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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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001. Theses 2814
002. NU Research Publications 10840
003. NU Research Institutes & Centres 586
004. NU Journals & Magazines 273
005. Forums, Conferences & Projects 1333

Recent Submissions

Access status: Open Access ,
Decision analysis for transporting critically ill patients with cardiovascular diseases
(Nazarbayev University School of Engineering and Digital Sciences, 2025-09-30) Kalpakov, Yerbolat; Abdildin, Yerkin; Viderman, Dmitriy
The transportation of critically ill patients with cardiovascular diseases is a critical aspect of emergency medical care, particularly in countries with vast territories, dispersed populations, and centralized healthcare services. In Kazakhstan, where specialized cardiac centers are concentrated in major urban areas, the need to transfer patients across long distances presents a complex decision-making problem that intertwines logistical, clinical, and economic challenges. Selecting the most appropriate mode of transport whether by airplane, helicopter, ambulance, train, or private clinical vehicle requires a careful evaluation of trade-offs between cost efficiency, transportation time, and patient safety during transit. This dissertation addresses this multifaceted problem by applying a rigorous decision-analytic framework grounded in Multiattribute Utility Theory (MAUT). The methodological contribution of this research lies in the development and application of a multiattribute utility function, U(X1, X2, X3), which incorporates three key attributes: transportation cost savings (X1), time savings (X2), and the medical impact of transportation on the patient’s health status (X3). The widely used APACHE II scoring system is used to measure how sick a patient is, which gives a more complete and accurate picture of their health than the color-based triage system that is currently used in Kazakhstan. We got our data from official sources, like the National Coordination Centre for Emergency Medicine (NCCEM), and we also got structured expert input from experienced medical professionals, such as an anesthesiologist who is familiar with transporting high-risk patients. The results show that the decision maker's preferences are not utility independent across the three attributes. This insight led to the construction of a utility function under partial utility independence (PUI) condition, using a functional form derived from the Utility Dependence Matrix (UDM) and elicited through certainty equivalent methods. The resulting analysis produced a ranking of the transportation alternatives under the PUI framework: airplane (1st), helicopter (2nd), ambulance (3rd), private clinical cars (4th), and train (5th). Further comparison was conducted using a multilinear utility function assuming complete utility independence and a series of single-attribute evaluations. These comparative analyses demonstrated the sensitivity of rankings to assumptions about attribute independence and underscored the value of employing a nuanced, realistic representation of decision maker’s preferences in high-stakes healthcare contexts. This study contributes to both the theoretical and applied domains of decision analysis. It offers a novel decision-support model tailored to the healthcare landscape of Kazakhstan, while also serving as a replicable framework for other nations facing similar geographical and infrastructural constraints. Ultimately, the proposed model enhances the objectivity and transparency of transport decision-making, supporting policymakers and emergency coordinators in their efforts to deliver timely, cost-effective, and clinically appropriate care to the most vulnerable patient populations.
Access status: Open Access ,
Packing, Flow and Heat Transfer of Non-Spherical Particles for Concentrated Solar Power Applications
(Nazarbayev University School of Engineering and Digital Sciences, 2025-10-14) Boribayeva, Aidana; Golman, Boris; Spotar, Sergey; Rojas-Solórzano, Luis Ramón; Curtis, Jennifer Sinclair
The demand for efficient and cost-effective thermal energy storage systems has intensified with the rapid expansion of renewable energy technologies, particularly concentrated solar power (CSP). In CSP systems, both packed beds utilized in thermal energy storage units and moving beds employed in moving bed heat exchangers are critical components that rely on granular solid materials for heat storage and transfer. Among these materials, bauxite has emerged as a promising candidate due to its thermal stability, abundance, and cost-effectiveness. The overall performance of packed and moving bed systems is strongly influenced by the flowability, packing structure, and heat transfer characteristics of the granular media. However, most previous studies have predominantly focused on idealized spherical particles, whereas real industrial materials often possess irregular morphology, resulting in more complex mechanical and thermal behavior compared to their spherical counterparts-a behavior that remains insufficiently understood. This doctoral thesis aims to fill this knowledge gap through a comprehensive experimental and numerical investigation of non-spherical particles in packed and moving bed configurations, with a particular focus on their flow dynamics and conductive heat transfer performance. This thesis provides a comprehensive framework for understanding the role of particle shape in granular packing, flow, and heat transfer in thermal energy storage systems. By integrating advanced experimental techniques, detailed shape analysis, and statistically calibrated numerical simulations, the work offers novel insights into the behavior of non-spherical particles in both static and dynamic systems. The findings emphasize the critical impact of particle geometry on packing structure, flow behavior, and heat transfer performance, offering practical guidelines for the selection and modeling of real granular materials in concentrated solar power and other energy storage applications.
