DSpace Repository

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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Recent Submissions

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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.
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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.
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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.
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Invisible images: missing disability representation in Kazakhstani primary school textbooks
(Globalisation, Societies and Education, 2026-01-01) Orynbassarov D.; Dukeyev B.
This article examines the underrepresentation of persons with disabilities in Kazakhstani primary school textbooks amid ongoing inclusive education reforms. Analysis of 155 textbooks containing approximately 19,783 images identified only seven depictions of persons with disabilities, often stereotypical and reinforcing able-bodied norms. Drawing on 23 interviews with textbook production stakeholders, the study highlights key challenges, including the absence of clear standards, prevailing societal attitudes, concerns about potential harm, and the legacy of Soviet-era ableism. Offering a Global South, postcolonial perspective, the findings call for expert involvement, professional training, greater transparency in textbook approval, and increased public engagement to address underrepresentation. © 2026 Informa UK Limited, trading as Taylor & Francis Group.
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The Influence of Parental Cultural Capital on Developing Professional Leadership Practices in Elite Schools
(British Journal of Educational Studies, 2026-01-01) Kesim E.; Aypay A.; Atmaca T.
This study investigates the ways that parental cultural capital influences the leadership behaviours of principals working in elite schools in Türkiye. Designed as a qualitative case study, the research reported here draws on data collected through semi-structured interviews with eight principals from long-established and academically ambitious schools in ?stanbul, Türkiye. The findings suggest that principals, over time, internalize the high standards and expectations embedded in their school cultures, which gradually transform their leadership dispositions. Furthermore, the study reveals that an administrative habitus begins to form and institutionalize in alignment with the dominant forms of cultural capital within these settings, enabling principals to lead in ways that transcend conventional administrative norms. © 2026 Society for Educational Studies.