Websites form the foundation of the Internet, serving as platforms for disseminating information and accessing digital resources. They allow users to engage with a wide range of content and services, enhancing the Internet's utility for all. The aesthetics of a website play a crucial role in its overall effectiveness and can significantly impact user experience, engagement, and satisfaction. This paper examines the importance of website design aesthetics in enhancing user experience, given the increasing number of internet users worldwide. It emphasizes the significant impact of first impressions, often formed within 50 milliseconds, on users' perceptions of a website's appeal and usability. We introduce a novel method for measuring website aesthetics based on color harmony and font popularity, using fuzzy logic to predict aesthetic preferences. We collected our own dataset, consisting of nearly 200 popular and frequently used website designs, to ensure relevance and adaptability to the dynamic nature of web design trends. Dominant colors from website screenshots were extracted using k-means clustering. The findings aim to improve understanding of the relationship between aesthetics and usability in website design.

In the modern world, the issues of preserving and popularizing the cultural heritage of a country and region remain relevant. This issue became particularly acute during and after the COVID-19 pandemic, when museums and galleries existed without active visitor attendance for an extended period. Modern technologies play a significant role in addressing this problem, both by facilitating and simplifying daily routine processes and by globally impacting the conceptual challenges of attracting visitors and expanding the functionality of cultural institutions. This article presents a project to develop a virtual museum in the form of a mobile application. The application will recognize exhibits in real-time using the device's camera and display their 3D models and contextual information in augmented reality mode. The work reviews the use of virtual and augmented reality technologies in museum applications, as well as machine learning algorithms for various purposes. The authors report preliminary results of recognizing some exhibits from a partner museum, describing the applied methodology and analyzing the effectiveness of the approaches used. Additionally, the results of testing a mobile application with real-time recognition capabilities under museum conditions are presented.

Lung cancer represents a significant health challenge both in Kazakhstan and globally, standing out as one of the most fatal forms of cancer. Diagnosis of lung cancer is challenging as symptoms often remain undetectable in the early stages. Furthermore, lung cancer shares clinical features with various other pulmonary conditions, complicating its accurate identification. Accurate diagnosis typically involves lung puncture for subsequent biopsy, a highly invasive and painful procedure for the patient. Therefore, it is crucial to distinguish false positive cases in the diagnostic stage of computed tomography scans. We conducted a comparative analysis of five machine learning models (Logistic Regression, Decision Tree, Random Forest, SVM, and Naïve Bayes Algorithms) based on radiological features extracted from annotated computed tomography scans. We opted for classical machine learning methods because their decision-making process is easier to control compared to neural networks. We evaluated the models in terms of binary and multi-class classification to determine whether a given nodule is related to calcifications or cancers, as well as its classification according to Lung-RADS, enabling the management of whether further biopsy or only routine monitoring is necessary. We used Precision to evaluate the number of False Positive predictions in the binary classification task. Precision emerged as a pivotal metric in our assessment, offering insights into the number of false positive predictions specifically in the binary classification task. For the multi-class classification aspect, we turned to Quadratic Kappa, a robust measure that accounts for the ordinal nature of the Lung-RADS classes. Our analysis was underpinned by a combination of local Kazakhstani data and the publicly available LIDC-IDRI dataset, underscoring our commitment to leveraging diverse data sources to bolster diagnostic capabilities.

Gene expression analysis has become a key component in understanding cellular behavior, disease mechanisms, and drug response. The advent of high-throughput sequencing, particularly single-cell RNA sequencing (scRNAseq), has expanded our ability to study cellular heterogeneity to an unprecedented level. Clustering algorithms needed to group genes or cells with similar expression profiles have become invaluable for analyzing the massive data sets generated by these technologies. This article reviews various clustering methods applied to gene expression data, particularly single-cell RNA sequencing. The analysis covers traditional methods such as hierarchical clustering and k-means, as well as more advanced approaches such as model-based clustering, machine learning-based methods, and deep learning methods. The primary challenges encompass handling high-dimensional data, mitigating noise, and achieving scalability for large datasets. Moreover, new advancements such as multi-omics data integration, deep learning-based clustering, and federated learning offer potential enhancements in accuracy and biological relevance for clustering applications in gene expression research. The review concludes with a discussion of clustering algorithms in handling increasingly complex gene expression data for more accurate biological insights.

