Search Results (1,378)

A synthetic methodology of the CuAAC “click” approach was exploited for the construction of 1,2-azolyltriazole quinine derivatives by the reaction of O-propargylquinine with azidomethyl-1,2-azoles in methanol. Quinine–piperidine and quinine–anabasine conjugates were obtained using a chloroacetate linker by reacting quinine chloroacetate with piperidine or anabasine in a diethyl ether medium. Cinchophene ester was obtained by the acylation of quinine with cinchophen acid chloride in methylene chloride. The antibacterial, fungicidal, analgesic and cytotoxic properties of the obtained compounds were examined. Full article

In the process of producing ferroalloys, a large amount of secondary raw materials is formed, including slag, aspiration dusts and sludge. The recycling of secondary raw materials can create resources and bring environmental and economic benefits. Wet secondary raw materials (WSRMs) are characterized by a high chromium oxide content (averaging 24%), but due to their high moisture levels, they cannot be directly used in arc furnaces. As a strategic approach, mixing WSRMs with drier, more chromium-rich dusts (up to 45% Cr₂O₃) has been proposed. This not only reduces the overall moisture content of the mixture but also enhances the metallurgical value of the charge material. This paper presents the results of laboratory studies on the agglomeration of secondary wet raw materials using briquetting, extrusion and pelletizing methods. The main factors influencing the quality of the resulting product were analyzed, including the method of agglomeration, the composition of the mixture, as well as the type and dosage of the binder component. The strength characteristics of the finished agglomerated samples were evaluated in terms of resistance to splitting, impact loads and falling. Notably, the selected binders are organic and polymer substances capable of complete combustion under metallurgical smelting conditions. Full article

Systemic sclerosis (SSc) is an autoimmune connective tissue disorder characterized by vascular abnormalities, immune dysfunction, and progressive fibrosis. One of the most common manifestations of SSc is interstitial lung disease (ILD), known by a progressive course leading to significant morbidity and mortality. Aim: to investigate autoantibodies, cytokines, and genetic markers in SSc-ILD through a systematic review and analysis of a Kazakh cohort of SSc-ILD patients. Methods: A PubMed search over the past 10 years was performed with “SSc-ILD”, “autoantibodies”, “cytokines”, and “genes”. Thirty patients with SSc were assessed for lung involvement, EScSG score, and modified Rodnan skin score. IL-6 was measured by ELISA, antinuclear factor on HEp-2 cells by indirect immunofluorescence, and specific autoantibodies by immunoblotting. Genetic analysis was performed using a 120-gene AmpliSeq panel on the Ion Proton platform. Results: The literature review identified 361 articles, 26 addressed autoantibodies, 20 genetic variants, and 12 cytokine profiles. Elevated levels of IL-6, TGF-β, IL-33, and TNF-α were linked to SSc. Based on the results of the systemic review, we created a preliminary immunogenic panel for SSc-ILD with following analysis in Kazakh patients with SSc (n = 30). Fourteen of them (46.7%) demonstrated signs of ILD and/or lung hypertension, with frequent detection of antibodies such as Scl-70, U1-snRNP, SS-A, and genetic variants in SAMD9L, REL, IRAK1, LY96, IL6R, ITGA2B, AIRE, TREX1, and CD40 genes. Conclusions: Current research confirmed the presence of the broad range of autoantibodies and variations in IRAK1, TNFAIP3, SAMD9L, REL, IRAK1, LY96, IL6R, ITGA2B, AIRE, TREX1, CD40 genes in of Kazakhstani cohort of SSc-ILD patients. Full article

This study explores fine-tuning methods and dataset structures for multilingual neural machine translation using the No Language Left Behind model, with a case study on Kazakh, English, and Russian. We compare single-stage and two-stage fine-tuning approaches, as well as triplet versus non-triplet dataset configurations, to improve translation quality. A high-quality, 50,000-triplet dataset in information technology domain, manually translated and expert-validated, serves as the in-domain benchmark, complemented by out-of-domain corpora like KazParC. Evaluations using BLEU, chrF, METEOR, and TER metrics reveal that single-stage fine-tuning excels for low-resource pairs (e.g., 0.48 BLEU, 0.77 chrF for Kazakh → Russian), while two-stage fine-tuning benefits high-resource pairs (Russian → English). Triplet datasets improve cross-linguistic consistency compared with non-triplet structures. Our reproducible framework offers practical guidance for adapting neural machine translation to technical domains and low-resource languages. Full article

