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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.

NU Research Portal - here you can find information about research projects, publications, and other activities of our 400+ researchers and faculty members.

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001. Theses 2817
002. NU Research Publications 11015
003. NU Research Institutes & Centres 586
004. NU Journals & Magazines 273
005. Forums, Conferences & Projects 1333

Recent Submissions

Access status: Open Access ,
IIoT Using AWS
(Nazarbayev University School of Engineering and Digital Sciences, 2024-04-24) Tubalykov, Aibat; Kizilirmak, Refik
Internet of Things (IoT) is a complex electronic device usually accompanied with sensors and software which enables the possibility for data exchange with other devices. One of the main purposes of IoT is data flow between devices and systems which in turn gives an opportunity to take data-driven decision making without interruption of human beings. There are several ways to avoid unwanted repercussions when it comes to accidents happening due to human factors and data redundancy. Nowadays, due to the development of high-tech devices people tend to trust machines to do the job when dealing with precise measurements which usually involve big amounts of data to be processed. The importance of cloud-based IoT is rapidly increasing due to the complex and vast amount of data handled in order to make right decisions on the correct time inter-val. As a result, the decision made on time can mitigate costs and avoid unpleasant or even fatal consequences. For example, miscalculation led to the Chernobyl disaster or recently happened in Ekibastuz thermal power plant malfunction leaving the city without heat water in the beginning of December 2022. The aim of this research project is to investigate how the use of Amazon Web Server (AWS) IoT in industry encourages optimisation of processes, cost saving and increased productivity. It is anticipated that research work findings will be published in the top-ranked international journals.
Access status: Open Access ,
Exploring supernova gravitational waves with machine learning
(Nazarbayev University School of Sciences and Humanities, 2025-12-09) Abylkairov Sultan; Takhanov, Rustem; Abdikamalov, Ernazar
Gravitational waves from core collapse supernovae offer a unique window into the physics of dense nuclear matter. The bounce signal from rotating stellar cores contains information about the nuclear equation of state, which controls the properties of matter at densities exceeding the density of atomic nuclei. Understanding how well we can extract the information about the equation of state from future detections requires a careful assessment of how detector noise affects machine learning algorithms on classification accuracy and what observational distances permit reliable inference. This thesis investigates the feasibility of constraining the nuclear equation of state through gravitational wave observations of rotating core collapse using machine learning techniques. We generate a comprehensive dataset of simulated waveforms spanning multiple equations of state, progenitor masses, and rotation rates. We employ both full general relativistic simulations and those using the general relativistic effective potential approximation to assess the impact of gravitational treatment on classification performance. We apply eight machine learning algorithms: two deep learning architectures (Convolutional Neural Networks and Recurrent Neural Networks) and six classical methods (Random Forest, Support Vector Machines, Naive Bayes, Logistic Regression, k-Nearest Neighbors, and XGBoost). For clean signals without detector noise, Support Vector Machines achieve the highest accuracy of 99.5% when distinguishing among four selected equations of state. A key finding is that models trained on waveforms from the effective potential approximation cannot reliably classify full general relativistic signals, achieving only 30% accuracy despite 99% performance within their own framework. This demonstrates that training data for equation of state inference must employ full general relativistic simulations rather than computationally cheaper approximations. To assess realistic observational scenario, we systematically inject detector noise corresponding to Advanced LIGO A+, Einstein Telescope, and Cosmic Explorer sensitivities. Classification accuracy smoothly decline with decreasing signal-to-noise ratio, reaching 87% at SNR = 200 and 68% at SNR = 70 for optimally oriented sources. Random source orientations reduce accuracy by approximately 10%. We establish the distance horizons for reliable equation of state classification: Advanced LIGO A+ can classify equations of state with better than 70% accuracy out to 20 kpc for optimal orientations but only to 10 kpc for random orientations. Next generation detectors extend these horizons to 80-100 kpc for optimal cases and 30 kpc for random orientations. We also investigate progenitor mass classification and find substantially lower accuracy (approximately 70% at SNR = 100) compared to equation of state identification, reflecting the weak dependence of bounce signals on progenitor mass. To address the challenge of extracting signals from noisy data, we develop a novel denoising methodology based on autoencoders with Jacobian rank constraints. Unlike conventional autoencoders with fixed dimensionality reduction, our approach incorporates soft rank constraints allowing adaptive adjustment to local data dimensionality. At SNR = 20, autoencoder denoising improves classification accuracy from 24% to 69%, with benefits persisting throughout the low to moderate SNR regime (SNR < 60) that encompasses most observable galactic events. Our analysis reveals that equation of state inference from core collapse gravitational waves is achievable with current generation detectors for the nearest galactic events under favorable conditions, but reliable classification for typical galactic distances and arbitrary source orientations will require next generation observatories. While our results should be interpreted as approximate upper limits due to simplifying assumptions, they provide quantitative targets for detector sensitivity requirements and demonstrate the scientific potential of multi-messenger observations. The methodology developed in this work establishes a foundation for equation of state inference from future core collapse supernova detections, contributing to our understanding of nuclear matter under the most extreme conditions accessible in nature.