This paper introduces a quantitative model designed to enhance the accuracy of vehicle repair cost estimations in the context of insurance claims. Motivated by the ubiquity of vehicle ownership and the frequent occurrence of vehicular damage, our research focuses on the development of a robust framework that integrates multiple variables affecting repair costs. These include parts pricing, labor charges, and the specifics of insurance policies. The proposed model leverages mathematical and computer modeling techniques to synthesize these elements into a predictive tool that aims to provide fair and precise repair cost forecasts. This tool is intended to facilitate equitable interactions between insurers and policyholders, ensuring that compensation aligns closely with actual repair expenses. The utility of this model is particularly significant in improving transparency and efficiency in handling insurance claims, thereby supporting better financial risk management and contributing to the stability of the insurance sector.

This paper examines the use of convolutional neural networks (CNNs) to improve traffic sign recognition systems, precisely in non-weather conditions. An extended dataset of the German Traffic Sign Recognition Test (GTSRB), based on a new CNN model, is also used, which contains more than fifty thousand labeled images covering more than forty categories. The model presents adaptive object selection layers designed to eliminate visibility problems caused by weather factors such as rain, fog, and snow. Advanced data augmentation techniques are applied to model different weather scenarios, which increases the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. This study not only examines the theoretical and practical improvements provided by CNNs for traffic sign detection in unfavorable conditions, but also verifies the effectiveness of the model through metrics such as accuracy, responsiveness, and F1 score. The results confirm the effectiveness of the model in minimizing false positives and accurately identifying traffic signs. The paper emphasizes the importance of careful dataset preparation, model optimization and improved training to enhance the performance of the detection system. This has positive implications for intelligent transportation systems, autonomous driving and road safety, indicating future progress in robust traffic sign recognition technologies.

This study explores the influence of Russian words on the development of the Kazakh language in social networks. The rapid advancement of information technology significantly impacts the language used in online communications. While the chaotic nature of online interactions can complicate language use and create confusion, it also accelerates the spread of information in Kazakh. This research examines how foreign words affect modern Kazakh internet discourse, including direct borrowings that enter the language without modification, mixedphrases that retain the lexical and semantic properties of foreign words, the emergence of new abbreviations, and the influence of barbarisms. The study utilizes machine learning methods to analyse social media content from Instagram and Facebook. This approach enabled the processing of over 100,000 posts, revealing key linguistic shifts associated with the integration of Russian borrowings into Kazakh. The use of machine learning algorithms, such as the Naive Bayes classifier, automated the data analysis process and uncovered hidden patterns, providing a deeper understanding of how these borrowings affect the Kazakh language in the digital environment.

Numerical modelling of compressible flows around moving solids is important for engineering applications such as aerodynamic flutter, rocket engines and landing gear. The penalty function method is particularly effective when using orthogonal structural meshes within a finite difference scheme and is widely used to solve both laminar and turbulent flow problems. The method is based on the direct application of the Navier-Stokes equations with added sources, which allows the boundary conditions to be set indirectly. This method facilitates the imposition of Dirichlet boundary conditions but complicates the application of Neumann conditions. Nevertheless, the method works well with both types of boundary conditions, making it suitable for thermal and compressible flows where Neumann conditions are often used. Despite its flexibility, the method requires a high degree of data management and additional coding. This paper presents results of a recently developed higher-order method for compressible subsonic flows, demonstrating accurate modeling of moving objects without numerical noise. The method has been tested on stationary and moving objects over a wide range of Reynolds and Mach numbers.

