and sedimentation of clay particles; filtration loss was 16-21 cm³/30 min. The presence of polyanionic cellulose in the suspension creates a gel structure, increasing the viscosity of the mud system and significantly reducing filtration loss to 7-9 cm³/30 min. [ABSTRACT FROM AUTHOR]

Islet transplantation represents a promising therapeutic approach for diabetes management, yet the isolation and evaluation of pancreatic islets remain challenging. This study focuses on the isolation of islets from rabbit pancreases, followed by a comprehensive assessment of their viability and functionality. We developed a novel method for isolating islet cells from the pancreas of adult rabbits. We successfully isolated viable islets, which were subsequently evaluated through a combination of viability assays, an insulin enzyme-linked immunosorbent assay (ELISA), and fluorescence lifetime imaging microscopy (FLIM). The viability assays indicated a high percentage of intact islets post-isolation, while the insulin ELISA demonstrated robust insulin secretion in response to glucose stimulation. FLIM provided insights into the metabolic state of the islets, revealing distinct fluorescence lifetime signatures correlating with functional viability. Our findings underscore the potential of rabbit islets as a model for studying islet biology and diabetes therapy, highlighting the efficacy of combining traditional assays with advanced imaging techniques for comprehensive functional assessments. This research contributes to the optimization of islet isolation protocols and enhances our understanding of islet functional activity dynamics in preclinical settings. [ABSTRACT FROM AUTHOR]

Soil salinity assessment methods based on remote sensing data are a common topic of scientific research. However, the developed methods, as a rule, estimate relatively small areas of the land surface at certain moments of the season, tied to the timing of ground surveys. Considerable variability of weather conditions and the state of the earth surface makes it difficult to assess the salinity level with the help of remote sensing data and to verify it within a year. At the same time, the assessment of salinity on the basis of multiyear data allows reducing the level of seasonal fluctuations to a considerable extent and revealing the statistically stable characteristics of cultivated areas of land surface. Such an approach allows, in our opinion, the processes of mapping the salinity of large areas of cultivated lands to be automated considerably. The authors propose an approach to assess the salinization of cultivated and non-cultivated soils of arid zones on the basis of long-term averaged values of vegetation indices and salinity indices. This approach allows revealing the consistent relationships between the characteristics of spectral indices and salinization parameters. Based on this approach, this paper presents a mapping method including the use of multiyear data and machine learning algorithms to classify soil salinity levels in one of the regions of South Kazakhstan. Verification of the method was carried out by comparing the obtained salinity assessment with the expert data and the results of laboratory tests of soil samples. The percentage of "gross" errors of the method, in other words, errors when the predicted salinity class differs by more than one position compared to the actual one, is 22–28% (accuracy is 0.78–0.72). The obtained results allow recommending the developed method for the assessment of long-term trends of secondary salinization of irrigated arable land in arid areas. [ABSTRACT FROM AUTHOR]

A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively. [ABSTRACT FROM AUTHOR]

Islet allotransplantation offers a promising cell therapy for type 1 diabetes, but challenges such as limited donor availability and immunosuppression persist. Microencapsulation of islets in polymer-coated alginate microcapsules is a favored strategy for immune protection and maintaining islet viability. This study introduces Poly [2-(methacryloyloxy)ethyl]trimethylammonium chloride (PMETAC) as an innovative coating material for microcapsules. PMETAC enhances biocompatibility and durability, marking a significant advancement in islet encapsulation. Our approach combines alginate with PMETAC to create Langerhans islet microcapsules, simplifying material composition and preparation and ultimately lowering costs and increasing clinical applicability. Our comprehensive evaluation of the stability (including osmotic stability, thermal stability, and culture condition stability) and cytotoxicity of a novel microencapsulation system based on alginate-PMETAC-alginate offers insights into its potential application in islet immunoisolation strategies. Microcapsules with PMETAC content ranging from 0.01 to 1% are explored in the current work. The results indicate that the coatings made with 0.4% PMETAC show the most promising outcomes, remaining stable in the mentioned tests and exhibiting the required permeability. It was shown that the islets encapsulated in this manner retain viability and functional activity. Thus, alginate microcapsules coated with 0.4% PMETAC are suitable for further animal trials. While our findings are promising, further studies, including animal testing, will be necessary to evaluate the clinical applicability of our encapsulation method. [ABSTRACT FROM AUTHOR]