Over the past few decades, lithium-ion batteries (LIBs) have gained significant attention due to their inherent potential for environmental sustainability and unparalleled energy storage efficiency. Meanwhile, polymer electrolytes have gained popularity in several fields due to their ability to adapt to various battery geometries, enhanced safety features, greater thermal stability, and effectiveness in reducing dendrite growth on the anode. However, their relatively low ionic conductivity compared to liquid electrolytes has limited their application in high-performance devices. This limitation has led to recent studies revolving around the development of poly(ionic liquids) (PILs), particularly imidazolium-mediated polymer backbones as novel electrolyte materials, which can increase the conductivity with fine-tuning structural benefits, while maintaining the advantages of both solid and gel electrolytes. In this study, a curated dataset of 120 data points representing eight different polymers was used to predict ionic conductivity in imidazolium-based PILs as well as the emerging ionene substructures. For this purpose, four ML models: CatBoost, Random Forest, XGBoost, and LightGBM were employed by incorporating chemical structure and temperature as the models’ inputs. The best-performing model was further employed to estimate the conductivity of novel ionenes, offering insights into the potential of advanced polymer architectures for next-generation LIB electrolytes. This approach provides a cost-effective and intelligent pathway to accelerate the design of high-performance electrolyte materials. Full article

Biotechnological research increasingly focuses on developing new drugs to counter the rise of antibiotic-resistant strains in hospitals. This study aimed to create bacterial lysates from antibiotic-resistant pathogens isolated from patients and medical instruments across hospital departments. Identification was performed based on morphological, cultural, and biochemical characteristics, as well as 16S rRNA gene sequencing using the BLAST algorithm. Strain viability was assessed using the Miles and Misra method, while sensitivity to eight antibacterial drug groups and biosafety between cultures were evaluated using agar diffusion. From 15 clinical sources, 25 pure isolates were obtained, and their phenotypic and genotypic properties were studied. Carbohydrate fermentation testing confirmed that the isolates belonged to the genera Escherichia, Citrobacter, Klebsiella, Acinetobacter, Pseudomonas, Staphylococcus, Haemophilus, and Streptococcus. The cultures exhibited good viability (10⁹–10¹⁰ CFU/mL) and compatibility with each other. Based on prevalence and clinical significance, three predominant hospital pathogens (Klebsiella pneumoniae 12 BL, Pseudomonas aeruginosa 3 BL, and Acinetobacter baumannii 24 BL) were selected to develop a bacterial lysate consortium. Lysates were prepared with physical disruption using a French press homogenizer. The resulting product holds industrial value and may stimulate the immune system to combat respiratory pathogens prevalent in Kazakhstan’s healthcare settings. Full article

Background: Access to safe and reliable water and sanitation remains a critical public health and development challenge, with rural and low-income communities being disproportionately affected by inadequate services and heightened exposure to waterborne diseases. Despite global efforts and infrastructure-based progress indicators, significant disparities persist, and these often overlook users’ perceptions of water quality, reliability, and safety. This study explores the determinants of household satisfaction with drinking water in rural areas, comparing subjective user feedback with official access data to reveal gaps in current monitoring approaches and support more equitable, user-centered water governance. Methods: This study was conducted in Kazakhstan’s Atyrau Region, where 1361 residents from 86 rural villages participated in a structured survey assessing household access to drinking water and perceptions of its quality. Data were analyzed using descriptive statistics and multinomial logistic regression to identify key predictors of user satisfaction, with results compared against official records to evaluate discrepancies between reported experiences and administrative data. Results: The field survey results revealed substantial discrepancies between official statistics and residents’ reports, with only 58.1% of respondents having in-house tap water access despite claims of universal coverage. Multinomial logistic regression analysis identified key predictors of user satisfaction, showing that uninterrupted supply and the absence of complaints about turbidity, odor, or taste significantly increased the likelihood of higher satisfaction levels with drinking water quality. Conclusions: This study underscores the critical need to align official water access statistics with household-level experiences, revealing that user satisfaction—strongly influenced by supply reliability and sensory water quality—is essential for achieving equitable and effective rural water governance. Full article

