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Nazarbayev University Repository (NUR) is an institutional electronic archive designed for the long-term preservation, aggregation, and dissemination of scientific research outcomes and intellectual property produced by the Nazarbayev University community and affiliated organizations.

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Access status: Open Access ,
Issue 1 - Nation-Building Narratives in the Kazakh Literature Curriculum for 11th Graders and Their Moral Towards Women
(2025-08) Azhibayeva, Mira
This paper takes as a subject for study the textbook for the governmental program in teaching Kazakh literature to 11th graders, looking at Smagul Elubay’s Hansulu and Talasbek Asemkulov’s Bektory female characters, and tries to investigate them through the theoretical framework on nation-building (Brubaker, Abashin, Anderson) and gender studies. These literary pieces were analysed according to the language utilized, the roles of main characters, their behavior and perspectives, and the issues raised in the course of the storytelling. The goal is to explore how moral narratives towards women are presented to the youth in the process of nation-building. There are brief summaries provided of the excerpts. The concepts of pa’kness (sinlessness) and idealized heroine are applied as a parallel for the stories about Hansulu and Bektory. This work eventually considers a modern woman in a world of western-style global norms competing with traditional ones, arguing for the search of balance between the two.
Access status: Embargo ,
Synthesis, characterization and photocatalytic properties of BiVO4 thin films
(Nazarbayev University School of Sciences and Humanities, 2026-05-12) Zhadyrassyn, Aruzhan; Kaikanov, Marat; Abduvalov, Alshyn
As the world’s population grows and industries expand, global energy demand continues to rise, making the need for clean, sustainable energy sources more urgent than ever. Hydrogen is considered a promising alternative to fossil fuels due to its high energy density and clean energy conversion (Yao et al., 2019). Among the different hydrogen production methods, photoelectrochemical (PEC) water splitting has emerged as a renewable approach that employs solar energy to produce hydrogen (Chen et al.,2022). Bismuth vanadate (BiVO4) is one of the widely studied materials for PEC applications due to its suitable band gap, chemical stability, and cost-effectiveness (Tayebi and Lee, 2019). However, its efficiency is still limited by poor charge carrier mobility and high recombination rate (Duan et al., 2022). Therefore, improving the intrinsic properties of BiVO4 remains an important research challenge. In this thesis, the effect of key fabrication parameters, including annealing temperature and thickness of the film, on the properties of BiVO4 thin films prepared by spin-coating method were systematically studied. The optimization of annealing temperature and film thickness was found to play a crucial role in improving crystallinity, light absorption, charge carrier mobility, and overall PEC performance. In addition, the effect of intense pulsed ion beam (IPIB) irradiation on BiVO4 photoelectrodes was investigated as a novel research data. The radiation tolerance of BiVO4 under IPIB irradiation was studied for the first time, and changes in film properties was quantitatively evaluated. Furthermore, the effect of IPIB irradiation on the PEC properties of BiVO4 was analyzed. These findings contribute to development of efficient BiVO4 based photoelectrodes for solar driven hydrogen production. Chapter 1 introduces the global need for sustainable hydrogen production and presents PEC water splitting as a promising renewable approach. It also outlines the significance of BiVO4 as a photoanode material, its current limitations, and the motivation for studying its performance using intense pulsed ion beam irradiation. Chapter 2 presents the theoretical background of PEC water splitting and reviews the development of BiVO4 photoanodes for hydrogen production. It also discusses the synthesis methods of BiVO4 thin films, strategies for improving their PEC per 2 formance, and other applications of BiVO4 semiconductor material. Chapter 3 describes the materials preparation, experimental procedures, and equipment used for the synthesis, annealing and characterization of BiVO4 thin films. It outlines the PEC measurement techniques and IPIB irradiation method applied to evaluate the performance of the photoelectrodes. Chapter 4 investigates the influence of annealing temperature and spin-coating speed on the structural, optical and photoelectrochemical behavior of BiVO4 thinfilms. The results