Monitoring of the water level and river discharge is an important task, necessary both for assessment of water supply in the current season and for forecasting water consumption and possible prevention of catastrophic events. A network of ground hydrometric stations is used to measure the water level and consumption in rivers. Rivers located in sparsely populated areas in developing countries of Central Asia have a very limited hydrometric network. In addition to the sparse network of stations, in some cases remote probing data (virtual hydrometric stations) are used, which can improve the reliability of water level and discharge estimates, especially for large mountain rivers with large volumes of suspended sediment load and significant channel instability. The aim of this study is to develop a machine learning model for remote monitoring of water levels in the large transboundary (Kazakhstan-People’s Republic of China) Ili River. The optical data from the Sentinel-2 satellite are used as input data. The in situ (ground-based) data collected at the Ili-Dobyn gauging station are used as target values. Application of feature engineering and ensemble machine learning techniques has achieved good accuracy of water level estimation (Nash–Sutcliffe model efficiency coefficient (NSE) >0.8). The coefficient of determination of the model results obtained using cross-validation of random permutations is NSE = 0.89. The method demonstrates good stability under different variations of input data and ranges of water levels (NSE > 0.8). The average absolute error of the method ranges from 0.12 to 0.18 meters against the background of the maximum river water level spread of more than 4 meters. The obtained result is the best current result of water level prediction in the Ili River using the remote probing data and can be recommended for practical use for increasing the reliability of water level estimation and reverse engineering of data in the process of river discharge monitoring.

Approximately 50% of the world’s uranium is mined in a closed way using underground well leaching. In the process of uranium mining at formation-infiltration deposits, an important role is played by the correct identification of the formation of reservoir oxidation zones (ROZs), within which the uranium content is extremely low and which affect the determination of ore reserves and subsequent mining processes. The currently used methodology for identifying ROZs requires the use of highly skilled labor and resource-intensive studies using neutron fission logging; therefore, it is not always performed. At the same time, the available electrical logging measurements data collected in the process of geophysical well surveys and exploration well data can be effectively used to identify ROZs using machine learning models. This study presents a solution to the problem of detecting ROZs in uranium deposits using ensemble machine learning methods. This method provides an index of weighted harmonic measure (f1_weighted) in the range from 0.72 to 0.93 (XGB classifier), and sufficient stability at different ratios of objects in the input dataset. The obtained results demonstrate the potential for practical use of this method for detecting ROZs in formation-infiltration uranium deposits using ensemble machine learning.

The uranium required for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in situ leaching (ISL). Uranium mining by in situ leaching is extremely attractive because it is economical and has a minimal impact on the region’s ecology. The effective use of ISL requires, among other things, the accurate assessment of the host rocks’ filtration characteristics. An accurate assessment of the filtration properties of the host rocks allows optimizing the mining process and improving the quality of the ore reserve prediction. At the same time, in Kazakhstan, this calculation is still based on methods that were developed more than 50 years ago and, in some cases, produce inaccurate results. According to our estimates, this method provides a prediction of filtration properties with a determination coefficient R2 = 0.32. This paper describes a method of calculating the filtration coefficient of ore-bearing rocks using machine learning methods. The proposed approach was based on nonlinear regression models providing a 20–75% increase in the accuracy of the filtration coefficient assessment compared with the current methodology. The work used different types of machine learning algorithms based on the gradient boosting technique, bagging technique, feed-forward neural networks, support vector machines, etc. The results of logging, core sampling, and hydrogeological studies obtained during the exploration stage of the Inkai deposit were used as the initial data. All used machine learning models demonstrated significantly better results than the old method. This resulted in improved results compared with previous studies. The LightGBM regressor demonstrated the best result (R2 = 0.710).

The nuclear decay of uranium is one of the cleanest ways to meet the growing energy demand. The uranium needed for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in-situ leaching (ISL). The effective use of ISL requires, among other things, the correct determination of the filtration characteristics of the host rocks. In Kazakhstan, this calculation is still based on methods that were developed more than 50 years ago, and in some cases, give inaccurate results. At the same time, knowledge of filtration characteristics is necessary for the calculation of recoverable reserves, prediction of production dynamics, calculation of the optimum number of wells, etc. This paper describes a method for calculating the filtration coefficient of ore-bearing rocks using machine learning. The proposed method is based on nonlinear regression models. It also allows the estimation of the filtration properties of rocks within the process acidification zone, where the existing method is not applicable. The proposed method applies to approximately half of the uranium mined in the world and makes it possible to significantly (by 22 %–70%) increase the accuracy of the filtration coefficient determination and, accordingly, improve the accuracy of recoverable reserves calculation and economic indicators of mining processes.

The review includes the results of analytical research on the problem of application of pancreatic islet encapsulation technologies for compensation of type 1 diabetes. We present a review of modern encapsulation technologies, approaches to encapsulation strategies, insulin replacement technologies: auto-, allo- and xenotransplantation; prospects for cell therapy for insulin-dependent conditions; modern approaches to β-cell encapsulation, possibilities of optimization of encapsulation biomaterials to increase survival of transplanted cells and reduce adverse consequences for the recipient. The main problems that need to be solved for effective transplantation of encapsulated islets of Langerhans are identified and the main strategies for translating the islet encapsulation technology into medical reality are outlined.