Cardiovascular aging poses a significant threat to the health and quality of life of individuals, especially those aged 65 years and older. This paper presents a way to predict cardiovascular aging using mathematical modeling. The developed model integrates various physiological and behavioral factors including blood pressure, cholesterol level, body mass index, smoking, physical activity and alcohol. The model is based on the application of iteration and Runge Kutta methods, which allows us to describe the dynamic interaction of these factors over time. Validation of the model was performed based on data from clinical studies of elderly patients' health. The results show that the model has high accuracy in predicting the progression of cardiovascular aging and allows to identify patients with increased risk of cardiovascular diseases. The proposed prediction method may become a valuable tool for physicians, helping to develop personalized prevention and intervention strategies in geriatrics, which, in turn, may improve treatment outcomes and prolong the healthy life of patients. Further refinement of the model parameters and expansion of its application to broader populations are planned for the future.

Hardy's inequality was formulated in 1920 and finally proved in 1925. Since then, this inequality has been significantly developed. The first development was related to the consideration of more general weights. The next step was to use more general operators with other kernels instead of the Hardy operator. Currently, there are many works devoted to Hardy-type inequalities with iterated operators. Motivated by important applications, all these generalizations of Hardy's inequality are studied not only on the cone of non-negative functions, but also on the cone of monotone functions. In this paper, we consider the problem of finding necessary and sufficient conditions for the fulfillment of a weighted Hardy-type inequality on the cone of monotone sequences for 1<p≤q<∞. The main method for solving the problem is the reduction method, which, using the Sawyer principle, allows us to reduce a Hardy-type inequality on the cone of monotone sequences to some inequality for all non-negative sequences.

In this paper, we consider a spectral problem for the Laplace operator with more general boundary conditions in a unit disk B₁. In the special cases, the boundary conditions inlude periodic and Samarskii-Ionkin type boundary conditions. The main important property of our problem is its non-self-adjointness, which causes number of difficulties in their analytical and numerical solutions. For example, the Fourier method of separation of variables cannot be applied directly to our problem. Therefore, the possibility of separation of variables is justified in this paper. Namely, we present a method that reduces solution of the problem to a sequential solution of two classical local boundary value problems. By using this method, we construct all eigenfunctions and eigenvalues of the problem in explicit forms. Moreover, completeness of the system of the eigenfunctions is proved in L² (B₁). Notably, our result generalises the special case of the result on the two-dimensional periodic boundary value problem for the Laplace operator obtained in [1–2].

In this paper, we present a weighted Hardy identity related to the Baouendi-Grushin vector fields and its applications in the context of differential inequalities. By selecting appropriate parameters, the Hardy identity related to the Baouendi-Grushin operator implies numerous sharp remainder formulae for Hardy type inequalities. In the commutative case, we obtain improved weighted Hardy inequalities in the setting of the Euclidean space. For example, in a special case, by dropping non-negative remainder terms, related to the Baouendi-Grushin operator, and choosing suitable parameters our identity allows us to derive an improved critical Hardy inequality for the radial derivative operator with a sharp constant that does not depend on the topological dimension. We employ the method of factorizing differential expressions, as used by Gesztesy and Littlejohn in [1]. In this paper, we demonstrate the application of the factorization method in the noncommutative Baouendi-Grushin setting. As an application of the Hardy identity related to the Baouendi-Grushin vector fields, we establish a Hardy inequality for the generalized Landau Hamiltonian (or the twisted Laplacian) with remainder terms.

The purpose of the article is to introduce and explore a wide class of doubly close-to-starlike functions, while demonstrating a unified approach to solving a certain range of extreme problems. The article defines a reference function of a general form – a starlike function, on the basis of which classes of close-to-starlike and doubly close-to-starlike functions can be constructed. On the basis of a general support function containing three parameters and new estimates of analytical functions, a generalization of various classes of close-to-starlike and doubly close-to-starlike functions is introduced, considered in a number of articles published in recent years, including the introduced class contains a generalized class of typically real functions. The properties of the introduced class of functions are studied, for example, the growth theorem, estimates of the modulus of the logarithmic derivative of the function and the radius of starlikeness are obtained, in particular cases leading to previously known results and representing new results. All the results of the article are accurate.