highlight those optimized conditions significantly improve crystallinity, light absorption, charge transport and overall PEC performance. Chapter 5 focuses on the effect of IPIB irradiation on BiVO4 thin films and assessing radiation tolerance. It provides detailed analysis on how IPIB irradiation influence morphological, structural, optical and overall PEC properties of BiVO4 thin films. Әлем халқының саны өсіп, өнеркәсіп қарқынды дамып келе жатқандықтан, жаһандық энергияға деген сұраныс артып, таза әрі тұрақты энергия көздеріне деген қажеттілік бұрынғыдан да өзекті болып отыр. Сутегі жоғары энергия тығыздығы және энергияны таза түрлендіру мүмкіндігіне байланысты қазба отындарға перспективалы балама ретінде қарастырылады (Yao et al., 2019). Сутегін өндірудің әртүрлі әдістерінің ішінде фотоэлектрохимиялық (PEC) суды ыдырату күн энергиясын пайдаланып сутегін өндіруге мүмкіндік беретін жаңартылатын әдіс ретінде кеңінен зерттелуде (Chen et al., 2022). Висмут ванадаты (BiVO4) қолайлы тыйым салынған аймақ ені, химиялық тұрақтылығы және экономикалық тиімділігіне байланысты PEC қолданбалары үшін кеңінен зерттелетін материалдардың бірі болып табылады (Tayebi and Lee, 2019). Алайда оның тиімділігі заряд тасымалдаушылардың төмен қозғалғыштығымен және зарядтардың жоғары рекомбинациялану жылдамдығымен шектеледі (Duan et al., 2022). Сондықтан BiVO4-тің ішкі қасиеттерін жақсарту маңызды ғылыми-зерттеу міндеттерінің бірі болып табылады. Бұл диссертацияда айналдыру арқылы қаптау (spin-coating) әдісімен дайындалған BiVO4 жұқа қабықшаларының қасиеттеріне негізгі дайындау параметрлерінің, соның ішінде күйдіру температурасы мен қабықша қалыңдығының әсері жүйелі түрде зерттелді. Күйдіру температурасы мен қабықша қалыңдығын оңтайландыру кристалдылықты, жарықты жұтуды, заряд тасымалдаушылардың қозғалғыштығын және жалпы PEC сипаттамаларын жақсартуда маңызды рөл атқаратыны анықталды. Сонымен қатар, қарқынды импульстік иондық сәулемен (IPIB) сәулелендірудің BiVO4 фотоэлектродтарына әсері жаңа зерттеу бағыты ретінде қарастырылды. IPIB сәулелендіру жағдайындағы BiVO4-тің радиациялық төзімділігі алғаш рет зерттеліп, қабықша қасиеттеріндегі өзгерістер сандық тұрғыдан бағаланды. Бұдан бөлек, IPIB сәулелендірудің BiVO4-тің фотоэлектрохимиялық қасиеттеріне әсері талданды. Алынған нәтижелер күн энергиясы негізінде сутегін өндіруге арналған тиімді BiVO4 негізіндегі фотоэлектродтарды дамытуға деген үлесі зерттелді.
Access status: Metadata only ,
Global Flood Vulnerability Model: Building-Level Assessment Using Multi-Source Remote Sensing
(Remote Sensing, 2026-01-01) Karaca F.; Varol H.A.; Satybaldiyeva D.; Serikkyzy A.; Sharipova A.; Olagunju S.O.
Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to water, building height, and basement depth) through geographic context classification to quantify vulnerability from terrain and structural characteristics across coastal, fluvial, and pluvial settings. Building heights are extracted primarily from the Global Building Atlas, with gaps filled using a ConvNeXt neural network trained on high-resolution Light Detection and Ranging (LiDAR) ground truth from four cities (within-city MAE 1.35 1.91 m, cross-city MAE 2.05 3.47 m). Terrain metrics are derived from a combination of hierarchical digital elevation models (DEM) (USGS 3DEP 10 m, AHN LiDAR 0.5 m, UK Environment Agency DTM 1 m, Australia 5 m) and global datasets (NASADEM 30 m, Copernicus GLO-30). Hydrographic networks are sourced from OpenStreetMap and Natural Earth. Implementation through Google Earth Engine requires only coordinates as input, returning a five-level vulnerability index with multi-hazard decomposition (fluvial, coastal, pluvial) and SHapley Additive exPlanations (SHAP)-based attribution identifying dominant drivers. Validation across 183 independent locations in Germany, UK, and USA demonstrates robust performance: Area Under Curve 0.855 for separating flooded from non-flooded sites, weighted Cohen s kappa 0.493 across regulatory zones, and Spearman ? 0.746 against Federal Emergency Management Agency (FEMA) classifications. Sensitivity analysis across 625 parameter configurations confirms stability, and DEM resolution experiments show that global 30 m elevation data produces category reclassification in only 5.3 8.6% of locations compared to high-resolution sources. Application to the 2024 Kazakhstan floods identifies 118 high-vulnerability locations across 581 assessment points, with vulnerability patterns matching documented inundation. GFVM advances remote sensing applications for disaster risk assessment by demonstrating that multi-source geospatial data fusion enables building-level vulnerability screening without local calibration or field surveys. © 2026 by the authors.