Access status: Embargo ,
Deep Learning Approaches for Subject-Independent EEG-Based Brain-Computer Interfaces
(Nazarbayev University School of Engineering and Digital Sciences, 2025-12-05) Otarbay, Zhenis; Abibullaev, Berdakh; Lee, Minho; S. Jamisola, Rodrigo
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) offer a noninvasive and promising pathway for facilitating direct communication between the human brain and external devices. However, traditional BCI systems often require subject-specific calibration, limiting their scalability and usability in real-world scenarios. This thesis addresses this limitation by developing and evaluating deep learning approaches for subject-independent EEG decoding, with a particular focus on two primary BCI paradigms: event-related potential (P300) classification and emotion recognition. The research presents a comprehensive investigation into how state-of-the-art deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and Transformer-based models, can be designed and optimized to enhance cross-subject generalization in EEG decoding tasks. Central to this work is the development of a series of hybrid models that integrate spatial, temporal, and frequency-domain feature representations using attention mechanisms and lightweight network designs. A systematic review of existing deep learning-based EEG classification techniques was first conducted to identify gaps in subject-independent performance. Then, several model architectures were proposed and evaluated on publicly available datasets, including BCI Competition datasets, BNCI Horizon 2020, EPFL, ALS, and SEED-IV. A novel architecture combining CNN, LSTM, and a support vector machine (SVM)-enhanced attention module was developed for P300 classification, demonstrating improved interpretability and robustness across participants. Additionally, a residual CNN and Transformer hybrid model was proposed for emotion classification using the SEED-IV dataset, achieving state-of-the-art performance with an accuracy of 95.5. The experimental methodology involved subject-wise data division using leave-one-subject-out (LOSO) cross-validation to simulate true generalization scenarios. The findings show that incorporating domain-agnostic architectural elements such as multi-head self-attention, residual connections, and adaptive dropout strategies significantly improves classification consistency across subjects. The work further explores training dynamics, optimization techniques (e.g., AdamW), and regularization methods that contribute to performance gains without requiring subject-specific tuning.
Access status: Open Access ,
Public History: Filling the Gaps of History through Historical Documentary Films in Contemporary Kazakhstan
(Nazarbayev University School of Sciences and Humanities, 2025-11-11) Koldeyeva, Dana; Bissenova, Alima; Schamiloglu, Uli
This thesis investigates the role of historical documentary films in shaping public history by addressing gaps in historical knowledge in contemporary Kazakhstan. Although nationhood and identity have been extensively explored, limited interdisciplinary research on documentary film production considers historians’ perspectives, narrative strategies, and audience reception. This study responds to that gap by examining the motivations and attitudes of participating historians, analyzing the content of selected documentaries, and exploring patterns of public engagement. The thesis applies cultural memory theory to examine historians’ roles in documentary film production. It also employs the documentary mode framework to analyze genre conventions, narrative structures, and their influence on the audience. The data for the study were collected through interviews with historians, content analysis, and analysis of social media comments. The findings indicate historians occupy dual roles in documentary production — serving as consultants and content reviewers and are at times involved in the selection of topics. While some embrace the identity of “public historians”, emphasizing their commitment to communicating with non-specialist audiences, others reject the label, expressing concerns about bias and the projection of particular perspectives. Despite these divergent positions, both groups are united by a shared aim: to revive historical memory and broaden public access to historical knowledge. The documentaries analyzed combine expository and participatory modes, employing affective storytelling to address historical gaps in ways that conventional academic narratives often do not. Audience engagement, assessed through YouTube comments, reveals recurring themes, including expressions of appreciation for the filmmakers and historians, critiques of historical inaccuracies, emotional reactions, and reflections on the films’ educational value.
Access status: Open Access ,
Қазақстан республикасы жоғары оқу орындарындағы гендерлік білімнің ғылыми концепциясы: теориялық- методологиялық негіздері
( "Булатова А.Ж.", 2025) Агабекова, Жазира Агабековна; Жаркынбекова, Шолпан Кузаровна; Калкеева, Қамарияш Райхановна; Сулейменова, Жаркынбике Нуаевна; Салимжанова, Айжан Сериковна; Камилова, Елизавета Ерболовна
Монографияның мақсаты – қазақстандық білім беру кеңістігінде қалыптасқан гендерлік білім берудің теориялық және методологиялық негіздерін тереңдету ғана емес, оны одан әрі дамытудың жаңа, ғылыми негізделген, заманауи әділеттік мен сын-қатерлерге бейімделген тұжырымдамасын ұсыну. Ұжымдық монографияның басты назары жоғары оқу орындарында гендерлік білім беруді енгізу және жетілдіру мәселелерін жан-жақты зерттеуге бағытталған. Монография барлық деңгейдегі және типтегі оқу орындарының оқытушыларына, гуманитарлық-педагогикалық бағыттағы магистранттар мен докторанттарға және гендерлік проблеманы зерттеушілерге арналған.