This article addresses the task of building personalized educational recommendations based on a heterogeneous knowledge graph that integrates data from university curricula, job vacancies, and online courses. To solve the problem of course recommendations by their relevance to a user’s competencies, a graph neural network (GNN)-based approach is proposed, specifically utilizing and comparing the Heterogeneous Graph Transformer (HGT) architecture, Graph Sample and Aggregate network (GraphSAGE), and Heterogeneous Graph Attention Network (HAN). Experiments were conducted on a heterogeneous graph comprising various node and relation types. The models were evaluated using regression and ranking metrics. The results demonstrated the superiority of the HGT-based recommendation model as a link regression task, especially in terms of ranking metrics, confirming its suitability for generating accurate and interpretable recommendations in educational systems. The proposed approach can be useful for developing adaptive learning recommendations aligned with users’ career goals. Full article

Barium titanate (BaTiO₃) has long been recognized as a promising photocatalyst for solar-driven water splitting due to its unique ferroelectric, piezoelectric, and electronic properties. This review provides a comprehensive analysis of atomistic simulation studies of BaTiO₃, highlighting the role of density functional theory (DFT), ab initio molecular dynamics (MD), and classical all-atom MD in exploring its photocatalytic behavior, in line with various experimental findings. DFT studies have offered valuable insights into the electronic structure, density of state, optical properties, bandgap engineering, and other features of BaTiO₃, while MD simulations have enabled dynamic understanding of water-splitting mechanisms at finite temperatures. Experimental studies demonstrate photocatalytic water decomposition and certain modifications, often accompanied by schematic diagrams illustrating the principles. This review discusses the impact of doping, surface modifications, and defect engineering on enhancing charge separation and reaction kinetics. Key findings from recent computational works are summarized, offering a deeper understanding of BaTiO₃’s photocatalytic activity. This study underscores the significance of advanced multiscale simulation techniques for optimizing BaTiO₃ for solar water splitting and provides perspectives on future research in developing high-performance photocatalytic materials. Full article

One of the promising research areas involving carborane derivatives is boron neutron capture therapy (BNCT). Due to the high boron atom content in carborane molecules, these compounds are considered potential candidates for BNCT-based cancer treatment. Despite ongoing studies on various biologically active carboranyl-containing compounds, the search continues for substances that meet the stringent requirements of effective BNCT agents. In this study, the synthesis of carboranyl-containing derivatives of β-arylaliphatic acids is described, along with the investigation of their reactivity with primary and secondary amines, as well as with metals and their hydroxides. The molecular structures of the synthesized compounds were confirmed using Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, elemental analysis, and mass spectrometry (LC-MS). Cytotoxicity of the water-soluble compound potassium 3-(2-isopropyl-1,2-dicarba-closo-dodecaboran-1-yl)-3-phenylpropanoate was evaluated using several cell lines, including HdFn and MCF-7. Full article

Reducing post-harvest losses of cereal crops is a key challenge for ensuring global food security amid the limited arable land and growing population. This study investigates the effectiveness of electron beam irradiation (5 MeV, ILU-10 accelerator) as a physical decontamination method for various cereal crops cultivated in Kazakhstan. Samples were irradiated at doses ranging from 1 to 5 kGy, and microbiological indicators—including Quantity of Mesophilic Aerobic and Facultative Anaerobic Microorganisms (QMAFAnM), yeasts, and molds—were quantified according to national standards. Experimental results demonstrated an exponential decline in microbial contamination, with a >99% reduction achieved at doses of 4–5 kGy. The modeled inactivation kinetics showed strong agreement with the experimental data: R² = 0.995 for QMAFAnM and R² = 0.948 for mold, confirming the reliability of the exponential decay models. Additionally, key quality parameters—including protein content, moisture, and gluten—were evaluated post-irradiation. The results showed that protein levels remained largely stable across all doses, while slight but statistically insignificant fluctuations were observed in moisture and gluten contents. Principal component analysis and scatterplot matrix visualization confirmed clustering patterns related to radiation dose and crop type. The findings substantiate the feasibility of electron beam treatment as a scalable and safe technology for improving the microbiological quality and storage stability of cereal crops. Full article