Access status: Metadata only ,
Enhanced Reservoir Performance Prediction Using a Pseudo-Pressure-Based Capacitance Resistance Model for Immiscible Gas Injection
(Energies, 2026-01-01) Pourafshary P.; Zhanabayeva M.
The capacitance resistance model (CRM) is an analytical tool widely used to forecast reservoir performance in enhanced oil recovery (EOR) methods. By representing flow dynamics and the connectivity between injection and production wells through the parameter of interwell connectivity, CRM offers fast computational processing and minimal input data requirements. These advantages make CRM a practical alternative for rapid reservoir analysis, especially when full-scale numerical simulations are infeasible due to time and budget constraints. CRM, rooted in material balance and productivity equations, uses injection/production rates and bottom-hole pressure data to construct reservoir models through optimization techniques, which can then be combined with oil fractional flow models for predictive purposes. Initially designed for waterflooding operations, CRM has seen limited but promising applications in gas injection projects, where research remains incomplete. This study presents a new formulation of CRM tailored for immiscible gas injection, incorporating the pseudo-pressure concept coupled with a saturation profile. The pseudo-pressure concept is a mathematical transformation that linearizes gas flow equations by accounting for variations in gas compressibility and viscosity with pressure. The proposed CRM was evaluated using a PUNQ-S3 benchmark reservoir model in the CMG IMEX black oil simulator, involving two injectors and four producers. History- matching results for fluid production rates showed that the newly developed CRM achieved the lowest NRMSE, outperforming other CRM models across a wide range of reservoir properties. Sensitivity analyses were conducted to examine the effects of gas and oil PVT properties on the model s performance. The newly developed CRM, incorporating the pseudo-pressure concept and saturation profiles, demonstrates superior performance in predicting fluid production rates, achieving an average NRMSE of 15.3% in a base case scenario, compared to other tested CRM models. Additionally, the sensitivity analysis on the effect of fluid properties shows that higher gas viscosity, lower gas formation volume factor, and increasing oil API gravity improve the CRM model s performance, and under all tested conditions the newly developed CRM provides the most accurate production history match. This study not only establishes the new CRM as a robust and accurate predictive tool for immiscible gas injection but also provides a comprehensive discussion on reservoir parameter ranges and model limitations, advancing the applicability of CRM in EOR processes. © 2026 by the authors.
Access status: Metadata only ,
Advanced security in fog environments using encryption and adaptive user activity tracking
(Scientific Reports, 2026-01-01) Kant S.; Abdallah H.A.; Agarwal S.; Agarwal N.; Razaque A.; Rai H.M.
The use of fog computing is on the rise, adding new dimensions to security and, more specifically, to data protection in fog cloud environments. Storing fog-computing data increases the likelihood of data exploitation when it is uploaded to fog-computing storage. In this paper, Adaptable User Activity Tracking (ASUT) is introduced, integrating AES-256, SHA-512, and user activity tracking (UAT). The need to integrate activity monitoring into the ASUT to collect statistical information on user actions has been stated. The file uploaded to the fog computing storage is encrypted using a 256-bit AES key. Then, this key is hashed with SHA-512 and stored in the fog cloud. The AES expansion is used to decrypt the data, while the SHA-512 hash of the AES key is used to verify that the user-provided key matches the original before decryption proceeds the hash is irreversible, and the original key is never stored in plaintext. The user must know the initial key to access the file further. When the client re-enters the fog, the algorithm compares the hashes of the two: the initial and the second entry keys. In parallel, the fog cloud broadcasts the user s actions to track any abnormal activity on the account. This mechanism helps mitigate risks of unauthorized data access and suggests ways to improve user protection. The proposed ASUT is designed using Python and PHP. Experimental results show that ASUT achieves 43.39% faster encryption, 66% faster decryption, and 19.86% higher throughput compared to the best-performing competing method, indicating improved computational efficiency and practical feasibility under the evaluated conditions. © The Author(s) 2026.