This article presents the results of a study on the production of active material for supercapacitor electrodes from graphene-like carbon obtained from tea waste, carbonization at a temperature of 550°C, followed by thermochemical activation using potassium hydroxide in a ratio of 1:4 at a temperature of 850°C in a quartz tube furnace. The structure and morphology of the resulting porous graphene-like carbon based on tea waste were investigated using scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET), X-ray diffraction, and Raman spectroscopy. The surface area of activated porous graphene-like carbon from tea waste was 2407 m2/g. Electrochemical characterization of the assembled supercapacitor using GLC-TW was performed on an Elins P-40X electrochemical workstation and showed high specific capacitance values of 182 F/g, as well as a Coulombic efficiency of 96% at a current density of 1 A/g and the material also demonstrated a low charge transfer resistance of about 1.5 Ohms. These results highlight the effectiveness of using graphene-like carbon derived from tea waste, demonstrating its potential as a promising material for supercapacitors.

In the study, according to the data of the national hydrometeorological service Kazhydromet, over the past 30 years, engineering and climatic calculations of Shymkent have been carried out in the context of annual, monthly and daily values, where the main purpose according to the data obtained was to determine favorable, unfavorable, permissible and unacceptable orientations, which were calculated based on the values of solar radiation and wind regime of the specified territory. As a result of the engineering and climatic calculation, a final comprehensive assessment of the climate analysis was compiled, where the south-eastern direction was set as the sector of favorable orientation for Shymkent between 140–200°, the sector of unacceptable orientation was set to the northern direction between 320–40°, the sector of permissible orientation was set to the north-western direction between 270–320°, the sector of unfavorable orientation was set to the south-western The direction is between 200–270°, and the optimal orientation is set to the east direction between 40–140°. It is noted that the obtained results of this study are relevant and can be used further in the study of the heat transfer process in external wall enclosing structures, taking into account the influence of solar radiation in the hot climate of the Republic of Kazakhstan.

The main disadvantage of traditional metal oxides, including zinc oxide (ZnO), is poor absorption of light in the visible range. Among the many ways to solve this problem, the creation of their composition with noble metal nanoparticles (NPs) is the most interesting from both practical and theoretical points of view. Due to the effect of localized surface plasmon resonance (LSPR), characterized by a light absorption band in the visible range, the functionality of oxide semiconductors can be significantly improved. This work presents the results of preparation of composite films based on ZnO with nanoparticles of noble metals (silver Ag, gold Au and their alloy AgAu) by magnetron sputtering, as well as the analysis of the LSPR effect in these composites. In ZnO:AgNPs films, the LSPR absorption was observed at 475 nm, while for ZnO:AuNPs at 535 nm. The AuAg alloy nanoparticles exhibit a maximum in the intermediate interval of these two values, i.e., in the region of 508 nm. The obtained data indicate that by controlling the composition of noble metal nanoparticles it is possible to effectively control the light absorption band in the visible range.

Among a large number of physical and chemical methods for obtaining materials with various functional characteristics, one of the very interesting and simple methods is sol-gel technology. Materials synthesized using sol-gel technology have high chemical homogeneity, which is definitely a big plus. And by changing the initial environmental conditions and solution parameters, it is possible to control the size and shape of the particles obtained, as well as the pore structure of the synthesized products. At present, much attention is paid to the study of hierarchical structures based on tin dioxide. Since they are distinguished by a large surface area, stable physicochemical properties, low cost of production, environmental friendliness of the method, as well as high surface permeability and low density. This article describes the results of the synthesis of hierarchical structures in thin films based on tin dioxide. The initial solution is a lyophilic film-forming system SnCl₄/EtOH/ NH₄OH. A direct dependence of the formation of hierarchical structures on the volume of ammonium hydroxide additive was found. This helps to control the shape and size of the synthesized structures when changing the ratio of the initial precursors. And as a consequence, it allows influencing the final physical and chemical characteristics of the obtained samples for their further use as transparent conductive coatings, sensors for various gases (including toxic ones), in solar panels, etc.