This article analyzes statistics on the failure of technological equipment, assemblies, and mechanisms of agricultural (and other) machines associated with the breakdown or failure of gear pumps. It was found that the leading causes of gear pump failures are the opening of gear teeth contact during pump operation, poor assembly, wear of bushings, thrust washers, and gear teeth. It has also been found that there is a problem related to the restoration, repair, and manufacture of parts in the conditions of enterprises serving the agro-industrial complex of the Republic of Kazakhstan (AIC RK). This is due to the lack of necessary technological equipment, tools, and instruments, as well as centralized repair and restoration bases equipped with the required equipment. This work proposes to solve this problem by applying AM technologies to the repair and manufacture of parts for agricultural machinery and equipment. The study results on the stress–strain state of support bushings under various pressures are presented, showing that a fully filled bushing has the lowest stresses and strains. It was also found that bushings with 50% filling and fully filled bushings have similar stress and strain values under the same pressure. The difference between them is insignificant, especially when compared to bushings with lower filling. This means that filling the bushing by more than 50% does not provide a significant additional reduction in stresses. In terms of material and printing time savings, 50% filling may also be the optimal option. Full article

This study evaluates the impact of digital systems and IT audit frameworks on tax performance and integrity within tax administrations. Using international data from organizations like the World Bank, OECD (Organisation for Economic Co-operation and Development), and IMF (International Monetary Fund), the research examines the relationship between tax revenue as a percentage of GDP, digital infrastructure, corruption perception, e-government development, and cybersecurity readiness. Quantitative analysis, including correlation, regression, and clustering methods, reveals a strong positive relationship between digital maturity, e-governance, and tax performance. Countries with advanced digital governance systems and robust IT audit frameworks, such as COBIT, tend to show higher tax revenues and lower corruption levels. The study finds that e-government development and anti-corruption measures explain over 40% of the variance in tax performance. Cluster analysis distinguishes between digitally advanced, high-compliance countries and those lagging in IT adoption. The findings suggest that digital transformation strengthens fiscal integrity by automating compliance and reducing human contact, which in turn mitigates bribery risks and enhances fraud detection. The study highlights the need for adopting international best practices to guide the digitalization of tax administrations, improving efficiency, transparency, and trust in public finance. Full article

The diffusion coefficient (DC) of CO₂ in brine is a key parameter in geological carbon sequestration and CO₂-Enhanced Oil Recovery (EOR), as it governs mass transfer efficiency and storage capacity. This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. The dataset, comprising 176 data points, spans pressures from 0.10 to 30.00 MPa, temperatures from 286.15 to 398.00 K, salinities from 0.00 to 6.76 mol/L, and DC values from 0.13 to 4.50 × 10⁻⁹ m²/s. The data was split into 80% for training and 20% for testing to ensure reliable model evaluation. Model performance was assessed using R², RMSE, and MAE. The RF model demonstrated the best performance, with an R² of 0.95, an RMSE of 0.03, and an MAE of 0.11 on the test set, indicating high predictive accuracy and generalization capability. In comparison, GBR achieved an R² of 0.925, and XGBoost achieved an R² of 0.91 on the test set. Feature importance analysis consistently identified temperature as the most influential factor, followed by salinity and pressure. This study highlights the potential of ML models for predicting CO₂ diffusion in brine, providing a robust, data-driven framework for optimizing CO₂-EOR processes and carbon storage strategies. The findings underscore the critical role of temperature in diffusion behavior, offering valuable insights for future modeling and operational applications. Full article