Access status: Open Access ,
Improbability Roller: A Hybrid Mobile Robot with Variable Diameter Wheels
(Nazarbayev University School of Engineering and Digital Sciences, 2025-12-12) Devappa, Gourav Moger; Varol, Huseyin Atakan; Rubagotti, Matteo; Erdemir, Erdem
This thesis presents the design, development, and experimental study of the Improbability Roller, a hybrid mobile robot that utilizes variable-diameter wheels to adapt to various terrains while maintaining a purely wheeled mode of operation. Wheeled robots typically struggle on uneven ground, whereas legged systems introduce mechanical and energy overhead. This work investigates wheel size modulation as a way to adjust geometry during motion without changing the locomotion mode. The first version used a cable-driven, passive spring-assisted mechanism that allowed continuous size adjustment with three actuators. Experiments in trajectory tracking, slope climbing, and cost of transport showed stable behavior across different terrains and lower energy use with larger diameters at higher speeds. Limitations in structural layout and maneuverability motivated a second iteration. The second version introduced a rhombus-folding wheel mechanism with a 1.87 size-change ratio, the highest reported among comparable systems, along with lighter composite components and two steering modes. Size-disparity steering provided better stability at higher speeds, while smaller diameters reduced vibration for indoor and sensor-sensitive tasks. Overall, the study demonstrates that adjusting wheel geometry without altering morphology can enhance terrain adaptability while maintaining simplicity in actuation and control, providing a practical approach for cluttered, deformable, and space-constrained environments.
Access status: Open Access ,
Curing Effects on the Durability of CSA Cement-Stabilized Soil Exposed to Wetting-Drying Cycles
(2025) Moon, Sung; Rauf, Ayesha; Kim, Jong; School of Engineering and Digital Sciences
Soil stabilization using calcium sulfoaluminate (CSA) cement presents a sustainable and effective solution for enhancing soil durability, particularly under cyclic wetting-drying (W-D) conditions. This study focuses on the durability and mechanical performance of soil stabilized with 5% CSA cement, subjected to curing durations of 3, 7, 14, and 28 days. Unconfined compressive strength (UCS) and ultrasonic pulse velocity (UPV) tests were conducted to evaluate strength development and resistance to degradation induced by W-D cycles. The results indicate a significant increase in strength with prolonged curing, with UCS values demonstrating progressive improvement over time. However, exposure to repeated W-D cycles resulted in a gradual decline in strength, highlighting the role of CSA cement in mitigating degradation effects. Scanning electron microscopy (SEM) analysis revealed microstructural changes, including the formation of cementitious bonds and reduced porosity, contributing to improved resilience against environmental stresses. The findings demonstrate that CSA cement stabilization with 5% content offers a balance between environmental sustainability and mechanical performance, making it a viable solution for geotechnical applications requiring enhanced resistance to cyclic moisture variations. Further investigation into field applications and long-term performance is recommended.
Access status: Open Access ,
The Impact of Phosphogypsum on Enhancing the Compressive Strength of CSA-Treated Sand
(2024) Loskutova, Anna; Kim, Jong; Moon, Sung; School of Engineering and Digital Sciences
Various additives such as fly ash, lime, fibers, and slag have been explored to enhance soil stabilization characteristics and fulfill specific performance criteria. The utilization of Calcium Sulfoaluminate (CSA) cement has garnered considerable attention due to its environmental friendliness compared to ordinary Portland cement (OPC) and its notable durability and early strength development. This study investigates the impact of replacing CSA with phosphogypsum (PG) on enhancing the compressive strength of sand while exploring the potential for recycling waste from phosphorus production. Chemical composition analysis of PG was conducted through X-ray fluorescence (XRF) and X-ray diffraction (XRD), revealing the predominant presence of calcium sulfate hemihydrate along with impurities like fluorine, phosphorus, silicon, and sulfur compounds. The mixture compositions were initially standardized to include 7% CSA and 10% water content for all samples. Subsequently, five types of mixtures were prepared, with CSA replaced by PG in proportions of 10%, 20%, 30%, 40%, and 50%. Uniaxial compressive strength (UCS) and ultrasonic pulse velocity (UPV) tests were conducted at intervals of 3, 7, 14, and 28 days to assess the influence of PG on soil stabilization characteristics. The findings indicate that the optimal replacement level of CSA with PG would be 30%, resulting in the highest strength development after 28 days of curing.