The leading edge of construction advancements is represented by 3D printing which creates durable and elaborate structures and minimizes resource wastage. As a viable substitute for regular cement materials highlights the ecological benefits and strong mechanical traits of fly ash geopolymers. This study investigates how the addition of polyethylene (PE) fibers alters these properties and how varying concentrations influence the flow behavior and capability of producing the composite material. The analysis of non-Newtonian behavior in these fiber-reinforced geopolymers is conducted using the Herschel-Bulkley model. By precisely measuring critical rheological factors  such as viscosity and flow behavior researchers can evaluate how they influence 3D printing processes. This research reveals that adding PE fibers boosts the material’s strength and improves resistance against cracking while also elevating the viscosity and yield stress that can hinder its passage through the printer’s nozzle. An optimal blend of fiber content emerges from controlled tests that align increased durability with controllable extrusion flow and structure reliability. The results offer deep practical applications that reveal methods for producing geopolymers that can maintain strength while meeting the exacting requirements of 3D printing methods. Research deepens the grasp of how adding fibers alters the properties of geopolymers and enriches the overall dialogue on green building materials. It opens doors for subsequent analysis of complex fiber systems and creative additive practices to boost the effectiveness and resilience of construction materials in practical use.

Silicon, one of the most abundant and cost-effective materials on Earth, holds significant promise for applications in water splitting and photovoltaics due to its suitable bandgap energy of approximately 1.12 eV, which allows absorption of ultraviolet, visible, and infrared light. However, the high reflectivity (~25%) of flat silicon surfaces limits its conversion efficiency, making it less efficient for photoelectrochemical (PEC) processes. To address this, nanostructured silicon has emerged as a solution to enhance light absorption, reduce substrate resistance, and improve hydrogen production efficiency. In this study, we fabricated nanostructured silicon photoelectrodes using the metal-assisted chemical etching (MACE) method. The resulting black silicon (b-Si) electrodes demonstrated superior light-harvesting capabilities, leading to significantly enhanced photocurrent densities. Notably, the b-Si photoelectrodes achieved a photocurrent density of 800 μA/cm² at 0V vs RHE (reversible hydrogen electrode), compared to 200 μA/cm² for planar silicon. Furthermore, the b-Si electrodes exhibited excellent long-term stability under continuous illumination for 16 hours. These results highlight the potential of nanostructured silicon as an efficient and stable material for solar-driven PEC water splitting and related renewable energy applications.

This study is devoted to optimizing the sustainability of production processes at «KazakhOil Aktobe» LLP, one of the leading companies in the oil and gas industry in Kazakhstan. In the context of global environmental challenges and the need to reduce the carbon footprint, the main goal of the study is to develop recommendations for improving the environmental and economic performance of the company. Analysis of current practices showed potential for significant improvements in energy efficiency, water management and waste management. The study results demonstrate that implementing energy management systems (EMS) and using renewable energy can reduce operating costs by 20–25% and carbon emissions by 30–35%. The introduction of modern wastewater treatment and recycling methods can reduce freshwater consumption by 30–40%, and the use of biotechnological waste management methods, such as anaerobic digestion, converts waste into useful resources and reduces its volume by 40–50%. The proposed recommendations provide concrete steps to achieve sustainable development and can be useful not only for «KazakhOil Aktobe» LLP, but also for other companies in the industry. The study confirms the need to implement advanced practices and technologies to promote environmental responsibility and economic prosperity in Kazakhstan’s oil and gas industry.

Natural surfactants such as gum and asphaltene in crude oil can form stable emulsions. Emulsions can cause significantly harm to crude oil storage, processing, product quality and equipment. Therefore, oilfield crude oil must undergo demulsification before being exported. However, conventional demulsifiers are difficult to dehydrate at low temperatures and the mechanism of action of low-temperature demulsifiers on oil-water interfaces is not clear.Therefore, this paper focused on the three low-temperature demulsifiers AR101, AR902, and AE405 selected from the Y block in X region, and used the interfacial rheological system of the interfacial tension meter to explore the low-temperature demulsification mechanism from the changes in oil-water interfacial tension. The results indicate that interfacial tension has a certain impact on crude oil demulsification, and the lower the interfacial tension value, the better the demulsification effect. As the concentration of the demulsifier increases, the interfacial tension value first decreases and then remains stable, indicating the existence of an optimal concentration that minimizes the interfacial tension. As the demulsification temperature increases, the interfacial tension between oil and water decreases, and the time required to reach stability becomes shorter, resulting in faster demulsification speed and better effectiveness. By studying the mechanism of low-temperature demulsification, theoretical guidance is provided for the on-site application of demulsifiers in oil fields.

This project explores the application of hydraulic fracturing methods to enhance reservoir productivity at the Amangeldi field. The study focused on the advanced HIWAY method, which optimizes proppant placement to improve hydrocarbon recovery. Mathematical models were developed to evaluate key parameters of this technique and implemented using FracPro software, enabling detailed simulation and analysis. Data for this study were gathered from publicly available sources, including OnePetro and ScienceDirect, ensuring a comprehensive review of current practices and innovations. Additional information was obtained during dual training at the Amangeldi field, providing practical insights and aligning the models with field-specific conditions. Contributions from scientific journals further enriched the study, supporting the integration of theoretical and empirical approaches. The findings aim to guide future hydraulic fracturing operations and highlight the potential of the HIWAY method to maximize efficiency, reduce operational costs, and mitigate environmental impact. This project underscores the importance of combining advanced modeling with hands-on field experience to address challenges in reservoir management.

The strategic utilization of Cloud Services by Airline company in Kazakhstan (hereinafter Airline company) to optimize report generation processes, alleviate strain on its ERP (Enterprise Resource Planning) system, and enhance overall data management efficiency is examined comprehensively in this study. With the increasing volume and complexity of data generated in modern business operations, the need for scalable and efficient data management solutions has become paramount. Through interviews with key stakeholders, analysis of system performance metrics, and observation of implementation processes, a thorough understanding of the challenges and strategies associated with cloud integration is attained. The findings underscore the pivotal role of Cloud Solutions in improving data management practices, ensuring reliable report generation processes, and facilitating seamless scalability to meet evolving business needs. The study demonstrates a significant reduction in report generation times (95% improvement) and highlights the challenges and mitigation strategies during the migration process. This case study offers valuable insights for organizations seeking to enhance their data management capabilities and leverage cloud technology for strategic advantage, showcasing the transformative potential of cloud-based solutions in optimizing operations, fostering innovation, and driving sustainable growth in today’s dynamic business landscape.

This study investigates the influence of digital marketing on the business performance of Kazakh Small and Medium-sized Enterprises (SMEs) managed by Muslims. The research employs a mixed-method approach, incorporating quantitative and qualitative data collection techniques. The findings reveal that social media marketing, search engine optimization (SEO), and email marketing are the most prevalent digital marketing practices adopted by Kazakh SMEs. However, challenges such as limited marketing budgets, difficulty measuring return on investment, and keeping up with trends hinder effective implementation. Despite these challenges, SMEs acknowledge the positive impact of digital marketing on their performance, reflected in areas like sales growth, customer acquisition, and brand awareness. The study concludes that digital marketing presents a powerful tool for Kazakh SMEs to enhance visibility, engage customers, and drive conversions, ultimately contributing to sustainable growth in the digital marketplace.